Constraints in different dairy based farming systems in Tamil Nadu

Introduction

India reigns supreme as the largest producer of milk in the world contributing to about 25 percent of the global milk production in 2025. The production of milk has gone up more than five fold since independence resulting in near doubling of per capita availability of milk. The per capita availability of milk now stands at 471 grams per day far above the global average of 322 grams. In 2024-2025 the livestock sector contributes 30.23 percent of agricultural GVA and 5.5 percent of the national economy (DAHD, Annual Report 2024-2025). Tamil Nadu, the southern most state of India is one of the major milk producing state. The state ranks 11 th in milk production (10.81 million tonnes, 2023-24), 2 nd in egg production (22.33 billion in 2023-24) and 6 th in meat production (0.68 million tonnes in 2023-24). The per capita availability of milk in Tamil Nadu is 384 g/day and 291 eggs/year, both exceeding ICMR recommendations, while per capita meat availability is 9.9 kg/year. A positive growth trend was observed for livestock production during the last decade. The co-operative dairying has effectively tapped the milk production potential in dairy development. Dairying has now emerged as an important income generating activity and a source of mass employment in rural arena. Farming system is a complex of inter related enterprises containing crops, dairy, sheep, goat, pigs, poultry, fishery etc., which interact among themselves. The judicious mix of the crops and animal enterprises must be based on the principle of minimizing the competition for resource and maximizing the returns among various enterprises (Behera and Mahapatra, 1999) [2] . Though dairy farming is providing employment to 80 million rural households, it has its own constraints. In resource poor Indian conditions, the farmers who are pursuing dairy as a single occupation is very meagre. Almost all the dairy farmers possess dairy along with agriculture, sheep, goat, poultry or others. In this scenario, it is essential to assess which dairy based system is more beneficial in different areas along with their constraints.

Methodology

Five farming systems were predominantly identified in the study area and they are as classified as Farming System-I (Only Dairy), Farming System-II (Dairy + Agriculture), Farming System-III (Dairy + Sheep/Goat), Farming System-IV (Dairy + Poultry) and Farming System-V (Dairy + Agriculture + Poultry). To analyse the constraints perceived by the farmers in production, financial and marketing, a list of constraints were enlisted and ranked using Garrett's ranking technique. By using this technique, the order of merits given by the respondents was changed into ranks by using the following formula

Where, R ij = Rank given for i th factor by j th individual N j = Number of factors ranked by j th individual

