Towards site specific management zones delineation in rotational cropping system: Application of multivariate spatial clustering model based on soil properties

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

“…Additionally, exploratory analyses and normality assessments were conducted, employing quantile-quantile (Q-Q) plots, histograms, and skewness values. Notably, some sampling locations exhibited atypical values or outliers that deviated from the general data set pattern, which were addressed to ensure accurate decision making [24,38]. The presence of non-normally distributed data with atypical values can introduce distortion, violating the principles of geostatistical theory [48,49].…”

“…It identifies the minimum data set (MDS) and generates new orthogonal variables known as principal components (PCs) [22,23]. PCA is widely employed as a data reduction method to derive smaller groups from multivariate data, consistently producing optimal outcomes in recent research endeavors focused on delineating MZs [22,[24][25][26].…”

Use of supervised and unsupervised approaches to make zonal application maps for variable-rate application of crop growth regulators in commercial cotton fields

“…(Pedersen and Lind, 2017). At the same time, many studies explore the importance of delineating zones for input management (Plant 2001;Taylor et al 2003;Nawar et al 2017;Ouazaa et al 2022), what field information to use to delineate the maps (Santi et al 2016;Ortuani et al 2019;Mazur et al 2022), the profitability of applications (Roberts et al, 2006;Boyer et al 2010) and statistical frameworks (Whelan and McBratney, 2000;Buttafuoco et al 2010).…”

“…They can be ready-to-use algorithms (e.g., obtained by research institutions), but it is important to consider local adaptation of methodologies, as this can captures local conditions and specificities (Liu et al, 2021;Vaz et al, 2023). Most studies use unsupervised multivariate techniques such as clustering techniques and principal component analysis (PCA) to delineate management zones (Fridgen et al, 2004;Córdoba et al, 2016;Damian et al, 2020;Ouazaa et al, 2022;Vaz et al, 2023). One of the main objectives of principal component analysis (PCA) is to reduce the dimensionality and retain the maximum possible variance of the variables, a task performed by creating a linear combination of conceptually relevant variables.…”

“…One of the main objectives of principal component analysis (PCA) is to reduce the dimensionality and retain the maximum possible variance of the variables, a task performed by creating a linear combination of conceptually relevant variables. The application of PCA in agriculture has been highlighted both by authors in the statistical field (Jolliffe, 2002) and by authors in the application areas, as in precision agriculture techniques (Córdoba et al, 2016;Ortuani et al, 2019;Ouazaa et al, 2022;Vaz et al 2023) for different crops and aspects of their management.…”