Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Climate change and associated uncertainties have serious direct and indirect consequences for crop production and food security in agriculture-based developing regions. Long-term climate data analysis can identify climate risks and anticipate new ones for planning appropriate adaptation and mitigation options. The aim of this study was to identify near-term (2030) and mid-term (2050) climate risks and/or opportunities in the state of Bihar, one of India's most populous and poorest states, using weather data for 30 years (1980-2009) as a baseline. Rainfall, maximum and minimum temperatures, and evapotranspiration will all increase in the near-and mid-term periods relative to the baseline period, with the magnitude of the change varying with time, season and location within the state. Bihar's major climate risks for crop production will be heat stress due to increasing minimum temperatures in the rabi (winter) season and high minimum and maximum temperatures in the spring season; and intense rainfall and longer dry spells in the kharif (monsoon) season. The increase in annual and seasonal rainfall amounts, and extended crop growing period in the kharif season generally provide opportunities; but increasing temperature across the state will have considerable negative consequences on (staple) crops by affecting crop phenology, physiology and plant-water relations. The study helps develop site-specific adaptation and mitigation options that minimize the negative effects of climate change while maximizing the opportunities.

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology as a function of soil, weather, and management is important. Mechanistic crop models are a major tool for such predictions. It has been shown that there is a large variability between predictions by different modeling groups for the same inputs, and therefore, a need for shared improvement of crop models. Two pathways to improvement are through improved understanding of the mechanisms of the modeled system, and through improved model parameterization. This article focuses on improving crop model parameters through improved calibration, specifically for prediction of crop phenology. A detailed calibration protocol is proposed, which covers all the steps in the calibration work-flow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values and diagnostics. For those aspects where knowledge of the model and target environments is required, the protocol gives detailed guidelines rather than strict instructions. The protocol includes documentation tables, to make the calibration process more transparent. The protocol was applied by 19 modeling groups to three data sets for wheat phenology. All groups first calibrated their model using their "usual" calibration approach. Evaluation was based on data from sites and years not represented in the training data. Compared to usual calibration, l calibration following the new protocol significantly reduced the error in predictions for the evaluation data, and reduced the variability between modeling groups by 22%.

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.

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