Socio-Economic Characteristics, Adoption, and Knowledge Level of Goat Farmers: A Study in Aspirational Districts of West Bengal, India

Abstract

A study on socio-economic condition and challenges encountered by the farmers was done in purposively selected five districts of West Bengal, India. About 5000 respondents were randomly chosen for this study. The study of various socioeconomic indicators suggests that most goat farmers in these districts were illiterate and belong to the lower economic status. It had been noted that women were primarily involved in raising goats, and their financial situation was precarious. Most of these farmers had educational levels below the 10th standard and were engaged in domestic labor. The majority of these farmers had not had any training in scientific animal husbandry techniques; therefore, offering appropriate training in this area could be beneficial for their ability to support themselves. Given that these farmers have limited land holding capacity, goat farming could be a potential alternative to improve their financial situation. As a whole promotion of goat husbandry could be an ideal intervention to improve the socioeconomic condition of these farmers whose livelihood was in stake. It has been observed that various obstacles, such as a lack of training initiatives and educational opportunities, were the main hindrances for improving the socioeconomic well-being of farmers in the selected districts of West Bengal.

1. Introduction

The rural economy’s backbone remains to be agriculture and related activities, particularly raising livestock, which is crucial for the socio-economic advancement, generating revenue, and general well-being of rural farmers of West Bengal. In the rural sector, livestock is crucial for boosting household income and creating productive employment. West Bengal’s economic activity has seen fundamental changes throughout the previous few decades. Agriculture and related industries’ share in the labour force is dwindling. India has the world’s richest animal wealth, yet the productivity of the livestock industry is low [1]. Another explanation for it can be variations in the socio-economic circumstances of the livestock farmers. Studying the socio-economic situation of the rural livestock farmers is important in this context.

Post-Green Revolution, with input-intensive agriculture, India has transformed from a food-deficit nation into a food-surplus one. Animal husbandry is an integral part of rural India’s symbiotic system of livestock production and constructs a significant part of the livestock wealth of the country. The socio-economic structure of the nation and the economy are both significantly influenced by animal husbandry. In India, cattle and poultry are an essential component in the rural economy, providing farmers with additional income. More processed foods, especially meat, are becoming more popular in terms of consumer preferences. The sector has enormous potential to increase farmer’s income because of its rapid growth. So, the need for livestock rearing for alternative livelihood is the major priority of this day.

India is predicted to raise millions out of poverty due to its high development trajectory. Although India is currently ranked 132 out of 188 nations according to the latest report (Human Development Report, 2022) by United Nations Development Programme, though many of its residents’ quality of life does not currently align with this growth story. A detailed examination of the data reveals significant variability in India’s living conditions. Significant differences exist inter-state and inter-district levels. India can advance in the Human Development Index by improving the districts that have made relatively less progress towards reaching the important social outcome. The Transformation of Aspirational Districts Programme aims to quickly and efficiently transform these districts. Institutional support (crop insurance, electronic markets, artificial insemination, animal vaccination, etc.) ultimately enables a large number of Indian youths to take up industry-relevant skill training that will help them in securing a better livelihood. It has always been believed that change happens when it begins at the grassroots. Innovative ideas, strategies, practices, and methods should be used to develop the 117 aspirational districts of India with a concentrated effort to transform these districts. In West Bengal, as per NITI Aayog (National Institution for Transforming India), the five Aspirational Districts (Nadia, Murshidabad, Birbhum, Maldah, South Dinajpur) are only under “Lower Gangetic Plains Region” under 15 agro-climatic zones in India.

Black Bengal goat is a very useful small livestock in this area distributed in almost every household in this area. It is also a great source of income for the poor people of this region. Small-scale Goat farming has a very important role in reducing unemployment and poverty. The costs of rearing Black Bengal goats are lower with scientific practices. This creates small-scale enterprise which has tremendous potential in this area in terms of nutritional security, gainful self-employment, and economic upliftment. More than 85% of the population of this region is non-vegetarian and chevon or chicken is preferred by most of the people. Improvement of Black Bengal Goat production is necessary to benefit the rural community and smallholder farmers. India has a large population of goats (16.7% of the world’s total) and a diverse genetic group [2]. In India, there are 23 recognised goat breeds [3]. On the other side, West Bengal, with 10.7% of the national total, has the second-highest goat population in the nation (148.90 lakhs) [4]. Poor people in India who live in a variety of climatic conditions depend heavily on goats for their livelihood. Goat keeping creates employment in rural areas at a rate of 4.2% annually [5]. Due to their accessible market demand, goats are seen as fixed deposits for the poorest of the poor, providing funds as and when necessary [6]. Goat management is done with domestic waste and requires only a small cost. Thus, they do not need to incur any more managerial expenses. The primary way that rural women are able to significantly contribute to the financial needs of themselves and their families is through goat rearing. Goat keeping is a highly essential function for women in rural communities.

Based on criteria including poverty, poor health, educational attainment, socio-economic characteristics, etc., the NITI Aayog has designated various districts throughout the nation as aspirational districts in light of the socio-economic development status. Five aspirational districts (Nadia, Murshidabad, Birbhum, Maldah, Dakshin Dinajpur) have been recognised in West Bengal by NITI Ayog. The State can improve its ranking in the Human Development Index by improving these districts that have made substantially less progress toward reaching the important social outcome. The position that each farmer holds in relation to the prevailing average standards, material ownership, social engagement, and other criteria is referred to as their socio-economic status [7]. In light of this context, an effort has been made to study the socio-economic situation and barriers experienced by the farmers in West Bengal’s several aspirational districts. Knowledge is the body of information that a person or a society possesses and is an intimate acquaintance with facts [8]. Adoption of any new or enhanced technology or methods is acquired from knowledge. An evaluation of the farmers’ existing knowledge and adoption level is necessary prior to the implementation of various entrepreneurship development programs in the study area.

2. Objectives

In the above-said context, certain objectives have been taken in this study. The specific objectives of this study are as follows:

1. To study the socio-economic profile of goat keepers,

2. To study the rearing challenges in goat rearing under village conditions in various aspirational districts of West Bengal,

3. To study the adoption level of the farmers, and,

4. To determine the knowledge level of the farmers in various aspirational districts of West Bengal.

3. Materials and methodology

3.1 Study area selection

As per NITI Aayog, Government of India, the aspirational districts under Lower Gangetic Plains Region in West Bengal are Birbhum, Nadia, Maldah, South Dinajpur, and Murshidabad, where this study was conducted (Figure 1).

Figure 1.

Study area map. Source: Government of India.

3.2 Sampling design

The five aspirational districts (identified by the NITI Aayog, Government of India) in West Bengal were purposively selected for this study because of their socio-economic condition, level of poverty, poor health, and educational status. Five Blocks were purposively selected from each District. These blocks are Ilambazar from Birbhum District, Haringhata from Nadia District, Bamongola from Maldah District, Kusmandi from South Dinajpur District, and Murshidabad-Jianganj from Murshidabad District. A total of 5000 respondents were randomly selected with a sample of 1000 farmers from each block of each district. The socio-economic status of randomly selected 5000 farmers from the Nadia, Murshidabad, Birbhum, Maldah, and Dakshin Dinajpur districts of West Bengal state was done between January 2020 to September 2022 under the Department of Biotechnology, Government of India, project entitled “Establishment of Biotech KISAN Hub” at West Bengal University of Animal and Fishery Sciences to ascertain the problems of the local farmers for linking available Science and Technology to the farmers of Aspirational Districts of West Bengal to provide a suitable and alternative solution for their better livelihood.

