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High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001–2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha− 1 with a maximum RUE trait value, compared to a control cultivar. No significant yield improvement (< 0.4 t ha− 1) was observed with increases in K and FE alone. Utilizing field-scale input data showed the greatest yield improvement per unit increment in RUE. Resolution of water related inputs (soil characteristics and precipitation) had a notably higher impact on simulated yield than of temperature. However, it did not alter the effects of high-yielding traits on yield. Simulated yields were only slightly affected by data aggregation for the different trait combinations. Warm-dry conditions diminished the benefits of high-yielding traits, suggesting that benefits from high-yielding traits depend on environments. The current findings emphasize the critical role of input data resolution and source in quantifying a large-scale impact of high-yielding traits. [ABSTRACT FROM AUTHOR]

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond. [ABSTRACT FROM AUTHOR]

Improved understanding of crops' response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO2 and H2O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models. [ABSTRACT FROM AUTHOR]

Improved understanding of crops' response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO2 and H2O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models. [ABSTRACT FROM AUTHOR]

Drought is a serious constraint to 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, 2018) with contrasting climatic conditions (low and high VPD). 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 compared to 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 was higher than stony soil. The 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 mimimum ψleaf in the stony soil was lower than in 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 less 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 of 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. [ABSTRACT FROM AUTHOR]

Drought is a serious constraint to 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, 2018) with contrasting climatic conditions (low and high VPD). 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 compared to 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 was higher than stony soil. The 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 mimimum ψleaf in the stony soil was lower than in 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 less 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 of 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. [ABSTRACT FROM AUTHOR]

The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil–plant continuum and the root images to develop and compare image analysis methods. [ABSTRACT FROM AUTHOR]

Background: Living labs are innovative platforms that bring together stakeholders (academic and non-academic actors from diverse disciplines), to collaboratively co-create, develop and test new technologies, products, services in real-life environments. As living labs become increasingly popular in addressing sustainability challenges in agriculture, it is essential to understand the potential of living labs to support innovation in the agricultural context. However, the existing knowledge is dispersed, and uncertainties remained regarding their approaches, methods, and outcomes. To address this gap, this study outlines a systematic review protocol of the existing literature on living labs in agricultural-related contexts. This will be done through questions focusing on the contributions of living labs to agricultural sustainability and innovation, their effectiveness, and strategies for development and implementation. Additionally, it will identify areas that require further research and development to advance our understanding of these initiatives. Method: This study will be conducted according to the RepOrting Standards for Systematic Evidence Syntheses (ROSES) review protocol. We will search databases, including Scopus, Web of Science (core collection) and Google Scholar, which will be limited to titles, abstracts, and keywords in English to eliminate irrelevant literature. The quality of the method used for each selected study will be evaluated using the Critical Appraisal Skills Program, Qualitative Research Checklist. Coding and data extraction will include for example bibliographic information, types of agricultural practices tested, dominant framework (e.g. open innovation, user innovation, and participatory design), the contribution of living lab in agriculture, stakeholder roles, etc. We will use thematic analysis to synthesise our findings. We will compare descriptive and narrative sub-groups to take into account differences in living lab concepts in relation to the context, outcomes, and limitations. Conclusions: The results of this review will provide a foundation for informing user-centred innovations in agriculture using living lab methodology. Researchers and practitioners working in areas such as co-design which incorporate user involvement, collaboration, and knowledge exchange, may also benefit from these findings. [ABSTRACT FROM AUTHOR]

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies. [ABSTRACT FROM AUTHOR]

France suffered, in 2016, the most extreme wheat yield decline in recent history, with some districts losing 55% yield. To attribute causes, we combined the largest coherent detailed wheat field experimental dataset with statistical and crop model techniques, climate information, and yield physiology. The 2016 yield was composed of up to 40% fewer grains that were up to 30% lighter than expected across eight research stations in France. The flowering stage was affected by prolonged cloud cover and heavy rainfall when 31% of the loss in grain yield was incurred from reduced solar radiation and 19% from floret damage. Grain filling was also affected as 26% of grain yield loss was caused by soil anoxia, 11% by fungal foliar diseases, and 10% by ear blight. Compounding climate effects caused the extreme yield decline. The likelihood of these compound factors recurring under future climate change is estimated to change with a higher frequency of extremely low wheat yields. [ABSTRACT FROM AUTHOR]

