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Determining the within-field yield variability from seasonally changing soil conditions

Crop yield variations are strongly influenced by the spatial and temporal availabilities of water and nitrogen in the soil during the crop growth season. To estimate the quantities and distributions of water and nitrogen within a given soil, process-oriented soil models have often been used. These models require detailed information about the soil characteristics and profile architecture (e.g., soil depth, clay content, bulk density, field capacity and wilting point), but high resolution information about these soil properties, both vertically and laterally, is difficult to obtain through conventional approaches. However, on-the-go electrical resistivity tomography (ERT) measurements of the soil and data inversion tools have recently improved the lateral resolutions of the vertically distributed measurable information. Using these techniques, nearly 19,000 virtual soil profiles with defined layer depths were successfully created for a 30 ha silty cropped soil over loamy and sandy substrates in Central Germany, which were used to initialise the CArbon and Nitrogen DYnamics (CANDY) model. The soil clay content was derived from the electrical resistivity (ER) and the collected soil samples using a simple linear regression approach (the mean R2 of clay = 0.39). The additional required structural and hydrological properties were derived from pedotransfer functions. The modelling results, derived soil texture distributions and original ER data were compared with the spatial winter wheat yield distribution in a relatively dry year using regression and boundary line analysis. The yield variation was best explained by the simulated soil water content (R2 = 0.18) during the grain filling and was additionally validated by the measured soil water content with a root mean square error (RMSE) of 7.5 Vol%.

Boenecke, E.; Lueck, E.; Ruehlmann, J.; Gruendling, R.; Franko, U. 2018. Determining the within-field yield variability from seasonally changing soil conditions. Precision Agriculture