3.3 Methodology used

All the selected variables were assessed either using a pre-existing scale or by creating a schedule. The socio-economic profile of the livestock farmers, including their caste, religion, level of education, family size, occupation, and annual income, has been studied. A pre-tested structured interview schedule was used to get the data. For a better understanding of the results, statistical techniques such as Percentage Analysis, Chi-Square Test, Standard Error of the Mean, Spearman Correlation Coefficient, and Analysis of Variance (ANOVA) have been measured. These tests were conducted using statistical tool, SPSS (Statistical Package for the Social Sciences), Version 20. Responses were collected between January 2020 to September 2022.

4. Results and discussion

As identified by NITI Aayog, the present study was carried out among 5000 farmers in five aspirational districts of West Bengal (Birbhum, Nadia, Maldah, South Dinajpur, and Murshidabad) in order to evaluate their socio-economic and demographic condition as well as the difficulties they face. This was done in order to identify the issues affecting the local farmers in the area. The analytical studies are shown in the following manner:

4.1 Demographic profile of the farmers

4.1.1 Gender

In the demographic study and in raising livestock, gender is a key factor. It has been noted that the majority of farmers (63.20%) engaged in livestock farming or animal husbandry activities. The Spearman correlation test showed that age, marital status, occupation, caste, and education are all positively and significantly (P < 0.01) linked with gender. Age and land ownership have a negative (P < 0.01) correlation with gender. This observation differs from the previous findings [9] since their study was conducted in a different location and the farmers were chosen on purpose. As a result, there is a clear contrast between this observation and prior findings.

4.1.2 Age

The data showed that the majority of farmers were in the 30–60 year age bracket, which is the most active age group (67.21%) and that they were most interested in raising goats. According to survey analysis, the percentage of young farmers, or those under 30 years old, was 26.00%, and the percentage of farmers over 60 years old was 6.79% (Table 1). The most active respondent groups were 67.25% in Birbhum, 66.98% in Nadia, 67.26% in Maldah, 67.26% in South Dinajpur, and 67.30% in the Murshidabad area. Age and occupation are highly and positively (P < 0.01) correlated factors, as are family income, education, and type of housing. Age has a negative (P < 0.01) correlation with marital status, religion, family size, and landholding. This result was consistent with previous studies [10, 11].

Characters	Category	Birbhum	Nadia	Malda	South dinajpur	Murshidabad	Overall	Chi-square value
		(%)	(%)	(%)	(%)	(%)	(%)
Sex	Male	36.76	36.8	36.8	36.79	36.87	36.8	66.64**
	Female	63.24	63.2	63.2	63.21	63.13	63.2
Age	Young group (up to 30 years)	25.98	26.15	26.02	25.95	25.88	26	94.92**
	Most active group (30–60 years)	67.25	66.98	67.26	67.26	67.3	67.21
	Elder group (above 60 years)	6.76	6.86	6.73	6.79	6.82	6.79
Religion	Hindu	91.37	45.92	50.51	88.93	23.99	61.96	160.44**
	Muslim	8.63	54.08	49.49	11.07	76.01	38.04
Marital status	Married	87.65	87.57	87.56	87.62	87.63	87.61	182.49**
	Unmarried	9.61	9.59	9.64	9.64	9.47	9.59
	Widow/widower	2.75	2.84	2.79	2.74	2.9	2.8
Gross family income/month	Below Rs. 2000	38.04	46.04	12.06	17.98	43.94	32	145.83**
	Rs. 2001–5000	41.96	29.94	59.9	47.98	30.05	41.87
	Rs. 5001–10,000	14.02	17.99	18.02	24.05	14.02	17.5
	Rs. 10,001 and above	5.98	6.04	10.03	10	11.99	8.63
Occupation	Labour	33.14	50.3	16.24	48.81	23.61	34.73	88.44^**
	Caste occupation	1.96	2.13	2.03	2.62	3.54	2.43
	Migrants labour	11.57	13.25	13.96	13.57	18.31	13.98
	Business	4.31	4.02	3.05	1.19	3.66	3.29
	Service	4.02	3.2	7.11	2.02	7.83	4.74
	Cultivation	45	27.1	57.61	31.79	43.06	40.84
Caste	General	65.39	37.51	53.43	48.33	14.27	44.9	102.94.**
	Schedule caste	14.51	6.51	4.95	26.19	6.06	11.9
	Schedule tribe	11.57	4.02	2.79	12.38	1.52	6.77
	Other backward caste	8.53	51.95	38.83	37.98	78.16	41.31
Education of the respondent	Illiterate	0.98	2.01	5.96	6.19	4.04	3.69	117.28**
	Can read only	20.59	6.04	4.06	17.98	11.62	12.51
	Can read and write	28.04	9.94	25.89	18.1	16.67	20.02
	Primary	15.69	15.74	35.91	13.69	20.33	19.88
	Middle school	15.49	35.98	12.06	12.14	20.08	19.09
	High school	9.71	22.37	4.06	21.9	17.93	15.08
	Graduate	9.51	7.93	12.06	10	9.34	9.73
Family type	Nuclear family	51.96	15.98	39.34	21.79	9.97	28.87	171.19**
	Joint family	48.04	84.02	60.66	78.21	90.03	71.13
Family size	Small	51.76	17.16	13.96	20.71	10.61	24.29	221.92**
	Medium	48.24	82.84	86.04	76.9	89.39	75.24
House type	No house	5.98	6.51	10.03	4.52	6.06	6.56	202.94**
	Hut	4.02	26.86	14.47	14.76	9.97	13.65
	Kutcha house	25.98	25.33	33.12	35.6	27.27	29.29
	Mixed house	59.51	35.27	33.5	39.05	49.12	44.01
	Pucca house	1.96	1.3	4.82	4.05	4.29	3.2
	Mansion	2.55	4.14	4.06	2.02	3.28	3.17
Land	No land/land less	16.47	35.98	15.99	22.02	44.95	26.58	105.84^**
	Up to 1 hectare	45.98	35.03	32.74	45	20.08	36.41
	Up to 2 hectares	25.59	26.04	43.4	25	25	28.73
	Above 2 hectares	11.96	2.37	7.87	7.98	9.97	8.17
Training received	Not received	15.78	12.9	14.09	18.57	17.93	15.85	89.96**
	Received	84.22	87.1	85.91	81.43	82.07	84.15
Farm power	No drought animal	72.75	72.78	72.84	72.74	72.73	72.79	117.28**
	1–2 drought animals	19.22	18.22	19.54	19.17	19.19	19.21
	3–4 drought/1 or more prestige animal	4.8	5.33	4.82	4.76	4.8	4.81
	5–6 Drought animals/tractors	3.14	3.2	3.17	3.21	3.16	3.2
Material possession	Bullock cart	30.78	30.77	30.84	30.83	30.81	30.81	145.83**
	Cycle	96.27	96.21	96.32	96.31	95.45	96.27
	Radio	34.22	34.2	34.26	34.29	34.22	34.26
	Television	69.71	69.7	72.59	69.76	69.7	69.73

Table 1.

