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In this study, we aimed to evaluate the monthly precipitation forecasts of JMA/MRI-CPS2, a global dynamical seasonal climate forecast (Dyn-SCF) system operated in the Japan Meteorological Agency, by comparing them with the forecasts of a statistical SCF (St-SCF) system using climate indices systematically and globally. Accordingly, we developed a new global St-SCF system using 18 climate indices and compared the monthly precipitation of this system with those of JMA/MRI-CPS2. Consequently, it was found that JMA/MRI-CPS2 forecasts are superior to St-SCFs around the equator (10° S-10° N) even for six-month lead forecasts. For one-month lead forecasts, the accuracy of JMA/MRI-CPS2 forecasts was higher than that of St-SCFs when viewed globally. In contrast, for forecasts made two months or longer in advance, St-SCFs had an advantage in global forecasts. In addition to evaluating the accuracy of JMA/MRI-CPS2 forecasts, the slow dynamics of the ocean and atmosphere, not reproduced by the JMA/MRI-CPS2 system, were determined by comparing the evaluations, and it was concluded that this could contribute to improving Dyn-SCF systems. [ABSTRACT FROM AUTHOR]

• There is an optimal rainfall for cowpea beyond which yield declined. • Cowpea yield response to water deficit and excess depends on soil type. • Projected heavy rainfalls would lead to excessive soil water even in dryland. • Excessive soil water poses as much of a threat as drought to legume production. • The need for countermeasures for both soil water deficit and excess is underscored. The intensification of the hydrological cycle has increased heavy rainfall and drought events in a changing climate. However, compared to drought, the impacts of heavy rainfall on crop production are under-studied. Using field experimental data and a calibrated crop model CYGMA, we showed that excessive soil water associated with heavy rainfall events is having a detrimental effect on cowpea yields, even in the dry environments of West Africa where cowpea is an important, protein-rich cash crop. Cowpea yields are susceptible to heavy rainfall in areas with poorly drained soils, and to drought in soils that have a low water-retention capacity. The crop model captured of the main characteristics of the observed development, growth, and yield, as well as the characteristics of root-zone soil water contents and how they vary by soil type. The analysis of d4PDF factual and counterfactual climate model simulations revealed that heavy rainfall events associated with anthropogenic climate change have increased in recent decades, and that they are projected to increase in future. Further, changes in seasonal rainfall and the number of dry days would be largely absent from CMIP6 climate projections by mid-century. Reductions in cowpea yields due to excessive soil water is projected to become more frequent, and the potential damage in a 1-in-100 extremely wet year would be comparable to the damage currently experienced in droughts, irrespective of soil types. Simulations of the projected damage due to drought show that the situation will be similar to current levels, with drought remaining a major climate hazard. However, excessive soil water is projected to be a serious threat to food security in the region. Our findings indicate that, even in dry environments, cropping systems need to be implemented in order to reduce the susceptibility of soils to both drought and excessive soil water. [ABSTRACT FROM AUTHOR]

Drought is a major risk in global agriculture. Building-up soil organic carbon (SOC) enhances soil fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC management demonstrates its benefit at various locations and is a promising means to achieve food security and climate mitigation at once. However, no global assessment of its potential and co-benefits gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global crop and soil datasets, we find that relatively small enhancement in topsoil (0–30 cm) organic carbon content (OCtop) could increase drought tolerance of the food production systems operating over 70% of the global harvested area (particularly drylands). By closing the gap between current and upper limit of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer's economic output in drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple development goals can be leveraged by SOC enhancement in carbon (C)-poor soils in drier regions around the world. [ABSTRACT FROM AUTHOR]

Droughts represent an important type of climate extreme that reduces crop production and food security. Although this fact is well known, the global geographic pattern of drought-driven reductions in crop production is poorly characterized. As the incidence of relatively more severe droughts is expected to increase under climate change, understanding the vulnerability of crop production to droughts is a key research priority. Here, we estimate the production losses of maize, rice, soy, and wheat from 1983 to 2009 using empirical relationships among crop yields, a drought index, and annual precipitation. We find that approximately three-fourths of the global harvested areas—454 million hectares—experienced drought-induced yield losses over this period, and the cumulative production losses correspond to 166 billion U.S. dollars. Globally averaged, one drought event decreases agricultural gross domestic production by 0.8%, with varying magnitudes of impacts by country. Crop production systems display decreased vulnerability or increased resilience to drought according to increases in per capita gross domestic production (GDP) in the countries with extensive semiarid agricultural areas. These changes in vulnerability accompany technological improvements represented by per capita GDP increases. Our estimates of drought-induced economic losses in agricultural systems offer a sound basis for subsequent assessments of the costs of adaptation to droughts under climate change. [ABSTRACT FROM AUTHOR]

