Climate Change and Extreme Weather Events; Establishing a

Prepared for: Defra Sustainable and Competitive Farming Strategy Area 3B Ergon House c/o 17 Smith Square London SW1P 3JR

Prepared by:

ADAS UK Ltd and University of Leeds

Date: June 2013

Climate Change and Extreme Weather Events; Establishing a Methodology for Estimating Economic Impacts on Agriculture

A Methodology for Estimating Economic Impacts of Extreme Weather Events on Agriculture

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Executive summary Extreme weather events are unusual, severe or unseasonal changes in weather patterns and have the potential to cause significant cost to society. The agricultural sector’s exposure to and reliance on the climate makes it particularly vulnerable. It has been well documented that there is an increasing incidence of extreme weather events which can be attributed to anthropogenic climate change. Models to estimate the impact of climate change generally predict only average changes to climate with limited ability to predict extreme weather events. As such defining the economic impacts and responses of agriculture to extreme weather events is difficult.

This project aims to address this limitation by establishing and testing a methodology to estimate the economic impacts of extreme weather events on agriculture in England using scenarios (rather than modelled outputs) of extreme weather events. The context for these scenarios was to explore ‘worst case scenarios’ for extreme weather, taking account of climate change in 2050.

The overall aim of the research is to develop a methodology to assess costs and impacts of extreme weather events to inform policy. The specific objectives of this study were to answer the following questions:

(a) What extreme events have the potential to incur substantial costs on agriculture?

(b) How do extreme events influence the behaviour of farmers, and the decision criteria (e.g. approach to risk) they use? In particular, are there systematic failures in the perception of risks?

(c) How can the economic cost associated with these events be estimated?

Five sequential but discrete tasks were carried out:

(i) A Rapid Evidence Assessment (REA) of past extreme weather events

(ii) Developing eight extreme weather scenarios for 2050

(iii) Estimating the impact of these scenarios on key agricultural sectors (Arable, Horticulture, Dairying, Sheep, Cattle, Pigs and Poultry)

(iv) Describing a method for using these impacts alongside economic datasets and spatial mapping to estimate economic impacts

(v) Consideration of adaptations.

The REA covers the period from the late nineteenth century to present day and focused on extreme weather events, economic impacts on agriculture and adaptive responses. It identified a number of extreme weather events which were then used to inform the development and characterisation of scenarios.

In developing the extreme weather scenarios for 2050 the insight and expertise of policymakers, climate scientists and industry stakeholders was pooled via a workshop. Using the workshop output alongside Met Office datasets and the REA findings, eight final scenarios were agreed. These scenarios included: mild winters, localised flooding, wet weather, seasonal dislocation and set combinations of extreme events, for example, a mild dry winter followed by severe spring frost. The magnitude of each weather event was defined using existing Met Office datasets and best available knowledge on feasible weather patterns by 2050.

Using the eight defined extreme weather scenarios, ADAS experts in key agricultural sectors (arable, horticulture, dairying, sheep, cattle, pigs and poultry) were tasked with estimating the impacts of extreme weather on their sector. The impact metrics considered fell under four key themes: environment, soil, lands and crops and livestock. Experts were given a common


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briefing to allow some standardisation and were questioned on sector-specific impacts and adaptations for each extreme weather scenario. Both quantitative and qualitative data was collected for completeness.

From the quantitative data provided by sector experts and using Farm Business Survey data as a baseline of economic performance an Excel-based model was developed. The model translated both direct and indirect effects of weather on agricultural outputs and inputs across nine robust farm types in England on a ‘per unit of production’ basis (per hectare or per head of livestock). The model also allows for price impacts due to supply changes (both nationally and globally). In order to scale up the impacts for particular weather events, published spatial datasets were used to define the boundaries of likely impact. Datasets included the Defra Census1, Environment Agency Flood zones and soils data (HOST soil wetness and drought prone soils).

Nine steps are set out in the report to detail how the data can be used to derive aggregate economic impacts. These are:

Step 1: Define the scenario weather event in terms of meteorological parameters, specifying spatial and temporal boundaries.

Step 2: Estimate the change in agricultural production parameters associated with the scenario for key sectors – enterprise yield, product quality, inputs and resources (soil, infrastructure etc) – using expert opinion and/or empirical evidence as available.

Step 3: Calculate the 3-year ‘average’ economic performance for robust farm types (FBS data) at farm level.

Step 4: Use robust farm type data (from Step 3) in combination with estimates of change in volume due to extreme weather (from Step 2) to estimate the unit value change in output for each crop or livestock type and for each input category.

Step 5: Define the spatial scale for the area affected by the weather event – administrative boundaries (regions, counties) – and overlay with the Defra Census dataset to calculate hectares of crop and head of livestock within that area.

Step 6: Use cropping and stocking data from (Step 5) to scale up the output for each crop or livestock type and for each input category.

Step 7: Adjust for price impacts at UK and global scale.

Step 8: Aggregate the scaled impacts for each enterprise and cost category to calculate total economic impact.

Step 9: Aggregate multiple year impacts

The analysis indicated that from the eight extreme weather scenarios considered, summer flooding and consecutive wet autumn/winters would have the most detrimental impact on economic performance per unit of agricultural land area. The impact of summer flooding (Scenario 1) was estimated to be a net economic loss of £776/ha while consecutive wet autumn/winters (Scenario 2) led to an estimated net economic loss of £537/ha.

1 Agricultural Census data is collected annually by survey from a sample of commercial farmers and growers; the data is confidential and is for Defra’s internal use only. Aggregated data is published online at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/structure-of-the-agricultural-industry#publications


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When per hectare data was scaled up using Census data and other spatial datasets, the most significant extreme weather event in terms of total economic impact was ‘seasonal dislocation’ (Scenario 5), a 12-month sequence of unseasonal weather events. The total economic impact of a seasonal dislocation scenario was estimated at a net loss of £1,361 million. This is attributed to it being a nationwide event, whilst flooding events are more localised and demonstrates the impact different extreme events can have over different scales. A number of inter-sector differences were also observed between extreme weather scenarios. Horticulture generally showed some of the biggest potential losses and poultry some of the smallest due to the largely indoor-based production system.

All scenarios were associated with a net economic loss apart from scenarios 3 (Mild Winters) and 7 (Drought with Extreme High Summer Temperatures), where commodity price increases offset volume production losses. The net impact was an estimated economic benefit of £184 million and £23 million for scenarios 3 and 7 respectively. These private benefits are associated with a societal loss as consumers will pay higher prices for food.

Adaptation measures were also considered on a farm scale for each extreme weather scenario for each farm sector. The scope for adaptations to be implemented varied widely depending on the extreme weather scenario and the farm sector. High value horticulture crops already demonstrated a number of adaptations and an ability to take up adaptations as required in the face on extreme weather events. A key issue with adaptation measures is uptake, as most measures incur a cost, while the benefits rely on the incidence of relevant extreme weather events. Whilst suggestions for sector adaptations showed overlap with the Defra list, there were additional ideas for adaptations which merit consideration and follow up work.

The analysis outlined offers a framework for quantifying impacts rather than presenting a definitive analysis of the impact of extreme weather. The eight scenarios outlined are not exhaustive and the model has to capacity to be used to test additional variants including policy changes and incorporate emerging evidence. The methodology has a number of limitations and areas for further work are highlighted.

The most challenging aspect was securing reliable quantitative estimates of impact by sector for the eight scenarios described due to the heterogeneity of farmland, production systems and management, as well as responses and adaptations. More work is necessary in this area and it is suggested that localised case studies would provide a suitable approach. There are also limitations in terms of datasets (detail and availability) and assessing medium-term impacts.


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Acknowledgements This report has been prepared by John Elliott, ADAS together with Claire Quinn and Dorian Speakman from the University of Leeds. Other members of the ADAS team include Lucy Wilson and Isabel Nias (spatial datasets), Camilla Durrant and a number of sector experts (sector impacts).

We wish to acknowledge all those involved in the sector workshop at the University of Leeds for their contribution to the development of extreme weather scenarios namely, Prof Tim Benton (UK Champion for Global Food Security), Alex Webb (EA Climate Change Adaptation Team), Andy Challinor (Professor of Climate Impacts, SEE), Suraje Dessai (Professor of Climate Change Adaptation, SEE), John Marsham (Academic Research Fellow, SEE), Dr Tom Osborne (National Centre for Atmospheric Science, University of Reading) and Nigel Penlington (Environment Programme Manager, BPEX).

We also wish to acknowledge members of the Defra steering group who provided valuable contributions and feedback to the research team and in particular Paul Bradley (Climate Adaptation, Defra), Marion Rawlins (Cereal and Hortic Policy, Defra) and Kathryn Humphrey (Committee on Climate Change) for their policy steer and Clemens Matt, Project Officer.


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Contents 1. Introduction ..............................................................................................1

1.1 Study objectives ........................................................................................................................2 1.2 Methodology ..............................................................................................................................3

2. Rapid Evidence Assessment (REA)..............................................................4

2.1 Flood events ..............................................................................................................................4 2.2 Drought events ..........................................................................................................................6 2.3 High summer temperatures .......................................................................................................8 2.4 High winter temperatures.........................................................................................................10 2.5 Severe winters.........................................................................................................................11 2.6 Evidence of adaptive response by farmers..............................................................................11 2.7 The economic and policy context for adaptation......................................................................14

3. Extreme weather scenarios......................................................................15

3.1 Extreme weather events in 2050 .............................................................................................15 3.2 Selection of Scenarios .............................................................................................................16

4. Establishing a method for estimating economic costs ..............................20

4.1 Key sectors and indicator enterprises......................................................................................20 4.2 Impact metrics .........................................................................................................................21 4.3 Baseline economics of production ...........................................................................................22 4.4 Approach for estimating economic impact ...............................................................................24 4.5 Validation of economic impact method ....................................................................................27

5. Economic analysis of scenarios ................................................................29

5.1 Yield and price assumptions....................................................................................................29 5.2 Economic impacts ...................................................................................................................30

6. The role of climate change adaptation.....................................................33

6.1 Adaptation measures for climate change.................................................................................33 6.2 Possible adaptation in response to extreme weather scenarios ..............................................33 6.3 Farmer uptake of climate change adaptation measures ..........................................................35

7. Discussion................................................................................................37

Appendix 1: Bibliography ..............................................................................38

Appendix 2: REA Evidence on past extreme weather events .........................41

Appendix 3: Scenario narratives....................................................................67


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Appendix 4: Impact of extreme weather on agriculture by sector .................76

Appendix 5: Spatial mapping methodology and datasets ..............................97

Appendix 6: A worked example of economic impacts (Scenario 1) ..............100

Appendix 7: Defra list of climate change adaptation measures ...................106

List of Tables Table 1: Characteristics of major drought and heat wave years in the UK .......6

Table 2: Gross Margin for major crops in 1995 compared with 1994 ...............7

Table 3: Impact of drought on yield and output of Other Crops in 1995..........8

Table 4: Summary of Costs of Extreme Events on Agriculture in the UK.........12

Table 5: Change to extreme rainfall intensity compared to a 1961‐90 baseline ........................................................................................................16

Table 6: Summary of Extreme Weather Scenarios .........................................18

Table 7: Indicator enterprises........................................................................20

Table 8: Climate change impacts from CCRA .................................................22

Table 9: Area of cropping affected by flooding of EA Flood Zone 3 ................27

Table 10: Summary yield impacts of extreme weather across agricultural sectors ..........................................................................................................29

Table 11: Estimated impacts of the eight extreme weather scenarios on output and input price ..................................................................................30

Table 12: Key climate change adaptation measures identified for extreme weather scenarios ...........................................................................34

Table 12: Keyword search terms ...................................................................41

Table 13: Summary of past mild winters .......................................................69

Table 14: Arable sector impacts by scenario..................................................76

Table 15: Horticulture sector impacts by scenario .........................................79


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Table 16: Dairy sector impacts by scenario....................................................84

Table 17: Cattle and sheep sector impacts by scenario ..................................87

Table 18: Pig sector impacts by scenario .......................................................90

Table 19: Poultry sector impacts by scenario.................................................94

Table 20: Soil wetness classes, defined by duration of wetness at depths of 40 and 70 cm.............................................................................................98

Table 21: Area of cropping affected by flooding using Soil Wetness Class......99

Table 22: Estimates of output and input change due to Scenario 1..............100

Table 23: FBS enterprise output for crops across robust farm types ............101

Table 24: Farm Business Survey (FBS) detailed outputs and inputs (3‐year average 2009/10, 2010/11 and 2011/12) ....................................................102

Table 25: Estimates of change in volume of variable and fixed cost categories under Scenario 1 ........................................................................103

Table 26: Scale of agricultural enterprises within EA Flood Zones 3 and 2 ...103

Table 27: Total impacts of Scenario 1 on farm enterprise output of enterprises within EA Flood Zones 3............................................................104

Table 28: Weighted change estimated for enterprise output across all robust farm types........................................................................................104

Table 29: Volume and price adjusted estimates of net economic impact of Scenario 1 (Year 1) ..................................................................................105

List of Figures Figure 1: Mean temperature (0c), 2005‐2011 ..................................................1

Figure 2: Rainfall in England (mm), 2005 – 2011 ..............................................1

Figure 3: Impact clusters for agriculture identified in the CCRA .....................21

Figure 4: Farm Business Income broken down by cost centre for livestock farms (2011/12) ............................................................................................23


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Figure 5: Estimated economic impact of extreme weather scenarios 1‐8 per hectare of land affected ..........................................................................30

Figure 6: Estimated total economic impact of extreme weather scenarios 1‐8 31

Figure 7: Met Office Summer rainfall in England from 1910‐2012..................67

Figure 8: Autumn rainfall totals in England....................................................68

Figure 9: Absolute Daily Maximum Temperatures: Heat Wave of August 2003..............................................................................................................73

Figure 10: Summer Heatwaves Daytime Maximum ºC: baseline for 1960‐2004..............................................................................................................74

Figure 11: Illustration of GIS mapping of Defra agricultural census and EA flood (Zone 3) data........................................................................................97


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1. Introduction Extreme weather events are unusual, severe or unseasonal changes in weather patterns that can occur on time scales as short as hours and include droughts, heat-waves, floods and storms. They can be defined as occurring less than 5% of the time and are much less predictable than climate change. Extreme events have the potential to cause significant cost to society and the agricultural sector’s exposure and reliance on the climate makes it particularly vulnerable. This research focuses on developing a methodology to assess the costs and impacts of extreme weather events to inform policy decisions on adaptation in Agriculture.

An indication of the seasonal variation in weather between years for the four seasons in England is shown in Figure 1 and Figure 2 in terms of temperature and rainfall data for recent years. However, this hides much greater variation at a shorter timescale, although much is within the bounds of what farmers and growers expect and plan for.

Figure 1: Mean temperature (0c), 2005-2011

Figure 2: Rainfall in England (mm), 2005 – 2011


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Extreme events such as flash flooding can occur on a timescale of minutes or hours, whilst heat waves require at least five consecutive days where temperature exceeds the average temperature by 5°C (World Meteorological Organisation definition (Frich et al, 2002)). The timescale of extreme weather events is also influenced by the type of extreme weather event with some typically lasting longer than others, for example storms typically occur over a short timescale whilst others, such as droughts can last for years (see Rapid Evidence Assessment and Benestad, 2005).

The increasing incidence of extreme events can be attributed to anthropogenic climate change (IPCC, 2012) and it is recognised that extreme events are likely to increase in the near future and pose an increasing threat. The Climate Change Risk Assessment (CCRA)2 was undertaken to consider the impact of longer term change in weather by sector (including agriculture) and provides a benchmark against which extreme weather events will take place. IPCC (2012) has concluded that it is “very likely” (90-100% probability) the length of warm spells or heat waves will increase over most land areas by the end of the 21st century. This indicates the duration of extreme weather events experienced currently and in the past will be longer by the 2050s and extreme events lasting for months or years rather than days could become more typical.

Recent research, such as that of Hansen et al (2012), demonstrates evidence for increasing frequency of temperature anomalies, a current 80% chance of a ‘hot’ summer due to climate change (‘hot’ defined as 3 standard deviations from the mean), and predictions of ‘hot’ summers becoming the norm and 5 standard deviation anomalies becoming the extreme. Other recent research on extreme rainfall in the UK such as the assessment by Jones et al (2012) and the spatial modelling by Atyeo & Walshaw (2012) is also relevant in scoping future extremes. While extreme events are challenging for climate models to predict and are currently far from reliable, improvements in extreme event prediction are underway (Walker Institute, 2009).

1.1 Study objectives Techniques to estimate the impact of climate change have mainly relied on models of projected average changes in climate which have limited capacity to account for extreme events. This project addresses this limitation by establishing and testing a methodology to estimate the economic impacts of extreme weather events on agriculture in England using scenarios (rather than modelled outputs) of future extreme events to provide information for analysis. The methodology also considers the influence that extreme weather events have on the attitudes, actions and approach to future business planning of farmers.

The project is expected to answer following questions:

1) What extreme events have the potential to incur substantial costs on agriculture? (based on Rapid Evidence Assessment (REA))

2) How do extreme events influence the behaviour of farmers, and the decision criteria (e.g. approach to risk) they use? In particular, are there systematic failures in the perception of risks? (based on REA)

3) How can the economic cost associated with these events be estimated?

2 http://www.defra.gov.uk/environment/climate/government/risk‐assessment/


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1.2 Methodology The research team from ADAS and the University of Leeds used the following approach:

Step 1: Rapid Evidence Assessment (REA) of past events. This review of the literature provided a basis for scoping the extent of different extreme weather events and associated impacts, and informed the development and characterisation of scenarios. The REA was led by the University of Leeds and is reported in chapter 2 with a detailed summary of evidence in Appendix 2.

Step 2: Scenarios development. A workshop was held at the University in February 2013 to capture the insight and expertise of policymakers, climate scientists and industry stakeholders in defining what extreme weather scenarios should be used. Follow-up work with the Defra steering group and referencing Met Office datasets was used by the University of Leeds to expand and define 8 scenarios. The scenarios are described in chapter 3 with a detailed narrative in Appendix 3.

Step 3: Expert elicitation. A group of ADAS agricultural experts were identified and tasked with estimating the impacts of extreme weather on key sectors. Initially this was a broadly based analysis which was subsequently linked to the eight scenarios developed in step 2. As each expert has sector-specific knowledge, the aim was not to seek consensus between them but to secure a consistent approach; this relied on providing a common briefing and framework, and using an iterative approach to challenge and validate responses. The estimated extreme weather impacts and possible adaptations are detailed in Appendix 4 (by sector).

Step 4: Modelling economic impacts. An excel-based model was developed by ADAS to translate the direct and indirect effects of weather on agricultural outputs and inputs across the nine robust farm types in England. It uses Farm Business Survey data as a baseline for economic performance and applies impacts to key parameters. The model uses the estimated impacts on sector outputs and inputs (as set out in Step 3) and allows for price impacts due to supply changes (nationally and globally). In scaling up the impacts, the model allows for the spatial extent of weather effects as well as the distribution of enterprises across England. The spatial modelling approach and links to relevant datasets is set out in appendix 5. The model approach is set out with detailed steps in chapter 4, with a worked example in Appendix 6.

A short overview of the economic analysis of all eight scenarios is given in Chapter 5.

Step 5: Adaptations. A summary analysis of adaptation responses is set out in chapter 6. This maps the proposed adaptations from the ADAS experts against a wider Defra list and looks at the evidence on uptake.

The analysis in this report offers a framework for quantifying impacts rather than presenting a definitive analysis of the impact of extreme weather. As such, the scenarios are not exhaustive and the model can be used to test additional variants, in response to policy priorities or emerging evidence on climate change and its impacts. These other issues are considered in a short discussion section in chapter 7.


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2. Rapid Evidence Assessment (REA) The REA focused on capturing evidence of past extreme weather events, with an initial reference period of 1950 to date. However, this was extended to the late nineteenth century in order to extend the evidence base. This section brings together the evidence by event type, focusing on agricultural impacts and highlighting economic consequences where recorded. The section also considers the aggregate evidence on adaptation issues. A detailed record of the references is available in Appendix 2.

The review was informed by documented impacts of extreme weather on agriculture rather than setting out to provide a comprehensive record of extreme weather events. It was limited to English language based records covering events in England (with one exception for Wales, as the case was analogous to England). The review is therefore not an inventory of weather events impacting on agriculture but rather it details specific events which have been reported or studied in order to highlight the scope and scale of impacts. This provides a basis for developing extreme weather event scenarios for 2050 (see chapter 3 of this report) alongside climate change projections. In turn, these scenarios represent worst case outcomes in response to single and combined weather events and provide a basis for developing the methodology for estimating economic impacts.

2.1 Flood events Flooding in summer: 2007

The floods of 2007 affected a wide area of the South Midlands, and South and East Yorkshire. The heavy rainfall occurred in late spring and late summer 2007. During May to July, rainfall in southern Britain was 223% the total of the 1961-1990 average for May to July. Flooding was more severe than the floods of 1947 which were the most widespread floods of the twentieth century (Marsh, 2008).

Crop damage and associated yield loss were the most reported impacts. In flooded areas cereal yields were down by approximately 40% and quality reduced due to soil contamination and sprouting where harvest was delayed.

As a consequence of the 2007 floods just under 8% (6 out of 78) of interviewed farmers reported that they were unable to plant winter crops or potatoes in the following spring. Soil compaction and a reduction in the earthworm population is thought to potentially reduce yields in the following years (Posthumus et al., 2009).

Straw yield and quality was also poor. Across the UK, winter wheat yields were 6% lower in 2007 than 2006. Additional costs resulted from the need to add agro-chemicals to make harvesting possible after flooding. There were additional harvesting costs and land reinstatement costs. In the aftermath of the summer 2007 floods there was a problem of soils staying waterlogged for a prolonged period, up until spring 2008 in some cases. It was found that a small number of farms suffered the highest losses and smaller farms suffered disproportionately. It was not stated by Posthumus et al. why this was the case; the greatest losses were incurred by those farms typed as ‘general cropping’, mostly due to reduced yields, or in some cases, total crop loss (Posthumus et al., 2009).

The second highest level of losses was from the loss of income from livestock and debris clearing costs. Grasslands were affected through losses of hay and silage as well as grazing. There were increased costs as a result of moving livestock and reseeding pasture. Other reported impacts were losses of livestock (due to drowning) and reduced milk production (causes not specified), additional labour, extra feed purchases, additional slurry disposal (due to livestock being kept indoors), and extra treatment costs due to disease. Livestock farms were affected indirectly with increased costs of moving livestock to shelter during the grazing season and additional costs arising from this, such as labour costs. Costs


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to livestock farmers were increased by the need to purchase extra feed. Whilst direct losses of livestock were low, there were increased costs for treatment of diseases, such as dairy mastitis and lameness. In addition, there were costs for repairing fences, gates and hedges, where the need is higher than for arable crops (Posthumus et al., 2009).

At a field level, the greatest losses affected horticultural produce, such as vegetable and salad crops, which were unfit for sale. Horticultural farms had relatively higher costs associated with repairs needed to irrigation equipment. The potato crop saw 2.6 % of the area spoiled by flooding though a larger proportion was lost to blight as a result of the wet weather conditions (Posthumus et al., 2009). In 2007, the floods had a significant impact on the potato crop. Yields were low and there was greening of potatoes. Around 2000 ha of crop was lost.

Flooding in summer: 1879 and 1880

In both 1879 and 1880 the weather was particularly wet in the main growing and ripening seasons. Abnormal rainfall was reported in the Midlands, central southern England and East Anglia. In 1879 the rainfall from May to July was 184% the average May-July period (1971-2000) for England and Wales (Marsh, 2008). Brown (1987) describes the impact on cereal yields as 50-75% of the average of the years 1873-77. The following year saw harvests similarly affected though there were areas of the country that were much less affected, such as Cornwall. Wheat was the worst affected crop. Livestock farming was affected as pastures were waterlogged. The problems were exacerbated by a dry spring in 1880 reducing grass growth and rain affecting haymaking. As a result livestock were reported to be underweight and “out of condition” Brown (1987). However, disease compounded the problems, such as foot rot and liver fluke affecting sheep, the latter reportedly killing nearly 10% of sheep in two years. The worst affected areas were the unusually wet Midlands such as Leicestershire, where there was as much as a 37% decline in sheep numbers by 1881 compared to 1878. There were also outbreaks of pleuro-pneumonia affecting cattle and in 1880-81 foot and mouth disease which started in eastern England.

Flood following a drought: 1912

The combination of a drought being followed by unseasonal heavy rainfall during the main growing season occurred with dramatic effects most notably in Norfolk, where up to 200mm rain fell in one day in August 1912. Generally the pattern of rainfall affected eastern regions most severely. There was “enormous damage” to hay and corn crops, with hay being washed out to sea (Mill and Salter, 1912). A large area of land remained underwater for the whole of the following winter. In Suffolk hay crops were spoiled and corn was stunted; in Essex much grain was reported as damaged. That which survived was “then subsequently swamped in August” resulting in threshed corn losing 35% of its weight (Mill and Salter, 1912).

In Hampshire, Mill and Salter (1912) report on the impacts from the dry Spring followed by the heavy rain. There was just 2.5mm rainfall recorded in April, which caused a “great check” to hay and straw. This was followed by an unusually wet June and resulted in an “immense loss” to farmers. The rest of the crop was so badly damaged it was only fit for animal feed. In stark contrast, 100mm above average rainfall fell in August, and as this was during harvest time agricultural losses were reported to be “heavy”. The wheat crop ended up being fed to pigs and the straw grew to 2 feet in height instead of the usual 3-4 feet (Mill and Salter, 1912).

Other parts of England also suffered. In Devon, the weather in June was reported as “disastrous” for hay and much of what would have otherwise been a good crop rotted on the ground. In August there was little or no corn saved, with a “great waste of the crop” with losses also reported in Herefordshire (Mill and Salter, 1912).


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Flooding and Contamination of agricultural land

In certain areas flooding can lead to contamination of river sediments on flood plain sites in agricultural use. This is a particular problem where nineteenth century mining activities in upland areas such as the Pennines results in higher concentrations of metals from spoil heaps or re-workings which leads to a washout of metals into the river system and deposition on land when flooded. One study on the River Swale in North Yorkshire, looking into the effect of the floods of 2000, found that concentrations of Cadmium, Lead and Zinc exceeded MAFF guidelines (Dennis et al., 2003). At 35% of monitoring sites along the Swale, lead was above guideline levels of 300 mg/kg (at over 530mg/kg).

A previous analysis in 1996 recorded very much higher concentrations of Cd, Pb, and Zn than after the 2000 floods; this was found to be most likely because of a dilution effect of greater sediment loads in the 2000 floods (ibid). Consequently catchments like the River Swale with historic mines pose a flood related risk of contaminated sediments on floodplains, and an increase in flood frequency would exacerbate that hazard (Dennis et al., 2003).

Flooding and soil erosion: Impacts from intense falls of rain

Soil erosion risk is posed by episodes of intense rainfall, although farmers may not directly perceive the loss of soil as a major cause of concern (Posthumus et al., 2008). Southern England is at risk from muddy floods after monthly rainfall totals of 200-300mm, which is twice or three times the average. Cereals are vulnerable if planted in large areas on slopes. Subsequently, once rills and gullies have been established, muddy floods can take place with much lower rainfall totals of 4mm per day. The link between an increase in muddy floods and a change in land use from pasture to arable has been highlighted by Boardman (2010). In a study of muddy floods in the Sussex Downs in late autumn 1982, in an area of 50 km², 66 sites reported erosion. One site incurred a clean-up cost of £64,000 which was met by the local council (Stammers and Boardman, 1984). Even gently sloping land experiencing heavy but by no means extreme falls of rain has been demonstrated to be vulnerable to muddy floods (Evans, 2004).

2.2 Drought events Wreford and Adger (2010) looked at the relative impact of droughts on UK agriculture over a time period from 1975 to 2006. The drought events studied are summarised in Table 1.

Table 1: Characteristics of major drought and heat wave years in the UK

Source: Wreford and Adger (2010:281)


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By studying how yields were impacted over successive major droughts this gave them insight as to whether there was adaptation and therefore increasing resilience shown by farmers in response to droughts.

The variability of yields of arable crops, potatoes, oilseed rape and, to a lesser extent, wheat decreased after each successive drought. Barley yields did not respond with any particular pattern through the succession of droughts.

Sugar beet is generally supposed to be drought resistant in the UK (Jaggard et al., 1998). However after having been very negatively affected by the 1975-6 drought, sugar beet continued to be adversely impacted by drought, and the level of impacts from successive major droughts did not reduce.(Wreford and Adger, 2010). Sugar beet in the UK, at around 52°N, (i.e. Cambridge) is more susceptible to summer drought than on land in mainland Europe at the same latitude; the European mainland tends to have higher summer rainfall totals and soils with greater water storage capacity. In the UK a maximum of 17% of land has been irrigated and this level was predicted to drop due to pressure on abstraction licenses. Average losses of sugar beet to drought are over 10%. Over the 16 years from 1980 to1995, losses due to drought ranged from zero to 365,000 tonnes, equivalent to 27.5% of production in 1995, a significant drought year. In 12 out of the 16 years, losses due to drought exceeded 2% of national yield averaging at 179,000 tonnes. In comparison with losses to disease (virus yellows) losses from droughts were almost always larger (Jaggard et al., 1998).

For livestock Wreford and Adger (2010) assumed that there was likely to be a delay effect from droughts as farmers sold stock after a poor year and effectively increased production in the short term. This seems to be reflected in sheep production where in the immediate year of drought, production increased but fell subsequently; overall the impact of drought on sheep production levels did not appear to reduce over time. For the pig sector, drought had an impact on production but since 1975-76 there has been a generally reduced level of impact. Poultry has shown an increased resilience to drought, so that by the 2003 production during drought actually increased (Wreford and Adger, 2010).

A summary of impacts of drought on key agricultural sectors is given below.

Arable crops The drought of 1995 exerted a positive impact on arable crop yields: wheat, barley and oilseed rape. Sugar beet reported the highest income surplus (due to higher prices) but this was countered by higher costs due to higher temperatures (Subak, 1997). In the study by Subak (ibid.) it is indicated that cereals have done moderately well in hotter years such as 1983, 1990 and 1995; 1976 was an exception where yields fell below the rising trend. In terms of net effect of the warm temperatures and drought in 1995, the additional costs for cooling of the crop were offset by lower expenses for herbicides, pesticides and fungicides. The Gross Margins (less area payments) for major crops are summarised below:

Table 2: Gross Margin for major crops in 1995 compared with 1994

Crop Net output change (£ million) Gross margin (£ million)Wheat +69 +128 Barley +16 +66 Oilseed Rape +20 +10 Sugar Beet -6 +30 Potatoes -40 +390 Total +59 +624 Source: Subak (1997:49)


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The autumn of 1989 was very dry for seed bed preparation with very high wear and tear on machinery (Harker, 1990). By May 1990, drought stress was affecting parts of central and southern England, which had 10% of the average rainfall for the month. Spring cereals and beans were most vulnerable. June rain benefited many crops but was not sufficient to improve those on light soils. Drought persisted in some southern areas with no rain for up to 24 consecutive days in July 1990, which was also warmer than average. Harvesting was reported to be excellent but yields from late sown crops were very variable (Harker, 1990).

In 1995 cereal disease increased and aphid and pea moth activity was unusually early (Subak, 1997).

Horticulture The drought of 1995 affected vegetable production due to yield impacts. Potatoes suffered the largest loss in terms of production whilst root crops such as carrots, parsnips and onions were also affected and additional pesticides were needed to counteract cutworms which thrived in the hotter weather (Subak, 1997). The yields and output values for other crops are summarised in Table 3:

Table 3: Impact of drought on yield and output of Other Crops in 1995

% yield change from1991-94 average

Output change (£ million)

Hops -14 ? Stock peas -2 -0.5 Stock beans -14 -5.6 Brussels sprouts -7 -1.7 Cabbage -5 -3.8 Cauliflower -19 -12.4 Forage (estimated impact on dairy herd) -20 -68.0 Carrots -2 -2.0 Beetroot -15 ? Onions -11 -11.0 Tomatoes +15 +10.1 Cucumbers +10 ? Ornamentals ? -3.3 Leeks ? -0.9 Source: Subak (1997:50)

Drought in 2006 also caused problems with drying out of soil to such an extent that it was impossible to transplant horticultural crops. Demand for water was so high for transplanting that irrigation was impossible (Collier et al., 2008).