Plant root traits play a crucial role in resource acquisition and crop performance when soil nutrient availability is low. However, the respective trait responses are complex, particularly at the field scale, and poorly understood due to difficulties in root phenotyping monitoring, inaccurate sampling, and environmental conditions. Here, we conducted a systematic review and meta-analysis of 50 field studies to identify the effects of nitrogen (N), phosphorous (P), or potassium (K) deficiencies on the root systems of common crops. Root length and biomass were generally reduced, while root length per shoot biomass was enhanced under N and P deficiency. Root length decreased by 9% under N deficiency and by 14% under P deficiency, while root biomass was reduced by 7% in N-deficient and by 25% in Pdeficient soils. Root length per shoot biomass increased by 33% in N deficient and 51% in P deficient soils. The root-to-shoot ratio was often enhanced (44%) under N-poor conditions, but no consistent response of the root-to-shoot ratio to Pdeficiency was found. Only a few K-deficiency studies suited our approach and, in those cases, no differences inmorphological traitswere reported. We encountered the following drawbacks when performing this analysis: limited number of root traits investigated at field scale, differences in the timing and severity of nutrient deficiencies, missing data (e.g., soil nutrient status and time of stress), and the impact of other conditions in the field. Nevertheless, our analysis indicates that, in general, nutrient deficiencies increased the root-length-to-shoot-biomass ratios of crops, with impacts decreasing in the order deficient P > deficient N > deficient K. Our review resolved inconsistencies that were often found in the individual field experiments, and led to a better understanding of the physiological mechanisms underlying root plasticity in fields with low nutrient availability. [ABSTRACT FROM AUTHOR]

The European Farm to Fork strategy strives to reduce pesticide use and risk by 50% by 2030, preserving agricultural productivity, biodiversity, and human health. Novel research on crop diversification and new field arrangements, supported by digital technologies, offers sustainable innovations for pest control. This study evaluates digital yellow water traps, equipped with a camera and associated artificial intelligence model for continuous pollen beetle monitoring in diversified agricultural landscapes. Data were collected in oilseed rape from three harvest years (2021-2023) at the experimental site patchCROP, a landscape experiment established to study the effects of spatial and temporal crop diversification measures on yield, ecosystem services, and biodiversity. In patchCROP, crops were planted in smaller, 0.5 ha (72 × 72 m) squares called "patches" with different pesticide management strategies and were compared to surrounding commercial fields. The digital yellow water traps and AI were evaluated and found to be useful for gauging pollen beetle immigration into the crop. Across all years, higher insect pest pressure was recorded in the patches compared to commercial fields but did not necessarily compromise yields. Implementation of pesticide management strategies, including targeted insecticide applications at specific insect pest thresholds, were not associated with reduced yields in patches with flower strips. Future studies should consider examining the role of field size and alternative diversification approaches to fine-tune insecticide reduction strategies at the landscape scale. [ABSTRACT FROM AUTHOR]

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures. [ABSTRACT FROM AUTHOR]

Intensive agriculture in Germany is not only highly productive but has also led to detrimental effects in the environment. Crop diversification together with new field arrangements considering soil heterogeneities can be an alternative to improve resource use efficiency (RUE), ecosystem services (ESS), and biodiversity. Agroecosystem models are tools that help us to understand and design diversified new field arrangements. The main goal of this study was to review the extent to which agroecosystem models have been used for crop diversification design at field and landscape scale by considering soil heterogeneities and to understand the model requirements for this purpose. We found several agroecosystem models available for simulating spatiotemporal crop diversification at the field scale. For spatial crop diversification, simplified modelling approaches consider crop interactions for light, water, and nutrients, but they offer restricted crop combinations. For temporal crop diversification, agroecosystem models include the major crops (e.g., cereals, legumes, and tuber crops). However, crop parameterization is limited for marginal crops and soil carbon and nitrogen (N). At the landscape scale, decision-making frameworks are commonly used to design diversified cropping systems. Within-field soil heterogeneities are rarely considered in field or landscape design studies. Combining static frameworks with dynamic agroecosystems models can be useful for the design and evaluation of trade-offs for ESS delivery and biodiversity. To enhance modeling capabilities to simulate diversified cropping systems in new field arrangements, it will be necessary to improve the representation of crop interactions, the inclusion of more crop species options, soil legacy effects, and biodiversity estimations. Newly diversified field arrangement design also requires higher data resolution, which can be generated via remote sensing and field sensors. We propose the implementation of a framework that combines static approaches and process-based models for new optimized field arrangement design and propose respective experiments for testing the combined framework. [ABSTRACT FROM AUTHOR]

