► Introduces the Agricultural Model Intercomparison and Improvement Project (AgMIP). ► Describes AgMIP Protocols for consistent research activities. ► Demonstrates AgMIP approaches using climate, ...crop, and economic model analyses. ► Wheat pilot results elucidate the relative uncertainties from crop and climate models. ► Outlines AgMIP crop-specific, regional, and global research activities.
The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones.
Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to CO2 with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with mid-century climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations’ resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.
The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic ...models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.
•Extreme events and future climate uncertainty represent risk for food production.•Crop models are largely able to simulate crop response to climate factors.•Adaptations are best evaluated in integrated assessment models (IAM).•Key limitations for crop models in IAM are low data availability and integration.•Cross-scale nature of IAM suggests novel modelling approaches are needed.
Ethiopia is one of the countries most vulnerable to the impacts of climate variability and change on agriculture. The present study aims to understand and characterize agro-climatic variability and ...changes and associated risks with respect to implications for rainfed crop production in the Central Rift Valley (CRV). Temporal variability and extreme values of selected rainfall and temperature indices were analysed and trends were evaluated using Sen's slope estimator and MannâKendall trend test methods. Projected future changes in rainfall and temperature for the 2080s relative to the 1971â90 baseline period were determined based on four General Circulation Models (GCMs) and two emission scenarios (SRES, A2 and B1). The analysis for current climate showed that in the short rainy season (MarchâMay), total mean rainfall varies spatially from 178 to 358 mm with a coefficient of variation (CV) of 32â50%. In the main (long) rainy season (JuneâSeptember), total mean rainfall ranges between 420 and 680 mm with a CV of 15â40%. During the period 1977â2007, total rainfall decreased but not significantly. Also, there was a decrease in the number of rainy days associated with an increase (statistically not significant) in the intensity per rainfall event for the main rainy season, which can have implications for soil and nutrient losses through erosion and run-off. The reduced number of rainy days increased the length of intermediate dry spells by 0·8 days per decade, leading to crop moisture stress during the growing season. There was also a large inter-annual variability in the length of growing season, ranging from 76 to 239 days. The mean annual temperature exhibited a significant warming trend of 0·12â0·54 °C per decade. Projections from GCMs suggest that future annual rainfall will change by +10 to â40% by 2080. Rainfall will increase during NovemberâDecember (outside the growing season), but will decline during the growing seasons. Also, the length of the growing season is expected to be reduced by 12â35%. The annual mean temperature is expected to increase in the range of 1·4â4·1 °C by 2080. The past and future climate trends, especially in terms of rainfall and its variability, pose major risks to rainfed agriculture. Specific adaptation strategies are needed for the CRV to cope with the risks, sustain farming and improve food security.
•We used multi-model crop growth simulation approach to characterize climate-induced variability and yield gaps of maize.•Maize yield shows high inter-annual variability and this variability is ...explained mainly by the variation in growing season rainfall.•Average farmers’ yields are only 28–30% of simulated water-limited yield.•Improved crop management and climate-proof strategies are needed to sustain production, increase yield and bridge existing yield gaps.•The use of more than one model provides some insight in uncertainty of simulating crop–climate interactions.
There is a high demand for quantitative information on impacts of climate on crop yields, yield gaps and their variability in Ethiopia, yet, quantitative studies that include an indication of uncertainties in the estimates are rare. A multi-model crop growth simulation approach using the two crop models, i.e. Decision Support System for Agro-Technology (DSSAT) and WOrld FOod STudies (WOFOST) was applied to characterize climate-induced variability and yield gaps of maize. The models were calibrated and evaluated with experimental data from the Central Rift Valley (CRV) in Ethiopia. Subsequently, a simulation experiment was carried out with an early maturing (Melkassa1) and a late maturing (BH540) cultivar using historical weather data (1984–2009) of three locations in the CRV. Yield gaps were computed as differences among simulated water-limited yield, on-farm trial yields and average actual farmers’ yields.
The simulation experiment revealed that the potential yield (average across three sites and 1984–2009) is 8.2–9.2 and 6.8–7.1Mg/ha for the late maturing and early maturing cultivars, respectively; ranges indicate mean differences between the two models. The simulated water-limited yield (averaged across three sites and 1984–2009) is 7.2–7.9Mg/ha for the late maturing and 6.1–6.7Mg/ha for the early maturing cultivar. The water-limited yield shows high inter-annual variability (CV 36%) and about 60% of this variability in yield is explained by the variation in growing season rainfall. The gap between average farmers yield and simulated water-limited yield ranges from 4.7 to 6.0Mg/ha. The average farmers’ yields were 2.0–2.3Mg/ha, which is about 1.1–3.1Mg/ha lower than on-farm trial yields. In relative terms, average farmers’ yields are 28–30% of the water-limited yield and 44–65% of on-farm trial yields. Analysis of yield gaps for different number of years to drive average yields indicates that yield gap estimation on the basis of few years may result in misleading conclusions. Approximately ten years of data are required to be able to estimate yield gaps for the Central Rift Valley in a robust manner.
