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The regional effects of local water storage are largely unknown. This study identifies, categorizes and discusses the challenges in assessing the potential of local water storage. These are illustrated using a structured method applied to a Dutch case. We conclude that the focus must shift from storage 'potential' (the quantity of water that can be stored) to storage 'feasibility', which depends on exploitability, purpose and interactions between storage alternatives. Spatial and temporal scale also influence feasibility. Finally, farmers' investment preferences are a factor, though these are shrouded in uncertainty. This overview is a first step towards improving storage assessment tools and processes. [ABSTRACT FROM AUTHOR]

A reliable method was developed to screen cereal crops for BYDV‐PAV resistance in glasshouse experiments. This also entailed the evaluation of traits associated with Barley yellow dwarf virus (BYDV) infection such as leaf discolouration, reduction in growth, biomass and yield traits, and percentage of virus‐infected plants, using enzyme‐linked immunosorbent assay (ELISA) and tissue blot immunoassay (TBIA). Four glasshouse experiments were conducted with eight wheat, barley and oat varieties inoculated with BYDV‐PAV at the 2‐leaf stage, using different numbers of viruliferous aphids and different inoculation periods and temperatures. Inoculation with 5–10 viruliferous aphids per plant for 4 days led to a high percentage of infection in susceptible varieties, indicating that this is an effective BYDV screening method when selecting for resistance in cereal crops. For barley and oat, visual evaluation of symptoms is considered adequate for assessing BYDV resistance. However, for wheat it is necessary to evaluate BYDV resistance by ELISA/TBIA tests and plant biomass (at early stage) and grain number and yield (at late stage) measurements. [ABSTRACT FROM AUTHOR]

Extreme climatic events such as heat waves, extreme rainfall and prolonged dry periods are a significant challenge to the productivity and profitability of dairy systems. Despite projections of more frequent extreme events, increasing temperatures and reduced precipitation, studies on the impact of these extreme climatic events on pasture-based dairy systems remain uncommon. The Intergovernmental Panel on Climate Change has estimated Australia to be one of the most negatively impacted regions with additional studies estimating Australian production losses of around 16% in the agricultural sector and 9-19% between the present and 2050 in the south-eastern dairy regions of Australia due to climate change. Here we review the literature on the impact of climate change on pasture-based dairy systems with particular focus on extreme climatic events. We provide an insight into current methods for assessing and quantifying heat stress highlighting the impacts on pastures and animals including the associated potential productivity losses and conclude by outlining potential adaptation strategies for improving the resilience of the whole-farm systems to climate change. Adapting milking routines, calving systems and the introduction of heat stress tolerant dairy cow breeds are some proposed strategies. Changes in pasture production would also include alternative pasture species better adapted to climate extremes such as heat waves and prolonged periods of water deficit. In order to develop effective adaptation strategies we also need to focus on issues such as water availability, animal health and associated energy costs. [ABSTRACT FROM AUTHOR]