Crop model intercomparison studies have mostly focused on the assessment of predictive capabilities for crop development using weather and basic soil data from the same location. Still challenging is the model performance when considering complex interrelations between soil and crop dynamics under a changing climate. The objective of this study was to test the agronomic crop and environmental flux‐related performance of a set of crop models. The aim was to predict weighing lysimeter‐based crop (i.e., agronomic) and water‐related flux or state data (i.e., environmental) obtained for the same soil monoliths that were taken from their original environment and translocated to regions with different climatic conditions, after model calibration at the original site. Eleven models were deployed in the study. The lysimeter data (2014–2018) were from the Dedelow (Dd), Bad Lauchstädt (BL), and Selhausen (Se) sites of the TERENO (TERrestrial ENvironmental Observatories) SOILCan network. Soil monoliths from Dd were transferred to the drier and warmer BL site and the wetter and warmer Se site, which allowed a comparison of similar soil and crop under varying climatic conditions. The model parameters were calibrated using an identical set of crop‐ and soil‐related data from Dd. Environmental fluxes and crop growth of Dd soil were predicted for conditions at BL and Se sites using the calibrated models. The comparison of predicted and measured data of Dd lysimeters at BL and Se revealed differences among models. At site BL, the crop models predicted agronomic and environmental components similarly well. Model performance values indicate that the environmental components at site Se were better predicted than agronomic ones. The multi‐model mean was for most observations the better predictor compared with those of individual models. For Se site conditions, crop models failed to predict site‐specific crop development indicating that climatic conditions (i.e., heat stress) were outside the range of variation in the data sets considered for model calibration. For improving predictive ability of crop models (i.e., productivity and fluxes), more attention should be paid to soil‐related data (i.e., water fluxes and system states) when simulating soil–crop–climate interrelations in changing climatic conditions. Core Ideas: We demonstrate the use of high precision weighable lysimeter for full model calibration and validation.Lysimeter data from translocated soils represent effects of changing climatic conditions.We compare calibration with blind forward simulations (fixed soil and calibrated crop parameter).We compare individual crop model predictions with multi‐model mean.We test the predictive ability of crop models and multi‐model mean. [ABSTRACT FROM AUTHOR]

Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30–60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions. [ABSTRACT FROM AUTHOR]

Leaf water pressure head (Ψleaf) and more specifically its critical thresholds (Ψthreshold) characterize stomatal control of transpiration, particularly for C4 plants, but this physiological process has rarely been integrated into dynamic crop models at the field scale. We further extended two coupled models with Feddes root water uptake (RWU) and Couvreur RWU models by adding the C4 photosynthesis model in order to be applicable to a maize (Zea mays L.) crop growing under field conditions. The Feddes RWU model relates RWU and plant stress directly to the root zone water hydraulic potential, whereas the Couvreur model explicitly represents hydraulic signals between soil and stomata. Model performance was evaluated with a comprehensive dataset including stomatal conductance, Ψleaf, sap flow, gross assimilation rate (GAR), soil water content (SWC), dry biomass, and leaf area index (LAI) from a two‐season maize experiment under contrasting environments and water regimes. For the Couvreur model, the RWU and dry biomass were more sensitive to the root hydraulic conductance parameters than to Ψthreshold and root growth parameters. The agreement index (I) for the Couvreur model after calibration was 0.91, 0.80, 0.81, and 0.69 for biomass, LAI, RWU, and GAR, respectively. The Feddes model performed similarly for the same metrics. The Feddes model simulated accurately the plant water stress in the first 45 d of the growing season, whereas the Couvreur model inaccurately predicted water stress, which resulted in lower agreement with observations (i.e., RMSEs of biomass simulated by Feddes and Couvreur model averaged from four validated plots were 0.171 and 0.214 kg m–2, respectively). The Feddes model showed the potential to be used for maize under water stress whereas the Couvreur model needs to be further evaluated and improved with an adequate estimation of root hydraulic conductance. A dynamic parameterization of normalized root system conductance and/or more accurate assimilate allocation to the roots, especially under drought stress, should be considered in order to apply this model in future studies to maize. Core Ideas: A C4 photosynthesis was introduced into two root–shoot models (Feddes and Couvreur models).Both coupled models were compared with a comprehensive dataset from field‐grown maize.Effects of crop properties were evaluated by a mechanistic Couvreur model.The Feddes model simulated water stress well, whereas the Couvreur model had less agreement with observations.Future use of the Couvreur model requires accurate estimates of root system conductance. [ABSTRACT FROM AUTHOR]

