Spatial-temporal Analysis of Soybean Productivity Using Geostatistical Methods

Gustavo Henrique Dalposso,Fernanda De Bastiani,Miguel Angel Uribe-Opazo

doi:10.5296/jas.v9i2.18494

Abstract

To have information about the soybean productivity over the crop years is essential to define strategies to increase profits and reduce costs and most important to reduce environmental impacts. One form of monitoring is the use of Geostatistical methods, which allow us to obtain maps with more accurate predictions. In this paper, an area of 127.16 ha was studied during six crop years between 2012/2013 and 2017/2018. We found that productivity values vary between crop years, mainly due to uncontrollable climatic factors. The removal of influential points caused changes in the predicted values showed in the maps, and the use of scaled semivariograms allowed us to obtain similar maps to those obtained considering the model without influential points, then there was no need to exclude observations. The use of a model with replicates helped to identify regions where productivity was lower. The use of explanatory variables allowed us to elaborate a more accurate thematic map in the 2017/2018 crop year, which was well evidenced by the prediction standard error map.

Highlights

Soybean (Glycine max (L.) Merrill) is a source of food, protein, and oil (Syed et al, 2019), and2021, Vol 9, No 2 one of the most important crops worldwide
The removal of influential points caused changes in the predicted values showed in the maps, and the use of scaled semivariograms allowed us to obtain similar maps to those obtained considering the model without influential points, there was no need to exclude observations
The average soybean productivity obtained in the agricultural year 2012/2013 (3.26 t ha-1) was satisfactory, considering that it was close to the average obtained in Paraná(3.35 t ha-1), the Brazilian state that received the second-highest average productivity in this crop year (CONAB, 2019)

Summary

Introduction

Soybean (Glycine max (L.) Merrill) is a source of food, protein, and oil (Syed et al, 2019), and2021, Vol 9, No 2 one of the most important crops worldwide. Given the importance of soy for agribusiness, research is often carried out to develop productivity maps, one of the most widely used precision farming techniques (Vega et al, 2019). Because the production of soybean has a high cost due to the use of fertilizers, pesticides, machines, and seeds (Costa and Santana, 2018), the use of productivity maps help in identifying regions with greater productive potential. These regions can be managed differently, optimizing inputs, improving profits, and reducing environmental impacts (Vatsanidou et al, 2017). Among the many methodologies used to create thematic maps, geostatistical methods have been widely applied in precision agriculture (Lamour et al, 2020)

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