Tradeoffs in the quest for climate smart agricultural intensification in Mato Grosso, Brazil
2018
Low productivity cattle ranching, with its linkages to rural poverty, deforestation and greenhouse gas (GHG) emissions, remains one of the largest sustainability challenges in Brazil and has impacts worldwide. There is a nearly universal call to intensify extensive beef cattle production systems to spare land for crop production and nature and to meet Brazil?s Intended Nationally Determined Contribution to reducing global climate change. However, different interventions aimed at the intensification of livestock systems in Brazil may involve substantial social and environmental tradeoffs. Here we examine these tradeoffs using a whole-farm model calibrated for the Brazilian agricultural frontier state ofMato Grosso, one of the largest soybean and beef cattle production regions in the world. Specifically, we compare the costs and benefits of a typical extensive, continuously grazed cattle system relative to a specialized soybean production system and two improved cattle management strategies (rotational grazing and integrated soybean-cattle) under different climate scenarios.We found clear tradeoffs in GHG and nitrogen emissions, climate resilience, and water and energy use across these systems. Relative to continuously grazed or rotationally grazed cattle systems, the integreated soybean-cattle system showed higher food production and lower GHG emissions per unit of human digestible protein, as well as increased resilience under climate change (both in terms of productivity and financial returns). All systems suffered productivity and profitability losses under severe climate change, highlighting the need for climate smart agricultural development strategies in the region. By underscoring the economic feasibility of improving the performance of cattle systems, and by quantifying the tradeoffs of each option, our results are useful for directing agricultural and climate policy.
Keywords:
- Correction
- Source
- Cite
- Save
- Machine Reading By IdeaReader
49
References
20
Citations
NaN
KQI