Data classification and criteria catalogue for data requirements

Data requirements for calibration and validation of agro-ecosystem models were elaborated and a classification scheme for the suitability of experimental data for model testing and improvement has been developed. The scheme enables to evaluate datasets and to classify datasets upon their quality to be used in crop modelling

Agricultural Landscapes in Brandenburg, Germany: An Analysis of Characteristics and Spatial Patterns

The increasing demand for agricultural commodities for food and energy purposes has led to intensified agricultural land management, along with the homogenization of landscapes, adverse biodiversity effects and robustness of landscapes regarding the provision of ecosystem services. At the same time, subsidized organic agriculture and extensive grassland use supports the provision of ecosystem services. Yet little is understood about how to evaluate a landscape’s potential to contribute to protecting and enhancing biodiversity and ecosystem services. To address this gap, we use plot-level data from the Integrated Administration and Control System (IACS) for Germany’s federal state of Brandenburg, and based on a two-step cluster analysis, we identify six types of agricultural landscapes. These clusters differ in landscape structure, diversity and measures for agricultural land management intensity. Agricultural land in Brandenburg is dominated by high shares of cropland but fragmented differently. Lands under organic management and those with a high share of maize show strong spatial autocorrelation, pointing to local clusters. Identification of different types of landscapes permits locally- and region-adapted designs of environmental and agricultural policy measures improves outcome-oriented environmental policy impact evaluation and landscape planning. Our approach allows transferability to other EU regions.Deutsche ForschungsgemeinschaftHumboldt-Universität zu Berlin (1034)Peer Reviewe

Identifying Agricultural Landscape Types for Brandenburg, Germany using IACS Data

The increasing demand for agricultural commodities for food and energy purposes has led to intensified agricultural production. This trend may manifest in agricultural compositions and landscape configurations that can have mixed and adverse impacts on the provision of ecosystem services. We rely on the EU’s plot-based data from the Integrated Administration and Control System (IACS) to identify different types of agricultural landscapes and their spatial distribution in Brandenburg, Germany, a study region strongly characterised by intensification trends. Based on a set of landscape metrics, we are able to characterise agricultural land use and identify six types of agricultural landscapes. We rely on a two-step cluster analysis for a hexagonal grid and find that agricultural land is dominated by cropland with different degrees of fragmentation. By providing a framework using landscape metrics derived from IACS data, our approach involves clustering to identify typologies that are transferable to other regions within the EU based on existing data. This framework can offer more tailored environmental and agricultural planning based on sophisticated measures that take into account local and regional characteristics

Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land

Explanation of header and data: Site is either west-facing or east-facing (see "Measurement site"); DistToEdge is the distance to the zero line (edge) in m, negative values are in the forest, positive values are in the arable land, zero is the edge; Repetition is the number of repetitions in the lab; Depth is measured in cm and is the depth of soil sampling ±3 cm; Ctot is the percentage (%) of total soil carbon content in the tested soil sample; Ntot is the percentage (%) of total soil nitrogen content in the tested soil sample and pH is the numeric scale to specify the acidity or basicity of the soil sample in solution

Tracking nitrogen losses in a greenhouse crop rotation experiment in North China using the EU-Rotate_N simulation model

Vegetable production in China is associated with high inputs of nitrogen, posing a risk of losses to the environment. Organic matter mineralisation is a considerable source of nitrogen (N) which is hard to quantify. In a two-year greenhouse cucumber experiment with different N treatments in North China, non-observed pathways of the N cycle were estimated using the EU-Rotate_N simulation model. EU-Rotate_N was calibrated against crop dry matter and soil moisture data to predict crop N uptake, soil mineral N contents, N mineralisation and N loss. Crop N uptake (Modelling Efficiencies (ME) between 0.80 and 0.92) and soil mineral N contents in different soil layers (ME between 0.24 and 0.74) were satisfactorily simulated by the model for all N treatments except for the traditional N management. The model predicted high N mineralisation rates and N leaching losses, suggesting that previously published estimates of N leaching for these production systems strongly underestimated the mineralisation of N from organic matter

Probabilistic modeling of crop-yield loss risk under drought: A spatial showcase for sub-Saharan Africa

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. © 2022 The Author(s). Published by IOP Publishing Ltd

Wassererosion auf Silomaisflächen – eine vergleichende Studie verschiedener Anbauverfahren

Der Anbau von Silomais als Substrat für Biogasanlagen hat in den letzten Jahren erheblich zugenommen. Dabei ist Silomais die mit Abstand am meisten eingesetzte Energiepflanze. Wie bei allen C4-Gräsern ist das Risiko für Wassererosion beim Maisanbau jedoch sehr hoch welches auf die langsame Jugendentwicklung mit geringer Bodenbedeckung bis in den Juli hinein, zurückzuführen ist. In diesem Artikel wird am Beispiel der Anbaufolge Winterweizen – Winterroggen als Winterzwischenfrucht – Silomais beschrieben werden, inwieweit der Anbau von Silomais als Haupt- oder Zweitfrucht die Gefährdung des Bodens durch Wassererosion beeinflusst

National-scale modelling of N leaching in organic and conventional horticultural crop rotations - policy implications

A method is presented to model N leaching in crop rotations on a national scale. Representative crop rotations for different regions and soil types are used in the cross-disciplinary, plant, soil, environment & economics model EU-Rotate_N. By comparing contrasting farming systems (organic and conventional) in the UK, their strengths and weaknesses in delivering environmental and economic sustainability can be assessed. Modelling results show that the annual leaching in different horticultural rotations and UK regions, using median weather, is within the range of 13-88 kg N/ha/year for organic and 54-130 kg N /ha/year for conventional. The weighted annual average figures are 39 kg N/ha/year for organic and 81 kg N/ha/year for conventional, respectively. It is concluded that organic horticultural rotations, with a current share of 6.1% already contribute to lower overall N losses from agriculture. However, on a UK national scale, only a large share of organic land use (e.g. >50%) has a large effect on reducing N losses. Similar reductions are also predicted by substantial cuts in conventional N inputs, giving a policy choice if pollution from agriculture steps up further on the political agenda

Modeling Intra‐ and Interannual Variability of BVOC Emissions From Maize, Oil‐Seed Rape, and Ryegrass

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

Field-level land-use data reveal heterogeneous crop sequences with distinct regional differences in Germany

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