Delineating Site‐Specific Management Zones and Evaluating Soil Water Temporal Dynamics in a Farmer's Field in Kentucky

Abstract

Core Ideas Clay content, soil organic matter, slope, NDVI, and topographic wetness index delineate management zones. Hard and fuzzy clustering analysis resulted in divisions of two and three areas. Wavelet analysis revealed variability in temporal soil water dynamics between zones. Due to spatial variability of soil genesis, topography, and resulting soil properties in farmers' fields, soil and crop processes vary in space and time. Therefore, optimum rates and timing of resource applications, such as nutrients and irrigation water, may vary as well. It remains a challenge to quantify the spatial variability of a field and to identify effective ways to manage fields in a site‐specific manner. The objective of this study was to delineate management zones within a farmer's field based on relatively easily obtainable information that is statistically integrated. Moreover, soil water temporal dynamics should be evaluated regarding their spatial differences in different zones. The set of direct and indirect observations included clay and silt content, apparent electrical conductivity, soil chemical properties (pH; organic matter; and total N, P, K, Ca, Mg, and Zn), satellite‐based normalized difference vegetation index (NDVI), and lidar‐based topographic variables in a western Kentucky field. Several key variables and their capability to describe spatial crop yield variability were identified by using principal component analysis: soil clay content, slope, soil organic matter content, topographic wetness index, and NDVI. Two types of cluster analysis were applied to delineate management zones. The cluster analyses revealed that two to three zones was the optimal number of classes based on different criteria. Delineated zones were evaluated and revealed significant differences in corn (Zea mays L.) yield and temporally different soil moisture dynamics. The results demonstrate the ability of the proposed procedure to delineate a farmer's field into zones based on spatially varying soil and crop properties that should be considered for irrigation management.

A soil moisture‐based framework for guiding the number and location of soil moisture sensors in agricultural fields

“…The weak correlations in our dataset agree well with previous studies also involving observations of soil moisture on the same day of the EC a survey, in which the correlation between EC a and gravimetric water content for a single survey were also weak (r 2 < .21; García et al, 2012). Single EC a surveys have been used to characterize the spatial variability of agricultural fields (Cambouris et al, 2006;Haghverdi et al, 2015; F I G U R E 6 Alternative delineation of management zones (MZs) using two soil moisture surveys (top), elevation (middle), and combined clay and sand fraction information (bottom) for Fields A (left), B (middle), and C (right) Johnson et al, 2003;Reyes et al, 2019), but MZs based on a single EC a survey can result in high intrazone soil moisture spatial variability and in highly fragmented zones of small area that would make precision farming operations difficult (Hedley & Yule, 2009). Recent evidence shows that multiple ECa surveys are required to improve the accuracy of soil moisture estimations based on EC a surveys (García et al, 2012;Huang et al, 2017).…”

“…As a result, the installation of SMS is often approached as a semipermanent setting (i.e., several growing seasons), where delineation of zones with more homogeneous soil moisture conditions relative to that of the entire field is a cost-effective alternative. Traditionally, the delineation of field management zones (MZs) has been aimed at guiding the application of fertilizers based on spatial patterns of grain yield and surface soil chemical properties (i.e., soil pH, P, organic matter; Schepers et al, 2004), apparent soil electrical conductivity in combination with soil texture and elevation (Fraisse et al, 2001;Peralta et al, 2015;Reyes et al, 2019), yield maps collected over multiple growing seasons and different phases of the crop rotation (Basso et al, 2007), and using proximal and remote canopy reflectance indices (e.g., normalized difference vegetation index [NDVI]) from unmanned aerial vehicles (Corti et al, 2020;Ohana-Levi et al, 2019) and satellite platforms (Boydell & McBratney, 2002). In recent years, advancements in variable rate irrigation technology coupled with the increasing need to optimize irrigation efficiency to conserve surface and groundwater resources has increased the focus on delineation of MZs with emphasis on soil moisture (Haghverdi et al, 2015;Hedley et al, 2012;Sadler et al, 2005).…”

Efeito Da Adubação Nitrogenada Na Atividade Enzimática Em Solos Cultivados Com Palma Forrageira

“…Por exemplo, Reyes et al (2019) usaram análise de clusters para delinear zonas de manejo e encontraram que o uso de algoritmos de cluster Fuzzi são os mais adequados e mais usados para agricultura de precisão.…”

Joint Simulation of Spatially Correlated Soil Health Indicators, Using Independent Component Analysis and Minimum/Maximum Autocorrelation Factors

“…Generally, soil variables are heterogeneous [4] and vary in space and time [5]. As such, it is in the best interest of decision-makers, scientists and other stakeholders to understand the spatial pattern of soil characteristics.…”