Livestock As a result of the drought of 1995 the livestock sector saw an increase in costs for purchased feeds in the South and East (Subak, 1997). Similarly, in 1989 drought in September led to a shortage of grazing and poor hay and silage crops (Harker, 1990). The 1995 drought was accompanied by a loss of fertility in pigs and poultry. However, population figures for sheep and cattle had been in decline over the last five years making the impact of the 1995 drought on livestock production difficult to discern (Subak, 1997).

2.3 High summer temperatures A summary of the evidence on impacts of high summer temperatures on key agricultural sectors is given below.


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Arable crops Overall, yields were down on the long term average in both 1989 and 1990. Winter cereals fared better from the hot dry summer due to having a good root system which had developed over the winter. Cereal development was advanced. The drought in 1990 and high temperatures reduced the period for grain filling, and some crops ripened prematurely with a high proportion of shrivelling, with the result that yields were down. However in northern England, crops yields were up, especially on the good soils in Humberside (Unsworth et al., 1993b).

The high temperatures and drought of 1990 resulted in a lower incidence of leaf diseases reliant on rainsplash such as mildew and leaf spot. For cereals brown rust was more of a problem than the usual yellow rust. Powdery mildew occurred earlier on sugar beet than previously and in 1990 foliar diseases had the largest impact. (Unsworth et al., 1993a). The root disease “take all” infecting roots and stems had a high incidence as a result of a mild winter, moist cool spring and an early dry summer affecting the 1989/90 crop (Unsworth et al., 1993b).

Viral diseases were a severe problem due to a high number of aphids. Aphids also caused direct damage to crops, though they declined during episodes of the hottest weather in summer 1990 (Unsworth et al., 1993b).

Horticulture If potatoes could be irrigated then yields and quality were good, though un-irrigated crops were affected by drought; water stress was a problem in the Midlands and North West. In 1990 maincrop potatoes were damaged by rain in September 1990. Many farmers opted to put potatoes into storage due to low prices in 1990. However, the temperature of the tubers was too high for immediate storage which led to sprouting and an older state of tubers at harvest (Unsworth et al., 1993b).

Damage by cutworms most frequently affects lettuce, though root crops such as beet and potatoes were the most reported hosts. Cutworm incidence is associated with warm dry weather, though migration by cutworms is another contributing factor. There were large outbreaks of cutworm in 1949 and 1976, and the incidence in1976 was accompanied by high temperatures: mean temperatures were in the order of 15-20°C. There is circumstantial evidence that rain or irrigation on potatoes reduced cutworm damage (Bowden et al., 1983).

High temperatures during development can cause yield loss of all horticultural crops. Vegetable crops were highly variable depending on soil type, management and if irrigation was available (Unsworth et al., 1993b). High temperatures in 1990 affected lettuce, cabbage and sprouts by causing bolting. Critical times of crops are during flowering and seed development stages.

For seeds, the hot summer of 2006 led to shortages of certain seed varieties in 2007. Furthermore, hybridisation is hampered by extreme high temperatures as...

“...breeders rely on simultaneous flowering for both parents and plant at different times to achieve this. This has proved to be increasingly difficult in recent years

(Unsworth 1993b:5).

However, heat waves can have a beneficial effect by inducing dormancy and delaying population growth in certain pests. Nevertheless a background of general warming has been predicted to initiate aphid activity 9 days earlier in the 2020s and 20 days earlier in the 2050s. The effect of rain and rain splash on pests and diseases will depend on the species and timing of the event (Collier et al., 2008)


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Grapes and fruit developed early after the mild winters of 1988 and 1989 but were very badly affected by the late frosts which exceeded the positive impact on yields of the 1990 summer – those areas unaffected by frosts had excellent yields (Unsworth et al., 1993a).

Livestock Milk output was affected negatively between April and October 1990. Livestock were affected by heat stress, although animal health was reportedly good – there may have been an impact on ewes and lambing. (Unsworth et al., 1993b)

2.4 High winter temperatures A key impact of high winter temperatures is an increase in pests and diseases in crops. A key example is the 1988-90 period, when there was an increased incidence of bird pests surviving the two mild winters, but damage was lower because of other natural food supplies not necessarily available in a severe winter. There was an upsurge in damage to emerging crops by mice, and slugs damaged soft fruit and vegetable crops. Because 1988/89 was relatively wet, re-sowing of crops was necessary. There was a high level of aphids despite use of aphicides (Unsworth et al., 1993a).

Many weeds survived the mild winters due to a lack of frosts though this did not affect early sown cereal and oil seed rape which outgrew the weeds. By October 1990 high levels of mildew were reported on barley and wheat (Harker, 1990, Unsworth et al., 1993a). There were also outbreaks of yellow rust and barley yellow dwarf virus (BYDV), necessitating unusually large volumes of crop protection sprays to be used. Crops experienced unusually early growth of autumn sown cereals with the result that the application of fertiliser and growth regulators had to be retimed (Unsworth et al., 1993a)

Arable crops Generally the incidence on powdery mildew is strongly related to the number of frost days during February and March (Asher and Williams, 1991). This exerts a stronger influence than frosts earlier in the winter and was found to be a stronger influence than other factors. Warm summer and infrequent rain favours the disease. Geographically the pattern of powdery mildew follows a “well defined pattern”. Beginning in the South East, in Essex, the diseases spread northwards into East Anglia by the end of August in most years. Its dispersal into western areas is only during more favourable years and it rarely occurs in the north of England (Asher and Williams, 1991).

The main problems were in storage facilities due to disease or early sprouting of seed crops (which could be offset if such tubers could be planted early too) (Unsworth et al., 1993a).

Horticultural crops Crops were advanced and yields were good, though unrefrigerated stores had problems with rot, especially onions. However some orchards were decimated by a late frost occurring in March and April 1990 after the mild winter, though others were unaffected. In April 1990, severe frosts damaged tender plants particularly oil seed rape, barley, and plum and pear blossom (Harker, 1990, Unsworth et al., 1993a).

Livestock Grass grew in most parts of the UK during the winters of 1988-89 but the summer was hot and dry leading to shortages in the south west where stock famers had to buy in feed stuff, and leave stock outside for longer. There was an increase in calf pneumonia and lugworm, and parasites affecting sheep in 1989 (Unsworth et al., 1993a).


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2.5 Severe winters The impact of the severe winter of 1947 was exacerbated by the previous summer of 1946 which was wet, particularly in August and September; the result was that there were severe shortages of food and fodder for livestock. The severe winter of 1947 caused thousands of sheep to be lost, as recorded in the community of Cwm Twyi in Wales. They could not be replaced with sheep from the lowlands as they needed to be bred in the landscape. On one farm, no ewes were sold until 1952. Many cows were on sale in the local market due to the shortage of fodder but prices were low and farmers could not help each other by sharing feed due to the shortages. The impact was acutely felt because of the dependency on hill farming in such communities (Jones et al., 2012).

2.6 Evidence of adaptive response by farmers Adaptation to Floods In the aftermath of the summer 2007 floods affecting the south Midlands and parts of Yorkshire many of those farmers interviewed by Posthumus et al. (2009) thought a repetition of the floods was very unlikely in the near future. However 33 out of 78 farmers questioned were considering a range of responses which included changing land use on floodplains (stopping potato growing or winter cereals) or converting arable land into grassland, improving drainage or ensuring enough silage or hay was available, reducing herd size or entering an environmental stewardship scheme. This group of farmers had suffered significantly higher losses related to damages to arable crops, buildings and machinery. In the West Midlands, there was a greater level of acceptance of flood risk by farmers than in both Yorkshire & the Humber and Oxfordshire and therefore greater emphasis was put on resilience (such as having buffer stocks of grazing ground or fodder) and warning as a adaptive response by farmers there (Posthumus et al., 2009).

In terms of soil erosion over arable land in the South Downs, this had been a recent phenomenon since the 1970s and policy responses were not yet in place (in the 1980s) to facilitate land use change or mitigation measures (Stammers and Boardman, 1984). Farmers can experience impacts from runoff causing muddy floods, and legal action may arise as has happened in the Isle of Wight where the council took action (Boardman, 2010) and in Suffolk where affected households took action (Evans, 2004).

In one North Yorkshire catchment overland flow was perceived to be a natural process as past flooding had been sporadic and not prolonged. Only farmers in the lower part of the catchment reported problems. Four of eleven farmers surveyed who experienced ponding in their fields had created ponds in response. As a result interviewees “did not feel a responsibility for flood risk management”. The interviewees felt that flooding was caused by factors such as urbanisation of the area and that an increase in hard surfaces for roads had exacerbated flood problems as well as river canalisation, intensive farming leading to soil compaction and increased drainage of moorland at the head of the catchment. The events up to 2008 had therefore generated little response and so the adaptive responses elicited were in response to degraded land scenarios presented to the stakeholders at a workshop. Reponses included reducing stocking at critical flood risk periods, creation of ponds and surrounding vegetation, planting trees along watercourses and removal of sediment from riverside ponds (Posthumus et al., 2008).

In the Sussex Downs, despite problems of soil erosion causing considerable muddy floods, the farmers in question refused to alter land use or to use contour ploughing in response to flooding, although spring sowing was under consideration (Stammers and Boardman, 1984).


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Table 4: Summary of Costs of Extreme Events on Agriculture in the UK

Event Date [Year, season] Location Return Period (Years) or % of Mean

Cost (2012 prices) or analogue

Source cost Source - event magnitude

Drought 1995 [April 1995 to

September 1996] England and Wales

60-90 England & Wales; 120-170 Yorkshire

£290 million Subak (1997) Marsh (1996)

Drought 1975-1976 UK >200 £430 million for major crops (1) £500 million (2)

(1)Subak (1997) (2) Met Office3

Marsh (1996)

Flood 2007 England: South Midlands, South and East Yorkshire

20* to over 150** years: *Worcester Oxford; ** Sheffield, Evesham, Ludlow.4

£76 million Posthumus et al 2009

Flood 2000 England and Wales

>150 years (most of England)

£603 million FRP (2012) after NFU

CEH &Met Office (2001)

Flood (storm and tidal surge)

1953 East Coast 250 years Unknown Zou and Reeve (2009)5

Drought 2011 £400 million FRP (2012) after NFU

Flood 2005 Cumbria 400 - 1800 years £400 million FRP (2012) after NFU

Hannaford et al. (2010)6

Flood 1879 England 151% of annual mean in Suffolk (1). 184% of May-July mean (2)

Cereal yields 50-75% of normal

Brown (1987) (1) Brown (1987); (2) Marsh (2008)7.

3 http://www.metoffice.gov.uk/news/releases/archive/2010/droughts-to-increase 4 Environment Agency http://:www.environment-agency.gov.uk/static/documents/Research/returnperiods_1918541.pdf 5 http://www.nerc.ac.uk/publications/planetearth/2009/autumn/aut09-clouds.pdf 6 http://www.ceh.ac.uk/news/news_archive/2010_news_item_47a.html 7 Marsh, T. 2008. A hydrological overview of the summer 2007 floods in England and Wales. Weather, 63, 274-279.


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In the South Downs many short-term reactive measures to control muddy flooding have been unsuccessful. Such measures were emergency engineering solutions (creation of retention ponds, banks, and drains, and straw bales) to slow runoff and filter out sediment. Whilst the costs were to be met by farmers themselves there was opposition to investing in amelioration measures (Boardman, 2010, Evans and Boardman, 2003). In contrast, publically funded agri-environmental schemes have allowed long term measures to be implemented, and these include buffer strips of vegetation, changes in land use or small dams on field boundaries (Boardman, 2010).

Adaptation to drought The level of irrigation UK wide has increased from 55,210m3 to 92,883m3 between 1982 and 2005. In addition, water storage capacity nearly doubled between 1984 and 1995 ((Wreford and Adger, 2010) after Orson 1996). The response of cereals to irrigation has been viewed as uneconomic and has led farmers to adapt the time of sowing or harvesting, or to introduce rapidly maturing cultivars (ibid). Investment in field drainpipes for irrigation increased by 6% from late 1994 to early 1995. This helped to reduce the level of losses compared to the drought of 1976. Farmers also responded in 1995 by changing applications of herbicides, fungicides and pesticides (Subak, 1997).

With increasing pressure on water supplies from competing uses there are limits to irrigation and the limited supply of water becomes a barrier to adaptation by means of irrigation. In the summer of 1995 restrictions on abstraction were in place in East Anglia, Herefordshire, Hampshire and Lancashire (Subak 1997). In the summer of 1990 there was an increasing need to irrigate crops whilst restrictions were placed on abstractions in the East and South East. Warnings were also made over Chlorine levels in eastern coastal areas (Unsworth et al., 1993b).

It is reported that farming of certain commodities dependent on irrigation (potatoes, sugar beet and vegetables) may be at the limits of being able to adapt (Wreford and Adger, 2010). They note that due to future limits on water availability, historic adaptation may not necessarily indicate future adaptive capacity. Repeated dry years could “force farmers to make irrigation priorities” (Subak, 1997) possibly in favour of high value crops such as carrots, potatoes and sugar beet. The benefits of irrigating cereals are perceived to be low (Subak, 1997). This example demonstrates the problem of path dependency in UK agriculture, where adaptation options are constrained by the farming system. As a result, adaptations (such as irrigation) might actually turn out to be maladaptations in the longer term.

Adaptation to Extreme Snow (following a wet summer) Upland areas, where farming is marginal, have been shown to be vulnerable to extreme events despite some resilience to disruption to transport and food supplies. The disruption and heavy losses wrought by the prolonged cold and extremely heavy snows of the winter of 1947 is argued by Jones et al. (2012) to have been so great for the Cwm Twyi community that the valley was abandoned. The Forestry Commission bought much of the land and the last family left in 1967. Marginal areas may represent farming systems where to scope to adapt is severely limited; continued climate change and/or a series of extreme weather events may therefore represent a tipping point where the only viable option left is land abandonment.

Small upland communities like Cwm Twyi in Wales had a certain level of resilience in the form of knowledge of previous hard winters (the same could be argued for upland communities across northern England); consequently the farms had a certain level of self-sufficiency for food. However, the poor summer of 1946, with poor harvests, coupled with the prolonged nature of the cold snowy conditions of winter 1947 in which many sheep were buried, led to heavy losses for communities dependent on hill sheep farming. As result of the


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hardships, the UK government decided to institute a subsidy for sheep farmers after 1947 (Jones et al., 2012).

More contemporary evidence will be available when the impacts of the severe winter of 2012/13, where extensive losses of sheep in the uplands has been reported, are evaluated.

2.7 The economic and policy context for adaptation The economic environment Cereal prices doubled in 2007 due to shortages in the world market and speculative commodity trading. The poor potato crop coincided with lower yields across Europe very likely leading to price increases. This was a repeat of 1995 when the low yields of potatoes resulted in large price increases in the UK. As a result of the high prices the potato sector reported gross margins of £624 million (Wreford and Adger, 2010).

The adaptive response of dairy farming to droughts was omitted by Wreford and Adger (2010) because of government policy influence affecting production levels.

The impact of the severe winter of 1947 would have been exacerbated by the economic conditions of the time: The UK was still under rationing in the post war period and was economically weakened with a debt burden (Jones et al., 2012).

The policy environment Barriers to adaptation by farmers may be financial or a combination of financial and attitudinal. The latter relates to the awareness or acceptance of responsibility for wider environmental impacts which relate to land management. In a North Yorkshire survey of landowners, it was found that financial assistance and an appropriate funding scheme would be required for farmers to carry out adaptation measures to reduce flooding, such as pond creation or planting (Posthumus et al., 2008).

Current schemes such as the CAP were seen as inappropriate if the benefit was for others. Also it was found that famers felt current agricultural schemes do not provide enough incentive for famers to change land management practices due to the level of cost involved. Whilst famers wished to be perceived by the public as stewards of the countryside they felt that they should receive 100% compensation for implementing measures which were “flood services for society”. This was in contrast to farmers’ attitudes to diffuse pollution from farms. Whilst they felt a responsibility for pollution because of the clear link between land management and diffuse pollution, farmers did not feel responsibility towards flood risk management (ibid.). However, bringing farmers together through workshops did have the effect of engaging farmers in good practice and success stories, and these were seen positively if this approach to flood control was used in combination with other objectives such as pollution control and conservation (ibid).

Efforts to alter farmer behaviour by government agencies have been reported as achieving limited success when local knowledge and experience was not seen as being valued (Wreford and Adger, 2010) (after Hall and Pretty 2008).


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3. Extreme weather scenarios 3.1 Extreme weather events in 2050 A range of possible future scenarios were identified by the stakeholder workshop held in February 2013 at the University of Leeds. These scenarios required parameters to be specified in order to generate ‘plausible’ scenarios which represent climate change influenced severe weather events. The concept of plausibility is contentious and some time was spent at the project workshop trying to develop a common understanding of what it means in the context of future extreme weather events. The Cambridge dictionary definition of plausible is ‘likely to be true or able to be believed’; however participants argued that this definition meant that there were no effective bounds for the scenarios. Ultimately a pragmatic view was taken, accounting for the likelihood as well as the possibility of future extreme weather events.

Additionally, it emerged that multi-event scenarios were of interest, such as the floods/drought of 2012. The next two sections outline the evidence for scenario parameterisation.

Rainfall

In the Norfolk floods of summer 1912, which followed a drought, rainfall was notable for exceeding 150mm in one day over a wide area. If such past events are used as analogues for the future they need to be augmented to take climate change into account. The extent that extreme values (rain, heat) increase for 2050 requires some rationale for the choice of magnitude. However, the evidence is not clear cut for the country as a whole. Rainfall intensities have been increasing during the first decade of 2000s in some parts of England, but decreases have been seen in others. For example, a 1 in 100 year two day rainfall event in South West England, which would have produced rainfall of 74.6mm in 1961-1990, produced 95.5mm in 2001-2009. In the South East this has decreased from 99.9mm to 72.9mm for a two day event (Jones et al., 2012). This highlights the difficulties of setting the magnitude of the scenario parameters.

Different approaches can be found for projecting rainfall into the future. In Australia, the Queensland Inland Flooding Study recommends a 5% increase in rainfall intensity per degree of global warming, which is estimated to be 2°C by 2050 (Wilby and Keenan, 2012). Therefore rain events with annual probabilities of 0.5% and 0.2% are projected to increase to 1% and 0.5% by 2050. As Wilby and Keenan (2012) point out, this is a different approach to that of using model projections of heavy precipitation for a given area.

In terms of changes to extreme rainfall the Met Office recommends that the UKCP09 projections are used for rainfall events with a frequency of up to 1 in 5 years. For rarer maximum daily rainfall, which is of interest in the context of severe and extreme weather events, peak rainfalls are considered more useful particularly for small catchments (Sanderson, 2010).

Whilst prediction of extreme rainfall is recognised as a key challenge for climate scientists, the Environment Agency (2011) gives flood managers the following guidelines for expected increases in extreme rainfall (Table 5).


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Table 5: Change to extreme rainfall intensity compared to a 1961-90 baseline

Applies across all of England Total potential change anticipated for 2020s

Total potential change anticipated for 2050s

Total potential change anticipated for 2080s

Upper end estimate 10% 20% 40% Change factor 8 5% 10% 20% Lower end estimate 0 5% 10%

Source: Environment Agency (2011:14)

For larger catchments an increase in peak river flows of up to 20% is advised (Prudhomme et al., 2010). The Met Office is working on high resolution models and also recommends looking at projections for 5 day rainfall for frontal events, which account for a large share of UK rainfall (Sanderson, 2010).

Model underestimation of precipitation extremes

Models have hitherto underestimated heavy rainfall events, particularly the top 10%, because climate models have been unable to replicate convective storms (Shiu et al., 2012). In their analysis the top 10% of precipitation totals will increase by 108% per °C, whilst lighter falls of rain, the lowest 30% to 60% of rainfall, would decrease by 20% per °C (ibid.). They find that globally, the top 10% of precipitation has increased by 80% already and will likely increase at a faster rate in the future. This has implications for increased downpours leading to erosion and landslips as well as an increasing occurrence of droughts (ibid.).

Projections of seasonal shifts in peak rainfall

In their analysis of the annual cycle of precipitation, Schindler et al. (2012) project that the pattern of peak rainfall in the year will change. The baseline period of 1961-2000 shows that eastern regions of England tend to have peak rainfall in August. Particularly for later in the century when indications are more consistent, it is projected that peak rainfalls will shift from summer to autumn in eastern England, whilst there will be little change in western England (Schindler et al., 2012).

Heat

Hansen et al. (2012) reviewed the temperature anomalies for the summer period (June, July August) over recent hot weather events. The anomalies were expressed in Standard Deviations from the mean from 1981 to 2010. They point out that 3x Standard Deviation anomalies were felt over 4-13% of the world in the years 2006-11, up from 0.1-0.2% during the period from 1951-80. Because the higher end of temperature distributions increased by more than 1 Standard Deviation with 0.5°C of warming over the past 30 years, a further +1°C of warming (expected within the next 50 years) would make 3x Standard Deviations the norm and 5x Standard Deviations anomalies more common (Hansen et al., 2012) .

3.2 Selection of Scenarios Table 6 details the main parameters for the 8 scenarios selected for study; narratives for these are provided in Appendix 3.

The workshop and review of the literature have identified single event scenarios which are not considered further in this study. However, their lack of selection does not necessarily indicate that they are less ‘plausible’ or unlikely to have a high impact, rather that in the

8 The change factors quantify the potential change (as either mm or percentage increase, depending on the variable) to the baseline (Environment Agency 2011).


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limited time this study has chosen to focus on the eight scenarios considered below and to prioritise multi-event scenarios.

Scenarios identified but not included are:

Winter flooding: Rainfall amounts exceed 200% above the average across England for the period from December – February. Temperatures remain average for the time of year. A 20% increase in rainfall intensity leads to widespread flooding, waterlogged soils and soil erosion. Winter crops either can’t be sown, or are slow to grow, weak, weedy and patchy.

Not considered separately; considered in S2 and combined with hot summer in S6

High summer temperatures: Summers that fall within the ‘extremely hot’ range of +5 standard deviations above the 2050 average become increasingly common (more than 1 in every 10 years). Although rainfall amounts remain average for the time of year the extreme heat increases rates of evaporation. Both crops and livestock are subject to significant heat stress.

Not considered separately; combined with drought in S7 and with winter flooding in S6

Severe winters: Severe winters may be less common but as such they constitute an event that will be considered more extreme in 2050 although their return period may extend. Mean maximum temperatures are 0⁰C or below for long periods of December through to February. Rainfall remains average for the time of year but falls predominantly as snow and hail. The whole of England is affected by the cold temperatures with significant snowfalls experienced in the west of England. The cold period is prolonged into March and fast thaws lead to localised flooding.

Not considered separately; forms part of the seasonal dislocation scenario S5

Summer storms: Conditions of rainfall and temperature remain average for the time of year but a large storm tracks across the country from west to east in the summer before harvest. Winds gusts range from 90 to 100 mph and cause widespread damage.

Not considered separately; combined with winter flooding and hot summer in S6


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Table 6: Summary of Extreme Weather Scenarios (Colours used for text reflect the type of event, and matching colours relate to the extreme event and the period used as an analogue)

Parameters Scenario

Precipitation Temperature Timing Area Other Analogue(s)

Localised summer flooding

200% above mean9. 20% increase in intensity. E & W Midlands & Eastern England: 354mm mean for summer. Over 150mm falls in one day in large parts of East.

16.8°C mean for Midlands (Central England10). 1.5°C cooler than mean for 2050.

June – August E & W Midlands, & Eastern England

1912

Two wet autumn/winters

NE: 462mm; NW: 550mm (200% of mean).

Average for 2050 or slightly warmer: 10°-13°C

September to November - two years

NE, NW & Yorkshire and the Humber. Impacts also countrywide

Autumn 2000

Mild winters South West: 200-240mm; North East: (includes Yorks & Humber) 320 – 380mm.11 Average for 2050 (0-20% increase on 1961-1990 baseline)12

Mean winter temp: 7.7⁰C South West;

6.3⁰C NE & Y&H

Two years SW, NE, Y&H 3σ of mean winter temperatures.

1989 and 1990 winters

9 Mean values based on baseline period 1981-2010, i.e. the most recent 30 year climate period. 10 Central England is based on the Central England Temperature dataset for the Hadley Centre. 11 Based on Mean values obtained from the Hadley Centre UK regional precipitation series (HadUKP). North East England includes Yorkshire & the Humber for HadUKP analysis. 12 Based on projections obtained from UKCP09. UKCP09 uses the 1961-1990 period as a baseline to account for climate change.


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Drought 70mm (England and Wales)*

East, E & W Midlands, SE, SW: 17.7⁰C mean summer temp.

June - August* SE and East of England, E & W Midlands

3σ of summer mean temperatures

1995 summer

Seasonal dislocation

(See narrative at Appendix 3) Early Winter: then mild; warm dry early spring; cold wet summer.

Country wide with summer flooding in E, SE, SW

Autumn/Winter 2010, January 1916, Spring Summer 1912 or 2012.

Wet Winter followed by hot summer plus Summer Atlantic storm**

Winter mean rainfall: 360mm England13.

17.7⁰C mean summer daily maxima

One Year Country wide but summer storms in SE

3σ of summer mean temperatures; 90-100 mph winds inland

1990 16 October 1987 type wind storm

Drought with extreme high summer temperatures

South East: 57.3mm Spring; 63.3mm Summer.

19.3⁰C mean summer temperatures (Jun to Aug) 32⁰ to 35⁰C for 7 days in southern England

One year South East and East of England

5σ of summer mean temperatures

1975-1976 drought + 2003 hot spell

Mild dry winter, severe late spring frosts

West Midlands: mean winter rainfall 78mm (34% of 2050 mean winter rainfall 14).

Mean winter temp 7.1⁰C. April: Rural temps at 0°C or below.

April frosts. West Midlands 3σ of winter mean temperatures. 6 days frost in April.

196415; 1990

13 http://www.metoffice.gov.uk/climate/uk/summaries/actualmonthly 14 2050 Rainfall assumed to be approximately 114% of current winter means, using UKCP09 mid range of medium emissions scenario data for the West Midlands area. 15 The rainfall in 1964 was 34% of the mean from 1961-1990.


4. Establishing a method for estimating economic costs 4.1 Key sectors and indicator enterprises Previous work on the impacts of climate and weather has focused on key indicator crops as a proxy for industry-level impacts, for example Defra projects CC036116 and AC030117. The latter reviewed a representative set of crops which are sensitive to environmental challenges, particularly temperature and water and their potential susceptibilities to pests and diseases. This included both established crops (cereals, oilseeds, peas, potatoes, vegetables, tomatoes, apples) and new crops which may be expected to become more important in the future (sunflower, maize and miscanthus). Similarly, the CCRA18 considered a range of reference enterprises based on their significance for land use and consumption and sensitivity to climate change and a range of ‘new crops’.

For this study, we have identified a number of specific crops and livestock enterprises as a focus for the estimation of impacts but in order to scope the aggregate impacts of extreme weather scenarios at a given spatial scale, it is necessary to consider all the main enterprises. Within this, the focus is on land use for food production rather than, for example, delivery of wider ecosystem services such as energy or water provision or climate regulation etc. This is a reflection of the scale of the project rather than the relevance of extreme weather to these services and in principle the methodology adopted for estimating impacts on food could be developed further to include these elements.

The proposed list for which an economic framework will be developed is shown in Table 7.

Table 7: Indicator enterprises

Crop enterprises Livestock enterprises Wheat Milk production Oilseed rape Cattle finishing (grass) Potatoes Upland cattle and sheep Carrots Indoor pig production Cauliflower Outdoor pig production Apples Indoor broiler meat production Strawberries Free range egg production

For some enterprises, notably indoor pigs and poultry, the direct impacts of weather may be limited e.g. additional energy costs for heating or cooling production units but there may be significant indirect impacts in terms of crop price change.

It is noted that by considering weather sequences over a period of 2-3 years, rotational issues will need to be accommodated.

16 ADAS (2008) Changes to Agricultural Management Under Extreme Events – Likelihood of Effects & Opportunities Nationally (CHAMELEON) 17 University of Warwick and Rothamsted Research (2007) Vulnerability of UK Agriculture to Extreme Events http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&ProjectID=14424&FromSearch=Y&Publisher=1&SearchText=ac03&SortString=ProjectCode&SortOrder=Asc&Paging=10#Description 18 Climate Change Risk Assessment: http://www.defra.gov.uk/environment/climate/government/risk-assessment/


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4.2 Impact metrics The CCRA considered a set of risk metrics to provide a measure of the impacts or consequences of climate change, related to specific climate variables or biophysical impacts. The work identified a wide range of impacts and potential consequences for agriculture (Figure 3) under four key themes:

1. Environment

2. Soil

3. Crops and livestock

4. Land

These risk metrics are equally applicable to extreme weather events and the typology will be used in this analysis as a framework for considering impacts, with a focus on production-related parameters.

Figure 3: Impact clusters for agriculture identified in the CCRA

A summary of climate change impacts based on the UKCP09 projections is shown in Table 8. While the scope and scale of impacts is not entirely appropriate for extreme weather events, again the analysis provides a useful reference point for isolating and quantifying impacts. This, together with the analysis of indicator enterprises from the literature, including


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Defra projects CC0361, AC0301 and the Economics of Climate Resilience report (CA040119) has been used as a more detailed prompt for our experts in considering the impacts of the eight extreme weather scenarios.

Table 8: Climate change impacts from CCRA

UKCP09 projected climate change Possible range of impacts on UK agriculture Carbon dioxide Concentration increases

Potential stimulated photosynthesis and yield (e.g. potatoes, wheat and forage) Changes in the quality and/or composition of land use (e.g. new crops, grassland)

Temperature Increase in winter and summer. Increases in number of ‘hot’ (20°C) and ‘very hot (27°C) days Marked decline in number of frosts

Heat benefits some crops (e.g. onions, legumes, carrots) Changes in crops grown (e.g. diversification into sunflowers, navy beans, soya, lupins, borage, grapevines etc, most notably in the SE) Less frost damage Lengthening of growing season leading to greater availability of UK grown produce throughout the year (e.g. soft fruit)

Precipitation Decrease in summer rainfall Increase in winter rainfall (regionally variable)

Drop in some crop yields Increased irrigation needs and changes in methods (e.g. potatoes) Decrease in summer soil moisture Changed poaching/water logging risk in some areas Late harvest problematic (e.g. increased drying costs and working on wet ground) Increased housing needed for livestock Increase in drainage systems Increase in wet weather related animal health problems/pest and disease problems

Weather extremes Increased frequency of extreme events, such as droughts and high temperatures, torrential rains and very strong winds

Crop damage/total crop loss (e.g. lodging of wheat, un-harvestable fields) Damage to agricultural buildings/change in building specifications Changing cropping practices Increased soil erosion Lack of grazing in drought events; Increased heat stress in livestock Increase in housing needed for livestock

Sea level rise Increase in sea level

Loss of coastal, estuary and floodplain agricultural land Erosion of land and salinisation of ground water

Other impacts Increase in cost and range of insurance; increasing diversification; New skills training/differing agricultural workload; changes in agricultural markets, demand and competition

4.3 Baseline economics of production The Defra Farm Business Survey (FBS) provides a baseline for farm-level returns and costs on the basis of robust farm type. These data are collected and published annually20 and can be updated readily.

A number of caveats apply to the use of the FBS datasets as follows:

19 Frontier Economics (2013) Economics of Climate Resilience Agriculture and Forestry Theme: Agriculture CA0401. A report prepared for Defra and the Devolved Administrations. February 2013. Available at: http://randd.defra.gov.uk/Default.aspx?Menu=Menu&Module=More&Location=None&Completed=0&ProjectID=18016 20 http://www.defra.gov.uk/statistics/foodfarm/farmmanage/fbs/publications/farmaccounts/


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- The data relates to a sample of full time commercial farms21. Within the population of farmers who are in scope, there is a high refusal rate (around 90%). Non-respondents may have significantly different characteristics from the potential population of respondents, leading to bias in the estimates of the full population. Calibration weighting is used to reduce this bias, but is unlikely to completely remove it;

- The data is year specific and will reflect seasonal changes in weather patterns, pest and disease burden etc as well as annual fluctuations in commodity and input prices. The element of price variability can be reduced by deflating outputs and costs by a price index and using a 3-year average price instead.

