Agricultural intensification and land use change: assessing country-level induced intensification, land sparing and rebound effect
Data files
May 06, 2020 version files 11.51 MB
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CroplandLR.do
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DATABASEclimate_(2).dta
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LRYield.do
Abstract
In the context of growing societal demands for land based products, crop production can be increased through expanding cropland or intensifying production on cultivated land. Intensification can allow sparing land for nature, but it can also drive further expansion of cropland, i.e. a rebound effect. Conversely, constraints on cropland expansion may induce intensification. We tested those hypotheses by investigating the bidirectional relations between changes in cropland area and intensity, using a global cross-country panel dataset over 1961-2016. We used a cointegration approach with additional tests to disentangle long and short-run causal relations between variables, and total factor productivity and yields as two measures of intensification. Over the long run we found support for the induced intensification thesis for low income countries. In the short run, intensification resulted in a rebound effect in middle-income countries, which include many key agricultural producers strongly competitive in global agricultural commodity markets. This rebound effect manifested for commodities with high price-elasticity of demand, including rubber, flex crops (sugarcane, palm oil and soybean), and tropical fruits. Over the long run, strong rebound effects remained for key commodities such as flex crops and rubber. Staple cereals such as wheat and rice manifested significant land sparing. In low-income countries, intensification driven by increases in total factor productivity was associated with a stronger rebound effect than yields increases. Agglomeration economies may drive yields increases for key tropical commodity crops. Our study design could allow addressing other complex long and short run causal dynamics in land and social-ecological systems.
Methods
This database has been created by gathering variables retreived from: i) Well known data sources such as the World Bank, the Food and Agriculture Organization and the United States Department of Agriculture; ii) Less well known data sources iii) Other researchers' work. These data was downloaded and stored on a commond folder. Afterwards, the data was gathered and homogenized (same format and country names) on a unique database using STATA sofware.
Usage notes
The database and the codes should be used after reading the "Supplementary information" of the paper this data was used for. Specially the section of the "Long and short run causal dynamics".