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Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models. Plain Language Summary: Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models. Key Points: Crop models show strong differences in input sensitivitiesStandardized modeling experiments reveal differences in emergent functional relationshipsNew standards in model evaluation are needed [ABSTRACT FROM AUTHOR]

Abstract Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models.

To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of five levels of atmospheric CO2 concentrations, seven temperature changes, five precipitation changes, together with five nitrogen application rates. Our results were in several aspects different from those of previous model inter-comparison studies for wheat, maize, rice, and potato crops. A partial model calibration only on phenology led to very poor simulation of aboveground biomass and seed yield of canola, even from the ensemble median or mean. A full calibration with additional data of leaf area index, biomass, and yield from one treatment at each site reduced simulation error of seed yield from 43.8 to 18.0%, but the uncertainty in simulation results remained large. Such calibration (with data from one treatment) was not able to constrain model parameters to reduce simulation uncertainty across the wide range of environments. Using a multi-model ensemble mean or median reduced the uncertainty of yield simulations, but the simulation error remained much larger than observation errors, indicating no guarantee that the ensemble mean/median would predict the correct responses. Using multi-model ensemble median, canola yield was projected to decline with rising temperature (2.5–5.7% per °C), but to increase with increasing CO2 concentration (4.6–8.3% per 100-ppm), rainfall (2.1–6.1% per 10% increase), and nitrogen rates (1.3–6.0% per 10% increase) depending on locations. Due to the large uncertainty, these results need to be treated with caution. We further discuss the need to collect new data to improve modelling of several key physiological processes of canola for increased confidence in future climate impact assessments. [ABSTRACT FROM AUTHOR]

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait‐based multi‐model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi‐model simulations targeting enhanced productivity, and aggregated results into model‐ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait‐parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP‐Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid‐century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context‐specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait‐based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options. [ABSTRACT FROM AUTHOR]

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate‐related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process‐based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no‐adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing‐zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies. [ABSTRACT FROM AUTHOR]

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change. [ABSTRACT FROM AUTHOR]

Abstract This study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield.

به دلیل افزایش جمعیت جهانی به همراه کاهش سطح زمین­های بارور و منابع آب، ارزیابی اثرات تغییر اقلیم بر بهره­وری کشاورزی به طور ویژه­ای دارای اهمیت می­باشد. به همین منظور این تحقیق در شش شهرستان از استان خراسان رضوی ( قوچان، گناباد، مشهد، سبزوار، تربت­حیدریه و تربت­جام) انجام شد. ابتدا اقلیم آینده (سال 2050) با استفاده از داده­های اقلیمی بلندمدت در دوره پایه (2010-1980) و با روش ارائه شده توسطAgMIP، تحت دو سناریوی اقلیمی RCP4.5 و RCP8.5 شبیه­سازی شد. مدل گردش عمومی مورد استفاده در این تحقیق MPI-ESM-MRبود و به منظور شبیه­سازی رشد و عملکرد گندم از مدل APSIM-Wheat در شرایط محدودیت آب استفاده شد. نتایج شبیه­سازی­ها نشان دادند که میانگین تغییرات دمای طول فصل رشد در منطقه مورد مطالعه در آینده نسبت به پایه (79/9 درجه سانتی­گراد) افزایشی بوده و تحت هر دو RCP به ترتیب برابر با 41/1 و 83/1 درجه سانتی­گراد می­باشد. هم­چنین نتایج نشان دادند که در آینده طول دوره رشد بین 13 تا 6/24 روز و فاصله زمانی کاشت تا گلدهی بین 2/13 تا 4/30 روز کاهش یافته و از سوی دیگر طول دوره پر شدن دانه بین 2/0 تا 4/4 روز افزایش می­یابد. در این تحقیق اثرات متقابل دما و CO2 و مجموع تغییرات رشد و نموی گندم آبی موجب تغییرات مثبت عمکرد دانه در آینده در بیشتر شهرستان­های مورد بررسی (1 تا 9/12 درصد) و کاهش عملکرد در تربت­حیدریه (1/4- درصد) شد. یافته­های این تحقیق همچنین نشان دادند تغییر تاریخ کاشت در آینده موجب افزایش بیشتر عملکرد خواهد شد.

