@article{KamaliLoriteWebberetal.2022, author = {Kamali, Bahareh and Lorite, Ignacio J. and Webber, Heidi A. and Rezaei, Ehsan Eyshi and Gabaldon-Leal, Clara and Nendel, Claas and Siebert, Stefan and Ramirez-Cuesta, Juan Miguel and Ewert, Frank and Ojeda, Jonathan J.}, title = {Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain}, series = {Scientific reports}, volume = {12}, journal = {Scientific reports}, number = {1}, publisher = {Macmillan Publishers Limited,}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-022-08056-9}, pages = {13}, year = {2022}, abstract = {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 R-2 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.}, language = {en} } @article{KerneckerFienitzNendeletal.2022, author = {Kernecker, Maria and Fienitz, Meike and Nendel, Claas and Paetzig, Marlene and Walzl, Karin Pirhofer and Raatz, Larissa and Schmidt, Martin and Wulf, Monika and Zscheischler, Jana}, title = {Transition zones across agricultural field boundaries for integrated landscape research and management of biodiversity and yields}, series = {Ecological solutions and evidence}, volume = {3}, journal = {Ecological solutions and evidence}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {2688-8319}, doi = {10.1002/2688-8319.12122}, pages = {7}, year = {2022}, abstract = {Biodiversity conservation and agricultural production have been largely framed as separate goals for landscapes in the discourse on land use. Although there is an increasing tendency to move away from this dichotomy in theory, the tendency is perpetuated by the spatially explicit approaches used in research and management practice. Transition zones (TZ) have previously been defined as areas where two adjacent fields or patches interact, and so they occur abundantly throughout agricultural landscapes. Biodiversity patterns in TZ have been extensively studied, but their relationship to yield patterns and social-ecological dimensions has been largely neglected. Focusing on European, temperate agricultural landscapes, we outline three areas of research and management that together demonstrate how TZ might be used to facilitate an integrated landscape approach: (i) plant and animal species' use and response to boundaries and the resulting effects on yield, for a deeper understanding of how landscape structure shapes quantity and quality of TZ; (ii) local knowledge on field or patch-level management and its interactions with biodiversity and yield in TZ, and (iii) conflict prevention and collaborative management across land-use boundaries.}, language = {en} } @article{RossoNendelGilardietal.2022, author = {Rosso, Pablo and Nendel, Claas and Gilardi, Nicolas and Udroiu, Cosmin and Chlebowski, Florent}, title = {Processing of remote sensing information to retrieve leaf area index in barley}, series = {Precision agriculture}, volume = {23}, journal = {Precision agriculture}, number = {4}, publisher = {Springer}, address = {Dordrecht}, issn = {1385-2256}, doi = {10.1007/s11119-022-09893-4}, pages = {1449 -- 1472}, year = {2022}, abstract = {Leaf area index (LAI) is a key variable in understanding and modeling crop-environment interactions. With the advent of increasingly higher spatial resolution satellites and sensors mounted on remotely piloted aircrafts (RPAs), the use of remote sensing in precision agriculture is becoming more common. Since also the availability of methods to retrieve LAI from image data have also drastically expanded, it is necessary to test simultaneously as many methods as possible to understand the advantages and disadvantages of each approach. Ground-based LAI data from three years of barley experiments were related to remote sensing information using vegetation indices (VI), machine learning (ML) and radiative transfer models (RTM), to assess the relative accuracy and efficacy of these methods. The optimized soil adjusted vegetation index and a modified version of the Weighted Difference Vegetation Index performed slightly better than any other retrieval method. However, all methods yielded coefficients of determination of around 0.7 to 0.9. The best performing machine learning algorithms achieved higher accuracies when four Sentinel-2 bands instead of 12 were used. Also, the good performance of VIs and the satisfactory performance of the 4-band RTM, strongly support the synergistic use of satellites and RPAs in precision agriculture. One of the methods used, Sen2-Agri, an open source ML-RTM-based operational system, was also able to accurately retrieve LAI, although it is restricted to Sentinel-2 and Landsat data. This study shows the benefits of testing simultaneously a broad range of retrieval methods to monitor crops for precision agriculture.