Novel Resilient and Sustainable Farm Profiles in Small Ruminant Production Systems Using Mathematical Programming Model
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodological Approach
- Revenues: Milk (yield × price), meat (yield × price); cheese (sales × price). Each type of product can include multiple sources, e.g., lamb meat and/or culled animal meat. All products do not apply to all breeds or production systems; e.g., in some systems, it is not typical for the farmers to produce cheese on farm, but to deliver their total production of milk to dairies. Prolificacy, weaning rate, mortality rate, and replacement rate are production traits that have been used in the calculation of the product yields per productive animal (ewe or goat). Yields of milk, meat, and cheese prices are expressed on an annual basis.
- Prices: For each product, prices are included separately in the model (in a separate column) and are linked to the constraint expressing product yields.
- Variable costs: The unit costs of all forms of variable capital are included (e.g., prices of purchased feedstuff, variable production costs of home-grown feed, which includes expenses for seeds, fertilizers, irrigation, pesticides, etc.). Veterinary expenses and drugs per productive animal are also included, as well as other variable expenses (water, electricity, detergents, additives, etc.).
- Land
- Labor
- Variable capital requirements
- Animal and flock-related constraints
2.2. Data
3. Results and Discussion
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Objective Function (Max) | Production | Breeding | Purchased Feed | Home-Grown Feed | Labor | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
MilkPrice | MeatPrice | CheesePrice | VCsheep | VCpur1 | … | VCpurN | VCcul1 | … | VCculN | HLAB | ||
AL | Y≥ | UL1 | … | ULN | ||||||||
AFL | Y≥ | RFL1 | RFL2 | RFL3 | … | RFLN | ||||||
0≥ | RHL1 | RHL2 | RHL3 | −1 | ||||||||
AHL | Y≥ | 1 | ||||||||||
AVC | Y≥ | VCBreed | 1 | … | 1 | 1 | … | 1 | ||||
0≥ | NutrReq | -NCpur1 | … | -NCpurN | -NCcul1 | … | -NCculN | |||||
0≥ | Prod_Milk | Prod_Meat | Prod_Cheese |
Impact on Farm Indicators | Type of Disease | Chios Sheep | Assaf Sheep | Boutsiko Sheep | Frizarta Sheep | Lacaune Sheep | Skopelos Goats |
---|---|---|---|---|---|---|---|
Prevalence in flock (%) | Mastitis | 15% 1 | 10% 12 | 27% 12 | 20% 20 | 22% 23 | 24% 4 |
GIN | 35% 4 | 43% 14 | 47% 17 | 35% 5 | 10% 23 | 12% 26 | |
Lameness | 7% 8 | 9% 7 | 9% 10 | 9% 22 | - | 9% 7 | |
Reduction in milk yield (%) | Mastitis | 38% 2 | 37% 13 | 43% 16 | 21% 21 | 10% 23 | 15% 24 |
GIN | 22% 5 | 11% 15 | 8.5% 18 | 22% 4 | 10% 23 | 5% 27 | |
Lameness | 19.3% 9 | 19.3% 9 | 19.3% 9 | 19.3% 9 | - | 19% 28 | |
Increase in vet/drug cost for treatment (in EUR/ewe/goat) | Mastitis | EUR 4 3 | EUR 4 3 | EUR 4 3 | 4 3 | EUR 3 23 | EUR 1.6 25 |
GIN | EUR 3 6 | EUR 3 6 | EUR 3 6 | EUR 3 6 | EUR 5 23 | EUR 4.5 29 | |
Lameness | EUR 4.26 10 | EUR 4.26 10 | EUR 4.26 10 | EUR 4.26 10 | - | EUR 4.26 10 | |
Extra time spent for treating disease (in h per ewe/goat) | Mastitis | 1 h 3 | 1 h 3 | 1 h 3 | 1 h 8 | 0.25 h 23 | 1 h 3 |
Lameness | 1.8 h 11 | 1.8 h 11 | 1.8 h 11 | 1.8 h 11 | - | 1.8 h 11 | |
Reduction in ewe/goat carcass due to disease (%) | GIN | 15% 7 | 2% 5 | 5% 19 | - | - | - |
Lameness | 8% 11 | 8% 11 | 8% 11 | 8% 11 | - | 8% 11 |
Chios Sheep (GR) | Current Situation | Optimal Situation | |
---|---|---|---|
Scenario 1 Diseases Present | Scenario 2 No Diseases | ||
Ewes | 233 | 387 | 387 |
Land 1 (ha) | 17 | 1.3 | 1.3 |
Labor (h) | 6425 | 6300 | 6300 |
Forage (tonnes) 2 | 82 (36.6%) | 123.7 (0.0%) | 123.6 (0.0%) |
Concentrates (tonnes) 2 | 47.2 (81.6%) | 30.9 (45.0%) | 30.9 (45.0%) |
Gross revenue (EUR) 3 | 79,919 (343) | 138,592 (358) | 155,300 (401) |
