N. P. Dalgliesh

Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate.

In Australia, a land subject to high annual variation in grain yields, farmers find it challenging to adjust crop production inputs to yield prospects. Scientists have responded to this problem by developing Decision Support Systems, yet the scientists’ enthusiasm for developing these tools has not been reciprocated by farm managers or their advisers, who mostly continue to avoid their use. Preceding papers in this series described the FARMSCAPE intervention: a new paradigm for decision support that had significant effects on farmers and their advisers. These effects were achieved in large measure because of the intensive effort which scientists invested in engaging with their clients. However, such intensive effort is time consuming and economically unsustainable and there remained a need for a more cost-effective tool. In this paper, we report on the evolution, structure, and performance of Yield Prophet®: an internet service designed to move on from the FARMSCAPE model to a less intensive, yet high quality, service to reduce farmer uncertainty about yield prospects and the potential effects of alternative management practices on crop production and income. Compared with conventional Decision Support Systems, Yield Prophet offers flexibility in problem definition and allows farmers to more realistically specify the problems in their fields. Yield Prophet also uniquely provides a means for virtual monitoring of the progress of a crop throughout the season. This is particularly important for in-season decision support and for frequent reviewing, in real time, of the consequences of past decisions and past events on likely future outcomes. The Yield Prophet approach to decision support is consistent with two important, but often ignored, lessons from decision science: that managers make their decisions by satisficing rather than optimising and that managers’ fluid approach to decision making requires ongoing monitoring of the consequences of past decisions.

Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops

Crop simulation models relevant to real-world agriculture have been a rationale for model development over many years. However, as crop models are generally developed and tested against experimental data and with large systematic gaps often reported between experimental and farmer yields, the relevance of simulated yields to the commercial yields of field crops may be questioned. This is the third paper in a series which describes a substantial effort to deliver model-based decision support to Australian farmers. First, the performance of the cropping systems simulator, APSIM, in simulating commercial crop yields is reported across a range of field crops and agricultural regions. Second, how APSIM is used in gaining farmer credibility for their planning and decision making is described using actual case studies. Information was collated on APSIM performance in simulating the yields of over 700 commercial crops of barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, and wheat monitored over the period 1992 to 2007 in all cropping regions of Australia. This evidence indicated that APSIM can predict the performance of commercial crops at a level close to that reported for its performance against experimental yields. Importantly, an essential requirement for simulating commercial yields across the Australian dryland cropping regions is to accurately describe the resources available to the crop being simulated, particularly soil water and nitrogen. Five case studies of using APSIM with farmers are described in order to demonstrate how model credibility was gained in the context of each circumstance. The proposed process for creating mutual understanding and credibility involved dealing with immediate questions of the involved farmers, contextualising the simulations to the specific situation in question, providing simulation outputs in an iterative process, and together reviewing the ensuing seasonal results against provided simulations. This paper is distinct from many other reports testing the performance and utility of cropping systems models. Here, the measured yields are from commercial crops not experimental plots and the described applications were from real-life situations identified by farmers. A key conclusion, from 17 years of effort, is the proven ability of APSIM to simulate yields from commercial crops provided soil properties are well characterised. Thus, the ambition of models being relevant to real-world agriculture is indeed attainable, at least in situations where biotic stresses are manageable.

Contributions of soil and crop factors to plant available soil water capacity of annual crops on Black and Grey Vertosols

Improved methods for field measurements of plant available soil water capacity (PAWC) of Black and Grey Vertosols in Australia’s north-eastern grain region were employed to characterise 83 soil–crop combinations over 7 depth intervals to 180 cm. Soil sub-order was shown to influence all components of PAWC (means of 224 and 182 mm in Black and Grey Vertosols, respectively) with drained upper limit (DUL), bulk density (BD), and crop lower limits (CLL) showing clear separation between soil sub-orders and a trend with soil depth. In addition to soil sub-order and soil depth effects, CLL showed crop effects such that expected PAWC of various crops when adjusted for soil sub-orders were: cotton 240 mm; wheat 233 mm; sorghum 225 mm; fababean 209 mm; chickpea 197 mm; barley 191 mm; and mungbean 130 mm. A total of 549 measured CLL values were used to develop a predictive model for estimating CLL from the soil sub-order, depth, DUL, and crop by predicting a CLL as a function of DUL and a depth-dependent variable for each crop–soil sub-order. The model CLL = DUL * (a + b * DUL) explained 85% of observed variation in the measured data with no significant bias between observed and predicted data. While properly measured data would be more reliable than estimated data, where specific site accuracy is less critical, this model may be used to estimate PAWC with an acceptable degree of accuracy.