However, current statistics will not account for anticipated future changes in both the scale and structure of the sector, including the impact of technology and indeed climate change.

The wider economic context for farming in 2050 is also relevant, given the significance of non-farm income streams for some farm types, notably upland livestock farms (see Figure 4). Thus, while much of the analysis will be presented in terms of absolute and percentage change in economic returns from farming, impacts need to be set in the context of overall income.

Figure 4: Farm Business Income broken down by cost centre for livestock farms (2011/12)

For the purposes of developing the economic impact model for this study, no assumptions have been made about farming in 2050 and no account has been taken of policy change, notably subsidies and income from agri-environment schemes. Similarly, diversified income will not be considered as part of this analysis although it is quite possible that this would be impacted e.g. reduced income from tourism-related activities in wet years.

21 FBS was re-designed starting from the 2010/11 accounting year; coverage of the survey is now restricted to those farms which have at least 25,000 Euros of output.


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4.4 Approach for estimating economic impact Having defined the scenarios in meteorological and temporal terms it is possible to estimate how this might impact on a number of agricultural sectors and systems through expert judgement. By defining the scenarios in spatial terms, impacts per unit of crop area or head of livestock can be combined with available datasets on land use (from the Defra Agricultural Census) to identify the area across which extreme weather will be felt, in order to scale up impacts. Further, financial performance data for agricultural systems (from FBS) provides a basis for converting percentage changes in crop yields and inputs into economic terms. Finally, it is possible to allow for price impacts related to weather, either at country level or globally. These steps are set out sequentially below, detailing the datasets available and how they can be used.


Scenarios 1-8 are defined in Table 6 with an accompanying narrative for each set out in Appendix 3.


In this study, expert opinion was used to estimate volume and price impacts of weather on agricultural output and input parameters. Two key approaches are available for eliciting expert opinion. In the first, researchers use simulations or multiple model outputs based on probabilistic functions to deal with the uncertainty that might be attached to expert opinion; this overcomes the ‘one step’ limitations in using expert opinion. The other is to use an iterative process where the expert opinions are used to run models and the outputs are then discussed with the experts in order to ‘reality check’ and refine the original parameters. This study has used the second process. It uses some of the benefits of the iterative process that underpins the Delphi approach but does not attempt to build consensus between experts from different sectors but rather it reality checks within sector expertise.

Based on the scenario description, ADAS agricultural experts were asked to provide an assessment of the likely impacts of wet weather combined with flooding across the main sectors. The full analysis for each scenario is detailed at Appendix 4 and yield (volume) change impacts summarised in Table 10 (page 29). These estimates are necessarily broad-based due to the degree of heterogeneity in most farming systems due to variation in context (geography, topology, soil type), farm systems (species, genotypes, planting dates, harvest dates, indoor/outdoor etc) and farm practices (reliance on inputs, uptake of technology, degree of climate adaptation etc).

The volume change is stated as a percentage change to the norm and applied to the baseline value for each parameter (from the FBS dataset). There is no attempt to deflate values by a price series to estimate volumes as this is difficult to apply across all parameters. It is possible to apply a price index to deflate values but this is more complex to deal with and not considered necessary.

Estimates of price change at UK level are based on experience of the sensitivity of markets to supply change. The model also allows for global price change which might exacerbate national price change or work in the opposite direction. These elements are combined in a single price co-efficient which is applied to the residual (volume-adjusted parameter value).


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Step 3: Calculate the 3year ‘average’ economic performance for robust farm types (FBS data) at farm level.

FBS farm-level data is available for the 9 robust farm types for recent financial years from the website. This allows data to be downloaded as excel files for cropping and stocking and for detailed output and input costs.

To adjust for single year bias in the FBS data, caused by seasonal anomalies (including weather effects, pest and disease incidence etc) or between-year price fluctuation, a 3-year average is taken using the three most recent FBS datasets22. While there is ongoing change in the sample of farms which make up the dataset, these effects are modest. A simple average of the three years data is taken as the baseline for measuring extreme weather impacts.


The unit output for key crop and livestock enterprises (£ per hectare or per head) can be calculated by dividing the farm level economic output change, represented by Δ Farm output (enterprise1; Robust Farm Type a) by the enterprise size (hectares of crop or numbers of livestock) for each robust farm type. The unit output or input is then multiplied by the estimated percentage volume change, represented by %Δ volume output (enterprise1) (see Appendix 4), to give unit change for each scenario. Thus for output:

Δ Unit output (enterprise1; Robust Farm Type a) = %Δ volume output (enterprise1) x (Farm output (enterprise1; Robust Farm Type a) / Enterprise size (enterprise1; Robust Farm Type a))

The calculated changes in output across each robust farm type vary and need to be weighted to provide the best estimate of change in volume of enterprise output at country level. Thus:

Weighted Δ unit output (enterprise1) = Σi Δ Unit output (enterprise1; Robust Farm Type i) x Enterprise size (enterprise1; Robust Farm Type i) / (Σ Enterprise size (Robust Farm Type i)), i=1,...,n

While this can be applied to output data, which is detailed by enterprise, variable costs, which are enterprise specific such as seed, feed, fertiliser, sprays etc are aggregated at category level (there is a single cost figure per farm) in the FBS dataset. As there may be differential impacts on variable costs across enterprises – possibly in opposite directions – it is necessary to differentiate between farm types. An assumption is made that by applying changes in inputs (e.g. seeds for arable crops) at enterprise group level (arable crops; horticulture; dairying; cattle & sheep; pigs; poultry) to the relevant farm types only, an estimate of input change by robust farm type can be calculated.

It is then possible to apply differential changes to an input category by multiplying the estimated percentage change in input use (e.g. seed) for each enterprise group (e.g. horticulture), represented by %Δ volume input (input a; enterprise group 1) by the average seed cost per hectare for that category and farm type (expenditure on seed on Horticulture units). Thus:

22 http://www.farmbusinesssurvey.co.uk/regional/


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Δ Unit input (input a; Robust Farm Type a) = %Δ volume input (input a; enterprise group 1) x (Farm input (Robust Farm Type a) / Σ Enterprise size (Robust Farm Type a))


Using relevant spatial datasets, calculate the area impacted by severe weather and overlay with the 1km2 land use dataset to provide areas of crops and numbers of livestock which will be impacted (see method and example at Appendix 6). This can be expressed at country level or at another spatial scale e.g. region, county or defined landscape area.


Scale up the enterprise impacts estimated at Steps 2 and 4 using the crop area or livestock number data from Step 5. Thus for outputs:

Δ Output (enterprise1) = Δ Unit value output (enterprise1) x Area (enterprise1)

For inputs there is an additional step to aggregate crop areas or livestock numbers into an enterprise group e.g. arable crops or horticultural crops as this is the basis on which unit changes have been calculated (rather than at single enterprise level). This is aggregated across the enterprise groups using the areas from Step 6. Thus:

Δ Input (input a) = Σi Δ Unit input (input a; enterprise group i) x Area (enterprise group i) , i=1,...,n


The previous steps allow for volume changes in outputs and inputs due to extreme weather but it is often the case that such changes in turn affect overall supply of a commodity and impact on market price. In this way, the economic impact of reduced yield can be (in part) offset by higher prices on the residual sales. There will also be cross-sector impacts. For livestock producers, for example, higher to prices for crops will be reflected in higher input costs (as feed) and will exacerbate the economic impact of any loss in livestock output. This step applies a percentage price adjustment, for example to enterprise 1, represented by (%Δ Price Output (enterprise1)) to the volume-adjusted, scaled up, economic value of outputs and inputs, represented by (Baseline Output (enterprise1)) (Δ Output(enterprise1)) for and similar for inputs. Thus the change in the value of output for enterprise 1 is represented by:

Δ Value Output (enterprise1) = {(1+%Δ Price Output (enterprise1)) x [(Baseline Output (enterprise1)) + (Δ Output(enterprise1))]} Baseline Output (enterprise1)

The baseline output and input data can be estimated by using the 3-year average FBS unit value data (farm-level data divided by area data), scaled up to the relevant spatial area for the scenario or taken from published Defra aggregate output and input data at http://www.defra.gov.uk/statistics/files/defra-stats-foodfarm-farmmanage-agriaccount-england-dataset-130117.xls The two approaches generate slightly different numbers but each is incomplete (published England-level FBS data is not available for all input categories while the aggregate input tables are not detailed for all FBS categories).

.


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The net economic impact of a scenario, represented by Net Economic Impact (Scenario n) is the simple sum of changes in outputs and inputs across all the categories, taking care to ensure that the fall in outputs are added to increases in input, allowing for some categories which may have changed in the opposite direction. Thus:

Net Economic Impact (Scenario n) = Σ i Δ Output (enterprise i) Σ i Δ Input (category i) , i=1,...,n

Step 9: Aggregate multiple year impacts

To allow for first and subsequent year effects of extreme weather, estimates of impacts for year 1 and subsequent years are added together by category e.g. wheat, seed etc. Thus:

Net Economic Impact (Scenario n (all years)) = Σ i Δ Net Economic Impact (Scenario 1(year i)), i=1,...,3

4.5 Validation of economic impact method The methodology has been informed by the scenarios and the associated estimated changes in output and input volume and value in addition to the availability and format of economic and spatial datasets. An example of the application of the method to Scenario 1 is shown at Appendix 6. The area affected is based on the Environment Agency Flood Zone 3, a total of 979,526 hectares, including 314,953 ha of rough grazing and permanent pasture. The localised summer flooding is restricted to the East of England, East Midlands and West Midlands, limiting the affected area to 442,961 or 5.6% of England agricultural area.

The areas affected are summarised in Table 9 below and highlights the disproportionate impact on higher value crops will lesser areas of grass and forage crops.

Table 9: Area of cropping affected by flooding of EA Flood Zone 3

Crop category % of England area

Cereals 323,503 13%

Oilseed rape 77,786 13%

Peas and beans 26,971 13%

Potatoes 21,894 22%

Sugar beet 25,691 22%

Horticultural crops 40,743 28%

Grass and forage crops 337,822 8%

The estimated net economic impact of this area being affected by summer flooding in Year 1 was estimated at £363 million before allowing for price effects; this reduced to £229 million (£516/ha) after allowing for a 10% price increase in the price of potatoes and 5% for horticultural produce due to the extent of area affected and subsequent impact on supply. If


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the price effect was as high as 20%, there would be a net economic gain across the sector, although this would obviously only benefit growers of these crops.

The ADAS analysis of the impacts of the 2007 summer floods on agriculture (Defra 2007) reported an estimated impact of between £8 million and £19 million on an affected area of 42,000 ha; this represents between £183 and £461 per hectare. A further analysis, based on interviews with affected farmers (Posthumus 2009) suggested a higher figure of £1207 per hectare, skewed by large impacts on a few farms incurring very high losses. The modelled scenario for this work estimates impacts over multiple years of £776 per hectare, but is extremely sensitive to supply-led price impacts.


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5. Economic analysis of scenarios This section presents a short overview of the outputs of the economic method for the eight extreme weather scenarios described in chapter 3 using the excel model developed by ADAS as part of this project. The analysis uses the following datasets:

- FBS data - Detailed Output and Input Costs England for 2011/12, 2010/11 and 2009/10

- Production, cost and price impacts - estimates of % change in output from baseline for each scenario from experts analysis and estimates of price impact on outputs and inputs of supply changes (over 3 years)

- Spatial datasets as follows: o Census data - full dataset used for scaling scenario impacts o EA Flood Zones - spatially mapped census data used for scaling scenario

impacts o HOST drought prone soils - spatially mapped census data used for scaling

scenario impacts o HOST wet soils - spatially mapped census data used for scaling scenario

impacts

- Livestock Unit calculations (used to apportion changes for cattle and sheep elements for variable and fixed cost changes)

- TIFF data to provide % change and FBS input weights - to supplement TIFF data

- API price data for reference

- FBS Gross Margins (key crop and livestock enterprises) for reference

5.1 Yield and price assumptions The impacts of extreme weather scenarios on crop and animal yields are summarised in Table 10 below. These are presented in terms of ranges in the table below but a single point value is used in the model. These estimates of yield change are subjective and can be adjusted in the model where further evidence is available.

Table 10: Summary yield impacts of extreme weather across agricultural sectors

Arable Horticulture (vegetables)α Dairying

Sheep & Cattle Pigs Poultry

Localised summer flooding -40 to 50% -50% -5 % -10 to 15% -4% 0 to -15%+++

Two consecutive wet autumn / winters -5 to -20% -5% No change No change -5% No change Mild winters

No change +5% +ve +ve +5% -5% Drought

-15 to 30% -10%* -10 to 25% +10% -5% -5% Seasonal dislocation

-10 to -20% -15% -5% -5 to -15% -10% -5% Wet Winter, then hot summer plus summer Atlantic storm** 0 to -7%*** 0 to -5% No change No change -10% No change Drought with extreme high summer temperatures -25 to -50% -10%* -10%++ -5% -5% -5% Mild dry winter, severe late spring frosts 0 to -10% -10% No change No change

No change -5%

α Estimates of yield impact for fruit was difficult to quantify, given the extensive range of crops * Providing water for irrigation is not restricted sunlight is beneficial, if water for irrigation is restricted negative impacts

dominate ** Disease impacts, hard to quantify *** Depends on if significant disease impacts occur + Depends on % early planting ++ If not housed +++ 30% figure if indoor birds housed for a long period and can’t sell eggs as free-range


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Price effects due to the supply changes highlighted above relate to both outputs and inputs (cereals are an output for arable farms but also an input for livestock farms). The expert estimates of scenario induced price changes are shown in Table 11. Again these are indicative and can be changed in the model and can be adjusted on the basis of wider global supply changes.

Table 11: Estimated impacts of the eight extreme weather scenarios on output and input price

Scenario 1 2 3 4 5 6 7 8 YEAR 1 Output Cereals 5% 10% 5% Oilseed rape 5% 10% 5% Peas and beans 5% 10% 5% Potatoes 10% 5% 25% 50% Sugar beet Other Crops (incl. hort.) 5% 5% 5% Milk and milk products Cattle Sheep and wool Pigs Eggs Broilers and other poultry Inputs Purchased feed & fodder 5% 10% 5%

5.2 Economic impacts By applying the yield and price data above to the farm performance data (FBS), estimates of economic impact have been calculated on a ‘per hectare’ basis (Figure 5).

-900

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-100

0

100

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SCENARIO 1 SCENARIO 2 SCENARIO 3 SCENARIO 4 SCENARIO 5 SCENARIO 6 SCENARIO 7 SCENARIO 8

£/ha

Figure 5: Estimated economic impact of extreme weather scenarios 1-8 per hectare of land affected


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The analysis indicates that summer flooding and consecutive wet autumn/winters (scenarios 1 and 2) are especially costly in terms of economic performance per unit of agricultural land area. Scenario 5 (seasonal dislocation) is the next most costly weather event. Scenario 3 (mild winters) and scenario 7 (drought with extreme high temperatures) result in positive impacts when both yield and price effects are considered.

These per hectare data have then been scaled up using the census data and spatial datasets for flooding and soils to provide estimates of total economic impact (Figure 6). The main difference from the ‘per hectare’ analysis is that scenario 5 (seasonal dislocation) is the most significant weather event; this reflects the fact that it is applied country-wide while the other scenarios are confined to 3-4 regions.

-1,600

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SCENARIO 1 SCENARIO 2 SCENARIO 3 SCENARIO 4 SCENARIO 5 SCENARIO 6 SCENARIO 7 SCENARIO 8

£m

Figure 6: Estimated total economic impact of extreme weather scenarios 1-8

Scenario 1 (localised summer flooding) is limited to three English regions, an area of 442,961 ha or 6% of the total agricultural area. While the extent of yield impact per hectare is high, especially on arable and horticultural crops (see Table 10, page 29), the overall supply impact would not be expected to have an effect on prices, other than for potatoes and some seasonal vegetable crops. However, the yield impacts are exacerbated by increased costs (in affected areas) for feedstuffs, fieldwork and clear up, taking the total to £776/ha and an economic loss of £344 million in total.

Scenario 2 (two wet autumn/winters) is again limited to three English regions (6% of total land area) and while the yield impacts are much less than for scenario 1, the weather event extends over a two year period. In this case, the impacts play out over three years and are dominated by increased costs rather than lost output. Price impacts are confined to the potato crop where yields impacts are more significant (estimated at up to 20%) and free range egg production where layers need to be housed, with prices discounted. The aggregate economic loss over the three year period is £537/ha and £272 million in total.

Scenario 3 (two mild winters) has an overall positive effect on output, mainly as increased vegetable yields, improved lambing and reduced losses from the outdoor pig sector, although a reduction in outdoor egg production is anticipated due to increased pests. No impact on


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prices is assumed. Savings in feed costs in the livestock and in energy costs are partially offset by increased crop protection costs in the arable and horticultural sectors. The net effect is a £62/ha economic gain but this applies across a large area (2.95 million hectares or 37% of the total agricultural area), an economic gain of £184 million in total.

Scenario 4 entails drought (June-August) across four English counties in the East and Midlands; using the HOST drought prone soils dataset the affected area is 1.26 million hectares or 16% of the total agricultural area. The drought would have a major impact on crop yields (down 15-30%) with a reduction in output of milk, pigs and eggs (and sheep in year 2). However, given the scale of the drought and its impacts, there would be some price response and this would more than offset the reduction in supply with a net increase in economic output over the three years. In terms of costs, the main effect is on feed costs for the livestock sector which lead to an overall economic loss of £47/ha or £59 million in total.

Scenario 5 represents seasonal dislocation, a series of ‘unseasonal’ weather events in a single year across all of England, an area of 7.9 million hectares. The weather is expected to have a significant impact on crop yields in year 2 (down 15-20%) with a reduction in livestock output, notably milk, cattle, sheep and pigs (also in year 2). In terms of cost, the main impact is the supply led increase in cereal prices feeding into livestock feed costs; there are also additional effects on infrastructure and other input costs. Given the widespread scale of the impacts, there would be some price response, notably for arable and horticultural crops and for lamb. This would offset some of the reduction in supply but overall the economic analysis is for an economic loss of £171/ha or £1,361 million in total.

Scenario 6 (wet winter, hot summer and summer storms) is also countrywide but most of the yield impacts are limited to storm damage. The model estimate of economic impact therefore uses yield impacts for the South East, apart from pigs where the losses are countrywide. Cost impacts are countrywide and largely related to additional inputs (crop protection, vet and contract costs) with additional infrastructure costs from storm damage limited to the South East (mainly horticulture and pigs). The net effect is an average £35/ha economic loss across all of England and represents an economic loss of £276 million in total.

Scenario 7 (drought with extreme temperatures) is similar in effect to scenario 4 but more severe but is limited to the South East and East of England (760,924 ha or 9.6% of the total agricultural area). Yields are impacted across all sectors except poultry with some savings in input costs (fertilisers, sprays and vet costs) but increased costs for purchased livestock feed and energy/water costs. The main factor with the modelling of this scenario is the extent to which large yield impacts feed into modest increases in prices – both for crops and animal feed – and offset the economic losses. Thus yield based effects and input effects in Year 1 lead to a reduction in economic return of £280/ha but after allowing for price increases for the residual output, this changes to an economic gain of £211/ha. Overall, the estimated economic gain is £31/ha or £23 million.

Scenario 8 (mild dry winter and severe late frosts) applies only to the West Midlands (863,870 hectares or 10.9% of the agricultural area). The main yield effects are on spring sown vegetable crops and potatoes and on oilseed rape which would be at pod set stage; additionally there would be increased losses in outdoor poultry systems due to the mild winter. There would be higher costs for crop protection on arable crops, labour costs in horticulture and bedding for outdoor pigs. No price impacts are anticipated due to the localised scale and moderate yield effects. Overall, the estimated economic loss is £57/ha or £49 million.

The scenarios have presented a wide range of effects both in terms of yield / input impacts but also in terms of prices. Further, the significance of the scale of the weather event is substantial. In particular, scenario 5 suggests that unseasonal weather across the country could cause the greatest economic losses.


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6. The role of climate change adaptation 6.1 Adaptation measures for climate change Defra has undertaken a recent economic analysis of climate change adaptation measures. This considered a broad range of measures (see Appendix 7), mapping them against climate change risks and scoring them in terms of economic and environmental effectiveness. Not all are relevant to this work – some are targeted at wider ecosystem services such as wildlife habitats and species or the welfare of farm workers – most are focused on mitigating impacts on food production and related impacts such as animal welfare, avoiding soil erosion etc. As such this analysis forms a useful basis for the assessment of extreme weather risks and we have mapped the adaptations cited by our experts (in their assessment of impacts) against this list.

6.2 Possible adaptation in response to extreme weather scenarios In the context of a future with increasing extreme weather events, climate change adaptation measures offer an opportunity to minimise potential losses, protect investments already made to on-site infrastructure and keep operations financially viable. There are a wide range of adaptation options available to farmers and different levels of adaptation depending on the severity of the extreme weather event. There is also demand for no-regret or low regret adaptation options23; measures which would be justified under all /almost all future climate scenarios and would not or be unlikely to lead to maladaptation. Which options are likely to be no or low regret is not easy to identify when so little is known about what future extreme weather events might look like.

A large range of possible adaption options has been reported by the agricultural experts. These are mapped against the Defra list of adaptations, highlighting coincidence and coherence (Table 12).

Some of the most common adaptations suggested related to simply shifting the timing of certain practices, for example, housing date, planting date or lambing date. These kinds of adaptations provide a usually relatively simple way to adapt to extreme weather. The main barriers to these adaptations are likely to be linked to farms accepting such a change in behaviour will be necessary and will not lead to unnecessary losses by moving from usual routine which in a typical year of weather will optimise yields. For example, shifting arable cropping from winter to spring, leads to a consequent decrease in yields. Another example is reducing the stocking density of poultry which reduces potential output but can prevent losses from heat stress. Changes in behaviour which lead to some loss of yield (but may prevent a more catastrophic loss of yield) could be encouraged by education, as analysing the trade offs can be complex given the current unpredictability of extreme weather events.

Convincing of a need to change remains a barrier for many adaptations; for some sectors after two or three years of extreme weather farmers would look to adapt whereas others may continue to be unconvinced. The level of adaptation which is feasible for each sector is largely determined by the market value of the agricultural produce, the cost of the adaptation and an analysis of whether there will be a return on investment. For example, irrigating during droughts and tunnelling to protect against cold temperatures are both adaptation practices which are feasible and already taking place in the horticulture sector (particularly for the higher value fruit crops) whereas this level of adaptation would not be financially logical for cereal crops. Any changes in the cost of adaptation measures and the achievable price for products by 2050 could influence the uptake of adaptations.

23 http://climatechange.worldbank.org/content/adaptation-guidance-notes-key-words-and-definitions


Table 12: Key climate change adaptation measures identified for extreme weather scenarios

Localised summer Flooding

Two wet autumn/winters Mild winters Drought

Seasonal dislocation

Wet Winter followed by hot summer plus summer Atlantic storm

Drought with extreme high summer temperatures

Mild dry winter, severe late spring frosts

Arable

Change timing of spring and autumn cultivation and harvest (38)

Increase water storage (17b)

Alter timing of granular fertiliser, manure and slurry application (42) Encouraging rooting by applying a liquid phosphate

Increase water storage (17b)

Change timing of spring and autumn cultivation and harvest (38) More fungicide applications Fleecing

Horticulture Build additional

drainage capacity (10) Use crop covers

Build additional drainage capacity (10) -Raised beds

Increase water storage (17b) Use irrigation Use crop covers Planting fruit crops later Spread suppliers

Build additional drainage capacity (10) Spring replanting Raised beds Strengthened tunnels Frost tolerant varieties (5)

Plant trees (15) Remove polythene from tunnels Spreading producer suppliers across country Wind breaks Strengthening posts

Use irrigation Novel irrigation methods Increase efficiency of irrigation (26) Crop hygiene to control pests Bio-control Venting tunnels

Fleecing Frost blasters Spreading producers groups Better weather forecasting

Dairy

Maintain tracks House animals (30)

House animals (30) Use temporary fencing (29)

Grow new or a greater variety of food crops (8) Housing design

House animals (30)

Move to autumn calving system, Keeping cool with sprinklers

Provide more shade for livestock (32) Plant trees (15) Improve water accessibility for livestock

Shifting housing turnout date

Cattle & Sheep

Buffer feed over the summer (34) Moving grazing area

House animals (30)

Buffer feed over the summer (34)

Changing lambing schedule

Buffer feed over the summer (34) House animals (30)

Increase water storage (17) In-field shelters Alter lambing date

Pigs

Build additional manure storage (18) House animals (30) Straw in reserve Sows kept on high ground Separating slurry and water Moving huts

Separating slurry and water storage House animals Increasing indoor area (new huts)

Improve insulation and ventilation Environmental enrichment

Inspect and mend leaks in pipes and tanks Plastic pipes (upgrading units)

Inspect and mend leaks in pipes and tanks More indoor area

Improve insulation and ventilation House animals (30)

Improve insulation and ventilation Provide mud wallows Upgrade water delivery system Shift area from bales

Improve insulation and ventilation Stockpile straw

Poultry House animals (30) Upgrading access Abandoning operations

Improve insulation and ventilation Wooden slatted area outside to help clean feet

Improve insulation and ventilation



Improve insulation and ventilation Reduce stocking numbers



More progressive and commercially focused farmers are likely to be the first to adapt when there is a clear return on investment; conversely those businesses that delay are likely to incur losses and may be less viable in the medium to long term. Some adaptations discussed included those that which aren’t currently viable in the UK but may be in the future as are used in countries with a more extreme climate.

The increasing incidence of several extreme weather events can also create issues in identifying the most effective adaptations when these extremes may be opposite in impact, for example when there is an increase in heat waves but also an increase in cold winters.

Some barriers to adaptations reflect local physical constraints such as the farm location and characteristics (soil type, topography, size, proximity to rivers etc.). Where farm characteristics and location prevent adapting we may see a shift in the distribution of farms as an adaptation. It is also suggested that perhaps on a larger scale an adaptation could be to have operations for a sector spread more uniformly across the country so if one area was hit by extreme weather yields could be compensated in areas not hit (this relies on extreme weather being localised).

For some adaptations it is an issue of timing, for example in housing design, farmers will be reluctant to upgrade current housing when it hasn’t reached the end of it’s useful life, but if new housing is being built they may be more willing to upgrade to minimise the impacts of extreme weather (e.g. improved ventilation) so adaptations could be built in over time. For the more costly adaptations where barriers are greater it was suggested that the use of legislation or grants would help uptake and that for some it was highly unlikely they would be implemented without such measures.

The adaptations identified by ADAS experts focus mainly on local farm adaptations but there is the opportunity for more strategic and overarching adaptations for UK agriculture to adapt to extreme weather events such as industry wide planning and these may offer greater opportunities to farmers unsure of how to best adapt in the face of increasing extreme events.

6.3 Farmer uptake of climate change adaptation measures A key issue with adaptation measures is uptake, as most measures incur a cost, while the benefits rely on the certainty and incidence of relevant extreme weather events. A key limitation is that the current generation of climate models and projections are more suited to simulation of long term climatology than extreme events. Extreme events are challenging for climate models to predict but improvements in extreme event prediction are underway (Walker Institute, 2009). Nevertheless, it is difficult for farmers and growers to quantify the benefits of adaptation in this context.

Changing farming practice is not straightforward. The beliefs and values of farmers impact on their behaviour and will facilitate or act as a barrier to changing practice (Holloway 1999). Farmers continuously adjust their practice in response to external stimuli (economic, social and political pressures); however, there is also risk aversion and inertia in the system (Holloway & Ilbery 1997). In the past, the emphasis has been on science and how to translate knowledge to farmers rather than really engaging with and understanding their belief systems. So unless farmers have positive feelings about scientific information they are less likely to use it in their decision making (Sharifzadeh et al 2012). The work of Fleming & Vanclay (2010) in Australia has demonstrated that farmers there hold at least four different views of the world (discourses) which determine their beliefs, values and behaviour in response to climate change. As such, each requires a different approach to ‘encourage’ changes in farming practice in response to climate change and extreme weather events. Similarly, in the UK four discourses (called attitudinal groups) were found in the livestock sector, which varied from believing that they were adaptation ready to needing support to implement adaptation (Hall & Wreford 2012).


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While work has been carried out to model farmer behaviour in the face of climate change (Gibbons & Ramsden 2008) this has been based on economic assumptions of rational behaviour, and does not consider extreme events. Work in the US suggests that experience is more likely to drive adaptation responses (Haden et al 2012), which means that the irregularity of extreme events may work against adaptation. Context is really important, farmers affected by climate effects like drought, but who have good health are more likely to instigate adaptation strategies, regardless of low incomes (Hogan et al 2011). But it is important to also consider the other risks that farmers have to make decisions under which might be more pressing than climate change (Knox et al 2010).

Research looking at long term averages/trends in climate has identified expansion of agricultural areas and increases in yield as adaptation responses (Bindi & Oleson, 2011). However, these changes in agricultural practice may not be conducive to adaptation for extreme weather events, particularly when water availability is severely restricted. Research into the impact of extreme weather under future climate change in the Mediterranean might provide some guidance for possible adaptation options beyond land abandonment such as changes in crop species, cultivar choices, sowing dates, fertilisation, irrigation, drainage, land allocation and changes to farming systems (Bindi &Oleson, 2011). However, this work does not examine the likelihood that farmers will change their behaviour and take up these adaptation options. Evidence from Germany suggests that farming practice is lagging behind the changes in trends such as earlier onset of spring (Menzel et al, 2006) but work in the Netherlands suggests that this is may be due to unforeseen factors such as fewer frost days (need frost days to create a crumbly soil good for sowing) (van Oort 2012), highlighting how extreme events need to be considered in the context of general trends in climate change. The ADAM project has looked at past adaptation responses in agriculture to extreme events in the UK. The researchers argue that while adaptation has happened in the past, the changes have been relatively easy and so future adaptation might not work quite so well (Merchler et al 2010; McEvoy 2010: special issue). Wreford & Adger 2010 have also reviewed adaptation responses to extreme weather events (heatwaves and droughts) on UK agriculture over the last 40 years where they found that the cost of damages has been reducing, suggesting that farmers have been adapting, but they also raise the question as to whether future adaptation will be so easy. Modelling work indicates that diversified farms will be the most robust by 2050 while marginal farms will need the most resource investment (e.g. irrigation) (Gibbons & Ramsden 2005).


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7. Discussion This study has concentrated on five sequential but discrete tasks; (i) the REA of past events, (ii) developing extreme weather scenarios for 2050, (iii) estimating the impact of these scenarios on key agricultural sectors, (iv) describing a method for using these impacts alongside economic datasets and spatial mapping to estimate economic impacts, and finally (iv) consideration of adaptations. The timescale and resources available for the work have necessarily confined the extent to which these tasks have been developed. As such, this work represents a ‘proof of concept’ for policymakers to consider the potential impacts of extreme weather events and adaptations, rather than a comprehensive analysis.

In the course of delivering the work a number of key issues have become evident. These are listed below along with some thoughts on how they might be resolved or researched further:

(i) The single most challenging aspect has been to secure reliable quantitative estimates of impact by sector for the eight scenarios described. While every attempt was made to be specific about the weather event, including the timing, extent and severity, much finer granularity is necessary to qualify how the impacts might play out at a local level as there is such heterogeneity between areas, farming systems and management. The same issue applies to making estimates of supply-induced price impact, notably for fresh produce. More work is necessary in this area and it is suggested that localised case studies would provide a suitable approach.

(ii) A number of highly relevant datasets exist which can provide the necessary economic data for this analysis e.g. census data, FBS data. These are not always detailed to the extent that very specific sector questions can be pursued but the data is generally sufficient given the wider assumptions required, notably on impact.

(iii) The spatially mapped Defra census data is critical to the approach proposed, as is the availability of other datasets to refine the spatial extent of a scenario impact. The latter has been limited in this study, notably flood zones and soil type data but this area should be researched further as spatial mapping is critical to the reliable scaling of estimated impacts.

(iv) It would have been helpful to have had access to more meteorological data, for example on the spatial distribution of peak temperatures or air frost days within the broad extreme weather events described. It is understood that much of this data is available but that resource is needed to access and analyse it to this degree of detail. Further work should explicitly involve the Met Office.

