Economic and Environmental Benefits / Risks of Precision Agriculture and Mosaic Farming

A report for the Rural Industries Research and Development Corporation by Lisa Brennan, Michael Robertson, Stuart Brown, Neal Dalgliesh, Brian Keating April 2007 RIRDC Publication No 06/018 RIRDC Project No CSW-34A

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© 2007 Rural Industries Research and Development Corporation. All rights reserved. ISBN 1 74151 281 6 ISSN 1440-6845 Economic and environmental benefits / Risks of precision agriculture and mosaic farming Publication No. 06/018 Project No. CSW-34A The information contained in this publication is intended for general use to assist public knowledge and discussion and to help improve the development of sustainable regions. You must not rely on any information contained in this publication without taking specialist advice relevant to your particular circumstances.

While reasonable care has been taken in preparing this publication to ensure that information is true and correct, the Commonwealth of Australia gives no assurance as to the accuracy of any information in this publication.

The Commonwealth of Australia, the Rural Industries Research and Development Corporation (RIRDC), the authors or contributors expressly disclaim, to the maximum extent permitted by law, all responsibility and liability to any person, arising directly or indirectly from any act or omission, or for any consequences of any such act or omission, made in reliance on the contents of this publication, whether or not caused by any negligence on the part of the Commonwealth of Australia, RIRDC, the authors or contributors.

The Commonwealth of Australia does not necessarily endorse the views in this publication.

This publication is copyright. Apart from any use as permitted under the Copyright Act 1968, all other rights are reserved. However, wide dissemination is encouraged. Requests and inquiries concerning reproduction and rights should be addressed to the RIRDC Publications Manager on phone 02 6272 3186.

Researcher Contact Details Dr Lisa Brennan CSIRO Sustainable Ecosystems / APSRU Queensland Bioscience Precinct 306 Carmody Rd St Lucia QLD 4067 Phone: 07 3214 2375 Fax: 07 3214 2308 Email : Lisa.Brennan@csiro.au

In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form. RIRDC Contact Details Rural Industries Research and Development Corporation Level 2, Pharmacy Guild House 15 National Circuit BARTON ACT 2600 PO Box 4776 KINGSTON ACT 2604 Phone: 02 6272 4819 Fax: 02 6272 5877 Email: rirdc@rirdc.gov.au. Website: http://www.rirdc.gov.au Published in April 2007 by canprint

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Foreword Some of the solutions to the market and environmental challenges facing Australian agriculture may lie in more diverse land use, in which enterprises and practices are better matched to soil and climate circumstances. Innovations that explicitly capitalise on spatial variability, such as precision agriculture and mosaic farming hold promise through their potential to increase production efficiency while reducing on-site degradation of soil resources and off-site environmental problems. This project aimed to provide improved tools and processes to evaluate the economic and environmental benefits, and risks, associated with technologies that address spatial variability in Australian farming systems. This publication has farmers and their advisors testing management alternatives that exploit variability. It highlights opportunities for mosaic farming designs and outlines topics for further investigation that change the mix and location of enterprises. This project was funded from RIRDC Core Funds which are provided by the Australian Government. The following report, an addition to RIRDC’s diverse range of over 1600 research publications, forms part of our Environment and Farm Management R&D program, which aims to support innovation in agriculture and the use of frontier technology to meet market demands for accredited sustainable production. Most of our publications are available for viewing, downloading or purchasing online through our website: • downloads at www.rirdc.gov.au/fullreports/index.html • purchases at www.rirdc.gov.au/eshop Peter O’Brien Managing Director Rural Industries Research and Development Corporation

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Acknowledgments The authors are indebted to the farmers and their advisers who actively participated in this research. We wish to thank Mike and Bev Smith, Paul and Wendy White, Bill Town and Graeme Sutton (Wesfarmers), Rob Kelly (DPI), members of Riverine Plains Inc., local consultants John Sykes, Tim Paramore, Peter Bains, staff at NSW Agriculture - Lisa Castlemaine, John Francis and Janelle Jenkins. We also acknowledge CSIRO colleagues Brett Cocks and Gordon McLachlan for in-field data collection, Chris Smith, John Ive and Hamish Cresswell for helpful discussion, particularly in the early stages of the research, Neil Huth for advice on the multi-point simulations, and Peter Carberry and Patrick Smith for providing valuable comments on an earlier draft of this report.

Abbreviations APSIM Agricultural Production Systems Simulator APSRU Agricultural Production Systems Research Unit DUL Soil water content at the drained upper limit (mm3 mm-3) GM Gross margin N Nitrogen PA Precision Agriculture PAWC Plant available water content of soils (mm)

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Contents Foreword iii

Acknowledgments iv

Abbreviations iv

Executive Summary vi

1. Introduction 9 1.1 Background 9 1.2 Objectives 10 1.3 Report structure 10

2. Research approach 11 2.1 Understanding the application of PA technologies in real situations 11 2.2 Defining the research questions 11 2.3 Characterisation of spatial variability 12 2.4 Interpretation of spatial variability 13 2.5 Management of spatial variability 13

3. Spatially-variable nitrogen management on a grain farm in the north-east Australian wheat-belt 14

3.1 Introduction 14 3.2 Describing spatial variability on “Bottom Tarnee” 14 3.3 Explaining variability 18 3.4 Managing spatial variability 30 3.5 Discussion 49

4. Mosaic farming in the south-east Murray Darling Basin 51 4.1 Introduction 51 4.2 Describing spatial variability 52 4.3 Explaining variability 57 4.4 Managing variability 68 4.5 Discussion 73

5. Implications and recommendations 75

6. References 77

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Executive Summary What the report is about Some of the solutions to the market and environmental challenges facing Australian agriculture may lie in more diverse land use, in which enterprises and practices are better matched to soil and climate circumstances. Innovations that explicitly capitalise on variability across paddocks, farms and catchments, such as precision agriculture and mosaic farming hold promise through their potential to increase production efficiency while reducing on-site degradation of soil resources and off-site environmental problems. Who is the report targeted at This report is target to Australian farmers looking to explore the value of technologies to increase production efficiency. Background Australian agriculture is confronted by the dual challenge of a market environment in which farmers’ terms of trade and the real net value of agricultural production have both shown strong and persisting downward trends (National Land and Water Resources Audit 2002) and a need to develop sustainable systems more in tune with Australia’s unique soil and climate conditions (Williams and Gascoigne 2003). Diversity was a prominent feature of many natural Australian landscapes, but all too often this diversity has been eliminated in the agriculture established since European settlement. Objectives This project aimed to provide improved tools and processes to evaluate the economic and environmental benefits, and risks, associated with technologies that address spatial variability in Australian farming systems. The research was based on two case studies and revolved around the decisions faced by farmers seeking to manage spatial variability, as observed through yield maps, on their grain farms. Such an approach allowed farmers to explore the value of the technologies in a real-life situation. Methods The first case study explored the profitability of spatially-variable nitrogen fertiliser management for a grains-based farm, near Moree, in the north-east Australian wheat belt. The second case study farm, in a cropping area in the Upper Murray Catchment, in the south-east Murray-Darling Basin, was selected to explore mosaic farming opportunities involving the incorporation of perennial crops into annual cropping systems for economic / environmental benefit. Two study groups were formed to consider the analyses conducted for each case-study farm. Each group included the farmer responsible for managing the farm, other local farmers and advisers (both private consultants and agronomists in local state agriculture departments). The research process commenced with farmers nominating their hypotheses of what was responsible for spatial variation on their farms. These hypotheses were then tested through the application of soil characterisation, crop monitoring and farming systems simulation. Farming systems simulation was conducted using a computer model called ASPIM, which can simulate the growth of a range of crops in response to a variety of management practices, crop mixtures, rotation sequences and, importantly, climatic conditions. The issue of interaction between spatial and temporal (climate-driven, seasonal) variability, and their respective interactions with a range of management options, was explored in the study groups, along with the implications of this for economic and environmental performance.

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Results For the case study on spatially-variable nitrogen management, both temporal (climate-driven) and spatial variability (in this case, attributed to variation in soil depth across the paddock) impacted on economically optimal nitrogen management practices. Optimal nitrogen rates were calculated for uniform and zone-based management under conditions of both full (perfect) and incomplete knowledge of the in-season climatic conditions. In some years, there was potential to make substantial economic returns and in others the benefits would unlikely outweigh the cost of the investment in the PA technology, even with perfect information. The biophysical response function relating nitrogen input to yield underpins the economic responsiveness of varying the level of nitrogen applied to a crop. If, for example, the shape of the simulated economic response surfaces of grain crops to nitrogen fertiliser was flat around the optimum nitrogen rate, the management implication was often that applying a ‘roughly right’ rate of nitrogen did not result in a high economic penalty. We suggest that any proposed application of PA technology to spatially-variable input management start with a thorough investigation into the nature of the biophysical response surface. This analysis also suggested that in an environment where the consequences of climate-driven temporal variability can exceed those of spatial variability, there is little value in applying spatially variable rates unless seasonal adjustments are also made. Conclusions Conclusions about the value of precision agriculture for varying inputs at the sub-paddock scale in the north eastern wheat belt should be further informed by sensitivity analysis considering variation in crop prices and input costs, the influence of nitrogen fertiliser and soil water carry over effects, and protein variation, in economic returns. An important question for mosaic farming is how to match the spatial location of the various enterprises of the mosaic (e.g. deep-rooted perennials in an annual cropping system) with landscape position and soil attributes. For the case study farm selected to explore mosaic farming, the ability to recognise spatial variation in economic returns across the paddock was found to be a necessary but not sufficient information requirement for mosaic farming design. The collaborating landholder noted that the uncertainties involved with interpreting variability made the final step of managing spatial variability, based on the data captured, a very difficult task to undertake with confidence. Historical climate records and simulation models assisted in explaining spatial and temporal variability, particularly by aiding diagnosis of possible constraints to yield such as frost, waterlogging and the influence of catchment-scale hydrological processes on yield. Simulation of management alternatives demonstrated that the economic and environmental outcomes from a mosaic farming system could vary within a farming landscape depending on where various elements of the mosaic farming system are located. Although this case study highlights opportunities for mosaic farming design, further research is needed to fully evaluate the implications of changing the mix and location of enterprises on this case study property. Scaling up implementation of mosaic farming to the farm scale requires that a greater range of factors be considered for further analysis. Examples provided during the discussions included the interactions between enterprises (e.g. crop and livestock activities) within the mosaic, risk factors, and costs and benefits that are incurred at whole-farm scale, such as redefining and / or re-fencing paddock boundaries to create feasible management zones. The impact that mosaic farming has on longer term sustainability factors must be also considered.

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Collaborating farmers reported that they were comfortable to make their own assessment of management alternatives, provided that they had confidence in understanding the biophysical processes taking place. One measure of this project’s success has been the collaborators’ improved understanding of their own or their client’s spatial variability from the soil characterisation and monitoring activities which provided reliable measurements of soil water, nitrogen and other physical and chemical properties. The discussion sessions using APSIM with landholders and their advisers have indicated strong interest in the potential of APSIM to complement PA technologies that sense variability and help explore spatially-variable management alternatives. When used with historical climate records, simulation models such as APSIM and can play an important role in interpreting the causes of variability and placing the performance of individual seasons in a long-term context, as well as assessing the likely responses to changed management practice.

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1. Introduction 1.1 Background Australian agriculture is confronted by the dual challenge of a market environment in which farmers’ terms of trade and the real net value of agricultural production have both shown strong and persisting downward trends (National Land and Water Resources Audit 2002) and a need to develop sustainable systems more in tune with Australia’s unique soil and climate conditions (Williams and Gascoigne 2003). Diversity was a prominent feature of many natural Australian landscapes, but all too often this diversity has been eliminated in the agriculture established since European settlement. Agriculture now tends to take place within paddocks that don’t reflect underlying soil variability, topography or landscape connectivity. Often times these paddocks are defined in terms of square or rectangular boundaries that have no meaning in terms of underlying soil, vegetation or landscape characteristics. Some of the solutions to the market and environmental challenges facing Australian agriculture may lie in more diverse land use, in which enterprises and practices are better matched to soil and climate circumstances. Innovations that explicitly capitalise on spatial variability, such as precision agriculture and mosaic farming hold promise through their potential to increase production efficiency while reducing on-site degradation of soil resources and off-site environmental problems. Precision agriculture is most commonly understood as the use of technologies that sense spatial variation in crop yields, electromagnetic conductivity, soil surface colour, elevation, topography and aspect, to manage within-paddock variation. One particularly high-profile application of these technologies is the use of the spatially-referenced information in conjunction with computer-controlled equipment to guide the precise application of inputs within a paddock. Common examples include herbicide application systems responsive to weed presence, spatially-variable fertiliser application or soil amendment associated with yield maps for soil information. Mosaic farming shares much in common with concept of precision agriculture, including the use of the same technologies. The key difference, however, is that mosaic farming concerns spatial variation in land management at the whole-farm or landscape scale, as opposed to the sub-paddock traditionally associated with precision agriculture. Mosaic farming creates agricultural landscapes made up of annual crops and pastures interspersed with deep-rooted perennial vegetation such as lucerne and/or trees. Each vegetation type is located so that its requirements are matched to landscape, vegetation and soil characteristics. An example might be the incorporation of trees or other deep-rooted perennials within grain/grazing farming systems to restrict dryland-salinity by reducing recharge, and to provide habitat for wildlife. Although many of the technologies for sensing spatial variation at the paddock and farm scale have been available to Australia farmers for around 10 years (Cook and Bramley 2001), they are not in widespread use (Rubzen and Rola-Rubzen, 2002; Cook et al 2002). In 2000, there were only 800 yield monitors in Australia, 500 of them in WA (Lowenberg-DeBoer, 2003a). Even where farmers have used yield maps and other spatially-referenced information, improvement in spatially-variable management has been slow, even though these technologies provide evidence of variation (Cook and Bramley 2001). In the USA, which leads in the development and use of PA technologies, low adoption and even low awareness of precision agriculture technologies have also been identified (Daberkow and McBride, 2003; Hudson and Hite, 2001). Precision agriculture technology is information intensive and expensive. It involves collecting, storing, manipulating, analysing and, most importantly, acting upon the vast amount of spatial information on the detailed characteristics of a farm field.

