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National WhopperCropper – risk management discussion support software

Howard Cox1,4, Graeme Hammer1,2, Greg McLean1 and Christine King3

1 Queensland Department of Primary Industries, PO Box 102 Toowoomba, Qld 4350
2
School of Land and Food Sciences, The University of Queensland, Brisbane, Qld 4072; graeme.hammer@dpi.qld.gov.au
3
University of Queensland, The University of Queensland, Brisbane, Qld 4072
4
Author for correspondence; mailto:howard.cox@dpi.qld.gov.au

Abstract

It is a paradox that in a country with one of the most variable climates in the world, cropping decisions are sometimes made with limited consideration of production and resource management risks. There are significant opportunities for improved performance based on targeted information regarding risks resulting from decision options. WhopperCropper is a tool to help agricultural advisors and farmers capture these benefits and use it to add value to their intuition and experience. WhopperCropper allows probability analysis of the effects of a range of selectable crop inputs and existing resources on yield and economic outcomes. Inputs can include agronomic inputs (e.g crop type, N fertiliser rate), resources (e.g soil water at sowing), and seasonal climate forecast (SOI phase). WhopperCropper has been successfully developed and refined as a discussion-support process for decision makers and their advisers in the northern grains region of Australia. The next phase of the project will build on the current project by extending its application nationally and enhancing the resource management aspects. A commercial partner, with over 800 advisor clients nationally, will participate in the project.

Media summary

Managing climate risk will now be easier because the WhopperCropper software package will soon be available nationally. With just a few mouse clicks, the effect of many crop management inputs and climate scenarios can be tested.

Keywords

Risk management, climate, decision support, APSIM

Introduction

Farmers in all regions of Australia endure widely varying rainfall conditions whilst needing to make critical management decisions prior to every cropping season. In a recent survey (Nelson et al., 2002), growers and advisors ranked both gross margin potential and production risk highly when asked about the decision of crop type. It was also reported that growers ranked erosion potential moderately highly as well. This indicated that growers are concerned with risk issues (both production and price) but also must synthesise price, production and resource management issues. Hence a tool that facilitates analysis of such issues could be of value. A degree of specificity is required because yield distributions vary in response to a multitude of factors and cannot be generalised (Pannell et al., 2000) but Cox (1996) contended that the most successful decision support packages were generic (not highly structured or constrained).

Important decisions can involve existing natural resources such as stored soil water, as well as controllable agronomic inputs, such as N fertiliser rate. Many of these inputs are important per se but they can also interact in a complex way. Currently, farmers must rely solely on experience and knowledge of district averages in order to set their ‘yield goal’ and hence manipulate their crop inputs. In addition it is very difficult to anticipate how input variables will interact to influence yield in an environment typified by widely varying rainfall. As long as the program is relevant to the farmers needs and is not so generic that it fails to match local conditions (McCown 2002), then it is expected that even experienced farmers could benefit from an easy-to-use information/scenario analysis system. It is desirable that input levels are optimised for the prevailing conditions in order that production is maximised whilst inputs are minimised. This has multiple benefits of minimising costs, maximising water use efficiency and minimising environmental impacts.

The APSIM crop simulation model (Keating et al., 2003) has the capability to simulate the effects of interacting resources and agronomic inputs. The modular format allows simulation of a large number of different crop types as well as the major inputs that affect yield such as soil water at sowing, nitrogen availability, plant density etc. Simple budgeting techniques can have an important role (Pannell 2000) and it is in this area that WhopperCropper has strength. WhopperCropper is an easy-to-use database of outputs from a factorial combination of locally-relevant inputs that can be used to create scenarios of interest. Such scenarios may involve comparative yield distributions as well as gross margin distributions that can facilitate simple budgeting or improved input allocations. Climate forecasting capability can also be used to indicate the how the current SOI phase (Stone et al., 1996) may result in a ‘shift’ of yield and gross margin probabilities.

Decision-support packages have had limited uptake in some instances (Hayman and Easedown 2002, McCown 2002). Attributes that dissuade users are: phobias involving computers, tedious data entry,

complicated set up process, lack of software support, lack of technical interpretation and application and local relevance (Cox 1996, Hayman and Easedown 2002, Hearn and Bange 2002, McCown 2002). This project addresses these issues by providing an easy-to-use package that is locally relevant with distribution and support via a trained ‘intermediary’ (field or sales agronomist or private consultant). The value of an intermediary has been described by Carberry et al. (2002) in their experimentation using a complex crop model with growers. Interest was high with what was described as ‘innovator/early adopter’ types but the remaining question was how to efficiently reach the ‘early majority’ farmer category.

Hayman 2004 suggested that the intermediaries are a favourable audience for decision support tools. WhopperCropper has the advantage that it is quick and simple to use. The capability of WhopperCropper has been sufficient for many users. In the future it is expected that it will also build the market for the use of more complex simulation tools. Software support and training will also be provided regionally. No on-going field monitoring is required and numerical data entry is only required for the gross margin section. Importantly, the model outputs will add value to a service (soil test interpretation) that is already in high demand; additional information and risk analysis capability will be available at critical decision times.

Materials and methods

The program

The type and value of relevant crop inputs for a district of interest are determined from local experts. Long-term meteorological data for each of the districts are readily available. The APSIM crop simulation model is run for every combination of these resource and management inputs for the length of the rainfall data. The resultant matrix of simulation runs is collated in a database. Individual scenarios are accessible via a graphical interface with multiple output formats. The program is quickly able to display the desired scenario. The current database contains 600,000 pre-run simulations. The decision components include; soil water-holding capacity, soil water at sowing, crop type, time of sowing, N fertiliser rate, crop maturity type, sowing density and SOI phase effect on yield. Simulations for 21 districts in Queensland and northern NSW are provided.