(v) In terms of adaptations, there was a reasonable degree of overlap between the suggestions made by the experts and those on the Defra list. However, there were some additional ideas which merit consideration. Many of the barriers to adaptation are cost-based and it is suggested that the cost-effectiveness analysis is revisited and extended as necessary within the context of this work to highlight areas where intervention is appropriate. Any analysis should account for wider ecosystem (dis)benefits of adaptation or its absence, notably for non-market goods.

(vi) Limited commentary has been made on the medium-term impact on key sectors of the various scenarios. This is in part because the broad analysis suggests that impacts, while severe locally, are temporally and spatially limited or that adaptations are available for many of the most significant impacts. However, this issue merits further discussion in a stakeholder forum.


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Appendix 1: Bibliography Asher, M. J. C. & Williams, G. E. 1991. Forecasting the national incidence of sugar-beet

powdery mildew from weather data in Britain. Plant Pathology, 40, 100-107.

Atyeo, J. and Walshaw, D. (2012), A region-based hierarchical model for extreme rainfall over the UK, incorporating spatial dependence and temporal trend. Environmetrics, 23: 509–521. doi: 10.1002/env.2155

Benestad, R.E., 2005, Can We Expect More Extreme Precipitation on the Monthly Time Scale? Journal of Climate 19: 630-637Bindi, M & Olesen, JE (2011) The responses of agriculture in Europe to climate change. Regional Environmental Change 11: S151-S158.

Boardman, J. 2010. A short history of muddy floods. Land Degradation & Development, 21, 303-309.

Bowden, J., Cochrane, J., Emmett, B. J., Minall, T. E. & Sherlock, P. L. 1983. A survey of cutworm attacks in England and Wales, and a descriptive population model for Agrotis segetum (Lepidoptera: Noctuidae). Annals of Applied Biology, 102, 29-47.

Brown, J. 1987. Agriculture in England. A survey of farming, 1870-1947, Manchester, Manchester University Press.

Collier, R., Fellows, J. R., Adams, S. R., Semenov, M. & Thomas, B. 2008. Vulnerability of horticultural crop production to extreme weather events. Aspects of Applied Biology, Vol.88, 3-14.

Dennis, I. A., Macklin, M. G., Coulthard, T. J. & Brewer, P. A. 2003. The impact of the October-November 2000 floods on contaminant metal dispersal in the River Swale catchment, North Yorkshire, UK. Hydrological Processes, 17, 1641-1657.

Evans, R. 2004. Outdoor pigs and flooding: An English case study. Soil Use and Management, 20, 178-181.

Evans, R. & Boardman, J. 2003. Curtailment of muddy floods in the Sompting catchment, South Downs, West Sussex, southern England. Soil Use and Management, 19, 223-231.

Fleming A & Vanclay F (2010) Farmer responses to climate change and sustainable agriculture. A review. Agronomy for Sustainable Development 30(1):11-19.

Frich, A.; L.V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A.M.G. Klein Tank, and T. Peterson (January 2002). "Observed coherent changes in climatic extremes during the second half of the twentieth century" (PDF). Climate Research 19: 193–212. Haden Van R, Niles MT, Lubell M, Perlman J & Jackson LE (2012) Global and Local Concerns: What Attitudes and Beliefs Motivate Farmers to Mitigate and Adapt to Climate Change? PLOS One 7(12).

Gibbons JM & Ramsden SJ (2008) Integrated modelling of farm adaptation to climate change in East Anglia, UK: Scaling and farmer decision making. Agriculture Ecosystems & Environment 127(1-2):126-134.

Harker, P. V. 1990. The weather in England and Wales - August 1989 to July 1990. Journal - Royal Agricultural Society of England, 151, 216-220.


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Hall C & Wreford A (2012) Adaptation to climate change: the attitudes of stakeholders in the livestock industry. Mitigation and Adaptation Strategies for Global Change 17(2):207-222.

Hogan A, Bode A & Berry H (2011) Farmer Health and Adaptive Capacity in the Face of Climate Change and Variability. Part 2: Contexts, Personal Attributes and Behaviors. International Journal of Environmental Research and Public Health 8(10):4055-4068.

Hollis, J. M. (1989) A methodology for predicting soil wetness class from soil and site properties. Soil Survey and Land Research Centre for MAFF.

Holloway L (1999) Understanding climate change and farming: scientific and farmers' constructions of 'global warming' in relation to agriculture. Environment and Planning A 31(11):2017-2032.

Holloway LE & Ilbery BW (1997) Global warming and navy beans: Decision making by farmers and food companies in the UK. Journal of Rural Studies 13(3):343-355.

IPCC, 2012: Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 1-19.Gibbons JM & Ramsden SJ (2005) Robustness of recommended farm plans in England under climate change: A Monte Carlo simulation. Climatic Change 68(1-2):113-133.

Jaggard, K. W., Dewar, A. M. & Pidgeon, J. D. 1998. The relative effects of drought stress and virus yellows on the yield of sugarbeet in the UK, 1980-95. Journal of Agricultural Science, 130, 337-343.

Jobson, J. D. and Thomasson, A. J. (1977) Soil water regimes. Soil Survey Technical Monograph, No. 11, Harpenden, 57 pp.

Jones, C. A., Davies, S. J. & MacDonald, N. 2012. Examining the social consequences of extreme weather: the outcomes of the 1946/1947 winter in upland Wales, UK. Climatic Change, 113, 35-53.

Jones, M. R., Fowler, H. J., Kilsby, C. G. and Blenkinsop, S. (2012), An assessment of changes in seasonal and annual extreme rainfall in the UK between 1961 and 2009. Int. J. Climatol.. doi: 10.1002/joc.3503

Knox J, Morris J & Hess T (2010) Identifying future risks to UK agricultural crop production: Putting climate change in context. Outlook on Agriculture 39(4):249-256.

Marsh, T. 2008. A hydrological overview of the summer 2007 floods in England and Wales. Weather, 63, 274-279.

Mechler R, Hochrainer S, Aaheim A, Salen H & Wreford A (2010) Modelling economic impacts and adaptation to extreme events: Insights from European case studies. Mitigation and Adaptation Strategies for Global Change 15(7):737-762.

Menzel A, von Vopelius J, Estrella N, Schleip C & Dose V (2006) Farmers' annual activities are not tracking the speed of climate change. Climate Research 32(3):201-207.

Mill, H. R. & Salter, C. 1912. British Rainfall 1912, London, Meteorological Office.


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Posthumus, H., Hewett, C. J. M., Morris, J. & Quinn, P. F. 2008. Agricultural land use and flood risk management: Engaging with stakeholders in North Yorkshire. Agricultural Water Management, 95, 787-798.

Posthumus, H., Morris, J., Hess, T. M., Neville, D., Phillips, E. & Baylis, A. 2009. Impacts of the summer 2007 floods on agriculture in England. Journal of Flood Risk Management, 2, 182-189.

Sharifzadeh M, Zamani Gh H, Khalili D & Karami E (2012) Agricultural Climate Information Use: An Application of the Planned Behaviour Theory. Journal of Agricultural Science and Technology 14(3):479-492.

Stammers, R. & Boardman, J. 1984. Soil Erosion and Flooding on Downland Areas. Surveyor, 164, 8-11.

Subak, S. 1997. Agriculture. In: Palutikof, J. P., Subak S, Agnew M D (ed.) Economic Impacts of the Hot Summer and Unusually Warm Year of 1995. Norwich: University of East Anglia.

Unsworth, M. H., Scott, R. K., Cox, J. S. & Bardell, K. 1993a. Impact on Agriculture and Horticulture. In: R, C. M. G. R. A. P. C. E. (ed.) Impacts of Mild Winters and Hot Summers in the United Kingdom 1988-90. London: HMSO.

Unsworth, M. H., Scott, R. K., Cox, J. S. & Bardell, K. 1993b. Impact on Agriculture and Horticulture. In: R, C. M. G. R. A. P. C. E. (ed.) Impacts of Mild Winters and Hot Summers in the United Kingdom 1988-90. London: HMSO.

Van Oort PAJ, Timmermans BGH & van Swaaij ACPM (2012) Why farmers' sowing dates hardly change when temperature rises. European Journal of Agronomy 40:102-111.

Walker Institute (2009) Improving predictions of extreme events available at: http://www.walker-institute.ac.uk/publications/factsheets/walker_factsheet_extremes.pdfWreford, A. & Adger, W. N. 2010. Adaptation in agriculture: historic effects of heat waves and droughts on UK agriculture. International Journal of Agricultural Sustainability, 8, 278-289.


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Appendix 2: REA Evidence on past extreme weather events The REA is based on a systematic literature review, which was focused initially on peer reviewed literature before looking at secondary sources as well as grey literature. Searches were based on the ISI Web of Science and Scopus databases using target key word searches. Certain first order terms were used as constants and in combination with second, third and fourth order terms. The keywords are listed in Table 12 below. These words have been selected based on key text quoted in the Defra documentation Vulnerability of UK Agriculture to Extreme Events by Warwick HRI (2008) and Climate Change Risk Assessment for the Agriculture Sector by Knox et al. (2012). In addition certain words have been added that are useful in the context of scenario development.

Table 12: Keyword search terms First Order Term

Second Order Term Third Order Term Fourth Order Term

agricult* AND AND AND farm* Extreme weather impact livestock OR rain sensitivity crop Severe drought stress heat disease precipitation quality flood yield warming response wind productivity temperature risk storm damage ice land suitability blizzard consequences snow pests landslip weeds water logging vulnerability fire erosion hail drainage variability cost attitude financ* response adapt* prepare* lowland arable fruit grass* moor* upland contingency risk grazing pasture

The key references are detailed in tabular format below.


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Ref [5.1.] Posthumus et al. 2009 Title Impacts of the summer 2007 floods upon agriculture in England Review rating (pre‐review) 5 (5) Topic(s) Flood risk management, rural agriculture, England, summer 2007 floods

Extreme weather hazard covered

Floods from heavy rain

Date / Year Summer (June - August) 2007 Duration of hazard 6 weeks average in each region

Geographic Area(s) Yorkshire & Humber, West Midlands and Oxfordshire Cost Total cost to agriculture in England: £50.7 million. Type of farming All Event Magnitude/severity/return period

42,000 hectares of land were flooded.

Abstract / overview Exceptional rainfall during the summer of 2007 caused widespread flooding in parts of England. While the focus of attention has been correctly placed on the impact on densely populated urban areas, large tracts of rural land were seriously affected by flooding. Summer flooding is particularly damaging to farming. This paper presents the results from an evaluation of the impacts of the summer 2007 flood events on agriculture. High financial losses were incurred in the horticultural sector. Arable farmers incurred direct losses in the form of crop loss or yield reduction due to flooding and associated waterlogging of fields. Livestock farmers incurred indirect losses in the form of additional housing and feeding costs for livestock. Although total costs to agriculture were small compared with urban flood costs, they were typically large at the individual farm scale. Such impacts should be properly acknowledged in future strategies for flood risk management.

Methods: Methodological quality; relevance of that research design; relevance of the study focus; Are the findings clear?

Relevance at the highest level. The Methodology involved a survey of 78 affected farmers. Structured questionnaires used to find out about nature flooding, crop and livestock, type and cost of damage and the attitudes of farmers towards adaptation to flood risk. Estimates of financial losses were according to estimates of physical damage and unit prices; average market prices for cereal, potatoes and field vegetables were used; other costs were accounted for such as extra labour, and machinery costs.

Scalability: Could the data/findings collected by scaled up to a regional or national-scale? Study done at regional scale.

Question 1: What direct impacts (on English farms) occurred? Why? OR were what indirect effects (i.e. weather influencing diseases or pests)?

Response 1 Crop damage and associated yield loss were the most reported impacts. The next reported of impact was loss of income from livestock and debris clearing. Crops were affected both in terms of yield and in quality. Additional costs were the result of the need to add agro-chemicals to maintain crop performance after flooding. There were additional harvesting costs and land reinstatement costs. Grasslands were affected through loss of hay and silage as well as grazing. There were increased costs in moving livestock and reseeded pasture. Losses of livestock (due to drowning) and reduced milk production, and additional labour, extra feed purchases, additional slurry disposal (due to livestock being kept indoors), and extra treatment costs due to disease. It was found that a small number of farms suffered the highest losses and smaller farms suffered disproportionately. At farm level the greatest losses were incurred by general crops, as a result of crop loss of reduced yields. At a field level the greatest losses affected horticultural produce such as vegetable and salad crops being unfit for sale. Horticultural farms had relatively higher costs associated with repairs needed to damaged irrigation equipment. Livestock farms were affected indirectly with increased costs of moving livestock to shelter during the grazing season and additional costs arising from this as well as labour. Costs to livestock farmers were increased by the need to purchase extra


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feed. Whilst direct losses of livestock were low, there were increased costs for treatment of diseases such as dairy mastitis and lameness. In addition there were cost for repairing fences, gates and hedges, where the need is higher than for arable crops. In flooded areas cereal yields were down by approximately 40%.Across the UK, winter wheat yields were 6% lower in 2007 than 2006. The potato crop saw 2.6 % of the area spoiled by flooding though a larger proportion was lost to blight as a result of the wet weather conditions.

Question 2: What was the socio-economic impact of these events on farms? Response 2 Average cost per farm £89,415. Cost per flooded hectare: £1,207 per ha. 82% of

this damage per hectare was due to flood damage to crops, the remaining 18% to farm assets, livestock production losses and indirect losses.

The highest losses were recorded by horticultural farms with an average cost of £6879 per ha. Mixed farms had the lowest at £411 per ha.

Question 3: Have, and if so how, such events triggered an adaptive response by farmers or altered attitudes towards business planning?

Response 3 There was a problem of soils staying waterlogged for a prolonged period, up until Spring 2008 in some cases. Many farms considered adaptation measure such as changing crop rotation which would incur no additional cost to the farm. Though an imminent increase in extreme event like the 2007 floods was seen as likely few saw that a change in land use would happen. Nearly half of those framers questioned were considering a change in land use on the floodplain (stopping potato growing or winter cereals) or converting arable land into grassland, improving drainage or ensuring enough silage or hay was available, reducing herd size or entering an environmental stewardship scheme. In the West Midlands there was a greater level of acceptance of flood risk than farmers in Yorkshire & the Humber and Oxfordshire and therefore greater emphasis was put on resilience and warning by farmers.

Question 4: Are there barriers to adaptation?

Response 4 Six cases were reported where farmers were unable to plant winter crops or potatoes in the spring. Soil compaction and a reduction in the earthworm population are thought to reduce yields for the following years.

Question 5: Is there a tipping point where adaptation is no longer viable?

Response 5 Question 6: Are there any affects (economic etc.) influencing the level of impact?

Response 6 Cereal prices doubled in 2007 due to shortages in the world market and speculative commodity trading. The poor potato crop (see Q1) coincided with lower yields across Europe very likely leading to price increases.

Full reference POSTHUMUS, H., MORRIS, J., HESS, T.M., NEVILLE, D., PHILLIPS, E. and BAYLIS, A., 2009. Impacts of the summer 2007 floods on agriculture in England. Journal of Flood Risk Management, 2(3), pp. 182-189.

Ref [5.2.] BOARDMAN, J. 2012 Title A Short History Of Muddy Floods Review rating (pre review) 4 (5) Topic(s) muddy flood; soil erosion; land-use change; flood protection; flood damage costs;

Europe Extreme weather hazard covered

Heavy rainfall

Date / Year Various; October to December for winter cereals. Duration of hazard Geographic Area(s) South Downs, Somerset, Dorset, East & West Midlands, Kent, East Anglia Cost For Breaky Bottom (South Downs) incident: cost to farmer’s insurers: €145 000.

Brighton suburbs (1987) €957,000 to insurers and local councils. Type of farming Arable Event Faringdon, Sussex: 75mm in 4 hours, Return period of 1 in 160 years. Southern


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Magnitude/severity/return period

England at risk from muddy floods after monthly rainfall totals of 200-300mm, which is twice or three times the average.

Abstract / overview Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear? Review of past muddy floods

Scalability: Could the data/findings collected by scaled up to a regional or national-scale?

Not this study - literature review.


Response 1 Soil erosion during episodes of intense rainfall. Cereals are vulnerable if planted in large areas on slopes. Muddy flooding becomes more likely after rainfall is two or three times the average. Subsequent once rills and gullies established muddy floods n can take place with much lower rainfall totals of 4mm per day.

Question 2: What was the socio-economic impact of these events on farms? Response 2 None directly. There is only a threat of litigation if it can be proved that the rainfall

event was “not exceptional but within the range of events that a farmer might be expected to encounter during a working life”. (p.5)


Response 3 In response to legal action by the Isle of Wight Council Question 4: Are there barriers to adaptation?

Response 4 Question 5: Is there a tipping point where adaptation is no longer viable?

Response 5 Many short term measures to control muddy flooding are unsuccessful such as the use of emergency engineering solutions retention ponds, banks, drains; straw bales have been used to slow runoff and filter out sediment.

Question 6: Are there any affects (economic etc.) influencing the level of impact?

Response 6 Full reference BOARDMAN, J. 2010. A SHORT HISTORY OF MUDDY FLOODS. Land

Degradation & Development, 21, 303-309.

Ref [5.3] Stammers R (1984) Title Soil erosion and flooding on downland areas. Review rating (pre) 5 – 2ND Topic(s) Flooding from arable fields Extreme weather hazard covered

Heavy rainfall

Date / Year 1982/83: November 27, December 7-8 1982. Duration of hazard 2 months Geographic Area(s) Lewes and Rottingdean, Sussex South Downs, SE. Cost £12000 to councils (1982 prices); [£64000 2012.] Type of farming Arable Event Magnitude/severity/return period

Two falls of over 30+mm/day in November; then two falls of 20-25mm/day in December event. In the study area 14 tonnes of soil was lost in two weeks, 1000 tonnes in 2 months. Over 50 km² 66 sites reported erosion.

Abstract / overview Change of land use making slopes more vulnerable to muddy flooding, erosions and soil loss.



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Scalability: Could the data/findings collected by scaled up to a regional or national-scale? To other regions with similar issues, if records exist of costs to councils.


Response 1 Loss of soil – impacts felt by house dwellers downslope and by local council. Question 2: What was the socio-economic impact of these events on farms? Response 2 Impact on farms only later felt (see Boardman 2012)


Response 3 This had been a recent phenomenon since the 1970s and policy responses were not yet in place to facilitate land use change.


Response 4 Economic and attitudinal – the farmer in question refused to alter land us or to use contour ploughing. Spring sowing was under consideration.


Response 5 None listed.


Response 6 Bankruptcy of owner allowed the local council to purchase the land and return the land to sheep grazing and reduce the risk of flooding and runoff “to zero”.

Full reference Stammers, R, Boardman, J (1984) SOIL EROSION AND FLOODING ON DOWNLAND AREAS Surveyor, 164 (4804), pp. 8-11.

Ref [5.4] Posthumus et al 2008. Title Agricultural land use and flood risk management: Engaging

with stakeholders in North Yorkshire Review rating (pre-review) 5 Topic(s) Flood risk management, Agriculture, Runoff, Stakeholders, FARM tool Extreme weather hazard covered

Flooding

Date / Year n/a Duration of hazard n/a Geographic Area(s) Skell & Laver catchments, eastern Yorkshire Dales, North Yorkshire, Yorkshire &

Humber Cost n/a Type of farming Pasture and moorland, oil seed rape, maize, cereals Event Magnitude/severity/return period

n/a

Abstract / overview Recent changes in agricultural and flood defence policies create new opportunities for involving rural land use, in particular agriculture, in flood risk management. This paper presents the results of a case study on land management and flooding in the Laver and Skell catchments in North Yorkshire. The perceptions of local stakeholders were explored through interviews with farmers and discussions among stakeholders that were held, supported by the Floods and Agriculture Risk Matrix (FARM) tool, during a stakeholder workshop. These stakeholder perceptions are reviewed against scientific evidence. Temporary storage of runoff water on farmland was found to have potential to mitigate flooding, but the participating stakeholders thought that this was beyond farmers’ responsibility of good farming practice. During the stakeholder workshop, it was therefore agreed among all participants that targeting funding is needed, as well as stakeholder engagement and demonstration farms, in order to successfully


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involve farmers in flood risk management. Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear? Scoping study in the form of semi structured interviews with eight farmers. This

was followed by structured interviews with 25 farmers (including the original eight). This was followed by a stakeholder workshop (n=23) involving farmers, NGOs (& Yorkshire Water), EA, English Nature, Forestry Commission, Defra, and Harrogate Borough Council. Reponses to the issue of flooding and its causes were introduced in the stakeholder workshop. These were in the form of scenarios showing illustrations of fields showing degradation and drainage problems. The “FARM” tool was used to grade the level of flood risk for various land units; it gauges the probability of runoff due to soil management practice and flow connectivity. Stakeholders were asked on what responses in land management would be needed for three scenarios to improve the situation.


Yes.


Response 1 Few, because overland flow was perceived to be a natural process as flooding had been sporadic and not prolonged. Only farmers in the lower part of the catchment reported problems of river bank erosion or debris on land caused by flooding. Eleven of the farmers reported that they had fields where ponding had occurred regularly.

Question 2: What was the socio-economic impact of these events on farms? Response 2 None reported, but the impact was felt downstream in 2000. [Flooding in Ripon

also occurred in 2007 after Environment Agency flood defence scheme was cancelled, and also occurred in September 2012.]


Response 3 Four of the eleven farmers who experienced ponding in their fields had created ponds in response. Generally surface runoff has been perceived to be a natural phenomenon. As a result interviewees “did not feel a responsibility for flood risk management” (p793). The interviewees found that flooding was caused by factors such as urbanisation of the area and an increase in hard surfaces for roads had exacerbated flood problems as well as river canalisation, intensive farming leading to soil compaction and increased drainage of moorland at the head of the catchment. The event up to 2008 had therefore generated little response and so the adaptive responses elicited were in response to the scenarios presented to the stakeholders at the workshop. Reponses included reducing of stocking at critical flood risk periods, creation of ponds and surrounding vegetation, plant trees along watercourses and removal of sediment from riverside ponds.


Response 4 The adaptation measures such as pond creation or planting would require financial assistance and an appropriate funding scheme is required. Current schemes such as the CAP were inappropriate if the benefit was for others. Also current schemes do not provide enough incentive to change land management practices due to the cost of changing. However bringing farmers together through such workshops did have the effect of engaging farmers to good practice and success stories, especially if this could be in combination of achieving other objective such as pollution control and conservation.



Response 6 Cuts to the Environment Agency in 2006 had delayed the implementation of flood defences for Ripon.

Full reference


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Ref 5.5 Semenov 2009 Title Impacts of climate change on wheat in England and Wales Review rating (pre-review) 3 (5) Topic(s) drought and heat stress; wheat simulation model; stochastic weather generator;

UKCIP02; LARS-WG; Sirius Extreme weather hazard covered

Drought and heat

Date / Year 2050s Duration of hazard Geographic Area(s) England and Wales Cost Type of farming Wheat Event Magnitude/severity/return period

Abstract / overview The frequency and magnitude of extreme weather events are likely to increase with global warming. However, it is not clear how these events might affect agricultural crops and whether yield losses resulting from severe droughts or heat stress will increase in the future. The aim of this paper is to analyse changes in the magnitude and spatial patterns of two impact indices for wheat: the probability of heat stress around flowering and the severity of drought stress. To compute these indices, we used a wheat simulation model combined with high-resolution climate scenarios based on the output from the Hadley Centre regional climate model at 18 sites in England and Wales. Despite higher temperature and lower summer precipitation predicted in the UK for the 2050s, the impact of drought stress on simulated wheat yield is predicted to be smaller than that at present, because wheat will mature earlier in a warmer climate and avoid severe summer drought. However, the probability of heat stress around flowering that might result in considerable yield losses is predicted to increase significantly. Breeding strategies for the future climate might need to focus on wheat varieties tolerant to high temperature rather than to drought.


Modelling of potential impacts of drought and heat waves on wheat.


On UK scale – modeling down may be of use.


Response 1 The research found that by the 2050s the impact of drought would be less because of warmer temperatures allowing wheat to mature earlier and thus be less affected by drought stress. Heat stress around flowering could result in major yield loss and this is predicted to increase significantly in probability.

Question 2: What was the socio-economic impact of these events on farms? Response 2


Response 3 Question 4: Are there barriers to adaptation?


Response 5


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Response 6 Full reference SEMENOV, M. A. 2009. Impacts of climate change on wheat in England and

Wales. Journal of the Royal Society Interface, 6, 343-350.

Ref 5.6 WREFORD, A. & ADGER, W. N. 2010 Title Adaptation in agriculture: historic effects of heat waves and droughts on UK

agriculture Review rating (pre-review) 5 (5) Topic(s) Adaptation, agriculture, climate change, drought, heat wave Extreme weather hazard covered

Drought; heat wave

Date / Year Various from 1970-2006. Major events covered 1976, 1983/4, 1990-2, 1995-7, 2003, 2004-6.

Duration of hazard Multi month to multi year Geographic Area(s) UK Cost … Type of farming Arable: potatoes, sugar beet, oil seed rape, wheat and barley. Livestock: beef,

cattle, sheep, pigs and poultry. Event Magnitude/severity/return period

Droughts classified as major (but criteria missing for definition of major - see Blenkinsop and Fowler (2007) J. Hydol. 342, 50-71).

Abstract / overview Extreme weather events are expected to increase in frequency and/or severity under climate change. Recent examples of these types of events, such as the heat wave in Europe in 2003, have caused considerable damage to crops and agriculture and substantial economic damage. If similar damage was incurred every time such an event occurred in the future, it would cause increasingly serious loss to social welfare and the economy as the frequency or intensity of these events increased. However, agriculture has a history of adapting to shocks, and in this paper we aim to determine whether there has been a systematic reduction in damage from historic extreme events over time in the agricultural sector in the UK. The impact of comparable droughts or heat waves over the past four decades is compared, and for many commodities there appears to have been a reduction in damage over time, to the point where recent events have had a minimal impact on production, indicating that the sector is relatively well adapted to the current climate. We discuss whether this type of adaptation can be sustained into the future under more rapid rates of change, or whether the 'low-hanging' fruits of adaptation have been picked.


Good records of drought and UK production data but not clear how severity or length of drought might have played a role in the level of impact.


Data could be scaled down to regional studies.


Response 1 For arable crops potatoes, oilseed rape and to a lesser extent wheat responded with a decreasing deviation to mean yields after each successive drought. However, sugar beet did not show a reduction in the level off impact from drought, having been very negatively affected by the 1975-6 drought. Barley yields did not respond with any particular pattern through the succession of droughts. For livestock it was assumed that there was likely to be a delayed effect from droughts as farmers sold stock after a poor year and effectively increase production in the short term. This seems to be reflected in the sheep production where in the immediate year of drought, production increased but fell subsequently; overall the authors’ note that the impacts do not appear to reduce


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over time. For the pig sector, the drought had an impact on production but since 1975-76 there has been a generally a reduced level of impact. Poultry had a more consistent pattern of reduction of impact from drought, so that by the 2003 drought production actually increased.

Question 2: What was the socio-economic impact of these events on farms? Response 2 See Q1.


Response 3 The level of irrigation UK wide has increased from 55210m3 to 92883m3 between 1982 and 2005. In addition, at a deeper level of adaptation to drought, water storage capacity has nearly doubled between 1984 and 1995 (after Orson 1996). Though the response of cereals to irrigation has been uneconomic, this has led farmers to adapt the time of sowing or harvesting, or introducing rapidly maturing cultivars (after Orson 1996).


Response 4 Theoretically the barriers include particular types of agriculture because of land quality constraints. Efforts to alter farmer behaviour by government agencies have been reported to not always be successful as local knowledge and experience was not seen as being valued (after Hall and Prety 2008).


Response 5 It is reported that some commodities may be the limits of being able to adapt. They also note that after irrigation has been implemented there may be no further irrigation possible, so historic adaptation may not necessarily indicate future adaptive capacity.


Response 6 Dairy farming was omitted because of policy influence. Full reference WREFORD, A. & ADGER, W. N. 2010. Adaptation in agriculture: historic effects of

heat waves and droughts on UK agriculture. International Journal of Agricultural Sustainability, 8, 278-289.

Ref [no.] 5.7 Brown (1987) Title Agriculture in England. A survey of farming, 1870-1947 Review rating (pre-review) 5 Topic(s) Impact of abnormal rainfall on agriculture in England Extreme weather hazard covered

Date / Year Best coverage on 1879. Also 1892. Duration of hazard Geographic Area(s) England Cost Type of farming All. Event Magnitude/severity/return period

See Marsh (2008): In 1879 the rainfall from May to July was 184% the average (1971 – 2000) for England and Wales (Marsh 2008).


study focus; Are the findings clear? Useful analogue for seasonal shift and abnormally heavy rain occurring in the

growing season. Sources of data could be useful for further study.



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Response 1 In both 1879 and 1880 the weather was particularly wet in the main growing and ripening seasons. Abnormal rainfall was reported in the Midlands, central southern England and East Anglia. Brown (1987) describes the impact on cereal yields was that yields were 50-75% of normal of the average of the years 1873-77. The following year saw harvest similarly affected though there were areas of the country that were much less affected such as Cornwall. Wheat was the worst affected crop. Livestock farming was affected as pastures were waterlogged. The problems were exacerbated by a dry spring in 1880 reducing grass growth and rain affecting haymaking. As a result stocks were reported to be underweight and “out of condition”. However, disease compounded the problems, such as foot rot affecting sheep, liver fluke; the latter reportedly killing nearly 10% of sheep in two years. Worst affected areas were the unusually wet Midlands such as Leicestershire, where there was as much as a 37% decline in sheep numbers by 1881 in comparison to 1878.





Response 5 … Question 6: Are there any affects (economic etc.) influencing the level of impact?

Response 6 Full reference

Ref [Grey 5.9] Bradley 2012 Title The Wet Cold Summer of 2012. A Farmers View. Review rating (pre-review) 5 Topic(s) Impact on single farm Extreme weather hazard covered

Multiple episodes of rain coupled with low temperatures. Cold Spring.

Date / Year 2012 Duration of hazard One year. Geographic Area(s) Ribblesdale, near Settle, western Yorkshire Dales, North Yorkshire, Yorkshire &

Humber. Cost n/a Type of farming Livestock Event Magnitude/severity/return period

n/a

Abstract / overview Impact of cold wet weather during the year of 2012 on a livestock farm. Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear? Anecdotal report. Gives a clear report on the impacts for an individual pasture farm

in northern England.



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Would need to be organised into a survey of farmers. See 2007 study.


Response 1 In Spring 2012, low temperatures prevailed and after a warmer interlude the rain was coupled with colder temperatures; this resulted in very low grass growth, and ewes lactated less milk so that lambs grew more slowly. Subsequently persistent rain, so that it was “continually wet” was coupled with lower temperatures. This had the effect that a grazing animal eats a lot of water with the grass and dilutes the energy from the grass, and animals cannot physically eat enough to satisfy their requirements. Fattening lambs has therefore taken much longer than usual to reach a marketable weight, an extension of three to four weeks in their development. In addition, the farm produced 10% less silage in 2012. In terms of grass, it was estimated that a month’s worth of grass was loss or a sixth of annual grass growth.

Question 2: What was the socio-economic impact of these events on farms? Response 2 The price rose early in the seasons because of the slow growth of the lambs. By

autumn there were a larger than usual number of autumn lambs that were lighter with less meat so the price dropped per lamb and per kilo.


Response 3 If there are to be likely to be wetter summers farmers are likely to increase reliance on grass production. Adaptation options might include reseeding with modern varieties of grass and closer to reduce the need for feeds and fertilizer and more flexibility of marketing of stock.


Response 4 If lambs could be marketed earlier this would alleviate the problem but because of the bad weather many farmers have lambs later in the season. Cereals to assist are expensive and can only be used “tactically rather than a blanket approach”.



Response 6 Cereals are at a historically high price due to world shortages and drought. Full reference Bradley A (2012) The Wet Cold Summer of 2012. A Farmers View. Yorkshire Dales

Review 121, 6.

