Holger Meinke

An overview of APSIM, a model designed for farming systems simulation

The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIMs structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.

Adapting agriculture to climate change

The strong trends in climate change already evident, the likelihood of further changes occurring, and the increasing scale of potential climate impacts give urgency to addressing agricultural adaptation more coherently. There are many potential adaptation options available for marginal change of existing agricultural systems, often variations of existing climate risk management. We show that implementation of these options is likely to have substantial benefits under moderate climate change for some cropping systems. However, there are limits to their effectiveness under more severe climate changes. Hence, more systemic changes in resource allocation need to be considered, such as targeted diversification of production systems and livelihoods. We argue that achieving increased adaptation action will necessitate integration of climate change-related issues with other risk factors, such as climate variability and market risk, and with other policy domains, such as sustainable development. Dealing with the many barriers to effective adaptation will require a comprehensive and dynamic policy approach covering a range of scales and issues, for example, from the understanding by farmers of change in risk profiles to the establishment of efficient markets that facilitate response strategies. Science, too, has to adapt. Multidisciplinary problems require multidisciplinary solutions, i.e., a focus on integrated rather than disciplinary science and a strengthening of the interface with decision makers. A crucial component of this approach is the implementation of adaptation assessment frameworks that are relevant, robust, and easily operated by all stakeholders, practitioners, policymakers, and scientists.

Seasonal and Inter-Annual Climate Forecasting: The New Tool for Increasing Preparedness to Climate Variability and Change in Agricultural Planning and Operations

Climate variability and change affects individuals and societies. Within agricultural systems, seasonal climate forecasting can increase preparedness and lead to better social, economic and environmental outcomes. However, climate forecasting is not the panacea to all our problems in agriculture. Instead, it is one of many risk management tools that sometimes play an important role in decision-making. Understanding when, where and how to use this tool is a complex and multi-dimensional problem. To do this effectively, we suggest a participatory, cross-disciplinary research approach that brings together institutions (partnerships), disciplines (e.g., climate science, agricultural systems science, rural sociology and many other disciplines) and people (scientist, policy makers and direct beneficiaries) as equal partners to reap the benefits from climate knowledge. Climate science can provide insights into climatic processes, agricultural systems science can translate these insights into management options and rural sociology can help determine the options that are most feasible or desirable from a socio-economic perspective. Any scientific breakthroughs in climate forecasting capabilities are much more likely to have an immediate and positive impact if they are conducted and delivered within such a framework. While knowledge and understanding of the socio-economic circumstances is important and must be taken into account, the general approach of integrated systems science is generic and applicable in developed as well as in developing countries. Examples of decisions aided by simulation output ranges from tactical crop management options, commodity marketing to policy decisions about future land use. We also highlight the need to better understand temporal- and spatial-scale variability and argue that only a probabilistic approach to outcome dissemination should be considered. We demonstrated how knowledge of climatic variability (CV), can lead to better decisions in agriculture, regardless of geographical location and socio-economic conditions.

Development of a generic crop model template in the cropping system model APSIM

The Agricultural Production Systems slMulator, APSIM, is a cropping system modelling environment that simulates the dynamics of soil-plant-management interactions within a single crop or a cropping system. Adaptation of previously developed crop models has resulted in multiple crop modules in APSIM, which have low scientific transparency and code efficiency. A generic crop model template (GCROP) has been developed to capture unifying physiological principles across crops (plant types) and to provide modular and efficient code for crop modelling. It comprises a standard crop interface to the APSIM engine, a generic crop model structure, a crop process library, and well-structured crop parameter files. The process library contains the major science underpinning the crop models and incorporates generic routines based on physiological principles for growth and development processes that are common across crops. It allows APSIM to simulate different crops using the same set of computer code. The generic model structure and parameter files provide an easy way to test, modify, exchange and compare modelling approaches at process level without necessitating changes in the code. The standard interface generalises the model inputs and outputs, and utilises a standard protocol to communicate with other APSIM modules through the APSIM engine. The crop template serves as a convenient means to test new insights and compare approaches to component modelling, while maintaining a focus on predictive capability. This paper describes and discusses the scientific basis, the design, implementation and future development of the crop template in APSIM. On this basis, we argue that the combination of good software engineering with sound crop science can enhance the rate of advance in crop modelling. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Impacts of the Madden-Julian Oscillation on Australian Rainfall and Circulation

