On-Farm trade-offs for optimal agricultural practices in Mato Grosso, Brazil

Publikations-Art

Kongressbeitrag

Autoren

Carauta, M., Libera, A. A. D., Chen, R. F. F., Hampf, A., Dantas, I. R. M., Silveira, J. M. F. J., Berger, T.

Erscheinungsjahr

2016

Veröffentlicht in

54° Congresso da Sociedade Brasileira de Economia, Administração e Sociologia Rural

Tagungsort

Maceió

Tagungsdatum

14 a 17 de agosto de 2016

Schlagworte

sustainability

Agricultural production in Mato Grosso (in mid-western Brazil) has been increasingstronglyoverthe past years, producing a quarter of the national grain production. In order to keep that path, farmers have been adopting several technological innovations withthe major share of this production gain coming from yield advances.However, the agricultural production system in Brazil has become complex and dynamic and it has experienced a large increase indecision variables which farmers need to tackle every year. Moreover, as those variables are widely spread across many distinct topics, bringing them together and summarizing information from diverse fields of research has become a difficult task in afarmer’s decision-making process. Therefore, we developed an Integrated Assessment simulation experiment with a region-specific bio-economic component in order to assess the trade-offs between different agricultural practices and production systems in Mato Grosso. We implemented our simulation in MPMAS, a multi-agent software package developed for simulating farm-based economic behavior and human-environment interactions in agriculture. The crop yields were simulated with the Model of Nitrogen and Carbon dynamics in Agro-ecosystems (MONICA).Our simulation results captured both regional difference between farms and between climate conditions, providing key insights into farmers’ decision-making process and comprehension about the interaction of those decision variables.We show that climate conditions are still a key variable to decision-making process and that farmers should assess those variables together in order to fully optimize their production and economic results.