Ref 5.10 Coulthard TJ (2012) Title Using the UKCP09 probabilistic scenarios to model the amplified impact of

climate change on drainage basin sediment yield Review rating (pre-review) 4 (5) Topic(s) Climate change sediment yield River Swale. Extreme weather hazard covered

Extreme rainfall

Date / Year 2070-2099 Duration of hazard n/a Geographic Area(s) River Swale catchment, North Yorkshire Y&H Cost … Type of farming Upland Event Magnitude/severity/return period

Change in intensity of high rainfall events: The 1 in 50 year event will increase form a baseline 85.6mm to 109.5mm with discharges of the river increasing from 114.4m3 s-1 to 168.54m3 s-1.

Abstract / overview Precipitation intensities and the frequency of extreme events are projected to increase under climate change. These rainfall changes will lead to increases in the magnitude and frequency of flood events that will, in turn, affect patterns of erosion and deposition within river basins. These geomorphic changes to river


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systems may affect flood conveyance, infrastructure resilience, channel pattern, and habitat status as well as sediment, nutrient and carbon fluxes. Previous research modelling climatic influences on geomorphic changes has been limited by how climate variability and change are represented by downscaling from global or regional climate models. Furthermore, the non-linearity of the climatic, hydrological and geomorphic systems involved generates large uncertainties at each stage of the modelling process creating an uncertainty "cascade". This study integrates state-of-the-art approaches from the climate change and geomorphic communities to address these issues in a probabilistic modelling study of the Swale catchment, UK. The UKCP09 weather generator is used to simulate hourly rainfall for the baseline and climate change scenarios up to 2099, and used to drive the CAESAR landscape evolution model to simulate geomorphic change. Results show that winter rainfall is projected to increase, with larger increases at the extremes. The impact of the increasing rainfall is amplified through the translation into catchment runoff and in turn sediment yield with a 100% increase in catchment mean sediment yield predicted between the baseline and the 2070-2099 High emissions scenario. Significant increases are shown between all climate change scenarios and baseline values. Analysis of extreme events also shows the amplification effect from rainfall to sediment delivery with even greater amplification associated with higher return period events. Furthermore, for the 2070-2099 High emissions scenario, sediment discharges from 50-yr return period events are predicted to be 5 times larger than baseline values. © Author(s) 2012.


UKCP09 weather generator to give probabilistic projection of precipitation. The weather generator applies monthly changer factors to observed statistics derived for 5 km cells across the UK. This was used for inputs into the CAESAR landscape evolution model to calculate catchment erosion and deposition. There may be some issues with rainfall totals being assumed across the catchment which could affect the results.


Could be done for larger river catchments, so long as rainfall data coverage is sufficient.


Response 1 Floods sizes and increased sediment loads will have significant effects on channel siltation, instability and channel pattern change. There will be knock on effects of bridges, flood defences and channel control structures being undermined or eroded. For the time period 2050, we can use the 2030-2069 period. For the 95 percentile, (or 1 in 20 year return period) maximum daily rainfall rises from a 77.6mm baseline to over 90mm both Medium and High Scenarios. The maxima of daily discharge at baseline is 98 m3 s-1 ; Under Medium and High Scenarios this rises to 128-9m3 s-1; sediment yield increase from just under 30 000 m3 to approximately 100m3 for both Medium and High Scenarios - this is over three times the baseline load. By the final time period of 2077-2099 a 95 percentile event could have a sediment load over four times in volume.






53



Ref 5.11. Subak 1997 Title Agriculture Review rating (pre‐review) 5 (5) Topic(s) Impact of the 1995 drought

Extreme weather hazard covered

Drought and high temperatures

Date / Year 1995 Duration of hazard

Geographic Area(s) UK Cost NB 1997 prices.

£180 million UK wide. Summary of Estimated losses and gains (£ millions)

Losses Gains Net

+30

-61 +120 +59

Arable Crops

Major Crops

Vegetable and minor crops -40 +10 -30

-207

-6 -6

-198 +4 -194

Livestock

Pigs

Cattle

Poultry -7 -7

(data from Subak 1997: 53)

Type of farming All Event Magnitude/severity/return period

n/a

Abstract / overview A study of the economic effects of the drought of 1995 on farming, with details on why yields were affected.


Recorded yields of arable crops, livestock and fish farming were compared against predicted yields based upon ten year trends, except for vegetables based on a five year trend. Major crops and some livestock populations were compared with 1976 yield and production. The economic impact was estimated using estimated yield surplus. Regional differences only highlighted in some cases.

Scalability: Could the data/findings collected by scaled up to a regional or national-scale? Study on a UK scale, so possible to scale down to regional a level.


Response 1 There were positive impacts on arable crops for the UK mains crops which includes


54

wheat, barley and oilseed rape. Sugar beet reported the highest surplus (due to higher prices) but this was countered by higher costs due to the high temperatures. Vegetable production suffered losses with low yields. Deficits were around 3 t/ha from predicted values. Potatoes suffered the largest loss in terms of production. Root crops such as carrots, parsnips and onions were affected and additional pesticides were needed to counter act cutworms which thrived in the hotter weather. The livestock sector saw an increase in costs for purchased feeds in the South and East. This was accompanied by a loss of fertility in pigs and poultry. As population figures for sheep and cattle population had been in decline over the last five years making the impact of the 1995 drought on livestock production difficult to discern.

Question 2: What was the socio-economic impact of these events on farms? Response 2 Losses due to the drought were far less than the 1976 drought, where yields from

crops were estimated to be £270 million.

Losses from reduced forage crops reduction led to costs of £68 million and an extra £12 million needed to be spent on feeds (After ADAS 1996).

Additional irrigation for 3000 ha of brassicas resulted in an extra cost of £1.0 million.


Response 3 Investment in field drainpipes for irrigation increased in late 1994 to early 1995 by 6%. This helped to reduce the level losses compared to the drought of 1976. Farmers also response in 1995 by changing applications of herbicides, fungicides and pesticides.


Response 4 There are limits to irrigation as restrictions on abstraction were in place in East Anglia, Herefordshire, Hampshire and Lancashire. Repeated dry years could “force farmers to make irrigation priorities” possibly in favour of high value crops such as carrots, potatoes and sugar beet. The benefits of irrigating cereals are seen to be low.


Response 5 Implicit in the study if there were to be repeated dry years a tipping point in terms of the type of farming practised may be reached.


Response 6 For potatoes the low yields resulted in large price increase in the UK, which resulted in gross margins of £624 million

Full reference Subak S (1997) Agriculture, in Economic Impacts of the Hot Summer and Unusually Warm Year of 1995, (Eds) Palutokof JP Subak S Agnew M D, UEA Norwich 45-55. pp178.

Ref [GREY 5.12.1 ] Unsworth et al. 1993a Title Impacts of the Mild Winter and Hot Summers in the United Kingdom in

1988-1990 Review rating (pre-review) 5 (5) Topic(s) Impact of warm winters on agriculture. Extreme weather hazard covered

Two successive mild winters.

Date / Year 1988-1990 Duration of hazard Two years Geographic Area(s) UK Cost In 1989, the cost of brown rust on barley yields was £12.2 million. Reduced

prices on some horticultural crops (see Q2). Type of farming All Event Magnitude/severity/return period


55

Abstract / overview … Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear?



Response 1 Pests and diseases There was an increase incidence of bird pests surviving the two mild winters, but damage was lower because of other natural food supplies not necessarily available in a severe winter. There was an upsurge un damage to crops by mice to emerging crops, and slugs damaged soft fruit and vegetable crops. Because 1988/89 was relatively wet, resowing of crops was necessary. There was a high level of aphids despite use of aphicides. Many weeds survived the mild winters due to a lack of frosts though this did affect early sown cereal and oil seed rape which outgrew the weeds. Cereals Some cereals were affected by mildew, yellow rust and barley yellow dwarf virus, necessitating unusually large volumes of fungicides to be used. Crops experienced unusually early growth of autumn sown cereals with the result that the timing of fertiliser and growth regulators had to be retimed. Potatoes The main problems were in storage facilities due to disease or early sprouting of seed crop (which could be offset if such tubers could be plated early too). Horticultural crops Crops were advanced and yields were good, though unrefrigerated stores had problems with rot, especially onions. However some orchards were decimated by a late frost occurring in March and April 1990 after the mild winter, though others were unaffected. Livestock Grass grew in most parts of the UK during the winters of 21988-89 but the summer was hot and dry leading to shortages in the south west where stock famers had to buy in feed stuff, and leave stock outside for longer. There was an increase in calf pneumonia and lugworm, and parasites affecting sheep in 1989.

Question 2: What was the socio-economic impact of these events on farms? Response 2 Between November 1988 and February 1989 the wholesale price of Brussel

sprouts was down between 4 and 35% compared with 1987/88. Carrots saw prices drop by 52% in the same period as yields were good but quality poor for both.





Response 6 High yields in horticultural crops coincided with a depressed market and storage affected the quality of produce as a result of disease or other damage.

Full reference Cannell M G R and Pitcairn C E R (Eds) (1993a) Impact on Agriculture and Horticulture, Chapter A4, 54-62, in Impacts of Mild Winters and Hot Summers in


56

the United Kingdom 1988-90, HMSO, London pp154.

Ref [GREY 5.12.2] Unsworth et al. 1993b Title Impacts of the Mild Winter and Hot Summers in the United Kingdom in

1988-1990 Review rating (pre-review) 5 (5) Topic(s) Hot summer impact on agriculture Extreme weather hazard covered

The hot summer of 1990 after two mild winters.

Date / Year 1989-1990 Duration of hazard Two Years Geographic Area(s) UK Cost n/a Type of farming All Event Magnitude/severity/return period

Some of the hottest temperatures recorded in the UK during August 1990.

Abstract / overview … Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear? Report on the various impacts on yield sand quality of agricultural products from

around the UK. The impact of the summer on a lot of crops in terms of yield relative to the norm was not given, so general impacts are described.


Already UK wide scale.


Response 1 With low rainfall there was an increasing need for irrigation and restrictions were placed in abstraction in the East and South East. Warnings were made over Chlorine levels in eastern coastal areas. Pests There was a lower incidence of leaf diseases reliant on rainsplash such as mildew and leaf spot. For cereals brown rust was more of a problem than the more usual yellow rust. Powdery mildew occurred earlier on sugar beet than previously and 1990 foliar diseases had the largest impact. The root disease “take all” infecting root and stems had a high incidence as a result of a mild winter, moist cool spring and an early dry summer affecting the 1989/90 crop. Viral diseases were a severe problem due to a high number of aphids. Aphids also caused direct damage to crops, though they declined during episodes of the hottest weather in summer 1990. Cereals Overall, yields were down on the long term average in both years 1989 and 1990. Winter cereals fared better from the hot dry summer due to having a good root system which had developed over the winter. Cereal development was advanced. Thee drought in 1990s and high temperatures reduced the period for grain filling, and some crops ripened prematurely with a high proportion of shrivelling, with the result that yields were down. However in northern England, crops yields were up, especially on the good soils in Humberside (see Q2). Potatoes If potatoes could be irrigated then yields and quality were good, though un-irrigated crops were affected by drought; water stress was a problem in the Midlands and North West. In 1990 maincrop potatoes were damaged by rain in September 1990. Many farmers opted to put potatoes into storage due to low


57

prices in 1990. However the temperature of the tubers was too high for immediate storage which led to sprouting and older state of tubers at harvest. Other Crops Sown and harvested early, nationally yields above average Oilseed rape had below average yields in 1989 and 1990, though crop quality was good. Vegetable crops were highly variable depending ion soil type, management and if irrigation was available. High temperatures did affect lettuce, cabbage and sprouts by causing bolting. Grapes and fruit developed early after the mild winters of 1988 and 1989 but were very badly affected by the late frosts which exceeded the positive impact on yields of the 1900 summer – those areas unaffected by frosts had excellent yields. Livestock Milk output was affected negatively between April and October 1990. Livestock was affected by heat stress, although animal health was reportedly good – there may have been an impact on ewes and lambing.

Question 2: What was the socio-economic impact of these events on farms? Response 2 Impact on Cereals

There were positive impacts on yields in the north compared to other regions which suffered due to the drought. There was double the amount of wheat with protein levels suitable for bread making.

Humberside 12.5 t / ha; Lighter drier soils in Cornwall, Norfolk and Suffolk: 3.8 t / ha; E Midlands some recording only 1.3 t / ha.

However, farmers were selling cereals at a reduced price than normal due to the lower moisture content on the crops in 1990. Some grain had to be cooled once harvested due to temperatures exceeding 30°C due to the risk of infestation and mould.

Other crops

Vegetable crops were down on average yield from 10 to 80% depending on crop and region. Tomatoes were affected by low humidity in the hot summer of 1990.


Response 3 Many farmers opted to put potatoes into storage due to low prices in 1990. However the temperature of the tubers was too high for immediate storage which led to sprouting and older state of tubers at harvest




Response 6 Full reference Unsworth M H, Scott R K, Cox J S, Bardell K (1993b) Impact on Agriculture and

Horticulture, Chapter B4, 127-139, in Impacts of Mild Winters and Hot Summers in the United Kingdom 1988-90, Cannell M G R and Pitcairn C E R (Eds) HMSO, London pp154.


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PRESS ARTICLE: FARMERS WEEKLY 1953

Ref [GREY 5.13] Anon Title 250,000 acres flooded by Sea Water; New Peril to 500 Square Miles; The next

Task - Getting Rid of the SALT Review rating (pre-review) 5 Topic(s) North Sea Storm Surge Extreme weather hazard covered

North Sea Storm Surge, flooding, salt damage.

Date / Year January 1953 Duration of hazard Up to 1 year Geographic Area(s) East Coast and immediate hinterland Cost Type of farming Grazing and arable Event Magnitude/severity/return period

n/a

Abstract / overview .. Methods: Methodological quality; relevance of that research design; relevance of the

study focus; Are the findings clear? List of immediate impacts of the flood, scale of losses were not yet known


No.


Response 1 250, 000 acres reported flooded by the Ministry of Agriculture. Damage greater than that reported for 1947 flood, even though it was a third of the total area flooded. Regionally Norfolk had 30,000 acres, mostly affecting arable land. In Suffolk at least 10 000 acres, and Essex had 30-50,000 acres flooded particularly in the south. East Riding of Yorkshire had 3000 acres flooded. In Kent 40 000 acres under water about 1/8 was arable land with 200 cattle and 4000 sheep reported lost in that area.








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Ref [4.1] Dennis et al 2003 Title The impact of the October–November 2000 floods on contaminant metal

dispersal in the River Swale catchment, North Yorkshire, UK Review rating (pre-review) 4 (4) Topic(s) Metal contamination of floodplain sediments Extreme weather hazard covered

Fluvial floods

Date / Year Autumn floods 2000 Duration of hazard Multi year Geographic Area(s) Swale catchment, North Yorkshire, Yorkshire & Humber. Cost n/a Type of farming Livestock (Dales) and arable, Vale of York. Event Magnitude/severity/return period

Highest floods in 375 years at York. Highest floods in the Yorkshire Dales since 1986.


study focus; Are the findings clear? Seventy samples of fine grained sediment were collected from 35 sites along the

River Swale. Overbank sediment was analysed within 10m of the bank line along the whole course of the River Swale. Concentrations of Lead, Zinc and Cadmium were measured. These were compared with previous analysis from the Swale and its tributaries.


Replication of study at other at risk catchments possible.


Response 1 Contamination of sediment occurred on flood plain sites in agricultural use. Due to mining activities which reached a peak in the nineteenth century washout of metals occurs. It was found that concentration of Cd Pb and Zn exceeded MAFF guidelines, ( though Cd and Zn were under Dutch safety levels). However Pb at 35% of sites was above danger levels. Previous analysis in 1996 recorded very much higher concentration of Cd, Pb, and Zn, most likely because of a dilution effect of greater sediment loads in the 2000 floods. Consequently catchments like the River Swale with historic mines pose a flood related risk of contaminated sediments on floodplains and more frequent flooding associated with climate change would exacerbate that hazard.

Question 2: What was the socio-economic impact of these events on farms? Response 2 n/a



Response 4 …. Question 5: Is there a tipping point where adaptation is no longer viable?

Response 5 …. Question 6: Are there any affects (economic etc.) influencing the level of impact?

Response 6 n/a Full reference


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Ref [4.2] Collier R (2008) Title Vulnerability of horticultural crop production to

extreme weather events Review rating (pre-review) (4-5) 4 Topic(s) Climate change, model, pest insect, crop Extreme weather hazard covered

Heat, drought and excessive rain

Date / Year Case studies from 2000, 2006 and 2007. Projections for the 2020s and 2050s Duration of hazard Months – year. Geographic Area(s) UK Cost Type of farming Horticulture Event Magnitude/severity/return period

n/a


study focus; Are the findings clear? Modelling study based on previous impacts. UKCIP02 models used with crop

models. No levels of impact on yield given as a result of running the weather generator. Some useful indications of yields reductions from examples of past events.


…


Response 1 High temperatures during development can cause yield loss of all horticultural crops. Critical times of crops are during flowering and seed development stages. For seeds the hot summer of 2006 led to shortages of certain seed varieties in 2007. Furthermore, hybridisation is hampered by extreme high temperatures as.. “… breeders rely on simultaneous flowering for both parents and plant at different times to achieve this. This has proved to be increasingly difficult in recent years (p.5). Drought in 2006 also caused problems with the drying out of the soil so such as extent that it was impossible to transplant horticultural crops. Such was the demand for water for transplant that irrigation was impossible. However heat waves can have a beneficial effect by inducing dormancy and delaying population growth in certain pests. Nevertheless a background of general warming has been predicted to initiate aphid activity by 9 days earlier in the 2020s and 20 days earlier in the 2050s. The effect of rain and rain splash on pests and diseases will depend on the species and timing of the event. In 2007, the floods had a significant impact on the potato crop. Yields were low and there was greening of potatoes. Around 2000 ha of crop was lost. In 2000, the heavy rains of autumn prevented harvest machinery from operating on the land, so that by Christmas about 25,000 ha (20% of the crop) had not been lifted.




Response 4


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Response 6 Full reference Collier, Rosemary, et al. "Vulnerability of horticultural crop production to extreme

weather events." Aspects of Applied Biology 88 (2008): 3-14.

Ref [4.3.] Jaggard 1998 Title The relative effects of drought stress and virus yellows on the yield of sugar

beet in the UK, 1980-95 Review rating (pre-review) 4 (4) Topic(s) Extreme weather hazard covered

Drought

Date / Year 1980-1995 Duration of hazard Geographic Area(s) Two sites in Suffolk and Nottinghamshire Cost Average loss to drought calculated to be £25.9 million at 1996 prices. Type of farming Sugar beet Event Magnitude/severity/return period

n/a


study focus; Are the findings clear? The losses of sugar beet production were analysed at two sites relating yield loss

with cumulative soil moisture deficit in combination with meteorological records, soil type and crop distribution data. The data from the sites were combined with annual survey data to calculate losses from disease.



Response 1 Sugar beet is generally supposed to be drought resistant in the UK, though yields are more susceptible to summer drought than mainland Europe at the same latitude of 52N which as higher summer rainfall totals and soils with greater water storage capacity. During a dry summer in the UK a maximum of 17% has been irrigated and this level was predicted to drop due to pressure on abstraction licenses. Average losses of sugar beet to drought are >10%. Over the 16 years from 1980-1995 losses ranged from zero to 365,000 tonnes or 27.5% of production in 1995, a significant drought year. In 12 out of 16 years, losses due to drought exceeded 2% of national yield averaging 179,000 tonnes. In comparison with losses to disease (virus yellows) losses from drought were almost always larger.

Question 2: What was the socio-economic impact of these events on farms? Response 2 Losses of £25.9 million on average based on average world sugar price of £200

per ton. In 12 out of 16 years the losses amounted to £33 million.


Response 3 n/a Question 4: Are there barriers to adaptation?


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Response 4 Cost is a barrier for sugar beet breeding companies to develop drought stress tolerant species.



Response 6 Full reference JAGGARD, K. W., DEWAR, A. M. & PIDGEON, J. D. 1998. The relative effects of

drought stress and virus yellows on the yield of sugar beet in the UK, 1980-95. Journal of Agricultural Science, 130, 337-343.

Ref [4.4.] Asher 1991 Title Forecasting The National Incidence of Sugar-Beet Powdery Mildew from

Weather Data in Britain Review rating (pre-review) 4 (4) Topic(s) Sugar beet Extreme weather hazard covered

Frost and summer temperatures

Date / Year 1980-89 Duration of hazard Geographic Area(s) England Cost Type of farming Arable: sugar beet Event Magnitude/severity/return period

n/a


study focus; Are the findings clear? Meteorological records from Brooms Barn agro-meteorological station to calculate

frost units for January to March and daily maximum minimum temperatures were used for average summer (April to August) temperatures; daily rainfall was measured for cumulative rainfall. The disease incidence was the percentage crop area infected by powdery mildew at the end of August. Initially, correspondence between disease incidence and each independent variable in turn was examined using simple linear regression. “Linear models were superior to higher order polynomials in all cases. Subsequently, stepwise multiple regression was used to test the contribution of additional variables to those models which had achieved statistical significance, thus identifying the model of best fit”. The area of sugar beet grown had been relatively constant in the period as it was subject to EEC quotas.


Results were scaled up to national level.


Response 1 The greatest influence on powdery mildew was found to be the number of frost days during February and March. This was a stronger influence than frosts earlier in the winter and was a stronger influence than other factors. Warm summer and infrequent rain favours the disease. Geographically the patter of powdery mildew follows a “well defined pattern”. Beginning in the South East, in Essex the diseases spread northwards into East Anglia by the end of August in most years. Its dispersal into western areas is only during more favourable years and rarely occurs in the north of England.

Question 2: What was the socio-economic impact of these events on farms?


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Response 2





Response 6 Full reference ASHER, M. J. C. & WILLIAMS, G. E. 1991. FORECASTING THE NATIONAL

INCIDENCE OF SUGAR-BEET POWDERY MILDEW FROM WEATHER DATA IN BRITAIN. Plant Pathology, 40, 100-107.

Ref [4.5.] Harker 1990 Title The weather in England and Wales - August 1989 to July 1990 Review rating (pre-review) 4 Topic(s) Extreme weather hazard covered

Extreme Gales plus sea inundation (in Wales); Spring frosts; drought during summer and heat in August.

Date / Year 1989-1990 Duration of hazard Multi event over one year. Geographic Area(s) UK Cost n/a Type of farming All. Event Magnitude/severity/return period

n/a


study focus; Are the findings clear?



Response 1 Effect of drought in September 1989 shortage of grazing and poor hay and silage crops. Very dry for seed bed preparation very high wear and tea on machinery. By October 1990, high levels of mildew were reported on barley and wheat. 25 January widespread extreme gales over 80kt [82 kt Leeds – DS] with damage to buildings, power lines and trees blown down. 26-27 February 1990 surge plus high winds caused sea water flooding in North Wales as sea defences were breached. High winds over Britain. [NB 14 people died from these gales gusts >80kts Yorkshire -DS]. April 1990 severe frosts damaged tender plant particularly oil seed rape, barley, and plum and pear blossom. May 1990 drought stress as parts of central and southern England had 10% average rainfall. Spring cereals and beans were most vulnerable. Cereal disease increased and aphid activity unusually early and pea moth earlier. June rain benefited many crops but not those on light soils. Drought persisted in some southern areas with no rain for up to 24 consecutive days in July 1990, which was


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drier and sunnier than normal. Harvesting excellent but yields from late sown crops was very variable. Root crops were under stress because of restrictions on water abstraction for irrigation. [August 2 1990: record heat in northern England: DS]






Response 6 Full reference Harker P V (1990) The weather in England and Wales - August 1989 to July 1990,

Journal of the Royal Agricultural Society of England 151, 216-220

Ref [4.6] Bowden 1983 Title A survey of cutworm attacks in England and Wales, and a descriptive

population model for Agrotis segetum (Lepidoptera: Noctuidae) Review rating (pre-review) 4 (4) Topic(s) Extreme weather hazard covered

Warm dry summer and cutworm epidemic

Date / Year 1976 and 1949 Duration of hazard Seasonal - summer Geographic Area(s) England and Wales Cost n/a Type of farming Arable Event Magnitude/severity/return period

Abstract / overview Surveys of larval populations and numbers of reports of damage showed that from 1945 to 1978 Agrotis segetum was the commonest species of cutworm, causing most damage, to a wide range of crops, from July to October. Attacks varied in extent and severity between years, the most severe damage occurring in 1949 and in 1976. A model has been devised to estimate an index of larval survival to third instar, based on temperature-rate of development relationships and mortality due to daily rainfall. This survival index is highly correlated with year-to-year changes in numbers of reported attacks by A. segetum. The model suggests that weather conditions alone were responsible for the last major cutworm outbreak in 1976.


Summaries of pest outbreak from MAFF were taken from 1945 and mapped by district level. A model was developed using meteorological data (rainfall and temperature) from a site in Suffolk to predict outbreaks. The relevance of study is due to its focus on the relationship of weather and pests rather than climate change.



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Response 1 Damage by cutworms most frequently affects lettuce though root crops such as beet and potatoes were the most reported hosts. Cutworm incidence is associated with warm dry weather, though migration is another contributing factor. There were large outbreaks of cutworm in 1949 and 1976, and the incidence of the 1976 was accompanied by daily high temperatures 15-20⁰C mean temperature. There is circumstantial evidence that rain or irrigation on potatoes reduced cutworm damage.

Question 2: What was the socio-economic impact of these events on farms? Response 2 n/a





Response 6 Full reference BOWDEN, J., COCHRANE, J., EMMETT, B. J., MINALL, T. E. & SHERLOCK, P.

L. 1983. A survey of cutworm attacks in England and Wales, and a descriptive population model for Agrotis segetum (Lepidoptera: Noctuidae). Annals of Applied Biology, 102, 29-47.

Ref [3.1] Van Dijk 2009 Title Climate change and infectious disease: helminthological challenges to

farmed ruminants in temperate regions Review rating (pre-review) 3 (4) Topic(s) Parasites and disease. Extreme weather hazard covered

Long term weather change.

Date / Year Multi year Duration of hazard Multi year Geographic Area(s) UK Cost Type of farming Event Magnitude/severity/return period


study focus; Are the findings clear? Reports on patterns of incidence of parasites and disease related to weather but

no correlation given. Incidence is expected to increase with climate change as long terms trends are illustrated, but extreme weather events are not covered except in a review of cases from tropical areas.


Study at UK scale.


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Response 1 Incidence is expected to increase with climate change, but extreme weather events are not covered. There has been a reported increase in incidence in the epidemiology of helminthes (parasitic worms). In recent years. The authors state that it is impossible to quantify the effects of rainfall on the development of parasitic and intermediate host for snail borne parasites. Nematodes incidence can be explained by temperature. Drought breaking rains are documented to increase the number of larval nematodes, using evidence from Queensland, Australia.






Response 6 Full reference VAN DIJK, J., SARGISON, N. D., KENYON, F. & SKUCE, P. J. 2010. Climate

change and infectious disease: helminthological challenges to farmed ruminants in temperate regions. Animal, 4, 377-392.


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Appendix 3: Scenario narratives S1: Summer flooding

In 2012 the UK overall had 371mm of summer rainfall, though the summer in 1912 was wetter (384 mm) and is the closest analogue. As the average in England is ~190mm, the 1912 value equates to a 200% increase.

Rainfall amounts exceed 200% above the mean for the East and West Midlands and the East of England for the period from June - August. Although temperatures remain around average for the time of year light levels remain consistently low because of long periods of cloud cover. With many convectional storms embedded in slow moving fronts rainfall intensity is +20% above the mean (mm per day).

The summer mean for this scenario is 354mm for the East and West Midlands and the East of England. Over 150mm falls in one day in large parts of the East of England. Unlike other scenarios, the analogue for this scenario has not been augmented. Below the pattern for summer rainfall between 1910 and 2010 shows the extent of the extreme rainfall in summer 1912.

Source: http://www.metoffice.gov.uk/climate/uk/summaries/actualmonthly

Figure 7: Met Office Summer rainfall in England from 1910-2012.

Impacts This leads to localised flooding, waterlogged soils and soil erosion as some locations experience falls of over 150mm in a 24 hour period. Some rivers draining upland areas have triple the erosive power of current floods and there are landslips in many locations and muddy floods from arable fields where there is little cover, even affecting gentle slopes. The risk of heavy metals and other pollutants being released is increased, e.g. from old lead spoil tips on the Pennines, leading to further accumulations in parts of the floodplain.

There are impacts on both spring-sown crops and crops still in the ground, including high-value crops such as potatoes and horticulture and late forage crops such as second/third cut silage or forage maize. Problems are created at harvest in terms of getting the crop in and drying it to the required moisture content for storage. Grain quality, particularly for milling


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wheat is likely to be affected. Harvesting in these conditions will also lead to soil compaction and ridging, leading to increased flooding problems and nutrient leaching.

In the livestock sector, farmers either have to house animals with requirements for additional feed with further impacts in terms of diseases such as mastitis, laminitis and increase in liver fluke, or consider moving them to flood-free ground. There are knock-on effects in the winter when a lack of straw and bedding and forage increases costs or welfare risks for animals particularly beef, dairy and sheep but also pigs and poultry when cereal prices may have increased. There are reduced finishing times as animals are housed and fed or increased costs to the farmer to get animals finished when they cannot find grazing. Slurry stores are likely to become overburdened as farmers are unable to spread.

S2: Two wet autumn/winters Rainfall amounts exceed 200% above the average across England for the period from September to November. The mean rainfall for the North East is 462mm while for the North West the mean is 550mm for the autumn period. In addition, the summers are relatively wet, keeping groundwater levels high. Temperatures remain close to the mean at 10-13⁰C for the time of year.

These conditions reflect a strong Atlantic pattern as slow moving deep depressions push far into Eastern Europe bringing gales and prolonged frontal rain to much of the continent. Consequently much of mainland Europe suffers similar wet conditions. In autumn 2000 rainfall was 179% of mean autumn rainfall in England and Wales. A 20% increase in rainfall totals would take the autumn total over 200% of the 1981-2010 mean.

In England, the pattern for autumn rainfall (September to November) is shown below. Unlike summer rainfall extremes which indicates a more cyclical pattern this appears to show an increasing trend for autumn extremes.

Source: http://www.metoffice.gov.uk/climate/uk/summaries/actualmonthly

Figure 8: Autumn rainfall totals in England


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Impacts A 20% increase in rainfall intensity and 450-550mm rainfall means leads to widespread flooding, waterlogged soils and soil erosion. Landslips occur and bridges are scoured in many river valleys. Winter crops either can’t be sown or are slow to grow, and are weak, weedy and patchy. Because of the flooding some crops need re-sowing. There is an increase in certain diseases such as mildew on cereals and pests such as aphids. Seed ends up in short supply as a result of loss of earlier sowings, or may become more expensive. Access to land is hampered by waterlogging, with compaction of soil and poaching of field entrances occurring. Land is compressed by traffic while water logged and so soil quality is compromised/hard to recover.

In livestock there is a rise in welfare issues, foot rot, production problems and disease transmission associated with housing animals and high humidity in housing. Reproduction problems are associated with poor quality feed. There are additional costs to the farmer associated with either housing or moving animals to flood free areas. Housing animals has a potential impact on slurry storage (especially if not separated from rainwater) and distribution (i.e. difficulty in spreading leading to a risk of non-compliance and damage to soil and watercourses).

S3: Mild winters A succession of two particularly mild winters with an absence of cold / wet winter conditions prompts early crop growth. The main interest in this scenario is the impact on pests and disease in both crops and livestock. Mean temperatures for winter in the South West are 7.7⁰C and 6.3⁰C for the North East and Yorkshire and the Humber. Frost becomes restricted to 2-3 days in a few locations and many milder locations have no frost at all.

For the 2050s Hansen et al. (2012) assume that extremely warm winter will be in the order of 3 Standard Deviations (+3σ) above the mean. The baseline (1951-1980) mean winter temperature for England is 3.6⁰C, but +3σ means that this has will have risen to a mean of 7.2⁰C. The closest analogue years are 1988-89, 1989-90 and 2007 with 1.4-1.7σ; although 1975 was cooler than 2007, there were three fewer frost days than 2007.