Demographic and socio-economic characteristics of farmers of aspirational districts of West Bengal (N = 5000).

**P < 0.01; *P < 0.05.

4.1.3 Religion

The findings showed that Hinduism is practiced by the majority of farmers (61.96%) (Table 1). Most respondents (91.37%) in Birbhum district, followed by South Dinajpur district (88.93%) and Maldah district (50.51%), were of the Hindu religion. Additionally, it was noted that the majority of respondents in the districts of Murshidabad (76.01%) and Nadia (54.08%) identified as Muslims. According to the chi-square test (Table 1), the observational difference between the various religions was statistically significant (P < 0.01). Due to the location of their studies, this observation differs from the previous findings [10, 12]. Additionally, because the farmers were chosen purposively, any differences or similarities between what has been observed and past results are clearly a natural occurrence.

4.1.4 Marital status

It is observed that altogether, 87.61% of respondents were married, compared to only 9.59% who were single and 2.80% who were widowed. Similar trends were seen in all of the districts. The findings indicated that the majority of respondents (87.65%) in the Birbhum district, followed by those in Murshidabad (87.63%), South Dinajpur (87.62%), Nadia (87.57%), and Maldah (87.56%), were married. According to the chi-square test (Table 1), the difference in observation of different marital statuses was statistically significant (P < 0.01). The majority of farmers were found to be older than 30 years old, therefore the present result of marital status was a natural occurrence in the study area.

4.1.5 Gross family income

According to the present study, the average farmer’s family income is between Rs. 2001 and Rs. 5000 per month, followed by 32% of respondents who earn less than Rs. 2000 per month and 17.50% of respondents who earn between Rs. 5001 and Rs. 10,000 per month. The results also showed that the income of farmers earning more than Rs. 10,001 is quite low (8.63%). In Maldah (59.60%), South Dinajpur (47.98%), and Birbhum district (41.96%), the majority of respondents belong to the monthly income group of Rs. 2001–5000, whereas in Nadia (46%), and Murshidabad district (44%) the majority of respondents belong to the monthly income group of below Rs. 2000. It had been noted that the majority of farmers in the districts of Maldah, South Dinajpur, and Birbhum were comparably more prosperous than those in the other two districts. The difference in value between the different monthly income groups of these farmers had a highly significant effect, according to the chi-square test (Table 1) (P < 0.01). Given that the majority of farmers (73.87%) had monthly incomes of less than Rs 5000, attention may be focused on helping these farmers to improve their financial situation. Numerous research conducted in this field revealed that the majority of farmers in rural India belonged to low-income categories. However, there are regional and individual differences in economic condition.

4.1.6 Occupation

The majority of farmers (40.84%) work primarily in agriculture, with the remainder working as labourers (34.73%) and migratory workers (13.98%). Aout 43.06% of respondents in Murshidabad district, 57.61% in Maldah district, and 45% in Birbhum district stated that their primary occupation was farming. 50.30% of respondents worked as labour in the Nadia district, while 48.81% did so in the South Dinajpur district. According to the results of the chi-square test (Table 1), there were statistically significant differences in the observations of the various occupations. While occupation is negatively correlated with family size, landholding, and training obtained (P < 0.01), it is positively correlated with education level, caste, family income, and house type (P < 0.05) (Table 2). This observation differs with previous studies [13, 14] which may have had a smaller sample size. Such findings were also made by some previous studies [10, 11, 15]. Due to their lower annual income of Rs. 30,000, the farmers’ ability to support themselves was at risk, so it was imperative to find an alternative arrangement. The key aim of the policy-making authorities is to take concrete action in this regard while also ensuring the living conditions of these farmers and inclusive growth for them.

	Sex	Age	Marital status	Religion	Occupation	Education Status	Caste	Family type	Family size	family income	Land holding	Training	House type
Sex	1	−0.248^**	0.356^**	0.111	0.280^**	0.361^**	0.171^**	−0.054	−0.065	0.002	−0.239^**	0.003	0.082
Age		1	−0.375^**	−0.278^**	0.336^**	−0.187^*	0.132	−0.009	−0.257^**	0.179^**	−0.307^**	0.344^**	0.174^**
Marital status			1	0.405^**	0.011	0.323^**	0.182^*	0.229^**	−0.154	0.308^**	0.073	0.064	−0.167
Religion				1	−0.077	0.083	0.069	0.566^**	−0.023	0.321^**	0.122^**	0.119^**	−0.098^**
Occupation					1	0.832^**	0.590^**	0.055	−0.373^**	0.451^**	−0.599^**	−0.212^**	0.127^*
Education status						1	0.530^**	0.012	0.010	0.118	−0.125^*	0.014	0.107
Caste							1	0.054	0.089	0.112^**	−0.265^**	0.017	0.109
Family type								1	−0.510	0.455^**	0.077	0.089	−0.211^**
Family size									1	−0.326^**	0.476^**	−0.117^**	0.389
Family income										1	−0.180^**	0.128*	0.086
Land holding											1	−0.054	−0.079
Training												1	0.509
House type													1

Table 2.

Spearman correlation coefficient of socio-economic characteristics of the farmers of aspirational districts of West Bengal (N = 5000).

**P < 0.01. *P < 0.05.

4.1.7 Caste

According to the observations, the majority of farmers belong to General Caste (44.9%), followed by OBC (41.31%) and Scheduled Caste (11.9%), only (6.77%) of farmers are ST (Table 1). The majority of respondents in the districts of Birbhum (65.39%), Maldah (53.43%), and South Dinajpur (48.33%) fell into the general category. In contrast, 51.95% of respondents in the Nadia district and 78.16% of respondents in the Murshidabad district identified as OBC. According to the results of the chi-square test (Table 1), there were statistically significant differences in the observations of the various castes. The results of this study are consistent with some previous studies [10, 12] but they are not consistent with other studies [16], who also claimed that the SC prefers the general trend of goat farming, which may be related to the location of their study.

4.1.8 Educational level

The study of the data showed that only 9.73% of farmers received graduation degree, whereas the majority of farmers can read and write (20.02%), have completed elementary school (19.88%), and middle school (19.09%) (Table 1). The findings (Table 1) showed that the majority of respondents (28.04%) in the Birbhum district can read and write. Among the sample respondents in the study area, the majority of respondents in the Nadia district (52%) had completed middle school, the majority in the Maldah district (35.91%) had completed primary level, the majority in the South Dinajpur district (21.90%) had completed high school, and the majority in the Murshidabad district (20.08%) had completed middle school. According to the results of the chi-square test (Table 1), there were statistically significant differences in the observations of people with different levels of education. Farmers’ education levels have a 0.01 significance level positive correlation with caste and a 0.05 significance level negative correlation with land ownership (Table 2). This study’s majority of farmers (62.8%) had lower educational levels, which is a rather clear finding. Previous studies [10, 12] have noted also a similar tendency. However, some studies [16] have made a different observation, which may have been caused by a different sample size and location.

