Nowadays, livestock and cropping systems tend to separate their activities resulting in a mismatch between nutrient demand and consumption. This makes these sectors to be two of the largest sources of greenhouse gas emissions (24% of total global emissions)(EPA, 2014). To bridge the gap between the two industries, several authors have focused on integrating livestock and crop management, both extensive (Bell et al., 2014) and intensive livestock (Taifouris & Martin, 2021). In these models, the animal feed is produced on the premises and the animal waste is used to produce fertilizers and biogas, favoring the circular economy of residues. However, location is a key factor in the sustainable design of these integrated systems since through climate and soil characteristics, fixes the availability, price, and yield of crops (Ngoune Liliane & Shelton Charles, 2020). In addition, there are locations more sensitive to nutrient pollution (Ministrerio de transición ecológica y el reto demográfico, 2021) or protected natural areas (Ministerio de transición ecológica y de reto demográfico, 2021). Therefore, the construction of these facilities should avoid these areas. Consequently, the selection of location affects both economic and environmental dimensions of farm design. Despite the importance of including selection of location in the optimization frameworks, to the best of the knowledge of the authors, this holistic approach has not been addressed in the literature. This work presents a three-step methodology to address simultaneously the selection of the optimal formulation of the feed, the number of animals, the annual crops distribution, the design of the nutrient recovery systems, as well as the location, from economic and environmental points of view. The methodology consists of three stages. First, prescreening discards the locations that do not meet a series of environmental conditions. Next, a mathematical model is formulated by integrating empirical models to estimate the nutritional and energy requirements of the animals, as well as the composition and amount of waste generated. In addition, empirical yields are used to design the nutrient recovery systems, the cultivation area, and fertilizer consumption. The location is integrated by using a series of economic (i.e. price, yield, and availability of land and water) and environmental (i.e. protected natural areas, nitrate vulnerable zones, and water scarcity) parameters. The problem becomes a very large mixed-integer linear programming (MILP) problem. This optimization framework is used to fix the optimal size of the farm following an economic approach. Once the size is fixed, the same optimization framework is also used to determine the optimal feed formulation, crop selection, and location following a multi-objective (economic and environmental) approach.
The methodology is applied to a case study in 345 agricultural districts in Spain, of which 145 are discarded in the first stage. The optimal size of the farm is 1200 initial animals. The selection of ‘Bureba-Ebro’ and a crop distribution that consumes 18% less fertilizer than the economic scenario (second stage of the methodology) achieves the reduction of 56% in the environmental impact. The sales of crops allow reducing the meat production cost from 8.69€/kg (1.6€/kg corresponds to the waste treatment) down to 2.25€/kg. The results show that only an integrated optimization framework that considered simultaneously the optimal design of the integrated livestock-cropping system and its locations can find a real trade-off between the economic and environmental optimums. The methodology is generic enough to be applied to different locations, crops, and animals.
References
Bell, L. W., Moore, A. D., & Kirkegaard, J. A. (2014). Evolution in crop–livestock integration systems that improve farm productivity and environmental performance in Australia. European Journal of Agronomy, 57, 10–20. https://doi.org/10.1016/J.EJA.2013.04.007
EPA. (2014). Global Greenhouse Gas Emissions Data. https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data
Ministerio de transición ecológica y de reto demográfico. (2021). La Red de Parques Nacionales. https://www.miteco.gob.es/es/red-parques-nacionales/la-red/
Ministrerio de transición ecológica y el reto demográfico. (2021). Zonas vulnerables. https://www.miteco.gob.es/es/cartografia-y-sig/ide/descargas/agua/zonas-...
Ngoune Liliane, T., & Shelton Charles, M. (2020). Factors Affecting Yield of Crops. In Amanullah (Ed.), Agronomy - Climate Change and Food Security. IntechOpen. https://doi.org/10.5772/INTECHOPEN.90672
Taifouris, M., & Martin, M. (2021). Toward a Circular Economy Approach for Integrated Intensive Livestock and Cropping Systems. ACS Sustainable Chemistry and Engineering, 9(40), 13471–13479. https://doi.org/10.1021/acssuschemeng.1c04014