The lack of spatially detailed crop calendars is a significant source of uncertainty in modeling, monitoring, and forecasting crop production. In this paper, we present a rule‐based model to estimate the sowing and harvesting windows of major crops over the global land area. The model considers field workability due to snow cover and heavy rainfall in addition to crop biological requirements for heat, chilling, and moisture. Using daily weather data for the period 1996–2005 as model input, we derive calendars for maize, rice, winter and spring wheat, and soybeans around the year 2000 with a spatial resolution of 0.5° in latitude and longitude. Separate calendars for rainfed and irrigated conditions and three representative varieties (short‐, medium‐ and long‐season varieties) are estimated. The daily probabilities of sowing and harvesting derived using the model well capture the major characteristics of reported calendars. Our modeling reveals that field workability is an important determinant of sowing and harvesting dates and that multicropping patterns influence the calendars of individual crops. The case studies show that the model is capable of capturing multicropping patterns such as triple rice cropping in Bangladesh, double rice cropping in the Philippines, winter wheat‐maize rotations in France, and maize‐winter wheat‐soybean rotations in Brazil. The model outputs are particularly valuable for agricultural and hydrological applications in regions where existing crop calendars are sparse or unreliable. Plain Language Summary: This manuscript describes a numerical model to estimate location‐specific sowing and harvesting dates of crops over the globe. Ten‐year‐long daily weather data and a few coefficients that represent the physiological characteristics of a crop (for instance, the amount of water needs to complete the life cycle of an annual crop) are only inputs to the model. Comparisons with the reported crop calendars indicate that the model well reproduces calendars of maize, rice, winter and spring wheat, and soybean around the year 2000 in major crop‐producing countries. We also find that snow cover and heavy rainfall, which influence field workability but have not considered in earlier modeling, are important to estimate sowing and harvesting dates and multicropping patterns (for instance, a combination of winter and summer crops) affect the calendar of individual crops. Our findings are useful when simulating the responses of crop calendars to climate change. Key Points: The model estimates the daily probabilities of sowing and harvesting in the year 2000Winter and spring wheat and rainfed versus irrigated conditions are differentiatedOur findings have implications to improve modeling multicropping patterns [ABSTRACT FROM AUTHOR]

The accumulated evidence indicates that agricultural production is being affected by climate change. However, most of the available evidence at a global scale is based on statistical regressions. Corroboration using independent methods, specifically process‐based modelling, is important for improving our confidence in the evidence. Here, we estimate the impacts of climate change on the global average yields of maize, rice, wheat and soybeans for 1981–2010, relative to the preindustrial climate. We use the results of factual and non‐warming counterfactual climate simulations performed with an atmospheric general circulation model that do and do not include anthropogenic forcings to climate systems, respectively, as inputs into a global gridded crop model. The results of a 100‐member ensemble climate and crop simulation suggest that climate change has decreased the global mean yields of maize, wheat and soybeans by 4.1, 1.8 and 4.5%, respectively, relative to the counterfactual simulation (preindustrial climate), even when carbon dioxide (CO2) fertilization and agronomic adjustments are considered. For rice, no significant impacts (−1.8%) are detected. The uncertainties in estimated yield impacts represented by the 90% probability interval that are derived from the ensemble members are −8.5 to +0.5% for maize, −8.4 to −0.5% for soybeans, −9.6 to +12.4% for rice and − 7.5 to +4.3% for wheat. Based on the yield impacts, the estimates of average annual production losses throughout the world for the most recent years of the study (2005–2009) account for 22.3 billion USD (B$) for maize, 6.5 B$ for soybeans, 0.8 B$ for rice and 13.6 B$ for wheat. Our assessment confirms that climate change has modulated recent yields and led to production losses, and our adaptations to date have not been sufficient to offset the negative impacts of climate change, particularly at lower latitudes. The presented study estimates the impacts of historical climate change on the global average yields of maize, rice, wheat and soybean in 1981–2010. The analysis uses the results of factual and counterfactual climate simulations performed with an atmospheric general circulation model that do and do not include anthropogenic forcings to climate systems, respectively, as inputs to a global gridded crop model. Based on the yield impacts, estimates of average annual economic production losses at the global level are quantified. [ABSTRACT FROM AUTHOR]