}, language = {en} } @article{KamaliJahanbakhshiDogaruetal.2022, author = {Kamali, Bahareh and Jahanbakhshi, Farshid and Dogaru, Diana and Dietrich, J{\"o}rg and Nendel, Claas and AghaKouchak, Amir}, title = {Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa}, series = {Environmental research letters}, volume = {17}, journal = {Environmental research letters}, number = {2}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/ac4ec1}, pages = {15}, year = {2022}, abstract = {Assessing the risk of yield loss in African drought-affected regions is key to identify feasible solutions for stable crop production. Recent studies have demonstrated that Copula-based probabilistic methods are well suited for such assessment owing to reasonably inferring important properties in terms of exceedance probability and joint dependence of different characterization. However, insufficient attention has been given to quantifying the probability of yield loss and determining the contribution of climatic factors. This study applies the Copula theory to describe the dependence between drought and crop yield anomalies for rainfed maize, millet, and sorghum crops in sub-Saharan Africa (SSA). The environmental policy integrated climate model, calibrated with Food and Agriculture Organization country-level yield data, was used to simulate yields across SSA (1980-2012). The results showed that the severity of yield loss due to drought had a higher magnitude than the severity of drought itself. Sensitivity analysis to identify factors contributing to drought and high-temperature stresses for all crops showed that the amount of precipitation during vegetation and grain filling was the main driver of crop yield loss, and the effect of temperature was stronger for sorghum than for maize and millet. The results demonstrate the added value of probabilistic methods for drought-impact assessment. For future studies, we recommend looking into factors influencing drought and high-temperature stresses as individual/concurrent climatic extremes.}, language = {en} } @article{HavermannGhirardoSchnitzleretal.2022, author = {Havermann, Felix and Ghirardo, Andrea and Schnitzler, J{\"o}rg-Peter and Nendel, Claas and Hoffmann, Mathias and Kraus, David and Grote, R{\"u}diger}, title = {Modeling intra- and interannual variability of BVOC emissions from maize, oil-seed rape, and ryegrass}, series = {Journal of advances in modeling earth systems}, volume = {14}, journal = {Journal of advances in modeling earth systems}, number = {3}, publisher = {American Geophysical Union}, address = {Washington}, issn = {1942-2466}, doi = {10.1029/2021MS002683}, pages = {22}, year = {2022}, abstract = {Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction-mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.}, language = {en} } @article{BlickensdoerferSchwiederPflugmacheretal.2022, author = {Blickensd{\"o}rfer, Lukas and Schwieder, Marcel and Pflugmacher, Dirk and Nendel, Claas and Erasmi, Stefan and Hostert, Patrick}, title = {Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany}, series = {Remote sensing of environment : an interdisciplinary journal}, volume = {269}, journal = {Remote sensing of environment : an interdisciplinary journal}, publisher = {Elsevier}, address = {New York}, issn = {0034-4257}, doi = {10.1016/j.rse.2021.112831}, pages = {19}, year = {2022}, abstract = {Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and environmental data. Our results show high overall accuracy and plausible class accuracies for the most dominant crop types across different years despite the strong inter-annual meteorological variability and the presence of drought and nondrought years. The maps show high spatial consistency and good delineation of field parcels. Combining optical, SAR, and environmental data increased overall accuracies by 6\% to 10\% compared to single sensor approaches, in which optical data outperformed SAR. Overall accuracy ranged between 78\% and 80\%, and the mapped areas aligned well with agricultural statistics at the regional and national level. Based on the multi-year dataset we mapped major crop sequences of cereals and leaf crops. Most crop sequences were dominated by winter cereals followed by summer cereals. Monocultures of summer cereals were mainly revealed in the Northwest of Germany. We showcased that high spatial and thematic detail in combination with annual mapping will stimulate research on crop cycles and studies to assess the impact of environmental policies on management decisions. Our results demonstrate the capabilities of integrated optical time series and SAR data in combination with variables describing local and seasonal environmental conditions for annual large-area crop type mapping.