TY - JOUR
T1 - Mapping crop producer perceptions: The role of global drivers on local agricultural land use in Brazil
AU - Dou, Yue
AU - da Silva, Ramon Felipe Bicudo
AU - Batistella, Mateus
AU - Torres, Sara
AU - Moran, Emilio
AU - Liu, Jianguo
N1 - Funding Information:
The authors wish to thank the local and state stakeholders for their active participation in the meetings and interviews, Dr. Rachel Keeton for her suggestions on FCM, and Sue Nichols for proof-reading and editing the draft. This work is supported in part by the US National Science Foundation ( 1518518 , 1924111 ), Michigan State University , and Michigan AgBioResearch . In addition, we gratefully acknowledge the funding support by the São Paulo Research Foundation (FAPESP, 14/50628-9 , 15/25892-7 , and 18/08200-2 ), and by the National Science Foundation-China ( 42001228 ). None of these agencies are responsible for the views expressed herein. They are the sole responsibility of the authors.
Publisher Copyright:
© 2023 The Authors
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Agricultural trade and climate change have altered land cover and land use worldwide. For example, the recent growth of international soybean demand has been associated with 1.3 Mha primary Amazon forest loss and up to 13-fold increase in double-cropping areas in Brazil. Many studies have tried to understand which and how global and local drivers affect deforestation and agricultural intensification processes at the landscape level, yet few have incorporated the direct perspectives of actual land users. Under the influence of a variety of social, economic, and cultural factors, producers are the ones who make decisions that will cause a significant impact on the environment. In this paper, we adopted Fuzzy Cognitive Maps (FCMs), a semi-quantitative modeling approach to represent complex decision-making systems, and we modeled land use and agricultural management perceptions of 27 crop producers from the three states - Mato Grosso, Goiás, and Tocantins - important soybean production and export areas in Brazil. We analyzed individual models and integrated them into aggregated regional models to compare individual and regional differences among the producers. In addition, we simulated how producers from the three states will make land-use decisions under more trade and extreme climatic events scenarios using the FCMs. Our results indicate that extreme climatic events are among the most important factors producers consider when it comes to the sustainability of their operations. Climate change scenarios have a stronger overall impact than trade scenarios on local land-use changes, causing a 12% reduction in total agricultural production. The improvement of technology packages can effectively mitigate climate change risks and has an overall positive impact on land-use intensification than expansion. On the other hand, sharing accurate climate information and socio-economic improvements such as credits have larger impacts on agricultural expansion than productivity itself. Moreover, the model complexity shows differences among the three states. Soybean trade has more weight in the perception of producers in Goiás and Tocantins than Mato Grosso. Based on the results, we discuss the importance of co-designing place-based, alternative policies and mitigation options for both agricultural intensification and environmental conservation, taken into consideration through the intertwined global and local forces.
AB - Agricultural trade and climate change have altered land cover and land use worldwide. For example, the recent growth of international soybean demand has been associated with 1.3 Mha primary Amazon forest loss and up to 13-fold increase in double-cropping areas in Brazil. Many studies have tried to understand which and how global and local drivers affect deforestation and agricultural intensification processes at the landscape level, yet few have incorporated the direct perspectives of actual land users. Under the influence of a variety of social, economic, and cultural factors, producers are the ones who make decisions that will cause a significant impact on the environment. In this paper, we adopted Fuzzy Cognitive Maps (FCMs), a semi-quantitative modeling approach to represent complex decision-making systems, and we modeled land use and agricultural management perceptions of 27 crop producers from the three states - Mato Grosso, Goiás, and Tocantins - important soybean production and export areas in Brazil. We analyzed individual models and integrated them into aggregated regional models to compare individual and regional differences among the producers. In addition, we simulated how producers from the three states will make land-use decisions under more trade and extreme climatic events scenarios using the FCMs. Our results indicate that extreme climatic events are among the most important factors producers consider when it comes to the sustainability of their operations. Climate change scenarios have a stronger overall impact than trade scenarios on local land-use changes, causing a 12% reduction in total agricultural production. The improvement of technology packages can effectively mitigate climate change risks and has an overall positive impact on land-use intensification than expansion. On the other hand, sharing accurate climate information and socio-economic improvements such as credits have larger impacts on agricultural expansion than productivity itself. Moreover, the model complexity shows differences among the three states. Soybean trade has more weight in the perception of producers in Goiás and Tocantins than Mato Grosso. Based on the results, we discuss the importance of co-designing place-based, alternative policies and mitigation options for both agricultural intensification and environmental conservation, taken into consideration through the intertwined global and local forces.
KW - ITC-ISI-JOURNAL-ARTICLE
KW - ITC-HYBRID
KW - UT-Hybrid-D
U2 - 10.1016/j.landusepol.2023.106862
DO - 10.1016/j.landusepol.2023.106862
M3 - Article
SN - 0264-8377
VL - 133
SP - 106862
JO - Land use policy
JF - Land use policy
M1 - 106862
ER -