Global agriculture is under pressure to meet increasing demand for food and agricultural products. There are several global assessments of crop yields, but we know little about the uncertainties of their key findings, as the assessments are driven by the single best yield dataset available when each assessment was conducted. Recently, two different spatially explicit, global, historical yield datasets, one based on agricultural census and the other largely based on satellite remote sensing, became available. Using these datasets, we compare the similarities and differences in global yield gaps, trend patterns, growth rates and changes in year-to-year variability. We analyzed maize, rice, wheat and soybean for the period of 1981 to 2008 at four resolutions (0.083°, 0.5°, 1.0° and 2.0°). Although estimates varied by dataset and resolution, the global mean annual growth rates of 1.7–1.8%, 1.5–1.7%, 1.1–1.3% and 1.4–1.6% for maize, rice, wheat and soybean, respectively, are not on track to double crop production by 2050. Potential production increases that can be attributed to closing yield gaps estimated from the satellite-based dataset are almost twice those estimated from the census-based dataset. Detected yield variability changes in rice and wheat are sensitive to the choice of dataset and resolution, but they are relatively robust for maize and soybean. Estimates of yield gaps and variability changes are more uncertain than those of yield trend patterns and growth rates. These tendencies are consistent across crops. Efforts to reduce uncertainties are required to gain a better understanding of historical change and crop production potential to better inform agricultural policies and investments. [ABSTRACT FROM AUTHOR]

Satellite-derived daily surface soil moisture products have been increasingly available, but their applicability to global gridded crop model (GGCM) evaluation is unclear. This study compares four different soil moisture products with the flux tower site observation at 18 cropland sites across the world where either of maize, soybean, rice and wheat is grown. These products include the first and second versions of Climate Change Initiative Soil Moisture (CCISM-1 and CCISM-2) datasets distributed by the European Space Agency and two different AMSR-E (Advanced Microwave Scanning Radiometer–Earth Observing System)-derived soil moisture datasets, separately provided by the Japan Aerospace Exploration Agency (AMSRE-J) and U.S. National Aeronautics and Space Administration (AMSRE-N). The comparison demonstrates varying reliability of these products in representing major characteristics of temporal pattern of cropland soil moisture by product and crop. Possible reasons for the varying reliability include the differences in sensors, algorithms, bands and criteria used when estimating soil moisture. Both the CCISM-1 and CCISM-2 products appear the most reliable for soybean- and wheat-growing area. However, the percentage of valid data of these products is always lower than other products due to relatively strict criteria when merging data derived from multiple sources, although the CCISM-2 product has much more data with valid retrievals than the CCISM-1 product. The reliability of the AMSRE-J product is the highest for maize- and rice-growing areas and comparable to or slightly lower than the CCISM products for soybean- and wheat-growing areas. The AMSRE-N is the least reliable in most location-crop combinations. The reliability of the products for rice-growing area is far lower than that of other upland crops likely due to the extensive use of irrigation and patch distribution of rice paddy in the area examined here. We conclude that the CCISM-1, CCISM-2 and AMSRE-J products are applicable to GGCM evaluation, while the AMSRE-N product is not. However, we encourage users to integrate these products with in situ soil moisture data especially when GGCMs simulations for rice are evaluated. [ABSTRACT FROM AUTHOR]

Replacing a rainfed cropping system with an irrigated one is widely assumed to be an effective measure for climate change adaptation. However, many agricultural impact studies have not necessarily accounted for the space-time variations in the water availability under changing climate and land use. Moreover, many hydrologic and agricultural assessments of climate change impacts are not fully integrated. To overcome this shortcoming, a tool that can simultaneously simulate the dynamic interactions between crop production and water resources in a watershed is essential. Here we propose the regional production and circulation coupled model (CROVER) by embedding the PRYSBI-2 (Process-based Regional Yield Simulator with Bayesian Inference version 2) large-area crop model into the global water resources model (called H08), and apply this model to the Songhua River watershed in Northeast China. The evaluation reveals that the model's performance in capturing the major characteristics of historical change in surface soil moisture, river discharge, actual crop evapotranspiration, and soybean yield relative to the reference data during the interval 1979-2010 is satisfactory accurate. The simulation experiments using the model demonstrated that subregional irrigation management, such as designating the area to which irrigation is primarily applied, has measurable influences on the regional crop production in a drought year. This finding suggests that reassessing climate change risk in agriculture using this type of modeling is crucial not to overestimate potential of irrigation-based adaptation. [ABSTRACT FROM AUTHOR]

