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

Abstract

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.

Scope for improved eco-efficiency varies among diverse cropping systems

“…4A), an Australian rainfed wheat crop from this database has a 71% probability of exceeding a technical efficiency of 0.8 (within 20% of the production frontier) and an 88% probability of exceeding a technical efficiency of 0.5. These findings affirm the assertion that many Australian farmers are currently operating close to their attainable production frontiers (17). Efficient performance might reflect that farmer subscribers to an advanced system such as Yield Prophet are likely elite farmers whose performance is superior to that of the normal farmer population.…”

“…Many crops yielded close to their attainable grain yield. This result reflects the situation in Australia, where APSIM is expected to closely simulate commercial crop yields (17) and in fact provides farmers with a reliable yield forecasting and crop management system (24). A proportion of crops demonstrated sizeable underpredictions and overpredictions, with some producing yields greater than what APSIM simulated, as could be expected given the available climate, soil, and fertilizer resources.…”

“…In this study, we accessed data on grain yields from 3,041 crops from single fields, along with associated site characteristics and management practices, for farms in China (irrigated wheatmaize double-cropping systems), Zimbabwe (rainfed maize systems), and Australia (rainfed wheat systems) ( Table 1). Y a and Y p values for these crops were simulated using the Agricultural Production Systems Simulator (APSIM) (16), which has been widely tested in the three case study systems (17)(18)(19) and was specifically parameterized for this study (SI Text). Our objective was to diagnose the state of agricultural production in these contrasting agricultural systems and suggest appropriate pathways for improving eco-efficiencies and closing yield gaps.…”

How does inclusion of weather forecasting impact in-season crop model predictions?

“…However, there are obstacles due to the lack of accurately forecasted weather data that is available in near real-time and compatible with crop model weather input requirements. Thus the majority of current crop model forecasting approaches rely on the combination of current and historical weather data to calculate yield probabilities in regions ranging from Australia (Carberry et al, 2009), to Canada (Chipanshi et al, 2015), and Europe (Williams and Falloon, 2015) as well as, Iowa-USA , and Nebraska-USA (Morell et al, 2016). A difference among the above listed crop model forecasting approaches is the number of historical weather years used, the structure of the crop models, the temporal resolution of weather data (hourly vs daily) and the number of weather variables (e.g.…”

Measuring and Modeling the Water Balance in Low-Rainfall Cropping Systems

“…In addition to such parameters, models also re- quire other parameters that describe hydraulic properties, including first and second stage evaporation constants, runoff, infiltration, and water movement between layers. Surprisingly, and despite the huge number of studies describing the use of APSIM for Australian (Carberry et al, 2009;Holzworth et al, 2014), African , and other systems, there are limited studies on defining soil parameterization or providing measures of the performance of soil water prediction. This study uses two unique longterm datasets collected in the low-rainfall Mallee region of South Australia and New South Wales to: 1.…”