}, language = {en} } @article{SchwiederWesemeyerFrantzetal.2022, author = {Schwieder, Marcel and Wesemeyer, Maximilian and Frantz, David and Pfoch, Kira and Erasmi, Stefan and Pickert, J{\"u}rgen and Nendel, Claas and Hostert, Patrick}, title = {Mapping grassland mowing events across Germany based on combined Sentinel-2 and Landsat 8 time series}, series = {Remote sensing of environment}, volume = {269}, journal = {Remote sensing of environment}, publisher = {Elsevier}, address = {New York}, issn = {0034-4257}, doi = {10.1016/j.rse.2021.112795}, pages = {16}, year = {2022}, abstract = {Spatially explicit knowledge on grassland extent and management is critical to understand and monitor the impact of grassland use intensity on ecosystem services and biodiversity. While regional studies allow detailed insights into land use and ecosystem service interactions, information on a national scale can aid biodiversity assessments. However, for most European countries this information is not yet widely available. We used an analysis-ready-data cube that contains dense time series of co-registered Sentinel-2 and Landsat 8 data, covering the extent of Germany. We propose an algorithm that detects mowing events in the time series based on residuals from an assumed undisturbed phenology, as an indicator of grassland use intensity. A self-adaptive ruleset enabled to account for regional variations in land surface phenology and non-stationary time series on a pixelbasis. We mapped mowing events for the years from 2017 to 2020 for permanent grassland areas in Germany. The results were validated on a pixel level in four of the main natural regions in Germany based on reported mowing events for a total of 92 (2018) and 78 (2019) grassland parcels. Results for 2020 were evaluated with combined time series of Landsat, Sentinel-2 and PlanetScope data. The mean absolute percentage error between detected and reported mowing events was on average 40\% (2018), 36\% (2019) and 35\% (2020). Mowing events were on average detected 11 days (2018), 7 days (2019) and 6 days (2020) after the reported mowing. Performance measures varied between the different regions of Germany, and lower accuracies were found in areas that are revisited less frequently by Sentinel-2. Thus, we assessed the influence of data availability and found that the detection of mowing events was less influenced by data availability when at least 16 cloud-free observations were available in the grassland season. Still, the distribution of available observations throughout the season appeared to be critical. On a national scale our results revealed overall higher shares of less intensively mown grasslands and smaller shares of highly intensively managed grasslands. Hotspots of the latter were identified in the alpine foreland in Southern Germany as well as in the lowlands in the Northwest of Germany. While these patterns were stable throughout the years, the results revealed a tendency to lower management intensity in the extremely dry year 2018. Our results emphasize the ability of the approach to map the intensity of grassland management throughout large areas despite variations in data availability and environmental conditions.}, language = {en} } @article{LischeidWebberSommeretal.2022, author = {Lischeid, Gunnar and Webber, Heidi and Sommer, Michael and Nendel, Claas and Ewert, Frank}, title = {Machine learning in crop yield modelling}, series = {Agricultural and forest meteorology}, volume = {312}, journal = {Agricultural and forest meteorology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2021.108698}, pages = {23}, year = {2022}, abstract = {Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50\% and 70\% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.}, language = {en} } @article{KamaliStellaBergMohnickeetal.2022, author = {Kamali, Bahareh and Stella, Tommaso and Berg-Mohnicke, Michael and Pickert, J{\"u}rgen and Groh, Jannis and Nendel, Claas}, title = {Improving the simulation of permanent grasslands across Germany by using multi-objective uncertainty-based calibration of plant-water dynamics}, series = {European journal of agronomy}, volume = {134}, journal = {European journal of agronomy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1161-0301}, doi = {10.1016/j.eja.2022.126464}, pages = {17}, year = {2022}, abstract = {The dynamics of grassland ecosystems are highly complex due to multifaceted interactions among their soil, water, and vegetation components. Precise simulations of grassland productivity therefore rely on accurately estimating a variety of parameters that characterize different processes of these systems. This study applied three calibration schemes - a Single-Objective (SO-SUFI2), a Multi-Objective Pareto (MO-Pareto), and, a novel Uncertainty-Based Multi-Objective (MO-SUFI2) - to estimate the parameters of MONICA (Model for Nitrogen and Carbon Simulation) agro-ecosystem model in grassland ecosystems across Germany. The MO-Pareto model is based on a traditional Pareto optimality concept, while the MO-SUFI2 optimizes multiple target variables considering their level of prediction uncertainty. We used measurements of leaf area index, aboveground biomass, and soil moisture from experimental data at five sites with different intensities of cutting