Our study suggests the possibility for transformational change in the productivity and risk profile of some of India's rainfed cropping systems. In the semi-arid regions of Southern India, farmers traditionally crop sorghum or chickpea on Vertisols during the post-rainy season, keeping the fields fallow during the rainy season. This practice avoids land management problems, but limits the potential for crop intensification to increase systems productivity. A long-term (15 year) experiment at ICRISAT demonstrated that cropping during the rainy season is technically feasible, and that grain productivity of double cropped sorghum + chickpea (SCP–SCP) and mung bean + sorghum (MS–MS) sequential systems were higher than their conventional counterparts with rainy season fallow, i.e. fallow + post-rainy sorghum (FS–FS) and fallow + post-rainy chickpea (FS–FCP). Without N application, mean grain yield of post-rainy sorghum in the MS–MS system was significantly greater (2520 kg ha −1 per two-year rotation) than in the FS–FS system (1940 kg ha −1 per two-year rotation), with the added benefit of the mung bean grain yield (1000 kg ha −1 per two-year rotation) from the MS–MS system. In the SCP–SCP system the additional grain yield of rainy sorghum (3400 kg ha −1 per two-year rotation) ensured that the total productivity of this system was greater than all other systems. Double cropping MS–MS and SCP–SCP sequential systems had significantly higher crop N uptake compared to traditional fallow systems at all rates of applied nitrogen (N). The intensified MS–MS and SCP–SCP sequential systems without any N fertilizer applied recorded a much higher median gross profit of Rs. 20,600 (US $ 375) and Rs. 15,930 (US $ 290) ha −1 yr −1 , respectively, compared to Rs. 1560 (US $ 28) ha −1 yr −1 ) with the FS–FS system. Applying 120 kg of N ha −1 considerably increased the profitability of all systems, lifting median gross profits of the sorghum + chickpea system over Rs. 60,000 (US $ 1091) ha −1 yr −1 and the conventional system to Rs. 20,570 (US $ 374) ha −1 yr −1 . The gross profit margin analysis showed that nitrogen is a key input for improving productivity, particularly for the double cropping systems. However, traditional systems are unviable and risky without N application in the variable climates of the semi-arid tropics. Together, our results show that on Vertisols in semi-arid India, double cropping systems increase systems’ productivity, and are financially more profitability and less risky than traditional fallow post-rainy systems while further benefits can be achieved through fertilizer application. [ABSTRACT FROM AUTHOR]

The main objective of this study was to assess the impact of biochar rate (0, 8, 16 and 32Mg ha-1) on the water retention capacity (WRC) of a sandy loam Dystric Plinthosol. The applied biochar was a by-product of slow pyrolysis (~450 °C) of eucalyptus wood, milled to pass through a 2000 μm sieve that resulted in a material with an intrinsic porosity ⩽10 μm and a specific surface area of ~3.2m² g-1. The biochar was incorporated into the top 15 cm of the soil under an aerobic rice system. Our study focused on both the effects on WRC and rice yields 2 and 3 years after its application. Undisturbed soil samples were collected from 16 plots in two soil layers (5-10 and 15-20 cm). Soil water retention curves were modelled using a nonlinear mixed model which appropriately accounts for uncertainties inherent of spatial variability and repeated measurements taken within a specific soil sample. We found an increase in plant-available water in the upper soil layer proportional to the rate of biochar, with about 0.8% for each Mgha-1 biochar amendment 2 and 3 years after its application. The impact of biochar on soil WRC was most likely related to an effect in overall porosity of the sandy loam soil, which was evident from an increase in saturated soil moisture and macro porosity with 0.5 and 1.6% for each Mgha-1 of biochar applied, respectively. The increment in soil WRC did not translate into an increase in rice yield, essentially because in both seasons the amount of rainfall during the critical period for rice production exceeded 650 mm. The use of biochar as a soil amendment can be a worthy strategy to guarantee yield stability under short-term water-limited conditions. Our findings raise the importance of assessing the feasibility of very high application rates of biochar and the inclusion of a detailed analysis of its physical and chemical properties as part of future investigations. [ABSTRACT FROM AUTHOR]

Abstract: For irrigation farmers, the deregulation of water markets and consequent emergence of water as a tradeable commodity calls for a method of comparing traditional on-farm water options (growing crops) with off-farm market options (selling water seasonally, or selling water licences permanently). The option to diversify farm income in this way is a desirable future adaptation strategy in response to decreased and more variable water supplies. We demonstrate a method for comparing such options based on their risk-return characteristics. A framework commonly used in the finance sector is adapted to agricultural water decisions, and illustrated using a case-study farm from Australia''s Riverina region. In our example, a range of potential farm management practices are examined for several future water availability scenarios, and then compared with a fixed-return option (selling water entitlements to the Australian Government''s current water buy-back scheme). We demonstrate how the attractiveness of the scheme for farmers depends on future water availability levels. For any future allocation level, the best way to use water on-farm varies with the value of the fixed-return option. The farmer''s decision on what portion of their water entitlement to sell provides them with the opportunity to tailor their operation''s risk-return performance. This method is universally applicable wherever there is a mix of variable and fixed-return options, and offers a framework to assist farmers in conceptualizing comparisons between traditional on-farm uses for water and newer, market-based options. [Copyright &y& Elsevier]