این تحقیق به منظور ارزیابی ریسک ناشی از تنش گرما در ذرت دانه‌ای استان خوزستان تحت شرایط افزایش دمای ناشی از تغییر اقلیم در شش شهرستان از استان خوزستان انجام شد. بدین منظور، ابتدا اقلیم آینده این شهرستان‌ها با استفاده از داده‌های اقلیمی بلندمدت دوره پایه و با استفاده از روش AgMIP تحت دو سناریوی اقلیمی RCP4.5 و RCP8.5 برای دوره 2069 -2040 تولید شدند و سپس از مدل APSIM برای شبیه‌سازی رشد و عملکرد ذرت دانه‌ای استفاده شد. نتایج این تحقیق نشان داد که میانگین دما در طول فصل رشد ذرت در استان خوزستان تحت RCP4.5 و RCP8.5 نسبت به دوره پایه (27.2 درجه‌ سانتی گراد) به‌ترتیب 8.5 و 34.57 درصد افزایش داشت. با در نظر گرفتن متوسط کل استان، میانگین عملکرد و تعداد دانه در مترمربع در دوره پایه 8.8 تن در هکتار و 2305.7 دانه در متر مربع بود. این مقدار در سال 2050 تحت RCP4.5 و RCP8.5 به ترتیب به 8.5 و 8.7 تن در هکتار و 2227.3 و 2254.3 دانه در متر مربع کاهش یافتند. با در نظر گرفتن متوسط همه تاریخ کاشت‌ها، مناطق و دوره‌های آینده تغییر اقلیم در استان خوزستان، احتمال تشکیل عملکرد اقتصادی، عملکرد غیر اقتصادی و عملکرد صفر به ترتیب 45.4، 13.5 و 41.2 درصد می‌باشد. به طور کلی نتایج این تحقیق نشان داد که تاریخ کاشت مرسوم در منطقه (سی‌ام بهمن) تاریخ کاشت مناسبی برای دوره پایه و آینده نیست و تاریخ کاشت زودهنگام (دوازده بهمن) در اکثر مناطق می‌تواند به عنوان یک راهکار سازگاری موثر برای کاهش ریسک ناشی از افزایش دماهای حدی هم در دوره پایه و هم در دوره آینده در نظر گرفته شود.

Despite considerable strides made in sugar industry since privatization in 1996, Tanzania still face an annual sugar deficit well above 300,000 tons, leading to an annual sugar import bill of USD$132 million. In addressing this challenge, Tanzania has embarked on ambitious program to expand the sugar industry by establishment of new sugar estates. In view of climate change challenges, the program should be aligned to the future conditions which will have bearing on the profitability of investments. A study was conducted to assess the effect of climate change on agricultural droughts in terms of water deficit index (WDI), throughout the near-term (2010–2039) at the newly established Mkulazi II Sugar Estate at Dakawa, Mvomero District Tanzania, under two greenhouse gas emission scenarios. Baseline climate indicates serious drought risk for sugarcane crop for months of July through November when WDI ≥ 0.75, and low risk during rainy season (March–May). Near-term period will become drier than the baseline climate for months of May–December while drought will subside during January–April with respect to the baseline climate. The projected changes in water deficit at the study site point to two facts: One is that rainfed production will not be possible, considering the projected decline in short rains (October–December), and the other is that water demand, in terms of crop water requirement, will increase due to increased drought conditions. In order to sustain sugarcane yields in the future, there will be a need to design an irrigation system that accounts for the climate change challenges. [ABSTRACT FROM AUTHOR]

This study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield. [ABSTRACT FROM AUTHOR]

APSIM model was used to investigate yield and water requirement of maize in different planting dates under two emission scenarios (RCP4.5 and RCP8.5) at the three locations of Kermanshah province (Kermanshah, Kangavar and Eslamabad-Gharb). Climatic parameters were predicted using the AgMIP methodology. Results of this study indicated that in the future, average maize grain yield will be reduced in all locations, scenarios and planting dates (70 percent) compare to the baseline. Reasons for yield loss are increasing temperature over growing season (15.7%), decreasing length of growing season (4.7%) and is likely to concurrency time of flowering with extreme temperature. In addition, maize water requirement, on average, will be increased 14 percent is comparison to the baseline in all locations, scenarios and planting dates mainly due to rising temperature. In conventional planting date (4 May), crop water requirement of maize on average increased 12 percent under two emission scenarios compared with the baseline while on earlier and later planting dates, crop water requirement increased 15 and 7 percent, respectively. Due to the amount of higher cumulative rainfall during the growing season (54.27) on earlier planting dates (4 and 19 April) as well as lower yield loss compare to other planting dates (56 percent), earlier planting dates can be explained as adaptation strategy in order to achieve appropriate yield. The results also showed that among study locations, Eslamabad-Gharb and Kermanshah were the most suitable areas in terms of grain yield (4221.8 Kg.ha-1) and water requirement (1489.2 mm), respectively.