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Despite the breadth of possible applications of precision agriculture technologies, most attention has focused on the technical aspects of these technologies (Bell, 2002; Stafford, 2000) in the absence of a capability to explain the variation that such technologies have detected. Developments in agronomy to explain the information have lagged behind the capacity to acquire it (Cook and Bramley (2001); Bell (2002); Stafford (2000) Whitney (2003)). An often difficult and potentially costly aspect of applying precision agriculture technology to guide spatially-variable management is the knowledge gap between the operation of precision agriculture technologies and the final step of taking informed action, based on the data captured, to manage spatial variability (Whelan (2001), Cook and Bramley (2001)). A significant issue is the interaction between spatial and temporal variability and their respective interactions with management practice and movements in commodity prices. Crop simulation models have had limited application in precision agriculture (Cook et al. 2002; Basso 2001) and have not been used for PA in Australia (Cook et al. 2002), but can play a valuable role in overcoming the difficulties imposed by temporal variation on empirical approaches in research into spatially variable management. Teamed with climate records, biophysical simulation models can play a role in interpreting the cause of spatial variability and assessing the significance of its impact on farm profitability and environmental outcomes in the long term. The complexities of applying precision agriculture to management, particularly when weighed up against uncertain / unproven economic and environmental benefits (Lambert and Lowenberg-DeBoerg, 2000; Lowenberg-deBoer and Swinton, 1997; Wolf and Buttel, 1996; Lowenberg-DeBoer and Boehlje, 1996; Stafford 2000 Lambert and Lowenberg-DeBoer (2000))), have been widely reported as a barrier to adoption in both Australia (Rubzen and Rola-Rubzen, 2002; Wylie, 2001) and overseas (Kitchen et al. 2002). Continuing growth in precision farming may provide the basis for sustainable agriculture through more closely matching resource use to resource capability, but it will rely on a capability to not only capture, but interpret and management variation, and to assess the economic and environmental benefits of specific applications of precision farming technologies. 1.2 Objectives This project aimed to provide improved tools and processes to evaluate the economic and environmental benefits, and risks, associated with technologies that address spatial variability in Australian farming systems specifically by: - The application of soil characterisation, crop monitoring and farming systems simulation

(APSIM) to address the issue of interaction between spatial and temporal variability and their respective interactions with management practice, economic and environmental performance.

- Achieving the above by collaborating with key stakeholders (farmers, agribusiness consultants and other researchers) in case studies of practical applications of precision agriculture and mosaic farming technology to interpret spatial variability and design tools and approaches to manage it.

1.3 Report structure Chapter 2 introduces the research approach taken in the project. Chapters 3 and 4 present the research activities and findings for each of two case studies, respectively. Chapter 5 discusses the implications of these findings and recommendations for future work.

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2. Research approach 2.1 Understanding the application of PA technologies in real situations This project investigates the application of PA technologies in the context of two individual cases. The research activities reported here revolve around the decisions faced by two farmers seeking to manage spatial variability on their farms. Such an approach provides a way to test approaches for guiding management decisions about PA in real-life situations. This draws on the concept of idiographic research – i.e. understanding a phenomenon in context. Two study groups were formed around each case-study farm. Each included the farmer responsible for managing the farm, other local farmers and advisers (both private consultants and agronomists in local state agriculture departments). Group members were involved in facilitated co-learning activities as part of the project. The two case studies are: a) Variable-rate nitrogen fertiliser application in the north-east Australian wheat belt A trend towards larger paddocks has resulted in rows running across soil types and crossing topographical features (Hayman, 2001). Dalgliesh and Foale (1998) reported that the increase in deep soil sampling and measurement of water holding capacity has increased the awareness of within paddock variability and sub-surface limitations. Variable-rate application of fertilisers, designed to exploit such within-paddock variability, is one of the most studied areas of PA-guided management, yet the economic benefits are still uncertain (Lowenberg-DeBoer, 2003b). In this project, the profitability of variable rate nitrogen application was explored for a grains-based farm in Gurley, near Moree, in the north-east Australia wheat belt – a region in which farmers are reluctant to fine-tune nitrogen rates (Henzel and Daniels, 1995; Hayman and Alston, 1999). b) Mosaic farming in the south-east Murray Darling Basin A case study farm located in the Upper Murray Catchment in the South East Murray-Darling Basin provided an opportunity to explore issues associated with mosaic farming in a region which is already involved in initiatives to explore land use change better matched to landscape characteristics. This case study farm in a cropping area of the Upper Murray catchment was selected to explore opportunities to "rezone" paddock boundaries to provide the greatest possible economic / environmental benefit from incorporating perennial crops into cropping systems. While much of the project work focuses on the sub-paddock scale, the approach illustrates the information requirements necessary to inform mosaic farming design, explore the issues relating to spatial x temporal interactions, and identify opportunities for changing the enterprise mix for economic and environmental benefit. 2.2 Defining the research questions

Our earliest interaction with our research collaborators involved discussions about the problematic aspects of applying PA and agreeing on a research approach to address the issues identified. All participants agreed that the project should begin with a mutual understanding of the research problem: that is, that while a lot of effort has been put into capture of information on spatial variability (as has been the situation on these case study farms), less effort has gone into the bio-physical analysis and interpretation of these data and too little into the economic evaluation of management strategies that make use of the extra information on spatial variability. A major issue that emerged in our discussions was the interaction between spatial and temporal variability and their respective interactions with management practice. Hence, the three major steps

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involved with applying precision agriculture technologies to a farm, outlined in Box 1, provided the framework for designing research activities with the project’s participating researchers, farmers and agribusiness collaborators. Box 1. Three stages in applying precision agriculture technologies to a farm

1. Data capture e.g., generating yield maps or some other spatially referenced information

2. Data interpretation e.g., how to explain the observed variation.

3. Taking informed action to manage spatial variability e.g. managing variation on the farm to achieve an economic and/or environmental benefit

Discussions between the authors and farmers about experiences in applying precision agriculture technology on farms revealed that while some of the drivers of variability (e.g. as observed in yield maps) were understood, the interpretation of spatial variability presented many uncertainties. Yield maps alone do not allow for full interpretation of observed variability, and year-to-year variation in crop performance, related to weather and management factors, complicated their ability to interpret their yield maps. Yield patterns may not be consistent from year to year and a management action to exploit observed spatial variability in one year may be inappropriate the next due to variation in seasonal conditions. A particularly problematic aspect of this situation is that Australian farmers have yield maps for a limited number of seasons. This problem of extreme variability seems to qualify as an ‘outcome irrelevant learning structure’ (OILS) of Einhorn (1982) in which outcome feedback (e.g. variability observed in yield maps) does not significantly influence subsequent action. Discussions of the complexities induced by temporal variability identified an opportunity to explore the potential of simulation models to aid the interpretation of spatial variability, such as that observed through yield maps, on the project’s two case study sites. As outlined in the Introduction, crop simulation models have had limited application in precision agriculture (Cook et al., 2002; Basso, 2001), but can play a valuable role in overcoming the difficulties imposed by temporal variation on empirical approaches in precision agriculture research. The value is in their ability to interpret and predict crop response to inputs in relation to weather variation, management practices and soil properties. Coupled to temporally-variable weather data, simulation models can predict temporal and spatial variation by modifying the crop and soil input parameters to account for spatial variability in a paddock. The research activities carried out in this project are described using the 3-step ‘describing-explaining-managing’ decision framework. 2.3 Characterisation of spatial variability For each farm studied, we accessed all existing spatially-referenced data (e.g. yield maps), information on crop management history, and crop and input prices. This information was supplemented with on-ground work on multiple sites on each farm to collect farm-specific soil and weather data. These data sets were necessary to enable model (APSIM) application.

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2.4 Interpretation of spatial variability Initial exploration of the causes of yield variation involved examination of yield variation patterns, visual and mathematical association of yield maps to each other and other maps and simple mathematical analysis (correlation, average, scatter plots, histograms). The project explored the potential of simulation models to aid the interpretation of spatial variability using APSIM. APSIM is a modelling environment that can simulate the growth of a range of crops in response to a variety of management practices, crop mixtures and rotation sequences (McCown et al., 1996). More recently, the APSIM framework was expanded to incorporate forestry systems (Huth et al., 2001) and a multi-point capability Wright et al. (1997). This project represents its first real application in the mosaic farming case study. For selected locations within paddocks on each of the case study farms, the extent to which APSIM explained the temporal and spatial variability observed on each location for past crop rotations was explored. During this period of initial model application and testing, APSIM was configured to represent the selected sites on each paddock using the soil characterization and crop monitoring data described above, as well as historical climate records, and the farmers’ crop management records. Simulated yields were compared with actual observed temporal/spatial variability. Meetings at the collaborating farmers’ properties were held to discuss the findings of the soil characterization activity, obtain feedback on the extent to which the APSIM-simulated crop yields compared with the observed spatial and temporal yield variability on farms, and explore the value of simulation in aiding the interpretation of observed variability. 2.5 Management of spatial variability The analysis of management alternatives was the final step in the decision framework applied to the application of PA technologies. The steps followed were:

Characterise the current farm production situation for relevant sites (e.g. field by field, zone by zone, within-field basis) using APSIM.

Identify alternative inputs/enterprises/management for investigation eg variable-rate nitrogen fertiliser application or lucerne for the mosaic farming case study. Determine and demonstrate appropriate APSIM configurations for modelling these management alternatives.

Identify management zones on farm and/or paddock for APSIM model application

Apply APSIM to explore alternative enterprises (step b) in the zones that have identified in step c) above.

Conduct biophysical and economic interpretation and analysis of alternative land use options and compare to the current situation.

Recent experience (Carberry et al. 2002) has demonstrated that simulation can provide a powerful learning tool in assisting farm managers to explore their farming system. As part of this project, a number of ‘what if’ analysis and discussion sessions using the farming systems model APSIM were run with farmers and their advisors to enable the interpretation of variability and exploration of the economic and environmental consequences of management alternatives of PA and mosaic farming to be explored in the context of the individual’s own farm. Chapters 3 and 4 report on the results and reactions from those meetings.

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3. Spatially-variable nitrogen management on a grain farm in the north-east Australian wheat-belt 3.1 Introduction “Tarnee” is a 1250 ha grains-based farm managed by Mike and Bev Smith in Gurley, south-east of Moree, in Northern NSW. Crops grown in recent years include wheat, sorghum, canola and chickpea. The average paddock size on the property is 114 ha. The project activity for this study explored the management of spatial variability on a 100 ha paddock on the farm known as “Bottom Tarnee”. The Smiths’ interest in precision agriculture came from the ability of a yield monitor to quantify yield spatially for the farm. According to Mike Smith, “We knew that our yields varied within fields, and that soil depth (plant available water capacity) was the most likely driver of this. If this pattern was to prove consistent then it was only common sense that fertiliser inputs could be varied, to achieve greater efficiency and balance in the system” (Smith, 2003). In this project case study, we investigated the potential to improve the profitability of “Bottom Tarnee” by changing from uniform to spatially-variable management of nitrogen fertiliser. The project activities carried out for this case study are reported using the 3-step ‘describing-explaining-managing’ decision framework outlined in Chapter 2. 3.2 Describing spatial variability on “Bottom Tarnee” Maps of crop yield (obtained using a yield monitor linked to GPS) dating back to 1996 and a soil-depth map for the “Bottom Tarnee” paddock were made available at the start of this project. Additional, spatially-referenced data were obtained through the soil characterisation activity carried out during the project. These data sets are described in this section. Yield maps The cropping history of the paddock since yield mapping commenced on “Bottom Tarnee” in 1996 is shown in Table 3.1. Spatially-variable nitrogen fertiliser application occurred in 1999 and 2003 seasons for wheat crops. The yield maps for a selection of these crops are shown in Fig 3.1. Table 3.1. Cropping history for “Bottom Tarnee”, 1996-2003

Sowing year Crop Management notes 1996 Wheat Uniform N management 1997 Sorghum N not applied 1998 Chickpea Crop failure due to disease 1999 Wheat Variable N application 2000 Sorghum N not applied 2001 Sorghum N not applied 2002 Chickpea N not applied 2003 Wheat Variable N application

The yield data presented in the yield maps are also presented as yield distribution functions (Fig 3.2), which indicate variation in yields within and between year– i.e. spatial and temporal variability.

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The 1999 and 2003 wheat seasons are not included in Fig. 3.2 to avoiding the confounding effect that the spatially-variable fertiliser application would have on the assessment of yield variability.

Figure 3.1. Yield maps showing spatial yield variability for annual crops in Bottom Tarnee for the a) 1997 sorghum crop, b) 2000 sorghum crop, c) 2001 sorghum crop and d) 2002 chickpea crop.

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Fig. 3.2. Yield distribution function demonstrating spatial yield variability (kg/ha) for annual crops in Bottom Tarnee: Sorghum 1997, Sorghum 2000, Sorghum 2001, Chickpea 2002. Soil depth map The undulating topography of the farm landscape is associated with variation in soil depth. Spatially-referenced, soil-depth measurements were recorded by the property owner to explore the hypothesis that variation in soil depth is causing variation in PAWC which in turn is the main factor responsible for the observed yield variation in the paddock. The soil depth map (Fig. 3.3) was constructed by inserting a steel push probe into wet soil to the depth of the underlying decomposing parent material (Fig 3.4). According to Mike Smith “our soils lent themselves to this process because of a decomposing rock layer underlying the black self mulching clay, which holds less water and was impenetrable to probe” (Smith 2003). The depth to this layer was measured to the nearest 15 cm graduation on the probe. This was performed at 375 geo-referenced locations across the paddock.

Fig. 3.3. Map showing spatial variability in soil depth (cm) on Bottom Tarnee. The black dots represent the actual locations where a soil depth measurement was taken using a push probe. These discrete measurements have been kriged to create the continuous, coloured areas.

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Fig. 3.4. Property owner Mike Smith measuring wet-soil depth using a push probe Soil characterisation In the seasons 2001-2003, determination of hydraulic characteristics of the soil was undertaken at three sites representing the variation in soil depth found across the paddock (90cm, 120cm and 180cm). Chemical and physical analysis of the soil was undertaken to determine differences, as well as in situ determination of the drained upper and lower limit of water extraction (Dalgliesh and Foale 1998). The locations of the sites are shown in Fig. 3.5. Soil characterisation at these sites identified that the main difference between the soils is the water holding capacity as the result of differences in soil depth. These values are given in Figure 3.6.

Fig. 3.5. Map showing location of soil characterisation sites for the shallow (90cm), medium (120cm) and deep (180cm) soils.