Pay-offs to zone management in a variable climate: an example of nitrogen fertiliser on wheat

Temporal variability affects the profitability of zone management of nitrogen, particularly in the north-eastern grain-growing region of Australia. This paper presents a framework for systematically investigating the effect of the interaction between spatial and temporal variability on economic performance, their relative importance, and the value of spatially variable nitrogen management to a farmer with and without knowledge about the coming season. The paper also addresses the degree to which economic performance is influenced by the relative sizes of management zones for fertiliser inputs, prices, and the shape of the biophysical response to fertiliser in each zone. The analysis was based on a single field exhibiting spatial variability. Scenario analysis of seasonally and/or spatially adjusted nitrogen management strategies was based on response functions generated by the cropping systems model APSIM. The analysis shows that seasonal and spatial interactions in nitrogen management are significant issues for decision makers. In this case, knowledge of the coming season is worth more than knowledge of spatial variability, but knowledge of both creates the greatest value. The functional relationship between yields and fertiliser levels for a given crop also determines the economic value of variable-rate nitrogen. A field may exhibit yield variability but this does not automatically present a case for spatially variable nitrogen management. If economic optima of different payoff curves are aligned then returns to zone management will be limited, despite significant differences in yield between different zones.

Tropical forage legumes provide large nitrogen benefits to maize except when fodder is removed

Abstract. Integration of tropical forage legumes into cropping systems may improve subsequent crop nitrogen (N) supply, but removal of legume biomass for forage is likely to diminish these benefits. This study aimed to determine: (i) under irrigated conditions, the potential N inputs that can be provided by different tropical forage legumes to a subsequent cereal crop; and (ii) the residual N benefits once fodder had been removed. Available soil mineral N following tropical forage legumes lablab (Lablab purpureus), centro (Centrosema pascuorum), butterfly pea (Clitoria ternatea) and burgundy bean (Macroptilium bracteatum) and grain legume soybean (Glycine max) was compared with a maize (Zea mays) control when legume biomass was retained or cut and removed (phase 1). An oat (Avena sativa) cover crop was then grown to ensure consistent soil-water across treatments (phase 2), followed by a maize grain crop (phase 3) in which N uptake, biomass production and grain yield were compared among the phase 1 treatments. To determine N-fertiliser equivalence values for subsequent maize crop yields, different rates of fertiliser (0–150 kg urea-N/ha) were applied in phase 3. Retained biomass of butterfly pea, centro and lablab increased phase 3 unfertilised maize grain yield by 6–8 t/ha and N uptake by 95–200 kg N/ha compared with a previous cereal crop, contributing the equivalent of 100–150 kg urea-N/ha. When legume biomass was cut and removed, grain yield in the phase 3 maize crop did not increase significantly. When butterfly pea, centro and lablab biomass was retained rather than removed, the maize accumulated an additional 80–132 kg N/ha. After fodder removal, centro was the only legume that provided N benefits to the phase 3 maize crop (equivalent of 33 kg urea-N/ha). Burgundy bean did not increase subsequent crop production when biomass was either retained or removed. The study found that a range of tropical forage legumes could contribute large amounts of N to subsequent crops, potentially tripling maize grain yield. However, when these legumes were cut and removed, the benefits were greatly diminished and the legumes provided little residual N benefit to a subsequent crop. Given the large N trade-offs between retaining and removing legume biomass, quantification of N inputs under livestock grazing or when greater residual biomass is retained may provide an alternative to achieving dual soil N–fodder benefits.