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

As the greatest water user in the world, the agricultural sector is vulnerable to changes in climate and water resource availability. Understanding the impact of these changes on crop yield is critical in order to achieve and maintain global food security. We analyze output from an ensemble of Agricultural Model Intercomparison and Improvement Project models to project the probability of rice, soybean, maize, and wheat yield failures across global and national breadbaskets through mid-century. The probability of crop yield failures is projected to be as much as 4.5 times higher by 2030 and up to 25 times higher by 2050 across global breadbaskets. Crop failures are projected to be more likely when effects of CO _2 fertilization are ignored. We utilize the open-source Aqueduct Water Risk Atlas to create a Water Scarcity Index composed of ten hydrological variables. The index reveals high water scarcity across crop breadbaskets in India, China, and the United States. If the ability to irrigate breadbaskets was eliminated due to water scarcity, the likelihood of crop failures would increase. Shifts in breadbaskets may cross national borders as crop yields will increase in Canada and decrease in the US as a response to a changing climate. Our analysis highlights top producing agricultural regions that have historically provided the global food system with large quantities of one or more major crops, but will face challenges in continuing to do so due to climate change and growing water scarcity.

Highlights • The Soil daTA Retrieval Tool (START) allows preparation of soil input data files for a local site. • Preparation of soil input files for multiple crop models was automated using START. • The source code of START can be reused to support different global soil databases. • Minimum efforts are needed to construct databases of soil input files using START. Abstract Soil profile data that characterize the physical and chemical properties of a soil are among the required set of inputs for ecological, crop and other dynamic simulation models. A web-based soil information system often provides the site-specific soil data of which formats are not readily compatible to crop models. The Soil daTA Retrieval Tool (START) was developed to automate a series of procedures for preparation of soil input data that includes the retrieval of soil profile data from the information system, reorganization of data, estimation of soil parameters, and creation of input files for simulation models. In a case study, the START was implemented to support the SoilGrids database operated by the International Soil Reference and Information Center. It took about 0.33% of time for the START to create soil input files compared with manual preparation. These results suggest that the START could provide an efficient approach for preparation of soil input files especially for sites where little soil information is available. [ABSTRACT FROM AUTHOR]

This study compares climate changes in major agricultural regions and current agricultural seasons associated with global warming of +1.5 or +2.0 °C above pre-industrial conditions. It describes the generation of climate scenarios for agricultural modeling applications conducted as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Coordinated Global and Regional Assessments. Climate scenarios from the Half a degree Additional warming, Projections, Prognosis and Impacts project (HAPPI) are largely consistent with transient scenarios extracted from RCP4.5 simulations of the Coupled Model Intercomparison Project phase 5 (CMIP5). Focusing on food and agricultural systems and top-producing breadbaskets in particular, we distinguish maize, rice, wheat, and soy season changes from global annual mean climate changes. Many agricultural regions warm at a rate that is faster than the global mean surface temperature (including oceans) but slower than the mean land surface temperature, leading to regional warming that exceeds 0.5 °C between the +1.5 and +2.0 °C Worlds. Agricultural growing seasons warm at a pace slightly behind the annual temperature trends in most regions, while precipitation increases slightly ahead of the annual rate. Rice cultivation regions show reduced warming as they are concentrated where monsoon rainfall is projected to intensify, although projections are influenced by Asian aerosol loading in climate mitigation scenarios. Compared to CMIP5, HAPPI slightly underestimates the CO 2 concentration that corresponds to the +1.5 °C World but overestimates the CO 2 concentration for the +2.0 °C World, which means that HAPPI scenarios may also lead to an overestimate in the beneficial effects of CO 2 on crops in the +2.0 °C World. HAPPI enables detailed analysis of the shifting distribution of extreme growing season temperatures and precipitation, highlighting widespread increases in extreme heat seasons and heightened skewness toward hot seasons in the tropics. Shifts in the probability of extreme drought seasons generally tracked median precipitation changes; however, some regions skewed toward drought conditions even where median precipitation changes were small. Together, these findings highlight unique seasonal and agricultural region changes in the +1.5 °C and +2.0 °C worlds for adaptation planning in these climate stabilization targets. [ABSTRACT FROM AUTHOR]