4.1.9 Family type

Analysis of the data showed that only 28.87% of the farmers were from nuclear families, whereas the majority of farmers (71.13%) came from joint families (Table 1). Most families with their entire family reside together in the village area. 90.03% of respondents in the Murshidabad district, 84.02% in the Nadia district, 78.21% in the South Dinajpur district, and 60.66% in the Maldah district belonged to a mixed family. However, the majority of responders (51.96%) came from nuclear families exclusively in the Birbhum district. The variation is extremely significant, according to the chi-square test (P < 0.01) (Table 1). It appears that family type has a 0.01 significance level of negative correlation with housing type but a 0.01 significance level of positive correlation with family income (Table 2). The results show that many economically active joint family members were eager to share more money from raising animals because these activities can support their living as a secondary source of income. Due to larger landholdings and alternative occupations, the annual family income for farmers in joint families is higher than for those in nuclear families overall. The results of previous studies [10, 17] are very similar to the findings of this study.

4.1.10 Family size

In the present study, it was found that 75.24% of farmers had families with a medium size. The remaining farmers (24.29%) had small family sizes. The majority of respondents had medium-sized families in Murshidabad (89.39%), Maldah (86.04%), Nadia (82.84%), and South Dinajpur district (76.90%). Only in Birbhum district (51.76%), most respondents have small families. The difference in value between the varied family sizes of these farmers had a highly significant effect, according to the chi-square test (Table 1) (P < 0.01).

4.1.11 House types

According to the comprehensive observations, the majority of the farmer’s houses were mixed houses (44.01%), followed by kutcha houses (29.59%), and pucca homes (3.20%) (Table 1). In every district, the pattern is consistent. Mixed-house respondents were found in Birbhum district 59.51%, Murshidabad district 49.12%, South Dinajpur district 39.05%, Nadia district 35.27%, and Maldah district 33.50%. The chi-square test (Table 1) demonstrated that the variation in value between the various housing types for these farmers had a highly significant effect (P < 0.01). Given that this is the most common circumstance in rural West Bengal, these results are consistent with other studies [12].

4.1.12 Landholding

The study of the data showed that the majority of farmers (36.41%) had land that was up to 1 hectare in size, while 28.73% had land that was up to 2 hectares, 26.58% had no land, and just 8.17% had land that was beyond 2 hectares. In the districts of Birbhum and South Dinajpur, respectively, 45.78% and 45% of respondents own up to one hectare of land. 43.40% of respondents in the Maldah district have land holdings of up to 2 hectares. While landless farmers predominated (36% of the population) in the districts of Murshidabad (44.95%) and Nadia (35.98%), respectively. The chi-square test revealed that the variation in these farmers’ landholding capacities had a highly significant impact (P < 0.01). The majority of farmers (62.99%) were found to be either landless or marginal, which offers some hope that they will make enough effort to enhance livestock farming and secure their livelihood. These results were supported by previous findings [12]. Different opinions may exist in this regard, which may be caused by the impact of different regions.

4.1.13 Training

According to the present study, the majority of farmers (84.15%) had received training in animal husbandry, with only 15.85% of farmers receiving no such training (Table 1). According to the results of the chi-square test, the variations are statistically significant (P < 0.01) (Table 1). Giving training is an important strategy to help the agricultural community find alternative sources of income, as seen in the present study, where training obtained was positively correlated with age and religion at the 0.01 significance level (Table 2), as well as with family income at P < 0.05 level (Table 2). It was evident in the present survey that livestock can help farmers make a living.

4.1.14 Farm power

The analysis of the data showed that the majority of farmers (72.79%) did not have any drought animals, while 19.21% of farmers did have one or two (Table 1). The majority of respondents in Birbhum (72.75%), Nadia (72.78%), Maldah (72.84%), South Dinajpur (72.74%), and Murshidabad district (72.73%) had no drought animals, while only a small percentage had one or two drought animals. According to the chi-square test (Table 1), the farmers’ varying farm power values had a very significant impact (P < 0.01).

4.1.15 Material possession

In the present study, it was found that 69.73% of farmers had televisions in their homes, with the majority of farmers (96.27%) having their own cycles(Table 1). In the Birbhum district, 96.27% of respondents stated having access to a cycle, while 69.71% reported having television and 34.22% having radio. In the Nadia district, 96.21% of participants answered owning a cycle, while 69.70% noted owning a television and 34.20% claimed owning a radio. In the Maldah district, 96.32% of respondents stated owning a cycle, while 72.59% reported owning a television and 34.26% reported owning a radio. In the South Dinajpur district, 96.31% of respondents stated owning a cycle, while 69.76% claimed owning a television and 34.29% claimed owning a radio. In the Murshidabad district, 95.45% of respondents stated owning a cycle, while 69.70% stated owning a television and 34.22% stated owning a radio. According to the chi-square test (Table 1), these farmers’ disparate material possessions had a highly significant impact (P < 0.01).

4.2 Socio-economic status

Table 3 has summarised the analysis of the 5000 livestock farmers’ economic conditions in West Bengal’s five aspirational districts (Nadia, Murshidabad, Birbhum, Maldah, and Dakshin Dinajpur). Additionally, analysis of variance was performed too to determine the effects of the kind of dwelling, ownership of land, employment, and training in animal husbandry techniques. According to the survey, farmers with larger landholdings have considerably (P < 0.01) higher monthly family incomes from agriculture, animal husbandry, and other sources (service/business) than those with smaller landholdings. According to the survey, farmers with mixed-house types, pucca houses, or mansions make significantly (P < 0.01) more money from agriculture, animal husbandry, or other sources (service/business) for their families than those with no homes or kutcha houses. Farmers with mansions make an average monthly income of Rs. 10,398.15, compared to Rs. 3123.84 for farmers without homes. It demonstrates that those with superior economic returns also possess better material goods. An important factor that affects the farmer’s financial growth is training. When compared to those who received no training, the agricultural community’s economic returns were considerably (P < 0.01) higher. Another significant factor that affects the farmer’s economic growth is the farmer’s occupation (Table 4). When compared to those who work in agriculture (Rs. 6849.30), the service sector (Rs. 11,004.85), the business sector (Rs. 7900.36), or as labour (Rs. 3123.84), people who are employed in these occupations are thought to have greater total monthly incomes (P < 0.01) (Table 5).