Table 13: Summary of past mild winters

Year Mean Winter Temp ⁰C

No. of days of airfrost in winter (England and Wales)

1990 6.1 12.5 1989 6.0 13.5 2007* 6.2 17.1 1975 6.0 14.2 Mean + 3σ 7.2

Impacts The mild winters result in an increase in pest incidence, e.g. aphids, with damage to cereals, fruit and vegetables. Vernalisation is hampered in many fruit trees leading to a decrease in budding, flowering and fruit production. Some crops also affected by the lack of cold temperatures for vernalisation. Crops also suffer increased competition from weeds, leading to lower crop yields (this is often in association with a dry period followed by sudden rain showers when weeds take advantage of the rain and are able to outgrow the young crops). Disease outbreaks also increase e.g. in the form of mildew and rust affecting cereals. There are problems of spoiling during storage for certain unprotected crops such as onions and potatoes and refrigeration costs increase.


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In the livestock sector animals suffer from an increase in parasites and vector borne diseases due to the high risk of survival of overwintering parasites on pasture, leading to high levels of internal parasitic infections in the following spring/summer. Livestock benefit from the absence of severe cold by not requiring early housing. It is more difficult to manage the nutrition of ewes in the run-up to lambing when they are not housed but are left out at pasture.

S4: Drought The drought begins in the previous winter and continues throughout the summer months. Weather fronts are weak and give meagre amounts of rain, mostly on western facing hills. High pressure dominates and in summer, temperatures average +3σ of the 1951-1980 baseline, constituting a hot, but not extremely hot, summer period. The drought extends to all parts of England particularly across southern England.

Mean summer temperatures for the East, East and West Midlands, South East and South West are 17.7⁰C. Mean maximum daily summer temperatures are 23ºC, 1ºC hotter than the 1976 summer, the hottest summer to date. (See Scenario 7 for discussion on likely high temperatures).

In 1995, the summer precipitation in England was around 70mm and this figure is used for this scenario. Drought was severe in August with 10-25% of normal range, with large part of central southern England receiving less than 5mm24.

Impacts Access to water is a problem in many locations in the South East, East Midlands and East of England where water demand is high, restrictions are enforced during the summer, and farmers lack water storage and management such as reservoirs. The West Midlands are particularly badly affected because of a lack of water infrastructure. As a result budding in many crops is reduced and yields of cereals, potatoes, sugar beet and horticultural crops suffer. The drought affects crops throughout the growth cycle. A lack of water results in soil capping and baking, leading to reduced water infiltration when it does rain.

Livestock farmers have to import feed as pastures for sheep and cattle become parched. Increased costs are experienced in providing additional water for outdoor poultry and pigs. Farmers may decide to house outdoor animals to improve access to water and feed and risk losing free-range status. Drinking and hose down water for livestock is severely limited.

S5: Seasonal Dislocation Analogues: Autumn/Winter 2010, January 1916, Spring Summer 1912 or 2012. Mean maximum temperatures are 0°C or below for long periods early in the winter (from November through December-January).

Rainfall remains average for the time of year but falls predominantly as snow and hail. The whole of England is affected by the cold temperatures with significant snowfalls. The weather pattern changes in January with strong south westerly winds leading to rapid thaws and localised flooding; mean temperatures are 7°C (6.9°C in January 1916).

This is followed by wet weather in February (150% of the mean) and then spring is early with a warm dry March: March mean temperatures are 9.6°C (Mean+3σ baseline). However, as spring progresses, low pressure dominates, with depressions taking a more southerly track bringing cool wet conditions. Rainfall amounts exceed 200% above the mean for the East,

24 http://www.ceh.ac.uk/data/nrfa/nhmp/monthly_hs.html


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South East and South West for the period from June-August. Although temperatures remain around average for the time of year light levels remain consistently low because of long periods of cloud cover. This multi-event scenario is less focused on the absolute extreme values than the dislocation of the seasons. It is plausible because a wide mixture of weather patterns were experienced in 1947 which had a severe winter, (which only become severe in February), followed by gales and a rapid thaw (and national scale flooding), with high temperatures in the summer.

Certain months from the above will fit the scenario and the suggested ones are all reported to be severe in their extent, early winter, a very dry and warm January and a dry period in spring before persistent heavy rainfall towards June accompanied by extreme daily amounts during the summer, as occurred in 1912 in much of eastern and southern and south western England.

This scenario is just one example of what could happen and there are likely to be multiple versions where seasonal shifts impact on agriculture because our crops and livestock systems are adapted to expected climate signals at certain times of the year. Other examples could include torrential rain just as fruit trees come into flower, destroying the potential for fruit production, or a frost during flowering, or overcast and wet days during grain harvest for cereals.

Impacts The impacts of such a pattern starts with the early onset of a severe winter in November, as livestock farmers lose some sheep in snow. There may be heavy losses of lambs in the early lambing flocks. Cleansing and disinfection of markets and livestock transport is seriously disrupted by the cold weather, increasing the risk of disease outbreaks. Harvesting of some crops and winter sowing of other crops is hampered, with additional problems of drying to the required moisture content for storage. Some crops, such as milling wheat, suffer a decrease in grain quality. The severe early winter has an impact on vegetable production too. Most winter vegetables are UK produced, e.g. carrots, brassicas (broccoli, sprouts, cauliflower), parsnips, onions, leeks, etc. They are planted to be harvested throughout the year as needed and they overwinter in the fields. A harsh early winter means that farmers cannot meet their contracted requirements for vegetables for retailers. This leads to additional costs for harvesting (if possible) and to the import of replacement crops.

Displacement of wild birds arriving for the migration season increases their contact with outdoor poultry and therefore increases the risk of disease transmission (avian influenza, Newcastle disease) of notifiable diseases. Water supply for outdoor pigs is made difficult by the extreme freezing weather as pipes freeze and water has to be carried by tankers, with a number of negative consequences (e.g. soil compaction, increased labour and fuel costs and potentially adverse effects on animal welfare).

With the warm weather in January, crop growth resumes but is checked by the dry conditions in March and the flowering season is disrupted by the cool wet weather later in spring. Heavy rainfall during the summer affects pasture land with livestock production (milk yields and growth rates) reduced, disease outbreaks, or production and reproductive diseases associated with poor forage and housing. There is an impact on seasonal breeders when the summer is prolonged and they may need to change markets. Flooded fields remain waterlogged right through until the next spring and gleying (when iron is concentrated within a thin horizon of soil) of some agricultural soils occurs due to the persistence of floodwater leading to anaerobic soil conditions. This results in spring cultivation being much later and more winter feed required for housed livestock as they are turned out to pasture late.

Seasonal dislocation leads to pollinators being out of sync with crops. Late spring rains prevent pollinators from flying and damages blossom reducing pollination of crops.


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S6: Wet winter followed by hot summer plus summer Atlantic storm The winter is wet and rainfall is widespread and prolonged throughout winter and spring. Rainfall amounts exceed 200% above the average across England with a winter mean over 360mm. Temperatures remain average for the time of year in the range of 10-13⁰C over the two regions. The increased rainfall leads to widespread flooding, waterlogged soils and soil erosion.

By late spring a dramatic change takes place as high pressure dominates bringing in hot dry air from the European mainland and this, together with temperatures consistently above 30⁰C for three weeks across much of England, leads to a summer drought. The mean summer daily maximum reaches 17.7⁰C. As harvesting gets under way at the end of August, an unusually vigorous depression tracks in from the Atlantic and intensifies very rapidly as it moves north-eastwards from Cornwall across central England bringing the strongest winds to the South East. Because the low is so intense, winds reach 90-100mph and cause serious damage; gales are accompanied by heavy rain and successive depressions follow, bringing wet and windy conditions from the end of August into September.

Impacts The wet winter leads to difficulties in getting farm machinery on the land for spring sowing, and autumn sown crops are in need of re-sowing because of water logging. Rain is intense enough to cause surface wash off of exposed soils in fields, with the associated loss of seeds and germinating seedlings. As spring progresses the hotter weather causes an initial spurt in growth but as summer continues water shortages begin to affect yields. The high temperatures also affect growth; the subsequent severe gales and rain flatten many ripening crops across a wide swathe of South East England. Straw cannot be collected leading to shortages and forage crops for livestock are severely affected.

Livestock turn out on to pasture is significantly delayed because of the wet winter. The hot summer leads to an increased risk of heat stroke in poultry and pigs and the animals need to be housed. Outdoor pigs are at risk of severe heat stress with sunburn, reduced sperm production and summer infertility. Dairy parlours need additional ventilation; housing and sheds need temperature control to avoid welfare issues. In high winds, outdoor poultry can be blown away and therefore need additional shelter.

S7: Drought with extremely high summer temperatures This year follows the pattern of 1975-76 with a prolonged drought forming the backdrop to the year, with an intensification of water stress in early August. For the South East mean rainfall is 57.3mm for the spring and 63.3mm for the summer. In a similar way to August 2003, a plume of very hot air with a long land track moves north from North Africa, across Spain and France, and inland areas of the South East and East of England experience record temperatures. The mean summer temperature reaches 19.3⁰C as widespread locations across the South East record over 40⁰C over 8 days. Some areas near the coasts escape the worst of the heat as sea breezes moderate the heat.

Summers that fall within the ‘extremely hot’ range of +5 standard deviations (5σ) above the 1981-2010 mean become increasingly common (more than 1 in every 10 years). Both crops and livestock are subject to significant heat stress. In terms of prolonged heat, the three hottest summers with highest mean daily maximum temperatures for England for all summer (June to August) are given below:

Year ⁰C 1976 17.6 2006 17.5 2003 17.4


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By the 2050s the variations in terms of standard deviation are listed below:

Statistic ⁰C Mean 15.2 Mean+3σ 17.7 Mean+5σ 19.3

Therefore, whilst a Mean+3σ summer will be 0.1⁰C warmer than 1976, a 5σ summer will be 1.7⁰C warmer25.

Whilst the mean of daily maxima for a year like 1976 is 22-23⁰C, the absolute maxima would be considerably higher.

During the long hot summer of 1976, temperatures exceeded 32°C (90 °F), somewhere in the UK, on 15 consecutive days starting on 23 June. In 2003, 32°C was exceeded in three consecutive days between 4 and 6 August and then on five consecutive days between 8 and 12 August, somewhere in the UK (temperatures failed to reach 32°C at any of the real‐time stations on 7 August).

Met Office 201226

Source: http://www.metoffice.gov.uk/climate/uk/interesting/aug03maxtemps.html

Figure 9: Absolute Daily Maximum Temperatures: Heat Wave of August 2003

25 Source: Original data taken from Met Office dataset summaries. http://www.metoffice.gov.uk/climate/uk/summaries/datasets 26 http://www.metoffice.gov.uk/climate/uk/interesting/aug03maxtemps.html

Absolute maximum temperatures: 10 August 2003. Temperatures exceeded 38⁰C in Kent, though northern Britain was cooling as a cold front brought an end to the heat wave.


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The UK Climate Projections Extreme Atlas27 shows large areas of southern England experiencing 28-31°C for a 7 day period for a 1 in 40 year event. (see Figure 10). If warming by the 2050s were to be in the order of 5σ above the mean summer temperature, this would be around 3.7°C warmer than the baseline used for the UK Climate Projections Extreme Atlas. Temperatures would therefore range 32-35ºC for a 7 day period for a similar 1 in 40 year heat event. In some locations it is likely that 40°C would be reached for a day, as it was over 38°C in 2003.

Source: UK Climate Projections Extremes Atlas.

Figure 10: Summer Heatwaves Daytime Maximum ºC: baseline for 1960-2004

Impacts The heat wave particularly affects the South East and East of England. Cereals are affected with lower moisture content and grain has to be cooled once harvested. As irrigation is restricted over much of the East and South East of England there are large drops in the yields of vegetables, root crops and cereals.

Livestock farmers report livestock (sheep, cattle, pigs, poultry, etc) suffering heat stress from the excessively high temperatures, losses are reported in milk yields and additional costs of importing feeds both during the summer and in the following winter (due to poor forage crop harvests) have to be met by farmers. Import costs are high due to heat waves affecting the North American Great Plains concurrently. Housed poultry can succumb to heat stroke very rapidly and sheds need to be ventilated. Additional costs are associated with housing pigs to avoid sunburn and heat stroke. High temperatures lead to infertility problems with breeding

27 http://ukclimateprojections.defra.gov.uk/22578


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males in a range of livestock species. There are increased costs for ventilating and cooling housing and transport where serious welfare issues can arise in hot weather.

S8: Mild dry winter, severe late spring frosts After a mild winter dry winter across the West Midlands with mean rainfall of 78mm and mean temperatures of 7.1⁰C28, and where spring growth has advanced, late spring frosts occur in April when mean temperatures fall below 0⁰C for a period of 6 days.

Such a pattern of weather occurred in 1990, when there were 6 days of air frost in April. Significantly 1990 had the second least number of frosts for the period 1961-201229, thus demonstrating how even during a mild year late frosts can occur.

In general, the trend for warmer springs and severe late frosts has seen a steady decline by the 2050s, making this a surprise event.

Impacts The late timing of the frost poses particular problems for spring sown crops and in particular fruit trees. The late frosts are localised in areas which are vulnerable, such as frost hollows and sandy soils, so not all farms are affected.

Slow growing grass means livestock turned out too early will need additional feed to compensate for the lack of forage. This means turnout times for sheep may need to change.

28 Temperature data for all Midlands area defined by Met. Office from http://www.metoffice.gov.uk/climate/uk/summaries/datasets 29 Data from http://www.metoffice.gov.uk/climate/uk/summaries/datasets


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Appendix 4: Impact of extreme weather on agriculture by sector The results of the expert elicitation, in terms of how the extreme weather scenarios might impact on key agricultural sectors is summarised in tables 14 to 19 below. These highlight the overall negative direction of impacts but also the many conditions which affect the degree of impact. The analysis highlights the degree of uncertainty in terms of quantifying both the spatial coverage and extent of extreme weather events.

1. Impact of Extreme Weather Scenarios on Arable Sector Arable sector adaptations are more limited than in the horticultural sector due to a reliance on relatively lower commodity prices. Thus adaptations that may reduce extreme weather losses such as irrigation may not be financially viable, unless also growing higher value crops. Potatoes are one of the most vulnerable arable crops to extreme weather situations, particularly under wet conditions and losses experienced can be some of the biggest of any sector (>50%) due to disease (blight) and difficulties harvesting. Arable field operation and drying costs can be one of the most significant extreme weather impacts. Most disease and pest issues can be managed.

Table 14: Arable sector impacts by scenario

SCENARIO 1 (Summer Flooding) Potential Impacts Implication (Driven by Impacts) Adaptations Barriers to

Adaptation Reductions in cereal yield (up to 40%) as flooding will inundate crop, smother roots, stifle growth, and make the crop un-harvestable in many cases due to lodging, wet straw and contamination from soil etc Potatoes worst affected (if roots wet for >12 hrs, will rot) – up to 50% yield loss

Industry-wide fall in yields of potato will be reflected in increased prices but not reflected in crops contracted for processing (crisping/chipping)

Upgrading field drainage Moving operations location

Cost – drainage is generally not cost-effective in most years

Reductions in cereal quality e.g. mycotoxins, milling quality etc where the crop can be salvaged.

Price impact Avoid planting on flood plain or other at risk sites

Land availability, attitude to risk

Extended harvest period (50% longer to harvest potatoes) or even salvaging if crop is un-harvestable.

Increased harvesting costs – fuel, labour and machinery costs Higher drying costs (10-30% higher)

Delayed planting of next crop Implications for the next crop, planted later, focus on spring rather than winter cereals

Farmers may not plant the next year (10-15%)

SCENARIO 2 (Two Wet Autumn/Winters) Potential Impacts Implication Adaptations Barriers to

Adaptation Difficulties planting winter cereals and winter OSR

Reduced winter drilling and switch to spring crops that are lower yielding.

Temporary switch to more spring cropping

Potential increase in price of spring seed due to increased demand leading to increase in fallow land

Increase in slug activity Increase in spend on slug pellets, failed crops and patchy establishment that will reduce overall yields and increase costs.

Drill earlier, switch to spring cropping

Earlier drilling dependent on conditions and can lead to other problems such as lodging

Harvesting issues for potatoes, impacted by wet as in S1

Yield loss and (partial) price response Potato growers stop growing on contracts if see repeated issues with harvesting

Uncertainty of weather patterns

Storage losses for potatoes and beet yields harvested in wet conditions

SCENARIO 3 (Mild Winters) Potential Impacts Implication Adaptations Barriers to

Adaptation


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Good autumn planting conditions and early establishment of crops

Early growth will increase lodging risk if not managed appropriately.

Winter crops establish well but at risk of increased disease pressure especially mildew and yellow rust on susceptible varieties. Aphid migration likely to be extended with increased risks for BYDV

Winter wheat crops could be sprayed in the autumn for Yellow rust and mildew (+£25-35/ha). Multiple aphicide treatments to prevent BYDV if the mild weather continues (cost £8.50/ha per spray plus application)

Increasing pesticide use

Minimal

Advanced crop growth and high tiller numbers could increase lodging risk

No issue where managed with growth regulators, and summer weather fine. In some situations lodging could be a problem with losses of up to 50% on the worst affected areas

Increased use of growth regulators

None

Higher cereal yields (if disease controlled) Potatoes and sugar beet less affected - delayed beet harvest with increased growing period higher yields

-

SCENARIO 4 (Drought) Potential Impacts Implication Adaptations Barriers to

Adaptation Potential cereal yield reductions of 25-50% on lightest soils depending on timing of drought. Could be 100% yield loss in spring crops where drought prevents crop establishment. Spring crops may have higher yield loss due to smaller root zone and capacity to exploit available water.

Loss of income and difficult management decisions which might lead to lower inputs.

Irrigation – through abstraction licences or farm reservoirs

Significant costs. 10-15% of potatoes growers with existing equipment would consider irrigating cereals Possible planning permission issues

Grain yield quality drop Price impacts

SCENARIO 5 (Early Winter: then mild; warm dry early spring; cold wet summer) Potential Impacts Implication Adaptations Barriers to

Adaptation Slow winter crop growth and development but no longer term effect on winter crops – but yields reduced by cold wet summer due to lack of sunlight – by up to 20%. Spring crops could have reduced establishment due to dry conditions – yields reduced by cold wet summer

Winter crops: some management problems due to winter and spring but main yield impact from the wet summer. Spring crops: potentially more serious yield impacts due to poor establishment and wet summer

Early drilling Irrigation for spring crop establishment

Knowing that it was going to be a cold winter or dry spring in advance

Increased disease in the summer due to wet Timing of crop protection

Biggest impact will be from disease so increased use of fungicides. Some yield impacts if timing of operations are affected but overall small impact

Bigger/faster machines

Cost/investment planning

Harvesting Wet harvest will result in increased drying costs

More efficient grain drying

SCENARIO 6 ( Wet winter followed by hot summer plus storm) Potential Impacts Implication Adaptations Barriers to

Adaptation Assuming a normal drilling window most winter crops will be well established and can cope with wet winter – although there may be an increase in pests such as slugs. There may be difficulties in applying crop protection products if soils remain wet.

Potentially an increase in slug control costs. Impacts from delays in applying crop protection could be slight yield losses especially if aphids or black-grass are not controlled. Most weeds can be controlled in the spring. May be more of a problem in field beans where there are few post-emergence options.

Investment in low pressure tyres Increase machinery capacity to cover wider area when conditions are fine

Costs

Hot summer should be advantageous to most crops unless it is accompanied by drought conditions. If temperatures are very high for a long period and especially if overnight temperatures stay high, water loss can be a problem and affect crop growth which will

Depends on the timing and duration of the hot weather. Positive side is reduction in wet weather diseases such as Septoria in cereals and blight in potatoes, but it could increase incidence of others such as brown rust in cereals.

Variety selection


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reduce yields. Higher levels of sunshine will help drive yields up in all crops (in absence of drought stress).

Summer storms are usually localised and can cause major damage depending on the timing such as pod shedding in oilseed rape, lodging in cereals and leaf damage to potatoes and sugar beet. In addition there could be soil erosion and crop damage from flooding and consequent difficulties in harvesting. Potatoes in particular may be affected by washing down of ridges and exposure of potatoes to light which turns them green.

Summer storms can cause up to 100% losses in oilseed rape crops if they hit the crop close to harvest. Lodging can cause up to 50% yield losses in affected areas if it occurs early in the season (June). Any reduction in canopy will reduce the photosynthetic capacity of a plant that will reduce yield potential. Green potatoes need to be graded out which will reduce saleable yield.

Insurance for some events such as hailstorm damage is available.

Cost

SCENARIO 7 (Drought with Extreme High Summer Temperatures) Potential Impacts Implication Adaptations Barriers to

Adaptation Yields of all crops likely to be affected unless irrigated. Crops on light land will be more severely affected than on heavier soil types. Shallow rooting spring sown crops likely to be more affected than winter crops.

Yields could be affected by 25-50%, with higher losses on lighter soils. Increase in sunlight could increase yields, but high temperatures will increase evapotranspiration and cause crop wilting at peak temperature that reduces efficiency of crop and affect yields. All crops affected.

Irrigation Economics for cereals and oilseeds are not supportive. Plastic lined farm reservoir could cost £450,000-£500,000 Planning permission can be an issue plus application costs

Harvesting costs Reduction in grain drying costs Reduction in wet weather diseases such as blight on potatoes, and Septoria in wheat.

Small reduction in fungicide costs likely

SCENARIO 8 ( Mild dry winter, severe late spring frosts) Potential Impacts Implication Adaptations Barriers to

Adaptation Biggest impact if potato crop is planted and emerged when frost occurs then defoliated and have to send out shoots again – greater impact on established crops. Winter oilseed rape may be flowering and setting pods and frost may cause some abortion. Limited impact on other crops unless frosts coincide with pesticide applications that can make them sensitive to frost. In winter cereals frost may affect development of ear.

Yield impacts Delayed planting of potatoes where high risk

Invest in fleece. Need to be convinced that would see a return on investment, particularly for fleece

Mild winter leads to an increase in pest and disease issues

Increased costs for fungicides/pesticides from increased autumn use

Select resistant varieties

Frosts on established beet plant can trigger the crop to bolt and run to seed rather than produce a beet

Yield impacts and seed return for future years

Later sowing but this could also reduce yields

2. Impact of Extreme Weather Scenarios on Horticulture Sector Horticulture is one of the most challenging sectors to quantify impacts for given the diversity of both fruit and vegetable crops and the year to year variability already experienced due to their sensitivity to weather. However given the crops are of higher value than arable crops more adaptations, such as tunnelling and irrigation are already in place and willingness to adapt to prevent losses of high value crops is high. Adaptations already in place provide some degree of resilience to extreme weather events, although adaptations may need upgrading to cope with increased severity of events. Pest and disease implications are greater for the horticulture sector than other sectors, particularly given the range of pests and diseases which can impact upon horticultural crops. Given the importance of prior adaptation and preparation for the horticulture industry, sequences of extreme events such as in Scenario 5 could represent some of the most damaging impacts.


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Table 15: Horticulture sector impacts by scenario

SCENARIO 1 (Summer Flooding) Potential Impacts Implication (Driven by

Impacts) Adaptations Barriers to Adaptation

Vegetables drilled and transplanted for winter cropping are set back or killed by swamping. Winter supplies of brassicas and onions greatly reduced. Carrots and parsnips tend to be grown on free draining sands and will be less affected.

If can’t access fields then will use tractor less. Labour for harvesting most vegetable crops would increase Increased cost for drying bulb onions, increased cost (fuel) for harvesting root crops Increase in land rental and cost of restoring rutted fields in subsequent years

For vegetable crops if growers are renting land could look to move

If moving land there will be some geographic limitations in terms of distance from packing facilities etc

Flooding will cause crop loss through waterlogged parts of field, usually tractor wheelings and reduced growth due to low sunshine levels.

Worse in the west as generally wetter

Improving drainage Drainage limited by cost (piped drainage cost in the region of £1730 to £2275 /ha to ditches already present); in areas where drainage hasn’t been improved for many years could be more costly

Outdoor non-tunnelled soft fruit will have high levels of berry rotting which makes picking slow and expensive.

Some crops will be totally lost; however the surviving areas will benefit from higher prices. Main impact is on delayed plant establishment and loss of growing season. In 2012 there was an overall loss of yield about 15% of normal

Crop tunnelling for the higher value crops (improves Class 1 yield and guarantees supply of English fruit to the supermarkets.) Without tunnels there would be high levels of imports. For vegetable crops would only look at tunnelling for the high value crops such as green beans and asparagus

Finance, but producer groups have greatly assisted investment in tunnels. Tunnelling is limited by return on investment so only used on high value crops

Higher humidity within the tunnels will increase leaf and berry diseases

Leaf spot on outdoor currants will need a more intensive spray programme. Similarly, apples will need more scab sprays and winter in store rots are likely to increase Soil borne diseases such as phytophthoras on soft fruit and apples will spread rapidly plantations will be irreversibly infected and their productive life shortened. Reliance on (mostly) root absorbed fungicides becomes more important

The main problem would be the establishment of crops (vegetables)

Market demand for summer fruits will slightly decline. High rainfall will increase soil erosion as polytunnels increase the flow rate off a field.

SCENARIO 2 (Two Wet Autumn/Winters) Potential Impacts Implication Adaptations Barriers to Adaptation

Harvesting of winter roots, e.g. carrots, parsnips and swede will increase damage to soil structure

Damaged soil structure can take several years to get back to the original state. 2 to 5 % increase in cost of harvesting and restoring soils

Improved drainage of site (expensive)

Grants used to be available but are no longer, reintroduction of grants could help

Winter leaf brassicas (cabbage cauliflower and sprouts) will suffer increased leaf spotting diseases.

Fungicides will need to be increased

Vegetables would see a drop in quality rather than yield so much, might get around a 5% drop in total Winter roots would be one of the worst impacted in vegetable sector

Planting top fruit and soft fruit on raised soil beds or completely isolated substrate (compost) grown crops.

Finance is more of a barrier to vegetables as have less high value crops


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Soil grown soft fruit crops that are infected with the water spread phytophthora group of diseases are likely to suffer greater crop losses

Geographic difference; sands in the East would recover quicker (lots of winter roots, get lots of roots damaged)

Irrigation availability to aid root zone recovery of damaged but healthy roots of perennial crops, e.g. raspberries.

High winter rainfall is very reassuring for those with reservoirs

Crop planting operations of top fruit (e.g. apples, plums and cherries) will be slower

Implications would depend on whether retailers relaxed their rules to allow produce of a lower quality than usual to be accepted (as has happened previously)

Soil erosion, plus possible pesticide and nutrient surface run off will increase from all crops, more so when associated with plastic ground cover. Soil erosion is always greatest in winter

Harvesting field vegetables is just harder work in wet soil and there is loss due to soil contamination, some extra disease and excessive wheelings

Grass strips along lower field boundaries by watercourses will reduce run off risk. They are well understood.

Few barriers, these are all affordable measures for high value crops and are already widely implemented.

Slugs thrive in wet mild winters, few crops escape damage. Activity being greatest in autumn and spring

Crop loss or damage. More sprays.

Wet winters can cause root death by suffocation. The crop can suffer if the following season is consistently dry and irrigation is not available

Increase in rent 5 to 10% per year, ultimately may run out of suitable soil

Most crops grown in summer would not be affected, harvesting of over wintered crops would be more difficult so yield of carrot/parsnip/brassicas would be reduced by around 2 to 5%

SCENARIO 3 (Mild Winters) Potential Impacts Implication Adaptations Barriers to Adaptation

Winter leaf brassicas (cabbage cauliflower and sprouts) will benefit from less winter losses to freezing and wet. Winter roots, e.g. carrots, parsnips and swede will benefit from reliable lifting schedules, as crops are not frozen into the ground.

Higher yields form winter harvested crops and higher than average yield the following summer for overwintered crops. However this must be balanced against the risk of untimely April and even May frosts that can destroy fruit blossom and yield.

Some blackcurrant varieties may suffer less bud beak in spring.

All non-irrigated crop yields will be reduced. Crop establishment delayed, reduced or killed.

Substrate crops, especially those on tabletops will come through winter unharmed and produce heavier crops maybe. Spring frosts can always destroy the flowers and yield potential.

Easier harvesting and less soil contamination, probably less fungal disease.

Over wintering weed seedlings will increase and make reliance on selective herbicides more important.

Increased costs

Main negative is pests overwintering, increase pesticide usage, which can be justified if winter is mild and see a good crop as will see a good return from pesticide usage. Aphid, caterpillars and slug numbers in all over wintering crops will rise.

Increased costs

This would be the opposite of S2. So an increase in yield of +2 to 5% of expected vegetable yield

Mild winters benefit vegetable production, the work is generally easier so less labour required 2% increase for drying cooling bulb onions and other short term stores

None needed None

SCENARIO 4 (Drought)


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Potential Impacts Implication Adaptations Barriers to Adaptation

Most crops would be affected, in particular salads in principle. Limited impact or an increase in yield overall for vegetables since drought conditions are usually associated with strong sunshine, provided water for irrigation is not restricted. Spring planted and drilled crops of all vegetables are at risk of failure without irrigation. Spring planted fruit (soft and top fruit) can die without irrigation. Yields will be reduced through summer except for irrigated crops.

In practice many high value crops have access to irrigation. A drought reduces the availability of water for irrigation. Depends on soils, for example brassicas are in Lincolnshire where the silt is not irrigated and the fenland soils have a high water holding capacity so would produce crops without irrigation but with a lower marketable yield – 50% less than normal. Sandy soils would suffer most. Yields could be increased by 10% of normal if water is plenty full but if restricted at time of plant establishment (spring time) then marketable yield could be zero.

A large % of horticultural land is already irrigated to might be looking at upgrading existing irrigation systems rather than implementing new ones Plant fruit crops in late autumn to allow the roots to settle and establish a little; the soil surface also settles. Both improve drought tolerance. Collecting rainwater off tunnel roofs and storing for irrigation. Reliable crop scheduling to match supermarket delivery schedules needs irrigated crops. Producer groups benefit form the spread of their suppliers, those in Scotland tending to avoid drought.

Increased powdery mildew levels in crops. Carrots and Parsnips might suffer the most in terms of vegetables

Increased frequency of powdery mildew sprays.

Berries are warmer on average at picking and need greater cooling costs of fruit.

Could get stress related diseases Heat would increase pests but drought would decrease diseases, trade offs. Western flower thrip (WFT) thrives in warm summers and can reduce yield by 40%, though not affecting all farms at present. Mite, thrip and powdery mildew damage reduce leaf performance or scar berries, which can make some crops unsalable. Mite pest damage of top and soft fruit crops will increase.

Probably more insecticides for summer pests no increase in fungicides due to routine application

Greater use of bio-control systems in tunnelled crops, as sprays have their limitations

Regulations for reservoir and borehole access. Investment costs of access, storage and distribution.

SCENARIO 5 (Early Winter: then mild; warm dry early spring; cold wet summer.) Potential Impacts Implication Adaptations Barriers to Adaptation

Overwintered root vegetables will not be lifted to schedule as the ground is frozen. Leafy vegetables will suffer freezing injury; even lose to drought as cold winds dry the leaves, whilst the ground is frozen.

Shortened life of winter-damaged perennial crops; damaged crops under-perform and are best replaced as hand picking costs are so high.

Improved drainage for some sites (main adaptation for veg.)

Leafy brassicas and alliums are highly susceptible to water-logging. See S1. Could also lose quite a lot of brassicas from a cold winter.

Winter freezing damage, followed by root loss to poor summer drainage will leave crops more vulnerable to further damage in the following winter.

Planting on raised beds significantly reduced the damage of winter or summer flooding.

Farm drainage may be impeded by neighbouring farms. Lack of grants to aid situation Resistance to changing the way cultivate

Soft fruit, such as strawberries raspberries lose tissue to frost damage and may even be killed outright in some fields.

Summer water-logging is more damaging than winter water-logging on perennial crops as the roots have a higher oxygen demand in higher temperatures. Spring replanting of strawberries killed by freezing damage.

Looking at frost tolerant varieties of crops, available but a case of finding the right one for site

Time to find right frost tolerant varieties


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Tree fruit trunks and branches can crack in extreme cold and a rapid thaw. Wounds can allow in diseases, though it is rare to see orchards being devastated.

Strong winds will flatten large scale tunnel installations

Lost tunnels can mean lost crops as the tunnels are not replaced adequately and yield is reduced once un-tunnelled

Some tunnel manufactures are looking at strengthening existing tunnel designs for better wind tolerance.

Cost of upgrading tunnels and the return you’d see from upgrading. Visual aspect of tunnels can create resistance

With this kind of seasonal dislocation could get continuity problems of responding to the wrong signals at the wrong times

Geographic variability - would purely depend on where there was frost. Early severe winter, would either affect bottoms of valleys or tops

Harder to prepare for such a string of events – more impact.

Impact as in S1. Main effect would be on plant growth in a cold wet summer. 10 to 15% loss of yield. Low sunshine always reduces yield of veg.

No real increase in vegetable labour as, wet periods always make harvesting harder work.

SCENARIO 6 ( Wet Winter Followed by Hot Summer plus Storm) Potential Impacts Implication Adaptations Barriers to Adaptation

Winter damage as described in S2. Mostly physical damage, fungicides protect very well against diseases in vegetables

Depends on losses seen as to worth adaptation

Irrigated high value crops will not suffer as S4. Though there will be the same pest problems.

Storm could impact on buildings

Spreading producer group suppliers across the country.

Storm could be disastrous on some farms for late summer tunnel fruit production. There is potentially another 50% of the strawberry and raspberry crop to harvest at this point.

Flattened fruit tunnels mean a stop to picking. There is no access. Taller Brassicas/sprouts crops would be most impacted, edge of field would be worst affected

Prompt removal of tunnel polythene in advance of gale warning. Wind breaks could be put up (1m high windbreak cost about £250/100m, hedge based on hawthorn costs about the same £250/270/100m but there are more substantial wind breaks around glasshouses which cost considerably more). This has been done for flower growers Strengthening of runner bean posts

Tunnel roof removal is well understood and practised already

Frost on spring flowers of top and soft fruit will significantly reduce yield.

The levels of damage are highly variable.

Spreading varieties wherever possible to spread the flowering season and reduce the impact of blossom loss to a short sharp frost.

Root crops and shorter crops would be affected Temporary damage to crops, mostly the salad types and summer vegetables such as runner beans. Most crops would recover or be replanted. There would be an increased risk from disease. Probably a 2% loss of yield.

Land rental increased Wet winter increases cost of harvesting

-

Heat in itself wouldn’t be an issue, water availability more of an issue

SCENARIO 7 (Drought with Extreme High Summer Temperatures) Potential Impacts Implication Adaptations Barriers to Adaptation

Irrigated high value crops will not suffer as S4. Though there will be the same pest problems.

As S4. If adequate water is available, few fruit and vegetable crops suffer badly - no impact or a positive increase since drought

Investment in winter storage for water Maintaining adequate water supplies, looking to improve efficiency of water supplies.

Upgrading irrigation not going to happen for field vegetables as the saving in water is small and cost is high. The


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conditions is usually associated with strong sunshine. Yields could be increased by 10% of normal if water is plenty full but if restricted at time of plant establishment (spring time) then marketable yield could be zero. This is also dependent on soil type, Lincolnshire silt is not irrigated and the fenland soils have a high water holding capacity so would produce crops with out irrigation but with a lower marketable yield – 50% less than normal

Upgrade rain guns to drip irrigation (used for strawberries) Some growers of wide row crops (runner beans and courgettes) on mains water use drip irrigation.

industry continues to invest in winter abstraction and storage. Cost £4k/ha and proved impractical in carrots for example.

Solarisation (sunburn) of leaves and berries can occur, i.e. berries and leaves are turned white as the sunny side is literally cooked in the sun.

Solarisation is more a curiosity than a crop loss phenomenon as only the very upper canopy of the crop is affected or the hot spell passes.

A hot summer is hard to foresee and infrequent, shading covers are not used

Some everbearer strawberry varieties can stop flowering due to 'thermodormancy'.

Thermodormancy does not affect all varieties and may reduce yield by 20-30% but this will not affect all farms. Thermodormancy wouldn’t be as much of an issue for veg.

Temperatures in tunnels are greatly reduced with venting, an established principle on farms already. Gangs of staff raise or lower the tunnel sides depending on the weather forecast.

Might be more likely to upgrade infrastructure for high value and perennial crops

Mildew, mite and thrip pests as scenario 4 could be extreme.

Reduced fungicide use is possible, though acaricides for mites may increase slightly.

Additional pest problems in a hot season cannot be foreseen, but it can be prepared for with good autumn and spring crop hygiene. Bio-control strategies are well suited warmer summers

Very high temperatures >30 °C will reduce effective pollination. This is commonly seen in tunnelled fruit if not vented

Pollinated crops would be impacted, beans and peas/legumes would be impacted

A hot season cannot be foreseen, in some cases this is to be welcomed, as consumers will buy more salad based and summer fruit products.

Some key pest predators are hindered >30 °C, whereas the pests are not. At these temperatures, powdery mildew stops developing.

Increase in insecticides due to high temperature favouring their development

Berries are warmer on average at picking and need greater cooling costs of fruit.

Increase in energy costs for short term storage and hydro-coolers

Dry heat will reduce berry rots to botrytis.

High light levels improve apple skin colour, favoured by supermarkets

SCENARIO 8 ( Mild Dry Winter, Severe Late Spring Frosts) Potential Impacts Implication Adaptations Barriers to Adaptation

A mild winter presents few problems as S3.

Lowered fruit density in a crop row means more expensive picking costs. In some cases a field is left unpicked, as the damage is so high.

Early crops are fleeced anyway so would fleece for longer if saw a spring frost Placing fleece over low growing strawberries when frosty nights are forecast.

No barriers, fleecing already done and has a reasonable cost

Spring planted and drilled crops can withstand frosts though herbicide use needs to be more careful. Herbicides could damage crops more if are weakened

South coast would be better as have coastal protection from sea

Frost blasters (tractor mounted diesel burners) are driven trough some tunnels crops during such nights, especially for cherries. Not used for veg.


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Crops develop quicker than normal in a mild early spring, especially if fleeced and tunnelled. This proportion of the crop is always at greater risk of April and May frosts are devastating to all soft and top fruit flowers. A flower may not be entirely killed but the resulting fruit is malformed and unsalable.

Spreading producer group farms across the country avoids localised frost damage.

Newly emerged spawn of raspberry and black berry (which crops the following year) can be severely damaged or killed off, though normally there is sufficient recovery if a healthy crop.

Better weather forecasting aids farm level decision making.

Some farms are prepared to pay for more refined forecast services.

Might lose some brassicas, via leaf scorch - would still grow but impacted. Frost damage to sensitive crops and bolting in Brassicas

Fleece/plastic covers cost £500/ha including disposal, there are different types of fleece and plastic covers and the £500/ha is from the system used on carrots.

Late frosts could kill summer types such as courgettes and runner beans but most vegetable crops would be unaffected.

Depending on the timing of frost some Brassicas may bolt prematurely. 2% loss of yield.

Could be an increased use of fleece and plastic crop covers

More overwintering pest, early part of spring would be a problem (pre-frost)

Diseases wouldn’t be a problem in this scenario

3. Impact of Extreme Weather Scenarios on Dairying The biggest impact upon the dairying sector from extreme weather is a need to house dairy cows to avoid the negative consequences of leaving them out (for example loss of animal condition and consequent impacts on milk yield and animal health). The additional costs associated with housing for longer periods are significant and include: labour to deal with slurry, purchased feed and bedding, vet costs and fuel or contract costs to transport silage and slurry. Continual extreme weather events could result in a year round housing system which would increase the costs associated with dairying and have consequent impacts on the viability of the sector and its image with consumers. Indirect impacts, notably on feed prices are potentially an important issue for this sector, especially for intensive systems.

Table 16: Dairy sector impacts by scenario

SCENARIO 1 ( Summer Flooding) Potential Impacts Implication (Driven by

Impacts) Adaptations Barriers to

Adaptation Cows require housing to avoid land poaching and sward damage.

Purchased feed costs will be 10-15% higher depending on flooding period. Additional labour required to deal with slurry (5-10%)

Permanently housing dairy cows Grazing land not suitable for cutting. Cost associated with all-year housing – forage making & storage; slurry storage and spreading.

Grass production impacted 10-15% decline in land productivity in subsequent year Cost of reseeding land damaged by poaching Less fertiliser used (~15%) Increased herbicides due to invasive weeds as a consequence of land poaching

Altering management system to utilise fields less prone to flooding Cow tracks (£42/m)

Access to land (limited by farm layout)

Contamination of crops if flooding occurs

Spoilage of stored silage Farmer access to grass may prevent cutting,


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delayed cutting leads to poorer quality silage

Difficulty in drying silage 15% more fuel required Land compaction Forage production impacts

in subsequent years Sub-soiling pastures Not visible

Disruption to grazing and impacts on feed intake and quality

Overall 5-10% impact on milk yield Fertility impacted (~10% herd affected) Additional vet costs from input on fertility, lameness and mastitis (~10%)


Adaptation Cows require earlier housing (possibly for 7 months rather than 6) to avoid land poaching and sward damage (implications as in S1)

Associated costs of longer housing (15-25% increase in associated labour for silage and slurry transport) Yields can only be maintained if additional feed purchased, cost implications

Housing cows earlier to avoid impacts of rain on yield

Costs – feed, forage, bedding, slurry, labour etc

Extended grazing farms (~20% dairy farms) most at risk

Up to 40% loss of grass production in subsequent year if switch to spring reseeding to compensate

Cow tracks Cost (£42/m)

Land compaction Loss of grass yield in subsequent years

Sub-soiling pastures Not visible

Late cut silage impacted. Accounts for less than 10% of overall silage so less of an issue than heavy rainfall in summer

Increased fuel costs of harvesting on poached land


Adaptation Can use extended autumn and spring grazing

Savings in housing and feed costs (bought feed, forage making, slurry handling)

Extend cow tracks Being flexible enough to adapt when mild winter occur and managing grazing

Disease implications likely to be fairly minimal; positive impacts from reduced housing (lameness, mastitis)


Adaptation Increased reliance on bought feed (extensive grazing systems most impacted)

Increased feed cost (20-30%) Increase in milk yield (10-15%) where greater 50% drop in fertiliser use Reduced silaging costs

Change to autumn calving Change to crops which perform better in drought (e.g. forage maize, tap root grass rather than rye grass); the cost of this is likely to be minimal

Likely to adapt forage over time.

Heat stress on cows Higher mortality, lower conception rates Associated costs of housing Water intakes increase

House cows to provide shade if heat stress is extreme Change in housing design (maybe changing dark roofs to lighter so reflect rather than absorb heat) probably at a significant cost Improve water supply infrastructure

Drought less common where dairy sector is concentrated (in the west)


Adaptation Cold winter is unlikely to impact upon cows directly

Accessibility may also lead to issues for milk collection during extended snowfall

365 day housing system (issues with slurry storage and other additional costs)

Cost of infrastructure and system


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Early autumn housing of cows but potentially earlier turnout

Early housing may increase work load and costs Overall input costs may be broadly similar - higher feed use in the autumn offset by lower cost from early turn out

Need to ensure good tracks infrastructure to enable the cows to graze all summer and as late into the autumn as possible

Low temperatures may affect grazing quality

Wet summer likely to result in poorer grass utilisation

Disruption to system likely to impact on production as not always planned for.

5-10% loss of yield

Wet impacts similar to Scenario 1

SCENARIO 6 ( Wet Winter Followed by Hot Summer plus Storm) Potential Impacts Implication Adaptations Barriers to

Adaptation Storm after dry period may lead to runoff (unlikely to influence soil structure as no penetration but may be some soil erosion on cropped fields i.e. maize)

Fences may be taken out by flowing water; impact on buildings - implications for infrastructure Wind may damage infrastructure

Improving infrastructure resilience Recognising which areas of farm infrastructure will be impacted and need for upgrading

Early housing needed No significant impact on milk yield or quality Alternative feed sources required Additional slurry storage and handling

Improve farm capability to handle winter rainfall to prevent slurry/water mixed stores overflowing

Without significant negative impacts adaptations are unlikely

Systems reliant on grazing (extensive and young stock) most impacted

High rainfall can damage reseeded fields

-Finding the capital to upgrade infrastructure (significant costs)

Soil dumped on land leading to contamination, crops can’t be used Flooding may damage some swards and / or increase weed ingression

Impacts on forage quality May not be able to do a second cut of forage, third cut delayed


Adaptation 10-30% loss of yield Spring calving herds likely to see greater impact Impact on milk quality – reduced milk fat levels by 5-10%. Protein levels will drop by a similar percentage.

Implications are dependent on calving system; a spring calving system might lose 25-30% yield unless additional feed available.

Move to autumn calving system Time taken to change calving pattern and associated cost

May get little growth in June, July August so rely on later cuts (Oct/Nov)

Reduced fertiliser spend 10-15% increase in labour due to hauling silage as cows now grazing

Grow alternative crops (maize/fodder beet)

Costs probably wouldn’t prevent a significant barrier

Heat stress Conception rates drop by up to 20% in hot weather If change milking to cooler time may see an increase in labour costs

Use of sprinklers to keep cows cool – but need water storage facilities

Cost of installing the sprinklers and water running costs

Need ample water Provide cool, clean water and enough trough space in all paddocks and at the dairy. Cows may drink 50% of their daily water intake straight after milking, so sufficient cool, clean water is needed at the dairy exit as well as in entry laneways and yards

Higher mortality. Interaction between temperature and humidity death occurs where temp is at 38 c+ and 100% humidity

Provide access to shade throughout the day. Shade can reduce radiant heat load from the environment by up to 50% Could plant trees to provide shade

Tree growing- need a long term view, takes a long time to grow to sufficient to provide shade


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The impact of hot, humid conditions on production may be further compounded by its impact on fertility (reproductive performance reduced above 26°C) with widespread poor conception and pregnancy rates

Reproductive performance impact would probably only affect 10% of herd if extreme conditions were just for a month assuming all year round calving

SCENARIO 8 ( Mild Dry Winter, Severe Late Spring Frosts) Potential Impacts Implication Adaptations Barriers to

Adaptation Early grass growth impacted by frost and with decrease in yield (first cut down by 10-15%) – this is often compensated for in later cuts.

If sufficient feed won’t see yield impacts Increased feed costs - modest

Use frost tolerant grass (longer term plan)

Not seen as a priority

Impact on cows will be influenced by whether cows have been turned out, may be brought back inside

If cows have to be kept in longer or brought back inside there will be associated costs (+5%)

Shifting turnout date Carry larger stocks of forage

Tactical rather than strategic change most likely

May delay housing in autumn Purchased feed cost savings (10%)

4. Impact of Extreme Weather Scenarios on Sheep & Cattle Sector As with dairying, the need to house cattle to avoid extreme weather scenarios is one of the biggest negative impacts and can present more of an issue for sheep, particularly upland sheep given the availability and distance to housing can be limiting. Lowland sheep systems have more of an opportunity to react to extreme weather scenarios and will likely see fewer impacts as a consequence. Generally more adaptations may be taken with beef cattle than sheep given their relative value. Indirect impacts, notably on feed prices are potentially an important issue for this sector.

Table 17: Cattle and sheep sector impacts by scenario

SCENARIO 1 (Summer Flooding) Potential Impacts Implication (Driven by Impacts) Adaptations Barriers to

Adaptation Sheep swept away in rising waters, affects all livestock but see the greatest % loss from sheep as seen of less of a priority given their worth (~£100/head) compared to say beef (~£1000/head) Impact greatest on growing animals

If flooding occurs may concentrate flock on a smaller area of land with insufficient grass which may lead to a drop in growth rate of up to 50% (without feed supplementation)

Providing access to higher land to avoid flood prone areas

Local factors, if land next to river if flat and fertile probably more willing to look at switching round sheep and silage areas

Lowland farms more susceptible to flooding, although upland farmers have the risk also

Hay may be wet with consequent impacts on subsequent winter feed period Contaminated herbage will be an issue

Impact on land and forage crops

Feed costs increase typically 10-15% but could be up to 100% in extreme situation from silage crop destruction/contamination Heavy metal contamination could become an issue

Cattle housed earlier Slurry / FYM spreading could be restricted

Infrastructure (farm buildings, fences etc) damaged from flooding and erosion

Loss of livestock in few cases Invest in maintenance / upgrade of buildings and infrastructure


Adaptation Pregnancy & lambing difficult Sediment loss will be an issue but

impacts on subsequent years productivity would be minimal

Reassessment of viability of sheep on flood prone land

System change unlikely – traditional sector


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Land impacts Water quality may become an issue, impacts of river biology Pollution may become an issue

Building housing if not currently have housing

Farms without housing unlikely to erect buildings due to capital and annual costs; either tolerate a loss in yield or change from sheep farming

Autumn critical for sheep condition to ensure good lambing Ewes lose condition

10-15% decrease in lambing potential due to abortions and re-absorptions Increased disease risk e.g. liver fluke

Improve rainwater storage / drainage infrastructure to prevent flooding,

Forage availability becomes an issue for finishing lambs

Autumn weather is important for a significant proportion of finishing lambs Abattoirs don’t want contaminated sheep

Up to 25% of forage crop unusable to lambs due to being soiled by flooding

10-15% increase in feed costs

Land poaching Land productivity in later years Animal welfare issues

Reduced stocking density Driven by economics

Cattle housed earlier Slurry / FYM spreading could be restricted

Difficult to harvest silage / hay Poor forage quality – higher feed costs or reduced livestock performance


Adaptation Good grass growth so can turn out cattle earlier

Grass grows longer, less purchased feed (10-15% less) Less manual labour distributing feed (10% less)

None required

Suckler cows can be kept out for longer

Lower costs – feed, bedding, energy etc Less need to harvest forage crops for overwinter feeding (reduced labour costs) Possible land poaching if out-winter

Out-wintering cattle May require some infrastructure

Improved animal performance; reduced mortality

Improved returns None required

Potential pest and disease issues but not significant, more significant if housed over mild winter

Vet costs for pneumonia etc Ventilation of buildings Awareness of risks and adaptations


Adaptation Warm, dry conditions mean that grazed cattle and sheep generally do well. Especially so in hill/upland areas as generally wetter

Additional purchased feed (amount depends on drought severity) If don’t supplement the loss of feed there will be performance consequences.

Greater forage stocks / reduced stock numbers e.g. sell as stores to regions less affected

Tactical rather than strategic Reluctance to reduce breeding stock numbers as seen as important to business size/viability

As grass availability decreases utilisation increases, some compensation

Less fertiliser used Few farmers would supplement feed, unless critical. Reliance on compensatory growth, especially in cattle

-

Reduced growth rates 15-25%loss of finished weight, not enough weight loss for mortality

Fire on open moorland/standing crops/hay

Significant impact on access to grazing. Damage to infrastructure

High risk areas of moorland already have plans in place.

Not seen as a responsibility of individual farmer


Adaptation


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Early winter for lowland sheep coincides with ewe lambing requires feed earlier to prevent lamb losses.

If don’t compensate with feed get 10-15% loss of lambs

Change lambing schedule

Upland sheep not yet be pregnant, more barren ewes Might expect 20-25% of ewes not to tup (less (5-10%) if kept near farmhouse)

-Labour costs reduced in dry mild spring, but heavy snow in the early winter will cause additional work

Early housing of cattle Affects feed costs

Extra manure storage capacity

Snow/blizzards lead to ewe losses

Start feeding earlier due to snow (higher feed costs, compensated in spring)

Mild dry spring may reduce lamb losses to partially compensate, reduced feed costs

Overall this set of conditions will make effective use of forage more challenging

10-15% impact on livestock growth rates

The delay in marketing the lamb crop will impact on the available grass for flushing ewes and so potentially reduce the next years lamb crop

More herbage fallowing and reduced grass growth from cloud cover and low summer temperatures

Finishing period longer (3-5 weeks) Additional silage drying costs from the wet summer Spreading of FYM could become a problem as the land is wet all winter and so unable to spread pre arable crops on lowland farms

Summer flood impacts see S1 (localised flooding, land poaching etc.)

SCENARIO 6 (Wet Winter Followed by Hot Summer plus Storm) Potential Impacts Implication Adaptations Barriers to

Adaptation See S2 for wet weather impacts, high levels of poaching (cattle can all be housed so more impact on sheep)

Fences may be taken out by flowing water, implications on infrastructure

Change housing schedule

Costs

Reduced intake in wet winter for grazing livestock

Reflected in drop in growth rates Increased disease risk e.g. liver fluke

Cattle housed earlier (increased feed costs (10-15%) and housing costs)

Increased labour costs from housing (transporting feed and slurry)

Extended grass growth in mild winter but soiling due to poaching

See S7 for heat stress impacts Storms damage buildings, trees, fences etc. Storms Impacts of flash flooding (loss of sheep as in S1)

Livestock not housed in summer so no losses

Improving infrastructure to withstand storm conditions

Storm after dry period may lead to runoff (unlikely to influence soil structure as no penetration but may cause soil erosion on non grass fields i.e. maize crops)

Soil dumped on land during storms leads to contamination)


Adaptation Reduced grass growth Upland thinner soils and river gravels worst affected

Water supply may become an issue as need more water to cope with heat Less fertiliser used

Irrigate grass where possible Grow more drought resistant crops

Not practical for most in this sector

20-30% drop in livestock growth rate depending on whether feed made available

Forced to sell mid-drought at a severely reduced price (40%)


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-Less field work undertaken - no reseeding and probably unable to establish cash crop

Increasing water storage capacity and access

Cost

Heat stress (as S4) Heat stress impacts on final yield, may be 2-3 weeks off their target finish date (assuming adequate forage) Up to 5% loss from mortality

Providing in-field shelters/shade or some structure for animals to hide under. Cheaper options such as temporary tents which could be moved around

Practicality e.g. whether the same fields are grazed each year.

Fire is a potential issue (as S4)

SCENARIO 8 ( Mild Dry Winter, Severe Late Spring Frosts) Potential Impacts Implication Adaptations Barriers to

Adaptation Mild dry winter would be good for all sheep systems

Overall impacts would probably be minimal as savings in feed over winter offset by extra feed requirement in spring

Could alter lambing/calving dates but costs impacts

Traditional sector

Good winter grass growth but checked in spring

Lower feed costs over winter but may be offset by late frost

Cold frosts wouldn’t be expected to lead to major lamb mortality providing not wet and windy

If frosts severe enough might impact upon water pipes

Turn out of cattle delayed but final sale weights/quality probably not reduced

Impacts will probably be localised

Dry conditions will allow good access to fields

5. Impact of Extreme Weather Scenarios on Pig Sector Currently 42% of the national pig breeding herd is outdoors although only about 10% of slaughter pigs are finished outside. Outdoor pigs are more impacted by extreme weather events than indoor pigs. Increased indoor pig housing presents many issues to the pig industry including: availability of suitable housing, heat stress, animal welfare issues, increased labour costs, disease impacts and the issue of tail biting due to stress and indoor housing ventilation systems. As a generally low input system costly adaptations, such as increasing housing capacity or implementing a cooling system are limited currently. Indirect impacts, notably on feed prices are potentially a major issue for this sector.

Table 18: Pig sector impacts by scenario

SCENARIO 1 (Summer Flooding) Potential Impacts Implication (Driven by

Impacts) Adaptations Barriers to Adaptation

Biggest impact on outdoor pigs Less impact on sandy soil, Northern areas will struggle more. In East Yorkshire and East Anglia reasonable soils so less impacts

Straw in reserve to bed up more

Sufficient years of weather event before adapt

More flies, pigs outdoors become wet and susceptible to chest infections, harbouring more salmonella & microorganisms (not a major impact). Bio-security becomes more of an issue, wash down more.

Location important (on very dry free draining land, drains quickly and there are less issues)

Keeping sows on higher ground to avoid flood risk areas

Lack of enforcing legislation

More skin problems - bacterial and abrasions cause problems

Slight hill is the worst location

Digging trenches (on outdoor pig units, most would have a JCB front-loader already, cost is minimal just labour costs)

Lack of planning when building paddocks, once wires etc. in hard to retrospectively install drainage (paddocks rebuild


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every 2 years, opportunity for change)

Bedding becomes damp and mouldy, have to re-bed more often (50% more). If straw bales are stored outside may have issues with damage and wastage

Pooling on flat land (don’t last long on light sandy soils) particular issue if drainage is poor

Mats to keep sows off the floor (reduce soil erosion)

Costs

Indoor pigs always have water diverting but diverting water will become a major issue

Contamination issues if water runs across land into water course.

Putting in buffer strips (again not the cost that would be an issue, sowing with a certain seed, cost relatively small)

Could get flooded housing particularly if pigs are digging around housing. Damage to land by digging and walking if also an issue, soil erosion

Pig farms tend to locate away from rivers which may minimise impacts

Bushes/planting trees (harder to do)

If renting land will have certain limitations

Higher mortality - sows come in wet, piglets getting wet; piglets drowning

May lose an extra piglet per litter drop (up to a 10% drop)

Moving huts prior to flooding, if can anticipate

Indoor pigs see less impact, if aren’t splitting their slurry and rainwater will see an impact of this if overflow becomes a risk

Increase storage capacity of slurry stores (special derogation to spread it may be required, starting to divert rainwater)


House pigs South & South West see the most impacts as less prepared

Storing water Convincing weather will continue long enough to be worth adapting

Pigs crammed inside wet, hot steamy conditions, more coughing and possible Pneumonia. Heat stress is also an issue when crammed inside

Look at building additional capacity for slurry and water storage

Cost

If outdoor pigs brought indoors insufficient room so may struggle to reach the food troughs so get variation in size depending upon access to food trough

Condition of animals decreases due to space restriction

Improve indoor areas for outdoor pigs (Cost: £600/ large huts (6 sows)) Abandoning pig operations

Some farms may look at cheaper ways to house e.g. straw bales with a roof

Typically indoor pigs would use a lot of outdoor space and only sleep inside so aren’t prepared to spend long periods indoors

Stress and loss of condition and stress related disorders

Increase in salmonella Drop in yield (could be hard to determine if via autumn infertility or via extreme weather)


Mild winter for indoor pigs beneficial, can ventilate well

Feed intake, water intake and growth rate and consequent yield all could be reduced by tail biting

Maintain/improve ventilation system

Tail biting could become an issue as get stressed in climate controlled housing when vents opened More flies, possibly an issue

If badly tail bitten reduced price for carcasses. Tail biting can cause whole group to become stressed and agitated, don’t eat as much. 0-45% of indoor pigs could be affected by tail biting in a mild winter

Environmental enrichment to reduce stress Build kennels with straw so protected from vent draughts (additional animal welfare benefits)

Minimal for environmental enrichment / kennels, easy to do.

For outdoor pigs, mild winter beneficial

SCENARIO 4 (Drought) Potential Impacts Implication Adaptations Barriers to Adaptation


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For indoor pigs not much of a problem (water readily available)

Trade off between fenders and keeping piglets inside and reduction in feeding and allowing outside and risk losses

Cost

For outdoor pigs require a good flow rate on drinkers, if this decreases get a decrease in milk yield and impact on piglets (small impact <1%)

Upgrading drinkers to ensure constant supply of water in drought

Outdoor sows spend more time outside farrowing huts in warm, if there’s a fender (stops pigs coming out), not always available to give piglets milk, 10% decrease in piglet body weight

Without fenders piglets outside, piglets can get badly sunburnt, discomfort. Piglets wander off (10% may be lost)

Fenders

Trade-offs of fenders vs. no fenders

SCENARIO 5 (Early Winter: then mild; warm dry early spring; cold wet summer.) Potential Impacts Implication Adaptations Barriers to Adaptation

Feed accessibility would be the biggest issue from snow

Might have to feed pigs non-specific feeds to cope with lack of delivery

Pipes unlikely to freeze indoors but an issue outdoors where pipes can freeze preventing water flow (one of biggest issues)

Impacts influenced by whether site exposed to wind chill and evaporative cooling

Changing to larger pipes and black pipes (easier to thaw out) with strong joints (don’t pop when freeze) Increased indoor space for pigs (or semi-indoors e.g. veranda area). If housed permanently would need a major change to infrastructure

Cost

Sows can wreck drinkers when frozen and drain entire system

Physically difficult to feed in snow (increased labour (up to 3 times more))

Bed up earlier (additional straw required with cost implications)

Welfare issues if can’t house adequately

Straw kept in reserve

Severe winter will reduce disease implications

Mild spring beneficial

Unseasonable and sudden changes in temperature may cause tail biting (see S3)

Cold summers cause sows to lose condition

15% drop in yield during that time (in regard to piglet yield or finisher weight)

Wet summer see S1, cold and wet worse than hot and wet


Very wet winter impacts outdoor pigs, need to bed up almost daily

-More indoor pigs -Pig prices being sufficiently high to be worth investing in adaptations

-Driving tractor difficult in such conditions, soil erosion and feeding up takes longer (additional labour costs)

- Hot summer expect a 15% drop in milk yield, reduced growth rate in finisher herds up to 40% drop in growth rate

-Cooling systems in indoor areas (e.g. refrigerated walls)

-Cost of cooling systems

Spend more time indoors (microorganisms can spread)

Spreading food into muddy fields is an issue

Land poaching Sows that are milking may suffer with mastitis

Flooding (issues as in S2) Diverting rainwater and slurry to prevent overflowing


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Heat stress, when too hot won’t eat, 50% drop in growth rate Mortality if extreme heat stress occurs

Housing cooling systems may be insufficient

Providing adequate water supply

Sunburn Milk yield drops (piglets most affected)

Storm conditions can damage piglets and infrastructure

Contamination issue after storm

Rotating farrowing paddock, if susceptible paddock then only use it when weather is good Plant trees to buffer storm effect from piglets


Outdoor pigs water availability not usually an issue but could be in extreme cases

Sows become agitated, break drinkers which costs money and time to fix

Using larger header tanks to reduce the likelihood of the tank running dry

Cost of the tank is the barrier – however, farms would buy one if they ran out of water for a few years.

Indoor sows/pigs will try to find wet areas to cool down – may cause damage to water drinkers to create wallows etc.

The cost of mending the broken drinkers and the cost of the water that has run down the drains

Top up water using a bowser from mains – again costly in both time and money, probably only used as emergency.

-Seeing enough hot summers to need to adapt

Feed intake will start to drop as appetite is reduced – so growth rate will reduce (reduced piglet size)

Cost of reduced growth rate means pigs will be on farm longer costing money – less throughput means less revenue per year on farm

Feeding wet feeds could mitigate this situation

Costs of adaptation to be able to use a wet feed system

Outdoor sows struggle to cool down without extra shade

Moving pigs in hot sun causes heat stress and some pigs may die

Cooling systems for the indoor pigs as practiced on the continent.

Heat stress Creating shade Legislation for shades to prevent welfare issues

Outdoor sows without shade will suffer sunburn and heat stress

Milk yield will reduce and the piglets will not grow as fast

Extra wallows may be necessary – which cost very little –just a hole filled with water (labour costs)

Cost of wages and UK does not like to work unsociable hour


For mild winter see S3 Ensure there is plenty straw to hand to allow for extra in extended cold spells

In late severe frost outdoor pigs would need to be kept warm

Extra bedding cost Use more straw Cost of straw and availability are limiting factors

Indoor pigs may struggle with ventilation – closed shut on the nights with frost but open wide during the day as it warms up – this can cause stress and sometimes tail biting

Tail bitten pigs or stressed pigs have reduced growth rate and cost the farmer money as he has to separate them and look after them.

Not much can be done to mitigate – check ventilation system is working correctly. Update the system to a newer version

New system is costly – industry is not making enough money to do this. Cost of getting an engineer out.

6. Impact of Extreme Weather Scenarios on Poultry Sector Some 95% of broilers and 50% of egg producing chickens are kept indoors; whilst provides some buffer against extreme weather impacts. For outdoor egg production and chickens reared outdoors there is greater vulnerability to extreme weather events and whilst there is a capacity to house them to avoid extreme weather events this then has consequent impacts on product price if eggs can no longer be sold as free range. For indoor poultry, managing ventilation and heating systems to maintain temperatures, restrict the spread of diseases and maintain suitable humidity is the main adaptation to extreme events. Potentially the biggest impacts for indoor poultry relate to (water and feed) availability and farm access; if water and feed are limited, this can lead to a significant drop in performance. Farm accessibility is key


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for feed delivery and product export. Indirect impacts, notably on feed prices are also a major issue.