Existing yield gaps indicate that there is scope for significantly increasing maize yield in the CRV and other, similar agro-ecological zones in Africa, through improved crop and climate risk management strategies. As crop models differ in detail of describing the complex, dynamic processes of crop growth, water use and soil water balances, the multi-model approach provides information on the uncertainty in simulating crop–climate interactions.
Grain yields of rainfed agriculture in Australia are often low and vary substantially from season to season. Assimilates stored prior to grain filling have been identified as important contributors ...to grain yield in such environments, but quantifying their benefit has been hampered by inadequate methods and large seasonal variability. APSIM-Nwheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), crop residues, crop growth and development. Model outputs were compared with detailed measurements of N fertilizer experiments on loamy soils at three locations in southern New South Wales, Australia. The field measurements allowed the routine for remobilization of assimilates stored prior to grain filling in the model to be tested for the first time and simulations showed close agreement with observed data. Analysing system components indicated that with increasing yield, both the observed and simulated absolute amount of remobilization generally increased while the relative contribution to grain yield decreased. The simulated relative contribution of assimilates stored prior to grain filling to grain yield also decreased with increasing availability of water after anthesis. The model, linked to long-term historical weather records was used to analyse yield benefits from assimilates stored prior to grain filling under rainfed conditions at a range of locations in the main wheat growing areas of Australia. Simulation results highlighted that in each of these locations assimilates stored prior to grain filling often contributed a significant proportion to grain yield. The simulated contribution of assimilates stored prior to grain filling to grain yield can amount to several tonnes per hectare, however, it varied substantially from 5–90% of grain yield depending on seasonal rainfall amount and distribution, N supply, crop growth and seasonal water use. High N application often reduced the proportion of water available after anthesis and decreased the relative contribution of remobilization to grain yield as long as grain yields increased, particularly on soils with greater water-holding capacity. Increasing the capacity or potential to accumulate pre-grain filling assimilates for later remobilization by 20% increased yields by a maximum of 12% in moderate seasons with terminal droughts, but had little effect in poor or very good seasons in which factors that affect the amount of carbohydrates stored rather than the storage capacity itself appeared to limit grain yield. These factors were, little growth due to water or N deficit in the weeks prior to and shortly after anthesis (when most of the assimilates accumulate for later remobilization), poor sink demand of grains due to low grain number as a result of little pre-anthesis growth or high photosynthetic rate during grain filling. Increasing the potential storage capacity for remobilization is expected to increase grain yield especially under conditions of terminal drought.
The combination of advances in knowledge, technology, changes in consumer preference and low cost of manufacturing is accelerating the next technology revolution in crop, livestock and fish ...production systems. This will have major implications for how, where and by whom food will be produced in the future. This next technology revolution could benefit the producer through substantial improvements in resource use and profitability, but also the environment through reduced externalities. The consumer will ultimately benefit through more nutritious, safe and affordable food diversity, which in turn will also contribute to the acceleration of the next technology. It will create new opportunities in achieving progress towards many of the Sustainable Development Goals, but it will require early recognition of trends and impact, public research and policy guidance to avoid negative trade-offs. Unfortunately, the quantitative predictability of future impacts will remain low and uncertain, while new chocks with unexpected consequences will continue to interrupt current and future outcomes. However, there is a continuing need for improving the predictability of shocks to future food systems especially for ex-ante assessment for policy and planning.
Wheat yields in the Mediterranean climate of Western and Southern Australia are often limited by water. Our measurements on a 70 ha growers field showed linear relationships between grain yield and ...the plant available soil water storage capacity (PAWc) of the top 100 cm of the soil profile. PAWc was linearly related to apparent soil electrical conductivity measured by proximal sensing using electromagnetic induction (EM38). The APSIM wheat model also employs PAWc as one of the systems parameters and simulated linear relationships between PAWc and yield. These relationships were used to transform an EM38-derived PAWc map of the field into yield maps for three major season types (dry, medium and wet) and nitrogen (N) fertiliser management scenarios. The results indicated that the main cause of temporal and spatial yield variability within the field was due to interactions of seasonal rainfall, PAWc and N fertiliser applications. Spatial variability was low in low rainfall years when yields across the field were low and the higher soil water storage capacity sites were often underutilised. With adequate N, spatial variability increased with seasonal rainfall as sites with higher PAWc conserved more water in wet seasons to give higher yield response than sites with low PAWc. The higher yield response of high PAWc sites to rainfall gave rise to larger temporal variability compared with sites with low PAWc. Provision of adequate N is required for the water limited yield potential to be expressed and this increased both spatial and temporal variability. Sites with low PAWc performed poorly irrespective of rainfall and N application. PAWc is inherently low on deep coarse sands; these sites should be considered for a change in land use. Elsewhere, strategic management interventions should aim to improve PAWc through sub-soil amelioration and deep root growth to increase the capital asset of the farm. The resulting increase in yields will occur in favourable seasons and with adequate fertiliser provisions. The largest grain yield response to water and N will be obtained on sites with the highest PAWc and it is at those sites that the greatest profits from fertiliser use could be achieved in wet seasons.
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.
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