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Agronomy 2015, 5(1), 89-106; doi:10.3390/agronomy5010089

Tools for Optimizing Management of a Spatially Variable Organic Field

Research Center of Spatial and Organizational Dynamics (CIEO), University of Algarve, Campus Gambelas, Faro 8005-139, Portugal
Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Katif research center for coastal deserts development, Ministry of Science Sedot Negev Academic Campus, Sedot 86200, Israel
Author to whom correspondence should be addressed.
Academic Editor: Ole Wendroth
Received: 6 January 2015 / Revised: 13 March 2015 / Accepted: 16 March 2015 / Published: 23 March 2015
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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. View Full-Text
Keywords: GRASS; raster images; principal component analysis; organic farming; precision agriculture; geostatistics GRASS; raster images; principal component analysis; organic farming; precision agriculture; geostatistics

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Panagopoulos, T.; de Jesus, J.; Ben-Asher, J. Tools for Optimizing Management of a Spatially Variable Organic Field. Agronomy 2015, 5, 89-106.

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