Bioeconomic models are analytical tools that integrate biophysical and economic models. These models allow for analysis of the biological and economic changes caused by human activities. The biophysical and economic components of these models are developed based on historical observations or theoretical relations. Technically these models may have various levels of complexity in terms of equation systems considered in the model, modeling activities, and programming languages. Often, biophysical components of the models include crop or hydrological models. The core economic components of these models are optimization or simulation models established according to neoclassical economic theories. The models are often developed at farm, country, and global scales, and are used in various fields, including agriculture, fisheries, forestry, and environmental sectors. Bioeconomic models are commonly used in research on environmental externalities associated with policy reforms and technological modernization, including climate change impact analysis, and also explore the negative consequences of global warming. A large number of studies and reports on bioeconomic models exist, yet there is a lack of studies describing the multiple uses of these models across different disciplines.
Ariel R. Angeli, Federico E. Bert, Sandro Díez-Amigo, Yuri Soares, Jaquelina M. Chaij, Gustavo D. Martini, F. Martín Montané, Alejandro Pardo Vegezzi, and Federico Schmidt
During the past two decades, extensive agriculture, particularly soybean production, has progressively replaced other crops in Argentina. This transformation was driven by economic, technological, environmental, and organizational factors, such as the increasing demand for agricultural commodities, technological advances, organizational innovations, and climate fluctuations. The expansion of soybean production has brought a substantial increase in agricultural revenue for Argentina. However, the predominance of soybean cultivation poses significant challenges, such as diminished soil fertility, reduction and increased variability in crop yields, ecological imbalance, increased greenhouse gas (GHG) emissions, and vulnerability to climate change. Crop rotation, particularly balanced crop rotation, may result in very large positive impacts on soybean yields, especially in unfavorable climatic conditions such as those experienced during the La Niña ENSO phase in Argentina. In addition to this positive impact on agricultural productivity and climate adaptation, in some contexts crop rotation may also contribute to the reduction of GHG emissions, increased input energy efficiency, and improved environmental outcomes. The 2018 Argentinian Association of Regional Consortia for Agricultural Experimentation and Inter-American Development Bank (AACREA-IADB) Integrated Crop Rotation Database compiled and harmonized the information from agricultural diaries kept by Regional Consortia for Agricultural Experimentation (CREA) members in Argentina from 1998 to 2016. This new consolidated data set has replaced previous regional templates, and it is expected to continue to be expanded with new information periodically, offering opportunities for further research on the impact of crop rotation on climate adaptation and on other topics in agricultural and environmental economics.