The percent position of each rank was converted into scores by referring table given by Garrett (Garrett and Woodworth, 1969) [4] . Then for each factor, the scores of individual respondents were added together and divided by the number of respondents for whom scores were added. These mean scores for all the factors were arranged in descending order and the constraints were ranked. [8] . This findings suggests that farmers lack exposure to efficient management practises, improved feeding methods, breeding technologies, disease prevention measures and also efficient utilization of resources. The overwhelming nature of this constraint indicates the importance of farmer training programmes, strengthening of extension services and skill development initiatives. Land availability was identified as the most significant constraint in FS-IV with a mean score of 81.92 and also ranked second in FS-III. This could be due to increased urbanization, fragmentation of land area and conversion of grazing land to other uses. Grazing area is one of the prime components that determines livestock fodder needs and productivity. Lack of fodder and grazing facility is the prime constraint in buffalo farming was observed by Kathiravan (2011) [5] . Inadequate veterinary facilities consistently showed up among the top ranked constraints across all farming systems with a mean score of 65.73, 66.52, and 76.01 in FS-II, FS-III, and FS-IV respectively. The score indicates that the farmers experience difficulty in availing timely veterinary care, vaccination and treatment facilities which would lead to decreased production and financial loss. Breeding problem constituted another major issue particularly in FS-I, FS-II. FS-III and FS-IV where they ranked second in FS-I and fourth in other farming systems respectively. Difficulties in obtaining AI facilities, superior bulls for breeding may contribute to lower breeding efficiency and performance. Unavailability of breeding bull was considered as an important constraint in dairy farming as said by Kumar et al. (2014) [6] . Non availability of paddy straw was ranked third in FS-I and second in FS-IV which demonstrates the importance of feed deficit in livestock based systems. Paddy straw is a major crop residue serving as an important feed resource and their shortage can have a negative impact on animal nutrition especially during dry seasons. Labour shortage was the most significant constraint in FS-V, where it ranked third with a mean score of 71.05. Integrated farming systems are generally labour intensive as they have to manage multiple enterprises simultaneously. Migration of labours, increasing wage rates may further enhance this problem. Outbreak of diseases were ranked second in FS-V but received relatively inferior ranks across all farming systems. This indicates integrating multiple livestock enterprises may have higher chances of disease transmission due to greater interaction of animals with each other along with complexity in managing them. Poor production of animals was ranked moderately low to lowest throughout all the farming systems. Although low production of animals was recognized as a concern, they observed it as an effect of other underlying constraints such as inadequate technical knowledge, shortage of feed, breeding issues and insufficient veterinary support. Financial constraints perceived by the farmers in the study are given in the Table 2. High cost of feed was found to be the most severe constraint in FS-1, FS-III and FS-IV with a Garrett score of 91.53, 87.51 and 84.81 respectively. With respective to FS-II and FS-V, financial constraint was ranked second with a mean score of 76.53 and 74.38 respectively. Steady high scores across all farming systems suggest that increasing feed cost significantly affect profitability and management decisions of the farm. High feed cost was also identified as a prime constraint in dairy farming stated by Akila (2012) [1] . High purchase cost of animals was considered as a major financial constraint in FS-III and FS-IV with a mean score of 84. 83 Marketing constraints perceived by the farmers are given in Table 3. Garrett ranking analysis revealed that the sequence of marketing constraints is almost same across all the farming systems (FS-I to FS-V) along with the order of ranking is same in each system. However, the severity of constraints varies slightly among different farming systems. Low procurement price for milk surfaced as the most severe constraint across all farming systems. The highest mean score was observed in FS-I followed by FS-III, FS-IV, FS-II and FS-V with Garrett score of 94.65, 93.53, 92.73, 91.73 and 90.43 respectively. The farmers across all farming systems experienced greater dissatisfaction regarding milk prices. The results are in line with Pradheeshwaran et al.

Results and Discussion

(2024) [8] . Exploitation by middleman was consistently ranked second among all farming systems. The problem was most severe in FS-III followed by FS-I, FS-IV, FS-II and FS-V with a mean score of 92.07, 91.54, 90.43, 86.15 and 84.65 respectively. Higher scores in FS-I. FS-III and FS-IV suggest greater dependence on intermediaries and low bargaining power of farmers across all the systems. Similar results were found by Meganathan et al. (2010) [7] . Lack of storage facilities occupied third rank throughout the farming systems and the mean score is high is FS-IV followed by FS-I, FS-III with mean score of 86.34, 85.91, 85.19 respectively. Lower scores in FS-V (81.87) and FS-II (80.47) indicates relatively better access to storage facilities or low perceived severity of the problem. Delay in payment from co-operative society was ranked fourth in all farming systems. The top most concern was reported in FS-III followed by FS-IV, FS-I, FS-V, and FS-II with a mean score of 84.67, 83.91, 80.52, 79.26 and 78.93 respectively. The result infers that the delay in payment was so problematic for the farmers in FS-III and FS-IV than other systems. Lack of marketing channels secured fifth rank in all farming systems with highest mean score in FS-III followed by FS-I, FS-IV, FS-II and FS-V with mean score of 81. Although this issue was perceived less, it still remains as a concern for dairy farmers.

References

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2. Behera UK, Mahapatra IC. Income and employment generation of small and marginal farmers through integrated farming systems. Indian J Agron. 1999;44(3):431-439.
3. Garrett HE, Woodworth RS. Statistics in Psychology and Education. Bombay: Vakils, Feffer and Simons Pvt Ltd; 1969. p. 329.
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5. Kumar N, Bishnoi P, Bishnai DK, Kumar J. Constraints analysis in adoption of improved dairy farming practices in Haryana. Asian J Dairy Food Res. 2014;33(2):136-140.
6. Meganathan N, Selvakumar KN, Prabu M, Pandian ASS, Kumar GS. Constraint analysis of tribal livestock farming in Tamil Nadu. Tamilnadu J Vet Anim Sci. 2010;6(1):12-18.
7. Pradheeshwaran U, Serma Saravana Pandian A, Prabu M. Constraints in buffalo farming in Tamil Nadu. Int J Vet Sci Anim Husb. 2024;9(4):641-644.