Análise do ganho de produtividade de soja inoculada com microrganismos solubilizadores de fósforo por meio de informações geoespaciais

Abstract

One of the major challenges of soybean cultivation in tropical regions is the low soil fertility, which limits phosphorus availability and leads to the intensive use of synthetic chemical fertilizers. This increases production costs, creates environmental impacts, and promotes dependence on imported inputs. In modern agriculture, beneficial microorganisms have emerged as alternatives for improving nutrient use efficiency, especially those capable of solubilizing low-solubility inorganic phosphates. This represents a promising, environmentally sustainable, and economically viable approach that contributes to increasing the availability of this nutrient to plants. The interaction between inoculants and plants, in addition to being complex, can manifest subtly. In such cases, classical statistical methods are generally unable to reveal productivity differences between treated areas, particularly when considering the full dynamics involved in the various developmental stages of the crop. In this context, geostatistical tools have been used to estimate spatial relationships between agricultural yield and different crop management parameters. The aim of this study is to evaluate soybean yield gains associated with inoculation using phosphorus-solubilizing microorganisms through statistical and geostatistical analysis of georeferenced yield data and, additionally, to determine whether spectral variables derived from multispectral images can explain and predict this yield using machine-learning algorithms. The results showed that the Random Forest algorithm provided the best soybean yield estimates with the lowest error, supported by multispectral images that incorporated information about the crop’s phenological dynamics, generating accurate estimates with reduced physical effort. With a large amount of training data, the kriging technique made it possible to generate a surface showing an average yield increase of 3.93%, validating the effectiveness of the P-solubilizing agent.