Table 19: Poultry sector impacts by scenario

SCENARIO 1 (Summer Flooding) Potential Impacts Implication Adaptations Barriers to Adaptation

Impacts on outside birds which may need to be housed

If outside birds need to be brought inside their eggs may no longer be able to be sold as free range (sold as barn eggs instead) cost implication with a 30% drop in achievable price There is the possibility Defra might make an exception in classification as they have for avian flu, to reduce impacts

With a small poultry farm if there were repeated floods preventing access, they would most likely pack up that operation

Cost would be the biggest barrier to adaptations, particularly on smaller sites

Road flooding and blocking supply of feed would likely by the biggest impact on indoor birds

No significant costs associated with additional housing during summer Feed generally brought by a UK supplier rather than locally so if flooding is localised less likely to have an impact

A large integrated company may consider upgrading access, if they have invested millions in on-farm infrastructure will want to protect this investment

With repeated flood events large poultry operations likely to adapt if necessary for their site

With a transport problem may also get stocking density if birds aren’t being taken away

Lack of feed leads to a decline in production and health and welfare issues

Stress could reduce output by 10% for outdoor birds housed indoors

Welfare issues if stocking densities increase


Humidity in poultry housing Costs of management of humidity impacts probably <1% increase but this could be significant as poultry farms operate tight margins Output may decrease by a few %

Ventilation to deal with humidity and prevent respiratory disease and consequent loss of productivity

Where ventilation not fully utilised due to trying to save costs require education as to the risks of respiratory diseases and how ventilation can reduce these risks

Outdoor birds may need to be housed

Increased stress from housing outdoor birds indoors Increased heat required to maintain temperature

Outdoor birds may trample mud into housing

Increased cleaning costs if mud trampled in (minimal cost implication) Bedding may need to be replaced if turned wet and muddy (costs fairly minimal)

Free Range Poultry have wooden slatted area outside to help clean feet before entering the indoor area (won’t eliminate risk but will reduce)

These adaptations are already taking place, indicates barriers to adapting can and are being overcome


Generally would probably see a positive impact

Around 5-6% of poultry costs are from heating so a decrease in gas cost will not have a very significant impact overall but can make a difference when margins are tight

The lack of cold temperatures may lead to more diseases and pests which overwinter rather than being wiped out

Overwintered pests could lead to a 10% reduction in egg production (not an issue for table chickens)

Adapting housing to have less insulation if continually mild winters

Housing adaptation is currently unlikely as would take many years of consistently mild winters to convince of an adaptation necessity as is a significant change

SCENARIO 4 (Drought)


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Potential Impacts Implication Adaptations Barriers to Adaptation

Temperature unlikely to present an issue for poultry

Cost implication of a drop in output (drop in output would depend on the amount of decrease in water and duration of water shortage)

Drought can have a huge impact Sites with backup water on site probably have 12 hours supply or less so beyond this there is an issue A 10% drop in water availability could lead to a 10% drop in performance

Backup supply of water (or increasing capacity of backup) Boreholes

Cost (around £10,000 for a 24 hour supply, unlikely to have longer than a 24 hour supply on-site practically) Boreholes may not work during a drought so may not be an effective adaptation

Birds kept indoors need a constant supply of water and water shortages lead to a significant and rapid drop in performance and quickly lead to a drop in growth rate, animal welfare issues, dehydration, disease and mortality

May need to find a way to get water transported in (likely to be at a high cost) For a site with 10 houses and 400,000 birds quickly becomes an issue

Turning water supply on and off to manage availability can create air locks and consequent complications

SCENARIO 5 (Early Winter: then mild; warm dry early spring; cold wet summer) Potential Impacts Implication Adaptations Barriers to Adaptation

This combination of events wouldn’t present any significant negative impacts

Snowfall could present an issue if transport was impacted

The mild spring may lead to humid conditions and ventilation issues as in S2.


A wet winter may lead to a dislocation of feed supplies with the associated health and welfare issues (see S2)

-New stocking density regulations (reduced to 38 kg/m2) mean there is less of an impact than under previous regulations

Additional fan capacity (adds 5-10% to cost of new building. 50/50 split of old and new housing.

Would only take a few years of extreme heat to occur before a large % of farmers would look to upgrade ventilation

Ventilation issues if humidity around winter

Heat stress leads to significant health, welfare, performance and economic impacts.

Retrofitting fans to older building for 30,000 birds would cost in the region of £15-30k (£0.50-£1.00/bird)

When putting up new buildings high probability of allowing for fan capacity; less likely to retrofit into older buildings

Hot summers present a problem for broilers as stocking density is fairly high

Heat could affect 60 million chicks per week of heat event

Ventilation will significantly reduce the impacts but not eliminates, as outside temperature goes up so will inside

Cost

Heat stress Can lead to widespread mortality

Misting system could be implemented

Laying hens, turkey and free range hens will have less issues in these conditions than broiler chickens

Adaptations would prevent significant welfare issues


See impacts from drought (S4) and extreme temperatures (S6)

Drought has less of an impact on hatcheries as eggs have their own water and feed supply, whilst live birds will be impacted

Reducing stocking density Reducing stocking density would reduce profitability; would need to experience significant impacts to trigger adaptation

The tight timescale on poultry production means production is constant and whereas other production systems may be able to delay harvesting by a month for example poultry cannot do this.

Fast turnover means a short term extreme event will have an impact

Additional capacity of water storage

-Cost (around £10,000 for a 24 hour supply, unlikely to have longer than a 24 hour supply practically)


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Ventilation issues Ventilation systems When putting up new buildings high probability of allowing for fan capacity; less likely to retrofit into older buildings


Mild dry winter would not present a problem

With poor ventilation can expect to see an increase in chronic disease

Education on ventilation and the need to maintain a minimum rate of ventilation

Drive to save costs may lead to not wanting to ventilate

During spring frost may see an increase in fuel costs

An increase in insulation to minimise heating costs (already seen an increase in insulation in the last 20 years from 4 inches and 100ml of fibreglass to double today)

Insulation, will want to know that will see a return on investment, hotter summers will also lead to drive to insulate as keeps buildings cool in summers

Humidity and a lack of cold winter to kill of parasites creates a risk of red mites which cause increased stress, birds may become anaemic or lead to mortality in extreme cases and will lead to a performance impact with laying hens.

Red mites can lead to aggression in birds and welfare issues Respiratory disease may occur if ventilation is inadequate (particularly if trying to prevent heat losses)

Treating parasites and keeping humidity lower to minimise parasite transmission


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Appendix 5: Spatial mapping methodology and datasets A spatial mapping approach is used to identify the geographical extent of an extreme weather event, according to a given criterion, for example EA Flood Zones (see Figure 11). This dataset is used in conjunction with land use maps based on Defra Agricultural Census data (2010). This provides cropping areas and livestock numbers on a 1 km2 grid across England, with results presented by county, region and or at country level. This dataset allows agricultural land use in flood zones (or another spatially defined area) to be quantified for the purposes of scaling up extreme weather impacts using per hectare output and input data for crop enterprises and per head data for livestock.

Figure 11: Illustration of GIS mapping of Defra agricultural census and EA flood (Zone 3) data

These data layers were imported into Esri ArcGIS as Esri shapefiles, along with regional boundaries. In order to obtain an estimate of each crop and livestock type in the flood zone areas in each region, the geoprocessing tool, Intersect, was used.

This created individual polygons with unique attributes (cropping / livestock information; region and flood zone). The cropping / livestock data were still the numbers associated with the whole 1 km2. Therefore, the areas (km2) of the intersected polygons were calculated, and this was used to scale the cropping / livestock data as a proportion of the original 1 km2 grid cell.

The crop areas and livestock numbers were summed by region in Microsoft Access, in order to give a summary of agricultural land use in the flood zones.

The ADAS 1 km2 cropping dataset is a statistical representation of the June Agricultural Census data that has undergone a large amount of processing to disaggregate it to a grid. The issue of disclosivity is therefore not as immediate as it would be for the raw census data. However there may be some issues of disclosivity for minor crops at a district level. There is unlikely to be a disclosivity issue when data are presented in tabular form at a regional level rather than on a map, since exact locations cannot be inferred.

For the avoidance of doubt, outputs displaying cropping areas for minor crops area and livestock numbers have been combined into more generic categories for the purpose of publication. For example, census categories A4-A7 (Oats; Mixed Grain; Rye; Triticale) are combined to form the category ‘Other cereals’ and categories A10 & A11 (Early Potatoes: Late Potatoes) are combined to form the category ‘Potatoes’. The London region has also been combined with the South-East region, since there are very small areas of most crops in London.


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Datasets available for spatial mapping

Soil water regime

Soil water regime is defined by six classes of soil wetness based on the duration of wetness at depths of 40 and 70 cm (Table 20). A wet soil is defined as containing water removable at a suction of less than 10 mb.

Table 20: Soil wetness classes, defined by duration of wetness at depths of 40 and 70 cm

I Wet within 70 cm depth for fewer than 30 days in most years;

II Wet within 70 cm depth for 30 to 90 days in most years;

III Wet within 70 cm depth for 90 to 180 days in most years;

IV Wet within 70 cm depth for more than 180 days, but not wet within 40 cm depth for more than 180 days in most years;

V Wet within 40 cm depth for more than 180 days and is usually wet within 70 cm depth for more than 335 days in most years;

VI Wet within 40 cm depth for more than 335 days in most years;

Source: Jobson and Thomasson (1977).

Notes: The number of days specified is not necessarily a continuous period ‘In most years’ is defined as more than 10 out of 20 years

For each 1 km2 grid cell, the Soil Wetness Class (SWC) of the soil series present in the cell was calculated using the Hydrology of Soil Types (HOST) and profile data provided by the NSRI National Soils Inventory, using the procedure developed by Hollis (1989).

For the purposes of this project, drought-prone soils were taken to be those in soil wetness class I and soils susceptible to waterlogging those in soil wetness classes V and VI.

ADAS cropping (ha) and livestock (head) statistics for 2010 at 1km spatial resolution were multiplied by the proportion of the grid cell that comprised soils that are (i) drought-prone and (ii) susceptible to waterlogging. This provided an estimate of the area of each crop and the numbers of each livestock category that were farmed on soils that are (i) drought-prone and (ii) susceptible to waterlogging. The assumption is made that the proportions of each soil wetness class are the same on agricultural land in the grid cell as for the entire grid cell.

The resultant agricultural statistics for cropping and livestock on soils that are (i) drought-prone and (ii) susceptible to waterlogging were summarised by region. Table 21 below shows the area and distribution of sectors using these datasets. This suggests fairly even distribution of cropping on drought prone soils at around 30% of all England cropping area which appears high. For soils prone to waterlogging there is a much smaller cropping area at around 5% of England area but a relatively higher proportion of grassland at 17%. These datasets need to be considered further as reliable estimates of drought and waterlogging.


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Table 21: Area of cropping affected by flooding using Soil Wetness Class

Soil Wetness Class I (drought) Soil Wetness Class V & VI (drought)

Crop area (ha) % of England area

Crop area (ha) % of England area

Cereals 788,297 32% 125,447 5%

Oilseed rape 178,546 30% 25,652 4%

Peas and beans 55,584 28% 8,238 4%

Potatoes 34,860 35% 3,920 4%

Sugar beet 40,109 34% 1,140 1%

Horticultural crops 43,945 30% 5,883 4%

Grass and forage crops 1,256,942 31% 697,775 17%


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Appendix 6: A worked example of economic impacts (Scenario 1) An example is set out here based on Scenario 1 (Localised Flooding). In order to indicate the flood plain land naturally affected by flooding (both river and coastal), the Environment Agency Flood Zones for England were used, namely:

- Flood Zone 3: areas affected by flooding (rivers and coastal), if there were no flood defences. The area could be flooded from a river flood with a ≥1% chance of happening each year, or a flood from the sea with a ≥0.5% chance of happening each year.

- Flood Zone 2: areas affected by extreme flooding (rivers and coastal), <0.1% chance of occurring each year.

Based on the methodology set out in the previous section, data are presented for Scenario 1 at each step below.


The meteorological conditions which are represented in this scenario are:

Country wide summer flooding, resulting from rainfall 200% above the 1981-2010 mean from June to August, with a 20% increase in rainfall intensity. Temperatures are assumed to be average for 2050 or slightly cooler. The analogue is 1912.


Impact estimates, expressed as percentage change in volume of outputs and inputs have been drawn from the expert analysis of Scenario 1 (tables 14-19) are summarised in below. The data highlights the significance of high value crops such as potatoes and horticultural crops in flood zones and the higher impact on crops relative to livestock. Year two effects on cereals and horticulture relate to issues of autumn crop establishment in flooded areas. Table 22: Estimates of output and input change due to Scenario 1

% yield loss

Year 1 Year 2 Year 3

Area/no. of livestock impacted

Agricultural output per ha/head Cereals, oilseeds etc -40.0% -20.0% 13% Potatoes and sugar beet -50.0% 22% Horticultural crops -50.0% -20.0% 22% Dairy -5.0% Other cattle enterprises -10.0% 8-9% Sheep -15.0% 6% Pigs -4.0% 12% Poultry (egg production) -15.0% 13% Agricultural inputs Fertiliser (horticulture) -10% Fertiliser (grassland) -25% Crop protection (horticulture) -10% Purchased feed & fodder +10% Vet and livestock sundries +10% Machinery fuel and oil +10% Water, electric and other +10% Fuel, electric and other fixed costs +10%


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Step 3: Calculate the 3year ‘average’ economic performance for robust farm types (FBS data) at farm level.

Detailed output and input costs for the nine robust farm types in England (Table 24, page 102) are used as a basis to calculate the impact of weather-related change for this country-wide scenario. For this example the single year data has been used but the methodology recommends use of 3-year average values to remove single year seasonal and/or price effects.


The percentage change in output and input has been applied to the unit value of each category used to calculate unit change in economic value for each output category across all robust farm types. Data is shown in Table 23 for unit change in enterprise outputs across all robust farm types for Scenario 1. Table 23: FBS enterprise output for crops across robust farm types

Cereals General

cropping Horticulture Mixed Dairy

Lowland Grazing

Livestock LFA Grazing

Livestock Pigs Poultry Output per ha of each crop Output per head of each livestock type

winter wheat -£468 -£483 -£556 -£453 -£467 -£483 -£1,341 -£490 -£478

winter barley -£334 -£309 -£278 -£331 -£349 -£359 -£887 -£459 -£411

spring barley -£341 -£351 -£212 -£394 -£336 -£310 -£729 -£581 -£309

other cereals -£238 -£172 -£305 -£205 -£250 -£248 -£521 -£157 -£242

oilseed rape -£469 -£513 -£516 -£474 -£460 -£519 £0 -£515 -£464

peas and beans -£260 -£349 -£272 -£249 -£235 -£269 -£220 -£302 -£169 potatoes

-£2,036 -£2,573 -£2,679 -£2,478 -£2,021 £0 £0 -£1,625 £0 sugar beet

-£972 -£973 -£1,078 -£1,021 -£690 £0 £0 -£848 -£765 Other Crops (incl. hort.) -£319 -£1,513 -£7,754 -£634 -£123 -£1,039 -£1,733 -£408 -£508 Milk and milk products -£72 -£128 £0 -£101 -£99 -£60 -£66 £0 -£48 dairy cattle

£26 £28 £0 £9 £6 £22 £13 £0 £7 other cattle

-£32 -£37 £0 -£36 -£44 -£34 -£33 -£33 -£30 sheep and wool -£15 -£16 £0 -£16 -£16 -£16 -£11 -£15 -£13 pigs

-£2 -£3 £0 -£6 -£8 -£2 -£6 -£7 -£2 eggs

-£3 -£3 £0 -£3 -£3 -£4 -£3 -£4 -£2 broilers and other poultry £0 £0 £0 £0 £0 £0 £0 £0 £0

For inputs, a single FBS figure is available for each category e.g. seed for each farm type but this is not split between individual enterprises as is the case for outputs. As the impacts of the weather will be discrete for each farm type or, individual estimates of impact are applied to each enterprise group (Table 25, page 103)


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Table 24: Farm Business Survey (FBS) detailed outputs and inputs (3-year average 2009/10, 2010/11 and 2011/12)

Detailed Output and Input Costs England - Baseline (3-year av) http://www.farmbusinesssurvey.co.uk/regional/ Cereals General cropping Horticulture Mixed Dairy

Lowland Grazing Livestock

LFA Grazing Livestock Pigs Poultry

Per Farm Per Farm Per Farm Per Farm Per Farm Per Farm Per Farm Per Farm Per FarmFarms In Sample 330 176 217 197 324 261 253 70 87

Agricultural output 195,256 351,388 359,723 208,914 356,103 72,953 69,036 474,694 634,230 Crop output (excluding subsidies) 169,462 312,351 350,679 89,013 24,698 11,192 6,301 29,552 17,518 winter wheat 85,293 80,318 7,412 35,146 11,689 2,128 1,051 13,167 8,298 winter barley 8,659 8,803 352 7,638 2,486 1,506 850 3,792 1,762 spring barley 7,581 10,190 387 6,959 1,733 1,361 784 1,970 386 other cereals 5,050 4,363 805 3,552 605 413 286 512 232 oilseed rape 36,087 23,097 735 10,929 2,144 147 103 4,475 2,140 peas and beans 6,008 5,594 161 2,194 278 72 37 801 114 potatoes 529 70,705 4,286 4,609 94 17 4 22 0 sugar beet 4,473 36,617 3,055 1,328 202 0 0 243 1,229 other crops 2,237 57,812 331,867 2,072 11 62 35 539 796 by-products, forage and cultivations (excl. set-aside) 11,781 14,112 2,229 13,554 5,135 5,411 3,118 3,890 2,422 Disposal of previous crops 1,765 740 -610 1,032 319 76 34 141 139 Livestock output (excluding subsidies and payments to agriculture) 9,399 16,600 2,488 108,425 325,351 57,134 59,229 442,430 614,204 milk and milk products 48 937 0 17,106 279,930 956 2,079 0 129 dairy cattle -17 -205 -31 -1,591 -16,867 -359 -396 0 -18 other cattle 5,161 8,650 1,498 39,054 56,595 37,839 31,109 3,145 3,210 sheep and wool 2,715 2,346 569 13,551 3,657 17,214 25,628 1,965 1,489 pigs 627 2,350 39 24,813 1,320 104 306 436,601 784 eggs 705 1,584 43 8,412 700 225 235 821 189,396 broilers and other poultry 148 923 366 6,705 13 67 8 -111 419,221 other livestock (including horses) 12 13 5 375 3 1,088 260 9 -8 Subsidies and payments to agriculture 377 368 40 532 1,376 329 315 48 17

Miscellaneous output (including agrcultural work done on other farms) 16,017 22,069 6,517 10,946 4,678 4,298 3,190 2,664 2,491 Output from Agri-environment activities and other payments 8,292 10,097 1,971 7,087 4,318 5,299 8,671 1,584 1,843 Output from diversification out of agriculture 18,816 14,796 26,204 20,031 7,960 9,035 4,500 6,485 14,180 Output from Single Payment Scheme 42,440 48,397 6,260 33,140 28,690 20,258 21,361 9,063 6,889

Agricultural costs 178,160 320,182 326,483 201,741 321,641 74,673 72,212 435,459 591,964 Variable costs 82,564 150,345 178,677 108,309 190,230 36,652 35,959 299,160 415,365 Crop specific costs 61,864 106,532 136,366 34,586 25,749 6,773 6,271 8,637 5,056 seed 8,748 24,707 62,382 5,879 3,998 956 581 1,385 1,009 fertilizers 26,771 33,233 12,527 15,384 15,200 4,054 4,350 3,299 1,705 crop protection 21,623 29,771 9,189 10,040 3,920 851 494 3,383 1,942 other crop costs 4,721 18,821 52,267 3,283 2,631 912 846 570 400 Livestock specific costs 4,931 8,453 1,198 59,080 142,059 23,955 24,781 278,445 397,871 purchased feed & fodder 2,001 3,638 529 32,725 93,089 11,507 13,295 230,010 354,433 home grown feed & fodder 889 2,132 202 10,767 7,848 2,828 1,613 8,627 1,072 veterinary fees & medicines 450 579 131 3,738 12,479 2,554 3,012 12,873 9,660 other livestock costs 1,590 2,104 337 11,849 28,642 7,066 6,861 26,934 32,706 Contract costs 13,025 21,601 4,981 11,681 18,694 4,809 3,637 9,333 8,832 Casual labour 1,938 12,255 34,827 2,635 3,604 982 1,101 2,744 3,593 Miscellaneous variable costs (including for work done on other farms) 807 1,503 1,305 327 124 132 168 1 13 Fixed costs 95,597 169,837 147,806 93,432 131,411 38,022 36,253 136,298 176,599 Regular labour 10,544 30,279 70,603 14,966 28,216 3,693 2,969 45,339 51,534 Machinery: fuels and oils (a) 9,251 16,278 7,260 8,828 10,281 3,833 3,947 7,898 5,887 Machinery: repairs and other (a) 9,388 18,235 8,956 9,530 12,688 4,197 3,860 11,029 10,197 Machinery depreciation 20,809 30,418 11,883 17,896 22,236 8,136 8,296 14,702 15,655 Depreciation of glasshouses & permanent crops 2 -254 3,662 -20 0 0 0 0 0 General farming costs 18,638 29,404 28,987 18,284 29,289 9,140 8,481 28,334 53,025 Bank charges & professional fees 4,548 5,699 4,933 3,566 5,271 1,954 1,809 3,548 6,853 Water, electricity and other general costs 10,573 18,282 21,079 11,310 18,207 5,694 5,226 17,852 38,763 Share of net interest payments 3,505 5,350 2,877 3,408 5,807 1,481 1,441 6,933 7,408 Write-off of bad debts 12 73 97 0 4 10 5 0 0 Land and property costs 16,952 31,354 14,396 16,991 25,447 6,643 6,841 27,877 38,880 Rent paid 13,522 26,514 10,993 12,480 16,956 5,055 5,306 18,221 14,001 Maintenance, repairs and insurance 547 828 610 402 520 204 211 736 1,036 Depreciation of buildings and works 2,883 4,012 2,793 4,109 7,971 1,384 1,325 8,920 23,842 Miscellaneous fixed costs (including for work done on other farms) 10,014 14,124 2,058 6,956 3,254 2,379 1,858 1,119 1,422 Costs of Agri-environment activities and other payments 1,731 1,923 413 1,187 688 1,200 1,848 269 504 Costs of diversification out of agriculture 7,534 7,147 16,044 12,123 3,827 4,743 2,298 3,886 5,783 Costs of Single Payment Scheme 3,744 4,034 539 3,044 2,259 2,168 2,462 630 530


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Table 25: Estimates of change in volume of variable and fixed cost categories under Scenario 1 Agricultural inputs Arable crops Horticulture Dairying Cattle & sheep Pigs Poultry Variable costs Seed Fertilizers -10.0% -15.0% -15.0% Crop protection -10.0% Other crop costs Livestock specific costs Purchased feed & fodder 10.0% 10.0% 10.0% 10.0% Home grown feed & fodder 10.0% 10.0% 10.0% 10.0% Veterinary fees & medicines 10.0% 10.0% 10.0% 10.0% Other livestock costs 10.0% 10.0% 10.0% 10.0% 10.0% 10.0% Contract costs 10.0% 10.0% 10.0% 10.0% 10.0% 10.0%


The EA Flood Zone datasets were overlaid with the Defra Census data, mapped at 1km2 to provide estimates of the area of crops and head of livestock impacted. Two sets of data were available; Flood Zone 3 was used in this instance.

Table 26: Scale of agricultural enterprises within EA Flood Zones 3 and 2 Flood Zone 3 Flood Zone 2 Agricultural output per ha/head Ha Ha winter wheat 260,364 288,061 winter barley 31,521 35,731 spring barley 22,587 25,809 other cereals 9,032 10,384 oilseed rape 77,786 86,200 peas and beans 26,971 30,157 potatoes 21,894 24,099 sugar beet 25,691 28,207 Other Crops (incl. hort.) 31,811 34,765 Head Head dairy cattle 90,649 107,621 other cattle 86,893 100,789 sheep and wool 335,743 389,338 pigs 122,800 138,442 eggs 2,466,635 2,784,906 broilers and other poultry 10,438,286 11,817,711


The scale data from Table 26 are then used to scale up the unit change estimates from Step 4. For scenario 1, enterprise impacts are shown in Table 27 below.

A similar process is applied to inputs to provide a full set of volume-adjusted economic impacts for the scenario. However, for inputs a broader set of farm types is used (Arable, Horticulture, Dairy, Grazing livestock, Pigs and Poultry). Aggregated Census data on hectares and numbers of livestock for each of these categories by region is combined with % change data for the input categories to provide a weighted estimate of changes inputs.


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Table 27: Total impacts of Scenario 1 on farm enterprise output of enterprises within EA Flood Zones 3

Average impact Flood Zone 3 Volume-adjusted Economic Impact Agricultural output per ha/head (£ per ha) Ha £ winter wheat -£476 162,630 -£77,369,099 winter barley -£346 14,171 -£4,907,080 spring barley -£364 10,277 -£3,744,218 other cereals -£210 3,859 -£809,725 oilseed rape -£485 44,602 -£21,641,974 peas and beans -£286 16,720 -£4,774,623 potatoes -£2,568 16,027 -£41,152,095 sugar beet -£971 23,188 -£22,519,466 Other Crops (incl. hort.) -£4,191 37,843 -£158,609,662 (£ per hd) Head milk and milk products -£99 19,787 -£1,952,244 dairy cattle £6 19,787 £126,294 other cattle -£37 123,280 -£4,579,620 sheep and wool -£14 89,159 -£1,215,226 pigs -£7 168,105 -£1,120,103 eggs -£2 1,366,844 -£3,250,007 broilers and other poultry £0 6,415,890 £0


Only prices for fresh produce (potatoes and horticultural crops) are expected to be impacted, with no wider global changes in supply or demand. The scale of price change allows for the relatively significant proportion of the crops affected but this is limited by the timing of the flooding. The resulting economic impacts are summarised in Table 28.

Table 28: Weighted change estimated for enterprise output across all robust farm types Volume-adj

Economic Impact

(a)

Baseline economic value (FBS 3-year av.)

(b)

Volume-adj economic

value (b-a)

Supply-led change in

market price (d)

Price and volume-adjusted economic value

(b-a)x(1+d)

Net economic impact of

weather event (b-a)x(1+d)-b

winter wheat -£77,369,099 £2,130,736,183 £2,053,367,083 0% £2,053,367,083 -£77,369,099

winter barley -£4,907,080 £277,245,609 £272,338,529 0% £272,338,529 -£4,907,080

spring barley -£3,744,218 £241,669,832 £237,925,613 0% £237,925,613 -£3,744,218

other cereals -£809,725 £1,784,853 £975,127 0% £975,127 -£809,725 oilseed rape

-£21,641,974 £727,289,832 £705,647,858 0% £705,647,858 -£21,641,974 peas and beans -£4,774,623 £143,467,474 £138,692,851 0% £138,692,851 -£4,774,623 potatoes

-£41,152,095 £512,719,980 £471,567,885 10% £518,724,674 £6,004,694 sugar beet

-£22,519,466 £230,104,910 £207,585,444 0% £207,585,444 -£22,519,466 Other Crops (incl. hort.)

-£158,609,662 £1,910,771,549 £1,752,161,887 5% £1,839,769,981 -£71,001,568

milk & milk products -£1,952,244 £2,284,020,804 £2,282,068,560 0% £2,282,068,560 -£1,952,244 dairy cattle

£126,294 -£147,756,720 -£147,630,426 0% -£147,630,426 £126,294 other cattle

-£4,579,620 £1,619,486,326 £1,614,906,706 0% £1,614,906,706 -£4,579,620 sheep and wool -£1,215,226 £644,042,122 £642,826,896 0% £642,826,896 -£1,215,226 pigs

-£1,120,103 £600,433,277 £599,313,173 0% £599,313,173 -£1,120,103 eggs

-£3,250,007 £356,904,383 £353,654,376 0% £353,654,376 -£3,250,007 broilers and other poultry £0 £769,381,280 £769,381,280 0% £769,381,280 £0


105

It can be seen that for potatoes and horticulture, the price effect more than offsets the volume loss, so the economic method is very sensitive to price change.


Summing the output and input changes before and after price adjustment provides an estimate of the net economic impact of Scenario 1. From Table 29 the figures for year 1 are £363 million (£820/ha) and £229 million (£516/ha) respectively, the latter reflecting the positive impact of higher fresh produce prices on the residual production.

Table 29: Volume and price adjusted estimates of net economic impact of Scenario 1 (Year 1)

Volume-adjusted Economic Impact Net economic impact of weather event Total impact -£363,281,375 -£228,516,492

Area affected (ha) 442,961 442,961

Total impact per ha -£820 -£516

Step 9: Aggregate multiple year impacts.

The model but considers the impact on volume of outputs and inputs, and on prices over a three year period to allow for the fact that some weather events come at the end of the year and others have a residual effect. A simple addition of annual effects is used. Thus, depending on the ability of land to recover from flooding and delayed harvest, some early sown cereal crops and oilseeds may be sown late or spring sown. Over the three year period, the net economic impact of Scenario 1 is estimated at £344 million (£776/ha).


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Appendix 7: Defra list of climate change adaptation measures No. Adaptation measures 1 Support networks to share information and experiences among farmers. 2 Planning to deal with changes and losses including natural disasters such as floods and fires. 3 Industry planning to take advantage of new opportunities. 4 Increased monitoring and of natural and agricultural changes including monitoring and forecasting of pests and

diseases. 5 Replace old crop varieties /livestock breeds with ones better suited to new (e.g. hotter, drier, more saline) conditions or

to take advantage of new opportunities such as hemp, wine, grapes, peaches, cherries. 6 Diversify crops/livestock to hedge bets against unpredictable weather. 7 Plant and animal breeding /selection for new adaptation traits (e.g. drought and pest/disease resistance. 8 New and diversified feed and forage crops. 9 Agro-forestry 10 Sustainable drainage systems (including porous surfaces, infiltration trenches, filter drains, ponds or wetlands, grass

buffers) to slow water flow, increase infiltration into soil and reduce flooding. 11 Restore Natural River Profiles. 12 Maintain/restore/create wetland, ponds and water meadows. 13 Maintain high water tables in peat lands to reduce loss of soil organic matter and block drains in peat. 14 Use land for flood storage 15 Plant trees (including for flood alleviation, shade and shelter, agro-forestry, new habitat), using appropriate tree species

to cope with future conditions. 16 Buffer strips of grass, scrub and/or trees around watercourses and other important habitat areas. 17 Increase water storage (ideally through 'softer' structures such as ponds. 17b Increase water storage – reservoirs. 18 Improve manure storage facilities to cope with wet winters. 19 Precision farming to optimise inputs (to reduce stress on the environment and improve its ability to adapt) 20 Livestock water pumps. 21 Facilities to dry crops. 22 Improve seed and crop storage facilities to deal with changes in moisture and temperature, as well as possible

increases in pests. 23 Reduce water wastage. 24 Reuse water. 25 Collect and store water runoff from buildings. 26 Improve irrigation efficiency. 27 Limit abstraction of water. 28 Measures to avoid soil erosion, compaction and runoff from grazing. 29a Keep livestock off wet soils where there is a risk of flooding. 29b Keep livestock off watercourses 30 Move livestock indoors or to a higher ground during times of flood risk 31 Provide appropriate livestock housing, transport and stocking numbers to maintain animal welfare during hot weather. 32 Provide sufficient shade/shelter for livestock. 33 Establish pasture with a diverse range of plant species that is more resilient to grazing pressure in poor conditions. 34 Provide extra silage when grazing options reduced by bad weather. 35 Measures to reduce erosion, compaction and runoff from arable farming and horticulture. 36 Mulches to conserve water used in cultivating crops. 37 Measures to increase soil organic matter. 38 Alter timing of activities (e.g. planting, harvest, silage cutting) to take advantage of/cope with changed conditions 39 Appropriate tillage to increase soil organic matter and moisture retention. 40 Measures to reduce damage to crops from storms (e.g. shade or hail netting, trees/hedges as shelter belts. 41 Minimise pesticide use e.g. through integrated pest management strategies and timing of crop planting (to reduce stress

on the environment.) 42 Measures to reduce leaching of fertiliser and pesticides including applying chemical inputs at times of lowest risk.

Documents

Climate Change and Extreme Weather Events; Establishing a