Degraded pastures: technical efficiency, stocking rates, pasture allocation and rural credit data

Degraded pasture is a major liability in Brazilian agriculture, but restoration and recovery efforts could turn this area into a new frontier to both agricultural yield expansion and forest restoration. Currently, rural properties with larger degraded pasture areas are associated with higher levels of technical inefficiency in Brazil. The recovery of 12 million ha of degraded pastures could generate an additional production of 17.7 million bovines while reducing the need for new agricultural land. Regional identification of degraded pastures would facilitate the targeting of agricultural extension and advisory services and rural credit efforts aimed at fostering pasture recovery. Since only 1% of Brazilian municipalities contain 25% of degraded pastures, focusing pasture recovery efforts on this small group of municipalities could generate considerable benefits. More efficient allocation of degraded and native pastures for meat production and forest restoration could provide land enough to fully comply with its Forest Code requirements, while adding 9 million heads to the cattle inventory. Degraded pasture recovery and restoration is a win–win strategy that could boost livestock husbandry and avoid deforestation in Brazil and have to be the priority strategy of agribusiness sector.

All data in this file is available at Brazilian Governmental sites,  exclusively in Portuguese.  In order to ensure greater transparency about the data used as well as to assist users with limited knowledge in Portuguese, we provided here detailed processes of data search and selection. 

For Efficiency, Stocking Rate and allocation data from Brazilian Agricultural Census 2017 were used. 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. Labels of variables are available in the spreadsheets "TEfficiency", "SRate" and "degraded native allocation".

For rural credit, data from The Rural Credit Data Matrix (MDCR) were used, which is an online platform of the Brazilian Central Bank (BCB) 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. Data is available in spreadsheet labeled as "rural credit for pastures".

Detailed information can be found in Supplemental Material of the manuscript under label rsos-201854.R1.

Original data on Brazilian Agricultural Census is available at https://sidra.ibge.gov.br/pesquisa/censo-agropecuario/censo-agropecuario-2017

Original data on Rural Credit in Brazil is available at https://olinda.bcb.gov.br/olinda/servico/SICOR/versao/v2/aplicacao#!/recursos

In our dataset, missing values are identified as "." Missing data is absence of information in the original source (Brazilian Agricultural Census and/or Rural Credit in Brazil).