Analysis of Four Delineation Methods to Identify Potential Management Zones in a Commercial Potato Field in Eastern Canada

Abstract

Management zones (MZs) are delineated areas within an agricultural field with relatively homogenous soil properties, and therefore similar crop fertility requirements. Consequently, such MZs can often be used for site-specific management of crop production inputs. This study evaluated the effectiveness of four classification methods for delineating MZs in an 8-ha commercial potato field located in Prince Edward Island, Canada. The apparent electrical conductivity (ECa) at two depths from a commercial Veris sensor were used to delineate MZs using three classification methods without spatial constraints (i.e., fuzzy k-means, ISODATA and hierarchical) and one with spatial constraints (i.e., spatial segmentation method). Soil samples (0.0–0.15 m depth) from 104 sampling points was used to measure soil physical and chemical properties and their spatial variation in the field were used as reference data to evaluate four delineation methods. Significant Pearson correlations between ECa and soil properties were obtained (0.22 < r < 0.85). The variance reduction indicated that two to three MZs were optimal for representing the field’s spatial variability of soil properties. For two MZs, most soil physical and chemical properties differed significantly between MZs for all four delineation methods. For three MZs, there was greater discrimination among MZs for several soil properties for the spatial segmentation-based method compared with other delineation methods. Moreover, consideration of the spatial coordinates of the data improved the delineation of MZs and thereby increased the number of significant differences among MZs for individual soil properties. Therefore, the spatial segmentation method had the greatest efficiency in delineation of MZs from statistical and agronomic perspectives.

Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System

“…Thus, the two methods are significantly influenced by the sample distance. Several previous studies have reported similar conclusions [ 11 , 21 , 48 ]. However, the GMM map was different from that of FCM and ISODATA.…”

“…First, fertilization practices significantly influence soil available nutrients (e.g., AP and AK), which contributes to the accumulation of soil available nutrients [ 39 ]. The fertilizer application rate varied greatly in intensive farming systems, resulting in high variability of soil AP and AK [ 11 ]. In addition, the crop rotation systems in this study are complicated; winter wheat and summer maize are the main cropping systems, and there are a few fields where wheat is rotated with peanuts or soybeans.…”

“…Management zones (MZs) have been delineated based on a variety of data: soil chemical properties [ 7 , 8 ], soil physical properties [ 9 , 10 ], apparent soil electrical conductivity (ECa) [ 11 ], historical yield data [ 12 ], satellite imagery [ 4 ], and others [ 13 ]. For medium- and small-scale farms, soil sampling is key to soil nutrient management [ 14 ].…”

“…Common clustering algorithms can be classified into two categories. The first clustering algorithm uses distance as the evaluation index of similarity to divide MZs, such as K-means [ 22 , 23 ], iterative self-organizing data analysis technique algorithm (ISODATA) [ 11 , 21 , 22 ], and fuzzy c-means clustering algorithm (FCM) [ 16 , 17 , 24 ]. FCM delineated MZs well in most cases, as confirmed in the aforementioned studies.…”

Delineation of management zones in integrated crop–livestock systems

“…These subregions may be called management zones (MZs), representing a combination of limiting factors that determine the spatial variability of soil properties and crop yield (Mallarino & Wittry, 2004). The soil's physical and topographical characteristics, vegetation biomass, and management practices are some of the most used data in cluster analysis algorithms to define MZs due to their stability over time (Khosla et al., 2002; Lajili et al., 2021; Schenatto et al., 2016). Monocultures such as soybean [ Glycine max (L.) Merr.…”