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]

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer's allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale. [ABSTRACT FROM AUTHOR]

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction. [ABSTRACT FROM AUTHOR]

Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis–stomatal conductance modeling approach. Those models estimate the effect of soil water stress on stomatal conductance directly from soil water content or soil hydraulic potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU) and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water hydraulic potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rainfed, and irrigated) and two soil types (stony and silty) in western Germany in 2016. The two models showed a relatively similar performance in the simulation of dry matter, leaf area index (LAI), root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (HILLFLOW–Couvreur's RWU–SLIMROOT–LINTULCC2) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil–plant model marks a promising approach but requires further testing for other scenarios regarding crops, soil, and climate. [ABSTRACT FROM AUTHOR]

Early vigour in wheat is a trait that has received attention for its benefits reducing evaporation from the soil surface early in the season. However, with the growth enhancement common to crops grown under elevated atmospheric CO2 concentrations (e[CO2]), there is a risk that too much early growth might deplete soil water and lead to more severe terminal drought stress in environments where production relies on stored soil water content. If this is the case, the incorporation of such a trait in wheat breeding programmes might have unintended negative consequences in the future, especially in dry years. We used selected data from cultivars with proven expression of high and low early vigour from the Australian Grains Free Air CO2 Enrichment (AGFACE) facility, and complemented this analysis with simulation results from two crop growth models which differ in the modelling of leaf area development and crop water use. Grain yield responses to e[CO2] were lower in the high early vigour group compared to the low early vigour group, and although these differences were not significant, they were corroborated by simulation model results. However, the simulated lower response with high early vigour lines was not caused by an earlier or greater depletion of soil water under e[CO2] and the mechanisms responsible appear to be related to an earlier saturation of the radiation intercepted. Whether this is the case in the field needs to be further investigated. In addition, there was some evidence that the timing of the drought stress during crop growth influenced the effect of e[CO2] regardless of the early vigour trait. There is a need for FACE investigations of the value of traits for drought adaptation to be conducted under more severe drought conditions and variable timing of drought stress, a risky but necessary endeavour. [ABSTRACT FROM AUTHOR]

Stomatal regulation and whole plant hydraulic signaling affect water fluxes and stress in plants. Land surface models and crop models use a coupled photosynthesis-stomatal conductance modelling approach and estimate the effect of soil water stress on stomatal conductance directly from soil water content or matrix potential without explicit representation of hydraulic signals between the soil and stomata. In order to explicitly represent stomatal regulation by soil water status as a function of the hydraulic signal and its relation to the whole plant hydraulic conductance, we coupled the crop model LINTULCC2 and the root growth model SLIMROOT with Couvreur's root water uptake model (RWU), and the HILLFLOW soil water balance model. Since plant hydraulic conductance depends on the plant development, this model coupling represents a two-way coupling between growth and plant hydraulics. To evaluate the advantage of considering plant hydraulic conductance and hydraulic signaling, we compared the performance of this newly coupled model with another commonly used approach that relates root water uptake and plant stress directly to the root zone water potential (HILLFLOW with Feddes' RWU model). Simulations were compared with gas flux measurements and crop growth data from a wheat crop grown under three water supply regimes (sheltered, rain-fed and irrigated) and two soil types (stony and silty) in Western Germany in 2016. The two models showed a relatively similar performance in simulation of dry matter, LAI, root growth, RWU, gross assimilation rate, and soil water content. The Feddes model predicts more stress and less growth in the silty soil than in the stony soil, which is opposite to the observed growth. The Couvreur model better represents the difference in growth between the two soils and the different treatments. The newly coupled model (LINTULCC2 - SLIMROOT - Couvreur - HILLFLOW) was also able to simulate the dynamics and magnitude of whole plant hydraulic conductance over the growing season. This demonstrates the importance of two-way feedbacks between growth and root water uptake for predicting the crop response to different soil water conditions in different soils. Our results suggest that a better representation of the effects of soil characteristics on root growth is needed for reliable estimations of root hydraulic conductance and gas fluxes, particularly in heterogeneous fields. The newly coupled soil-plant model marks a promissing approach but requires further testing for other scenarios regarding crop, soil, and climate. [ABSTRACT FROM AUTHOR]

