Abstract

The pasturelands areas of Brazil constitute an important asset for the country, as the main food source for the world’s largest commercial herd, representing the largest stock of open land in the country, occupying ~21% of the national territory. Understanding the spatio-temporal dynamics of these areas is of fundamental importance for the goal of promoting improved territorial governance, emission mitigation and productivity gains. To this effect, this study mapped, through objective criteria and automatic classification methods (Random Forest) applied to MODIS (Moderate Resolution Imaging Spectroradiometer) images, the totality of the Brazilian pastures between 2000 and 2016. Based on 90 spectro-temporal metrics derived from the Red, NIR and SWIR1 bands and distinct vegetation indices, distributed between dry and wet seasons, a total of 17 pasture maps with an approximate overall accuracy of 80% were produced with cloud-computing (Google Earth Engine). During this period, the pasture area varied from ~152 (2000) to ~179 (2016) million hectares. This expansion pattern was consistent with the bovine herd variation and mostly occurred in the Amazon, which increased its total pasture area by ~15 million hectares between 2000 and 2005, while the Cerrado, Caatinga and Pantanal biomes showed an increase of ~8 million hectares in this same period. The Atlantic Forest was the only biome in which there was a retraction of pasture areas throughout this series. In general, the results of this study suggest the existence of two relevant moments for the Brazilian pasture land uses. The first, strongly supported by the opening of new grazing areas, prevailed between 2000 and 2005 and mostly occurred in the Deforestation Arc and in the Matopiba regions. From 2006 on, the total pasture area in Brazil showed a trend towards stabilization, indicating a slight intensification of livestock activity in recent years.

Highlights

  • Food production was driven by population expansion, consumption and increased per capita income, which gradually raised the global food demand [1]

  • Despite previous works having mapped these areas throughout the Brazilian territory [13,16], the absence of recurrent maps and the methodological differences between these initiatives make a temporal analysis of the Brazilian pasturelands difficult; its dynamics have important territorial, economic and environmental implications [17,18]

  • The pasture area mapped in 2002 and 2015 was ~169 and ~176 million hectares, respectively, while the Landsat-based mappings show, for the same years, ~149 and ~179 million hectares of pastures. In addition to these relatively close area values, the mapped areas have a very similar distribution pattern, which indicates that the proposed method is producing, in general, coherent and spatially consistent results, even though it is based on moderate spatial resolution data

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Summary

Introduction

Food production was driven by population expansion, consumption and increased per capita income, which gradually raised the global food demand [1] In this context, Brazil has tremendous importance for food production, as it is the largest world beef exporter [2] and is responsible for 35% of the world soybean exportation, considering grain and derived products [3]. Brazil has tremendous importance for food production, as it is the largest world beef exporter [2] and is responsible for 35% of the world soybean exportation, considering grain and derived products [3] This increase in the Brazilian agricultural production occurred by the conversion of natural ecosystems into planted pastures, mainly altering the Amazon [4] and Cerrado [5] biomes and through the soybean expansion over natural ecosystems and planted pastures [6,7]. Despite previous works having mapped these areas throughout the Brazilian territory [13,16], the absence of recurrent maps and the methodological differences between these initiatives make a temporal analysis of the Brazilian pasturelands difficult; its dynamics have important territorial, economic and environmental implications [17,18]

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