TY - JOUR A1 - Berg-Mohnicke, Michael A1 - Nendel, Claas T1 - A case for object capabilities as the foundation of a distributed environmental model and simulation infrastructure JF - Environmental modelling & software with environment data news N2 - With the advent of increasingly powerful computational architectures, scientists use these possibilities to create simulations of ever-increasing size and complexity. Large-scale simulations of environmental systems require huge amounts of resources. Managing these in an operational way becomes increasingly complex and difficult to handle for individual scientists. State-of-the-art simulation infrastructures usually provide the necessary re-sources in a centralised setup, which often results in an all-or-nothing choice for the user. Here, we outline an alternative approach to handling this complexity, while rendering the use of high-performance hardware and large datasets still possible. It retains a number of desirable properties: (i) a decentralised structure, (ii) easy sharing of resources to promote collaboration and (iii) secure access to everything, including natural delegation of authority across levels and system boundaries. We show that the object capability paradigm will cover these issues, and present the first steps towards developing a simulation infrastructure based on these principles. KW - Cap'n proto KW - Scientific collaboration KW - Co -development KW - Communication KW - protocol KW - Object capability Y1 - 2022 U6 - https://doi.org/10.1016/j.envsoft.2022.105471 SN - 1364-8152 SN - 1873-6726 VL - 156 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Blickensdörfer, Lukas A1 - Schwieder, Marcel A1 - Pflugmacher, Dirk A1 - Nendel, Claas A1 - Erasmi, Stefan A1 - Hostert, Patrick T1 - Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany JF - Remote sensing of environment : an interdisciplinary journal N2 - 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. KW - agricultural land cover KW - analysis-ready data KW - time series KW - large-area mapping KW - optical remote sensing KW - SAR KW - big data KW - multi-sensor Y1 - 2022 U6 - https://doi.org/10.1016/j.rse.2021.112831 SN - 0034-4257 SN - 1879-0704 VL - 269 PB - Elsevier CY - New York ER - TY - JOUR A1 - Ghafarian, Fatemeh A1 - Wieland, Ralf A1 - Lüttschwager, Dietmar A1 - Nendel, Claas T1 - Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data JF - Environmental modelling & software with environment data news N2 - Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting - a Machine Learning technique - to predict the microclimate of forest sites in Brandenburg, Germany, using seasonal data comprising weather features. The analysis was amended by applying a SHapley Additive explanation to show the interaction effect of variables and individualised feature attributions. We evaluate model performance in comparison to artificial neural networks, random forest, support vector machine, and multi-linear regression. After implementing a feature selection, an ensemble approach was applied to combine individual models for each forest and improve robustness over a given single prediction model. The resulting model can be applied to translate climate change scenarios into temperatures inside forests to assess temperature-related ecosystem services provided by forests. KW - cooling effect KW - machine learning KW - ensemble method KW - ecosystem services Y1 - 2022 U6 - https://doi.org/10.1016/j.envsoft.2022.105466 SN - 1364-8152 SN - 1873-6726 VL - 156 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Ghafarian, Fatemeh A1 - Wieland, Ralf A1 - Nendel, Claas T1 - Estimating the Evaporative Cooling Effect of Irrigation within and above Soybean Canopy JF - Water N2 - Vegetation with an adequate supply of water might contribute to cooling the land surface around it through the latent heat flux of transpiration. This study investigates the potential estimation of evaporative cooling at plot scale, using soybean as example. Some of the plants' physiological parameters were monitored and sampled at weekly intervals. A physics-based model was then applied to estimate the irrigation-induced cooling effect within and above the canopy during the middle and late season of the soybean growth period. We then examined the results of the temperature changes at a temporal resolution of ten minutes between every two irrigation rounds. During the middle and late season of growth, the cooling effects caused by evapotranspiration within and above the canopy were, on average, 4.4 K and 2.9 K, respectively. We used quality indicators such as R-squared (R-2) and mean absolute error (MAE) to evaluate the performance