[en] Detailed knowledge of the intra-field variability of soil properties and crop characteristics is indispensable for the establishment of sustainable precision agriculture. We present an approach that combines ground-based agrogeophysical soil and aerial crop data to delineate field-specific management zones that we interpret with soil attribute measurements of texture, bulk density, and soil moisture, as well as yield and nitrate residue in the soil after potato (Solanum tuberosum L.) cultivation. To delineate the management zones, we use aerial drone-based normalized difference vegetation index (NDVI), spatial electromagnetic induction (EMI) soil scanning, and the EMI–NDVI data combination as input in a machine learning clustering technique. We tested this approach in three successive years on six agricultural fields (two per year). The field-scale EMI data included spatial soil information of the upper 0–50 cm, to approximately match the soil depth sampled for attribute measurements. The NDVI measurements over the growing season provide information on crop development. The management zones delineated from EMI data outperformed the management zones derived from NDVI in terms of spatial coherence and showed differences in properties relevant for agricultural management: texture, soil moisture deficit, yield, and nitrate residue. The combined EMI–NDVI analysis provided no extra benefit. This underpins the importance of including spatially distributed soil information in crop data interpretation, while emphasizing that high-resolution soil information is essential for variable rate applications and agronomic modeling.
Disciplines :
Agriculture & agronomy
Author, co-author :
von Hebel, Christian ; Forschungszentrum Jülich, Institute of Bio- and Geosciences, Jülich, Germany ; Agro-BioTech Gembloux, Univ. Liege, Gembloux, Belgium
Reynaert, Sophie; Soil Service Belgium (SSB), Gembloux, Belgium
ERA-NET Cofund WaterWorks2015 to support research on the sustainable management of water resources in agriculture, forestry, and freshwater aquaculture sectors. The WaterWorks2015 proposal responded to the Horizon 2020 (H2020) Societal Challenge 5 2015 Call topic Water-3 [2015]. We thank Benjamin Mary for editor responsibilities, as well as Guiseppe Calamita and an anonymous reviewer for significantly improving the manuscript. We thank Joschka Neumann and Ayhan Egmen, respectively working at the Institute of Engineering and Analytics, Technology (ZEA-1) and at the Institute for Bio-und Geosciences (IBG) of Forschungszentrum Jülich, for constructing and maintaining the EMI sleds. For help in the field, thank you to Gaël Dumont, Philipp Steinberger, Michael Iwanowitsch, Jessica Schmäck, Nicole Höring, Yenni Paloma Villa Acuna, Durra Saputera, Nassim Nassar, and Carlos Manuel Ocampo Ortiz. For discussing the data, thank you to Yves Brostaux and Hélène Soyeurt. We thank the Belgian farmers Koen van Eyck, Bert Peurteners, Patrick van Oeckel, and the Dutch farmer Jacob van den Borne for committing their fields and their interest in our studies.
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