Investigation of Effect of Labour and Road Infrastructure on Fermented Dairy Production Capacity in Upstream Fermented Dairy Supply Chain

Abstract

Scope of the study focuses on fermented dairy products because, they constitute very large and important part of human diet. These products are very healthy because, they have some health benefits. 2731 fermented dairy processors in 227 sub-sectors in 48 cities have been investigated to determine the effect of the length of roads, the number of white-collar and blue-collar employees on fermented dairy production capacity. To do that, Generalized Linear Model has been utilized. Consequently, results state that the number of blue-collar employees has a positive effect on fermented dairy production capacity at 0.001 significance while the number of white-collar employees does not have a significant effect on the fermented dairy production capacity. On the other hand length of road has a positive effect on the fermented dairy production capacity at 0.10 significance level.

Keywords

• Fermented Dairy Products Supply Chain
• Labour
• Road Infrastructure
• Fermented Dairy Production Capacity
• Production Efficiency

References

1. Acemoglu D (1998). Why do new technologies complement skills? Directed technical change and wage inequality. Quarterly Journal of Economics 4(113): 1055-1089. Available at https://doi.org/10.1162/003355398555838 (accessed January 9, 2023).
2. Akin A & Cevger Y (2019). Analysis of factors affecting production costs and profitability of milk and dairy products in Turkey. Food Science and Technology 39: 781-787. Available at https://doi.org/10.1590/fst.28818 (accessed February 28, 2023).
3. Anderson C J, Verkuilen J & Johnson T (2010). Applied Generalized Linear Mixed Models: Continuous and Discrete Data. Springer, New York, USA. pp. 170-172
4. Bewley J, Palmer R W & Jackson-Smith D B (2001). Modelling milk production and labor efficiency in modernized Wisconsin dairy herds. Journal of Dairy Science 84(3): 705-716. Available at https://doi.org/10.3168/jds.S0022-0302(01)74525-0 (accessed January 27, 2023).
5. Cortignani R, Carulli G, Dono G (2020). COVID-19 and labour in agriculture: Economic and productive impacts in an agricultural area of the Mediterranean. Italian Journal of Agronomy 15(2): 172-181. Available at https://doi.org/10.4081/ija.2020.1653 (accessed May 4. 2023).
6. Dani S (2015). Food Supply Chain Management and Logistics: From Farm to Fork. Kogan Page Publishers, Great Britain pp. 13.6
7. Du X Tejeda, H Yang Z & Lu L (2022). A general-equilibrium model of labor-saving technology adoption: Theory and evidences from robotic milking systems in Idaho. Sustainability 14(7683): 1-17. Available at https://doi.org/10.3390/su14137683 (accessed December 28, 2022).
8. Eurostat (2017). Available from https://ec.europa.eu/eurostat (accessed: 15.January.2023).

