No. 18 (2005)
Articles

Spatially Continuous GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.)

Published March 4, 2005
János Tamás
Debreceni Egyetem Agrártudományi Centrum, Mezőgazdaságtudományi Kar, Víz- és Környezetgazdálkodási Tanszék, Debrecen
István Buzás
Kecskeméti Főiskola, Környezettudományi Intézet, Kecskemét
Ildikó Nagy
Debreceni Egyetem Agrártudományi Centrum, Mezőgazdaságtudományi Kar, Víz- és Környezetgazdálkodási Tanszék, Debrecen
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APA

Tamás, J., Buzás, I., & Nagy, I. (2005). Spatially Continuous GIS Analysis of Sampling Points Based on Yield and Quality Analysis of Sugar Beet (Beta vulgaris L.). Acta Agraria Debreceniensis, (18), 56-61. https://doi.org/10.34101/actaagrar/18/3248

The homogeneity of a study area of 20x20 m used for beetroot production in North-West Hungary was analysed with geo-statistical methods on the basis of measured plant and soil parameters. Based on variogram calculations (Equation 1 and 2), the yield surface showed homogeneity in North-South direction. Considering the results, decrease of sampling distance to 17 m can be suggested. The direction of the variability of yield (Figure 1) could be modelled with a direction variogram based on analysis of the variogram surface. In the study, developed methodological processes are presented for the analysis of spatial relationship between measured production and soil parameters. 5 spatial evaluation methods for yield surface were compared (Table 1). On the basis of the analysed methods, it can be stated that different methods (LP, RBF) should be used when the reasons for locally extreme yields are in focus than in case when the yield surface of the whole area is estimated (IDW, GP). Using adequate parameters the kriging method is applicable for both functions. Similarly to the results of an ordinary Pearson correlation analysis, spatial correlation analysis was shown using soil pH and Cu concentration data. The results of cross variogram analysis (Equation 2) and the North-South direction of the variogram surface showed negative correlation (Figure 3). Based on simulation calculations, decrease of 30% in sampling points resulted in increase of 12% in error for the total sample number considering Cu concentration. The method provides a tool to decrease the cost of sampling and sample analyses of spatially correlating features, and to increase the reliability of spatial estimation using a better sampling strategy with the same sample number.

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