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  1. Article: Data sets of spatial variable data from Precision Agriculture data

    Kersebaum, Kurt Christian

    FACCE MACSUR Reports, 7:C1.1.1-D

    2016  

    Abstract: Data sets from two fields in Germany and Italy with spatial variable soil and management information were distributed to crop growth modellers to investigate the site sensitivity of crop models. For the field in Germany data from 60 grid points were ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract Data sets from two fields in Germany and Italy with spatial variable soil and management information were distributed to crop growth modellers to investigate the site sensitivity of crop models. For the field in Germany data from 60 grid points were provided while the Italian data set consists of 100 data points. Spatial yield observations for wheat were available for three years to be compared with model outputs.
    Keywords Precision Agriculture ; crop growth models ; data ; site sensitivity
    Language English
    Document type Article
    DOI 10.4126/FRL01-006415898
    Database Repository for Life Sciences

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  2. Article: Empirical analysis on crop-weather relationships

    Kersebaum, Kurt Christian

    FACCE MACSUR Reports, 6:D-C2.5

    2015  

    Abstract: There have been several studies, where process-based crop models are developed, used and compared in order to project crop production and corresponding model uncertainties under climate change. Despite many advances in this field, there are some ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract There have been several studies, where process-based crop models are developed, used and compared in order to project crop production and corresponding model uncertainties under climate change. Despite many advances in this field, there are some correlations between climate variables and crop growth, such as pest and diseases, that is often absent in process-based models. Such relationships can be simulated using empirical models. In this study, several statistical techniques were applied on winter oilseed rape data collected in some European countries. The empirical models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Results suggest that newly developed regression techniques such as shrinkage methods work well both in yield projections and finding the influential climatic variables. Many of regression techniques agree in terms of yield prediction; however, choice of significant climate variables is rather sensitive to the choice of regression technique.
    Language English
    Document type Article
    DOI 10.4126/FRL01-006415900
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  3. Article: Results of uncalibrated model runs available

    Kersebaum, Kurt Christian

    FACCE MACSUR Reports, 3:C1.5

    2014  

    Abstract: The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments. Data sets for 5 locations in Europe were distributed to 19 interested modeller groups ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract The study ROTATIONEFFECT aims to compare the output of different models simulating field data sets with multi-year crop rotations including different treatments. Data sets for 5 locations in Europe were distributed to 19 interested modeller groups comprising a total of 201 crop growth seasons. In a first step only minimal information for calibration were provided to the modellers. In total 14 modelling teams sent their “uncalibrated” results as single-year calculations and/or calculations of rotation depending on the capability of the model. 7-10 models were capable to run the rotations as continuous runs. Up to 12 models provided single year simulations of at least one crop. Comparing results of models which provided both single year and continuous runs, show a little lower root mean square error for the continuous rotations runs. Cereal crop yields were generally better simulated than tuber/beet yields. Additionally, the models’ response to various treatments (irrigation/rainfed, nitrogen level, CO2 level, residue management/ tillage, catch crops) were compared to observed differences. First indicators of model performance have been developed and presented at international conferences.
    Language English
    Document type Article
    DOI 10.4126/FRL01-006413576
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  4. Article: Guidelines on extending on-going experiments with additional measurements to support crop modelling – Field experimental protocol

    Kersebaum, Kurt Christian

    FACCE MACSUR Reports, 3:C2.3

    2013  

    Abstract: The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) are listed. A list of possible seasonal observations/measurements that could ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) are listed. A list of possible seasonal observations/measurements that could be carried out in existing experiments to increase their potential for crop modelling studies is also provided. The general methodology suitable to be used is outlined, but in all cases the selected method depends strongly on the experimental set-up and facilities/instruments at the disposal of the experimentalists. Such methodologies needs to be documented and preferably benchmarked against standard methods.
    Language English
    Document type Article
    DOI 10.4126/FRL01-006413632
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  5. Article: Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations

    Kersebaum, Kurt Christian / Nendel, Claas

    PLOS ONE, 11(4): e0151782

    2016  

    Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Potsdam-Institut für Klimafolgenforschung
    Abstract We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.
    Keywords Cereal crops ; Agricultural soil science ; Germany ; Maize ; Seasons ; Simulation and modeling ; Wheat ; Winter
    Language English
    Document type Article
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  6. Article: Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture

    Kersebaum, Kurt Christian / Lana, Marcos / Nendel, Claas

    FACCE MACSUR Reports, 10:C1.1-D2

    2017  

    Abstract: Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables.
    Language English
    Document type Article
    DOI 10.4126/FRL01-006415896
    Database Repository for Life Sciences

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  7. Article: Data format for model in- and output

    Kersebaum, Kurt Christian / Bindi, Marco

    FACCE MACSUR Reports, 3:C1.3

    2013  

    Abstract: A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the ... ...