Different factors		From agriculture (Rs)	From animal husbandry (Rs)	From other (service/businesses) (Rs)	Total income (Rs)
Overall		2024.95 ± 16.05	971.98 ± 12.36	4059.62 ± 12.66	7056.55 ± 13.63
Districts	Birbhum	2240.52 ± 19.23	1075.45 ± 14.81	4055.34 ± 15.70	7371.31 ± 16.91
	Nadia	2018.09 ± 15.88	968.68 ± 12.22	4237.99 ± 12.41	7224.76 ± 13.36
	Maldah	2026.23 ± 16.148	972.59 ± 12.46	3849.84 ± 12.91	6848.66 ± 13.91
	South Dinajpur	1857.67 ± 13.13	891.68 ± 10.11	4086.87 ± 6.59	6836.23 ± 7.09
	Murshidabad	1930.79 ± 13.74	926.78 ± 10.58	4054.66 ± 10.38	6912.23 ± 11.18
Sex	Male	2260.19 ± 19.54	1068.20 15.05	4949.50 ± 15.95	7311.29 ± 17.18
	Female	1698.09 ± 4.69	866.02 ± 3.61	2884.22 ± 4.00	5448.33 ± 4.31
Age	Young group (up to 30 years)	2046.49 ± 17.10	982.32 ± 13.16	3849.84 ± 13.67	6878.65 ± 14.72
	Most active group (30–60 years)	2285.33 ± 19.85	1290.54 ± 15.28	4866.41 ± 16.61	8442.28 ± 17.89
	Elder group (above 60 years)	1977.73 ± 15.26	949.31 ± 11.75	4153.23 ± 11.90	7080.26 ± 12.82
Religion	Hindu	2228.85 ± 18.93	1069.85 ± 14.58	4234.82 ± 15.44	7533.53 ± 16.63
	Muslim	1671.90 ± 4.33	802.51 ± 3.33	3678.18 ± 23.95	6152.60 ± 25.80
Marital status	Married	2127.54 ± 17.71	1021.22 ± 13.63	4042.33 ± 14.43	7191.09 ± 15.54
	Unmarried	1996.30 ± 15.57	537.73 ± 11.99	3613.31 ± 12.15	6147.34 ± 13.09
	Widow/widower	1836.46 ± 12.82	910.56 ± 9.87	3178.49 ± 4.26	5925.51 ± 4.59
Family type	Nuclear family	1467.56 ± 3.72	749.01 ± 2.86	4495.56 ± 3.09	6712.13 ± 3.32
	Joint family	2431.48 ± 20.15	1167.11 ± 15.52	4234.82 ± 16.89	7833.41 ± 18.19
Education	Illiterate	2041.58 ± 16.79	873.85 ± 12.93	2615.60 ± 13.42	5531.02 ± 14.45
	Farmers’ education up to middle school (class 10)	2107.28 ± 17.40	1069.85 ± 13.40	3926.84 ± 14.18	7103.97 ± 15.27
	Farmers’ education more than middle school (class 10)	1931.98 ± 14.44	980.85 ± 11.12	7356.37 ± 11.14	10269.19 ± 12.00
Training	Training received	2191.86 ± 18.62	1052.09 ± 14.34	4471.04 ± 15.19	7714.99 ± 16.36
	Training not received	1702.03 ± 5.73	719.72 ± 4.41	4311.82 ± 5.55 (3606)	6733.57 ± 5.98 (3606)
Occupation	Labour	598.77 ± 2.41	480.85	2054.22	3123.84
	Caste occupation	1655.25 ± 3.95	840.12 ± 3.04	5002.28 ± 6.65 (1040)	7497.65 ± 7.17 (1040)
	Migrants labour	1702.03 ± 5.13	719.72 ± 3.95	4311.82 ± 3.59	6733.57 ± 3.87
	Business	1902.86 ± 13.43	849.19 ± 10.34	5148.31 ± 14.20	7900.36 ± 15.30
	Service	1931.98 ± 14.35	980.85 ± 11.05	8092.02 ± 10.89	11004.85 ± 11.72
	Cultivation	2105.62 ± 17.13	1055.14 ± 13.19	3688.54 ± 13.92 (1750)	6849.30 ± 15.00 (1750)
Caste	General	2155.15 ± 18.01	1040.72 ± 13.87	5002.28 ± 14.68 (1924)	8198.15 ± 15.82 (1924)
	Schedule caste	1958.33 ± 14.65	719.72 ± 11.28	4311.82 ± 11.39	6989.87 ± 12.27
	Schedule tribe	1722.12 ± 6.70	934.18 ± 5.16	3533.11 ± 11.98	6189.41 ± 12.90
	Other backward caste	1731.68 ± 8.02	880.48 ± 6.18	4865.17 ± 4.75	7477.33 ± 5.11
House Type	No house	588.77 ± 2.20	480.85 ± 1.69	2054.22 ± 2.22	3123.84 ± 2.40
	Hut	1805.62 ± 12.52	905.14 ± 9.64	2988.75 ± 3.89	5699.51 ± 4.19
	Kutcha house	1958.33 ± 14.96	719.72 ± 11.52	4001.82 ± 11.65	6679.87 ± 12.54
	Mixed house	1722.12 ± 7.94	934.18 ± 6.11	3533.11 ± 28.40	6189.41 ± 30.59
	Pucca house	1931.47 ± 14.04	988.25 ± 10.81	5837.17 ± 10.63	8756.89 ± 11.45
	Mansion	2155.15 ± 18.32	1040.72 ± 14.11	7202.28 ± 14.94	10398.15 ± 16.09
Land Holding	No land/land less	1029.25 ± 2.68	719.72 ± 2.07	3012.21 ± 1.99	4761.17 ± 2.15
	Up to 1 hectare	1698.24 ± 4.83	934.18 ± 3.72	3533.11 ± 3.28	6165.53 ± 3.53
	Up to 2 hectares	2031.45 ± 16.49	1018.25 ± 12.69	5837.17 ± 13.17	8886.87 ± 14.18
	Above 2 hectares	2550 ± 20.4	1040.72 ± 15.75	6801.128 ± 17.17	10391.85 ± 18.49

Table 3.

Monthly economic return (in rupees) (Mean ± SEM) of the farmers of aspirational districts of West Bengal (N = 5000).

SOV	df	From agriculture	From animal husbandry	From other (service/businesses)	Total income	From agriculture	From animal husbandry	From other (service/businesses)	Total income
		Mean square				F value
Sex	1	26.8	44.8	62.7	44.8	1.14	1.62	5.56**	1.14
Age	2	81.1	57.9	46.2	51.8	3.32	1.12	4.10*	1.32
Religion	1	68.8	56.7	23.5	47.6	2.89	1.81	1.99	1.21
Marital status	2	78.2	39.5	125.9	81.13	3.64*	1.03	11.87**	2.76
Family type	1	96.7	67.5	109.3	117.4	6.98**	2.84	9.70**	2.99
Education	2	55.9	78.6	158.4	104.9	2.52	2.48	17.38**	2.67
Training	1	63.7	187.43	197.0	142.29	2.54	6.89**	14.89**	3.66*
Occupation	5	164.3	93.7	233.5	169.7	5.10*	3.05	29.55**	4.02**
Caste	3	66.4	38.7	22.1	32.21	1. 49	1. 80	1.79	0.87
House Type	5	89.34	88.71	212.31	133.3	3.97*	3.14*	19.51**	3.39*
Land Holding	3	761.6	167.32	81.4	310.5	28.52**	6.40**	7.35**	7.90**
Error	5424	32.654	29.576	13.026	28.226

Table 4.

ANOVA of monthly economic return of different category of the farmers under aspirational districts of West Bengal.