regimes (from two to five cutting events per season) to evaluate model performance. Both MO-Pareto and MO-SUFI2 outperformed SO-SUFI2 during calibration and validation. The comparison of the two MO approaches shows that they do not necessarily conflict with each other, but MO-SUFI2 provides complementary information for better estimations of model parameter uncertainty. We used the obtained parameter ranges to simulate grassland productivity across Germany under different cutting regimes and quantified the uncertainty associated with estimated productivity across regions. The results showed higher uncertainty in intensively managed grasslands compared to extensively managed grasslands, partially due to a lack of high-resolution input information concerning cutting dates. Furthermore, the additional information on the quantified uncertainty provided by our proposed MO-SUFI2 method adds deeper insights on confidence levels of estimated productivity. Benefiting from additional management data collected at high resolution and ground measurements on the composition of grassland species mixtures appear to be promising solutions to reduce uncertainty and increase model reliability.}, language = {en} } @article{ZappaSchlafferBroccaetal.2022, author = {Zappa, Luca and Schlaffer, Stefan and Brocca, Luca and Vreugdenhil, Mariette and Nendel, Claas and Dorigo, Wouter}, title = {How accurately can we retrieve irrigation timing and water amounts from (satellite) soil moisture?}, series = {International journal of applied earth observation and geoinformation}, volume = {113}, journal = {International journal of applied earth observation and geoinformation}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1569-8432}, doi = {10.1016/j.jag.2022.102979}, pages = {12}, year = {2022}, abstract = {While ensuring food security worldwide, irrigation is altering the water cycle and generating numerous environmental side effects. As detailed knowledge about the timing and the amounts of water used for irrigation over large areas is still lacking, remotely sensed soil moisture has proved potential to fill this gap. However, the spatial resolution and revisit time of current satellite products represent a major limitation to accurately estimating irrigation. This work aims to systematically quantify their impact on the retrieved irrigation information, hence assessing the value of satellite soil moisture for estimating irrigation timing and water amounts. In a real-world experiment, we modeled soil moisture using actual irrigation and meteorological data, obtained from farmers and weather stations, respectively. Modeled soil moisture was compared against various remotely sensed products differing in terms of spatio-temporal resolution to test the hypothesis that high-resolution observations can disclose the irrigation signal from individual fields while coarse-scale satellite products cannot. Then, in a synthetic experiment, we systematically investigated the effect of soil moisture spatial and temporal resolution on the accuracy of irrigation estimates. The analysis was further elaborated by considering different irrigation scenarios and by adding realistic amounts of random errors in the soil moisture time series. We show that coarse-scale remotely sensed soil moisture products achieve higher correlations with rainfed simulations, while high-resolution satellite observations agree significantly better with irrigated simulations, suggesting that high-resolution satellite soil moisture can inform on field-scale (similar to 40 ha) irrigation. A thorough analysis of the synthetic dataset showed that satisfactory results, both in terms of detection (F-score > 0.8) and quantification (Pearson's correlation > 0.8), are found for noise-free soil moisture observations either with a temporal sampling up to 3 days or if at least one-third of the pixel covers the irrigated field(s). However, irrigation water amounts are systematically underestimated for temporal samplings of more than one day, and decrease proportionally to the spatial resolution, i.e., coarsening the pixel size leads to larger irrigation underestimations. Although lower spatial and temporal resolutions decrease the detection and quantification accuracies (e.g., R between 0.6 and 1 depending on the irrigation rate and spatio-temporal resolution), random errors in the soil moisture time series have a stronger negative impact (Pearson R always smaller than 0.85). As expected, better performances are found for higher irrigation rates, i.e. when more water is supplied during an irrigation event. Despite the potentially large underestimations, our results suggest that high-resolution satellite soil moisture has the potential to track and quantify irrigation, especially over regions where large volumes of irrigation water are applied to the fields, and given that low errors affect the soil moisture observations.}, language = {en} }