Abstract
Management zones (MZs) are used in precision agriculture to diversify agronomic management across a field. According to current common practices, MZs are often spatially static: they are developed once and used thereafter. However, the soil–plant relationship often varies over time and space, decreasing the efficiency of static MZ designs. Therefore, we propose a novel workflow for time-specific MZ delineation based on integration of plant and soil sensing data. The workflow includes four steps: (1) geospatial sensor measurements are used to describe soil spatial variability and in-season plant growth status; (2) moving-window regression modelling is used to characterize the sub-field changes of the soil–plant relationship; (3) soil information and sub-field indicator(s) of the soil–plant relationship (i.e., the local regression slope coefficient[s]) are used to delineate time-specific MZs using fuzzy cluster analysis; and (4) MZ delineation is evaluated and interpreted. We illustrate the workflow with an idealized, yet realistic, example using synthetic data and with an experimental example from a 21-ha maize field in Italy using two years of maize growth, soil apparent electrical conductivity and normalized difference vegetation index (NDVI) data. In both examples, the MZs were characterized by unique combinations of soil properties and soil–plant relationships. The proposed approach provides an opportunity to address the spatiotemporal nature of changes in crop genetics × environment × management interactions.
Highlights
Crop yields and resource use efficiency have a strong spatial component, which can be observed over a wide range of scales, from regional to subfield [1]
We aim to present a novel workflow for the selection of time-specific management zones (MZs) according to in-season spatial measurements of crop growth status and its relationship with high-resolution soil spatial information
For soil tillage, sowing, and early vegetation stages when surface reflectance is mostly determined by soil, a static MZ delineation approach based on the spatial variability of soil properties (e.g., [9]) may be more adequate
Summary
Crop yields and resource use efficiency (e.g., nutrients and water) have a strong spatial component, which can be observed over a wide range of scales, from regional to subfield [1]. Yield variability in uniformly managed fields is often related to the spatial variability of soil properties. Agronomy 2018, 8, 253 and their impact on plant growth [2,3,4,5,6] This variability can be addressed using precision agriculture practices [7], such as variable rate management (VRM) [8]. According to VRM principles, efficiency or crop production can be increased by varying agronomic inputs over a field according to varying soil and crop conditions [9]. The use of MZs for VRM has been shown to increase productivity, decrease costs, and/or reduce environmental impacts of agronomic practices [12,13]. Several authors have shown that within-MZ management should change over time [14,15,16,17,18,19,20], such an approach is often referred to as “dynamic VRM” [14]
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