Agricultural intensification increased crop productivity but simplified production with lower diversity of cropping systems, higher genetic uniformity, and a higher uniformity of agricultural landscapes. Associated detrimental effects on the environment and biodiversity as well as the resilience and adaptability of cropping systems to climate change are of growing concern. Crop diversification may stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity, but a shared understanding of crop diversification including approaches towards a more systematic research is lacking. Here, we review the use of 'crop diversification' measures in agricultural research. We (i) analyse changes in crop diversification studies over time; (ii) identify diversification practices based on empirical studies; (iii) differentiate their use by country, crop species and experimental setup and (iv) identify target parameters to assess the success of diversification. Our main findings are that (1) less than 5% of the selected studies on crop diversification refer to our search term 'diversification'; (2) more than half of the studies focused on rice, corn or wheat; (3) 76% of the experiments were conducted in India, USA, Canada, Brazil or China; (4) almost any arable crop was tested on its suitability for diversification; (5) in 72% of the studies on crop diversification, at least one additional agronomic measure was tested and (6) only 45% of the studies analysed agronomic, economic and ecological target variables. Our findings show the high variability of approaches to crop diversification and the lack of a consistent theoretical concept. For better comparability and ability to generalise the results of the different primary studies, we suggest a novel conceptual framework. It consists of five elements, (i) definition of the problem of existing farming practices and the potential need for diversification, (ii) characterisation of the baseline system to be diversified, (iii) definition of the scale and target area, (iv) description of the experimental design and target variables and (v) definition of the expected impacts. Applying this framework will contribute to utilizing the benefits of crop diversification more efficiently. [ABSTRACT FROM AUTHOR]

Agroecosystem models need to reliably simulate all biophysical processes that control crop growth, particularly the soil water fluxes and nutrient dynamics. As a result of the erosion history, truncated and colluvial soil profiles coexist in arable fields. The erosion‐affected field‐scale soil spatial heterogeneity may limit agroecosystem model predictions. The objective was to identify the variation in the importance of soil properties and soil profile modifications in agroecosystem models for both agronomic and environmental performance. Four lysimeters with different soil types were used that cover the range of soil variability in an erosion‐affected hummocky agricultural landscape. Twelve models were calibrated on crop phenological stages, and model performance was tested against observed grain yield, aboveground biomass, leaf area index, actual evapotranspiration, drainage, and soil water content. Despite considering identical input data, the predictive capability among models was highly diverse. Neither a single crop model nor the multi‐model mean was able to capture the observed differences between the four soil profiles in agronomic and environmental variables. The model's sensitivity to soil‐related parameters was apparently limited and dependent on model structure and parameterization. Information on phenology alone seemed insufficient to calibrate crop models. The results demonstrated model‐specific differences in the impact of soil variability and suggested that soil matters in predictive agroecosystem models. Soil processes need to receive greater attention in field‐scale agroecosystem modeling; high‐precision weighable lysimeters can provide valuable data for improving the description of soil–vegetation–atmosphere process in the tested models. [ABSTRACT FROM AUTHOR]