Climate change is estimated to exacerbate existing challenges faced by smallholder farmers in Sub-Sahara Africa. However, limited studies quantify the extent of variation in climate change impact under these systems at the local scale. The Decision Support System for Agro-technological Transfer (DSSAT) was used to quantify variation in climate change impacts on maize yield under current agricultural practices in semi-arid regions of Senegal (Nioro du Rip) and Ghana (Navrongo and Tamale). Multi-benchmark climate models (Mid-Century, 2040–2069 for two Representative Concentration Pathways, RCP4.5 and RCP8.5), and multiple soil and management information from agronomic surveys were used as input for DSSAT. The average impact of climate scenarios on grain yield among farms ranged between −9% and −39% across sites. Substantial variation in climate response exists across farms in the same farming zone with relative standard deviations from 8% to 117% at Nioro du Rip, 13% to 64% in Navrongo and 9% to 37% in Tamale across climate models. Variations in fertilizer application, planting dates and soil types explained the variation in the impact among farms. This study provides insight into the complexities of the impact of climate scenarios on maize yield and the need for better representation of heterogeneous farming systems for optimized outcomes in adaptation and resilience planning in smallholder systems.

We present the Representative Temperature and Precipitation (T&P) GCM Subsetting Approach developed within the Agricultural Model Intercomparison and Improvement Project (AgMIP) to select a practical subset of global climate models (GCMs) for regional integrated assessment of climate impacts when resource limitations do not permit the full ensemble of GCMs to be evaluated given the need to also focus on impacts sector and economics models. Subsetting inherently leads to a loss of information but can free up resources to explore important uncertainties in the integrated assessment that would otherwise be prohibitive. The Representative T&P GCM Subsetting Approach identifies five individual GCMs that capture a profile of the full ensemble of temperature and precipitation change within the growing season while maintaining information about the probability that basic classes of climate changes (relatively cool/wet, cool/dry, middle, hot/wet, and hot/dry) are projected in the full GCM ensemble. We demonstrate the selection methodology for maize impacts in Ames, Iowa, and discuss limitations and situations when additional information may be required to select representative GCMs. We then classify 29 GCMs over all land areas to identify regions and seasons with characteristic diagonal skewness related to surface moisture as well as extreme skewness connected to snow-albedo feedbacks and GCM uncertainty. Finally, we employ this basic approach to recognize that GCM projections demonstrate coherence across space, time, and greenhouse gas concentration pathway. The Representative T&P GCM Subsetting Approach provides a quantitative basis for the determination of useful GCM subsets, provides a practical and coherent approach where previous assessments selected solely on availability of scenarios, and may be extended for application to a range of scales and sectoral impacts. [ABSTRACT FROM AUTHOR]

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]

We discuss an on-line tool that facilitates access to the large collection of climate impacts on crop yields produced by the Agricultural Model Intercomparison and Improvement Project. This collection comprises the output of seven crop models which were run on a global grid using climate data from five different general circulation models under the current set of representative pathways. The output of this modeling endeavor consists of more than 36,000 publicly available global grids at a spatial resolution of one half degree. We offer flexible ways to aggregate these data while reducing the technical barriers implied by learning new download platforms and specialized formats. The tool is accessed trough any standard web browser without any special bandwidth requirement. [ABSTRACT FROM AUTHOR]

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.

