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Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. The projected impact of 1.5 and 2.0°C warming above the pre‐industrial period on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impacts of warming of <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. [ABSTRACT FROM AUTHOR]

The perfect knowledge of yield before harvest has been a wish puzzling human being since the beginning of agriculture because seasonal forecast of crop yield plays a critical role in decision making for different stakeholders--from farmers to policy makers to governments for food security, to commodities traders. Different methods have been used to forecast yield with different levels of granularity, accuracy and timing. This chapter presents a critical review of the current seasonal crop yield forecasting methods found in the scientific literature. Extensive research has been conducted on crop yield forecast, particularly for wheat, maize, rice, barley, and soybean. Yield forecast are mainly based on field surveys, statistical regressions between historical yield and in-season variables (agrometeorological, or remotely sensed data), crop simulation models, or on integration between statistical modeling with dynamic process-based crop simulation models. A low number of studies rely on field surveys as a means to forecast yield, but they remain the main methods of yield forecast and estimation in several countries (i.e., USA). This chapter aims to report results found in peer-review journals for different crops, methods, geographies, and accuracies, and to end with a critical perspective on the advantage and disadvantage of the different methods currently employed by researchers and stakeholders. [ABSTRACT FROM AUTHOR]

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1% points, representing a relative change of −8.6%. [ABSTRACT FROM AUTHOR]

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e‐mean and e‐median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e‐mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2–6 models if best‐fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e‐mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e‐mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e‐mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations. One way of estimating the projected impact of climate change on crops is to use crop models. There is a large variability in results of different crop models, but it has been observed that the mean or median of a multimodel ensemble (MME) often gives good agreement with observed data. We used empirical data from several MME studies, plus theoretical arguments, to better understand why and when the MME mean will be a good predictor. This should help modelers decide how to create and use MMEs for climate impact assessment. [ABSTRACT FROM AUTHOR]

The Crop Environment Resource Synthesis (CERES) models have been developed and utilized for the last 30 years to simulate crop growth in response to climate, soil, genotypes and management across locations throughout the world. We reviewed 215 papers found in the literature that contained field observed data where the CERES models were tested. Over 30 simulated variables of the CERES models have been tested in 43 different countries under various experimental treatments. Across all testing conditions, the CERES models simulated grain yield with a root mean square error (RMSE) of less than 1400 kg/ha (~10% relative error, RE), 1200 kg/ha (~20% RE) and 800 kg/ha (~10% RE) for maize, wheat, and rice, respectively. Phenological development was simulated with less than 7 days difference from the observations in most studies. The CERES models simulated aboveground biomass, harvest index, evapotranspiration, and soil water reasonably well too. The simulations of grain number (up to 4340 root mean square error, RMSE), grain weight (up to 22% error), intercepted photosynthetically active radiation (IPAR, up to 0.41 MJ/plant), leaf area index (LAI, 31.9% error), soil temperature (over 10°C difference), and nitrogen (N) dynamics (up to 80% error) were less accurate. In fact the average error of CERES model simulations tends to be higher under marginal crop growing conditions such as extreme heat or cold, water and nutrient deficit conditions. [ABSTRACT FROM AUTHOR]

The response of wheat crops to elevated CO2 ( eCO2) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha−1 and 1600 to 3900 kg ha−1, respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol−1 CO2) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm ( P = 0.10) by eCO2, increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY- CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency ( RUE) or transpiration efficiency ( TE) was not critical to define the overall response to eCO2. However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2, water and temperature is required to resolve these model discrepancies. [ABSTRACT FROM AUTHOR]

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield ( GY) and grain protein concentration ( GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. [ABSTRACT FROM AUTHOR]

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g·N·m-2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g·N·m-2) (r2 = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately. [ABSTRACT FROM AUTHOR]

