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Crop models are essential tools for assessing the threat of climate change to local and global food production. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 degrees C to 32◦ degrees C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each degree C of further temperature increase and become more variable over space and time.

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.

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.

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.

For highly productive regions such as Germany, the increase of wheat grain yields observed throughout the 20th century is largely attributed to the progress in crop breeding and agronomic management. However, several studies indicate a strong variability of the genetic contribution across locations that further varies with experimental design and variety selection. It is therefore still unclear to which extent management conditions have promoted the realization of the breeding progress in Germany over the last 100+ years. We established a side-by-side cultivation experiment over two seasons(2014/2015 and 2015/2016)including 16 winter wheat varieties released in Germany between 1895 and 2007. The varieties were grown using 24 different long-term fertilization treatments established since 1904 (Dikopshof, Germany). Averaged over all cultivars and treatments mean yields of 6.88 t ha−1 and 5.15 t ha−1were estimated in 2015 and 2016, respectively. A linear mixed effects analysis was performed to study the treatment-specific relation between grain yields and year of variety release. Results indicate a linear increase in grain yields ranging from 0.025 to 0.032 t ha−1 yr−1 (0.304 to 0.387% yr−1 )in plots that were treated with combined synthetic-organic fertilizers without signs of a leveling-off. Yields from low or unfertilized plots do not show a significant progress in yield. Responsiveness of mean yields to fertilizer management increases with year of release and indicates small yield penalties under very low nutrient supply. Results highlight the need to consider the importance of long-term soil fertilization management for the realization of genetic gains and the value of long-term fertilization experiments to study interactions between genetic potential and management.

Research on the impact of climate change on agricultural production has mainly focused on the effect of climate and its variability on individual crops, while the potential for adapting to climate change through crop substitution has received less attention. This is surprising because the proportions of individual crops in the total crop area have changed considerably over periods of time much shorter than those typically investigated in climate change studies. The flexibility of farmers to adapt to changing socioeconomic and environmental conditions by changing crop type may therefore also represent an alternative option to adapt to climate change. The objective of this case study was to investigate the potential of crop substitution as an adaptation strategy to climate change. We compared biomass yield and water use efficiency (WUE) of maize ( Zea mays L) and pearl millet ( Pennisetum americanum L.) grown in the semi-arid northeast of Iran for fodder production under present and potential future climatic conditions. Climate change projections for the baseline period 1970–2005 and two future time periods (2011–2030 and 2080–2099) from two emission scenarios (A2 and B1) and four general circulation models were downscaled to daily time steps using the Long Ashton Research Station-Weather Generator (LARS-WG5). Above-ground biomass was simulated for seven research sites with the Decision Support System for Agrotechnology Transfer (DSSAT 4.5) model which was calibrated and tested with independent experimental data from different field experiments in the region. The analysis of observations across all study locations showed an inverse relationship between temperature and biomass yield for both pearl millet and maize. Biomass yield was most sensitive to the duration of the phenological phase from floral initiation to end of leaf growth. For this phase we also found the highest negative correlation between mean temperature and biomass yield, which was more pronounced for pearl millet than for maize. This relationship was well reproduced by the crop model, justifying its use for the assessment. Due to the higher sensitivity of pearl millet to temperature increase, simulations suggest that the maximum benefit of crop substitution for biomass yield and WUE is to be gained for present-day conditions and would decline under future warming. The simulated increase in biomass yield due to substitution of maize by pearl millet was nevertheless larger than the yield decrease from potential climate change. Therefore, substituting maize by pearl millet should be considered as a measure for increasing fodder production in the investigated region. Differences in yields of crops that may substitute for each other because of similar use have been shown for other regions under current and potential future climatic conditions as well, so that we suggest that our findings are of general importance for climate change research. More research is required to quantify the effects for other crop combinations, regions, and interactions with other adaptation measures.