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha-1 is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment. [ABSTRACT FROM AUTHOR]

Abstract: Around the globe farmers managing irrigated crops face a future with a decreased and more variable water supply. To investigate generic adaptation issues, a range of on-farm strategies were evaluated for apportioning limited water between fields and enterprises using a typical case-study farm from Australia''s Riverina region. These strategies are compared for a range of seasonal water availability levels. The analysis did not address investment in new irrigation technologies or new crops, but focussed on irrigation intensity and crop choice amongst existing enterprises. Participatory engagement and whole-farm simulation modelling were our primary tools of research. The adaptation options found to best suit irrigation farming in years of high water availability were substantially different to those when water supplies were low. This illustrates strategic differences between irrigation farming in land-limited circumstances and water-limited circumstances. Our study indicates that the cropping and irrigation strategy leading to greatest farm returns changes on a season-by-season basis, depending primarily on the water availability level. [Copyright &y& Elsevier]

Global changes including increases in temperature, atmospheric greenhouse gases, soil degradation and competition for land and water resources, will have multiple impacts on rice production systems in Africa. These changes will affect weed communities, and management approaches must be adapted to take this into account. Higher temperatures and limited water availability will generally advantage C4 over C3 plants (e.g. rice). Conversely, elevated carbon dioxide (CO2) levels will improve the competitiveness of rice relative to C4 weeds, which comprise many of the problem weeds of rice. Increased atmospheric CO2 levels may also improve tolerance of rice against parasitic weeds, while prevalence of parasitic species may be amplified by soil degradation and more frequent droughts or floods. Elevated CO2 levels tend to promote growth below-ground relative to above-ground, particularly in perennial (C3) species. This may render mechanical control of weeds within a cropping season less effective or even counterproductive. Increased CO2 levels, rainfall and temperature may also reduce the effectiveness of chemical control, while the implementation of adaptation technologies, such as water-saving irrigation regimes, will have negative consequences for rice–weed competition. Rain-fed production systems are prevalent throughout Africa and these are likely to be most vulnerable to direct effects of climate change (e.g. higher temperatures and changes in rainfall patterns). Effective weed management strategies in these environments could encompass off-season tillage, the use of well-adapted cultivars (i.e. those with drought and heat tolerance, high weed competitiveness and parasitic weed resistance or tolerance) and rotations, intercropping or short, off-season fallows with weed-suppressive legumes including those that suppress parasitic weeds. In irrigated, non-flooded rice systems, weeds are expected to become more serious. Specifically, perennial rhizomatous C3 weeds and species adapted to hydromorphic conditions are expected to increase in prevalence. By implementing an integrated weed management strategy primarily targeted at weed prevention, dependency on flood water, herbicides and mechanical control can be lessened. Off-season deep tillage, stale seed bed techniques, use of clean seeds and irrigation water, competitive cultivars, timely transplanting at optimum spacing and judicious fertilizer timings are suitable candidate components for such a strategy. Integrated, novel approaches must be developed to assist farmers in coping with the challenges of weed control in the future. [ABSTRACT FROM PUBLISHER]

Abstract: This paper discusses the key information needs to reduce the negative impacts of weather variability and climate change on land degradation and food security, and identifies the opportunities and barriers between the information and services needed. It suggests that vulnerability assessments based on a livelihood concept that includes climate information and key socio-economic variables can overcome the narrow focus of common one-dimensional vulnerability studies. Both current and future climatic risks can be managed better if there is appropriate policy and institutional support together with technological interventions to address the complexities of multiple risks that agriculture has to face. This would require effective partnerships among agencies dealing with meteorological and hydrological services, agricultural research, land degradation and food security issues. In addition a state-of-the-art infrastructure to measure, record, store and disseminate data on weather variables, and access to weather and seasonal climate forecasts at desired spatial and temporal scales would be needed. [ABSTRACT FROM AUTHOR]