Abstract: Wheat yield production in Mediterranean environment is highly affected by rainfall and amount of soil water stored into the soil before and during the growing season. Agricultural fields have been always considered as uniform entities and managed accordingly. However, uniform agronomic management in fields where spatial variability is present, is economically and environmentally inefficient. The objectives of this study were to: (i) identify spatially and temporally stable areas throughout the field, (ii) understand the influence of fallow and growing season rainfall on spatial and temporal variability of wheat yield. The study was carried out on a 12ha field located in Foggia, Southern Italy during five years wheat monoculture. One hundred geo-referenced points were sampled for deriving spatial maps of soil texture and organic carbon. Spatial maps of grain yield, normalized difference vegetation index (NDVI), soil electrical resistivity tomography (ERT) were collected non-destructively. Total growing season rainfall was correlated with grain yield after dividing it into long fallow (June–November), short fallow (September–November), growing season (December–May), vegetative (December–February), reproductive (March–May). The spatial maps were used to define spatial and temporal yield variability and to identify three stable zones within the field, “low yield stable” (LS), “average yield stable” (AS), “high yield stable” (HS). Long and short fallow rainfall was highly correlated with grain yield of HS zone with correlation coefficients ranging between 0.5 and 1. Growing season rainfall was mostly correlated with the AS zone. The crop response to rainfall was a result of dynamic interaction of spatial static properties such as soil texture, position in the landscape and dynamic properties (soil water content, infiltration and crop water use). [Copyright &y& Elsevier]

Abstract: Wheat yield and protein content in a field are spatially variable due to inherent variability of soil properties and landscape. In Mediterranean environments yield variability in space and time is caused by irregular weather patterns, particularly rainfall, and by position in the landscape. A tested crop simulation model, SALUS, was used to select optimal nitrogen fertilizer rates using strategic and tactical approaches in a spatially variable field where three distinct management zones had been previously identified. The crop model was tested and then used to simulate seven N rates from 0 to 180kgNha−1 with a 30kgNha−1 increments for 56 years using historical weather data. The available soil water at the time of N sidedressing each year and each management zone was correlated with yield response to N to evaluate the possibility of using the stored soil water to tactically determine N rates. Assuming recent production costs and grain prices the simulations helped identify an optimal N rate for each of the zones based on agronomic, economic and environmental sustainability of N management. Results showed the high yielding zone had a maximum economic return and minimal environmental impact in terms of nitrate leaching by applying 90kgNha−1annually. On the other hand, the low yielding zone had little economic returns for application higher than 30kgNha−1. When simulated soil root-zone water was low at sidedressing, a lower fertilizer rate increased profit and decreased N leaching in the medium and high yielding zones. [Copyright &y& Elsevier]

Abstract: Appropriate nitrogen management is one of the main challenges of agricultural production and for the environment. The objectives of this study were to evaluate the efficiency of crop N uptake in a long-term wheat crop in a Mediterranean environment of Southern Italy, and to identify optimal N rate for reasonable economic returns and minimum nitrate leaching using SALUS crop simulation model. The study was part of a long-term monoculture wheat system that started in 1991/1992 season, with two levels of nitrogen (0 and 90kgNha−1). Simulations of the treatment with no nitrogen (0N) and 90kgNha−1 (90N) were performed using the SALUS crop model for wheat. The model was tested against measurements of harvested grain yield, final N uptake, soil water content and total soil N. Long-term simulation over 56 years showed that grain yield median value was 3435kgha−1 for 0N and 3876kgha−1 for 90N. Simulation scenarios with different N rates (0, 30, 60, 90, 120, 180kgNha−1) showed that yield response was higher for 120N (3528kgha−1), with the 60 and 90N yields giving the same response, 3010 and 3054kgha−1, respectively. The most profitable treatments were 120N (302Euroha−1), followed by the 60N (220Euroha−1). The simulation results showed that nitrate leaching was higher for the N rate of 120 and 180 with a mean annual value of 49 and 81kgha−1, respectively. Results suggest that in such environment 60kgNha−1 can be the most appropriate as an N fertilization management due to the best trade-off between leaching and economic. Since N fertilization rates are linked to nitrous oxide (N2O) emissions and N leaching, a trade-off between N fertilization rates profit and grain yield should be thought as way to reduce environmental pollution while keeping productivity and profit. The adoption of simulation models to approximate the best N rate for durum wheat in rainfed Mediterranean environment proved to be a useful tool for supporting management decisions through quantifying the temporal variability related to weather uncertainty as it influences on the yield and nutrient dynamics. [Copyright &y& Elsevier]

