Skip to main content
Dryad logo

Degraded Pastures in Brasil: dataset used in econometric models

Citation

Feltran-Barbieri, Rafael; Féres, Jose Gustavo (2021), Degraded Pastures in Brasil: dataset used in econometric models, Dryad, Dataset, https://doi.org/10.5061/dryad.76hdr7sv8

Abstract

Degraded pasture restoration is the major liability in Brazilian agriculture but could be the main asset. Here we show the technical inefficiency of livestock activity in Brazil is around 19% in which degraded pastures being the main factor of diseconomy of scale. On the other hand, the recovery of 12 million hectares of degraded pastures could generate an additional production of 16.9 million bovines. There is a large regional concentration of degraded pastures, which would facilitate the targeting of Technical Assistance and Rural Extension (ATER) and Rural Credit efforts to foster pasture recovery, as these two factors have a significant impact in reducing inefficiency. 1% of Brazilian municipalities concentrate 25% of degraded pastures that could generate almost 30% of the total increment of the additional herd via pasture recovery.  At the municipality level, even if most of the degraded pastures were allocated to forest recovery in order to comply with the Forest Code, an increase of 9 M of cattle would be possible due to the recovery of degraded pastures that would exceed the minimum necessary to comply with the environmental law. Redirecting investment credits, especially for resources with controlled interest rate. Degraded pasturelands are the new frontier that could boost livestock activities and avoid deforestation in Brazil.

Methods

All econometric models - Stochastic Frontier for estimating livestock efficiency, and SEM and Robust Regression models for estimating regional stocking rates - were estimated using variables collected from the Agricultural Census 2017. It is the most recent Brazilian census, conducted by the Brazilian Institute of Geography and Statistics. The census covered the entire national territory, covering the 5.03 million rural properties in 5,563 municipalities, 26 states and the Federal District, with data reported from October 1, 2016 to September 30, 2017, and published in 2019. The census adopted the collection and content premises suggested by the World Programme for the Census of Agriculture 2020 , implemented by the Food and Agriculture Organization - FAO, and the International Standard Industrial Classification of all Economic Activities - ISIC - Revision 4. The census is divided into 13 themes with 137 interactive tables that allow advanced data selection and information combination.  Each table has 1 or more variables that can be combined with a series of search criteria, including producer profile, production type, geographical level, etc. The search results can be downloaded in 7 formats (different extensions). The census did not provide microdata - producer-level responses. The most detailed territorial level is the municipality.

We also used data from The Rural Credit Data Matrix (RCDM) which is an online platform of the Brazilian Central Bank (CBB) that allows access to information on the value and number of rural credit contracts signed by the official credit system, allowing search combined with different criteria, including month and year of operation, municipality, economic activity group, purpose (investment, costs etc.), product (pasture, milk, soybeans etc.), credit program and subprogram, sources of resources etc. The time series is updated monthly and covers the period from January 1, 2013 to the present, and generates results for free donwload in 4 formats (extensions). 

Usage Notes

Dataset - in original websites - are mostly available exclusively in Portuguese, and there is no official English version. In order to ensure greater transparency about the data used as well as to assist users with limited knowledge in Portuguese, we provided in the Supplemental Material a comprehensive guide on how to access and download the data, including links to all variables used in our models.

Dataset is in Excel Spreadsheet.