Using Geospatial Information to Map Yield Gain from the Use of <i>Azospirillum brasilense</i> in Furrow
George Deroco Martins,
Laura Cristina Moura Xavier,
Guilherme Pereira de Oliveira,
Maria de Lourdes Bueno Trindade Gallo,
Carlos Alberto Matias de Abreu Júnior,
Bruno Sérgio Vieira,
Douglas José Marques,
Filipe Vieira da Silva
Affiliations
George Deroco Martins
Instutute of Geography, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
Laura Cristina Moura Xavier
Post Graduate Program in Agriculture and Geospatial Information, Institute of Agrarian Sciences, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
Guilherme Pereira de Oliveira
Lallemand Soluções Biológicas LTDA, Patos de Minas 38706-420, BR-MG, Brazil
Maria de Lourdes Bueno Trindade Gallo
Cartography Departament, Faculdade de Ciências e Tecnologia, Universidade Estadual Paulista, São Paulo 19060-900, Brazil
Carlos Alberto Matias de Abreu Júnior
Post Graduate Program in Agriculture and Geospatial Information, Institute of Agrarian Sciences, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
Bruno Sérgio Vieira
Institute of Agrarian Sciences, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
Douglas José Marques
Institute of Agrarian Sciences, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
Filipe Vieira da Silva
Instutute of Geography, Unversidade Federal de Uberlândia, Monte Carmelo 38500-000, BR-MG, Brazil
The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense.