Determination of potential management zones from soil electrical conductivity, yield and crop data

Li, Yan; Shi, Zhou; Wu, Cifang; Li, Hongyi; Feng, Li

doi:10.1631/jzus.b071379

Cited by 50 publications

(31 citation statements)

References 33 publications

(35 reference statements)

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“…For the practical use of these results, it would be recommended to include the weather data into the procedure and soil verifications. This was also suggested by Flemming and Westfall (2000), King et al (2005) and Li et al (2008).…”

Section: Methodssupporting

confidence: 74%

Application of Clustering Method to Determine Production Zones of Field

Ingeli

Galambošová

Prokeinová

et al. 2015

Acta Technologica Agriculturae

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Determining the production zones of field is an important analysis in the precision farming technology as these may be used to control field operations in site-specific application. The aim of this paper was to evaluate the potential to identify the yield potential zones based on historical yield maps and to evaluate the procedure over the growing extent of input data. Standardized yield values from six growing seasons were considered. Suitable datasets were created, and hierarchical and non-hierarchical clustering methods were applied to create clusters. Results showed that using the data from commercial combine monitoring systems enables determining the zones. Multiple yield data are recommended as the values of analyses increase with the increased number of input datasets. However, commercial data have limitations in terms of complexity.

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Section: Methodssupporting

confidence: 74%

Application of Clustering Method to Determine Production Zones of Field

Ingeli

Galambošová

Prokeinová

et al. 2015

Acta Technologica Agriculturae

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show abstract

“…Thus, alternative methods are sought to replace crop records (Blackmore, 2000;Milne et al, 2012), soil properties (Kitchen et al, 2005;Chang et al, 2014), and their combination (Li et al, 2008;Mann et al, 2011), which are still widely used at present.…”

Section: Introductionmentioning

confidence: 99%

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

et al. 2017

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Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixel TM chlorophyll index and PurePixel TM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha -1 ) and a high performing one (GY>8 Mg ha -1 ). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

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“…Data on the two harvests did not reveal any gain by implementing the CVI index, due to the similar performances of FPI and MPE. However, specialized literature (Li, Shi, Wu, Li, & Li, 2008;Valente et al, 2012) reports instances in which the above failed to occur and that CVI would provide a less subjective decision on the best interpolation or on the number of management areas.…”

Section: Resultsmentioning

confidence: 99%