Crop residue exploitation for bioenergy can play an important role in climate change mitigation without jeopardizing food security, but it may be constrained by impacts on soil organic carbon (SOC) stocks, and market, logistic and conversion challenges. We explore opportunities to increase bioenergy potentials from residues while reducing environmental impacts, in line with sustainable intensification. Using the case study of North Rhine‐Westphalia in Germany, we employ a spatiotemporally explicit approach combined with stakeholder interviews. First, the interviews identify agronomic and environmental impacts due to the potential reduction in SOC as the most critical challenge associated with enhanced crop residue exploitation. Market and technological challenges and competition with other residue uses are also identified as significant barriers. Second, with the use of agroecosystem modelling and estimations of bioenergy potentials and greenhouse gas emissions till mid‐century, we evaluate the ability of agricultural management to tackle the identified agronomic and environmental challenges. Integrated site‐specific management based on (a) humus balancing, (b) optimized fertilization and (c) winter soil cover performs better than our reference scenario with respect to all investigated variables. At the regional level, we estimate (a) a 5% increase in technical residue potentials and displaced emissions from substituting fossil fuels by bioethanol, (b) an 8% decrease in SOC losses and associated emissions, (c) an 18% decrease in nitrous oxide emissions, (d) a 37% decrease in mineral fertilizer requirements and emissions from their production and (e) a 16% decrease in nitrate leaching. Results are spatially variable and, despite improvements induced by management, limited amounts of crop residues are exploitable for bioenergy in areas prone to SOC decline. In order to sustainably intensify crop residue exploitation for bioenergy and reconcile climate change mitigation with other sustainability objectives, such as those on soil and water quality, residue management needs to be designed in an integrated and site‐specific manner. [ABSTRACT FROM AUTHOR]

Improving wheat (Triticum aestivum L.) yields to meet the projected demand for food in the future requires the talents of diverse scientists. In this article, we present the rationale for crop‐model‐driven, trait‐focused collaborative research emphasizing radiation use efficiency (RUE). Improving RUE is one of the promising avenues to substantially increase potential yields. A collaboration of crop modelers, physiologists, geneticists, plant breeders, and system agronomists is proposed to efficiently create genetic improvements that, when taken to the field, will have regional and global yield impacts. The effort will require a large, internationally integrated science project and related infrastructure such as was recently developed in the Wheat Initiative. The Wheat Initiative is an international consortium of scientists and public and industry funders to sustainably improve global wheat production. The proposed integrated research is applicable to explore other traits and trait combinations and could become a model for yield improvements for major food crops. [ABSTRACT FROM AUTHOR]

Although the root length density (RLD) of crops depends on their root system architecture (RSA), the root growth modules of many 1D field crop models often ignored the RSA in the simulation of the RLD. In this study, two model set‐up scenarios were used to simulate the RLD, above‐ground biomass (AGB) and grain yield (GY) of water‐stressed spring wheat in Germany, aiming to investigate the impact of improved RLD on AGB and GY predictions. In scenario 1, SlimRoot, a root growth sub‐model that does not consider the RSA of the crop, was coupled to a Lintul5‐SlimNitrogen‐SoilCN‐Hillflow1D crop model combination. In scenario 2, SlimRoot was replaced with the Somma sub‐model which considered the RSA for simulating RLD. The simulated RLD, AGB and GY were compared with observations. Scenario 2 predicted the RLD, AGB and GY with an average root mean square error (RMSE) of 0.43 cm/cm3, 0.59 t/ha and 1.03 t/ha, respectively, against 1.03 cm/cm3, 1.20 t/ha and 2.64 t/ha for scenario 1. The lower RMSE under scenario 2 shows that, even under water‐stress conditions, predictions of GY and AGB can be improved by considering the RSA of the crop for simulating the RLD. [ABSTRACT FROM AUTHOR]

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

During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled 'Crop Modeling for the Future', held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders. [ABSTRACT FROM AUTHOR]

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

The Agricultural Model Intercomparison and Improvement Project (AgMIP) has developed novel methods for Coordinated Global and Regional Assessments (CGRA) of agriculture and food security in a changing world. The present study aims to perform a proof of concept of the CGRA to demonstrate advantages and challenges of the proposed framework. This effort responds to the request by the UN Framework Convention on Climate Change (UNFCCC) for the implications of limiting global temperature increases to 1.5°C and 2.0°C above pre-industrial conditions. The protocols for the 1.5°C/2.0°C assessment establish explicit and testable linkages across disciplines and scales, connecting outputs and inputs from the Shared Socio-economic Pathways (SSPs), Representative Agricultural Pathways (RAPs), Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) and Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble scenarios, global gridded crop models, global agricultural economics models, site-based crop models and within-country regional economics models. The CGRA consistently links disciplines, models and scales in order to track the complex chain of climate impacts and identify key vulnerabilities, feedbacks and uncertainties in managing future risk. CGRA proof-of-concept results show that, at the global scale, there aremixed areas of positive and negative simulated wheat and maize yield changes, with declines in some breadbasket regions, at both 1.5°C and 2.0°C. Declines are especially evident in simulations that do not take into account direct CO2 effects on crops. These projected global yield changes mostly resulted in increases in prices and areas of wheat and maize in two global economics models. Regional simulations for 1.5°C and 2.0°C using site-based crop models had mixed results depending on the region and the crop. In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan, resulted in an increase in vulnerable households and the poverty rate. [ABSTRACT FROM AUTHOR]