Predicting rice ( Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [ CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [ CO2] and temperature. [ABSTRACT FROM AUTHOR]

The AgMERRA and AgCFSR climate forcing datasets provide daily, high-resolution, continuous, meteorological series over the 1980–2010 period designed for applications examining the agricultural impacts of climate variability and climate change. These datasets combine daily resolution data from retrospective analyses (the Modern-Era Retrospective Analysis for Research and Applications, MERRA, and the Climate Forecast System Reanalysis, CFSR) with in situ and remotely-sensed observational datasets for temperature, precipitation, and solar radiation, leading to substantial reductions in bias in comparison to a network of 2324 agricultural-region stations from the Hadley Integrated Surface Dataset (HadISD). Results compare favorably against the original reanalyses as well as the leading climate forcing datasets (Princeton, WFD, WFD-EI, and GRASP), and AgMERRA distinguishes itself with substantially improved representation of daily precipitation distributions and extreme events owing to its use of the MERRA-Land dataset. These datasets also peg relative humidity to the maximum temperature time of day, allowing for more accurate representation of the diurnal cycle of near-surface moisture in agricultural models. AgMERRA and AgCFSR enable a number of ongoing investigations in the Agricultural Model Intercomparison and Improvement Project (AgMIP) and related research networks, and may be used to fill gaps in historical observations as well as a basis for the generation of future climate scenarios. [ABSTRACT FROM AUTHOR]

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [ CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames ( USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [ CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [ CO2] among models. Model responses to temperature and [ CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. [ABSTRACT FROM AUTHOR]

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies. [ABSTRACT FROM AUTHOR]

Climate change is projected to push the limits of cropping systems and has the potential to disrupt the agricultural sector from local to global scales. This article introduces the Coordinated Climate-Crop Modeling Project (C3MP), an initiative of the Agricultural Model Intercomparison and Improvement Project (Ag MIP) to engage a global network of crop modelers to explore the impacts of climate change via an investigation of crop responses to changes in carbon dioxide concentration ([ CO2]), temperature, and water. As a demonstration of the C3MP protocols and enabled analyses, we apply the Decision Support System for Agrotechnology Transfer ( DSSAT) CROPGRO-Peanut crop model for Henry County, Alabama, to evaluate responses to the range of plausible [ CO2], temperature changes, and precipitation changes projected by climate models out to the end of the 21st century. These sensitivity tests are used to derive crop model emulators that estimate changes in mean yield and the coefficient of variation for seasonal yields across a broad range of climate conditions, reproducing mean yields from sensitivity test simulations with deviations of ca. 2% for rain-fed conditions. We apply these statistical emulators to investigate how peanuts respond to projections from various global climate models, time periods, and emissions scenarios, finding a robust projection of modest (<10%) median yield losses in the middle of the 21st century accelerating to more severe (>20%) losses and larger uncertainty at the end of the century under the more severe representative concentration pathway (RCP8.5). This projection is not substantially altered by the selection of the AgMERRA global gridded climate dataset rather than the local historical observations, differences between the Third and Fifth Coupled Model Intercomparison Project ( CMIP3 and CMIP5), or the use of the delta method of climate impacts analysis rather than the C3 MP impacts response surface and emulator approach. [ABSTRACT FROM AUTHOR]

There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and net ecosystem exchange varied significantly according to the length of the modeler's experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in "trial-and-error" calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler's assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.

Climate change is projected to negatively impact biophysical agricultural productivity in much of the world. Actions taken to reduce greenhouse gas emissions and mitigate future climate changes, are thus of central importance for agricultural production. Climate impacts are, however, not unidirectional; some crops in some regions (primarily higher latitudes) are projected to benefit, particularly if increased atmospheric carbon dioxide is assumed to strongly increase crop productivity at large spatial and temporal scales. Climate mitigation measures that are implemented by reducing atmospheric carbon dioxide concentrations lead to reductions both in the strength of climate change and in the benefits of carbon dioxide fertilization. Consequently, analysis of the effects of climate mitigation on agricultural productivity must address not only regions for which mitigation is likely to reduce or even reverse climate damages. There are also regions that are likely to see increased crop yields due to climate change, which may lose these added potentials under mitigation action. Comparing data from the most comprehensive archive of crop yield projections publicly available, we find that climate mitigation leads to overall benefits from avoided damages at the global scale and especially in many regions that are already at risk of food insecurity today. Ignoring controversial carbon dioxide fertilization effects on crop productivity, we find that for the median projection aggressive mitigation could eliminate ∼81% of the negative impacts of climate change on biophysical agricultural productivity globally by the end of the century. In this case, the benefits of mitigation typically extend well into temperate regions, but vary by crop and underlying climate model projections. Should large benefits to crop yields from carbon dioxide fertilization be realized, the effects of mitigation become much more mixed, though still positive globally and beneficial in many food insecure countries.