Impacts of the Madden-Julian oscillation (MJO) on Australian rainfall and circulation are examined during all four seasons. The authors examine circulation anomalies and a number of different rainfall metrics, each composited contemporaneously for eight MJO phases derived from the real-time multivariate MJO index. Multiple rainfall metrics are examined to allow for greater relevance of the information for applications. The greatest rainfall impact of the MJO occurs in northern Australia in (austral) summer, although in every season rainfall impacts of various magnitude are found in most locations, associated with corresponding circulation anomalies. In northern Australia in all seasons except winter, the rainfall impact is explained by the direct influence of the MJOs tropical convective anomalies, while in winter a weaker and more localized signal in northern Australia appears to result from the modulation of the trade winds as they impinge upon the eastern coasts, especially in the northeast. In extratropical Australia, on the other hand, the occurrence of enhanced (suppressed) rainfall appears to result from induced upward (downward) motion within remotely forced extratropical lows (highs), and from anomalous low-level northerly (southerly) winds that transport moisture from the tropics. Induction of extratropical rainfall anomalies by remotely forced lows and highs appears to operate mostly in winter, whereas anomalous meridional moisture transport appears to operate mainly in the summer, autumn, and to some extent in the spring.

Near‐global impact of the Madden‐Julian Oscillation on rainfall

The accuracy of synoptic-based weather forecasting deteriorates rapidly after five days and is not routinely available beyond 10 days. Conversely, climate forecasts are generally not feasible for periods of less than 3 months, resulting in a weather-climate gap. The tropical atmospheric phenomenon known as the Madden-Julian Oscillation (MJO) has a return interval of 30 to 80 days that might partly fill this gap. Our near-global analysis demonstrates that the MJO is a significant phenomenon that can influence daily rainfall patterns, even at higher latitudes, via teleconnections with broadscale mean sea level pressure (MSLP) patterns. These weather states provide a mechanistic basis for an MJO-based forecasting capacity that bridges the weather-climate divide. Knowledge of these tropical and extra-tropical MJO-associated weather states can significantly improve the tactical management of climate-sensitive systems such as agriculture.

Potential deep drainage under wheat crops in a Mediterranean climate. I. Temporal and spatial variability

High rates of deep drainage (water loss below the root-zone) in Western Australia are contributing to groundwater recharge and secondary salinity. However, quantifying potential drainage through measurements is hampered by the high degree of complexity of these systems as a result of diverse soil types, a range of crops, different rainfall regions, and in particular the inherent season-to-season variability. Simulation models can provide the appropriate means to extrapolate across time and space. The Agricultural Production Systems Simulator (APSIM) was used to analyse deep drainage under wheat crops in the Mediterranean climate of the central Western Australian wheatbelt. In addition to rigorous model testing elsewhere, comparisons between simulated and observed soil water loss, evapotranspiration, and deep drainage for different soil types and seasons confirmed the reasonable performance of the APSIM model. The APSIM model was run with historical weather records (70–90 years) across 2 transects from the coast (high rainfall zone) to the eastern edge of the wheatbelt (low rainfall zone). Soils were classified as 5 major types: deep sand, deep loamy sand, acid loamy sand, shallow duplex (waterlogging), and clay soil (non-waterlogging). Simulations were carried out on these soil types with historical weather records, assuming current crop management and cultivars. Soil water profiles were reset each year to the lower limit of plant-available water, assuming maximum water use in the previous crop. Results stressed the high degree of seasonal variability of deep drainage ranging from 0 to 386 mm at Moora in the high rainfall region (461 mm/year average rainfall), from 0 to 296 mm at Wongan Hills in the medium rainfall region (386 mm/year average rainfall), and from 0 to 234 mm at Merredin in the low rainfall region (310 mm/year average rainfall). The largest amounts of drainage occurred in soils with lowest extractable water-holding capacities. Estimates of annual drainage varied with soil type and location. For example, average (s.d.) annual drainage at Moora, Wongan Hills, and Merredin was 134 (73), 90 (61), and 36 (43) mm on a sand, and 57 (64), 26 (43), and 4 (18) mm on a clay soil, respectively. These values are an order of magnitude higher than drainage reported elsewhere under native vegetation. When not resetting the soil each year, carry-over of water left behind in the soil reduced the water storage capacity in the subsequent year, increasing long-term average deep drainage, depending on soil type and rainfall region. The analyses revealed the extent of the excess water problem that currently threatens the sustainability of the wheat-based farming systems in Western Australia.

MODELLING GLOBAL CHANGE IMPACTS ON WHEAT CROPPING IN SOUTH-EAST QUEENSLAND, AUSTRALIA

The wheat module, I_WHEAT, from the APSIM cropping system model was used to investigate the impacts of changes in atmospheric CO2 concentrations on wheat crops by modifying radiation use efficiency, transpiration efficiency, specific leaf area and critical nitrogen concentrations. The effects of several combinations of atmospheric CO2, climate change and crop adaptation strategies on wheat production in the Burnett region were studied. Mean wheat yields were increased under doubled CO2, with the response relative to ambient CO2 greatest in dry years. Higher temperatures under the climate change scenarios moderated the yield gains achieved with increasing CO2 and in some instances reversed them under the reduced rainfall scenario. The status of the region as a producer of prime hard wheat may be at risk due to reduced grain protein levels under doubled CO2 and the increased likelihood of “heat shock” in the climate scenarios used.

