Combining Biophysical and Price Simulations to Assess the Economics of Long-term Crop Rotations

Biophysical simulation models (e.g. APSIM) using historical rainfall data are increasingly being used to provide yield and other data on crop rotations in various regions of Australia. However, to analyse the economics of these rotations it is desirable to incorporate the other main driver of profitability, price variation. Because the context was that APSIM was being used to simulate an existing trial site being monitored by a farmer group Gross Margin output was considered most appropriate. Long-run rotational gross margins were calculated for the various rotations with yields (and other physical outputs) derived from APSIM simulations over a period of 100+ years and prices simulated in @Risk based on subjective triangular price distributions elicited from farmers in the group. Rotations included chickpeas, cotton, lucerne, sorghum, wheat and different lengths of fallow. Output presented to the farmers included mean annual gross margins and distributions of gross margins presented as probability distributions, cumulative probability distributions and box and whisker plots. Cotton rotations were the most profitable but had greater declines in soil fertility and greater drainage out of the root zone. Introduction This study was conducted with a farmer group on the Jimbour Plain of Queensland. The Jimbour Plain is north of Dalby in SE Queensland and has an annual rainfall of 676mm. The soils are deep, self-mulching black vertosols and the main crops in the area are dryland and irrigated cotton, maize, wheat, barley, sorghum, chickpea, mungbean and sunflower. The group had an existing trial looking at various rotations and management systems and were familiar with simulation modelling of the biophysical system using APSIM or Agricultural Production Systems Simulator (Keating and McCown 2001). APSIM is a farming systems simulator that combines climate risk analysis with the prediction of long-term consequences of farming practice on the soil resource. Farmers involved in this project were interested in the long-term benefits and costs of the various rotations with and without lucerne. This paper addresses the issue of developing a method of analysis for the information generated from these simulations that could be presented to farmers so that it allowed them to assess the returns and risks associated with each of the rotations. The main risks to be considered were yield and price risk, although sustainability indices such as subsoil drainage and run-off were included as well. Economics of perennial and long-term rotations Bathgate and Pannell (2002, p. 118) point out that ‘high quality economic analysis of perennial plant-based enterprises is not straightforward’. This is because of the complexities of interactions between crops including the complementary and supplementary effects, the trade-offs between short-term returns and long-term sustainability factors, and the various options for incorporating perennials such as lucerne in the cropping systems. Another complicating factor is that lucerne on the Jimbour Plain has to be incorporated in rotations that include chickpeas, cotton, mungbeans, sorghum, wheat and different lengths of fallow. More importantly these crops have different sowing windows, moisture requirements, climatic responses and different price levels and variations. Consequently there are different levels of profitability and risk associated with the crops and rotations that can influence the decision to adopt a rotation. Some crops may be cropped opportunistically (i.e. not growing a crop if there is insufficient stored soil moisture) while others such as cotton require substantial fallow periods to be successful. Farmer decision making The context of this study was that farmers were attempting to incorporate lucerne in their rotations to lift the long-term profitability of the cropping operations by improving soil properties. There is also an element of improving environmental sustainability of their operations by decreasing run-off and subsoil drainage. Risk preferences also influence farmers’ decisions. There is strong evidence that Australian farmers are risk averse (Bond and Wonder 1980; Abadi Ghadim and Pannell 2000). This means farmers take account of the variability of return as well as the level of return and accept lower returns in order to reduce their risks. There is also evidence that people respond differently in situations of Knightian uncertainty and ambiguity (Camerer and Weber 1992; Murray-Prior and Wright 2001). Consequently the decision to make a particular strategic change in a cropping rotation will depend upon the context set by the manager’s higher-level strategies. The relationship between these factors will interact with the riskiness of the particular decision and the manager’s attitude to risk. Since the purpose of this study was to provide information that could be used in a discussion with farmers, the information provided needed to be in a form that farmers could easily incorporate in their decision processes (Murray-Prior 1996). This implied separating level of profitability, variability of profitability and sustainability factors so that farm managers could evaluate trade-offs between these factors according to their own preferences. Method The analysis was conducted in four steps: Simulating biophysical data in APSIM, simulating prices in @Risk, combing these simulations to calculate rotational gross margins in Excel spreadsheets and finally comparing various rotational choices in an additional Excel spreadsheet (Figure 1). Figure 1: Representation of stages in calculating rotational gross margins and comparing rotations Excel (Calculate Rotation 1 GM) Excel (Calculate Rotation 2 GM) Excel (Calculate Rotation 3 GM) Excel @Risk (year, prices) Excel Comparison of Rotations 1, 2 & 3 From @Risk to Excel file APSIM (Rotation 1 crop info) Linked Import Output Copy APSIM (Rotation 2 crop info) APSIM (Rotation 3 crop info)