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Overview of the study design and respective four working steps from ‘Step 1’ listing the data sets used, ‘Step 2’ defining phenological growing periods from different data sets, ‘Step 3’ aggregation of spatiotemporal weather indices (WIs), ‘Step 4’ integration of all data into one comprehensive dataset and ‘Step 5’ statistical analysis.
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Heat and drought are major abiotic stressors threatening cereal yields, but little is known regarding the spatio-temporal development of their yield-effects. In this study, we assess genotype (G) × environment (E) × management (M) specific weather-yield relations utilizing spatially explicit weather indices (WIs) and variety trial yield data of win...
Citations
... The list of the GCMs dataset can be found in Table 1 (Riedesel et al., 2024) and wildfire (Sharples et al., 2021). ...
Heatwaves pose significant threats to diverse sectors, including agriculture and forestry. This extreme weather event is characterized by prolonged periods of exceptionally high air temperatures and has caused substantial economic damage and affecting millions. During heatwave events, agricultural and forest lands are affected by intensified thermal stress and water scarcity, impacting plant health, productivity, and ecosystem stability. This study revealed the projected heatwave changes in frequency and duration over agricultural and forest areas in Türkiye based on the ensemble mean of 23 general circulation models through the two latest CMIP6 climate change scenarios (SSP3-7.0 and SSP5-8.5). Agricultural and forest lands are projected to experience dramatic increases in summer heatwave events and prolonged durations throughout two long-term periods (2041-2070 and 2071-2100) during 21st century, particularly between 36°N and 38°N latitudes. Trend analysis using the triple-ITA method confirms unstable positive trends in historical heatwave metrics over these ecosystems, transitioning to stable positive trends in future projections. These findings emphasize the escalating risk of extreme heat events for critical ecosystems in Türkiye.
... To calculate RYL in each rice variety, the yield of non-inoculated plants (NinAMF) in optimal conditions (at the recommended dose of NPK (150:60:60 kg NPK/ha) under continuous flooding (CF)) was taken as yield reference. For each treatment, the following formula was used to estimate the RYL 53 : ...
Arbuscular mycorrhizal fungi (AMF) enhance the uptake of water and nutrients by host plants. In this study, we examined the response of six rice varieties from two ecotypes (three irrigated and three rainfed upland varieties) to AMF inoculation at five fertilizer levels, under continuous flooding (CF) and alternate wetting and drying (AWD) irrigation over two consecutive years in field conditions. Both irrigated and upland rice varieties experienced significant yield losses with AWD irrigation and reduced NPK fertilizer levels, with irrigated rice being more severely affected. Under AWD irrigation, AMF inoculation mitigated relative yield losses, especially when half of the recommended fertilizer dose was applied. In CF conditions, AMF inoculation often fully compensated for yield losses caused by reduced NPK levels. Furthermore, irrigation regime, fertilizer levels, and ecotype were significant sources of variation in the effects of AMF inoculation on several yield-related traits, such as total biomass, tiller number, panicle number, fertility, and maturity dates. Our findings suggest that AMF inoculation could be integrated with AWD irrigation and/or low NPK inputs to contribute to fertilizer and water savings in both irrigated and upland rice production systems.
... Half of the experts agreed that "site conditions" determine an agroecosystem's resilience to prolonged dry periods (e.g. Riedesel et al., 2024) and determine the need for management improvements. These four out of six experts assigned "site conditions" a high weight, of whom one weighed it equally to "management". ...
Ecosystem services (ES) − the benefits people obtain from ecosystems − are affected by agricultural management. Often, they are degraded because of practices that solely aim at maximizing yield regardless of their impact on other ES, such as water regulation or habitat provision for biodiversity. Therefore, the spatial targeting of suitable agri-environmental schemes (subsidized by the Common Agricultural Policy of the EU) is needed to address degraded ES in specific areas. To study the interrelations and spatial patterns of ES and agricultural management, there is no compromise between time-and data-intense process-based models and rather simple ES maps based on land use types and single proxies. Therefore, we propose an opportunity map approach, which enables both regional overview and field-specific evaluation on where ES can be improved through agricultural management changes. For this purpose, we developed evaluation criteria and scores for site conditions, management data from 2022 and other spatial environmental parameters based on literature research and expert interviews. These evaluation criteria were assigned scores indicating the opportunity to improve the ES "pro-vision of clean water", "habitat provision", "carbon sequestration" and "water regulation" (approximated by drought protection) through altered management. Individual criteria were developed for each ES and weights for the ES criteria resulted from expert interviews using the Analytic Hierarchy Process. Except for the management data, all spatial data sets were publicly available. The study region of Northwest Saxony, Germany-an intensively cultivated agricultural area-showed an overall high opportunity to improve ES through management changes. The highest opportunities were identified for the ES "provision of clean water" and "habitat provision". This novel approach is fast, reproducible and can be transferred to and adapted for other German regions and ES. Therefore, it helps decision-makers to spatially target management suggestions and to support information campaigns or subsidy schemes for management practices with positive impacts on ES.