Abstract: Much has been learned in the interpretation and use of climate information since the 1997/1998 El Niño event that garnered so much attention. Seasonal-to-interannual forecasts are now produced around the world. However, mismatches in their scales, specificity or communication (of forecast content and uncertainties) with decision-maker needs still hinder their use. More work is needed to improve a) the utility of models, b) access to observational and model/forecast data, c) understanding and communication of the opportunities and limitations of forecasts, and d) methods by which decision systems use climate predictions – both through modifications of decision systems and more tailored forecast information. This white paper discusses these issues and recent advances in providing climate information needed in effective climate risk management. The paper advocates for the establishment and/or strengthening of “chains of experts and communications” to better enable appropriate and effective dissemination, assimilation and further use and application of climate information. [ABSTRACT FROM AUTHOR]

Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, runoff) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty. [ABSTRACT FROM AUTHOR]

Abstract: The possible impact of climate change on frequency and severity of weather extremes is hotly debated among climate scientists. Weather extremes can have a significant impact on agricultural production, but their effect is often unclear; this due to interaction with other factors that affect yield and due to lack of precise definitions of relevant weather extremes. We show that an empirical analysis of historical yields can help to identifying such rare, high impact climate events. A reconstructed time series of ware potato production in Flevoland (The Netherlands) over the last 60 years (1951–2010) enabled us to identify the two main yield affecting weather extremes. In around 10% of the years yield anomalies were larger than −20%. We found that these anomalies could be explained from two weather extremes (and no other), namely a wet start of the growing season and wet end of the growing season. We derived quantitative, meteorological definitions of these extremes. Climate change scenarios for 2050 show either no change or increased frequency of the two extremes. We demonstrate there is large uncertainty about past and future frequencies of the extremes, caused by a lack of sufficiently long historical weather records and uncertainties in climate change projections on precipitation. The approach to identify weather extremes presented here is generally applicable and shows the importance of long term crop and weather observations for investigating key climatic risks to production. [Copyright &y& Elsevier]

Groundwater levels in the North China Plain (NCP), the bread basket of China, have dropped more than one meter per year over the last 40 years, putting at risk the long term productivity of this region. Groundwater decline is most severe in the Alluvial Fans where our study site is located. Avoiding a foreseeable systems collapse requires region-wide changes in crop systems management, underpinned by sound environmental policies. Here, we explore the potential of crop system adaptation to remedy the excessive water use and quantify the likely yield penalties associated with more sustainable water use practices. Using simulations with the APSIM cropping systems model we explore production opportunities in an area within the NCP with intensive cropping and no access to irrigation from rivers. We estimate the attainable production levels for wheat and maize if agriculture were made groundwater neutral, through changes in crop sequence, irrigation practices and water conservation technologies (e.g. mulching with plastic film). Total grain production would drop by 44% compared to current practice if agriculture were made groundwater neutral. Water conservation by plastic film could limit this reduction to 21–33% but possible environmental impacts of plastic film need attention. This analysis facilitates a much needed debate on alternative agronomic practices and incentives through a quantitative comparison of adaptation options. Our biophysical analysis needs to be complemented with socio-economic considerations and discussions with all stakeholders. Similar analyses in other parts of the NCP are possible but require more accurate modelling of landscape hydrology and (towards the coast) risk of salt water intrusion. [ABSTRACT FROM AUTHOR]

Highlights: [•] A long-term evaluation of four cropping systems for effective management of safflower wilt. [•] Safflower yield was higher in S+SF system following S+CP system in the rotation. [•] Wilt incidence was significantly different among cropping systems but not years. [•] Built-up of Fusarium propagules was significant among cropping systems and years. [•] Safflower yield was not affected by wilt, and higher nitrogen rates improved yield. [•] Replacing safflower with chickpea in S+SF system in alternate years controls wilt. [ABSTRACT FROM AUTHOR]

Highlights: [•] We used a two-step approach to estimate phenology parameters of four rice genotypes. [•] Reciprocal transfer experiment resulted in a robust set of photoperiod parameters. [•] All rice genotypes were photoperiod-sensitive, but all to a different extent. [•] Time to flowering was overestimated when using default cardinal temperatures. [•] Development rate was overestimated below 31°C, and underestimated above 31°C. [ABSTRACT FROM AUTHOR]