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Figure 3.6. Profiles of drained upper limit (DUL) and lower limit (LL) for the 3 sites characterised in the paddock: (a) shallow, (b) medium (c) deep. 3.3 Explaining variability Drawing on the spatially-referenced data assembled, we ask what is the cause of the spatial variability observed in the yield maps and is there evidence to support the hypothesis that the variable soil depth is responsible for the variability in yield observed across the paddock? Furthermore, how consistent are the patterns of variability from year to year, and can areas of consistently high or consistently low yield be identified that indicate potential for spatially-variable management? The steps applied to interpreting paddock variability - pattern analysis of yield map data, correlations between measured soil depth and yield, and the application of simulation modelling are discussed below. Yield patterns in Bottom Tarnee To explore year-to-year consistency of yield variability in Bottom Tarnee - that is, the extent to which the patterns of variability are repeated each year - the yields corresponding with each pixel in a yield map were assigned to ‘clusters’ describing yield performance. For example, in the three-cluster maps (Fig. 3.7) pixels were assigned to one of three categories - either a ‘high’, ‘medium’ or ‘low’ yield cluster. For the four-cluster maps (Fig. 3.8) pixels were assigned to a ‘high’, ‘medium’, ‘low’ or ‘lowest’ yield cluster. The method for creating yield clusters for each year was by the fuzzy k-means clustering algorithm (Fridgen et al. 2000; Whelan and McBratney 2003).

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Fig. 3.7. Yield cluster maps presented on a 3-cluster basis for the (a) 1997 sorghum, (b) 2000 sorghum, (c) 2001 sorghum and (d) 2002 chickpea seasons. Cursory visual inspection of the yield cluster maps suggests that large areas of the paddock do not maintain the same yield performance, relative to other parts of the paddock, on a year-to-year basis. For example, considering yields on a three cluster basis, almost one third (29%) of the paddock area has occupied all three yield clusters – meaning that over the four years, these areas have been the highest, middle and lowest yielding parts of the paddock. Just 7% of the paddock area, however, yielded consistently over four years – that is, remained consistently low, consistently medium or consistently high. The majority of the paddock (64%) occupied 2 of the 3 yield clusters. Figure 3.9 shows the location of these areas.

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Fig. 3.8. Yield cluster maps presented on a 4-cluster basis for the (a) 1997 sorghum, (b) 2000 sorghum, (c) 2001 sorghum and (d) 2002 chickpea seasons.

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Fig. 3.9. Map showing consistency of pixel yield over 4 years on a three-cluster basis. The green areas indicate areas of the paddock that have occupied the same yield cluster category (high, medium or low) consistently over 4 years. The yellow areas are those where yields have belonged to any two of the three cluster categories at any time of the 4 years. The red areas are those that have been the low, medium and high yields at various times over 4 years. The analysis of yield patterns does not in itself provide explanations of the cause of variability, but does provide a starting point in identifying the opportunities for spatially-variable management. For example, the identification of areas that yield consistently lower or higher than other parts of the paddock suggests spatially-variable management opportunities. However, the question of what management to apply remains and the answer depends on knowledge of the cause of the variability. Without this understanding, a high degree of yield inconsistency makes it difficult to identify management zones that may respond to differential treatment. If the hypothesis of soil depth as the cause of yield variability is correct, then it would be expected that yield variability would exhibit consistency over time, in spatial patterns that reflect that spatial variation in soil depth. When discussing this issue with the property owner, it was revealed that the year-to-year inconsistency in yield patterns could be explained. Crop establishment problems, resulting in variable plant population densities, were believed to be responsible for patterns of yield variability not related to soil depth, and were particularly problematic in the 2001 and 2002 seasons. Model application and testing Crop yields were simulated for each of the three characterised sites for sorghum (2001), chickpea (2002) and wheat (2003). The APSIM simulations used the soil characterisation data collected for each site. For each crop the corresponding weather record in that year was used. Automatic weather stations were installed in the paddock to obtain rainfall and temperature data. Radiation data were obtained from the nearest Bureau of Meteorology station (Gurley). Key simulation assumptions appear in Table 3.2.

22

Table 3.2 Details of crops simulated at “Bottom Tarnee” Season Crop

species Cultivar Sowing

date Established plant populat’n (plants/m2)

Fertiliser N applied (kg/ha)

Maturity date

2001-02 Grain sorghum

Buster 21 Oct 2002

5.9 None 19 Feb 2002

2002 Chickpea Jimbour 15 May 2002

27 None 1 Nov 2002

2003 Durum wheat

Walloroi 26 May 2003

70 80 24 Oct 2003

Fig. 3.10, compares simulated yield and measured yields at quadrats at each site (90 cm -shallow, 120cm - medium and 180cm - deep soil) for the crops in these three seasons. Table 3.3 also summarises these results. There was generally good agreement between simulated and observed yields for the three soil depths. APSIM simulated and observed soil water and soil nitrate are presented in Fig. 3.11 and Fig. 3.12 respectively. Table 3.3 Comparison of APSIM-simulated yield and measured quadrat yield for sorghum 2001, chickpea 2002 and wheat 2003 on the shallow, medium and deep soils

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23

Fig. 3.10. Comparison of APSIM-simulated (indicated by lines) and observed yield (indicated by squares) for sorghum 2001, chickpea 2002 and wheat 2003 on the (a) shallow soil, (b) medium soil and (c) deep soil On the deep soil, simulated soil water under-predicted observed in April 2003 by about 50 mm (Fig. 3.11c). As this was not due to simulation of excessive amounts of runoff, a reset was performed (Fig 3.11d) so that the simulation of the 2003 crop yield could be conducted with correct starting water. The simulated wheat yield on the deep soil for 2003 is presented with and without (in brackets) the effect of the soil water reset.

24

Fig 3.11. APSIM simulated (indicated by lines) and observed (indicated by squares) soil water (mm) for sorghum (2001), chickpea (2002) and wheat (2003) for the (a) shallow, (b) medium, (c) deep soil on Bottom Tarnee and (d) deep soil with soil water reset in the simulation. Rainfall (mm) over the same period is also represented

25

Fig 3.12. APSIM simulated (indicated by lines) and actual (indicated by squares) soil nitrate (kg/ha) for sorghum (2001), chickpea (2002) and wheat (2003) for the (a) shallow, (b) medium and (c) deep soil on Bottom Tarnee

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Relationships between yield and soil depth Fig 3.13 shows the relationship between the soil depths measured with the push probe and corresponding yield at each measurement, as recorded by the yield monitor. One yield monitor pixel representing an area of 11m x 11m corresponds with every push probe point. For the 1997, 2000, 2001 and 2002 seasons, APSIM estimates of yield responses for different soil depths (30, 60, 90, 120, 150cm) were generated. The simulated yield responses are also shown in Fig. 3.13. The APSIM configuration for these simulations was based on the chemical and physical properties of the ‘deep’ soil. Measurements of soil organic carbon, pH and mineral soil N indicated that there were not any substantial differences to justify parameterising differently. The variation in soil depth was represented in APSIM by restricting the depth to which roots could extract water. Actual crop sowing dates were used in the simulations (sorghum 1997 –30 September, sorghum 2000 – 1 December, sorghum 2001 – 22 October, chickpea 2002 – 15 May). No nitrogen fertiliser was added to the crop. Actual soil water was used for the 2001 and 2002 simulations, but assumed values were used for 2000 and 1997.

Fig. 3.13. Relationship between push-probe measured soil depth and corresponding yield (kg/ha) from the yield map compared with model-simulated yield for a) sorghum, 1997, b) sorghum 2000, c) sorghum 2001, d) chickpea 2002 For all years displayed, there was a wide variation of yields achieved for any given soil depth. Simulated yields and yield-monitor observations in 1997 and 2000 followed a general trend of yields increasing with soil depth. Of note is the weak correlation between soil depth and yield-monitor observations for the years of particularly poor crop establishment in the 2001 the 2002 season, even through model-simulated yields increase with increasing soil depths. One possible explanation of the

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high yield-monitor observations at shallow depths could be potential errors in the soil depth measurement taken by the push probe. Further model application addressed the following questions: 1. To what extent does variable plant population density explain the wide variation in yield

corresponding to any particular soil depth? We addressed this by simulating yields using the expected variation in plant population densities (3, 6 and 10 plants/m2) for the 1997 and 2000 sorghum crop.

2. Is it possible that the soil depth to which roots can penetrate is actually deeper than what was measured using the push probe? The simulated yields recorded against each soil depth on Fig 3.13 were modified to account for a 30 cm increase in rooting depth additional to the depth recorded by the push probe.

With the adjustments to rooting depth and plant population, Fig. 3.14 suggests that most of the observed variation in the observed paddock yields could be explained by soil depth and plant population density.

Fig. 3.14. Relationship between adjusted soil depth and corresponding yield (kg/ha) from the yield map compared with model-simulated yield for a) sorghum, 1997, b) sorghum 2000 for three plant population densities – 3, 6 and 10 plants per square metre

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Simulated yield maps The model application was extended to the whole-paddock scale, enabling the creation of model-simulated yield maps for 1997 and 2000. Figs. 3.15 and 3.16 show the model-simulated maps (based on the same plant population density over the entire paddock at sowing) and compares this to the actual yield map for the 1997 and 1999 sorghum crops.

Fig. 3.15. Actual (a) and simulated (b) yield map for sorghum, 1997

Fig. 3.16. Actual (a) and simulated (b) yield map for sorghum, 2000 As APSIM is a point-scale model, the process of creating a simulated yield map involved conceiving pixels in the yield map as simulation points, with each paddock pixel assigned a simulated yield based on soil depth. The Bottom Tarnee yield maps have 13 523 pixels. Therefore, 13 523 simulation points were also created. In order to allocate a soil depth to each yield pixel, the 375 spatially-referenced push probe measurements were kriged in order to generate a soil depth for each of 13 523 pixels. Taking the simulations for the selection of soil depths - 30, 60, 90, 120, 150, and 180 cm, a process of interpolation between these simulated values enabled a simulated yield to be calculated for every pixel.

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Figs. 3.17 and 3.18 summarise the comparison between simulated and actual yield maps with ‘difference’ maps for the 1997 and 2000 sorghum crops, respectively – that is, each pixel on a difference map represents the difference between the actual recorded yield and the corresponding simulated yield. The first set of difference maps (Fig. 3.18) shows areas of the paddock where the model is within 10 per cent of the observed yield and Fig. 3.17 shows simulated areas that are within 30 per cent of the observed yield.

Fig. 3.17. ‘Difference’ maps showing the areas where actual and simulated yield are less than or greater than 10 per cent of each other for (a) sorghum 1997 and (b) sorghum 2000

Fig. 3.18. ‘Difference’ maps showing the areas where actual and simulated yield are less than or greater than 30 per cent of each other for (a) sorghum 1997 and (b) sorghum 2000 The model does not account for all of the observed spatial variation. However, in both years, most (78% in 1997 and 63% in 2000) of the simulated paddock pixel yields are within 30% of the observed yield. The simulations were conducted by varying only one parameter in the model configuration - i.e. soil depth, and therefore it is to be expected that not all of the variation in the paddock can be accounted for by the model in each year. The areas where the difference is greater than 30% tend to fall on parts of the paddock where there were few actual measurements taken with the push probe (see Fig. 3.3), meaning that estimated kriged soil depth may not be an accurate representation of depth in such areas. Although it is not possible to say conclusively what accounts for the > 30% difference between observed and simulated yield, the likely explanations are ‘patchy’ crop establishment and potential measurement inaccuracies from the push probe.

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Another possible consideration is the potential for inaccuracies of the yield monitor. Also, models do not address all of the factors responsible for variation, such as pests and diseases. Although other factors are likely to be contributing to yield variation, it was felt that continuing the analysis on the premise that soil depth is the inherent soil characteristic responsible for yield variation was still valuable. Importantly, the collaborating farmer had sufficient confidence in the model - i.e. simulation was able to explain and represent much of the yield variability on Bottom Tarnee. He could usually explain the discrepancies between observed and simulated yields, and was comfortable with proceeding with analyses of spatially-variable nitrogen management options based on the premise that soil depth (plant available water capacity) causes spatial yield variation.

3.4 Managing spatial variability Opportunities to improve the profitability of Bottom Tarnee by changing from uniform to spatially-variable management of nitrogen fertiliser were explored. Specific questions addressed included: 1. How do the returns from uniformly-applied nitrogen fertiliser compare to spatially-variable

nitrogen management options?If management zones are used in fertiliser application, how many will give the best returns?

3. How variable are the returns to spatially-variable N management from year to year? Given a known variation in soil depth across the paddock, simulation was used to explore the likely consequence of this soil depth variation on yield performance, nitrogen requirements and paddock gross margins and whether it is possible to manage different parts of the paddock (with different soil depths) differently for economic benefit. Spatially-variable N management was explored for both sorghum and wheat crops. Model estimates of yield and gross margin responses for soil depth x N rate APSIM simulations were run for sorghum and wheat crops over a ten-year weather record (1991 to 2001) to determine the average yield response to four nitrogen rates (0, 50, 100, 150 kg N/ha) for the six soil depths previously simulated (30, 60, 90, 120, 150, 180cm). As before, the APSIM configuration for these simulations was based on the chemical and physical properties of the ‘deep’ soil, with the variation in soil depth represented in APSIM by restricting the depth to which roots could extract water. Other than the investigated variations in N rate, the same crop management was applied in every simulation. The sorghum crop simulations were configured using a sowing window between 22 October and 15 December (which is suitable for northern NSW) conditional upon 25mm falling in a 3- day period. Two soil moisture profiles were examined for the sorghum crop – 60 and 100% full, however given the similarities between the two sets of results, only the 100% case is presented here. The soil water was reset to these levels at the commencement of the crop in each year. The wheat crop was also assumed to have a full moisture profile at the commencement of each season. N in the soil at the time of planting was assumed to be 20kg/N each season. Both water and nitrogen were reset on the April 1 each year. The sowing date for the wheat simulations was determined within a sowing window from 1 May to 30 June, conditional upon 25mm falling in a 7-day period. Depending on the date, an early or late variety was sown. Long-term climate records for the nearest town, Gurley, were used. The simulated yields were averaged over the 10 years (Fig. 3.19). Fig. 3.20 highlights the impact of temporal variability by showing the range of expected yields for just one soil depth and N rate combination. The curves1 fitted to the average simulated yields in Fig 3.19 show the yield response ‘flattening’ out – that is, becoming less responsive to increases in N rate, particularly for the shallow soil depths. 1 Mitscherlich equation fitted

31

Figure 3.19. Model estimates of (a) sorghum and (b) wheat yield (t/ha) response to N rates (0, 50, 100, 150 kg N/ha) x soil depth (30, 60, 90, 120, 150, 180cm), averaged over ten years 1991 to 2001 for “Bottom Tarnee” paddock

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Fig. 3.20. Simulated yield response (kg/ha) of a wheat crop to one N fertiliser rate (100 kg/ha) on one soil depth (120cm), over 10 years (1991 to 2001) The sorghum and wheat gross margins ($/ha) across the range of soil depths and nitrogen rates in Fig 3.21 are derived from the yield-nitrogen curves presented in Fig. 3.21, based on the assumptions in Table 3.4. In this analysis, we did not quantify protein variation and therefore assumed a constant wheat price. Across the range of soil depths investigated, the optimal N rate ranges from 34 to 94 kg/ha for the sorghum, and from 62kg/ha to 142kg/ha for the wheat. Table 3.4 Economic assumptions used in the gross margin calculations for wheat and sorghum Sorghum Wheat Crop price ($/t) 133 180 Variable costs ($/ha) (excluding N)

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Figure 3.21. Model estimates of (a) sorghum and (b) wheat gross margin for ten year average yields (1991 to 2001) in response to N rates (0, 50, 100, 150 kg N/ha) x soil depth (30, 60, 90, 120, 150, 180 cm) for “Bottom Tarnee” paddock Optimal N rate for uniform paddock management in an ‘average’ year The amount of N fertiliser that would maximise the paddock’s profit when applied at a uniform rate was calculated. The calculation of the profit maximising N rate for the paddock sets a benchmark against which spatially-variable management options can be compared. In calculating the optimal N rate that, when applied uniformly, maximises the paddock GM, we needed take into account that different parts of the paddock will respond differently to a given rate of N because of the variation in soil depth.