Reliable regional-scale representation of crop growth and yields has been increasingly important in earth system modeling for the simulation of atmosphere-vegetation-soil interactions in managed ecosystems. While the parameter values in many crop models are location specific or cultivar specific, the validity of such values for regional simulation is in question. We present the scale dependency of likely parameter values that are related to the responses of growth rate and yield to temperature, using the paddy rice model applied to Japan as an example. For all regions, values of the two parameters that determine the degree of yield response to low temperature (the base temperature for calculating cooling degree days and the curvature factor of spikelet sterility caused by low temperature) appeared to change relative to the grid interval. Two additional parameters (the air temperature at which the developmental rate is half of the maximum rate at the optimum temperature and the value of developmental index at which point the crop becomes sensitive to the photoperiod) showed scale dependency in a limited region, whereas the remaining three parameters that determine the phenological characteristics of a rice cultivar and the technological level show no clear scale dependency. These results indicate the importance of using appropriate parameter values for the spatial scale at which a crop model operates. We recommend avoiding the use of location-specific or cultivar-specific parameter values for regional crop simulation, unless a rationale is presented suggesting these values are insensitive to spatial scale. [ABSTRACT FROM AUTHOR]

Aim Recent changes in crop yields have implications for future global food security, which are likely to be affected by climate change. We developed a spatially explicit global dataset of historical yields for maize, soybean, rice and wheat to explore the historical changes in mean, year-to-year variation and annual rate of change in yields for the period 1982-2006. Location This study was conducted at the global scale. Methods We modelled historical and spatial patterns of yields at a grid size of 1.125° by combining global agricultural datasets related to the crop calendar and harvested area in 2000, country yield statistics and satellite-derived net primary production. Modelled yields were compared with other global datasets of yields in 2000 ( M3-Crops and Map SPAM) and subnational yield statistics for 23 major crop-producing countries. Historical changes in modelled yields were then examined. Results Modelled yields explained 45-81% of the spatial variation of yields in 2000 from M3-Crops and Map SPAM, with root-mean-square errors of 0.5-1.8 t ha−1. Most correlation coefficients between modelled yield time series and subnational yield statistics for the period 1982-2006 in major crop-producing regions were greater than 0.8. Our analysis corroborated the incidence of reported yield stagnations and collapses and showed that low and mid latitudes in the Southern Hemisphere (0-40° S) experienced significantly increased year-to-year variation in maize, rice and wheat yields in 1994-2006 compared with that in 1982-93. Main conclusions Our analyses revealed increased instability of yields across a broad region of the Southern Hemisphere, where many developing countries are located. Such changes are likely to be related to recent yield stagnation and collapses. Although our understanding of the impacts of recent climate change, particularly the incidence of climate extremes, on crop yields remains limited, our dataset offers opportunities to close parts of this knowledge gap. [ABSTRACT FROM AUTHOR]

