Project ModelMeat – A model for the optimization of product environmental and nutritional performance as a decision support service for the extensive animal production sector

Objective: The goal of Project ModelMeat is to develop a decision support service (DSS) for integrated sustainability management in the extensive livestock production sector, starting with a sample of 1,000 farmers in... [ view full abstract ]

Objective: The goal of Project ModelMeat is to develop a decision support service (DSS) for integrated sustainability management in the extensive livestock production sector, starting with a sample of 1,000 farmers in Portugal. The project began in November 2016 and runs until October 2019. In this work we present the activities and methods of the project, as well as preliminary results obtained during the first year.

Method: ModelMeat is divided in three components:

(1) The animal will be modelled using Dynamic Energy Budget (DEB) theory. This model simulates the dynamics of feed, growth, maintenance and reproduction of livestock. It will allow farmers to obtain an estimate of the animal characteristics and of the ensuing products (meat and milk), as well as greenhouse gas (GHG) emissions. Grazing feed will be quantified using field measurements and correlating observations with remote sensing instruments such as aerial pictures rom drone flights and satellite data.

(2) Upstream, feeds and management practices will be characterized in the second block of the project. Practices will be analyzed environmentally and economically, depending on the consumption of raw materials and energy, including concentrate feeds and others produced away from the farm, using parametric Life Cycle Assessment (LCA). LCA will also determine the indirect GHG emissions from these inputs.

(3) Downstream from the farm, the third block of the project is the determination of the nutritional, quality and health and sensory features of the products, as well as their economic valuation by consumers.

The model will initially be calibrated using data from the Alentejo region, but the project will establish methods for data collection, modelling and analysis that can be replicated elsewhere.

Results: In the first months the project established a partnership with an initial group of farmers for data collection and methodological calibration. An online system was developed for data collection. These farmers were surveyed to obtain a list of relevant materials consumption and management practices. For these activities, data was collected from LCA databases to establish the simplified parametric LCA calculation tool and benchmark data from other systems and countries. Grazing exclusion cages were installed in all grazing land in these farms to estimate yield. The DEB model is currently being built. In the near future, the other activities will start. At the end of the project a software tool will be developed with the integrated implementation of ModelMeat.

Conclusion: Project ModelMeat is an innovative approach to surveying that will produce relevant methods (e.g. parametric LCA, correlation between remote sensing of grasslands and yield) and a DSS for future use by farmers and other actors in the agri-food sector. The DSS will allow farmers to estimate the environmental, economic and nutritional consequences of management decisions (e.g. selection of production systems such as conventional or organic) using the integrated on- and off-farm framework of ModelMeat.