    Abstract A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the modellers, those providing empirical data of the experiments and those analysing the simulation results. The input format facilitates the model application in a way that each cropping-system to be modelled will be defined in the same way. Data will be delivered in EXCEL sheets with sub-tables for each block of inputs. Tables are mostly organized in a way that allows export and sequential read-in by the models. The common output format enables effective processing of results estimating model performance indicators.
    Keywords Climate Change ; Agriculture ; Food Security
    Language English
    Document type Article
    DOI 10.4126/FRL01-006413630
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  8. Article: Coherent multi-variable field data set of an intensive cropping system for agro-ecosystem modelling from Müncheberg, Germany

    Mirschel, Wilfried / Kersebaum, Kurt Christian / Nendel, Claas

    Open data journal for agricultural research, 2(1): 1-10

    2016  

    Abstract: A six-year (1993-1998) multivariable data set for a four-plot intensive crop rotation (sugar beet - winter wheat - winter barley - winter rye - catch crop) located at Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract A six-year (1993-1998) multivariable data set for a four-plot intensive crop rotation (sugar beet - winter wheat - winter barley - winter rye - catch crop) located at Leibniz Centre for Agricultural Landscape Research (ZALF) Experimental Station, Müncheberg, Germany, is documented in detail. The experiment targets crop response to water supply on sandy soils (Eutric Cambisol), applying rain-fed and irrigated treatments. Weather as well as soil and crop processes were intensively monitored and management actions were consistently recorded. The data set contains coherent data for soil (water, nitrogen contents), crop (ontogenesis, plant, tiller and ear numbers, above-ground and root biomasses, yield, carbon and nitrogen content in biomass and their fractions, sugar content in beet), weather (all standard meteorological variables) and management (soil tillage, sowing, fertilisation, irrigation, harvest). In addition, observation methods are briefly described. The data set is available via the Open Research Data Portal at ZALF Müncheberg and is published under doi:10.4228/ZALF.1992.271. The data set was used for model intercomparison within the crop modelling part (CropM) of the international FACCE MACSUR project.
    Keywords Müncheberg (Germany) ; crop-soil-weather-management database ; agro-ecosystem modelling ; field experiment ; intensive cropping system
    Language English
    Document type Article
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  9. Article: Food security: Frost risk by dwindling snow cover

    Kersebaum, Kurt Christian

    Nature climate change

    2022  Volume 12, Issue 5, Page(s) 421

    Language English
    Document type Article
    ZDB-ID 2614383-5
    ISSN 1758-678x
    Database Current Contents Nutrition, Environment, Agriculture

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  10. Article: Variability in the Water Footprint of Arable Crop Production across European Regions

    Gobin, Anne / Kersebaum, Kurt Christian / Ventrella, Domenico / Zoumides, Christos

    Water, 9(2): 93

    2017  

    Abstract: Crop growth and yield are affected by water use during the season: the green water footprint (WF) accounts for rain water, the blue WF for irrigation and the grey WF for diluting agri-chemicals. We calibrated crop yield for FAO’s water balance model “ ... ...

    Institution Leibniz-Zentrum für Agrarlandschaftsforschung
    Abstract Crop growth and yield are affected by water use during the season: the green water footprint (WF) accounts for rain water, the blue WF for irrigation and the grey WF for diluting agri-chemicals. We calibrated crop yield for FAO’s water balance model “Aquacrop” at field level. We collected weather, soil and crop inputs for 45 locations for the period 1992–2012. Calibrated model runs were conducted for wheat, barley, grain maize, oilseed rape, potato and sugar beet. The WF of cereals could be up to 20 times larger than the WF of tuber and root crops; the largest share was attributed to the green WF. The green and blue WF compared favourably with global benchmark values (R2 = 0.64–0.80; d = 0.91–0.95). The variability in the WF of arable crops across different regions in Europe is mainly due to variability in crop yield ( cv¯¯¯ = 45%) and to a lesser extent to variability in crop water use ( cv¯¯¯ = 21%). The WF variability between countries ( cv¯¯¯ = 14%) is lower than the variability between seasons ( cv¯¯¯ = 22%) and between crops ( cv¯¯¯ = 46%). Though modelled yields increased up to 50% under sprinkler irrigation, the water footprint still increased between 1% and 25%. Confronted with drainage and runoff, the grey WF tended to overestimate the contribution of nitrogen to the surface and groundwater. The results showed that the water footprint provides a measurable indicator that may support European water governance.
    Keywords Europe ; cereals ; crop water use ; arable crops ; water footprint ; yield
    Language English
    Document type Article
    Database Repository for Life Sciences

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