Daily global solar radiation (SR) is one of essential weather inputs for crop, hydrological, and other simulation models to calculate biomass production and potential evapotranspiration. The availability of long-term observed SR data is, however, limited, especially in developing countries. This hinders climate applications in various sectors in these countries. To overcome this difficulty, we here propose a method to infer the reasonable daily SR condition for past decades from global reanalysis and limited observed SR data. The method consists of the regression-based statistical downscaling method and two empirical models for estimating the SR condition (i.e. the S-model and the DTR/RH-model). These empirical models were independent in terms of the variables explaining the SR condition. The regression models were trained on the basis of the SR conditions estimated by the S-model and the DTR/RH-model instead of the observed SR data. The Markov Chain Monte Carlo (MCMC) technique was applied to determine the parameter values of these models that guide the models to provide SR conditions that are close in value to each other at both the site and domain-mean scales. After that, we computed the SR condition over the 30 years from 1978 through 2007 at 17 sites in the Vietnam Mekong Delta area using the determined parameter values. The inferred SR condition was close in value to the corresponding observations available from the literature. This suggests that the proposed method yielded a reasonable inference of the SR condition at the sites despite the limited availability of observed SR data. The provided estimates of the daily SR condition over the past 30 years are useful for climate applications in agricultural, hydrological, and other sectors in this area. Copyright © 2011 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Projecting the impacts of climate change includes various uncertainties from physical, biophysical, and socioeconomic processes. Providing a more comprehensive impact projection that better represents the uncertainties is a priority research issue. We used an ensemble-based projection approach that accounts for the uncertainties in climate projections associated with general circulation models (GCMs) and biophysical and empirical parameter values in a crop model. We applied the approach to address the paddy rice yield change in Japan in the 2050s (2046-2065) and 2090s (2081-2100) relative to the 1990s (1981-2000). Seventeen climate projections, nine (eight) climate projections performed by seven (six) GCMs conditional on the Special Report on Emission Scenarios (SRES) A1B (A2), were included in this projection. In addition, 50 sets of biophysical and empirical parameter values of a large-scale process-based crop model for irrigated paddy rice were included to represent the uncertainties of crop parameter values. The planting windows, cultivation practices, and crop cultivars in the future were assumed to be the same as the level in the baseline period (1990s). The resulting probability density functions conditioned on SRES A1B and A2 indicate projected median yield changes of + 17.2% and + 26.9% in Hokkaido, the northern part of Japan, in the 2050s and 2090s with 90% probability intervals of (− 5.2%, + 40.3%) and (+ 6.3%, + 51.2%), relative to the 1990s mean yield, respectively. The corresponding values in Aichi, on the Pacific side of Western Japan, are 2.2% and − 0.8%, with 90% probability intervals of (− 15.0%, + 14.9%) and (− 33.4%, + 17.9%), respectively. We also provided geographical maps of the probability that the future 20-year mean yield will decrease and that the future standard deviation of yield for 20 years will increase. Finally, we investigated the relative contributions of the climate projection and crop parameter values to the uncertainty in projecting yield change in the 2090s. The choice of GCM yielded a relatively larger spread of projected yield changes than that of the other factors. The choice of crop parameter values could be more important than that of GCM in a specific prefecture. [ABSTRACT FROM AUTHOR]

This study quantifies the ranges of climate model biases in surface air temperature for July and August (summer temperature) and daily total insolation for May–October (warm-season insolation) that can give simulated regional paddy rice yields with a bias within ±2.5% of the 20-yr mean observed regional yield. The following four sets of three meteorological elements (daily maximum and minimum temperatures and daily total insolation) from daily climate model outputs were used as meteorological inputs for a large-scale crop model for irrigated paddy rice: 1) raw climate model outputs of all meteorological elements, 2) bias-corrected temperatures and raw climate model outputs of insolation, 3) bias-corrected insolation and raw climate model outputs of temperatures, and 4) bias-corrected climate model outputs of all meteorological elements. These meteorological inputs were sourced from seven coupled general circulation models, one regional climate model, and one reanalysis dataset. Crop model simulations with artificially biased meteorological inputs were also used. By using the approximation formula derived from these crop model simulation results and the Monte Carlo simulation technique, it was found that climate model outputs with biases within ±0.6°C and ±3% for summer temperature and warm-season insolation, respectively, could result in a simulated regional paddy rice yield with a bias within ±2.5% of the 20-yr mean observed regional yield. The simulated regional yield was less biased not only when the biases of two meteorological inputs were small but also when the cold or warm bias of summer temperature and the overestimation of warm-season insolation were balanced through the crop model processes. The methodology presented here will lead to a better and more comprehensive understanding of the nature of error propagation from a climate model to an application model and will facilitate the selection of climate models suitable for specific applications. [ABSTRACT FROM AUTHOR]

Abstract: A Bayesian approach, the Markov Chain Monte Carlo (MCMC) technique, was applied to a newly developed large-scale crop model for paddy rice to optimize a new set of regional-specific parameters and quantify the uncertainty of yield estimation associated with model parameters. The developed large-scale model is process-based and up-scaled from a conventional field-scale model to meet the intended spatial-scale of the large-scale model to the typical grid size of high-resolution climate models. The domain of the large-scale model covers all of Japan, but the crop simulation is conducted for each local governmental area in Japan. The MCMC technique exhibits powerful capability to optimize multiple parameters in a nonlinear and fairly complex model. The application of the Bayesian approach is useful to quantify the uncertainty of model parameters in a comprehensive manner when researchers on crop modeling analyze the uncertainty of yield estimation associated with model parameters under given observations. A sensitivity analysis of the large-scale model was conducted with the obtained posterior distribution of parameters and warming conditions that have never been experienced before to demonstrate the change in the uncertainty of yield estimation associated with the uncertainty of parameters of the large-scale model. The uncertainty of yield estimation under warming conditions was larger than that obtained under climate conditions that have been experienced before. This raises a concern that the uncertainty of impact assessment on crop yield may increase if future climate projections are fed to crop models with parameters optimized under current climate conditions. [Copyright &y& Elsevier]