Development

The WhopperCropper software has been developed in the northern cropping region of Australia in association with private and public agronomists, farmers, and educational institutions (Nelson et al., 2002). Feedback from these agronomists shaped the technical content of the program and the training process. Improvements and additions have been made in successive versions. Several ‘markets’ for the program have been successfully accessed including private and public-funded agronomists, farmers and secondary and tertiary education establishments.

Training and accreditation process

The current project includes a workshop to enable discussion of cropping options and soil and water resource effects and options as well as instruction on the use of the software and APSIM simulation modelling processes. The training workshop and program has been provided for a nominal charge in return for feedback regarding technical content, usability and workshop process (Table 1). Seventy five percent of participants indicated that WhopperCropper was easy to learn and use. Approximately half of the respondents said the program provided accurate yield outcomes.

Seventeen training workshops have been conducted since 1999 with 201 agronomists trained. Participants have included agricultural consultants, farmers, extension officers, high school teachers and students. The package is emerging as a capable training resource for this wide range of clients.

Evaluation

Evaluation was conducted as participants completed the training and accreditation workshops (Table 1).

Table 1. Percentage of respondents indicating degree of satisfaction with WhopperCropper (n=44)

Skills

Agree or strongly agree
(%)

Increased knowledge of crop simulation

95

Increased knowledge of seasonal climate forecasting

82

Understand concepts of probability

65

Ability to explore management options

89

Will use Whopper Cropper

72

Increased understanding of how Whopper Cropper works and limitations

100

The program

 

Whopper Cropper is easy to learn and use

75

The program provides accurate yield distributions

49

The graphics and presentation is easy to learn and use.

77

A follow-up survey of 55 participants indicated its usage after the training. The evaluation has indicated that the program was subsequently used by 65% of extension (officers) and consultants surveyed (Figure 1). The consultants and public extension officers were the group that most frequently used WhopperCropper.

Figure 1. Use rate of WhopperCropper after training workshop (total of 55 respondents).

The future

The new challenge is to facilitate the distribution of WhopperCropper to other regions and states in Australia. A new project will enable building on the current project to extend its application nationally. The Agricultural Production Systems Research Unit (APSRU) has engaged a commercial delivery partner (Nutrient Management Systems Pty Ltd), to train and support their advisors in the use of WhopperCropper. Nutrient MS has a network of more than 600 advisors (field agronomists, sales agronomists and private consultants) currently using soil test interpretation software. The WhopperCropper capability will be used as an adjunct to the fertiliser advisory capability to improve management of fertiliser and other inputs within an environment of high climate variability and risk.

Collaboration with research and extension agronomists from all regions in Australia will enable development of valid APSIM simulations to be conducted for inclusion in the WhopperCropper database.

Conclusion

This project takes crop modelling and climate science capability from the research realm to field application by agronomists. The ability to examine the magnitude and variability of response to crop inputs and SOI phases will assist risk management. Because a trained agronomist will use the system in conjunction with farmer clients, it is expected that the inherit capability will be more widely utilised than occurred with some previous decision-support systems.

“Of all the decision support tools coming from DPI, WhopperCropper has the greatest capacity to really be useful
“ (farmer quote).

References

P. S. Carberry, Z. Hochman, R. L. McCown, N. P. Dalgliesh, M. A. Foale, P. L. Poulton, J. N. G. Hargreaves, D. M. G. Hargreaves, S. Cawthray, N. Hillcoat and M. J. Robertson 2002. The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring, simulation, communication and performance evaluation. Agricultural Systems 74 141-178.

Cox, P.G. 1996. Some issues in the design of agricultural decision support systems. Agricultural Systems 51 1- 27.

Hayman, P.T 2004 Decision support systems in Australian dryland farming: A promising past, a disappointing present and uncertain future. Fourth International Crop Science Congress, Brisbane (In press).

Hayman, P.T and Easedown, W. J. 2002. An ecology of a DSS:reflections on managing wheat cropsin the northeastern Australian grains region with WHEATMAN. Agricultural Systems 74 57-77. Agricultural Systems 74 27-56.

Hearn, A.B and Bange, M.P 2002 SIRATAC and CottonLogic: persevering with DSSs in the Australian cotton industry. Agricultural Systems 74 27-56.

Keating, B. A., Carberry P. S., Hammer, G. L., Probert, M. E., Robertson, M. J., Holzworth, D., Huth, N. I, Hargreaves, J. N. G, Meinke, H, Hochman, Z, McLean, G., Verburg, K., Snow,V,. Dimes, J. P, Silburn, M., Wang, E., Brown, S., Bristow, K. L., Asseng, S., Chapman, S., McCown, R. L., Freebairn, D. M., and Smith, C. J. 2003. An overview of APSIM, a model designed for farming systems. European Journal of Agronomy 18 267-288.

McCown ,R.L 2002. Changing Systems for supporting farmer’ decisions: problems, paradigms and prospects. Agricultural Systems 74 179-220.

Nelson, R.A., Holzworth, D.P., Hammer, G. L., and Hayman, P.T. 2002 Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software. Agricultural Systems 74 393-414.

Pannell, D.J., Malcolm, L.R. and Kingwell, R.S. 2000. Are we risking too much? Perspectives on risk in farm modelling and farm management. Agricultural Economics 23, 69-78.

Stone, R.C., Hammer, G.L. and Marcussen, T. 1996. Prediction of global rainfall probabilities using phases of the Southern Oscillation Index. Nature 384: 252 - 255.

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