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As reported earlier, the process of interpolation of yield response between the simulated soil depths enabled yields to be calculated for every pixel, of a given soil depth. For each of the ten, simulated years (1991-2001), the average of 13 523 pixel yields was calculated to determined an average paddock yield (t/ha) for each of the four N rates (0, 50, 100, 150 kg/ha) for the wheat and sorghum crops. The 11 average paddock pixel yields (i.e. 11 years of simulation) were then averaged to calculate the 11-year average paddock yield for each N rate. Table 3.5 shows the simulated results for wheat. Note the temporal variation.

Fig. 3.22. Model estimates of average paddock yield (a) sorghum and (b) wheat (t/ha) response to uniformly-applied nitrogen rates averaged over ten years 1991 to 2001) and corresponding gross margin ($/ha), “Bottom Tarnee” paddock. The profit maximizing nitrogen application rate is highlighted Table 3.5. Average paddock wheat yield (kg/ha) for each N rate (0,50,100 and 150kg/ha) in each year (1991-2001)

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Yield (kg/ha) Year 0 kg N/ha 50 kg N/ha 100 kg N/ha 150 kg N/ha 1991 773 1795 1540 1387 1992 939 1944 1715 1373 1993 927 3106 4998 5418 1994 686 436 393 393 1995 851 2056 1557 1276 1996 747 2768 4426 4888 1997 971 1356 774 576 1998 792 2988 4817 6168 1999 1000 2551 2571 2417 2000 675 1192 846 647 2001 750 2524 3084 2891 Average 828 2065 2429 2494

A mitscherlich equation was fitted through the 11-year average simulations to create a paddock N response curve (Fig 3.22). Fig 3.22 also shows the corresponding gross margin curve. The profit-maximizing rate of 80.1kg N/ha nitrogen for the wheat crop and 43 kg N ha for the sorghum was calculated from these curves. The analysis above showed that the general shape of the economic response surface of a grains crop to nitrogen fertiliser is generally flat around the optimum. As shown in Fig. 3.22, between 25 and 65 kg/ha of N for sorghum and 60 and 105kg/ha wheat are rates on the relatively flat part of the gross margin curve. Applying N at 50 per cent of the optimal rate still yields over 96 per cent of the potential profit for the sorghum and 87 per cent for the wheat. Optimal N rate for spatially-variable N management in an ‘average’ year The optimal N rate for a uniformly-managed paddock was compared to the maximum profits achievable by dividing the paddock into management zones based on soil depth and applying the economically optimal N rate to each. The same procedure which was used to calculate the optimal N rate for uniform N application was applied on a zone-by-zone basis – i.e. the analysis was repeated for each zone, with each zone essentially treated as a new, but smaller, paddock for analysis. Figures 3.23 show the spatial arrangement of management zones on Bottom Tarnee corresponding with 2, 3 and 4 zone management. Spatial statistical techniques were used to create these zones, with the clustering of pixels based on soil depth. This was done using the fuzzy k-means clustering algorithm (Fridgen et al. 2000; Whelan and McBratney 2003). This had the effect of assigning each pixel to a particular zone. It was assumed that there were no extra variable fertiliser application costs associated with additional management zones. Figure 3.24 and 3.25 show the maximum whole-paddock gross margin ($/paddock) that could be achieved for four spatially-variable nitrogen managements options (one zone / uniform N application, two, three and four zones) for sorghum and wheat crops respectively based on simulations over eleven years (1991-2001). For each zone, the figures shows the average soil depth, the proportion of the total paddock area occupied by the zone, the predicted average crop yield, the profit-maximising N application rate and the gross margin.

36

Fig. 3.23. Maps showing the spatial arrangement of the two, three and four-zone management system The three main conclusions drawn from this analysis were: 1. The economic benefits from uniform to zone management appear low – moving from uniform

paddock management to a four-zone N management system resulted in returns of $392 for the sorghum crop in Bottom Tarnee and just $132 for the wheat crop.

2. There were decreasing marginal returns to increasing precision – Figures 3.24 and 3.25 show that most of the addition returns to the spatially variable N were achieved by moving from uniform management to the 2 zone system. Beyond this point each extra zone resulted in less and less additional return.

3. Small areas of the paddock cost money to crop – in case of the sorghum crop in a 4 zone system, one zone (4a in Fig 3.23) cost money to crop (i.e. had a negative gross margin). The area was 16% of paddock (16ha).

These results were discussed with the project’s farmer and agribusiness collaborators. The feedback received was that the returns from this analysis would be too low to offset the investment in variable rate fertiliser application technology. However, there was a reaction of disbelief that the returns could be so low. A strong desire was expressed to extend to this analysis to consider recent, individual years, allowing project collaborators to overlay their own experiences into the discussion of the results. The sentiment expressed was that the analysis based on an 11-year average scenario was hiding the potential for substantial gains from variable rate N management in some individual years. This acknowledges the substantial temporal variability in the production systems in the north-eastern Australian wheat belt.

37

Figure 3.24. Maximum gross margin ($/paddock, highlighted in red) that could be achieved for four spatially-variable nitrogen managements options (one zone i.e. uniform N application, two, three and four zones) for the sorghum crop based on simulations over ten years (1991-2001). For each zone, the figure shows the average soil depth, the proportion of the total paddock area occupied by the zone, the predicted average sorghum yield, the profit-maximising N application rate and the gross margin.

Figure 3.25. Maximum gross margin ($/paddock, highlighted in red) that could be achieved for four spatially-variable nitrogen managements options (one zone i.e. uniform N application, two, three and four zones) for the wheat crop based on simulations over ten years (1991-2001). For each zone, the figure shows the average soil depth, the proportion of the total paddock area occupied by the zone, the predicted average wheat yield, the profit-maximising N application rate and the gross margin.

Depth (cm) 88Area 100%Yield (kg/ha) 3114N opt (kg/ha) 43GM ($) $14,078 $14,078

Depth (cm) 62 Depth (cm) 113Area 48% Area 52%Yield (kg/ha) 2429 Yield (kg/ha) 3792N opt (kg/ha) 17 N opt (kg/ha) 64GM ($) $3,650 GM ($) $10,960 $14,610

Depth (cm) 50 Depth (cm) 85 Depth (cm) 120Area 27% Area 36% Area 37%Yield (kg/ha) 2110 Yield (kg/ha) 3074 Yield (kg/ha) 3980N opt (kg/ha) 10 N opt (kg/ha) 34 N opt (kg/ha) 71GM ($) $1,111 GM ($) $5,164 GM ($) $8,423 $14,698

Depth (cm) 42 Depth (cm) 70 Depth (cm) 97 Depth (cm) 123Area 16% Area 27% Area 27% Area 30%Yield (kg/ha) 1842 Yield (kg/ha) 2703 Yield (kg/ha) 3378 Yield (kg/ha) 4060N opt (kg/ha) 5 N opt (kg/ha) 23 N opt (kg/ha) 46 N opt (kg/ha) 74GM ($) 172$ GM ($) $2,911 GM ($) $4,600 GM ($) $7,047

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Depth (cm) 88Area 100%Yield (kg/ha) 2326N opt (kg/ha) 80.1GM ($) $23,851 $23,851

Depth (cm) 62 Depth (cm) 113Area 48% Area 52%Yield (kg/ha) 1838 Yield (kg/ha) 2773N opt (kg/ha) 69.5 N opt (kg/ha) 87.6GM ($) $7,725 GM ($) $16,229 $23,954

Depth (cm) 50 Depth (cm) 85 Depth (cm) 120Area 27% Area 36% Area 37%Yield (kg/ha) 1620 Yield (kg/ha) 2276 Yield (kg/ha) 2897N opt (kg/ha) 68.8 N opt (kg/ha) 72.7 N opt (kg/ha) 91.8GM ($) $3,369 GM ($) $8,449 GM ($) $12,155 $23,973

Depth (cm) 42 Depth (cm) 70 Depth (cm) 97 Depth (cm) 123Area 16% Area 27% Area 27% Area 30%Yield (kg/ha) 1497 Yield (kg/ha) 1968 Yield (kg/ha) 2500 Yield (kg/ha) 2952N opt (kg/ha) 71.7 N opt (kg/ha) 67.1 N opt (kg/ha) 77.7 N opt (kg/ha) 93.8GM ($) $1,608 GM ($) $5,092 GM ($) $7,220 GM ($) $10,063

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38

Optimal N rate for spatially-variable paddock management for individual years Given the strong influence of temporal variability on yields, the main question arising from the discussion about the apparent lack of economic benefits to moving from uniform paddock management to a four-zone N management system was how might the economic returns vary on a year-to year-basis. Following the same procedure as before, simulations were run for selected individual wheat seasons nominated by the collaborating farmer – two lower-yielding years (1991 and 1992), a medium (1999) and high-yielding years (1996) for the same set of N rates described above (0,50,100 and 150kg/ha). The weather record corresponding to the year of simulation was used in the model. The returns from uniform N management were again compared to spatially-variable N management by zones in each year. Table 3.6 shows the maximum gross margin ($/paddock) that could be achieved for uniform and four-zone spatially-variable nitrogen management for the four years investigated. The returns from the ‘average’ year are also included in this table. Table 3.6. Returns from uniform N management compared with those from four-zone spatially-variable nitrogen management for a wheat crop for the four years investigated (1991, 1992, 1996 and 1999). The 10-year average is also displayed. Year and season type

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1991 (poor) $14 048 $1 560 29 11% 1992 (poor) $17 333 $2 865 10 17% 1996 (good) $63 742 $1 012 5 2% 1999 (medium) $30 753 $509 1.3 2% ‘average’ year $23 851 $131 0 1% For individual years investigated, the increase in paddocks returns attributable to range from $509 (1999) to $2 865 (1992), representing increases in paddock return of 2% to 17% respectively. The returns to spatially-variable N management in the 10-year average year were low in comparison, and the largest relative gains were made in the poor years. An inspection of the gross margin responses to varying N rates in the four zones in the seasons 1992, 1996 and 1999 curves (Fig. 3.26) provides an explanation for the difference in returns. In the 1992 season, the N rate that maximised paddock returns for uniformly-applied N could deviate markedly from the N rate that maximised returns in individual zones. In comparison, the difference between the uniformly-applied and zonally-applied optimal N rates were less significant in the higher-yielding seasons - 1996 and 1999.

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(c) Fig 3.26 shows the yield response curve fitted to simulated yields for N rates (0, 50, 100 and 150) applied to each zone for the (a) 1992, (b) 1996 and (c) 1999 seasons. The corresponding gross margin response is also presented and the optimum N rate for uniform paddock management is indicated with a vertical line.

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Perfect vs imperfect knowledge of seasonal conditions The individual-year analysis showed that temporal variability impacts on economically optimal N management practices. It is important to note that the results of optimal N rates for both uniform and zone-based management are for conditions of full or perfect knowledge of the in-season climatic conditions. In some years, there is potential to make substantial economic returns and in other years the benefits would unlikely outweigh the cost of the investment in the PA technology, even with perfect information. In the years where potential gains to spatially-variable N management were evident, the question is how achievable are these gains in practice? In the practical situation, the farmer does not have perfect weather information at the time of N application. Even if the relationship between nitrogen fertiliser input and crop yield is well understood across a range of soil depths, the challenge is the uncertainty relating to climatic conditions that the farmer faces each year when deciding what level of crop yield to target. It means that the outcome of spatially-variable management is risky and that realisation of the high returns of this analysis would be difficult to achieve in practice. Taking the 1992, 1999, and 1996 seasons as representative of poor, medium, and good years, respectively, the management alternative reported above, which utilise perfect weather information, are compared against management options that might be implemented when the climatic conditions are not known. Table 3.7 sets out the following comparisons: 1. ‘Perfect management’ – economically-optimal N rates applied to each zone and adjusted for each

season, based on full knowledge of the in-season climatic conditions 2. ‘Seasonal management’ – uniform application of N (no zones) with economically-optimal N rate

seasonally adjusted using perfect seasonal knowledge 3. ‘Zonal’ management – management by zones, but rates in each zone not seasonally adjusted. In

each year, the economically optimal N rates for each zone in the ‘medium’ year were applied in all years.

4. ‘Uniform management’ - No spatial or seasonal adjustments were made to the N rate for this option. The economically optimal N rate for uniform N management in the ‘medium’ year was applied in all years.

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Table 3.7. N fertiliser rates (kg/ha) applied to each zone for each management option, corresponding simulated yields (t/ha), resultant N fertiliser excess or deficit (kg/ha), yield gain or loss (t/ha), total paddock returns ($), and penalty ($) relative to the ‘perfect management’ option.

