Abstract
This paper aimed to analyze the spatial autocorrelation of soybean yield and its bivariate spatial correlation with theagrometeorological variables rainfall, mean temperature, and mean global solar radiation in 2014/2015, 2015/2016, and 2016/2017 crop years in the West of the State of Paraná – Brazil. To achieve this objective, techniques of spatial statistics of areas were used, which, through autocorrelation and spatial correlation indices, sought to identify patterns of association between soybean yield and agrometeorological variables. This research is justified because in addition to the soybean crop being the main source of food protein and vegetable oil in the world and the agrometeorological variables being the factors that most influence it, the western mesoregion of Paraná stands out with the highest production values in the state. Thus, it is important to monitor its development through spatial analysis to obtain information that will support decision making. The global and local Moran’s indices showed that soybean yield is self-correlated in the municipalities of Western Paraná, identifying clusters to the west and east of the mesoregion. The significance of the bivariate spatial correlation indices confirmed the influence of the variables rainfall, mean temperature, and average global solar radiation on soybean yield.
Highlights
Similar to the history of GIS – Geographic information system, spatial statistics emerged independently, scattered across a diverse set of research domains (Chan-Tack, 2014)
The objective of this paper is to analyze the spatial autocorrelation of soybean yield and its bivariate spatial correlation with the agrometeorological variables rainfall, mean temperature, and mean global solar radiation in the 2014/2015, 2015/2016, and 2016/2017 crop years in the West of the State of Paraná – Brazil
In the southern region of Brazil, climatic conditions were adequate for soybean development, in the western region of Paraná, irregular precipitation had an impact on the crop (CONAB, 2015a)
Summary
Similar to the history of GIS – Geographic information system, spatial statistics emerged independently, scattered across a diverse set of research domains (Chan-Tack, 2014) They play a fundamental role in understanding variables related to the most diverse areas of knowledge, such as public health (Silva et al, 2020), climatology (Barboza et al, 2020), and aquaculture (Francisco et al, 2020). The second largest Brazilian soybean producer is the state of Paraná, which still in the 2016/2017 cycle achieved an average yield of 3,508 t ha-1 In this harvest year, the mesoregion of the West Paraná State, which obtained an average yield higher than the national average, with 3,874 t ha-1 (SEAB, 2018), stood out. As explained by Araujo et al (2014), areas spatial statistics techniques aim to describe and analyze the behavior of phenomena that occur in space to understand the spatial distribution of data, patterns of association, or different spatial regimes, through aggregated data (polygons) with geographic references (Seffrin, 2017)
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