Software development for spatial variability mapping in agriculture and environment
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Abstract
Diverse agronomic and environmental data are collected with geographical position systems enabling the construction of maps that describe the variation of the studied properties over the space. Uni and multivariate spatial variability has been used to understand observation variation within crop fields and between sites in a large territory, allowing the delimitation of homogeneous areas at different scales. The use of spatial statistics methods, both regionally and on a small scale, demands specialized software programs. FastMapping is an interactive web application developed to process spatial data. It allows users to preprocess georeferenced data (clean, standardize and re-scale), it facilitates spatial interpolation and the generation of spatial variability maps quasi-automatically. This work compares the multivariate zoning of agricultural fields according to soil properties produced by FastMapping and that obtained from commercial software used for precision agriculture. FastMapping allows to import, clean, analyze and visualize spatial data, in a single environment, provide an easy-to-use framework for the effective implementation of spatial statistics methods.
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Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis, 27 (2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Anselin, L. (1996). The Moran scatterplot as an ESDA tool to assess local instability in spatial association. En Manfred Fischer, Henk J. Scholten y David Unwin (Eds.), Spatial AnalyticalPerspectives on GIS Vol. 4 (p. 121-138). London, United Kingdom: Taylor & Francis.
Betzek, N. M., Souza, E. G. de, Bazzi, C. L., Schenatto, K., Gavioli, A. y Magalhães, P. S. G. (2019). Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps. Computers and Electronics in Agriculture, 157, 49–62. https://doi.org/10.1016/j.compag.2018.12.004
Bezdek, J. C., Coray, C., Gunderson, R. y Watson, J. (1981). Detection and characterization of cluster substructure I. Linear structure: Fuzzy c-lines. SIAM Journal on Applied Mathematics, 40(2), 339–357.
Bivand, R. S. y Wong, D. W. S. (2018). Comparing implementations of global and local indicators of spatial association. TEST, 27, 716–748. https://doi.org/10.1007/s11749-018-0599-x
Chang, W., Cheng, J., Allaire, J. J., Xie, Y. y McPherson, J. shiny: Web Application Framework for R (version 1.4.0.2) [Software de cómputo]. Recuperado de: https://cran.r-project.org/package=shiny
Córdoba, M., Bruno, C., Costa, J. L. y Balzarini, M. (2013). Subfield management class delineation using cluster analysis from spatial principal components of soil variables. Computers and Electronics in Agriculture, 97, 6–14. https://doi.org/10.1016/j.compag.2013.05.009
Córdoba, M. A., Bruno, C. I., Costa, J. L., Peralta, N. R. y Balzarini, M. G. (2016). Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems Engineering, 143, 95–107. https://doi.org/10.1016/j.biosystemseng.2015.12.008
Córdoba, M., Paccioretti, P., Giannini Kurina, F., Bruno, C. y Balzarini, M. (2019). Guía para el análisis de datos espaciales. Aplicaciones en agricultura.Córdoba, Argentina: Brujas.
Cressie, N., y Wikle, C. K. (2011). Statistics for spatio-temporal data. New York, USA: John Wiley & Sons.
Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G.…y Wagner, H. H.. adespatial: Multivariate Multiscale Spatial Analysis (versión 0.3-8) [Software de cómputo]. Recuperado de:https://cran.r-project.org/package=adespatial
Dray, S., Saïd, S. y Débias, F. (2008). Spatial ordination of vegetation data using a generalization of Wartenberg’s multivariate spatial correlation. Journal of Vegetation Science, 19 (1), 45–56. https://doi.org/10.3170/2007-8-18312
Fridgen, J. J., Kitchen, N. R. y Sudduth, K. A. (2004). Management zone analyst (MZA): Agronomy Journal, 96 (1), 100–108. https://www.soils.org/publications/aj/abstracts/96/1/100
Frogbrook, Z. L. y Oliver, M. A. (2007). Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data. Soil Use and Management, 23 (1), 40–51. https://doi.org/10.1111/j.1475-2743.2006.00065.x
Galarza, R., Mastaglia, M. N., Albornoz, E. M. y Martınez, C. (2013). Identificación automática de zonas de manejo en lotes productivos agrıcolas. V Congreso Argentino de Agroinformática (CAI) y 42da. Jornadas Argentinas de Informática. Córdoba, Argentina. http://sinc.unl.edu.ar/sinc-publications/2013/GMAM13
Giannini Kurina, F., Hang, S., Córdoba, M. A., Negro, G. J. y Balzarini, M. G. (2018). Enhancing edaphoclimatic zoning by adding multivariate spatial statistics to regional data. Geoderma, 310, (170-177). https://doi.org/10.1016/j.geoderma.2017.09.011
Hiemstra, P. H., Pebesma, E. J., Twenhöfel, C. J. W. y Heuvelink, G. B. M. (2009). Real-time automatic interpolation of ambient gamma dose rates from the Dutch radioactivity monitoring network. Computers & Geosciences, 35(8), 1711–1721. https://doi.org/10.1016/j.cageo.2008.10.011
Hijmans, R. J. raster: Geographic Data Analysis and Modeling (versión 3.1-5) [Software de cómputo]. Recuperado de: https://cran.r-project.org/package=raster
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. En XXIV International Joint Conference on Artificial Intelligence, (2), 1137–1145. San Francisco, CA, United States: Morgan Kaufmann Publishers Inc.
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. y Leisch, F. e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien (versión 1.7-3) [Software de cómputo]. Recuperado de: https://cran.r-project.org/package=e1071
Moral, F. J., Terrón, J. M. y Marques da Silva, J. R. (2010). Delineation of management zones using mobile measurements of soil apparent electrical conductivity and multivariate geostatistical techniques. Soil and Tillage Research, 106 (2), 335–343. https://doi.org/10.1016/j.still.2009.12.002
Nychka, D., Furrer, R., Paige, J. y Sain, S.fields: Tools for spatial data (version 10.3) [Software de cómputo]. Boulder, Colorado, U.S.: University Corporation for Atmospheric Research.
Odeh, I. O. A., McBratney, A. B. y Chittleborough, D. J. (1992). Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Science Society of America Journal, 56 (2), 505–516. https://doi.org/10.2136/sssaj1992.03615995005600020027x
Ping, J. L. y Dobermann, A. (2003). Creating Spatially Contiguous Yield Classes for Site-Specific Management. Agronomy Journal, 95 (5), 1121-1131. https://doi.org/10.2134/agronj2003.1121
R Core Team. (2019). R: A Language and Environment for Statistical Computing (versión 3.6.3) [Software de cómputo]. Viena, Austria. Recuperado de: https://www.r-project.org/
Ribeiro, P. J. y Diggle, P. J. (2018). geoR: Analysis of Geostatistical Data. (version 1.8-1) [Software de cómputo] Recuperado de: https://cran.r-project.org/package=geoR
Stein, M. L. (1999). Interpolation of Spatial Data.Some Theory for Kriging.New York, United States: Springer. https://doi.org/10.1007/978-1-4612-1494-6
Sudduth, K. A. y Drummond, S. T. (2007). Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal, 99 (6), 1471–1482. https://doi.org/10.2134/agronj2006.0326
Taylor, J. A., McBratney, A. B. y Whelan, B. M. (2007). Establishing Management Classes for Broadacre Agricultural Production. Agronomy Journal, 99(5), 1366-1376. https://doi.org/10.2134/agronj2007.0070
Vega, A., Córdoba, M., Castro-Franco, M. y Balzarini, M. (2019). Protocol for automating error removal from yield maps. Precision Agriculture,19(102), 1–15.
Webster, R. y Oliver, M. A. (2007). Geostatistics for Environmental Scientists(2da ed.).New York, USA: John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470517277
Xie, X. L. y Beni, G. (1991). A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(8), 841–847. https://doi.org/10.1109/34.85677