Abstract: Photosynthetic aquatic biomass (PAB – algae and other floodwater flora) is a significant source of organic carbon (C) in rice-based cropping systems. A portion of PAB is capable of fixing nitrogen (N), and is hence also a source of N for crop nutrition. To account for this phenomenon in long term simulation studies of rice-based cropping systems, the APSIM modelling framework was modified to include new descriptions of biological and chemical processes responsible for loss and gain of C and N in rice floodwater. We used well-tested algorithms from CERES-Rice, together with new conceptualizations for algal dynamics, in modelling the contribution of PAB to maintenance of soil organic C and soil N-supplying capacity in rice-based cropping systems. We demonstrate how our new conceptualization of PAB growth, turnover, and soil incorporation in flooded rice systems facilitates successful simulation of long-term soil fertility trials, such as the IRRI Long Term Continuous Cropping Experiment (35+ years), from the perspectives of both soil organic carbon levels and yield maintenance. Previous models have been unable to account for the observed maintenance of soil organic C in these systems, primarily due to ignoring inputs from PAB as a source of C. The performance of long-term rice cropping system simulations, with and without inclusion of these inputs, is shown to be radically different. Details of our modifications to APSIM are presented, together with evidence that the model is now a useful tool to investigate sustainability issues associated with management change in rice-based cropping systems. [Copyright &y& Elsevier]

Abstract: Water shortages in many rice-growing regions, combined with growing global imperatives to increase food production, are driving research into increased water use efficiency and modified agricultural practices in rice-based cropping systems. Well-tested cropping systems models that capture interactions between soil water and nutrient dynamics, crop growth, climate and management can assist in the evaluation of new agricultural practices. The APSIM model was designed to simulate diverse crop sequences, residue/tillage practices and specification of field management options. It was previously unable to simulate processes associated with the long-term flooded or saturated soil conditions encountered in rice-based systems, due to its heritage in dryland cropping applications. To address this shortcoming, the rice crop components of the ORYZA2000 rice model were incorporated and modifications were made to the APSIM soil water and nutrient modules to include descriptions of soil carbon and nitrogen dynamics under anaerobic conditions. We established a process for simulating the two-way transition between anaerobic and aerobic soil conditions occurring in crop sequences of flooded rice and other non-flooded crops, pastures and fallows. These transitions are dynamically simulated and driven by modelled hydraulic variables (soil water and floodwater depth). Descriptions of floodwater biological and chemical processes were also added. Our assumptions included a simplified approach to modelling O2 transport processes in saturated soils. The improved APSIM model was tested against diverse, replicated experimental datasets for rice-based cropping systems, representing a spectrum of geographical locations (Australia, Indonesia and Philippines), soil types, management practices, crop species, varieties and sequences. The model performed equally well in simulating rice grain yield during multi-season crop sequences as the original validation testing reported for the stand-alone ORYZA2000 model simulating single crops (n =121, R 2 =0.81 with low bias (slope, α =1.02, intercept, β =−323kgha−1), RMSE=1061kgha−1 (cf. SD of measured data=2160kgha−1)). This suggests robustness in APSIM''s simulation of the rice-growing environment and provides evidence on the usefulness of our modifications and practicality of our assumptions. Aspects of particular strength were identified (crop rotations; response to applied fertilizers; the performance of bare fallows), together with areas for further development work (simulation of retained crop stubble during fallows, greenhouse gas emissions). APSIM is now suitable to investigate production responses of potential agronomic and management changes in rice-based cropping systems, particularly in response to future imperatives linked to resource availability, climate change, and food security. Further testing is required to evaluate the impact of our simplified assumptions on the model''s simulation of greenhouse gas emissions in rice-based cropping systems. [Copyright &y& Elsevier]