• The global gridded crop model (GGCM) performance is evaluated at national level. • Years of irrigated and rainfed simulations need be matched with national reports. • Planting window is considered using the planting date ensemble simulations. • The quality of crop statistics, misreports and conversion errors are explored. • This study focuses on rice but has implications for evaluations for other crops. Global gridded crop models (GGCMs) are increasingly used for climate risk assessments and adaptation planning in agriculture. GGCM historical simulation performance is therefore crucial for such applications. However, GGCM performance is lower for rice than for other crops at an aggregated administrative unit level despite the lack of a clear difference in model performance at the site level. Here, we present key factors that need to be considered in the spatial and temporal aggregation of GGCM outputs to improve the evaluation of GGCM historical rice simulations at the country scale. The factors include an adjustment for the harvesting year in GGCM rainfed and irrigated simulations, the removal of misreports from reference data, a consideration of the quality of national crop statistics (census reports), and the explicit incorporation of a planting window. The effect of each individual factor is demonstrated by analyzing a multi-GGCM dataset and performing a planting date ensemble simulation of two GGCMs. We reveal that among others, aligning the harvesting year in the GGCM simulations with that in national reports is pivotal. Although our analysis focuses specifically on rice, the findings of this study are useful for improving the country-level evaluations of GGCM historical simulations for other crops. [ABSTRACT FROM AUTHOR]

The authors constructed the framework for a preliminary assessment of climate change impact on the rice insurance payout in Japan. The framework consisted of various models ranging from climate projection downscaling, rice yield estimation, yield loss assessment, and rice insurance payout estimation. In this study, a simulation was conducted based on the dynamically downscaled regional climate projection with a lateral boundary condition given by the global climate projection of the Meteorological Research Institute Coupled General Circulation Model, version 2 (MRI CGCM2), under the A2 scenario of the Special Report on Emission Scenarios (SRES). Results indicated that rice yield in the 2070s will decrease slightly in central and western Japan and increase in northern Japan. The increase in yield was derived from a significant reduction in yield loss caused by cool-summer damage; on the other hand, the decrease in yield was caused by the increase in yield loss caused by heat stress and the shortening of the growth period induced by the temperature rise. The increase in the atmospheric CO2 concentration resulted in an increase in paddy rice biomass because of the fertilization effect; however, the increase in biomass was not enhanced much as a result of shortening of the growth period if early planting was not considered as an adaptation practice. Reflecting such changes in yield, the rice insurance payout significantly decreased in northern Japan but only slightly increased in the areas of central and western Japan. In total, the 9-yr mean payout in Japan in the 2070s decreased to 120.2 billion yen; the value corresponded to 87% of the payout averaged over 9 yr in the 1990s (1991–99). [ABSTRACT FROM AUTHOR]

Climate impacts on crop production components other than yield, i.e., area and cropping intensity, remain under-studied. Here, we clarify climate-crop area relationships by analyzing subnational census area and yield data for six multi-rice cropping countries in South and Southeast Asia. Extreme climate has a greater influence on the departure of area and yield from long-term trends than the average seasonal climate; precipitation and temperature in the sowing period of the wet/rainfed season have a greater influence on variability of the total annual area than in the growing period. In 57% of the country-scenario cases showing significant changes in area and/or yield, the directions of the area and yield responses to climate are not synchronized, deriving non-significant production changes under projected climates. Climate-area relationships not only limit production shocks, but also clarify uncertainties associated with climate mitigation of agricultural land, where area markedly affects the scale of mitigation. [ABSTRACT FROM AUTHOR]