Potential soil water extraction by sunflower on a range of soils

Abstract A conceptual framework of the time course of soil water depletion under water limiting conditions was used to quantify water extraction by sunflower (Helianthus annuus L.) on a wide range of soil types. The framework takes account of the maximum plant-available soil water content (MAWC) in each layer, the rate at which the soil water extraction front descends through the soil profile (EFV) and the rate of water extraction within each soil layer (kl.). Total plant-available soil water for the profile (TPAW) is defined as the sum for all layers of the difference in volumetric soil water between the drained upper limit (θu, determined in a separate infiltration experiment)_and the lower limit (θl, determined at the end of the experiment after water extraction by the crop). To quantify parameter values, soil water content was measured frequently in sunflower crops grown entirely on stored soil moisture. Measurements were made in an environment with high temperature and vapour pressure deficit to determine the potential rate of water extraction. The parameters θu, θl and kl were determined for each 20-cm depth increment for each of five soil types. The rate of progression of the soil water extraction front through the profile (EFV) and time at which the extraction front commences its descent (t0) were also determined for each soil type. The framework described actual water extraction on each soil type well. Values for TPAW ranged from 77 to 210 mm for the five soils. This was a result of differences in maximum depht of extraction (EFmax) and MAWC). Values for MAWC in the surface were related to clay content and were greatest (24%) at clay contents near 55%. On heavy textured soils, MAWC decreased linearly with depth whereas on soils low in clay content it did not changed with depth. The decline was caused by an increase in θ1 with depth. It is possible that preferred root pathways resulted in a lower than expected water extraction at depth and deeper soil layers were consequently not fully exploited. Therefore, values for TPAW are a combination of plant and soil factors. Maximum depth of water extraction (EFmax) ranged from 80 to 180 cm. The potential EFV across soil types was 3.6 cm day−1. Values of EFmax related to either crop factors (time to flowering) or soil factors (decline in MAWC). The extraction rate kl increased linearly with clay content of the soil profile. Cumulative water extraction varied among soil types depending on MAWC, EFmax and kl.

Improving wheat simulation capabilities in Australia from a cropping systems perspective III. The integrated wheat model (I_WHEAT)

Previous work has identified several short-comings in the ability of four spring wheat and one barley model to simulate crop processes and resource utilization. This can have important implications when such models are used within systems models where final soil water and nitrogen conditions of one crop define the starting conditions of the following crop. In an attempt to overcome these limitations and to reconcile a range of modelling approaches, existing model components that worked demonstrably well were combined with new components for aspects where existing capabilities were inadequate. This resulted in the Integrated Wheat Model (I_WHEAT), which was developed as a module of the cropping systems model APSIM. To increase predictive capability of the model, process detail was reduced, where possible, by replacing groups of processes with conservative, biologically meaningful parameters. I_WHEAT does not contain a soil water or soil nitrogen balance. These are present as other modules of APSIM. In I_WHEAT, yield is simulated using a linear increase in harvest index whereby nitrogen or water limitations can lead to early termination of grainfilling and hence cessation of harvest index increase. Dry matter increase is calculated either from the amount of intercepted radiation and radiation conversion efficiency or from the amount of water transpired and transpiration efficiency, depending on the most limiting resource. Leaf area and tiller formation are calculated from thermal time and a cultivar specific phyllochron interval. Nitrogen limitation first reduces leaf area and then affects radiation conversion efficiency as it becomes more severe. Water or nitrogen limitations result in reduced leaf expansion, accelerated leaf senescence or tiller death. This reduces the radiation load on the crop canopy (i.e. demand for water) and can make nitrogen available for translocation to other organs. Sensitive feedbacks between light interception and dry matter accumulation are avoided by having environmental effects acting directly on leaf area development, rather than via biomass production. This makes the model more stable across environments without losing the interactions between the different external influences. When comparing model output with models tested previously using data from a wide range of agro-climatic conditions, yield and biomass predictions were equal to the best of those models, but improvements could be demonstrated for simulating leaf area dynamics in response to water and nitrogen supply, kernel nitrogen content, and total water and nitrogen use. I_WHEAT does not require calibration for any of the environments tested. Further model improvement should concentrate on improving phenology simulations, a more thorough derivation of coefficients to describe leaf area development and a better quantification of some processes related to nitrogen dynamics