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Agricultural innovation and climate change policy in the Brazilian Amazon: Intensification practices and the derived demand for pasture

Caviglia-Harris, Jill L.

Journal of Environmental Economics and Management, 2018, vol. 90, issue C, 232-248

Abstract: Tropical deforestation in Brazil is a major source of greenhouse gas emissions that contribute to climate change. Brazil has taken several steps to reduce emissions associated with deforestation including the development of policies to promote and reward cattle ranching intensification. Intensification practices are hypothesized to reduce the conversion of tropical forests while simultaneously increasing the productivity of ranching on current pasture lands. This paper assesses the impact of intensification on the demand for productive land. A theoretical model of land use, which considers the degree of intensification in the estimation of the derived demand for pasture, is tested with data from small-scale landowners who operate dairy farms and calving operations in the greater Ouro Preto do Oeste region of Rondônia. Results suggest different trajectories for beef and dairy production. The intensification of cattle production exhibits a nonlinear relationship with the demand for productive land: first as farms become more intensive the demand for newly cleared land increases, but then decreases with further intensification. The results are different for dairy intensification, which is found to be correlated with reduced deforestation. Findings suggest that the reliance on policies that promote intensification can be a risky way of achieving climate change objectives.

Keywords: Deforestation; Agricultural intensification; Climate change; Cattle; Pasture; Brazilian Amazon (search for similar items in EconPapers)
JEL-codes: Q23 Q18 Q15 (search for similar items in EconPapers)
Date: 2018
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Journal of Environmental Economics and Management is currently edited by M.A. Cole, A. Lange, D.J. Phaneuf, D. Popp, M.J. Roberts, M.D. Smith, C. Timmins, Q. Weninger and A.J. Yates

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