@misc{SchmidtJochheimKersebaumetal.2017, author = {Schmidt, Martin and Jochheim, Hubert and Kersebaum, Kurt-Christian and Lischeid, Gunnar and Nendel, Claas}, title = {Gradients of microclimate, carbon and nitrogen in transition zones of fragmented landscapes - a review}, series = {Agricultural and forest meteorology}, volume = {232}, journal = {Agricultural and forest meteorology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2016.10.022}, pages = {659 -- 671}, year = {2017}, abstract = {Fragmentation of landscapes creates a transition zone in between natural habitats or different kinds of land use. In forested and agricultural landscapes with transition zones, microclimate and matter cycling are markedly altered. This probably accelerates and is intensified by global warming. However, there is no consensus on defining transition zones and quantifying relevant variables for microclimate and matter cycling across disciplines. This article is an attempt to a) revise definitions and offer a framework for quantitative ecologists, b) review the literature on microclimate and matter cycling in transition zones and c) summarise this information using meta-analysis to better understand bio-geochemical and bio-geophysical processes and their spatial extent in transition zones. We expect altered conditions in soils of transition zones to be 10-20 m with a maximum of 50 m, and 25-50 m for above-ground space with a maximum of 125 m.}, language = {en} } @article{SchmidtLischeidNendel2019, author = {Schmidt, Martin and Lischeid, Gunnar and Nendel, Claas}, title = {Microclimate and matter dynamics in transition zones of forest to arable land}, series = {Agricultural and forest meteorology}, volume = {268}, journal = {Agricultural and forest meteorology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0168-1923}, doi = {10.1016/j.agrformet.2019.01.001}, pages = {1 -- 10}, year = {2019}, abstract = {Human-driven fragmentation of landscapes leads to the formation of transition zones between ecosystems that are characterised by fluxes of matter, energy and information. These transition zones may offer rather inhospitable habitats that could jeopardise biodiversity. On the other hand, transition zones are also reported to be hotspots for biodiversity and even evolutionary processes. The general mechanisms and influence of processes in transition zones are poorly understood. Although heterogeneity and diversity of land use of fragments and the transition zones between them play an important role, most studies only refer to forested transition zones. Often, only an extrapolation of measurements in the different fragments themselves is reported to determine gradients in transition zones. This paper contributes to a quantitative understanding of agricultural landscapes beyond individual ecotopes, and towards connected ecosystem mosaics that may be beneficial for the provision of ecosystem services.}, language = {en} } @article{SchmidtNendelFunketal.2019, author = {Schmidt, Martin and Nendel, Claas and Funk, Roger and Mitchell, Matthew G. E. and Lischeid, Gunnar}, title = {Modeling Yields Response to Shading in the Field-to-Forest Transition Zones in Heterogeneous Landscapes}, series = {Agriculture}, volume = {9}, journal = {Agriculture}, number = {1}, publisher = {MDPI}, address = {Basel}, issn = {2077-0472}, doi = {10.3390/agriculture9010006}, pages = {15}, year = {2019}, abstract = {In crop modeling and yield predictions, the heterogeneity of agricultural landscapes is usually not accounted for. This heterogeneity often arises from landscape elements like forests, hedges, or single trees and shrubs that cast shadows. Shading from forested areas or shrubs has effects on transpiration, temperature, and soil moisture, all of which affect the crop yield in the adjacent arable land. Transitional gradients of solar irradiance can be described as a function of the distance to the zero line (edge), the cardinal direction, and the height of trees. The magnitude of yield reduction in transition zones is highly influenced by solar irradiance-a factor that is not yet implemented in crop growth models on a landscape level. We present a spatially explicit model for shading caused by forested areas, in agricultural landscapes. With increasing distance to forest, solar irradiance and yield increase. Our model predicts that the shading effect from the forested areas occurs up to 15 m from the forest edge, for the simulated wheat yields, and up to 30 m, for simulated maize. Moreover, we estimated the spatial extent of transition zones, to calculate the regional yield reduction caused by shading of the forest edges, which amounted to 5\% to 8\% in an exemplary region.}, language = {en} } @article{WebberLischeidSommeretal.2020, author = {Webber, Heidi and Lischeid, Gunnar and Sommer, Michael and Finger, Robert and Nendel, Claas and Gaiser, Thomas and Ewert, Frank}, title = {No perfect storm for crop yield failure in Germany}, series = {Environmental research letters}, volume = {15}, journal = {Environmental research letters}, number = {10}, publisher = {IOP Publ. Ltd.}, address = {Bristol}, issn = {1748-9326}, doi = {10.1088/1748-9326/aba2a4}, pages = {14}, year = {2020}, abstract = {Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.}, language = {en} } @article{HampfNendelStreyetal.2021, author = {Hampf, Anna and Nendel, Claas and Strey, Simone and Strey, Robert}, title = {Biotic yield losses in the Southern Amazon, Brazil}, series = {Frontiers in plant science : FPLS}, volume = {12}, journal = {Frontiers in plant science : FPLS}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {1664-462X}, doi = {10.3389/fpls.2021.621168}, pages = {16}, year = {2021}, abstract = {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.}, language = {en} } @article{JaenickeGoddardSteinetal.2022, author = {J{\"a}nicke, Clemens and Goddard, Adam and Stein, Susanne and Steinmann, Horst-Henning and Lakes, Tobia and Nendel, Claas and M{\"u}ller, Daniel}, title = {Field-level land-use data reveal heterogeneous crop sequences with distinct regional differences in Germany}, series = {European journal of agronomy}, volume = {141}, journal = {European journal of agronomy}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1161-0301}, doi = {10.1016/j.eja.2022.126632}, pages = {12}, year = {2022}, abstract = {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.}, 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{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{GhafarianWielandLuettschwageretal.2022, author = {Ghafarian, Fatemeh and Wieland, Ralf and L{\"u}ttschwager, Dietmar and Nendel, Claas}, title = {Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data}, series = {Environmental modelling \& software with environment data news}, volume = {156}, journal = {Environmental modelling \& software with environment data news}, publisher = {Elsevier}, address = {Oxford}, issn = {1364-8152}, doi = {10.1016/j.envsoft.2022.105466}, pages = {11}, year = {2022}, abstract = {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.}, language = {en} } @article{BergMohnickeNendel2022, author = {Berg-Mohnicke, Michael and Nendel, Claas}, title = {A case for object capabilities as the foundation of a distributed environmental model and simulation infrastructure}, series = {Environmental modelling \& software with environment data news}, volume = {156}, journal = {Environmental modelling \& software with environment data news}, publisher = {Elsevier}, address = {Oxford}, issn = {1364-8152}, doi = {10.1016/j.envsoft.2022.105471}, pages = {12}, year = {2022}, abstract = {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.}, language = {en} }