Delineation of Management Zones for Site-Specific Information about Soil Fertility Characteristics through Proximal Sensing of Potato Fields

Abstract

The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.

Clustering of the chemical properties of small‐scale farm soils to develop fertilizer blend ratios

“…Similarly, the pH in these clusters was the lowest necessitating the liming of the farms in that cluster. Similar results were achieved using cluster analysis to delineate the region of high and low potato yields, thus enhancing fertility management (Khan et al., 2020). Moreover, the archetypal analysis yielded similar grouping (van Erp et al., 2014).…”

Reducing Topdressing N Fertilization with Variable Rates Does Not Reduce Maize Yield

“…The UAVs [17][18][19][20][21] are massively used in agricultural systems [17][18][19][20][21]. Proximal sensing equipment also used for VR fertilization is represented by Greenseeker [22][23][24] and OptRx [25].…”