Abstract: Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was −4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. [ABSTRACT FROM AUTHOR]

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population. [ABSTRACT FROM AUTHOR]

The Agroforestry Tree Domestication Program was globally launched in the 1990s by the World Agroforestry Center to increase farmers’ living standards and slow deforestation. After 20 yr of operation, we screen the impacts on farmers’ livelihoods and prospects in one of the implementation sites: the Ucayali region of the Peruvian Amazon. We contrast the viewpoints of two sets of stakeholders: (a) “outsiders”—scientists, technicians, and development workers; and (2) “insiders”—local farmers organized and not organized. Group and semistructured interviews were used for data collection, and system analyses were conducted for their integration and interpretation. Results diverge by subset. “Outsiders” praised the program lastingness but questioned its dependence on external parties for management; they considered land property and product marketing keys to persist and succeed. In contrast, “insiders” considered the program financially not viable as well as third-party and locally dependent. As an alternative they had initiated activities, both forestry related and otherwise, to profit from their acquired technical and managerial skills. Based on these findings, broadening stakeholders’ diversity and set conveniently roles and decision power during the rationale and implementation phases of similar initiatives could prevent such divergences. [ABSTRACT FROM PUBLISHER]

In rangelands, grazing management is a main driver of rangeland condition. Due to masking effects of seasonal climate fluctuations, little is known about (dis)similarity of management effects on rangeland condition and forage provision across major dryland biomes. Taking a macro-ecological perspective, we analysed if management effects differed between South Africa’s central grassland and Kalahari savanna biomes. We recorded proxies of forage provision (phytomass, vegetation cover and their ratio) over five seasons, annual rainfall to account for seasonal climate fluctuations, and rangeland condition (through relative abundances of increaser and decreaser species). Regarding forage provision, we found effects of management for the savanna, where, irrespective of rainfall, rotational grazing management resulted in higher phytomass and phytomass–cover ratios than management with continuous grazing. In the grassland, however, this difference was only discernible for phytomass–cover ratio in two years with above-average antecedent rainfall. This suggests that management effects are biome-dependent and that modulating effects of annual rainfall are stronger in the grassland. In either biome, management effects on the dominance of increaser and decreaser species were negligible, i.e. rangeland condition did not differ across management types in either biome. We conclude that investigations on management effects should account for interactions with biome and rainfall. [ABSTRACT FROM AUTHOR]

Globally, yam ( Dioscorea spp.) is the fifth most important root crop after sweet potatoes ( Ipomoea batatas L.) and the second most important crop in Africa in terms of production after cassava ( Manihot esculenta L.) and has long been vital to food security in sub-Saharan Africa (SSA). Climate change is expected to have its most severe impact on crops in food insecure regions, yet very little is known about impact of climate change on yam productivity. Therefore, we try estimating the effect of climate change on the yam (variety: Florido) yield and evaluating different adaptation strategies to mitigate its effect. Three regional climate models REgional MOdel (REMO), Swedish Meteorological and Hydrological Institute Regional Climate Model (SMHIRCA), and Hadley Regional Model (HADRM3P) were coupled to a crop growth simulation model namely Environmental Policy Integrated Climate (EPIC) version 3060 to simulate current and future yam yields in the Upper Ouémé basin (Benin Republic). For the future, substantial yield decreases were estimated varying according to the climate scenario. We explored the advantages of specific adaptation strategies suggesting that changing sowing date may be ineffective in counteracting adverse climatic effects. Late maturing cultivars could be effective in offsetting the adverse impacts. Whereas, by coupling irrigation and fertilizer application with late maturing cultivars, highest increase in the yam productivity could be realized which accounted up to 49 % depending upon the projection of the scenarios analyzed. [ABSTRACT FROM AUTHOR]