**P < 0.01; *P < 0.05.

Category	Different factors	Adoption index	Adoption level (%)
		(Mean ± SEM)	Low	Medium	High	Chi square value
			(<5.00)	(>5.00–6.5)	(>6.5)
Overall		5.95 ± 1.05	58.35	32.80	8.85	72.14**
Districts	Birbhum	5.57 ± 0.93	62.10	28.30	9.60	60.44**
	Nadia	5.20 ± 1.16	57.86	31.46	10.67
	Maldah	6.02 ± 1.01	60.66	29.37	9.96
	South Dinajpur	6.07 ± 0.99	46.97	39.60	13.43
	Murshidabad	6.01 ± 1.03	54.08	34.29	11.63
Age	Young group (up to 30 years)	6.09 ± 0.92	52.71	35.31	11.98	82.49**
	Most active group (30–60 years)	5.90 ± 1.03	56.45	32.52	11.03
	Elder group (above 60 years)	5.88 ± 0.93	55.07	33.55	11.38
Religion	Hindu	5.86 ± 1.01	58.28	31.15	10.57	75.33**
	Muslim	5.12 ± 1.13	57.83	31.49	10.68
Marital Status	Married	5.41 ± 0.96	58.67	30.86	10.47	64.84^**
	Unmarried	5.19 ± 1.05	60.35	29.61	10.04
	Widow/widower	5.45 ± 1.28	58.36	31.09	10.55
Occupation	Labour	5.99 ± 0.72	46.59	39.88	13.53	68.94**
	Caste occupation	5.06 ± 1.00	61.34	28.87	9.79
	Migrants labour	5.46 ± 0.98	56.95	38.40	4.65
	Business	5.04 ± 1.10	61.49	28.75	9.75
	Independent	5.55 ± 0.89	57.60	31.66	10.74
	Cultivation	5.95 ± 1.09	62.18	28.24	9.58
Caste	General	5.82 ± 1.02	58.59	30.92	10.49	67.28**
	Schedule caste	5.52 ± 0.97	57.83	31.49	10.68
	Schedule tribe	5.37 ± 0.88	58.97	30.64	10.39
	Other backward caste	4.74 ± 1.06	63.78	27.04	9.17
Education of the farmers	Illiterate	5.04 ± 1.05	61.49	28.75	9.75	74.44**
	Can read only	5.29 ± 1.02	59.58	30.18	10.24
	Can read and write	5.41 ± 1.06	58.67	30.86	10.47
	Primary	5.72 ± 1.02	56.30	32.63	11.07
	Middle school	5.85 ± 1.04	55.30	33.37	11.32
	High school	6.09 ± 0.86	53.47	34.74	11.79
	Graduate	6.22 ± 1.12	60.12	29.78	10.10
Family type	Nuclear family	5.85 ± 1.12	55.33	33.35	11.31	104.22**
	Joint family	5.96 ± 1.06	49.97	37.36	12.67
Family Size	Small	5.66 ± 0.99	47.17	39.45	13.38	79.94**
	Medium	5.59 ± 1.00	47.82	38.96	13.22
	Large	5.57 ± 1.11	48.01	38.82	13.17
House Type	No house	5.49 ± 0.97	42.22	43.14	14.63	47.84^**
	Hut	5.72 ± 0.92	43.81	41.96	14.23
	Kutcha house	5.55 ± 1.08	48.20	38.68	13.12
	Mixed house	6.02 ± 1.01	43.81	41.96	14.23
	Pucca house	6.09 ± 0.87	43.16	42.44	14.40
	Mansion	5.32 ± 1.04	50.34	37.08	12.58
Land Holding	No land/land less	5.54 ± 1.07	48.29	38.61	13.10	77.74**
	Up to 1 hectare	5.92 ± 1.09 (1560)	44.74	41.26	14.00
	Up to 2 hectares	6.45 ± 1.09	39.77	44.97	15.26
	Above 2 hectares	7.63 ± 0.92	34.35	49.02	16.63
Training	Training received	7.89 ± 1.08	42.24	39.82	17.94	87.22**
	Training not received	5.42 ± 1.01	56.95	38.40	4.65

Table 5.

Adoption index (mean ± SEM) and adoption level of the farmers of aspirational districts of West Bengal (N = 5000).

**P < 0.01; *P < 0.05.

4.3 Adoption level of the farmers in the aspirational districts

4.3.1 Adoption index of the farmers in the aspirational districts

With regard to improved farm practices of animal husbandry, such as vaccination against contagious diseases, cultivation of green fodder, deworming for parasitic disease, feeding of concentrate mixture, feeding of green fodder, feeding of urea and molasses treated straw, feeding of colostrum to new-born kid, castration by bradizzo castrator, value addition of milk and meat, Adoption Index (mean and standard error of mean [SEM]) [18] has been generated among the farmers in the Aspirational Districts of West Bengal. Analysis of variance in respect of gender, age of the farmers, religion, marital status, occupation, caste, education, family type, family size, house type, training received, and land-holding capacities have been assessed and depicted in Table 6.

SOV	df	Mean square from	F value
Sex	1	10.92	1.16
Age	2	8.07	0.86
Religion	1	10.13	1.07
Marital status	2	15.00	1.59
Family type	1	12.98	1.38
Education	2	44.49	4.72**
Training	1	130.51	13.84**
Occupation	5	43.54	4.62**
Caste	3	18.05	1.91
House type	5	15.28	1.62
Land holding	3	40.83	4.33**
Error	4254	9.43

Table 6.

ANOVA of adoption index of different category of the farmers of aspirational districts of West Bengal.

**P < 0.01; *P < 0.05.

It had been observed that education, training received, occupation and landholding had highly (P < 0.01) significant effect on adoption index. It had been appeared that adoption index among the farmers of South Dinajpur district (6.14 ± 0.99) was significantly higher than the other four districts namely Maldah (6.11 ± 1.01), Murshidabad (6.01 ± 1.03), Birbhum (5.98 ± 0.93), and Nadia (5.35 ± 1.16). The result indicated that the farmers of South Dinajpur district were more adapted to improved animal husbandry practices. The farmers who were graduate or had higher educational degree were having significantly (P < 0.01) higher adoption index (7.41 ± 1.09) in comparison to those with lower educational level. Similarly, who were worked as labour and engaged in cultivation having significantly (P < 0.01) higher adoption index than other occupations.

Farmers, who had received training, were significantly (P < 0.01) higher adoption index (8.65 ± 1.06) in comparison to those who had not received any training (6.54 ± 1.08). It was clear that the farmers who had received training were more familiar with new methods of animal husbandry. These farmers, therefore, favored the deployment of improved technology. The adoption of more advanced technologies was chosen by the farmers in terms of land-holding patterns.

The adoption index of farmers with land holdings larger than 2 acres was considerably (P < 0.01) higher (8.35 ± 0.93) than that of landless (6.34 ± 1.02), marginal (7.81 ± 1.07), and small farmers (6.89 ± 1.06) groups in the study. Farmers with land, especially agricultural land larger than 2 acres, preferred to raise animals as a form of secondary income. Their ability to raise livestock scientifically required improved technology, which is why the adoption rate was comparatively higher. Other variables like age, religion, education, caste, marital status, family size, and type of housing had no significant impact on those farmers’ adoption rates.

4.3.2 Adoption level of the farmers in the aspirational districts

A major indicator of how important the improved technology was to these farmers was the adoption rate. The value was shown in Table 5 and the adoption level was divided into three categories: low, medium, and high. In comparison to the other four districts, it was found that the majority of farmers in Birbhum district (67.07%) had a larger percentage of low adoption levels. Unmarried person engaged in cultivation (66.27%) were also having higher frequency of low-level adoption in comparison to married person (64.45%). Farmers of other backward caste were having higher frequency of low level of adoption (67.46%) in relation to other caste (General caste 62.35%). Illiterate (67.25%) persons were also having higher frequency of low-level adoption in comparison to higher educated persons (65.37%).

