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The biophysical and socio-economic dimension of yield gaps in the southern Amazon – A bio-economic modelling approach

Anna C. Hampf, Marcelo Carauta, Evgeny Latynskiy, Affonso A.D. Libera, Leonardo Monteiro, Paulo Sentelhas, Christian Troost, Thomas Berger and Claas Nendel

Agricultural Systems, 2018, vol. 165, issue C, 1-13

Abstract: Farmers in the State of Mato Grosso are among Brazil's most productive soybean, maize and cotton producers, but are still far away from achieving potential yields as measured on experimental sites. The objective of this study was to decompose yield gaps in the Southern Amazon into their biophysical and socio-economic dimensions. In order to achieve this, the process-based MOdel of NItrogen and Carbon dynamics in Agro-ecosystems (MONICA) was coupled with the Mathematical Programming-based Multi-Agent Systems (MPMAS) software. Soybean, maize and cotton yield gaps were simulated for five macro-regions in Mato Grosso considering different climatic, edaphic and crop management conditions. The impact of socio-economic constraints on crop yields was assessed in form of full factorial design in which each factor was set to a baseline and unconstrained level. The simulation results show that biophysical yield gaps (due to water and nutrient deficit) account for 24% of potential yields (Yp), whereas an unrestricted access to machinery, labour, credit and technological innovation would lead to a reduction of yield gaps by 6.1% and an expansion of cropland by 22%. Yield gaps can be reduced through improved water- and nutrient management, appropriate cultivar-sowing date combinations and in part by a removal of socio-economic constraints. However, each solution comes with its own limitation either in form of increased pressure on limited environmental resources or incompatibility with individual farmer objectives. Future yield gap closure will depend on the access to arable land, environmental regulations preventing further deforestation as well as political and economic incentives for sustainable intensification.

Keywords: Potential yield; Water-limited yield; Crop modelling; Agent-based modelling; Integrated assessment; State of Mato Grosso (search for similar items in EconPapers)
Date: 2018
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