Abstract: This paper examines the idea that plasticity in farm management introduces resilience to change and allows farm businesses to perform when operating in highly variable environments. We also argue for the need to develop and apply more integrative assessments of farm performance that combine the use of modelling tools with deliberative processes involving farmers and researchers in a co-learning process, to more effectively identify and implement more productive and resilient farm businesses. In a plastic farming system, farm management is highly contingent on environmental conditions. In plastic farming systems farm managers constantly vary crops and inputs based on the availability of limited and variable resources (e.g. land, water, finances, labour, machinery, etc.), and signals from its operating environment (e.g. climate, markets), with the objective of maximising a number of, often competing, objectives (e.g. maximise profits, minimise risks, etc.). In contrast in more rigid farming systems farm management is more calendar driven and relatively fixed sequences of crops are regularly followed over time and across the farm. Here we describe the application of a whole farm simulation model to (i) compare, in silico, the sensitivity of two farming systems designs of contrasting levels of plasticity, operating in two contrasting environments, when exposed to a stressor in the form of climate change scenarios;(ii) investigate the presence of interactions and feedbacks at the field and farm levels capable of modifying the intensity and direction of the responses to climate signals; and (iii) discuss the need for the development and application of more integrative assessments in the analysis of impacts and adaptation options to climate change. In both environments, the more plastic farm management strategy had higher median profits and was less risky for the baseline and less intensive climate change scenarios (2030). However, for the more severe climate change scenarios (2070), the benefit of plastic strategies tended to disappear. These results suggest that, to a point, farming systems having higher levels of plasticity would enable farmers to more effectively respond to climate shifts, thus ensuring the economic viability of the farm business. Though, as the intensity of the stress increases (e.g. 2070 climate change scenario) more significant changes in the farming system might be required to adapt. We also found that in the case studies analysed here, most of the impacts from the climate change scenarios on farm profit and economic risk originated from important reductions in cropping intensity and changes in crop mix rather than from changes in the yields of individual crops. Changes in cropping intensity and crop mix were explained by the combination of reductions in the number of sowing opportunities around critical times in the cropping calendar, and to operational constraints at the whole farm level i.e. limited work capacity in an environment having fewer and more concentrated sowing opportunities. This indicates that indirect impacts from shifts in climate on farm operations can be more important than direct impacts from climate on the yield of individual crops. The results suggest that due to the complexity of farm businesses, impact assessments and opportunities for adaptation to climate change might also need to be pursued at higher integration levels than the crop or the field. We conclude that plasticity can be a desirable characteristic in farming systems operating in highly variable environments, and that integrated whole farm systems analyses of impacts and adaptation to climate change are required to identify important interactions between farm management decision rules, availability of resources, and farmer''s preference. [Copyright &y& Elsevier]

Abstract: The parasitic weed Orobanche crenata inflicts major damage on faba bean, lentil, pea and other crops in Mediterranean environments. The development of methods to control O. crenata is to a large extent hampered by the complexity of host–parasite systems. Using a model of host–parasite interactions can help to explain and understand this intricacy. This paper reports on the evaluation and application of a model simulating host–parasite competition as affected by environment and management that was implemented in the framework of the Agricultural Production Systems Simulator (APSIM). Model-predicted faba bean and O. crenata growth and development were evaluated against independent data. The APSIM-Fababean and -Parasite modules displayed a good capability to reproduce effects of pedoclimatic conditions, faba bean sowing date and O. crenata infestation on host–parasite competition. The r 2 values throughout exceeded 0.84 (RMSD: 5.36 days) for phenological, 0.85 (RMSD: 223.00gm−2) for host growth and 0.78 (RMSD: 99.82gm−2) for parasite growth parameters. Inaccuracies of simulated faba bean root growth that caused some bias of predicted parasite number and host yield loss may be dealt with by more flexibly simulating vertical root distribution. The model was applied in simulation experiments to determine optimum sowing windows for infected and non-infected faba bean in Mediterranean environments. Simulation results proved realistic and testified to the capability of APSIM to contribute to the development of tactical approaches in parasitic weed control. [Copyright &y& Elsevier]

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 APSIM''s 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. [Copyright &y& Elsevier]

The Agricultural Production Systems sIMulator, 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. [Copyright &y& Elsevier]