Toward high‐resolution agronomic soil information and management zones delineated by ground‐based electromagnetic induction and aerial drone data. Issue 4 (11th February 2021)

• Record Type: Journal Article
• Authors: von Hebel, Christian; Reynaert, Sophie; Pauly, Klaas; Janssens, Pieter; Piccard, Isabelle; Vanderborght, Jan; van der Kruk, Jan; Vereecken, Harry; Garré, Sarah
• Is Part Of: Vadose zone journal. Volume 20:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-02-11
• Subjects: Soil science -- Periodicals; Zone of aeration -- Periodicals; Groundwater flow -- Periodicals; Groundwater flow; Zone of aeration; Periodicals; Electronic journals; 631.4
• DOI: 10.1002/vzj2.20099
• Languages: English
• ISSNs: 1539-1663

Abstract

Abstract: 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. Core Ideas: Electromagnetic induction (EMI) enables delineation of high‐resolution management zones. Management zone delineation using aerial crop data depends on acquisition time. Management zone delineation based on EMI data seems time independent. Important for variable rate applications, EMI‐based delineated zones differ agronomically.