Tools for Optimizing Management of a Spatially Variable Organic Field

Thomas Panagopoulos,Jorge De Jesus,Jiftah Ben-Asher

doi:10.3390/agronomy5010089

Abstract

Geostatistical tools were used to estimate spatial relations between wheat yield and soil parameters under organic farming field conditions. Thematic maps of each factor were created as raster images in R software using kriging. The Geographic Resources Analysis Support System (GRASS) calculated the principal component analysis raster images for soil parameters and yield. The correlation between the raster arising from the PC1 of soil and yield parameters showed high linear correlation (r = 0.75) and explained 48.50% of the data variance. The data show that durum wheat yield is strongly affected by soil parameter variability, and thus, the average production can be substantially lower than its potential. Soil water content was the limiting factor to grain yield and not nitrate as in other similar studies. The use of precision agriculture tools helped reduce the level of complexity between the measured parameters by the grouping of several parameters and demonstrating that precision agriculture tools can be applied in small organic fields, reducing costs and increasing wheat yield. Consequently, site-specific applications could be expected to improve the yield without increasing excessively the cost for farmers and enhance environmental and economic benefits.

Highlights

Wheat (Triticum turgidum var. durum) is cultivated over more than 13 million hectares worldwide [1]
This parameter was detrended, and the residuals were used for the semivariogram creation and interpolation
The data show that durum wheat yield is strongly affected by soil parameter variability, and the average production can be substantially lower than its potential

Summary

Introduction

Wheat (Triticum turgidum var. durum) is cultivated over more than 13 million hectares worldwide [1]. The management regime of those crops has undergone a series of changes as a result of an increase in average field size. New tools are required to enable a global view of these larger-sized fields and to determine the heterogeneous zones that often appear within them. The use of yield prediction maps is an important tool for the delineation of within-field management zones. Yield prediction maps are of great importance to ensure that yields are maximized with fewer inputs, less waste and less environmental impact. Yield monitoring and mapping have given producers a direct method for measuring spatial variability in yield [1]. Producers have expressed increased interest in characterizing soil variability

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