of the model simulation. The performance of the model in this study was better above the canopy (R-2 = 0.98, MAE = 0.3 K) than below (R-2 = 0.87, MAE = 0.9 K) due to the predefined thermodynamic condition used to estimate evaporative cooling. Moreover, the study revealed that canopy cooling contributes to mitigating heat stress conditions during the middle and late seasons of crop growth. KW - canopy cooling effects KW - shading cooling KW - canopy-air temperature KW - energy KW - balance KW - the Penman-Monteith equation Y1 - 2022 U6 - https://doi.org/10.3390/w14030319 SN - 2073-4441 VL - 14 IS - 3 PB - MDPI CY - Basel ER - TY - JOUR A1 - Hampf, Anna A1 - Nendel, Claas A1 - Strey, Simone A1 - Strey, Robert T1 - Biotic yield losses in the Southern Amazon, Brazil BT - making use of smartphone-assisted plant disease diagnosis data JF - Frontiers in plant science : FPLS N2 - Pathogens and animal pests (P&A) are a major threat to global food security as they directly affect the quantity and quality of food. The Southern Amazon, Brazil's largest domestic region for soybean, maize and cotton production, is particularly vulnerable to the outbreak of P&A due to its (sub)tropical climate and intensive farming systems. However, little is known about the spatial distribution of P&A and the related yield losses. Machine learning approaches for the automated recognition of plant diseases can help to overcome this research gap. The main objectives of this study are to (1) evaluate the performance of Convolutional Neural Networks (ConvNets) in classifying P&A, (2) map the spatial distribution of P&A in the Southern Amazon, and (3) quantify perceived yield and economic losses for the main soybean and maize P&A. The objectives were addressed by making use of data collected with the smartphone application Plantix. The core of the app's functioning is the automated recognition of plant diseases via ConvNets. Data on expected yield losses were gathered through a short survey included in an "expert" version of the application, which was distributed among agronomists. Between 2016 and 2020, Plantix users collected approximately 78,000 georeferenced P&A images in the Southern Amazon. The study results indicate a high performance of the trained ConvNets in classifying 420 different crop-disease combinations. Spatial distribution maps and expert-based yield loss estimates indicate that maize rust, bacterial stalk rot and the fall armyworm are among the most severe maize P&A, whereas soybean is mainly affected by P&A like anthracnose, downy mildew, frogeye leaf spot, stink bugs and brown spot. Perceived soybean and maize yield losses amount to 12 and 16%, respectively, resulting in annual yield losses of approximately 3.75 million tonnes for each crop and economic losses of US$2 billion for both crops together. The high level of accuracy of the trained ConvNets, when paired with widespread use from following a citizen-science approach, results in a data source that will shed new light on yield loss estimates, e.g., for the analysis of yield gaps and the development of measures to minimise them. KW - plant pathology KW - animal pests KW - pathogens KW - machine learning KW - digital KW - image processing KW - disease diagnosis KW - crowdsourcing KW - crop losses Y1 - 2021 U6 - https://doi.org/10.3389/fpls.2021.621168 SN - 1664-462X VL - 12 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Hannigan, Sara A1 - Nendel, Claas A1 - Krull, Marcos T1 - Effects of temperature on the movement and feeding behaviour of the large lupine beetle, Sitona gressorius JF - Journal of pest science N2 - Even though the effects of insect pests on global agricultural productivity are well recognised, little is known about movement and dispersal of many species, especially in the context of global warming. This work evaluates how temperature and light conditions affect different movement metrics and the feeding rate of the large lupine beetle, an agricultural pest responsible for widespread damage in leguminous crops. By using video recordings, the movement of 384 beetles was digitally analysed under six different temperatures and light conditions in the laboratory. Bayesian linear mixed-effect models were used to analyse the data. Furthermore, the effects of temperature on the daily diffusion coefficient of beetles were estimated by using hidden Markov models and random walk simulations. Results of this work show that temperature, light conditions, and beetles' weight were the main factors affecting the flight probability, displacement, time being active and the speed of beetles. Significant variations were also observed in all evaluated metrics. On