The chi-square test (Table 5) found that the difference in value in adoption level of these farmers based on different category had a highly significant effect (P < 0.01). The adoption of technology by mainly small and marginal farmers in the underdeveloped districts (Aspirational Districts) of West Bengal was the prime focus of the present study. The result had showed low level of adoption among these farmers who indicated their lacking in application of improved animal husbandry practices. The present study conducted mainly with small and marginal farmers in the underdeveloped districts (Aspirational Districts) of West Bengal.

4.3.3 Relationship between independent variables with adoption level of farmers in the aspirational districts

Table 7 provides the correlation coefficients for each personal and socio-economic characteristic with the respondents’ adoption level. Overall, it was found that seven independent variables—religions, occupations, education, family type, house type, land holding, and training—showed highly significant correlation (P < 0.01) with the adoption level of farmers in five Aspirational Districts in West Bengal. Caste, however, showed a significant correlation with this variable at the 0.05 level of probability. The adoption rate of farmers in five aspirational districts in West Bengal was not significantly correlated with the other three variables, district, age, or marital status. Marital status had a negative correlation with adoption index, but it was not statistically significant.

Factors	Districts	Age	Religion	Marital status	Occupation	Caste	Education of the farm ers	Family type	Training	House type	Land holding	Adoption index
Districts	1.00	−0.158*	0.142*	−0.640**	0.383**	0.301**	−0.628**	0.08	0.09	0.234**	−0.12	0.10
Age		1.00	0.01	0.164**	−0.11	−0.396**	0.150*	0.12	0.11	−0.03	0.10	0.07
Religion			1.00	−0.269**	0.403**	0.127*	0.333**	0.418**	0.11	−0.07	0.03	0.362**
Marital status				1.00	−0.358**	−0.223**	0.528**	−0.12	0.10	−0.218**	0.317**	−0.06
Occupation					1.00	0.444**	0.01	0.353**	0.245**	−0.01	0.353**	0.440**
Caste						1.00	−0.153*	0.07	−0.08	0.07	0.203**	0.162*
Education of the farmer							1.00	0.267**	0.41**	−0.337**	0.401**	0.273**
Family type								1.00	0.11	0.06	0.125*	0.356**
Training									1.00	−0.05	0.248**	0.256**
House type										1.00	−0.323**	−0.269**
Land holding											1.00	0.373**
Adoption index												1.00

Table 7.

Spearman correlation of adoption index with different factors among the farmers of aspirational districts of West Bengal (N = 5000).

**P < 0.01; *P < 0.05.

4.3.4 Adoption level of different technologies among the farmers in the aspirational districts

Adoption level of different technologies is depicted here (Table 8 and Figure 2). Hence in future days, comprehensive farmers’ trainings and awareness programmes need to be generated for improvement of their livelihood through livestock rearing. In South Dinajpur vaccination (82.14%) had highest adoption level followed by Murshidabad district (75.96%) and Nadia districts (73.97%), Maldah (71.97%) and Birbhum (67.68%). Cultivation of Green fodder adoption level have higher frequency in Murshidabad (37.12%) in comparison to other districts namely Maldah (36.69%), Birbhum (35.73%), Nadia (35.72%), and South Dinajpur (26.69%). Value addition of Milk and Meat frequency have higher adoption level in South Dinajpur Districts (33.10%) followed by Murshidabad (32.19%), Maldah (26.15%), Nadia (25.55%), and Birbhum (21.88%) (Figure 3).

Category	Birbhum	Nadia	Maldah	South Dinajpur	Murshidabad	Overall
	(%)	(%)	(%)	(%)	(%)	(%)
Vaccination against contagious disease	67.94	73.96	71.95	82.14	76.01	74.14
Deworming for parasitic control	66.08	64.02	66.5	71.67	67.93	67.19
Cultivation of green fodder	35.88	35.74	36.68	26.67	37.12	34.42
Feeding of green fodder	36.18	29.23	32.23	39.52	36.49	34.8
Feeding of concentrate mixture	43.82	26.51	34.14	52.98	42.05	40.09
Feeding of colostrum to newborn kid	27.84	20	26.78	30.83	28.16	26.74
Feeding of urea and molasses treated straw	24.02	18.22	23.35	23.93	24.49	22.82
Castration by bradizzo castrator	30	25.33	25.13	34.05	26.39	28.31
Value addition of milk and meat	21.96	25.56	26.14	33.1	32.2	27.51

Table 8.

Adoption of different technologies among the farmers of aspirational districts of West Bengal (N = 5000).

Figure 2.

Adoption index among the farmers of different aspirational districts (developed by the authors).

Figure 3.

Adoption of different technologies among the farmers of aspirational districts of West Bengal (developed by the authors).

4.4 Knowledge level of the farmers in the aspirational districts

4.4.1 Knowledge index of the farmers in the aspirational districts

Knowledge Score (Mean and SEM) generated using standard practice [19] among the farmers in the Aspirational Districts of West Bengal in relation to improved farm practices of animal husbandry had been depicted in Table 9. Table 10 shows the analysis of variance by religion, age of the farmers, marital status, caste, occupation, and education of the farmers, as well as by family size, family type, land holding capacities, and house type. All the factors, including districts, marital status, age of the farmers, occupation, religion, caste, education, family type, house type, family size, and land holding capacities, had not been shown to have a significant impact on knowledge scores (Table 10). However, it showed that South Dinajpur had the lowest Knowledge Score (30.16 ± 3.03) and Nadia district had the highest knowledge score (37.37 ± 3.55) among farmers, compared to Birbhum (35.07 ± 2.85), Maldah (34.18 ± 3.09), and Murshidabad (33.51 ± 3.15).