This study evaluates the impacts of projected climate change on irrigation requirements and yields of six crops (winter wheat, winter barley, rapeseed, grain maize, potato, and sugar beet) in Europe. Furthermore, the uncertainty deriving from consideration of irrigation, CO2 effects on crop growth and transpiration, and different climate change scenarios in climate change impact assessments is quantified. Net irrigation requirement ( NIR) and yields of the six crops were simulated for a baseline (1982-2006) and three SRES scenarios (B1, B2 and A1B, 2040-2064) under rainfed and irrigated conditions, using a process-based crop model, SIMPLACE < LINTUL5, DRUNIR, HEAT>. We found that projected climate change decreased NIR of the three winter crops in northern Europe (up to 81 mm), but increased NIR of all the six crops in the Mediterranean regions (up to 182 mm yr−1). Climate change increased yields of the three winter crops and sugar beet in middle and northern regions (up to 36%), but decreased their yields in Mediterranean countries (up to 81%). Consideration of CO2 effects can alter the direction of change in NIR for irrigated crops in the south and of yields for C3 crops in central and northern Europe. Constraining the model to rainfed conditions for spring crops led to a negative bias in simulating climate change impacts on yields (up to 44%), which was proportional to the irrigation ratio of the simulation unit. Impacts on NIR and yields were generally consistent across the three SRES scenarios for the majority of regions in Europe. We conclude that due to the magnitude of irrigation and CO2 effects, they should both be considered in the simulation of climate change impacts on crop production and water availability, particularly for crops and regions with a high proportion of irrigated crop area. [ABSTRACT FROM AUTHOR]

The nitrogen (N) dilution curve is a useful tool for farmers to assess the effectiveness of fertilizer application. The N dilution curve describes the decrease in plant N as biomass increases. This concept has not yet been tested for its applicability and robustness under different cutting regimes in grasslands. We conducted a principal components analysis on biomass yield and N concentration data to discern relationships among experimental, climatic, and management factors. Next, two N dilution curve parameters were calibrated for different cutting frequencies. We compared N uptake using four different methods utilizing calibrated N dilution curves, a reference curve, and different cutting regimes representing different physiological ages of the crops at cutting. Our results show that excluding cutting frequency information overestimates values of N uptake. Calibration of the N dilution curve according to cutting frequency improves N uptake estimates relative to observed values. Therefore, N uptake is better estimated using both the N dilution curve and the cutting regime information. [ABSTRACT FROM AUTHOR]

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

Despite our growing knowledge on plants’ functional responses to grazing, there is no consensus if an optimum level of functional aggregation exists for detecting grazing effects in drylands. With a comparative approach we searched for plant functional types (PFTs) with a consistent response to grazing across two areas differing in climatic aridity, situated in South Africa’s grassland and savanna biomes. We aggregated herbaceous species into PFTs, using hierarchical combinations of traits (from single- to three-trait PFTs). Traits relate to life history, growth form and leaf width. We first confirmed that soil and grazing gradients were largely independent from each other, and then searched in each biome for PFTs with a sensitive response to grazing, avoiding confounding with soil conditions. We found no response consistency, but biome-specific optimum aggregation levels. Three-trait PFTs (e.g. broad-leaved perennial grasses) and two-trait PFTs (e.g. perennial grasses) performed best as indicators of grazing effects in the semi-arid grassland and in the arid savanna biome, respectively. Some PFTs increased with grazing pressure in the grassland, but decreased in the savanna. We applied biome-specific grazing indicators to evaluate if differences in grazing management related to land tenure (communal versus freehold) had effects on vegetation. Tenure effects were small, which we mainly attributed to large variability in grazing pressure across farms. We conclude that the striking lack of generalizable PFT responses to grazing is due to a convergence of aridity and grazing effects, and unlikely to be overcome by more refined classification approaches. Hence, PFTs with an opposite response to grazing in the two biomes rather have a unimodal response along a gradient of additive forces of aridity and grazing. The study advocates for hierarchical trait combinations to identify localized indicator sets for grazing effects. Its methodological approach may also be useful for identifying ecological indicators in other ecosystems. [ABSTRACT FROM AUTHOR]