Toward high‐resolution agronomic soil information and management zones delineated by ground‐based electromagnetic induction and aerial drone data

Christian Von Hebel,Pieter Janssens,Sophie Reynaert,Klaas Pauly,Jan Vanderborght,Sarah Garré,Jan Van Der Kruk,Isabelle Piccard,Harry Vereecken

doi:10.1002/vzj2.20099

Abstract

AbstractDetailed 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.

Highlights

We study the use of electromagnetic induction (EMI), normalized difference vegetation index (NDVI), and the combined EMI–NDVI datasets for delineating agronomic management zones
Though De Benedetto et al (2013) found that crops can be more sensitive to the management practices than to the intrinsic soil property, this study indicates that clear management zone delineation based on NDVI data depends on the data acquisition timing
We proposed a joint interpretation of ground-based EMI soil scanning data together with aerial drone-based crop data of NDVI and soil sampling data

Summary

Introduction

Machinery, and using monoculture cropping systems and homogeneous agronomic management This led to lower production costs, higher profits, vibrant agricultural supply sectors, and low food prices. To turn the system and act sustainable, agriculture needs to scan the soil, measure the crops, and act on the intra-field variable soil conditions that drive the specific cropping areas. To support this shift and advocate sustainability, we need detailed knowledge of the soil, the crops, and the soil–crop interactions in the specific environmental and climatological conditions. At certain zones within the field, the soil holds vital plant resources where the crops need less or even no extra fertilizer, irrigation water, and/or agro-chemicals. Knowing the intra-field soil variability helps to save resources, since this information enables applying fertilizers, water, or pesticides only where needed (Cassman, 1999; De Benedetto et al, 2013)

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