9. Fassio L H, Reis R. P & Geraldo L G (2006).Technical and economical effectiveness of milk production from Minas Gerais state, Brazil. Ciência e Agrotecnologia 30: 1154-1161. Available at https://doi.org/10.1590/S1413-70542006000600018 (accessed March 3, 2023).
10. Fox J (2015). Applied Regression Analysis and Generalized Linear Models, Sage Publications, California, USA pp. 103-105
11. Frick F, Jantke C & Sauer J (2019). Innovation and productivity in the food vs. the high-tech manufacturing sector. Economics of Innovation and New Technology 28(7): 674-694. Available at https://doi.org/10.1080/10438599.2018.1557405 (accessed March 1, 2023).
12. García-Burgos M, Moreno-Fernández J, Alférez M J, Díaz-Castro J & López-Aliaga I (2020). New perspectives in fermented dairy products and their health relevance. Journal of Functional Foods 72: 1-11. Available at https://doi.org/10.1016/j.jff.2020.104059 (accessed February 28, 2023).
13. Garcia‐Covarrubias L, Läpple D, Dillon E & Thorne F (2024). The role of hired labour on technical efficiency in an expanding dairy sector: The case of Ireland. Australian Journal of Agricultural and Resource Economics. Available at https://doi.org/10.1111/1467-8489.12553 (accessed May 6, 2024).
14. Goldin C & Katz L F (1998). The origins of technology-skill complementarity, Quarterly Journal of Economics 113(3): 693-732. Available at https://doi.org/10.1162/003355398555720 (accessed March 1, 2023).
15. Grodach C & Martin D (2021). Zoning in on urban manufacturing: industry location and change among low-tech, high-touch industries in Melbourne, Australia. Urban Geography 42(4): 458-480. Available at https://doi.org/10.1080/02723638.2020.1723329 (accessed March 1, 2023).
16. Hardin J W & Hilbe J M (2007). Generalized Linear Models and Extensions, Stata Press, Texas, USA. 108 pp Hussain M, Ghafoor A & Saboor A (2010). Factors affecting milk production in buffaloes: a case study. Pakistan Vet. J 30(2): 115-117. Available at www.pvj.com.pk (accessed December 27, 2022).
17. Janssen B (2013). Herd management: labor strategies in local food production. Anthropology of Work Review 34(2): 68-79. Available at https://doi.org/10.1111/awr.12011 (accessed February 18, 2023).
18. Kiambi S, Onono J O, Kang’ethe E, Aboge G O, Murungi M K, Muinde P & Alarcon P (2020). Investigation of the governance structure of the Nairobi dairy value chain and its influence on food safety. Preventive Veterinary Medicine 179: 1-15. Available at https://doi.org/10.1016/j.prevetmed.2020.105009 (accessed January 28, 2023).
19. Krampe C & Fridman A (2022). Oatly, a serious ‘problem’for the dairy industry? A case study. International Food and Agribusiness Management Review 25(1): 157-171. Available at https://doi.org/10.22434/IFAMR2021.0058 (accessed June 21, 2023).
20. Kumari B, Chandel B S & Lal P (2020). Economic analysis of milk production in eastern region of India. Indian Journal of Dairy Science 73(5). Available at https://doi.org/10.33785/IJDS.2020.v73i02.010 (accessed December 28, 2022).
21. Leonardo W J, van de Ven G W, Udo H, Kanellopoulos A, Sitoe A & Giller K E (2015). Labour not land constrains agricultural production and food self-sufficiency in maize-based smallholder farming systems in Mozambique. Food Security 7(4): 857-874. Available at https://doi.org/10.1007/s12571-015-0480-7 (accessed February 5, 2023).
22. Lindsey J K (2000). Applying Generalized Linear Models. Springer Science & Business Media, New York, USA, 52 pp
23. Liu C, Li Y & Shen W (2014). Integrated manufacturing process planning and control based on intelligent agents and multi-dimension features. The International Journal of Advanced Manufacturing Technology 75: 1457-1471. Available at https://doi.org/10.1007/s00170-014-6246-0 (accessed March 2, 2023).
24. Malanski P D, Schiavi S, Dedieu B & Damasceno J C (2022). Labor in agrifood value chains: a scientometric review from Scopus. International Food and Agribusiness Management Review 25(3): 449-468. Available at https://doi.org/10.22434/IFAMR2021.0066 (accessed July 1, 2023).
25. Martinelli R R, Damasceno J C, de Brito M M, da Costa V D V, Lima P G L & Bánkuti F I (2022). Horizontal collaborations and the competitiveness of dairy farmers in Brazil. Journal of Co-operative Organization and Management 10(2): 1-11. Available at https://doi.org/10.1016/j.jcom.2022.100183 (accessed February 3, 2023).
