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► Spatio-temporal patterns of oat phenological development are analyzed. ► The warming trend enhanced phenological stages and shortened phenological phases. ► Results suggest spatial ...and temporal variability in used varieties. ► Temperature sum concept allows sufficient description of large scale phenology.
Phenological development of crops has been extensively studied in field experiments but less so at larger scales for which data availability is often limited. To what extent the spatio-temporal variability of crop development can be explained by relationships derived from field studies such as the temperature sum concept used in many crop models is unclear but the question could entail the large scale application of these models. The aim of this study was to analyze the spatio-temporal patterns of crop phenological development in response to temperature and day length. We used a comprehensive dataset (656,234 phenological observations at 6019 observation sites) about the phenology of oat (
Avena sativa L.) and related climate data from Germany for the period 1959–2009.
Our results show that the statistically significant warming trend since 1959 resulted in an earlier onset of all phenological stages and a shortening of most phenological phases with a 17-day earlier onset of yellow ripeness and a shortening of the “sowing to yellow ripeness” phase by 14 days. There was also a distinct spatial pattern in phenological development, with differences among eco-regions in the occurrence of development stages of 15–26 days and the length of the phases between stages of 6 and 21 days. Most of this spatio-temporal variability could be explained through the effects of temperature and day length. However, temperature sums (thermal times) and day length corrected temperature sums (photo-thermal times) also varied in time and space, pointing to the use of different varieties over time and across eco-regions. A considerable part of this variability in temperature sums and photo-thermal times could be explained by the mean temperature during the development periods. This may provide a means of modelling farmers’ adaptation to climate change using varieties of different maturity types; but it requires further investigation. The good agreement of the thermal and photo-thermal requirements of oat computed in this study with relationships known from field experiments supports the use of the temperature sum concept for large scale application to simulate crop phenology in response to temperature and day length. The analysis should be extended to other crops and regions to further evaluate the observed spatio-temporal patterns in crop phenology and the relationships explaining these patterns.
► Introduces the Agricultural Model Intercomparison and Improvement Project (AgMIP). ► Describes AgMIP Protocols for consistent research activities. ► Demonstrates AgMIP approaches using climate, ...crop, and economic model analyses. ► Wheat pilot results elucidate the relative uncertainties from crop and climate models. ► Outlines AgMIP crop-specific, regional, and global research activities.
The Agricultural Model Intercomparison and Improvement Project (AgMIP) is a major international effort linking the climate, crop, and economic modeling communities with cutting-edge information technology to produce improved crop and economic models and the next generation of climate impact projections for the agricultural sector. The goals of AgMIP are to improve substantially the characterization of world food security due to climate change and to enhance adaptation capacity in both developing and developed countries. Analyses of the agricultural impacts of climate variability and change require a transdisciplinary effort to consistently link state-of-the-art climate scenarios to crop and economic models. Crop model outputs are aggregated as inputs to regional and global economic models to determine regional vulnerabilities, changes in comparative advantage, price effects, and potential adaptation strategies in the agricultural sector. Climate, Crop Modeling, Economics, and Information Technology Team Protocols are presented to guide coordinated climate, crop modeling, economics, and information technology research activities around the world, along with AgMIP Cross-Cutting Themes that address uncertainty, aggregation and scaling, and the development of Representative Agricultural Pathways (RAPs) to enable testing of climate change adaptations in the context of other regional and global trends. The organization of research activities by geographic region and specific crops is described, along with project milestones.
Pilot results demonstrate AgMIP's role in assessing climate impacts with explicit representation of uncertainties in climate scenarios and simulations using crop and economic models. An intercomparison of wheat model simulations near Obregón, Mexico reveals inter-model differences in yield sensitivity to CO2 with model uncertainty holding approximately steady as concentrations rise, while uncertainty related to choice of crop model increases with rising temperatures. Wheat model simulations with mid-century climate scenarios project a slight decline in absolute yields that is more sensitive to selection of crop model than to global climate model, emissions scenario, or climate scenario downscaling method. A comparison of regional and national-scale economic simulations finds a large sensitivity of projected yield changes to the simulations’ resolved scales. Finally, a global economic model intercomparison example demonstrates that improvements in the understanding of agriculture futures arise from integration of the range of uncertainty in crop, climate, and economic modeling results in multi-model assessments.
Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. ...However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
High temperature and drought stress are projected to reduce crop yields and threaten food security. While effects of heat and drought on crop growth and yield have been studied separately, little is ...known about the combined effect of these stressors. We studied detrimental effects of high temperature, drought stress and combined heat and drought stress around anthesis on yield and its components for three wheat cultivars originating from Germany and Iran. We found that effects of combined heat and drought on the studied physiological and yield traits were considerably stronger than those of the individual stress factors alone, but the magnitude of the effects varied for specific growth‐ and yield‐related traits. Single grain weight was reduced under drought stress by 13%–27% and under combined heat and drought stress by 43%–83% but not by heat stress alone. Heat stress significantly decreased grain number by 14%–28%, grain yield by 16%–25% and straw yield by 15%–25%. Cultivar responses were similar for heat but different for drought and combined heat and drought treatments. We conclude that heat stress as imposed in this study is less detrimental than the effects of those other studied stresses on growth and yield traits.
Rice is mainly grown under rainfed conditions in West Africa. Unpredictable and variable rainfall, poor soil quality, and suboptimal crop management practices are the main determinants of low ...productivity. We assessed the effects of soil water availability and fertilizer application, and their interaction on the yield of rainfed rice in Glazoué, Department of Zou-Collines, central Benin between 2010 and 2013. On-farm fertilizer management trials and field surveys were conducted in 13–39 farmers’ fields per year. Field water conditions were visually assessed three times per week during the rice-growing season and flood and drought indices were calculated on the basis of number of days with ponded water and dry surface soil relative to the total number of days for the vegetative, the reproductive and whole rice-growing period. Variations in flood and drought indices were related to the sand content of the soil. While nitrogen was the most limiting nutrient, average response to N fertilizer application was low with an agronomic N use efficiency of only 7–9 kg grain per kg of N applied. Year-to-year variation in rainfall and spatial variation in field water status affected both rice yield and response to N fertilizer. Some 47% of the observed yield variation was explained by field water status and the amounts of N fertilizer applied, with rice response to N fertilizer being less when water was limited. We conclude that the prevailing blanket fertilizer recommendations are unlikely to contribute to yield increases in rainfed systems of West Africa. There is a need for field-specific recommendations that consider soil texture and the spatial–temporal dynamics of water availability.
Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date ...has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
The projected impact of 1.5 and 2.0°C warming above the pre‐industrial period on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impacts of warming of <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.
The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic ...models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.
•Extreme events and future climate uncertainty represent risk for food production.•Crop models are largely able to simulate crop response to climate factors.•Adaptations are best evaluated in integrated assessment models (IAM).•Key limitations for crop models in IAM are low data availability and integration.•Cross-scale nature of IAM suggests novel modelling approaches are needed.
AIM: To derive location‐specific parameters that reflect the geographic differences among cultivars in vernalization requirements, sensitivity to day length (photoperiod) and temperature, which can ...be used to simulate the phenological development of wheat and maize at the global scale. LOCATION: Global. METHODS: Based on crop calendar observations and literature describing the large‐scale patterns of phenological characteristics of cultivars, we developed algorithms to compute location‐specific parameters to represent this large‐scale pattern. Vernalization requirements were related to the duration and coldness of winter, sensitivity to day length was assumed to be represented by the minimum and maximum day lengths occurring at a location, and sensitivity to temperature was related to temperature conditions during the vegetative development phase of the crop. RESULTS: Application of the derived location‐specific parameters resulted in high agreement between simulated and observed lengths of the cropping period. Agreement was especially high for wheat, with mean absolute errors of less than 3 weeks. In the main maize cropping regions, cropping periods were over‐ and underestimated by 0.5–1.5 months. We also found that interannual variability in simulated wheat harvest dates was more realistic when accounting for photoperiod effects. MAIN CONCLUSIONS: The methodology presented here provides a good basis for modelling the phenological characteristics of cultivars at the global scale. We show that current global patterns of growing season length as described in cropping calendars can be largely reproduced by phenology models if location‐specific parameters are derived from temperature and day length indicators. Growing seasons can be modelled more accurately for wheat than for maize, especially in warm regions. Our method for computing parameters for phenology models from temperature and day length offers opportunities to improve the simulation of crop productivity by crop simulation models developed for large spatial areas and for long‐term climate impact projections that account for adaptation in the selection of varieties.
Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under ...varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.
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