There is concern about positive feedbacks between climate change and methane (CH 4 ) emission from rice paddies. However, appropriate water management may mitigate the problem. We tested this hypothesis at six field sites in central Thailand, where the irrigated area is rapidly increasing. We used DNDC-Rice, a process-based biogeochemistry model adjusted based on rice growth data at each site to simulate CH 4 emission from a rice-rice double cropping system from 2001 to 2060. Future climate change scenarios consisting of four representative concentration pathways (RCPs) and seven global climate models were generated by statistical downscaling. We then simulated CH 4 emission in three water management practices: continuous flooding (CF), single aeration (SA), and multiple aeration (MA). The adjusted model reproduced the observed rice yield and CH 4 emission well at each site. The simulated CH 4 emissions in CF from 2051 to 2060 were 5.3 to 7.8%, 9.6 to 16.0%, 7.3 to 18.0%, and 13.6 to 19.0% higher than those from 2001 to 2010 in RCPs 2.6, 4.5, 6.0, and 8.5, respectively, at the six sites. Regionally, SA and MA mitigated CH 4 emission by 21.9 to 22.9% and 53.5 to 55.2%, respectively, relative to CF among the four RCPs. These mitigation potentials by SA and MA were comparable to those from 2001 to 2010. Our results indicate that climate change in the next several decades will not attenuate the quantitative effect of water management practices on mitigating CH 4 emission from irrigated rice paddies in central Thailand. [ABSTRACT FROM AUTHOR]

A negative effect of increasing temperature on wheat production in the coming decades has been projected for Sudan, which is a major wheat producer in Sub-Saharan Africa. Wheat is susceptible to high temperature, so trend analysis of historical yields together with observed temperature is critical for understanding the effect of climate change. The objective of this study was to determine the association between yield of irrigated wheat in hot drylands of Sudan and temperature during the growing season (November–February). Regional-scale yield data in three major wheat-producing areas (Northern State, Gezira State, and Kassala State) in 48 crop seasons (1970/71–2017/18) were used to determine the correlation of yield with maximum (TMAX) and minimum temperatures (TMIN) at representative meteorological stations (Dongola, Wad Medani, and New Halfa, respectively). Frequencies of days with maximum temperature above 35 °C (THD) and minimum temperature above 20 °C (THN) were also used for correlation analysis. In all three areas, regression analysis detected upward trends in the growing-season temperature. The increase in temperature was particularly evident at Dongola, although no such trend has been reported previously. The yields were negatively correlated with the growing-season temperature, particularly THN in Northern State, TMAX in Gezira State, and TMIN in Kassala State. These results confirm that the recent increase in the growing-season temperature might have reduced the yield to some extent in the breadbasket of Sudan. [ABSTRACT FROM AUTHOR]

Process-based models are powerful tools for simulating the economic impacts of climate change, but they are computationally expensive. In order to project climate-change impacts under various scenarios, produce probabilistic ensembles, conduct online coupled simulations, or explore pathways by numerical optimization, the computational and implementation cost of economic impact calculations should be reduced. To do so, in this study, we developed various emulators that mimic the behaviours of simulation models, namely economic models coupled with bio/physical-process-based impact models, by statistical regression techniques. Their performance was evaluated for multiple sectors and regions. Among the tested emulators, those composed of artificial neural networks, which can incorporate non-linearities and interactions between variables, performed better particularly when finer input variables were available. Although simple functional forms were effective for approximating general tendencies, complex emulators are necessary if the focus is regional or sectoral heterogeneity. Since the computational cost of the developed emulators is sufficiently small, they could be used to explore future scenarios related to climate-change policies. The findings of this study could also help researchers design their own emulators in different situations. [ABSTRACT FROM AUTHOR]

In order to project climate-change impacts under a wide-range of scenarios or conduct on-line coupled simulations, the computational and implementation cost of economic impact calculations should be reduced. To do so, in this study, we developed various emulators that mimic simulation outputs, namely economic models coupled with bio/physical process-based impact models. Their performance was evaluated for multiple sectors and regions. Among the tested emulators, those composed of artificial neural networks, which can incorporate nonlinearities and interactions between variables, performed better particularly when finer input variables were available. Although simple functional forms were effective for approximating general tendencies, complex emulators are necessary if the focus is regional or sectoral heterogeneity. The developed emulators could be used to explore future scenarios related to climate-change policies, and the findings of this study could also help researchers design their own emulators under different situations. [ABSTRACT FROM AUTHOR]

