Using Geospatial Information to Map Yield Gain from the Use of Azospirillum brasilense in Furrow

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 al...

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Bibliographic Details
Published in:Agronomy (Basel) 2023-03, Vol.13 (3), p.808
Main Authors: Martins, George Deroco, Xavier, Laura Cristina Moura, de Oliveira, Guilherme Pereira, de Lourdes Bueno Trindade Gallo, Maria, de Abreu Júnior, Carlos Alberto Matias, Vieira, Bruno Sérgio, Marques, Douglas José, da Silva, Filipe Vieira
Format: Article
Language:English
Subjects:
Agricultural production
Algorithms
Azospirillum brasilense
Bacteria
biological product
Biological products
Cereal crops
Corn
Crop yield
Crops
geospatial data analysis
Machine learning
Neural networks
Physiology
Precision farming
Root-mean-square errors
Spatial data
Spatial distribution
Statistical significance
Training
Variance analysis
yield gain distribution map
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Summary: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.
ISSN:2073-4395
2073-4395
DOI:10.3390/agronomy13030808