N applied (kg/ha) Simulated yield (t/ha) N excess (kg/ha) Yield gain (deficit) (t/ha) Management Zone Area (ha) Good Medium Poor Good Medium Poor Good Medium Poor Good Medium Poor Perfect 1 (shallow) 16 118 44 0 4.1 1.3 0.8 management 2 (medium) 27 129 73 29 4.5 2.3 1.2 3 (deep) 27 150 84 68 5.1 3.1 2.2 4 (extra deep) 30 150 96 80 5.6 3.6 3.0 Seasonal 1 (shallow) 16 148 84 62 4.1 1.5 0.5 30 40 62 0.0 0.1 -0.3 management 2 (medium) 27 148 84 62 4.6 2.3 1.3 19 11 34 0.0 0.0 0.1 3 (deep) 27 148 84 62 5.1 3.1 2.2 -3 0 -5 0.0 0.0 0.0 4 (extra deep) 30 148 84 62 5.6 3.5 2.8 -3 -12 -18 0.0 -0.1 -0.2 Zonal 1 (shallow) 16 44 44 44 2.6 1.3 0.6 -74 0 44 -1.5 0.0 -0.3 management 2 (medium) 27 73 73 73 3.6 2.3 1.3 -56 0 44 -0.9 0.0 0.1 3 (deep) 27 84 84 84 4.0 3.1 2.3 -66 0 16 -1.1 0.0 0.0 4 (extra deep) 30 96 96 96 4.4 3.6 3.0 -55 0 16 -1.2 0.0 0.0 Uniform 1 (shallow) 16 84 84 84 3.6 1.5 0.4 -34 40 84 -0.4 0.1 -0.4 management 2 (medium) 27 84 84 84 3.9 2.3 1.3 -45 11 55 -0.6 0.0 0.1 3 (deep) 27 84 84 84 4.0 3.1 2.3 -66 0 16 -1.1 0.0 0.0 4 (extra deep) 30 84 84 84 4.1 3.5 3.0 -66 -12 4 -1.6 -0.1 0.0

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Paddock returns by zone $ $ penalty relative to Option 1 Zone Good Medium Poor Total Good Medium Poor Total Perfect 1 (shallow) $ 8,452 $ 1,554 $ 778 management 2 (medium) $ 15,987 $ 6,376 $ 2,580 3 (deep) $ 17,613 $ 9,820 $ 6,295 4 (extra deep) $ 22,708 $ 13,520 $ 10,549 total $ 64,760 $ 31,270 $ 20,202 $ 116,232 Seasonal 1 (shallow) $ 7,930 $ 1,282 -$ 1,265 -$ 522 -$ 272 -$ 2,043 management 2 (medium) $ 15,658 $ 6,274 $ 2,166 -$ 328 -$ 103 -$ 413 3 (deep) $ 17,597 $ 9,820 $ 6,274 -$ 16 $ - -$ 21 4 (extra deep) $ 22,549 $ 13,273 $ 10,168 -$ 159 -$ 246 -$ 381 total $ 63,734 $ 30,649 $ 17,344 $ 111,727 -$ 1,025 -$ 621 -$ 2,858 -$ 4,505 Zonal 1 (shallow) $ 5,217 $ 1,554 -$ 717 -$ 3,235 $ - -$ 1,494 management 2 (medium) $ 13,035 $ 6,376 $ 1,858 -$ 2,952 $ - -$ 722 3 (deep) $ 14,230 $ 9,820 $ 6,059 -$ 3,384 $ - -$ 236 4 (extra deep) $ 17,793 $ 13,520 $ 10,246 -$ 4,915 $ - -$ 303 total $ 50,275 $ 31,270 $ 17,446 $ 98,991 -$ 14,485 $ - -$ 2,756 -$ 17,241 Uniform 1 (shallow) $ 7,775 $ 1,291 -$ 1,851 -$ 677 -$ 263 -$ 2,629 management 2 (medium) $ 14,135 $ 6,313 $ 1,464 -$ 1,852 -$ 64 -$ 1,116 3 (deep) $ 14,235 $ 9,820 $ 6,064 -$ 3,378 $ - -$ 231 4 (extra deep) $ 16,306 $ 13,424 $ 10,531 -$ 6,402 -$ 96 -$ 18 total $ 52,451 $ 30,847 $ 16,208 $ 99,506 -$ 12,309 -$ 423 -$ 3,994 -$ 16,726

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The relative contribution that seasonal and spatial adjustment each make to the returns are also teased out. The seasonal management option had the effect of under-fertilising the deep soils and over-fertilising the shallow soils within each season, sometimes resulting in a yield decline associated with the crop ‘haying off’ with too much nitrogen. The zone management option under-fertilised in the good years and over-fertilised in the poor years. The economic significance of N excess was minor compared to N deficits because the price of nitrogen is low relative to the value of the crop. Over the 3-year period, the ‘seasonal management’ strategy generated a greater proportion of the potential ‘perfect management’ returns than the ‘zonal management’ and ‘uniform management’ options. The failure of the ‘zonal management’ option to match the returns from the ‘seasonal management’ option, and even the ‘uniform management’ option, in the good year was its inability to exploit the potential yields of the good year with sufficient N. The penalty for ignoring the season in the N management decision - failing to adjust rates to account for seasonal variation - was, in this case, greater than the penalty for ignoring spatial variability. To put these differences in context, it should be noted that the poorest performing strategy over the 3-year period did actually generate from 80-99% of the returns achievable from perfect management, suggesting that the penalty from a fixed N regime (uniform management) is not high. Implications for practical farm management Lawrence et al (2000, p.527) outline the nature of the challenge for dryland cereal cropping in Northern Australia:

“A dependence on stored soil moisture and unreliable in-crop rainfall creates uncertainty at planting about yield and protein expectations, the crop’s subsequent N requirements, and the most appropriate rates of fertiliser. Despite this uncertainty, most nitrogen nutrition decisions are made before, or at, planting because of the unreliability of follow up rain limits opportunities for in-crop applications.”

The four management options were discussed with the collaborating farmer in terms of the implications of spatial and temporal variability for practical N management decisions. In particular, we explored the question of what N management decision to make given a well-understood relationship between nitrogen fertiliser input and crop but uncertain weather? A simple spreadsheet-based framework was developed to facilitate a discussion to scope and evaluate N management options based around the four options presented above for different seasonal conditions – poor, medium, good. Taking the ‘perfect management’ simulated yields and N rates as the starting point, alternative N management options were nominated by the farmer. In order to provide flexibility to conduct immediate ‘what if’ analyses, and allow for the ongoing, independent use of the spreadsheet by the collaborating farmer, simple ‘rule of thumb relationships’ were incorporated into the spreadsheet to represent the yield response to N – in other words, linear approximations of the response surfaces up to the economically optimal N rate were derived based on the simulated yields reported above. It was assumed that yield increases beyond the optimal N rate were negligible, however provision was made to factor in yield losses associated with excessively high N rates. In the discussion it was pointed out that such an approach does have the effect of slightly over-estimating yield losses that may occur at rates of N just under the optimal. However, such an assumption was accommodated in order to quickly and easily explore alternative management options. As part of the interactive process the farmer also nominated starting soil N, protein estimates, input costs and crop prices.

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The 4 scenarios reported above were explored using this spreadsheet tool, along with what the farmer considered to be two practically-achievable variations to the ‘zone management’ option. Both variations were designed to reduce the risk of under-fertilising in the good year. This was partly in reaction to the analysis presented which demonstrated the penalties to ‘missing out’ on the good season but also consistent with the farmer’s 30:70 rule - i.e. 30 % of the years contribute 70% of the profit. The implication for him is to be well positioned to take advantage of good years. In the first variation to ‘zone management’, different N rates were applied in each zone and then fixed for the three years – poor, medium and good. However, the rate applied targeted a better-than-average year in each zone- in this case a 60th percentile year was targeted. In the second variation of the ‘zone management’ option, the economically optimal rates for each zone for the good season were also applied in the medium season. Given that the farmer had some confidence in being able to anticipate a poorer season– for example, from a late sowing, low starting soil water and/or negative SOI at the time of the N application decision - N rates were reduced in each zone to correspond with those of the medium year. Hayman (2001) reported that it is common for up to half the supply of water for the crop to be stored in the heavy clay soils from the previous summer fallow. Farmers have long been encouraged to determine a potential yield range by measuring stored soil water at sowing. More recently, the value of a seasonal climate forecast to wheat farming in southern Queensland has been shown. As Hayman and Turpin (1998) report, not only can farmers respond tactically to the state of the stored soil water, they can also respond to the state of the atmosphere. Table 3.8 summarises the results of the customised analysis. Assumptions made include 60kg/ha starting N and a ‘rule of thumb’ yield response of 33kg N produces one tonne of grain. Protein values were nominated by the property owner. Wheat prices were modified to reflect protein variation: values greater than 13% attracted $180/t, 11.5-13% were $160/t, 10-11.5 were $150 and less than 10% was $120/t. Although these prices were not current at the time of the analysis, the highest price was consistent with that used for earlier analyses. Sensitivity analyses are recommended to explore the implications of alternative prices. Both additional strategies nominated by the farmer, improved the returns over the original zone management option. The first variation applied excess N in the medium and poor years - the extra fertiliser cost in these years was more than adequately offset by a smaller yield loss in the good years. In the second variation, the returns exceeded those for the ‘season management’ option. These two additional management options were later analysed using simulated yield responses to N (not shown) and the same conclusions could be drawn – that is, the first variation of zonal management was greater than the returns to the original zone management option, but less than the seasonal management option. The second variation to zone management resulted in returns greater than the seasonal management option.

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Table 3.8. Fertiliser rates(kg/ha) applied to each zone for each management option, corresponding wheat yields derived by ‘rule of thumb’2(t/ha), protein, resultant N fertiliser excess or deficit (kg/ha), yield gain or loss (t/ha), total paddock returns ($),and penalty ($) relative to the ‘perfect management’ option. N fertiliser applied (kg/ha) N fertilser excess/deficit (kg/ha) Yield gain / deficit (t/ha) Total yield (t/ha) Protein % Zone Good Medium Poor Good Medium Poor Good Medium Poor Good Medium Poor Good Medium Poor

Perfect 1 (shallow) 114 24 7 0 0 0 0.0 0.0 0.0 4.1 1.3 0.8 13 13.5 15 management 2 (medium) 130 55 21 0 0 0 0.0 0.0 0.0 4.5 2.3 1.2 12 12.5 14 3 (deep) 148 82 54 0 0 0 0.0 0.0 0.0 5.1 3.1 2.2 11 11.5 13 4 (extra deep) 165 99 78 0 0 0 0.0 0.0 0.0 5.6 3.6 3.0 10 10.5 12 Seasonal 1 (shallow) 142 70 42 28 46 35 0.0 0.0 -0.1 4.1 1.3 0.7 14.5 15 17 management 2 (medium) 142 70 42 13 15 21 0.0 0.0 0.0 4.5 2.3 1.2 12 12.5 15 3 (deep) 142 70 42 -5 -11 -13 -0.2 -0.3 -0.4 4.9 2.7 1.9 10 11 11.5 4 (extra deep) 142 70 42 -23 -29 -36 -0.7 -0.9 -1.1 4.9 2.7 1.9 9 10 11 Zonal 1 (shallow) 24 24 24 -90 0 17 -2.7 0.0 0.0 1.3 1.3 0.8 9 13.5 16 management 2 (medium) 55 55 55 -75 0 34 -2.3 0.0 0.0 2.3 2.3 1.2 9 12.5 15.5 3 (deep) 82 82 82 -66 0 27 -2.0 0.0 0.0 3.1 3.1 2.2 9 11.5 13.5 4 (extra deep) 99 99 99 -66 0 21 -2.0 0.0 0.0 3.6 3.6 3.0 9 10.5 12.5 Uniform 1 (shallow) 70 70 70 -44 46 63 -1.3 0.0 -0.2 2.7 1.3 0.6 10.5 15 17 management 2 (medium) 70 70 70 -60 15 49 -1.8 0.0 -0.1 2.7 2.3 1.1 9 12.5 16 3 (deep) 70 70 70 -77 -11 16 -2.3 -0.3 0.0 2.7 2.7 2.2 8 11 13.5 4 (extra deep) 70 70 70 -95 -29 -8 -2.9 -0.9 -0.2 2.7 2.7 2.7 8 10 11.5 Zonal 1 (shallow) 15 15 15 -99 -9 8 -3.0 -0.3 0.0 1.1 1.1 0.8 9 14 17 management 2 (medium) 23 23 23 -107 -32 2 -3.2 -1.0 0.0 1.3 1.3 1.2 9 13 16 variation 1 3 (deep) 64 64 64 -83 -17 10 -2.5 -0.5 0.0 2.5 2.5 2.2 9 12 15 4 (extra deep) 90 90 90 -75 -9 12 -2.3 -0.3 0.0 3.3 3.3 3.0 9 12 13 Zonal 1 (shallow) 114 114 24 0 90 17 0.0 -0.3 0.0 4.1 1.1 0.8 13 14 17 management 2 (medium) 130 130 55 0 75 34 0.0 -0.3 0.0 4.5 2.0 1.2 12 14 16 variation 2 3 (deep) 148 148 82 0 66 27 0.0 -0.1 0.0 5.1 3.0 2.2 11 14 13.5

4 (extra deep) 165 165 99 0 66 21 0.0 -0.1 0.0 5.6 3.5 3.0 10 14 13

2 33kg N = 1 tonne of grain

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Paddock returns by zone $ $ penalty relative to Option 1

Management Zone Good Medium Poor Total Good Medium Poor Total Perfect 1 (shallow) $ 8,514 $ 1,887 $ 663 management 2 (medium) $ 13,495 $ 5,645 $ 2,790 3 (deep) $ 13,637 $ 8,247 $ 6,650 4 (extra deep) $ 17,220 $ 10,192 $ 8,840 total $ 52,866 $ 25,971 $ 18,943 $ 97,781 Seasonal 1 (shallow) $ 8,052 $ 1,125 -$ 202 -$ 462 -$ 762 -$ 864 management 2 (medium) $ 13,153 $ 5,227 $ 2,230 -$ 342 -$ 418 -$ 560 3 (deep) $ 13,137 $ 6,350 $ 4,161 -$ 500 -$ 1,897 -$ 2,489 4 (extra deep) $ 10,368 $ 7,137 $ 4,121 -$ 6,852 -$ 3,055 -$ 4,719 total $ 44,710 $ 19,839 $ 10,311 $ 74,859 -$ 8,156 -$ 6,132 -$ 8,632 -$22,921 Zonal 1 (shallow) $ 580 $ 1,887 $ 388 -$ 7,934 $ - -$ 275 management 2 (medium) $ 3,182 $ 5,645 $ 1,873 -$ 10,314 $ - -$ 918 3 (deep) $ 4,981 $ 8,247 $ 5,925 -$ 8,656 $ - -$ 725 4 (extra deep) $ 6,967 $ 10,192 $ 8,210 -$ 10,254 $ - -$ 630 total $ 15,709 $ 25,971 $ 16,395 $ 58,075 -$ 37,157 $ - -$ 2,548 -$39,705 Uniform 1 (shallow) $ 3,937 $ 1,125 -$ 967 -$ 4,577 -$ 762 -$ 1,630 management 2 (medium) $ 4,284 $ 5,227 $ 964 -$ 9,211 -$ 418 -$ 1,826 3 (deep) $ 4,177 $ 6,350 $ 6,230 -$ 9,460 -$ 1,897 -$ 420 4 (extra deep) $ 4,695 $ 7,137 $ 7,951 -$ 12,525 -$ 3,055 -$ 889 total $ 17,094 $ 19,839 $ 14,178 $ 51,111 -$ 35,772 -$ 6,132 -$ 4,765 -$46,670 Zonal 1 (shallow) $ 202 $ 1,249 $ 531 -$ 8,312 -$ 639 -$ 132 management 2 (medium) $ 899 $ 3,021 $ 2,738 -$ 12,596 -$ 2,625 -$ 52 variation 1 3 (deep) $ 3,758 $ 6,461 $ 6,389 -$ 9,880 -$ 1,786 -$ 261 4 (extra deep) $ 6,262 $ 10,238 $ 10,245 -$ 10,958 $ 47 $ 1,405 total $ 11,121 $ 20,969 $ 19,903 $ 51,993 -$ 41,746 -$ 5,003 $ 960 -$45,788 Zonal 1 (shallow) $ 8,514 -$ 340 $ 388 $ - -$ 2,228 -$ 275 management 2 (medium) $ 13,495 $ 3,613 $ 1,873 $ - -$ 2,032 -$ 918 variation 2 3 (deep) $ 13,637 $ 7,652 $ 5,925 $ - -$ 595 -$ 725 4 (extra deep) $ 17,220 $ 10,897 $ 9,978 $ - $ 706 $ 1,137 total $ 52,866 $ 21,821 $ 18,163 $ 92,851 $ - -$ 4,150 -$ 780 -$ 4,930

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To conclude the discussion, the property owner indicated that the presence of temporal variability means it will not be not possible to provide the optimal N rates, but some intermediate state based on knowledge of soil water, a seasonal climate forecast, and the ability to tactically manage N in-season by splitting the application, means that the gains lie somewhere in between the ‘perfect management’ and ‘uniform management’ options. If budgetary constraints or environmental implications on the amount of N that applied are not considered, there is an incentive to be generous, rather than conservative, in N application. However, we suggest reserving such conclusions until further analysis is done to explore the influence of N carryover effects and the relative frequency of the representative years used in this study over a longer time frame. Although supra-optimal rates reduce the ‘downside risk’ of not applying enough fertiliser, for many farmers, a constrained N budget will mean this is not an achievable management strategy. 3.5 Discussion Put simply, managing crop nitrogen nutrition is to balance crop demand with the supply from the soil and fertilisers. The presence of temporal climatic variability presents significant challenges to land managers seeking to gain an economic benefit from implementing PA technologies for spatially-variable management, particularly in cases when the consequences of temporal variability exceeds those of spatial variability. The analysis reported in this chapter showed that both temporal and spatial variability impact on economically optimal N management practices. Optimal N rates were calculated for both uniform and zone-based management under conditions of full or perfect knowledge of the in-season climatic conditions. In some years, there was potential to make substantial economic returns and in others the benefits would unlikely outweigh the cost of the investment in the PA technology, even with perfect information. It was not the intention of this activity, however, to provide recommendations, but rather to provide improved processes to assist landholders make judgments about spatially-variable management. Collaborating farmers reported that they were comfortable to make their own assessment of management alternatives, provided that they had confidence in understanding the biophysical functions taking place. This is consistent with the recognition of the importance of supporting farmers’ own planning (Brennan and McCown, 2003) rather than normative analyses which identify the best courses of action. For this reason, collaborators identified that the soil characterisation and monitoring activities were particularly valuable in improving their understanding of their own, or their client’s, production system. The biophysical response function relating N input to yield underpins the economic responsiveness of varying the level of N applied to a crop. The shape of the simulated economic response surfaces of grain crops to nitrogen fertiliser was generally flat around the optimum. The management implication that could often be concluded was that applying a ‘roughly right’ rate of N did not result in a high economic penalty. These findings are consistent with those of Hayman and Turpin (1998) and Turpin et al (1998) who examined the returns to fixed and variable rates of nitrogen based on assessments of available N, available water and seasonal forecasts in the North East Australian wheat belt. These analyses concluded there was less to be gained from being ‘precisely right’ than first imagined. The ‘flatness’ of the crop/fertiliser response curve around the optimum is a well-documented finding in agricultural economics literature, dating back to the 1950s (Pannell, 2004). Anderson (1975) cited discussions on the topic, commencing with Hutton and Thorne (1955), Heady and Pesek (1955) and Hutton (1955) and poetically proclaimed “precision is pretence and great accuracy is absurdity”.

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The reactions from collaborating farmers and advisers of the insensitivity of economic returns around the optimum did trigger a recollection of Jardine’s (1975) account of presenting information to agronomists about flat payoff curves for fertilisers where he “observed such reactions as complete disbelief, blank incomprehension, incipient terror, and others less readily categorised” (p.200). Pannell (2004) suggests that these responses would be among many current agronomists, but notes that there has been little recent debate on the topic, even among agricultural economists for which the ‘far-reaching’ but ‘under-recognised’ implications of flat payoff functions have largely disappeared from the economics agenda. Pannell (2004) claimed that in cases where precision agriculture technology results in low to moderate changes in input levels at a given location within the field, there is a high probability that the improvement in profit will be very low due to a flat payoff function. Also consistent with our findings, Pannell (2004) wrote that flat pay-off functions generally mean that there are diminishing marginal returns to precision in decisions about agricultural inputs. We suggest that any proposed application of PA technology to spatially-variable input management start with a thorough investigation into the nature of the biophysical response surface. We also believe that simulation modelling and historical climate records can play an important role in interpreting the causes of variability and placing the performance of individual seasons in a long-term context, as well as assessing the likely responses to changed management practice. This analysis suggests that in an environment where the consequences of temporal variability exceed those of spatial variability, there is little value in applying spatially variable rates unless seasonal adjustments are also made. However, conclusions about the value of PA for varying inputs at the sub-paddock scale in the north eastern wheat belt can not be fully formed until further analyses have been conducted on the influence of inter-annual variation, such as N fertiliser and soil water carry over effects, and protein variation on economic returns. Further sensitivity analysis considering a variation in crop prices and input costs is also required.

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4. Mosaic farming in the south-east Murray Darling Basin 4.1 Introduction The second case-study farm is located in the Upper Murray Catchment in the south-east Murray Darling Basin. The farm has operated as a mixed sheep and cropping farm, but in more recent years has been devoted to cropping activities due to the higher economic returns. The main enterprises in the area are dryland cropping, grazing of cattle, and sheep for wool and prime lambs. The key environmental challenges facing the Murray-Darling Basin are represented here: rising water tables, declining water quality and loss of biodiversity. A recent economic study of the local area predicted a decline in the value of the farm asset base and falling farm profits as a result of these issues (Frankenberg 1999). Although it has been recognized that management of these problems will involve more trees and perennial pasture, there is uncertainty how much of these are needed to address the environmental challenges (Frankenberg 1999). The central concept of mosaic farming is recognition of spatial variation in the farming landscape and, associated with this, recognition of the spatial variation in economic returns across the farm, and the potential benefits of withdrawing inputs from poorer-performing areas for reallocation to the more responsive areas, whilst simultaneously converting some land to vegetation systems with, for example, greater water use. Such deployment of deep-rooting perennials in carefully targeted locations may provide environmental benefits and profitable opportunities, even though they may not be economically viable for use on a wider scale. A key challenge in implementing a mosaic farming system is determining how to match the spatial location of the various enterprises of the mosaic with landscape position and soil attributes within a farm in a multiple-paddock or sub-paddock context. Before deciding on an alternative land use, it is essential in mosaic farming design to recognise the reason an area might be under-performing relative to other areas. The current productivity of an area should be assessed against potential productivity and the costs and benefits of moving to this state. Devising the best possible placement of these land uses in terms of salinity control, minimising acidification, productivity and maintenance of native biodiversity will require a thorough understanding of landscape process and ecosystems function, and spatially-referenced data of landscape characteristics. A preliminary investigation into the feasibility of implementing mosaic farming systems is presented here in the sub-paddock context. The activities reported concentrate on alternative management strategies on one 50ha paddock. This paddock is managed uniformly, although the landholder is interested in exploring the opportunities for exploiting the spatial variability on the farm. Although paddock scale, the analyses illustrate the information requirements necessary to inform mosaic farming design, explore the management issues relating to spatial x temporal interactions, and identify opportunities for changing the enterprise mix for economic and environmental benefit. From the viewpoint of mosaic farming we are interested in applying precision agriculture technologies to identify areas where current production is not profitable, or areas where small tradeoffs in profit result in large environmental gains. The observed variation in the paddock opens up the possibility that some areas of the paddock could be managed differently to others with the aim of increasing overall economic and environmental outcomes.

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4.2 Describing spatial variability Yield maps Yield maps have been generated for five years for the following crops and seasons: canola 1999, wheat 2000, triticale 2001, lupins 2002 and wheat 2003. These are presented in Fig. 4.1.

The yield map data have also been presented as probability distributions for the 2000, 2002 and 2003 seasons (Fig 4.2). The 1999 and 2001 seasons were omitted because of ‘gaps’ in the yield maps – i.e. incomplete data sets due to errors in equipment operation, technical failures etc. These gaps are not evident on the yield maps in Fig. 4.1 because the process of kriging statistically generates a yield estimate where there are missing values. Incomplete yield maps create frustration for both farmers and researchers, particularly as the data have been collected for so few years. Both views of the yield data reveal spatial variation occurring each year, and it is particularly pronounced in the wheat crops. Of note is an apparent ‘flip flop’ effect in the 2000 and 2003 wheat crops – that is, some of the lowest yielding areas in the 2000 season are the highest yielding in 2003, and vice versa. Patterns in the yield maps are explored further in the next section. Other spatially-referenced data In addition to yield maps, maps of electrical conductivity (EC) (Fig. 4.3) and elevation (Fig. 4.4) have been generated. Electrical conductivity is measured by EM38 and represents variation in soil attributes that are expressed in a difference in the electrical conductivity of the soil (e.g. soil water content, salt levels). The elevation map presents spatial variability in terms of metres above sea level. The difference between the highest and lowest part of the paddock is 17m. The elevation map reveals similar patterns to the yield maps, particularly the 2000 wheat crop. Soil characterisation In the seasons 2001-2003, characterisation of hydraulic characteristics of the soil was done on 4 locations within the paddock representing variation in elevation (and yield as shown in Fig. 4.5 against the 2000 wheat map). Soil characterisation was done using chemical and physical analysis of deep soil cores, as well as in situ determination of the drained upper and lower limit of water extraction (Dalgliesh and Foale, 1998). These values are given in Fig. 4.6. Soil characterisation at these sites identified that the main difference between the soils at the four locations was the plant available water capacity of the soils. Site 1was generally associated with the highest PAWC (140 – 159 mm to 160 cm). Site 4, at the lowest part of the paddock, was the next highest (131 mm to 130 cm), followed by sites 2 and 3 which were similar to each other (108 – 119 mm to 130 cm). Fig. 4.7 shows that the soil texture at locations 2, 3 and 4 was similar. The soil at Site 1 was a sandier soil. Its higher PAWC was a function of deeper water extraction. With the exception of the second sampling point, the annual crops had similar lower limits of water extraction. There is no apparent reason why the lower limit for triticale is lower than that for wheat and lupin at the second sampling point. This resulted in a PAWC of 101 and 108 mm for lupin and wheat compared to 157mm for triticale. Based on the differences between the soil types it would be expected that site 1 would yield highest, particularly in dry years when plant available water capacity would limit yield.

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Figure 4.1. Yield map demonstrating spatial variability in the case study paddock for a) canola, 1999 season b) wheat, 2000 season, c) triticale, 2001 season, d) lupins, 2002 season, e) wheat, 2003 season

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Fig. 4.2. Probability distributions demonstrating spatial variability for annual crops in the case study paddock for wheat (2000), lupins (2002) and wheat (2003)

Figure 4.3. Variation in electrical conductivity (EM38 readings) across the case study paddock

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Fig. 4.5. Location of soil characterisation sites 1-4 on the case study paddock

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Figure 4.6. Profiles of estimated drained upper limit (DUL) and lower limit (LL) for annual crops (wheat, tricitale and lupin) for the four characterised soils

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Fig 4.7. Proportions (%) of sand, silt and clay at depths up to 180cm for the soils at the four characterised sites 4.3 Explaining variability We hypothesise that the cause of yield variation observed in the yield maps is due to areas of the paddock differing in inherent soil characteristics, particularly plant available water capacity, and that part of the cause is also due to difference in internal soil drainage associated with soil properties and topography. This introduces the concept that landscape-scale processes are causing the variation in yield. Variation in topography, quantified as elevation data, represents a number of factors potentially causing spatial yield variability – for example, soils changed texture with elevation, elevation affects lateral water flows and the distribution of water within the paddock, and the temperatures recorded differed with elevation. Volume-area relationships A first step in assessing the potential for spatially variable management is identifying parts of the paddock that perform differently to each other. For example, do areas of the paddock consistently contribute disproportionately large/small volumes of yield relative to their area? If yes, is a) the variation sufficient to provide a case for managing each area differently and b) the cause of variability understood in order to manage it?

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Based on our hypothesis outlined above, we consider elevation as the basis for delineating 4 potential management zones (Fig. 4.8) within the paddock and consider the yield performance of each zone relative to its area (Fig 4.9). The fuzzy k-means clustering algorithm (Fridgen et al. 2000; Whelan and McBratney 2003) was used to delineate zones based on elevation. Fig. 4.8 also reveals the location of the soil characterisation sites - each falls within a different elevation-based management zone.