Variable cost (EUR) 3 | 29,125 (125) | 29,063 (75) | 28,287 (73) |
Gross margin (EUR) 3 | 50,794 (218) | 109,530 (283) | 127,013 (328) |
AssafΕ sheep (GR) | Current situation | Optimal situation | |
Diseases present | No diseases | ||
Ewes | 490 | 835 | 857 |
Land 1 (ha) | 15 | 15 | 15 |
Labor (h) | 4,820 | 8,400 | 8,400 |
Forage (tonnes) 2 | 275.0 (0%) | 652.9 (0%) | 670.0 (0%) |
Concentrates (tonnes) 2 | 72.3 (37.4%) | 163.2 (20.2%) | 167.6 (19.7%) |
Gross revenue (EUR) 3 | 128,250 (262) | 228,560 (274) | 254,743 (297) |
Variable cost (EUR) 3 | 74,185 (151) | 103,008 (123) | 104,081 (121) |
Gross margin (EUR) 3 | 54,065 (111) | 125,553 (151) | 150,662 (176) |
Frizarta sheep (GR) | Current situation | Optimal situation | |
Diseases present | No diseases | ||
Ewes | 240 | 263 | 270 |
Land 1 (ha) | 35 | 26 | 26 |
Labor (h) | 4200 | 4200 | 4200 |
Forage (tonnes) 2 | 48.0 (100%) | 36.0 (68.4%) | 38.8 (62.8%) |
Concentrates (tonnes) 2 | 31.1 (3.5%) | 28.4 (79.6%) | 28.8 (79.6%) |
Gross revenue (EUR) 3 | 60,936 (254) | 69,381 (264) | 79,817 (296) |
Variable cost (EUR) 3 | 25,012 (104) | 21,487 (82) | 21,740 (81) |
Gross margin (EUR) 3 | 35,924 (150) | 47,894 (182) | 58,076 (215) |
Chios Sheep (GR) | Current Situation | Optimal Situation | |
---|---|---|---|
Scenario 1 Diseases Present | Scenario 2 No Diseases | ||
Ewes | 108 | 86 | 87 |
Land 1 (ha) | 16 | 48 | 48 |
Labor (h) | 5250 | 3150 | 3150 |
Forage (tonnes) 2 | 1.3 (0%) | - | - |
Concentrates (tonnes) 2 | 9.7 (0%) | 7.6 (0%) | 7.7 (0%) |
Gross revenue (EUR) 3 | 12,806 (118) | 9077 (106) | 10,320 (119) |
Variable cost (EUR) 3 | 9093 (84) | 3238 (38) | 3026 (35) |
Gross margin (EUR) 3 | 3713 (34) | 5839 (68) | 7239 (84) |
AssafΕ sheep (GR) | Current situation | Optimal situation | |
Diseases present | No diseases | ||
Ewes | 400 | 468 | 468 |
Land 1 (ha) | 70 | 46 | 46 |
Labor (h) | 3500 | 3500 | 3500 |
Forage (tonnes) 2 | 102.0 (100%) | 101.0 (100%) | 101.0 (100%) |
Concentrates (tonnes) 2 | 80.0 (100%) | 73.0 (88%) | 73.0 (88%) |
Gross revenue (EUR) 3 | 137,986 (345) | 170,766 (365) | 175,179 (374) |
Variable cost (EUR) 3 | 42,000 (105) | 37,863 (81) | 37,320 (80) |
Gross margin (EUR) 3 | 95,986 (240) | 132,903 (284) | 137,860 (295) |
Frizarta sheep (GR) | Current situation | Optimal situation | |
Diseases present | No diseases | ||
Ewes | 300 | 399 | 399 |
Land 1 (ha) | 15 | 28 | 28 |
Labor (h) | 8825 | 8566 | 8429 |
Forage (tonnes) 2 | 15 (0%) | - | - |
Concentrates (tonnes) 2 | 45 (0%) | 47.1 (45%) | 47.1 (45%) |
Gross revenue (EUR) 3 | 29,736 (99) | 45,392 (114) | 47,663 (119) |
Variable cost (EUR) 3 | 15,520 (52) | 17,030 (43) | 16,279 (41) |
Gross margin (EUR) 3 | 14,216 (47) | 28,362 (71) | 31,384 (79) |
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Theodoridis, A.; Ragkos, A.; Vouraki, S.; Arsenos, G.; Kominakis, A.; Coppin, S.; Thenard, V.; Byrne, T.J. Novel Resilient and Sustainable Farm Profiles in Small Ruminant Production Systems Using Mathematical Programming Model. Sustainability 2023, 15, 11499. https://doi.org/10.3390/su151511499
Theodoridis A, Ragkos A, Vouraki S, Arsenos G, Kominakis A, Coppin S, Thenard V, Byrne TJ. Novel Resilient and Sustainable Farm Profiles in Small Ruminant Production Systems Using Mathematical Programming Model. Sustainability. 2023; 15(15):11499. https://doi.org/10.3390/su151511499
Chicago/Turabian StyleTheodoridis, Alexandros, Athanasios Ragkos, Sotiria Vouraki, Georgios Arsenos, Antonis Kominakis, Stephanie Coppin, Vincent Thenard, and Tim J. Byrne. 2023. "Novel Resilient and Sustainable Farm Profiles in Small Ruminant Production Systems Using Mathematical Programming Model" Sustainability 15, no. 15: 11499. https://doi.org/10.3390/su151511499