Background and Aims: The problem of increasing CO2 concentration [CO2] and associated climate change has generated much interest in modelling effects of [CO2] on plants. While variation in growth and productivity is closely related to the amount of intercepted radiation, largely determined by leaf area index (LAI), effects of elevated [CO2] on growth are primarily via stimulation of leaf photosynthesis. Variability in LAI depends on climatic and growing conditions including [CO2] concentration and can be high, as is known for agricultural crops which are specifically emphasized in this report. However, modelling photosynthesis has received much attention and photosynthesis is often represented inadequately detailed in plant productivity models. Less emphasis has been placed on the modelling of leaf area dynamics, and relationships between plant growth, elevated [CO2] and LAI are not well understood. This Botanical Briefing aims at clarifying the relative importance of LAI for canopy assimilation and growth in biomass under conditions of rising [CO2] and discusses related implications for process-based modelling. Model: A simulation exercise performed for a wheat crop demonstrates recent experimental findings about canopy assimilation as affected by LAI and elevation of [CO2]. While canopy assimilation largely increases with LAI below canopy light saturation, effects on canopy assimilation of [CO2] elevation are less pronounced and tend to decline as LAI increases. Results from selected model-testing studies indicate that simulation of LAI is often critical and forms an important source of uncertainty in plant productivity models, particularly under conditions of limited resource supply. Conclusions: Progress in estimating plant growth and productivity under rising [CO2] is unlikely to be achieved without improving the modelling of LAI. This will depend on better understanding of the processes of substrate allocation, leaf area development and senescence, and the role of LAI in controlling plant adaptation to environmental changes.

Projections of climate change impacts on crop yields are inherently uncertain(1). Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate(2). However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models(1,3) are difficult(4). 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.

Drought is a serious constraint on crop growth and production of important staple crops such as maize. Improved understanding of the responses of crops to drought can be incorporated into cropping system models to support crop breeding, varietal selection, and management decisions for minimizing negative impacts. We investigate the impacts of different soil types (stony and silty) and water regimes (irrigated and rainfed) on hydraulic linkages between soil and plant, as well as root : shoot growth characteristics. Our analysis is based on a comprehensive dataset measured along the soil–plant–atmosphere pathway at field scale in two growing seasons (2017 and 2018) with contrasting climatic conditions (low and high vapor pressure deficit). Roots were observed mostly in the topsoil (10–20 cm) of the stony soil, while more roots were found in the subsoil (60–80 cm) of the silty soil. The difference in root length was pronounced at silking and harvest between the soil types. Total root length was 2.5–6 times higher in the silty soil than in the stony soil with the same water treatment. At silking time, the ratios of root length to shoot biomass in the rainfed plot of the silty soil (F2P2) were 3 times higher than those in the irrigated silty soil (F2P3), while the ratio was similar for two water treatments in the stony soil. With the same water treatment, the ratios of root length to shoot biomass of silty soil were higher than for stony soil. The seasonally observed minimum leaf water potential (ψleaf) varied from around −1.5 MPa in the rainfed plot in 2017 to around −2.5 MPa in the same plot of the stony soil in 2018. In the rainfed plot, the minimum ψleaf in the stony soil was lower than in the silty soil from −2 to −1.5 MPa in 2017, respectively, while these were from −2.5 to −2 MPa in 2018, respectively. Leaf water potential, water potential gradients from soil to plant roots, plant hydraulic conductance (Ksoil_plant), stomatal conductance, transpiration, and photosynthesis were considerably modulated by the soil water content and the conductivity of the rhizosphere. When the stony soil and silt soil are compared, the higher “stress” due to the lower water availability in the stony soil resulted in fewer roots with a higher root tissue conductance in the soil with more stress. When comparing the rainfed with the irrigated plot in the silty soil, the higher stress in the rainfed soil resulted in more roots with a lower root tissue conductance in the treatment with more stress. This illustrates that the “response” to stress can be completely opposite depending on conditions or treatments that lead to the differences in stress that are compared. To respond to water deficit, maize had higher water uptake rate per unit root length and higher root segment conductance in the stony soil than in the silty soil, while the crop reduced transpired water via reduced aboveground plant size. Future improvements in soil–crop models in simulating gas exchange and crop growth should further emphasize the role of soil textures on stomatal function, dynamic root growth, and plant hydraulic system together with aboveground leaf area adjustments.

Food production must adapt in the face of climate change. In Europe, projected vulnerability of food production to climate change is particularly high in Mediterranean regions. Increasing agricultural diversity has been suggested as an adaptation strategy, but empirical evidence is lacking. We analyzed the relationship between regional farm diversity (i.e., diversity among farm types) and the effects of climate variability on regional wheat (Triticumspp.) productivity. An extensive data set with information from more than 50 000 farms from 1990 to 2003 was analyzed, along with observed weather data. Our results suggest that the diversity in farm size and intensity, particularly high in Mediterranean regions, reduces vulnerability of regional wheat yields to climate variability. Accordingly, increasing regional farm diversity can be a strategy through which regions in Europe can adapt to unfavorable conditions, such as higher temperatures and associated droughts.