The objective of this work was to develop a Decision-Support System (DSS) in order to support the decision makingprocess by campesino farmers of Central Mexico. Two biological models, one socio-economic model and a survey database form the DSS. The CERES-Maize model simulated the yield response of three local land-races of maize to different management systems. The second biological model, a cow model (dynamic hybrid model), was used tosimulate alternative feedingsystems. A multi-period mathematical programming model integrated the outputs of the previous models with the survey database. This model was used to find the optimal combination of resources and technologies that maximised farmers’ income. This model consists of 15,698 structural columns and 612 rows. The DSS successfully reproduced the functioningof the farming system’s main components. More importantly, it simulated the complex interactions observed between the farmers and their crops and cattle, including traditional maize management practices.
The main objective of this work was to develop a ‘‘Decision-Support System’’ based on the integration of three simulation models, with a survey database on the campesino maize–cattle production systems of the Toluca Valley. Most of Mexico’s food production, particularly maize, depends on the smallholder or campesino farmingsector (SARH, 1993). Mexico’s membership to the North American Free Trade Agreement (NAFTA) has meant important changes that have led the sector to a crisis (de Ita, 1997). To adapt to these changes, campesino maize farmers are lookingtoward alternative production systems and better uses of their land to help them adjust to new scenarios. There is also the need for scientists, in collaboration with farmers, to identify and develop viable technical options which will serve alternative production systems and, more importantly, probably help farmers in the process of decision makingwhen deciding whether to adopt or reject new technology.