Delineating site-specific management zones for precision agriculture

SUMMARYDelineating site-specific management zones within fields can be helpful in addressing spatial variability effects for adopting precision farming practices. A 3-year (2008/09 to 2010/11) field study was conducted at the Postgraduate Agricultural Research Station, University of Agriculture, Faisalabad, Pakistan, to identify the most important soil and landscape attributes influencing wheat grain yield, which can be used for delineating management zones. A total of 48 soil samples were collected from the top 300 mm of soil in 8-ha experimental field divided into regular grids of 24 × 67 m prior to sowing wheat. Soil and landscape attributes such as elevation, % of sand, silt and clay by volume, soil electrical conductivity (EC), pH, soil nitrogen (N) and soil phosphorus (P) were included in the analysis. Artificial neural network (ANN) analysis showed that % sand, % clay, elevation, soil N and soil EC were important variables for delineating management zones. Different management zone schemes ranging from three to six were developed and evaluated based on performance indicators using Management Zone Analyst (MZA V0·1) software. The fuzziness performance index (FPI) and normalized classification entropy NCE indices showed minimum values for a four management zone scheme, indicating its appropriateness for the experimental field. The coefficient of variation values of soil and landscape attributes decreased for each management zone within the four management zone scheme compared to the entire field, which showed improved homogeneity. The evaluation of the four management zone scheme using normalized wheat grain yield data showed distinct means for each management zone, verifying spatial variability effects and the need for its management. The results indicated that the approach based on ANN and MZA software analysis can be helpful in delineating management zones within the field, to promote precision farming practices effectively.

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Identificación de zonas de manejo sitio-específico a partir de la combinación de variables de suelo

Ciencia y Tecnología Agropecuaria ◽

10.21930/rcta.vol13_num1_art:239 ◽

2012 ◽

Vol 13 (1) ◽

pp. 47 ◽

Cited By ~ 2

Author(s):

Mariano Córdoba ◽

Mónica Balzarini ◽

Cecilia Bruno ◽

José Luis Costa

Keyword(s):

Spatial Variability ◽

Principal Component ◽

Production Potential ◽

Site Specific ◽

Management Zones ◽

Soil Variables ◽

Regionalized Variables ◽

Several Variables ◽

Specific Management

El manejo sitio-específico demanda la identificación de sub-regiones homogéneas, o zonas de manejo (ZM), dentro del espacio productivo. Sin embargo, definir ZM suele ser complejo debido a que la variabilidad espacial del suelo puede depender de varias variables. La zonificación o delimitación de ZM puede realizarse utilizando una variable de suelo a la vez o considerando varias variables simultáneamente. Entre los métodos de análisis multivariado, difundido para la zonificación, se encuentra el análisis de conglomerados fuzzy k-means (KM) y el análisis de componentes principales (PCA). No obstante, como otros métodos multivariados, éstos no han sido desarrollados específicamente para datos georreferenciados. Una nueva versión del PCA, conocido como MULTISPATI-PCA (PCAe), permite contemplar la autocorrelación espacial entre datos de variables regionalizadas. El objetivo de este estudio fue proponer una nueva estrategia de análisis para la identificación de ZM, combinando la aplicación KM y PCAe sobre datos de múltiples variables de suelo. La capacidad del método propuesto se evaluó en base a la comparación de los rendimientos promedios alcanzados en cada zona delimitada, tanto para la combinación de KM con PCA, la aplicación tradicional de KM sobre las variables originales y la nueva propuesta KM-PCAe. Los resultados mostraron que KM-PCAe fue el único método que permitió distinguir zonas estadísticamente diferentes en cuanto al potencial productivo. Se concluye que la combinación propuesta constituye una herramienta importante para el mapeo de la variabilidad espacial y la identificación de ZM a partir de datos georreferenciados. Identification of site-specific management zones from combination of soil variablesSite-specific management demands the identification of homogeneous subfield regions within the field or management zones (MZ). However, due to the spatial variability of soil variables, determination of MZ from several variables, is often complex. Although the zonification or delimitation of MZ may be univariate, it is more appropriate to consider all variables simultaneously. Fuzzy k-means clustering (KM) and principal component analysis (PCA) are multivariate analyses that have been used for zonification. Nevertheless, PCA and KM have not been explicitly developed for georeferenced data. Novel versions of PCA, known as MULTISPATI-PCA (PCAe), incorporate spatial autocorrelation among data of neighbor sites of regionalized variables. The objective of this study was to propose a new analytical tool to identify homogeneous zones from the combination of KM and PCAe on multiple soil variable data. The performance of proposed method was assessed through comparison of the average yields obtained in each zone delimited by combination of KM with PCA, as well as KM on the original variables and the new proposed method KM-PCAe. The results showed that KM-PCAe was the only method able to identify zones statistically different in terms of production potential. PCAe and its combination with KM are useful tools to map spatial variability and to identify MZ within fields from georeferenced data.

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