WEATHER, CLIMATE, AND FARMERS: AN OVERVIEW : Roger Stone

Expert meeting 15-18 November 2004.

Some key underlying points:

•Improved seasonal to interannual climate prediction offers farmers and agricultural industry the opportunity to protect, or even to increase, economic well-being.

• These advances in science in meteorology and climatology should enable society to deal with the effects of weather and climate variability more effectively than ever before.

• ‘The effectiveness of forecast information depends strongly on the systems that distribute the information, the farmer’s modes of understanding and judgement about the information sources, and the ways in which the information is presented’ (after Stern and Easterling, 1999).

(From Stone et al; Nature, Nov 1996)

We now have the capability to predict seasonal rainfall in many world regions

Forecasts are prepared in a variety of ways

                                                                                     

                                         

It may be the case that the manner in which forecasts are prepared and disseminated has a major bearing on how or whether these forecasts can be utilised.

The value of forecasts to farmers will depend not only on their accuracy but also on the management options available to the user to take advantage of the forecasts (Nicholls, 1991).

Climate and weather information may have no value unless it changes management decisions.

Management decisions require management

tools

Climate Information and Forecasts and Decision Making

Farm Harvest, Transport, Mill Catchment Marketing PolicyFarm Harvest, Transport, Mill Catchment Marketing Policy

Scale Axis Scale Axis

In

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C l i m a t e Scale Information C l i m a t e Scale Information

•Irrigation•Fertilisation•fallow practice• land prep • planting• weed manag.• pest manag.

• Improved Planning for wet weather disruption – season start and finish•Crop size forecast•CCS, fibre levels•Civil works schedule

• Land & Water Resource Management

•Environmental Management

• Water allocation•Planning and policy associated with exceptional Events

Industry

Business and Resource ManagersBusiness and Resource Managers

GovernmentGovernment

• Crop size Crop size ForecastForecast•Early Season Early Season SupplySupply•Supply PatternsSupply Patterns-ShippingShipping-Global SupplyGlobal Supply

Need to consider the whole value chain Need to consider the whole value chain

Understanding issues across the whole value chainUnderstanding issues across the whole value chain

The CanePlant

Sugarcane Production

Harvest & Transport

Raw Sugar Milling

Marketing & Shipping

•• Best use of scarce/costlyBest use of scarce/costlywater resourceswater resources

•• Better decisions onBetter decisions onfarm operationsfarm operations

•• Improved planningImproved planningfor wet weatherfor wet weather

disruptiondisruption•• Best cane supplyBest cane supply

arrangementsarrangements-- crush start andcrush start and

finish timesfinish times

•• Better schedulingBetter schedulingof mill operationsof mill operations-- crop estimatescrop estimates-- early seasonearly season

cane supplycane supply

•• Better marketing decisions basedBetter marketing decisions basedon likely sugar qualityon likely sugar quality

•• More effective forward sellingMore effective forward sellingbased on likely crop sizebased on likely crop size

•• Improved efficiency of sugarImproved efficiency of sugarshipments based on supplyshipments based on supplypattern during harvest seasonpattern during harvest season

Case study example from RSA: An integrated climate-farming/cropping systems forecast

Planting date: 1 November Planting date: 1 November (El Niño Years)(El Niño Years)

Probability (%) of exceeding maize yields of 2.5 t/haProbability (%) of exceeding maize yields of 2.5 t/ha

Planting date: 1 November Planting date: 1 November (La Niña Years)(La Niña Years)

Forecasting the Australian Grain Crop; example of a fully integrated agrometeorological system

Rainfall up to date and Rainfall up to date and Climate ForecastClimate Forecast

Simple Agro- Simple Agro- climatic modelclimatic model

Geographical Geographical InformationInformation

SystemSystem

Drought ProbabilityDrought Probability

5 7 9 11 13 5 7 9 11 13

5 7 9 11 13

Month

0.5

1.0

1.5

2.0

2.5

0.5

1.0

1.5

2.0

2.5

NSW QLD SA

VIC WA AUS

Wheat outlook for the 1999 season

10%Pred50%Pred90%PredABARE10%NoP90%NoPLTmed

Spatial StatisticsSpatial Statistics

Crop OutlookCrop Outlook

…simulateswater STRESS...

The Model - Simple water balance

Crop available Soil water

SoilDepth

EvaporationEvaporation

Run-off &Run-off &

Deep drainage Deep drainage

Compare to reference yield

expectation

Causes Effects Corrective action

Credibility Previous forecasts are perceived as being ‘wrong’ and the communicator is not generally trusted.

Users will ignore the forecasts

Give probabilistic forecasts and rely on trusted communicators

Legitimacy Forecasts are perceived as superceding users local knowledge

Users will ignore the forecasts and reject any associated advice

Attempt to incorporate local knowledge into the forecast and important to involve users in developing the advice information

Scale Forecasts provide no information about events in their local area

Users will not incorporate forecasts into their decision-making processes

Need to work with users to analyse the implications for the local area. Attempt to provide regional or local scale forecast information, in probabilistic format.

Cognition Forecasts are new in format, confusing, and different.

Users will either not incorporate forecasts or they will do so in a way that is counterproductive .

Need to work repetitively with users to decipher the meaning of forecasts for their local region and to correct mistakes.

Procedures Forecasts finally produced at the wrong time, to the wrong people, or is unexpected

Users will not incorporate forecasts

Repeat communication to resolve the timing, involvement of relevant key players, And aim to ensure consistency.

Choices Forecast information does not contain enough information to alter any specific decision.

Users (farmers) will not changes decisions in response to a forecast.

Need to improve forecast skill and encourage users to make incremental decisions (‘lean’ rather than ‘jump’).

Terms of Reference for the Expert Team on Weather, Climate and Farmers

  (a) To review and develop recommendations for enhancing more effective and regular communication, and dialogue for training and demonstration between agrometeorological services and farmers at the local level to provide better services to farmers;

 

(b) To review the use of weather and climate data and make recommendations for improvements in applications of agrometeorological products, and advisories and forecasts for both short-term daily operational decisions and long-term strategic planning at the farm level;

 

(c) To establish procedures and guidance for the proper use of agrometeorological information for crop, livestock, forestry and fisheries management;

 

(d) To describe, using case studies from Member countries, successful applications of weather and climate for agriculture, and review the strengths, weaknesses and limitations for more general use; and

 To prepare reports for operational applications in accordance with timetables established by the OPAG and/or MG.

Thank youThank youThank youThank you