... Agronomy 2025,15, 241 ...
This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R² and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R² = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R² = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices.
... Nendel et al., 2023;Webber et al., 2020) with a finer temporal stress-disaggregation, could further assist in increasing the robustness of the results. A finer separation of phenological phases, as done by Bönecke et al. (2020) and Riedesel et al. (2024), would help to better identify critical phenological phases, which are particularly sensitive to adverse climate impacts. Considering only the vegetative and the reproductive season, as done in this study, does not resolve impacts at important phenological steps, e.g. ...
Whether hydroclimatic extremes cause yield losses or failures not only depends on their intensity but also on local environmental conditions. These conditions shape the capacity to buffer climatic shocks and thus necessitate a regionally specific impact assessment and adaptation planning. However, the degree to which different environmental conditions affect climate impacts on yields and its spatiotemporal variability across Germany is relatively unknown. In this study, we use a regression-based crop-climate modelling approach for 71 regions, classified according to soil and climate characteristics and investigate region-specific vulnerabilities of winter wheat yields to hydroclimatic extremes for the period 1991-2019. We account for the co-occurrence of temperature and moisture impacts (i.e. compound effects) as well as for local soil-climate conditions. On average, our models can explain approx. 67 % of past winter wheat yield variations. Despite the rather homogeneous climate in Germany, the results reveal clear geographic differences across different soil-climate regions. While the northeastern regions show a clear dominance of drought impacts, southern regions show stress due to moisture excess. Heat impacts can clearly be linked to the warm regions along the western part of the country. Overall, compound dry-hot extremes pose the strongest and most widespread risk for winter wheat yields in Germany, being responsible for approx. 38 % and in some regions for up to 50 % of past yield variations. Based on the identified regional differences in hydroclimate susceptibility, we can define four geographic risk clusters, which exhibit vulnerability to climatic extremes such as summer droughts, winter droughts, summer heat waves, and winter moisture excess. The identified risk clusters of heat and moisture stresses could inform regional-specific adaptation planning.
... Spatially explicit time series datasets based on interpolated observations of phenological events are available for Germany. These datasets have been used to characterize phenological windows (Möller, Boutarfa, and Strassemeyer 2020), to calculate phenological and phase-specific weather indices (Möller et al. 2019), and to disentangle extreme weather effects on crop yields (Bucheli, Dalhaus, and Finger 2022;Riedesel et al. 2023Riedesel et al. , 2024. ...
Detailed and accurate statistics on crop productivity are key to inform decision-making related to sustainable food production and supply ensuring global food security. However, annual and high-resolution crop yield data provided by official agricultural statistics are generally lacking. Earth observation (EO) imagery, geodata on meteorological and soil conditions, as well as advances in machine learning (ML) provide huge opportunities for model-based crop yield estimation in terms of covering large spatial scales with unprecedented granularity. This study proposes a novel yield estimation approach that is bottom-up scalable from parcel to administrative levels by leveraging ML-ensembles, comprising of six regression estimators (base estimators), and multi-source geodata, including EO imagery. To ensure the approach’s robustness, two ensemble learning techniques are investigated, namely meta-learning through model stacking and majority voting. ML-ensembles were evaluated multi-annually and crop-specifically for three major winter crops, namely winter wheat (WW), winter barley (WB), and winter rapeseed (WR) in two German federal states, covering 140,000 to 155,000 parcels per year. ML-ensembles were evaluated at the parcel and district level for two German federal states against official yield reports, ranging from 2019 to 2022, based on metrics such as coefficient of determination (RSQ) and normalized root mean square error (nRMSE). Overall, the most robustly performing ensemble learning technique was majority voting yielding RSQ and nRMSE values of 0.74, 13.4% for WW, 0.68, 16.9% for WB, and 0.66, 14.1% for WR, respectively, through cross-validation at parcel level. At the district level, majority voting reached RSQ and nRMSE ranges of 0.79–0.89, 7.2–8.1% for WW, 0.80–0.84, 6.0–9.9% for WB, and 0.60–0.78, 6.1–10.4% for WR, respectively. Capitalizing on ensemble learning-based majority voting, examples of unprecedented high-resolution crop yield maps at spatial resolution are presented. Implementing a scalable yield estimation approach, as proposed in this study, into crop yield reporting frameworks of public authorities mandated to provide official agricultural statistics would increase the spatial resolution of annually reported yields, eventually covering the entire cropland available. Such unprecedented data products delivered through map services may improve decision-making support for a variety of stakeholders across different spatial scales, ranging from parcel to higher administrative levels.
