Data fusion techniques for delineation of site-specific management zones in a field in UK

Data fusion techniques for delineation of site-specific management zones in a field in UK Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Data fusion techniques for delineation of site-specific management zones in a field in UK

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-015-9417-6
Publisher site
See Article on Publisher Site

Abstract

Fusion of different data layers, such as data from soil analysis and proximal soil sensing, is essential to improve assessment of spatial variation in soil and yield. On-line visible and near infrared (Vis–NIR) spectroscopy have been proved to provide high resolution information about spatial variability of key soil properties. Multivariate geostatistics tools were successfully implemented for the delineation of management zones (MZs) for precision application of crop inputs. This research was conducted in a 18 ha field to delineate MZs, using a multi-source data set, which consisted of eight laboratory measured soil variables (pH, available phosphorus (P), cation exchange capacity, total nitrogen (TN), total carbon (TC), exchangeable potassium (K), sand, silt) and four on-line collected Vis–NIR spectra-based predicted soil variables (pH, P, K and moisture content). The latter set of data was predicted using the partial least squares regression (PLSR) technique. The quality of the calibration models was evaluated by cross-validation. Multi-collocated cokriging was applied to the soil and spectral data set to produce thematic spatial maps, whereas multi-collocated factor cokriging was applied to delineate MZ. The Vis–NIR predicted K was chosen as the exhaustive variable, because it was the most correlated with the soil variables. A yield map of barley was interpolated by means of the inverse distance weighting method and was then classified into 3 iso-frequency classes (low, medium and high). To assess the productivity potential of the different zones of the field, spatial association between MZs and yield classes was calculated. Results showed that the prediction performance of PLSR calibration models for pH, P, MC and K were of excellent to moderate quality. The geostatistical model revealed good performance. The estimates of the first regionalised factor produced three MZs of equal size in the studied field. The loading coefficients for TC, pH and TN of the first factor were highest and positive. This means that the first factor can be assumed as a synthetic indicator of soil fertility. The overall spatial association between the yield classes and MZs was about 40 %, which reveals that more than 50 % of the yield variation can be attributed to more dynamic factors than soil parameters, such as agro-meteorological conditions, plant diseases and nutrition stresses. Nevertheless, multivariate geostatistics proved to be an effective approach for site-specific management of agricultural fields.

Journal

Precision AgricultureSpringer Journals

Published: Oct 27, 2015

References

  • A comparison of geostatistical methodologies used to estimate snow water equivalent
    Carroll, SS; Cressie, N
  • Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics
    Castrignanò, A; Giugliarini, L; Risaliti, R; Martinelli, N
  • Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation
    Castrignanò, A; Wong, MTF; Stelluti, M; Benedetto, D; Sollitto, D

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