Delineation of Management Zones Using Soil Properties and a Multivariate Geostatistical Approach

Abstract

Agricultural fields naturally have within-field variations that may cause spatial variability in crop yields. Delineating a field into a few management zones (MZ) is therefore a practical management approach for precision agriculture. In this study, some soil properties (clay content, organic matter content and water content measured at different dates) and a multivariate geostatistical approach were used to delineate management zones. The temporal stability of soil water content spatial patterns may be an additional approach in delineating management zones. The present study was conducted on a 120000 m 2 durum wheat field in Southern Italy, where soil properties and soil water content were measured at different times over three crop seasons (2005-06, 2006-07, and 2007-08) at 100 locations. The multivariate spatial and temporal data were analysed by Factor Kriging Analysis (FKA). All simple and cross model variograms were calculated by fitting a linear model of coregionalization including the nugget effect and two spatial structures at shorter (70 m) and longer range (500 m). The application of factor kriging allowed us to identify one regionalized factor at longer scale and to partition the field from the point of view of temporal stability of soil water content, into contiguous zones to be submitted to site-specific management. The method showed a great flexibility to combine data from several sources in the delineation of management zones.

The Tasseled Cap Transformation for RapidEye data and the estimation of vital and senescent crop parameters

“…Recent studies pointed out that site specific harvesting bears potential to increase wheat quality and net return rates in quality incentive markets (Morari et al 2013;Diacono et al 2012;Meyer-Aurich et al 2008;Wright et al 2004;Freeman et al 2003). In accordance with previous studies, Diacono et al (2012) reported that climatic conditions, especially available soil water, play a major role in affecting the spatial patterns of yield as well as other quality parameters of wheat crops in rain fed agricultural systems (Buttafuoco et al 2010;Ibrahim & Huggins 2011;Basso et al 2007). Practically, the longer the duration and slower the rate of senescence, the greater is the probability for higher yields (Herbek & Lee 2009).…”

Delineating site-specific management zones for precision agriculture

“…The delineation of site-specific management zones can play an important role in addressing the spatial variability effects by defining homogenous sub-field areas within an entire field. Defining the sub-field areas is difficult due to complex interactions between many factors such as climate, topography and soil properties (Buttafuoco et al 2010;Moral et al 2011). In some cases, interaction between these factors induces spatial variability and affects crop yield (Bakhsh et al 2000;Mzuku et al 2005;Bakhsh & Kanwar 2008).…”

“…Defining the sub-field areas is difficult due to complex interactions between many factors such as climate, topography and soil properties (Buttafuoco et al . 2010; Moral et al . 2011).…”

Hierarchical Factor Models for Large Spatially Misaligned Data: A Low‐Rank Predictive Process Approach

“…The choice of the number of spatial processes and their respective scales is a critical point in geostatistical models. In the applications of LMC to multivariate spatial analysis (see, e.g., Webster et al, ; Castrignanó et al, ; Buttafuoco et al, ), the spatial correlation functions , the parameters , and r are obtained from empirical estimates of the auto‐ and cross‐variograms before any modeling. This approach ignores the uncertainty in the estimates of the spatial parameters and may yield dubious inference.…”