Fig. 4.8. Spatial configuration of four management zones, based on elevation Building on the data presented in the form of yield maps, this view of the data does not present a consistent picture of year-to-year paddock performance. In 2003 all parts of the paddock performed reasonably evenly. In 2000 and 2002, yields differed between zones, but not in consistent ways. Yields (t/ha) decreased in the wheat 2000 crop with decreasing elevation – i.e. lower positions in the paddock. For the 2002 lupin crop yields also varied from zone to zone, but within a narrower range than the wheat and with the first and third zone, outperforming the other two zones. When individual years are considered in isolation from the others, we conclude that only one year (2000) presents a strong case for spatially variable management.

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Fig. 4.9. Yield (t/ha and % of whole paddock yield volume) and area (% of whole paddock) contributed by each zone for wheat (2000 and 2003) and lupins (2002). (Note that canola 1999 and triticale 2001 are omitted due to missing data from the yield map) Model application and testing APSIM simulations were conducted for the four monitored points in the paddock over three season to check if model parameters for soil PAWC interacting with season and management (eg sowing date, fertiliser N applied) could account for the observed yield variation. Crop yields were simulated at the four characterised sites for triticale (2001), lupin (2002) and wheat (2003). These were compared with the observed yields cut from quadrats at each site. The APSIM simulations used the soil characterisation data collected for each site. Measurements of soil organic carbon, pH and mineral soil N indicated that there were not any substantial differences in the soil N supplying power of the soils to warrant different parameterisation. Automatic weather stations were installed in 2001 at the top and bottom of the slope in the paddock and daily minimum and maximum temperature was recorded throughout the three seasons of detailed monitoring. Rainfall data in conjunction with these temperatures and solar radiation from the Albury Bureau of Meteorology station were used to drive the simulation. Key simulation assumptions appear in Table 4.1. Simulations were initialised with starting soil water and nitrate in 2001 and allowed to run through the following three cropping seasons without re-setting. Crops were sown at the recorded established densities and fertiliser N was applied on the specified dates. Crop residues were retained during fallows, and fallows and crops were assumed to be weed-free. These assumptions were considered reasonable after checking with the farmer.

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Nutrients other than nitrogen were applied to ensure non-limiting growth, so only the potential limitations of N on growth are simulated. Where model parameters for the crop varieties were not available, the closest variety available in terms of phenology was chosen for the simulation. Simulations were conducted without any effects of waterlogging, lodging, frost at flowering impacting on yield. While it was possible that waterlogging may have constrained yield in some seasons, lack of undertanding of the landscape-scale hydrology of the paddock prevented any confident parameterisation of a water table build up in wet seasons. Table 4.1. Details of crops simulated for the case study paddock

Season Crop species

Cultivar Sowing date Established plant populat’n (Plants/m2)

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1999 Canola Pinnacle 8 June 1999 40* 80* Unknown 2000 Wheat Dollarbird 25 May 2000 140* 80* Unknown 2001 Triticale Credit 15 June 2001 110 40 12 Dec ‘01 2002 Lupin Wonga 24 April 2002 31 None 12 Nov ‘02 2003 Wheat Chara 20 May 2003 230 76 12 Dec ‘03

* not measured and therefore was assumed Figs 4.10 to 4.13 compare simulated and measured values for yield, biomass, soil water and soil nitrate at the 4 sites. Simulated water and nitrogen stress is also presented. Given the generally good agreement between observed and simulated values, it was concluded that the model can sufficiently represent the agronomic processes taking place in the paddock to explore the performance of other seasons and management options. Simulations were then run for the 1999 and 2000 seasons.

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Fig 4.10. APSIM simulated (indicated by lines) and actual (indicated by squares) (a) crop yields and biomass for triticale (2001), lupins (2002) and wheat (2003) for measurement site 1 on the case study paddock, (c) soil water and rainfall and (d) soil nitrate for triticale (2001), lupins (2002) and wheat (2003) for measurement site 1. Simulated water and nitrogen stress is also represented (b).

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Fig 4.11. APSIM simulated (indicated by lines) and actual (indicated by squares) (a) crop yields and biomass for triticale (2001), lupins (2002) and wheat (2003) for measurement site 2 on the case study paddock, (c) soil water and rainfall and (d) soil nitrate for triticale (2001), lupins (2002) and wheat (2003) for measurement site 2. Simulated water and nitrogen stress is also represented (b).

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Fig 4.12. APSIM simulated (indicated by lines) and actual (indicated by squares) (a) crop yields and biomass for triticale (2001), lupins (2002) and wheat (2003) for measurement site 3 on the case study paddock, (c) soil water and rainfall and (d) soil nitrate for triticale (2001), lupins (2002) and wheat (2003) for measurement site 3. Simulated water and nitrogen stress is also represented (b).

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Fig 4.13. APSIM simulated (indicated by lines) and actual (indicated by squares) (a) crop yields and biomass for lupins (2002) and wheat (2003) for measurement site 4 on the case study paddock, (b) soil water and rainfall and (c) soil nitrate for triticale (2001), lupins (2002) and wheat (2003) for measurement site 4. Simulated water and nitrogen stress is also represented (d).

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Fig. 4.14 summarises the comparisons between simulated and observed yield and also introduces the yield map data corresponding to each characterisation site an additional source of data for comparison.

Fig 4.14. Yield estimates: simulated vs quadrat measurement vs yield map for (a) canola 1999 (b) wheat 2000 (c) triticale 2001 (d) lupin 2002 (e) wheat 2003. Note that quadrat observations commenced in 2001 and are not available for canola 1999 and wheat 2000. In all but the wheat 2000 season, there is generally good agreement between the simulated, yield map and quadrat observed yields, with the exception of the 2000 wheat crop. Consistent with the area-volume analysis reported above, in three of the 5 years (1999, 2002 and 2003) there did not appear to be yield differences between the measurement points that would provide a clear indication that these points should be managed differently.

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The greatest discrepancy between simulated yields and those measured in the field occurred in the 2000 season wheat crop – the same season which had the most pronounced difference in yields in each zone. The model testing was extended to explain the difference between simulated and observed yields in the lowest part of the paddock for the wheat 2000 crop and identify the cause of the production constraint in the lowest parts of the paddock. Two possible constraints to production are explored: Frost The landholder suspected that frost could provide an explanation for the low observed yields in the 2000 wheat crop, although he was vague as to whether frost damage was suffered in this paddock versus other paddocks on the property. Inspection of the daily minimum and maximum temperature records in 2001-03 from the automatic weather stations installed at the top and bottom of the slope indicated that a minimum temperature of 1oC that could have been damaging to wheat yield, would occur if the minimum temperature at the top of the slope reached 3oC. Temperature data for the bottom of the slope were not available for the 2000 wheat crop as detailed monitoring did not commence until 2001. However, the Albury Bureau of Meteorology station record indicates that a 3oC minimum was reached close to the date of flowering of wheat in 2000 and therefore this would more than likely have translated to a 1oC at the bottom position and probable frost damage to flowering wheat heads. In the wheat crop in 2003 and the triticale crop in 2001, no likely yield-damaging minimum temperatures were recorded close the flowering, nor in the lupin crop of 2002. Long-term simulations for wheat sown in mid-May (Fig. 4.15) indicate that in 17 out of 40 simulated seasons a 30C minimum temperature (indicative of a possible frost in the lower part of the paddock) would fall within 2 days either side of simulated anthesis date – indicating a high chance of frost damage. In only 2 seasons out of 40 would a 1oC minimum (indicative of a frost in the high part of the paddock) occur. This suggests that the lower part of the paddock is particularly frost-prone and that sowing a later flowering wheat variety in this zone may help to minimise this frost risk.

Fig. 4.15. Frost risk over the historical climate record for the case study paddock

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Waterlogging The extent to which waterlogging could explain depressed yield in lower parts of paddock was also explored. The landholder re-called bogging his vehicle in the lower part of the paddock in 2000 and hence believes that excess water could have depressed growth below the APSIM-simulated potential for that zone in that season. After some discussion with the landholder it was clear that excess water likely to be contributing to a waterlogging event would come from both up-slope of this lower zone within the paddock, but also from outside the paddock boundary. He also made it clear that water would be able to move out the the bottom zone along a drainage line but that this movement would be impeded somewhat by a railway line running along the paddock boundary. A elevation map of the farm was used to construct a possible catchment area that might contribute excess water to the lower zone in wet seasons (Fig. 4.16).

Fig. 4.16 a) Hillshade and b) contour maps of the case study paddock both constructed from digital elevation data. The arrows on (b) indicate the direction of surface water flow. A multi-point APSIM simulation was run to explore the degree of contribution of excess water from the catchment area in different seasons, particularly 2000 when waterlogging during the winter growing seasons might have occurred. The multipoint simulation consisted of five points (zones), four within the paddock and one outside the paddock. Each zone was assigned an area to allow inter-conversions between depth (used in the water balance in each zone) and volumes (used to transport the water between zones) of water. Soil hydraulic properties for the area outside the paddock were assumed that same as site 1 (i.e. top of the slope) inside the paddock. Runoff curve numbers were assumed the same for each of the five zones, as was crop history. Excess water was generated through runoff (in APSIM this is a function of daily rainfall and soil water content in the upper part of the profile) and routed from the four upper zones onto the lowest zone. While the actual route of excess water movment may not have been surface runoff (e.g. sub-surface flows may have also been responsible) the runoff term in the model was used as an indicator of the likely magnitude of excess water moving laterally. Runoff was not permitted from the lowest zone, so the lowest zone had extra water infiltrating in excess of that provided by rainfall. Fig 4.17 shows the seasonal variation of the extra water infiltration experienced by the lowest zone. Infiltration in 2000 was significantly higher (nearly 100 mm for the year) than in the recent seasons when waterlogging had not been cited as a possible cause of spatial variability in yield. Inspection of the seasonal distribution of the excess water showed that much of the excess water was generated at times of the season when the 2000 wheat crop would have been potentially growing actively and hence susceptible to being checked by a growth limitation such as waterlogging.

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Fig. 4.17. Water infiltration in the lowest part of the case study paddock contributed from the catchment delineated in Fig. 4.16. 4.4 Managing variability The analysis presented above indicated that the lowest zone (Zone 4) in the paddock may be subject to excess water accumulation and possible waterlogging damage in wet years. One management option is to grow deep-rooted perennial vegetation in this zone to de-water the soil, create a dry buffer that would absorb excess water in wet years, and provide an alternative productive use for part of the paddock. One such deep-rooted perennial is lucerne, which was of interest to the collaborating landholder. Long-term lupin performance – exploring the opportunities for managing parts of the paddock differently

The first management alternative explores the performance of lupin crops in the case study paddock. Lupins are particularly sensitive to waterlogging and hence would be expected to yield poorly in the lower lying areas of the paddock where the soil has poor internal drainage and water tends to collect at wet times of the season. The economic implications of not growing lupin in these parts of the paddock were considered with a view to potentially freeing up these areas for an alternative land use such as lucerne.

Based on the soil characterisation data collected in each zone, simulations with APSIM were conducted in each zone using climate records over the period 1973-2002 with lupin crops (cv. Belara) sown in each season between 4th May and 19th July whenever at least 20 mm of rainfall fell over a 10 day period. This sowing window is typical for Southern NSW. Belara has similar phenology to the Wonga cultivar grown in 2002. Waterlogging was assumed to potentially only occur in the bottom-most zone of the paddock, as this is where water accumulates in wet years. It should be noted that these simulations were not conducted using the multi-point simulation option which accounts for the excess water flows from the upper parts of the paddock. However, a waterlogging option in the model was activated to account for excess water. Considering zones separately, simulations showed the high degree of spatial and temporal variability that would be expected in the paddock (Figure 4.18).

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In some seasons (e.g. 1974, 1982, 1993, 1996, 2000) the yield of zones were similar (except from zone 4 with waterlogging) while in other season there was marked differences between zones (e.g. 1977, 1981, 1987, 1991, 1997, 2001). The zones always yielded in a consistent rank, with zone 1 highest and zone 4 lowest, over a wide range of seasonal conditions. The fact that zone 4 (prone to waterlogging) always yielded consistently below the other zones is the basis for considering managing this zone differently to the rest of the paddock, i.e. not sowing it to lupins.

Fig. 4.18. Simulated lupin yield in zones 1 to 4 over 30 years in the paddock. Zone 4 is simulated with and without waterlogging effects “turned on” in the model so as to illustrate the magnitude of the simulated waterlogging effect. Grain yields for each zone were used to calculate gross margins based on growing costs of $150/ha and a grain price of $250/t. Gross margin was calculated on both a per zone and per paddock basis to see how much each zone was contributing to the overall profitability (or otherwise) of the paddock. Scenarios were considered where either the bottom-most zone (zone 4) or the next zone (zone 3) were not sown to lupins. The impact on paddock gross margin was re-calculated for these scenarios. Figure 4.19 shows the contribution of each zone to the overall paddock gross margin. For zones 1-4 the average gross margin in each zone was $4 033, $3 613, $2 774 and $339, respectively. The average paddock gross margin is about $11000 and the bulk of this comes from zones 1, 2 and 3. Zone 4 makes a loss in 1 season out of three, whereas there is only one season when there is a loss in the other three zones. The seasons in which zone 4 made a loss was not predictable based on the yielding level of that season. Zone 4 lost money in both high income (e.g. 1990) and low income (e.g. 1977) seasons. The impact of dropping out zone 4 permanently from production was to increase the average paddock grain yield from 1.59 t/ha to 1.94 t/ha. However, economically this increase in average yield did not completely offset the return forgone from withdrawing zone 4 from production, so that paddock gross margin fell slightly to $10 420. After zone 4, zone 3 was the next poorest contributor to paddock gross margin. However, dropping this zone as well as zone 4 permanently out of production had the impact of also increasing average paddock yield to 2.13 t/ha but decreasing gross margin markedly to $7646.

