Spatiotemporal dynamics of soybean crop in the Matopiba region, Brazil (1990–2015)
Introduction
Because of its considerable territorial extent and its favorable climate, topography, and soil physical properties that support extensive rainfed crop production, Brazil has become one of the main exporters of agricultural commodities, such as soybean, corn, coffee, sugarcane, and cotton (MAPA, 2016a; Mueller and Mueller, 2016). In 2015, the annual crops that presented the largest planted areas in Brazil were soybeans (32 million ha), maize (16 million ha), and sugarcane (10 million ha) (IBGE, 2015). Currently, Brazil and the United States are the leading producers of soybeans (MAPA, 2015). In 2015, Brazil exported approximately 57 million tons of soybeans (An and Ouyang, 2016), primarily to China (Lima et al., 2017). In 2016/2017, Brazil produced 114 million tons of soybeans (USDA, 2017). Analysis by the United Nations Food and Agriculture Organization has indicated that soybean production in Brazil will increase by 37% over the next 10 years (OCDE/FAO, 2015).
Soybean planting began in Brazil in the 1940s as an option for crop rotation with wheat (Brown et al., 2005), and the crop adapted well in the southernmost part of the country because of the temperate climate. According to Paludzyszyn Filho et al. (1993), soybean became important in Rio Grande do Sul State because of the exportation. Gradually, because of investments by the Brazilian government in research institutions, soybean plants were genetically modified to improve adaptation in other regions of the country with tropical climate (Guimarães and Leme, 1997; Andersen et al., 2002). In addition, the use of chemical fertilizers was implemented to correct the predominantly acidic soils with low natural fertility found mainly in central parts of the country (Delgado, 1985). Brazilian Agricultural Research Institute (Embrapa Cerrados), created in 1973 by the Ministry of Agriculture, played a key role here since its research allowed remarkable increase in the soybean production in Brazil (Mueller and Mueller, 2016).
At the same time, because of the approved agrarian reform legislation, large landowners started to prevent against land loss for small farmers, rural workers, and landless peasants by increasing their production and investing in mechanization (Mueller and Mueller, 2016). Other relevant factors that contributed to the expansion of soybean in Central Brazil included the public tax incentives to open new areas for soybean and for establishment of companies for grain storage and processing, the availability of large areas with flat topography (plateaus), that is, favorable for mechanization, the relatively high precipitation conditions for rainfed agriculture, and the relatively good economic and technological levels of the farmers from southern part of the country that migrated to Cerrado (Dall’Agnol (2008); Campos, 2010).
Since 1990, the Brazilian government reduced its direct involvement in agriculture. On the other hand, private sector started to invest strongly in this sector (Alston et al., 2016). The country also implemented a series of more open and predictable political and economic institutional arrangements (Alves and Pastore, 1978). This lower need for direct political intervention in areas such as credit and price management allowed the country to grow even more in the agricultural sector (Mueller, 2009).
New agricultural frontiers were created in the Cerrado biome, e.g., regions of western Bahia State, southeastern Goiás State (municipalities of Jataí and Rio Verde), and the central region of Mato Grosso State (municipalities of Lucas do Rio Verde, Sinop, and Sorriso). Agricultural frontier is defined as a region dominated by natural vegetation that started to face intensive agriculture-related land occupation. The most recent agricultural frontier of the Cerrado is the Matopiba region, a continuous zone formed by the states of Maranhão, Tocantins, Piauí, and Bahia (Miranda et al., 2014). In this region, infrastructure is poor, land prices are cheap, and the climate and topographic relief are favorable for rainfed agriculture. Currently, soybean is the main agricultural crop of Matopiba (MAPA, 2017).
Following the rapid agricultural expansion in Matopiba, the Brazilian government issued Federal Decree No. 8,447 on May 6, 2015, establishing an Agricultural Development Plan for Matopiba. The purpose of this decree was to promote and coordinate public policies for economic and sustainable development of agricultural and livestock activities in the Matopiba region. The plan proposes guidelines for federal programs, projects, and actions to be undertaken with the objectives of improving both the living standards of the local population and the economic growth of the country. For this plan to succeed, it is of great relevance to understand the spatiotemporal dynamics of the crops produced in this region, with emphasis on the spatial clusters that might eventually appear over time. This understanding will assist the agricultural, environmental, and economic planning activities of the region.
Despite the recent increase in the number of studies focused on the Matopiba region, the use of statistics such as the Moran index for spatial analysis has not been explored yet, leading to some unanswered questions. 1) Does soybean production and yield follow spatial patterns that reflect local or regional aptitude? 2) If spatial patterns of production and yield occur, do they vary systematically over time, demonstrating the migration of aptitude to soybean cultivation? 3) Is there any spatial correlation between soybean production and yield? The answers to these questions will assist both the preparation of local and regional agricultural planning and the ongoing research and analysis in fields such as agriculture, the environment, and logistics. The primary objective of this research was to analyze the spatiotemporal dynamics of soybean production in the Matopiba region (1990–2015) by applying spatial statistics techniques on time series data.
Section snippets
Study area
The selected study area was the Matopiba region, which extends over parts of the states of Maranhão, Tocantins, Piauí, and Bahia (Miranda et al., 2014; Santos Filho et al., 2016). The Matopiba region comprises 10 mesoregions (5 in the state of Maranhão, 2 in Tocantins, 1 in Piauí, and 2 in Bahia) and 337 municipalities (Fig. 1). A mesoregion corresponds to a subdivision of a Brazilian state and it can encompass a varying number of municipalities with high levels of economic and social
Spatiotemporal dynamics
The Matopiba region showed significant growth in soybean production during the time series (Fig. 2): from 260,624 t in 1990 to 10,758,927 t in 2015, an increase of 4028% in 25 years. The municipality of São Desidério (Extremo Oeste Baiano mesoregion) presented the highest increase in production, rising from 1999 t in 1990 to 1,134,000 t in 2015 (an increase of more than 28,000%). This municipality presents average mean precipitation of 1145 mm (Tropical Rainfall Measuring Mission data,
Discussion
To make reliable decisions regarding regional agricultural planning and development, knowledge of the spatiotemporal patterns of crops and their performance over time is essential. Therefore, the analyses performed in this study, evidencing the spatial patterns of soybean cultivation in the Matopiba region over 25 years, could provide important support for the development of agriculture in the region, particularly within the context of environmentally sustainable progress. The spatial
Conclusions
This study identified important zones of soybean production in the Matopiba region of Brazil based on the spatial statistics of the global and local Moran indices. The most productive areas of soybean cultivation were found in the Extremo Oeste Baiano, Sul Maranhense, and Sudoeste Piauiense mesoregions. Areas of low soybean yield were found located in the central eastern parts of Maranhão, western Maranhão, and northern Tocantins, as well as in the municipality of Wanderley in Bahia State, all
Conflicts of interest
None.
Acknowledgments
We acknowledge two anonymous reviewers for valuable comments. The authors thank the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for providing the Master of Science scholarship for the first author.
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