Future changes in the climate system could have significant impacts on the natural environment and human activities, which in turn affect changes in the climate system. In the interaction between natural and human systems under climate change conditions, land use is one of the elements that play an essential role. On the one hand, future climate change will affect the availability of water and food, which may impact land-use change. On the other hand, human-induced land-use change can affect the climate system through biogeophysical and biogeochemical effects. To investigate these interrelationships, we developed MIROC-INTEG-LAND (MIROC INTEGrated LAND surface model version 1), an integrated model that combines the land surface component of global climate model MIROC (Model for Interdisciplinary Research on Climate) with water resources, crop production, land ecosystem, and land-use models. The most significant feature of MIROC-INTEG-LAND is that the land surface model that describes the processes of the energy and water balance, human water management, and crop growth incorporates a land use decision-making model based on economic activities. In MIROC-INTEG-LAND, spatially detailed information regarding water resources and crop yields is reflected in the prediction of future land-use change, which cannot be considered in the conventional integrated assessment models. In this paper, we introduce the details and interconnections of the submodels of MIROC-INTEG-LAND, compare historical simulations with observations, and identify various interactions between the submodels. By evaluating the historical simulation, we have confirmed that the model reproduces the observed states well. The future simulations indicate that changes in climate have significant impacts on crop yields, land use, and irrigation water demand. The newly developed MIROC-INTEG-LAND could be combined with atmospheric and ocean models to develop an integrated earth system model to simulate the interactions among coupled natural–human earth system components. [ABSTRACT FROM AUTHOR]

Achieving food security goals in West Africa will depend on the capacity of the agricultural sector to feed the rapidly growing population and to moderate the adverse impacts of climate change. Indeed, a number of studies anticipate a reduction of the crop yield of the main staple food crops in the region in the coming decades due to global warming. Here, we found that crop production might have already been affected by climate change, with significant yield losses estimated in the historical past. We used a large ensemble of historical climate simulations derived from an atmospheric general circulation model and two process-based crop models, SARRA-H and CYGMA, to evaluate the effects of historical climate change on crop production in West Africa. We generated two ensembles of 100 historical simulations of yields of sorghum and millet corresponding to two climate conditions for each crop model. One ensemble is based on a realistic simulation of the actual climate, while the other is based on a climate simulation that does not account for human influences on climate systems (that is, the non-warming counterfactual climate condition). We found that the last simulated decade, 2000–2009, is approximately 1 °C warmer in West Africa in the ensemble accounting for human influences on climate, with more frequent heat and rainfall extremes. These altered climate conditions have led to regional average yield reductions of 10–20% for millet and 5–15% for sorghum in the two crop models. We found that the average annual production losses across West Africa in 2000–2009 associated with historical climate change, relative to a non-warming counterfactual condition (that is, pre-industrial climate), accounted for 2.33–4.02 billion USD for millet and 0.73–2.17 billion USD for sorghum. The estimates of production losses presented here can be a basis for the loss and damage associated with climate change to date and useful in estimating the costs of the adaptation of crop production systems in the region. [ABSTRACT FROM AUTHOR]

Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño–Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks. [ABSTRACT FROM AUTHOR]

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28). For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises. [ABSTRACT FROM AUTHOR]

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark. [ABSTRACT FROM AUTHOR]

Abstract: Change in temperature sensitivity of soil organic carbon (SOC) decomposition with change in soil qualities (i.e. decomposability or lability) is one of the most important issues to be evaluated for projection of future CO2 emissions from soils. We inversely estimated the temperature sensitivity of SOC decomposition rate by applying a hybrid of the Metropolis-Hasting algorithm and the particle filter method to the extended Rothamsted carbon model (RothC), together with long-term (9 years) experimental data on SOC obtained at five sites in Japanese upland soils. Contrary to the prediction of the Arrhenius kinetics theory, we found no significant differences in temperature sensitivity among soils with different qualities (represented as soil compartments in the RothC model). We also confirmed that there was a positive correlation between the relative temperature sensitivity of the humus compartment and future total CO2 emissions. The RothC model with default parameterization tended to overestimate future total CO2 emissions relative to the calibrated model, and the degree of overestimation was larger than that of underestimation. [Copyright &y& Elsevier]

• Systematically examines climatic forcing data in agricultural model performance • Explores uncertainty across up to 91 climate data / crop model combinations • Isolates key climatic features driving interannual yield variation in each region • Quantifies performance of climatic forcing datasets for top countries and crop species • More extensive bias correction improves climatic forcing datasets for crop models We present results from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI) Phase I, which aligned 14 global gridded crop models (GGCMs) and 11 climatic forcing datasets (CFDs) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. Results show that CFDs demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. Countries where simulations correlate highly with reported FAO national production anomalies tend to have high correlations across most CFDs, whose influence we isolate using multi-GGCM ensembles for each CFD. Correlations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). Bias-adjusted CFDs most often were among the highest model-observation correlations, although all CFDs produced the highest correlation in at least one top-producing country. Analysis of larger multi-CFD-multi-GGCM ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. Our analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple CFDs as long as a top-performing CFD is utilized for the focus region. [ABSTRACT FROM AUTHOR]