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Dropping zones out of production may or may not result in a decrease in paddock gross margin, depending upon economic prospects for the use the retired land is put to. In this case it is possible and probably desirable (for instance to minimise weed problems and erosion risk) to grow an

Figure 4.19. Lupin gross margin ($/zone) simulated for each zone in the paddock and the paddock as a whole for 30 simulated seasons in the paddock. alternative vegetation type preferably with low establishment and management cost and capable of making a return. The possibility of growing lucerne in this zone means that some biomass could be grown with the additional prospect of being able to reduce deep drainage and associated risks of salinity and leaching. Companion cropping in Zone 4 In order to minimise the income foregone by changing the land use of this zone from cropping to permanent lucerne it is possible to adopt a companion cropping strategy, whereby annual crops such as wheat are grown together in an intercrop with lucerne. Such a management option is being explored by the CRC for Plant-Based Management of Dryland Salanity as a means to retain lucerne as a permanent feature of a cropping system, together with its hydrological benefits, but continue to grow crops to provide cash income above that provided by the lucerne. A number of livestock-based and cropping-based farmers in Australia are currently companion cropping. While not conducted in this project, elsewhere APSIM has been validated against experimental measurements of crop and lucerne productivity and soil water in companion cropping systems (Table 4.2).

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Table 4.2. Grain crop yields (t/ha) observed and simulated for monocrop and companion crop systems over two seasons at Grogan, NSW. Values in parentheses are companion crop grain yields as a percent of that in the corresponding monocrop. (Robertson, Gaydon, Peoples and Swan, CSIRO, unpublished) 2002 2003 Observed Simulated Observed Simulated Monocrop canola 0.87 0.79 1.52 1.95 Companion crop canola 0.00 (0%) 0.23 (29%) 1.23 (81%) 1.54 (79%) Monocrop wheat 1.72 1.17 3.28 3.38 Companion crop wheat 0.20 (12%) 0.34 (29%) 1.72 (52%) 1.60 (47%) Long-term simulations were conducted to explore seasonal variation in productivity and water balance, this time for wheat-lucerne companion cropping systems for the lowest zone in the paddock. Although the analysis above was based on lupins, the collaborating farmer was interested in the performance of wheat when grown with lucerne. This analysis utilised the multi-point simulation capability to capture the effect of excess water being added to the zone from other parts of the catchment. Continuous wheat and lucerne systems were also simulated for the comparison with the companion system. Lucerne was simulated with a typical density (350 stems/m2) as well as a low density (50 stems/m2) to examine the potential for low density to confer the same hydrological benefits but at a lower penalty for wheat yield. Soil and root system parameters had not been measured for lucerne in the paddock and so these had to be estimated from the lower limits of the annual crops measured and knowledge of the difference in lower limits between annual crops and lucerne that has been found in other experimental situations in southern NSW. As expected, companion cropping reduced wheat yield compared to monocrop wheat due to competition for resources (light, water and nitrogen). On average wheat yielded 4.7 t/ha when grown conventionally (with waterlogging impacts simulated) and 1.7 t/ha when grown as a companion crop at high lucerne density. Use of a low density of lucerne raised average wheat yield to 3.5 t/ha (Fig. 4.20). The use of long term averages obscure the considerable inter-annual variability in yields and yield penalties in the two systems. In some seasons there is no reduction in yield in companion cropping. In a few seasons there is a slight increase in yield probably due to better N supply to wheat from the accompanying lucerne. In a few years the yield of wheat is almost completely suppressed (< 1t/ha).

Fig. 4.20. Simulated annual wheat yield (kg/ha) for solid wheat, the high and low lucerne density companion cropping options from 1960 to 2003.

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Simulated total annual lucerne harvested biomass was 10.5, 6.9 and 3.8 t/ha in the sole lucerne, companion cropping at high lucerne density and companion cropping at low lucerne density, respectively (Fig 4.21). There was some discussion with the farmer regarding the feasibility of being able to harvest all of the biomass grown in a companion cropping system (see below). Also, as expected incorporating lucerne into the system reduced water excess (deep drainage) from a mean annual total of 227 mm (annual wheat), to 84 mm (companion cropping at low lucerne density), 25 mm (companion cropping at high lucerne density) and 6 mm (sole lucerne) (Fig. 4.22). Hence a reduction in lucerne productivity through companion cropping had no impact on water excess at high lucerne density, but at low lucerne density some control over water excess was lost. The benefit of growing the lucerne in zone 4 in place of the lupins could be potentially valued in a variety of ways. The biomass produced could be valued as livestock feed (if the zone were to be fenced off), or as hay if grown ‘solid’ rather than in companion cropping. Other values of the lucerne include the provision of ground cover for weed control and protection from erosion.

Fig. 4.21. Simulated annual lucerne yield (kg/ha) for the high and low lucerne density companion cropping options, and solid lucerne from 1960 to 2003.

Fig. 4.22. Simulated drainage (mm) under the solid wheat, high and low lucerne density companion cropping options, and solid lucerne from 1960 to 2003

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A change in the mix of enterprises in a mosaic farming system will affect the dynamic interactions between crops. Interactions between legume and cereal grain crops in the short to medium term, for example, include nitrogen fixation from a legume crop and yield boost / depression from other factors in a crop rotation. Yield boosting factors can stem from breaking pest cycles, particularly soil borne diseases and pests. 4.5 Discussion The three-step decision framework of applying PA technologies to a farm – i.e. sensing, interpreting and managing variability was applied to explore the potential for changing the mix and location of enterprises on the case study property. Although this activity was focussed on the sub-paddock scale, the approach illustrated the information requirements necessary to inform mosaic farming at broader scales. The analysis demonstrated that the economic and environmental outcomes from a mosaic farming system could vary within a farming landscape depending on where various elements of the mosaic farming system are located. An important question then for mosaic farming is how to match the spatial location of the various enterprises of the mosaic with landscape position and soil attributes. The ability to recognise spatial variation in economic returns across the paddock was found to be a necessary but not sufficient information requirement for mosaic farming design. The collaborating landholder noted that the uncertainties involved with interpreting variability made the final step of managing spatial variability, based on the data captured, a very difficult task to undertake with confidence. Historical climate records and simulation models assisted in explaining spatial and temporal variability, particularly by aiding diagnosis of possible constraints to yield such as frost, waterlogging and the influence of catchment-scale hydrological processes on yield. When asked to provide feedback on different aspects of the research activity, the collaborating landholder and advisers reported that the most valuable aspect of the project was their improved understanding of variability in the case study paddock, particularly aided by the soil characterisation activity. Although they acknowledged that interpretation of soil characterisation ‘doesn’t tell you what to do about variability’, improved understanding of system was valued highly. Collaborators indicated that the model was credible – that is, able to represent the agronomic processes on the farm, and could play a role in guiding future management practice. However, the collaborating landholder did not intend to change management practice in the short term based on the results of the simulated management alternatives. In explaining this, the complexity of changing the mix of enterprises on the farm was revealed. The timing of the lucerne pasture in a companion cropping system, for example, would need to be compatible with livestock operations. The feasibility of being able to harvest all of the biomass grown amongst a wheat crop was also questioned. Another issue concerns the feasible size of management units. The landholder indicated ‘zone 4’ in the paddock was not of an appropriate size and location to manage separately. It should be considered that in practice mosaic farming applies at scales larger than the individual paddock, providing the possibly of incorporating several adjoining paddocks into new production areas and hence increasing the scale of field operations. The landholder recognised that the current paddock boundaries are not well aligned with the variations in the landscape which cause yield variability. Redefining paddock boundaries so that similar areas to Zone 4 could be managed as a single unit was the idealised management scenario, but the costs associated with preparing and re-fencing new paddock boundaries made the idea less attractive in practice, particularly where cropping and grazing are joint enterprises. Although this case study highlights opportunities for mosaic farming design, further research is needed to fully evaluate the implications of changing the mix and location of enterprises on this case

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study property. The impact that mosaic farming systems have on longer term sustainability factors – for example, the time taken for the landscape to become degraded or the time taken for the management alternative to prevent or reverse / decline damage, must be considered. Scaling up implementation of mosaic farming to the farm scale requires that a much greater range of factors be considered than those outlined in this activity. As discussed in Brennan et al (2004) these include not just short-term profitability factors, but dynamic factors incorporating the temporal interactions between enterprises within the mosaic, long-term aspects concerned with the temporal separation between costs and income associated with perennial enterprises, inter-enterprise factors relating the spatial interactions between the enterprises, risk factors, and costs and benefits that are incurred at whole-farm scale.

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5. Implications and recommendations This project aimed to provide improved tools and processes to evaluate the economic and environmental benefits, and risks, associated with technologies that address spatial variability in Australian farming systems. The research was based on case studies of practical applications of PA technology. An early observation in the project was the difficulty of describing and interpreting variability using yield maps alone. The soil characterisation and monitoring activities provided reliable measurements of soil water, nitrogen and other physical and chemical properties. One measure of this project’s success has been the collaborators’ improved understanding of their own or their client’s spatial variability from the these activities. Such benefits are not confined to the PA context, as soil characterisation and monitoring have widely benefited farmers’ production systems understanding (Carberry et al. 2002). The farming systems simulation model APSIM was used in a process that commenced with farmers nominating their hypotheses of what was responsible for spatial variation. These hypotheses were then tested through monitoring and simulation, and explored in interactive discussion sessions with project collaborators. The discussion sessions using APSIM with landholders and their advisers have indicated strong interest in the potential of APSIM to complement PA technologies that sense variability and help explore spatially-variable management alternatives. APSIM was applied to address the issue of interaction between spatial and temporal variability and their respective interactions with management practice. The project is a pioneering study in the application of dynamic, production systems simulation to the evaluation of spatially-variable management alternatives for Australian agricultural landscapes. The research undertaken by this project has advanced the development of APSIM’s new, multi-point simulation capability to assess the impact of landscape-scale processes, such as surface water flows, as the potential determinant of variable production performance at the sub-paddock scale. When teamed with soil characterisation and monitoring, the value of the simulation model lies in its ability to interpret and predict crop response to inputs in relation to weather variation, management practices and soil properties. Economic analyses are important to highlight the significance of the biophysical responses. Although a common starting point for many PA analyses is to start with the assumption that soil variation is the determinant of spatial variability in yields, the use of simulation and microclimate analysis was able to identify alternative, non-soil-based factors (e.g. frost) as the reason for variation. Simulation models can predict temporal and spatial variation by modifying the crop and soil input parameters to account for spatial variability on a paddock. There remain, however, some challenges to utilizing simulation in PA applications. APSIM is a ‘point’-scale model. We note the issues discussed by Cook and Bramley (1998) and Hansen and Jones (2000) concerned with applying point-scale crop simulation models to larger scales that encompass spatial heterogeneity. The difficulties lie in obtaining crop and soil data that account for the full extent of variation across the paddock. With the hypothesis that soil depth was the main driver of variation in one of the case studies, the task of simulating crop yields in a spatially-variable paddock was greatly assisted by the availability of a layer of a geo-referenced soil-depth data which enabled the model to be configured to reflect this variability across the whole paddock. However, even with only one soil factor driving the variation, other variables may confound yield variation, limiting the extent to which models will be able to perfectly reproduce yield maps. The multifactorial nature of variability means that much is to be gained by incorporating farmer knowledge of a paddock into the interpretation of variability and design of management zones.

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APSIM can dynamically simulate the growth of crops, trees and their main interactions in response to the climate, soil and management options applicable at any selected location in Australia. While this research focussed on the management of only two farms, the tools and processes developed can potentially be made available to many more landholders around Australia via established commercial advisory services and public extension activities (Carberry et al., 2002) and can potentially be well utilized within established PA advisory services for landholders. More information about the commercial delivery of APSIM as an agricultural planning and advisory tool can be viewed at www.farmscape.cse.csiro.au.

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6. References Anderson, JR 1975, ‘One more or less cheer for optimality’, Journal of the Australian Institute of Agricultural Science, vol. 41, pp.195-197. Basso, B, Ritchie, JT, Pierce, FJ, Braga, RP, Jones, JW 2001, ‘Spatial validation of crop models for precision agriculture’, Agricultural Systems, vol. 68, pp. 97-112. Brennan, L, Robertson, M, Dalgliesh, N, Brown, S, Keating, B, Smith, C, Ive, J (in press). Mosaic Farming Feasibility, A report of the Heartlands Initiative, Technical Report No. 3, Publication No. HL11-03. Brennan, LE & McCown, RL 2003 ‘Making farm management research relevant to farm management practice’ Invited paper presented to the 47th Annual Conference of the Australian Agricultural and Resource Economics Society, Fremantle, 11-14 February. Bell, CJ 2002, Internet Delivery of Short Courses for Farmers. A case study of a course on Precision Agriculture, RIRDC Publication No 02/085, RIRDC, Canberra, 16pp. Carberry, PS, Hochman, Z, McCown, RL, Dalgliesh, NP, Foale, MA, Poulton, PL, Hargreaves, JNG, Hargreaves, DMG, Cawthray, S, Hillcoat, N, Robertson, MJ, 2002, ‘The FARMSCAPE approach to decision support: Farmers,’ Advisers,’ Researchers’ Monitoring, Simulation, Communication, And Performance Evaluation’, Agricultural Systems, vol. 74, pp. 179-220. Cook, SE & Bramley, RGV 2001 ‘Is agronomy being left behind by precision agriculture?’, In: Proceedings of the 10th Australian Agronomy Conference, Australian Agronomy Society, Hobart. Cook, SE & Bramley, RGV 1998, ‘Precision agriculture – opportunities, benefits and pitfalls of site-specific crop management in Australia’, Australian Journal of Experimental Agriculture, vol. 38, pp. 753-63. Cook, SE, Adams, ML, Bramley, RGV, Whelan, BM (in press), ‘State of precision agriculture in Australia’ in A Srinivasan (ed), Precision Farming - a global perspective, Haworth Food Products Press, New York. Daberkow, SG & McBride, WD 2003, ‘Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US’, Precision Agriculture, vol. 4, pp. 163-177. Dalgliesh, N & Foale, M 1998, Soil Matters – Monitoring soil water and nutrients in dryland farming, Agricultural Production Systems Research Unit, Toowoomba. Einhorn, H 1982, ‘Learning from experience and suboptimal rules in decision making’, In: D Kahneman, P Slovic, A Tversky (eds), Judgement under uncertainty: Heuristics and biases, Cambridge University Press, Cambridge, pp. 268-283. Frankenberg, J 1999, West Hume Landcare Group, New South Wales. Landcare, The University of Melbourne. Available: http://www.landcareweb.com/lcdirectory/westhume.html Fridgen, JJ, Fraisse, CW, Kitchen, NR, Sudduth, KA 2000, 'Delineation and analysis of site-specific management zones', Paper presented to the Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, Florida, 10-12 January. Hansen, JW & Jones, JW 2000, ‘Scaling-up crop models for climate variability applications’, Agricultural Systems, vol. 65, pp. 43-72.

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