Novel Resilient and Sustainable Farm Profiles in Small Ruminant Production Systems Using Mathematical Programming Model

Alexandros Theodoridis (), Athanasios Ragkos, Sotiria Vouraki, Georgios Arsenos, Antonis Kominakis, Stephanie Coppin, Vincent Thenard and Tim J. Byrne
Additional contact information
Alexandros Theodoridis: Laboratory of Livestock Production Economics, School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Athanasios Ragkos: Agricultural Economics Research Institute, Hellenic Agricultural Organization—DIMITRA, Kourtidou 56-58, 11145 Athens, Greece
Sotiria Vouraki: Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Georgios Arsenos: Laboratory of Animal Husbandry, School of Veterinary Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Antonis Kominakis: Laboratory of Animal Breeding & Husbandry, Agricultural University of Athens, 11855 Athens, Greece
Stephanie Coppin: Institut de l’Elevage, 31321 Auzeville-Tolosane, France
Vincent Thenard: AGIR, Institut National de La Recherche Agronomique, Université de Toulouse, 31326 Castanet-Tolosan, France
Tim J. Byrne: AbacusBio International Limited, Roslin Innovation Centre, University of Edinburgh Easter Bush Campus, Midlothian EH25 9RG, UK

Sustainability, 2023, vol. 15, issue 15, 1-16

Abstract: In this study, a farm-scale mathematical programming model for sheep and goat farms is proposed to simulate economic performance, including new resilience sheep traits that allow animals to counteract the presence of infectious and noninfectious diseases. The model was developed in the Small Ruminants Breeding for Efficiency and Resilience (SMARTER) Horizon 2020 project. The SMARTER model is a comprehensive and adaptable linear programming model that enables the assessment of hypothetical scenarios/challenges related to animal traits that prevent infectious and noninfectious diseases. The optimal performance and the structure of the farm are modeled under the presence of infectious and noninfectious diseases (disease plan) and under conditions where no diseases occur (future plan). A comparison of the model solutions, between presence and absence of diseases, provides suggested adjustments to the farming system and insights into the potential shape of new sustainable farm system profiles for the sheep and goat sector. Technical and economic data from five different sheep farms and one goat farm in Greece and France were used in this empirical application to assess different scenarios in the presence of mastitis, parasitism, and lameness in the flocks. The results showed that the profitability and sustainability of the farms are significantly improved when the resilience of animals reduces the impact of the diseases (the highest increase in gross margin was 23.5%). However, although there is substantial improvement in the economic performance of the farms that rear healthy animals, this does not affect the production and management plan of the farmer and does not alter the farm’s structure.

Keywords: sheep and goat sector; farm modeling; linear programming; economic performance; infectious and noninfectious diseases (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/15/11499/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/15/11499/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:15:p:11499-:d:1201983

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().