average, beetles exposed to light conditions and higher temperatures had higher mean speed and flight probability. However, beetles tended to stay more active at higher temperatures and less active at intermediate temperatures, around 20 degrees C. Therefore, both the diffusion coefficient and displacement of beetles were lower at intermediate temperatures. These results show that the movement behaviour and feeding rates of beetles can present different relationships in the function of temperature. It also shows that using a single diffusion coefficient for insects in spatially explicit models may lead to over- or underestimation of pest spread. KW - Agricultural pests KW - Diffusion KW - Hidden Markov models KW - Movement ecology Y1 - 2022 U6 - https://doi.org/10.1007/s10340-022-01510-7 SN - 1612-4758 SN - 1612-4766 SP - 389 EP - 402 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Havermann, Felix A1 - Ghirardo, Andrea A1 - Schnitzler, Jörg-Peter A1 - Nendel, Claas A1 - Hoffmann, Mathias A1 - Kraus, David A1 - Grote, Rüdiger T1 - Modeling intra- and interannual variability of BVOC emissions from maize, oil-seed rape, and ryegrass JF - Journal of advances in modeling earth systems N2 - 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. KW - biogenic volatile organic compounds KW - process-based modeling KW - Zea mays KW - Brassica napus KW - Lolium multiflorum KW - plant ontogenetic stage Y1 - 2022 U6 - https://doi.org/10.1029/2021MS002683 SN - 1942-2466 VL - 14 IS - 3 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Jänicke, Clemens A1 - Goddard, Adam A1 - Stein, Susanne A1 - Steinmann, Horst-Henning A1 - Lakes, Tobia A1 - Nendel, Claas A1 - Müller, Daniel T1 - Field-level land-use data reveal heterogeneous crop sequences with distinct regional differences in Germany JF - European journal of agronomy N2 - Crop cultivation intensifies globally, which can jeopardize biodiversity and the resilience of cropping systems. We investigate changes in crop rotations as one intensification metric for half of the croplands in Germany with annual field-level land-use data from 2005 to 2018. We proxy crop rotations with crop sequences and compare how these sequences changed among three seven-year periods. The results reveal an overall high diversity of crop sequences in Germany. Half of the cropland has crop sequences with four or more crops within a seven-year period, while continuous cultivation of the same crop is present on only 2% of the cropland. Larger farms tend to have more diverse crop sequences and organic farms have lower shares of cereal crops. In three federal states, crop rotations became less structurally diverse over time, i.e. the number of crops and the number of changes between crops decreased. In one state, structural diversity increased and the proportion of monocropping decreased. The functional diversity of the crop sequences, which measures the share of winter and spring crops as well as the share of leaf and cereal crops per sequence, remained largely stable. Trends towards cereal-or leaf -crop dominated sequences varied between the states, and no clear overall dynamic could be observed. However, the share of winter crops per sequence decreased in all four federal states. Quantifying the dynamics of crop sequences at the field level is an important metric of land-use intensity and can reveal the patterns of land-use intensification. KW - crop production KW - crop rotation KW - cropping diversity KW - IACS KW - intensification KW - land-use intensity Y1 - 2022 U6 - https://doi.org/10.1016/j.eja.2022.126632 SN - 1161-0301 SN - 1873-7331 VL - 141 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kamali, Bahareh A1 - Jahanbakhshi, Farshid A1 - Dogaru, Diana A1 - Dietrich, Jörg A1 - Nendel, Claas A1 - AghaKouchak, Amir T1 - Probabilistic modeling of crop-yield loss risk under drought: a spatial showcase for sub-Saharan Africa JF - Environmental research letters N2 - 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. KW - Copula theory KW - crop model KW - drought stress KW - joint probability KW - risk Y1 - 2022 U6 - https://doi.org/10.1088/1748-9326/ac4ec1 SN - 1748-9326 VL - 17 IS - 2 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Kamali, Bahareh A1 - Lorite, Ignacio J. A1 - Webber, Heidi A. A1 - Rezaei, Ehsan Eyshi A1 - Gabaldon-Leal, Clara A1 - Nendel, Claas A1 - Siebert, Stefan A1 - Ramirez-Cuesta, Juan Miguel A1 - Ewert, Frank A1 - Ojeda, Jonathan J. T1 - Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain JF - Scientific reports N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-08056-9 SN - 2045-2322 VL - 12 IS - 1 PB - Macmillan Publishers Limited, CY - London ER -