Positive forest cover effects on coffee yields are consistent across regions
González-Chaves, Adrian; Carvalheiro, Luísa; Garibaldi, Lucas; Metzger, Jean Paul (2021), Positive forest cover effects on coffee yields are consistent across regions, Dryad, Dataset, https://doi.org/10.5061/dryad.612jm644g
We obtained productivity data from the Brazilian Institute of Geography and Statistics (IBGE, http://www.ibge.gov.br/), we calculated coffee yield (productivity) for each year per municipality by dividing the total production (tons) by the total coffee area (ha) planted per municipality per year. Mean coffee yields were calculated from three consecutive years for each municipality. The years considered for each municipality depended on data availability of the coffee fields’ maps, which was different for each state. To determine forest cover surrounding coffee plantations, we used coffee maps from the National Company of supply (CONAB, http://www.conab.gov.br/), which compiled maps from the five leading coffee producing states within the Atlantic forest. Additionally, we used annual forest remnants maps from MapBiomas (Project of annual mapping of land-use and land-cover of Brazil, http://mapbiomas.org/), both with a resolution of 30x30 m.
We gathered 19 bioclimatic variables from the Worldclim 30 seconds resolution database (www.worldclim.org). We calculated the mean values per municipality by extracting the climatic values from the coffee field maps. The bioclimatic variables include annual mean temperature and precipitation, and extreme or limiting factors relevant to coffee production (Table S1). Regarding soil properties, we obtained physical (bulk density, clay coarse and silt content at different depths) and chemical (cation exchange capacity, soil organic carbon content and soil pH) data from SoilGrids at 250 m (www.soilgrids.org), and then extracted mean values for the coffee fields at the municipality level (Fig. S1).
Coffee agricultural management practices are from the IBGE database, based on a field survey done in 2006 that assessed the number of coffee farms under a particular management practice. We considered variables associated with management intensity and calculated the percentage of farms that use: irrigation, mechanical harvest, organic or chemical manure, and pesticides. Additionally, we also calculated the percentage of organic farms within each municipality.
For model selection, transformation of the variables were made. For instance, yield values were log transformed. As well as coffee cover and forest cover variables. The rest of climatic and management were scaled before creating the full model.
Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2013/234567-6
Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2017/14911-1
Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2018/06330-6