Data fusion techniques for delineation of site-specific management zones in a field in UK
Tóm tắt
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.
Tài liệu tham khảo
Carroll, S. S., & Cressie, N. (1996). A comparison of geostatistical methodologies used to estimate snow water equivalent. Journal of the American Water Resources Association, 32(2), 267–278.
Castrignanò, A., Costantini, E. A. C., Barbetti, R., & Sollitto, D. (2009). Accounting for extensive topographic and pedologic secondary information to improve soil mapping. Catena, 77, 28–38.
Castrignanò, A., Giugliarini, L., Risaliti, R., & Martinelli, N. (2000). Study of spatial relationships among some soil physico-chemical properties of a field in central Italy using multivariate geostatistics. Geoderma, 97, 39–60.
Castrignanò, A., Wong, M. T. F., Stelluti, M., De Benedetto, D., & Sollitto, D. (2012). Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation. Geoderma, 175–176(2012), 78–89.
Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. R. (2001). Near-infrared reflectance spectroscopy-principal components regression analyses of soil properties. Soil Science Society of America Journal, 65, 480–490.
Chilès, J. P., & Delfiner, P. (1999). Geostatistics: Modelling spatial uncertainty. New York: Wiley. 695 pp.
Cohen, M. J., Prenger, J. P., & DeBusk, W. F. (2005). Visible-near infrared reflectance spectroscopy for rapid, nondestructive assessment of wetland soil quality. Journal of Environmental Quality, 34, 1422–1434.
Durrant-Whyte, H. (2001). Multi Sensor Data Fusion. Australian Centre for Field Robotics, University of Sydney. Version 1.2.
Geovariances. (2013). Isatis technical ref., ver. 2013.1. France: Geovariances & Ecole Des Mines De Paris.
Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University Press. 483 pp.
Guastaferro, F., Benedetto, D., Sollitto, D., Troccoli, A., & Cafarelli, B. A. (2010). Comparison of different algorithms for the delineation of management zones. Precision Agriculture, 11, 600–620.
Halcro, G., Corstanje, R., Mouazen, A.M. (2013). Site-specific land management of cereal crops based on management zone delineation by proximal soil sensing. In J. Stafford (Ed.), Precision Agriculture 2013, Proceedings of the 10th European Conference on Precision Agriculture (pp. 475–481). Wageningen: Wageningen Academic Publishers.
Journel, A. G., & Huijbregts, C. J. (1978). Mining geostatistics. London: Academic Press.
Khosla, R., & Alley, M. (1999). Soil-specific nitrogen management on mid-Atlantic coastal plain soils. Better Crops, 83(3), 6–7.
Khosla, R., Westfall, D. G., Reich, R. M., Mahal, J. S., & Gangloff, W. J. (2010). Spatial variation and site-specific management zones. In M. A. Oliver (Ed.), Geostatistical applications for precision agriculture (pp. 195–219). Dordrecht, Heidelberg, London, New York: Springer.
Martens, H., & Naes, T. (1989). Multivariate calibration. Chichester: Wiley.
Molin, J. P., & Castro, C. N. (2008). Establishing management zones using soil electrical conductivity and other soil properties by the fuzzy clustering technique. Scientia Agricola, 65, 567–573.
Moral, F. J., Terrón, J. M., & Silva, J. R. M. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research, 106, 335–343.
Morari, F., Castrignanò, A., & Pagliarin, C. (2009). Application of multivariate geostatistics in delineating management zones within a gravelly vineyard using geo-electrical sensors. Computers and Electronics in Agriculture, 68, 97–107.
Mouazen, A.M. (2006). Soil Survey Device. International publication published under the patent cooperation treaty (PCT). World Intellectual Property Organization, International Bureau. International Publication Number: WO2006/015463; PCT/BE2005/000129; IPC: G01N21/00; G01N21/00.
Mouazen, A. M., De Baerdemaeker, J., & Ramon, H. (2005). Towards development of on-line soil moisture content sensor using a fibre-type NIR spectrophotometer. Soil and Tillage Research, 80, 171–183.
Mouazen, A. M., De Baerdemaeker, J., & Ramon, H. (2006). Effect of wavelength range on the measurement accuracy of some selected soil constituents using visual-near infrared spectroscopy. Journal of Near Infrared Spectroscopy, 14, 189–199.
Mulla, D. J., Bhatti, A. U., Hammond, M. W., & Benson, J. A. (1992). A comparison of winter wheat yield and quality under uniform versus spatially variable fertilizer management. Agriculture, Ecosystems and Environment, 38, 301–311.
Murphy, J., & Riley, J. P. (1962). A modified single solution method for determination of phosphate in natural waters. Analytica Chimica Acta, 27, 31–36.
Olsen, S. R., Cole, C. V., Watanabe, F. C., and Dean, L. S. (1954). Estimation of available phosphorus in soil by extraction with sodium bicarbonate. Circular/United States Department of Agriculture, 939.
Rivoirard, J. (2001). Which models for collocated cokriging. Mathematical Geology, 33, 117–131.
Shepherd, K. D., & Walsh, M. G. (2002). Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal, 66, 988–998.
Soil Survey Division Staff. (1993). Soil survey manual. Soil Conservation Service. U.S. Department of Agriculture Handbook, 18.
Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89. doi:10.1016/S0034-4257(97)00083-7.
Stenberg, B., Viscarra Rossel, R. A., Mouazen, M. A., & Wetterlind, J. (2010). Visible and near infrared spectroscopy in soil science. Advances in Agronomy, 107, 163–215.
Wackernagel, H. (1988). Geostatistical techniques for interpreting multivariate spatial information. In C. F. Chung, A. G. Fabbri, & R. Sinding-Larsen (Eds.), Quantative analysis of mineral and energy resources (pp. 393–409). Dordrecht: Reidal.
Wackernagel, H. (2003). Multivariate geostatistics: An introduction with applications (3rd ed.). Berlin: Springer.