Abstract: The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies. [Copyright &y& Elsevier]

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981–2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R 2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. [ABSTRACT FROM AUTHOR]

Abstract: Soil organic carbon sequestration rates over 20 years based on the Intergovernmental Panel for Climate Change (IPCC) methodology were combined with local economic data to determine the potential for soil C sequestration in wheat-based production systems on the Indo-Gangetic Plain (IGP). The C sequestration potential of rice–wheat systems of India on conversion to no-tillage is estimated to be 44.1MtC over 20 years. Implementing no-tillage practices in maize–wheat and cotton–wheat production systems would yield an additional 6.6MtC. This offset is equivalent to 9.6% of India''s annual greenhouse gas emissions (519MtC) from all sectors (excluding land use change and forestry), or less than one percent per annum. The economic analysis was summarized as carbon supply curves expressing the total additional C accumulated over 20 year for a price per tonne of carbon sequestered ranging from zero to USD 200. At a carbon price of USD 25MgC−1, 3MtC (7% of the soil C sequestration potential) could be sequestered over 20 years through the implementation of no-till cropping practices in rice–wheat systems of the Indian States of the IGP, increasing to 7.3MtC (17% of the soil C sequestration potential) at USD 50MgC−1. Maximum levels of sequestration could be attained with carbon prices approaching USD 200MgC−1 for the States of Bihar and Punjab. At this carbon price, a total of 34.7MtC (79% of the estimated C sequestration potential) could be sequestered over 20 years across the rice–wheat region of India, with Uttar Pradesh contributing 13.9MtC. [Copyright &y& Elsevier]

• A large multi-model study predicting wheat phenology in Australia was performed. • Calibration and evaluation datasets were independently drawn from the same population. • Mean absolute prediction error ranged from 6 to 20 days (median 9 days). • Two thirds of modeling groups predicted better than a simple temperature sum. • Variability between groups using the same model structure was substantial. Predicting wheat phenology is important for cultivar selection, for effective crop management and provides a baseline for evaluating the effects of global change. Evaluating how well crop phenology can be predicted is therefore of major interest. Twenty-eight wheat modeling groups participated in this evaluation. Our target population was wheat fields in the major wheat growing regions of Australia under current climatic conditions and with current local management practices. The environments used for calibration and for evaluation were both sampled from this same target population. The calibration and evaluation environments had neither sites nor years in common, so this is a rigorous evaluation of the ability of modeling groups to predict phenology for new sites and weather conditions. Mean absolute error (MAE) for the evaluation environments, averaged over predictions of three phenological stages and over modeling groups, was 9 days, with a range from 6 to 20 days. Predictions using the multi-modeling group mean and median had prediction errors nearly as small as the best modeling group. About two thirds of the modeling groups performed better than a simple but relevant benchmark, which predicts phenology by assuming a constant temperature sum for each development stage. The added complexity of crop models beyond just the effect of temperature was thus justified in most cases. There was substantial variability between modeling groups using the same model structure, which implies that model improvement could be achieved not only by improving model structure, but also by improving parameter values, and in particular by improving calibration techniques. [ABSTRACT FROM AUTHOR]

• 27 modeling groups were evaluated for wheat phenology predictions for France. • Calibration and evaluation data were sampled from the same target population. • The calibration and evaluation data have neither year nor site in common. • The best groups had a mean absolute error comparable to the measurement error. • Model structure alone does not determine prediction accuracy. Predicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population. [ABSTRACT FROM AUTHOR]