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Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed. Core Ideas: Assessment of climate change impacts on agriculture requires a systems approach.Agriculture is weather dependent, and changes in climate can have a drastic impact on food production.Crop models can extend the results of physical experiments to better assess long‐term climate change effects on agriculture.Crop models are critical in assessing climate change impacts and developing adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

Rainfed agriculture in Senegal is heavily affected by weather-related risks, particularly timing of start/end of the rainy season. For climate services in agriculture, the National Meteorological Agency (ANACIM) of Senegal has defined an onset of rainy season based on the rainfall. In the field, however, farmers do not necessarily follow the ANACIM's onset definition. To close the gap between the parallel efforts by a climate information producer (i.e., ANACIM) and its actual users in agriculture (e.g., farmers), it is desirable to understand how the currently available onset definitions are linked to the yield of specific crops. In this study, we evaluated multiple onset definitions, including rainfall-based and soil-moisture-based ones, in terms of their utility in sorghum production using the DSSAT–Sorghum model. The results show that rainfall-based definitions are highly variable year to year, and their delayed onset estimation could cause missed opportunities for higher yields with earlier planting. Overall, soil-moisture-based onset dates determined by a crop simulation model produced yield distributions closer to the ones by semi-optimal planting dates than the other definitions, except in a relatively wet southern location. The simulated yields, particularly based on the ANACIM's onset definition, showed statistically significant differences from the semi-optimal yields for a range of percentiles (25th, 50th, 75th, and 90th) and the means of the yield distributions in three locations. The results emphasize that having a good definition and skillful forecasts of onset is critical to improving the management of risks of crop production in Senegal. [ABSTRACT FROM AUTHOR]

• An agro-climate decision support tool, SIMAGRI was developed. • SIMAGRI seamlessly integrates climatology or seasonal climate forecast and crop models. • SIMAGRI can produce information relevant to agricultural decision making. • SIMAGRI's graphical user-interface helps easy analysis of "what-if" scenarios. Managing climate-related risks is one of the key components of enhancing agricultural resilience and productivity under increasingly variable and changing climate. In this study, a decision support tool, SIMAGRI, was developed to translate climate information into agricultural and economic terms that can support strategic and tactical decisions in crop production e.g., crop choices, agricultural management practices, insurance needs, among others. The SIMAGRI helps users to run crop simulation models for three target crops (maize, soybean and wheat) with representative management options, based on historical climate data and probabilistic seasonal climate forecasts. The user-friendly interface of SIMAGRI allows users to compare results of several management practices (e.g., planting dates, crops and cultivars, fertilizer/irrigation application, etc.) given a climate condition, and thus help identify appropriate mixes of production options, which can increase (reduce) positive (negative) impacts of current and future climatic conditions. The SIMAGRI can be used for different purposes (e.g., initial assessment or operational use for generating crop yield outlook, etc.). It can enhance better communications with stakeholders, and for identifying their needs. [ABSTRACT FROM AUTHOR]

[Display omitted] • SMAP soil moisture data assimilation was implemented in the SWAT model. • Post-assimilation error for soil moisture predictions was moderately reduced. • Assimilation increased total streamflow but affected flow components differently. • Impact on predicted water quality and crop yield was both strong and complex. • Changes to available water modified both nutrient transport and transformation. Model soil moisture predictions are routinely updated with independent observations using data assimilation techniques. While the impacts of these updates on modeled hydrology are well studied, impacts on predicted water quality and crop yield are unresolved. In this study, we evaluated data assimilation effects for a range of predictions in the Soil and Water Assessment Tool (SWAT). We used satellite surface soil moisture data products from the Soil Moisture Active/Passive (SMAP) mission to update SWAT soil moisture for two U.S. experimental watersheds. We addressed possible limitations to the vertical transfer of surface soil moisture updates to deeper layers in the model by additionally testing a modified soil percolation approach that relies on relative saturation rather than the original thresholding behavior of SWAT. Results at both watersheds demonstrated that data assimilation greatly impacted water quality and crop yield predictions. Modifying the soil percolation algorithm, however, did not improve assimilation results. Assimilation increased median soil moisture (+2% to +6 %) which in turn increased total streamflow (+0.43 to +1.70 m3/s daily). Critically, streamflow alone was not a sufficient predictor for the assimilation changes to water quality predictions as flow component contributions and seasonality differed between sites. A varied response in annual water quality predictions to soil moisture updates was identified (+1.70 to +123 kg/ha total nitrogen; −0.11 to −0.57 kg/ha total phosphorous; +8.1 to +50.0 kg/ha sediment) that was dependent upon the timing of the updates, site characteristics, and change to specific transport and transformation processes. Crop yield predictions were similarly impacted by data assimilation from changes to both water and nutrient availability that varied by crop type. The overall strong and diverse response of water quality and crop yield predictions to soil moisture updates provides evidence that assimilation can impact a range of model predictions. Efforts to improve soil moisture simulations, therefore, have potential for meaningful improvements in targeted, non-hydrologic predictions. [ABSTRACT FROM AUTHOR]

Seasonal climate prediction can potentially contribute to achieving a more resilient cropping system management. This can help alleviate food insecurity and the economical sustainability of farming at large. For this purpose, seasonal climate forecasts are used to generate crop forecasts. This study assesses two methods for linking seasonal climate forecasts with crop models to improve crop yield predictability in the Iberian Peninsula (IP). Crop models usually require daily weather data and therefore, we tested two methods to disaggregate seasonal climate forecasts into daily weather realizations: (1) a conditional stochastic weather generator (predictWTD) and (2) a simple forecast probability resampler (FResampler1).These methods were evaluated under three seasonal rainfall forecasts by analyzing the impacts on rainfed wheat yield and on irrigation requirements and yields of maize crop. In addition, we estimated the gross margins (€ ha − 1 ) and the production risks associated with contrasting scenarios of seasonal rainfall forecasts (dry and wet). Both methods provided comparable predictability and therefore, both seem feasible options for using seasonal forecasts to establish yield forecasts and irrigation requirements. The large impact of crop prices and of irrigation cost on gross margins for both crops suggests that using a combination of information on expected market prices and crop forecast based on seasonal climate forecasts can be an effective tool for farmer's decision-making, especially under dry forecast situation and/or in locations with low annual precipitation. These methods can help to quantify the benefits and risks from the seasonal weather forecasts to farmers in the IP. The anticipation of risks and the opportunity that skillful climate and crop forecast provide allows for windows of opportunity to prepare and pre-empt mitigating actions. [ABSTRACT FROM AUTHOR]