26. McCullogh P & Nelder J A (1989). Binary Data. In Generalized Linear Models. Springer, US. 99 pp Nahar A, Mila F A, Culas R J & Amin M. R (2022). Assessing the factors and constraints for value chain development of dairy food products in Bangladesh. Heliyon 8(10): 1-8. Available at https://doi.org/10.1016/j.heliyon.2022.e10787 (accessed February 28, 2023).
27. Nelder J A & Wedderburn R W M (1972). Generalized linear models. Journal of the Royal Statistical Society 135: 370–384. Available at https://doi.org/10.2307/2344614 (accessed December 29, 2022).
28. Nmadu J N & Akinola A (2015). Farm labour supply and utilization for food crop production in Nigeria. In Proceedings of the 2nd International Conference on Education and Social Sciences (INTCESS15), Istanbul, Turkey, February, 2-4, 2015 pp.311-320
29. Ojo S O (2004). Improving labour productivity and technical efficiency in food crop production: A panacea for poverty reduction in Nigeria. Journal of Food Agriculture and Environment 2: 227-231. Available at www.world-food.net (accessed January 3, 2023).
30. Rutledge Z & Mérel P, (2022). Farm labor supply and fruit and vegetable production. American Journal of Agricultural Economics 105(2): 644-673. Available at https://doi.org/10.1111/ajae.12332 (accessed January 8, 2023).
31. Saravana Pandian A, Selvakumar K N, Prabu M & Ganeshkumar B (2013). Assessing the productivity of resources in milk production in Tamil Nadu-an econometric analysis. Indian Journal of Animal Research 47(3): 541-559. Available at https://www.arccjournals.com/ (accessed February 14, 2023).
32. Sargent, J & Matthews L (2008). Capital intensity, technology intensity, and skill development in post China/WTO maquiladoras. World Development 36(4): 541-559. Available at https://doi.org/10.1016/j.worlddev.2007.04.015 (accessed March 1, 2023).
33. Shafiee Z & Sharifi G (2017). Comparing the effect of resistance, aerobic, and concurrent exercise program on the level of resistin and high reactive protein c of overweight and obese women. Int Arch Health Sci, 4: 1-6. Available at DOI: 10.4103/iahs.iahs_4_17 (accessed January 8, 2023).
34. Silvi R, Pereira L G R, Paiva, C A V, Tomich, T R, Teixeira V A, Sacramento J P & Dórea, J R R (2021).Adoption of precision technologies by Brazilian dairy farms: The farmer’s perception. Animals 11(3488): 1-16. Available at https://doi.org/10.3390/ani11123488 (accessed January 8, 2023).
35. Smit, G (2003). Dairy Processing: Improving Quality. Cambridge, England, Woodhead Publishing in Food Science and Technology 545: 103
36. Střeleček F, Zdeněk R & Lososová J (2007). Influence of farm milk prices in the EU 25 on profitability and production volume indicators. Agricultural Economics–Czech 53: 545-557. Available at DOI: 10.17221/1224-AGRICECON (accessed February 9, 2023).
37. Subburaj M, Babu T R & Subramonian B S (2015). A study on strengthening the operational efficiency of dairy supply Chain in Tamilnadu, India. Procedia-Social and Behavioral Sciences 189: 285-291. Available at https://doi.org/10.1016/j.sbspro.2015.03.224 (accessed January 10, 2023).
38. Talukder B, Agnusdei G P, Hipel K W & Dubé L (2021). Multi-indicator supply chain management framework for food convergent innovation in the dairy business. Sustainable Futures 3: 1-14. Available at https://doi.org/10.1016/j.sftr.2021.100045 (accessed January 18, 2023).
39. Tereszczuk M & Mroczek R (2018). Labor productivity and concentration of food production in the Polish food industry against the EU-28. Zeszyty Naukowe Szkoły Glównej Gospodarstwa Wiejskiego w Warszawie-Problemy Rolnictwa Światowego 18(1): 299-308. Available at 10.22630/PRS.2018.18.1.27 (accessed January 21, 2023).
40. Tostivint C, de Veron S, Jan O, Lanctuit H, Hutton Z V & Loubière M (2017). Measuring food waste in a dairy supply chain in Pakistan. Journal of Cleaner Production 145: 221-231. Available https://doi.org/10.1016/j.jclepro.2016.12.081 (accessed February 18, 2023).
41. Tse C, Barkema H W, DeVries T J, Rushen J & Pajor E (2018). Impact of automatic milking systems on dairy cattle producers’ reports of milking labour management, milk production and milk quality. Animal 12(12): 2649-2656. Available at https://doi.org/10.1017/S1751731118000654 (accessed March 3, 2023).
42. Zhang W (2005). Generalized linear models in family studies, Journal of Marriage and Family 67(4): 789-1101. Available at https://doi.org/10.1111/j.1741-3737.2005.00192.x (accessed February 15, 2023).