Category	Different factors	Knowledge score	Knowledge level (%)
		(Mean ± SEM)	Low	Medium	High	Chi square value
			(<30.00)	(>30.00–35)	(>35)
Overall		34.98 ± 3.22	59.15	31.75	9.10	64.14**
Districts	Birbhum	35.07 ± 2.85	62.10	28.30	9.60	62.85**
	Nadia	37.37 ± 3.55	57.86	30.00	12.00
	Maldah	34.18 ± 3.09	60.00	30.00	9.96
	South Dinajpur	30.16 ± 3.03	48.00	39.60	12.00
	Murshidabad	33.51 ± 3.15	54.08	34.29	11.63
Age	Young group (up to 30 years)	34.09 ± 2.82	53.29	34.88	11.83	104.33**
	Most active group (30–60 years)	33.85 ± 3.15	53.66	34.60	11.74
	Elder group (above 60 years)	34.28 ± 2.85	53.03	35.08	11.90
Religion	Hindu	34.78 ± 3.09	52.34	35.59	12.07	74.28**
	Muslim	33.45 ± 3.46	54.16	34.23	11.61
Marital status	Married	32.89 ± 2.94	54.93	33.65	11.42	57.58^**
	Unmarried	33.45 ± 3.21	54.16	34.23	11.61
	Widow/widower	34.74 ± 3.92	52.40	35.55	12.06
Occupation	Labour	33.97 ± 2.20	53.45	34.76	11.79	59.66**
	Caste occupation	35.01 ± 3.06	52.03	35.82	12.15
	Migrants labour	33.63 ± 3.00	61.18	26.46	12.37
	Business	32.22 ± 3.37	55.85	32.97	11.18
	Independent	34.08 ± 2.72	53.30	34.87	11.83
	Cultivation	35.04 ± 3.34	51.98	35.85	12.16
Caste	General	34.85 ± 3.12	52.25	35.66	12.10	64.65**
	Schedule caste	33.36 ± 2.97	54.29	34.13	11.58
	Schedule tribe	32.98 ± 2.69	54.81	33.75	11.45
	Other backward caste	35.19 ± 3.24	51.78	36.01	12.21
Education of the farmers	Illiterate	35.55 ± 3.21	51.29	36.37	12.34	58.36**
	Can read only	33.61 ± 3.12	53.94	34.39	11.67
	Can read and write	33.65 ± 3.24	53.89	34.43	11.68
	Primary	33.07 ± 3.12	54.68	33.84	11.48
	Middle school	33.76 ± 3.18	53.74	34.54	11.72
	High school	34.03 ± 2.63	53.37	34.82	11.81
	Graduate	36.2 ± 3.43	50.40	37.04	12.56
Family type	Nuclear family	33.75 ± 3.43	53.75	34.53	11.71	98.25**
	Joint family	35.1 ± 3.24	53.27	34.89	11.84
Family size	Small	33.77 ± 3.03	60.28	29.85	9.87	58.22**
	Medium	35.45 ± 3.06	54.16	34.23	11.61
House type	No house	33.28 ± 3.40	54.40	34.05	11.55	44.22^**
	Hut	33.65 ± 2.97	53.89	34.43	11.68
	Kutcha house	34.06 ± 2.82	53.33	34.85	11.82
	Mixed house	34.77 ± 3.30	52.35	35.58	12.07
	Pucca house	36.5 ± 3.09	49.98	37.35	12.67
	Mansion	37.1 ± 2.66	49.16	37.96	12.88
Land holding	No land/land less	33.36 ± 3.18	54.29	34.13	11.58	47.64**
	Up to 1 hectare	34.52 ± 3.27	52.70	35.32	11.98
	Up to 2 hectares	36.02 ± 3.34	51.88	35.93	12.19
	Above 2 hectares	36.24 ± 3.34	50.34	37.08	12.58
Training	Training received	33.21 ± 2.82	62.10	28.30	9.60	45.88**
	Training not received	37.07 ± 3.30	52.71	35.31	11.98

Table 9.

Knowledge score (mean ± SEM) and knowledge level of the farmers of aspirational districts of West Bengal (N = 5000).

**P < 0.01; *P < 0.05.

SOV	df	Mean square from	F value
Sex	1	12.96	1.13
Age	2	12.42	1.08
Religion	1	20.03	1.75
Marital status	2	41.40	3.62
Family type	1	8.44	0.74
Education	2	26.64	2.33
Training	1	5.19	0.45
Occupation	5	12.76	1.11
Caste	3	26.15	2.28
House type	5	25.37	2.22
Land holding	3	27.01	2.36
Error	4254	11.45

Table 10.

ANOVA of knowledge score of different category of the farmers of aspirational districts of West Bengal.

**P < 0.01 *P < 0.05.

The findings suggested that farmers in the Nadia district knew more about modern methods of animal husbandry. More intriguingly, it was found that farmers with agricultural land holdings larger than 2 acres (36.24 ± 3.34), and Pucca Houses (36.5 ± 3.09), or mansions (37.1 ± 2.66), had higher knowledge level about animal husbandry. These results were regarded as a natural phenomenon of a higher knowledge score since they considered modern technology. Farmers who had land, especially agricultural land area larger than 2 acres, and were financially stable, preferred raising animals as a source of additional family income. Improved technology for raising livestock scientifically is crucial for increasing economic gain, because of that their Knowledge Score was comparatively higher. The Knowledge Score of those farmers was unaffected by other variables such as age, religion, occupation, education, caste, marital status, family size, or type of housing.

4.4.2 Knowledge level of the farmers in the aspirational districts

Level of knowledge (Table 9) is an important factor to assess utilisation of the improved technology among farmers. The farmers engaged in livestock farming were classified into three level of knowledge namely low (<30), medium (>30 –35), and high (>35). The result of knowledge level of these three groups namely low, medium, and high and the value was depicted in Table 9.

It had been observed that majority of farmers in Birbhum district (62.10%) were having a higher percentage of low knowledge levels in comparison to other four districts. In Nadia districts (57.86%) farmers had low level of knowledge followed by medium (30%) and high (12%) level of knowledge in improved livestock farming practices. In Maldah district (60%) farmers had low level of knowledge followed by medium (30%) and high (9.96%) level of knowledge in improved livestock farming practices. Most interestingly, the farmers of South Dinajpur district had less low level of knowledge (48%) but higher medium (39.60%) and high (12%) level of knowledge which indicated that these farmers were befitted for improved animal farming practices.

In comparison to other farmers, those who owned agricultural land larger than 2 acres and Pucca House or another mansion tended to have higher or medium levels of knowledge about animal husbandry. In addition to the farmers’ education, financial stability was a key aspect in learning information. The chi-square test (Table 9) showed a highly significant effect (P < 0.01) of the difference in value in knowledge level of these farmers based on distinct categories (Table 10).

In order to create an effective work plan for a sustainable livelihood through livestock rearing, it was necessary to examine the level of livestock farming expertise among the farmers in these aspirational districts. The result showed low level of knowledge among these farmers which indicate their lacking in application of improved animal husbandry practices.

4.4.3 Relationship between independent variables with the knowledge level of farmers

Table 11 provides the correlation coefficients for each personal and socio-economic characteristic with the respondent’s knowledge level (Figure 4). The study indicated that, generally, out of 11 independent variables, family type, occupation, and religion showed highly negative correlation (P < 0.01) with knowledge level of farmers in five districts (Figure 5). The two variables marital status and training showed highly positive correlation whereas the remaining variables namely, age, caste, education, house type, and land holding did not establish any significant relationship with the knowledge level of farmers in five Aspirational Districts in West Bengal (Figure 6).

Factors	Districts	Age	Religion	Marital status	Occupation	Caste	Education of the farm women	Family type	Training	House type	Land holding	Knowledge level
Districts	1.0	−0.16*	0.142*	−0.640**	0.383**	0.301**	−0.628**	0.01	−0.290**	0.234**	−0.1	−0.137*
Age		1.0	0.02	0.164**	−0.1	−0.396**	0.150*	0.1	0.1	0.01	0.1	0.001
Religion			1.0	−0.269**	0.403**	0.127*	0.333**	0.418**	0.170**	−0.1	0.0	−0.336**
Marital status				1.0	−0.358**	−0.223**	0.528**	−0.1	0.245**	−0.218**	0.317**	0.220**
Occupation					1.0	0.444**	0.01	0.353**	0.1	0.0	0.353**	−0.242**
Caste						1.0	−0.153*	0.1	0.01	0.1	0.203**	−0.109
Education of the farm women							1.0	0.267**	0.407**	−0.337**	0.401**	−0.108
Family type								1.0	0.608**	0.1	0.125*	−0.258**
Training									1.0	−0.1	0.248**	0.358**
House Type										1.0	−0.323**	0.094
Land Holding											1.0	0.024
Knowledge level												1.0

Table 11.

Spearman correlation of knowledge level of the farmers of aspirational districts of West Bengal.

*P < 0.05; **P < 0.01.

Figure 4.

Knowledge level among the farmers of different aspirational districts (developed by the authors).

Figure 5.

Knowledge level among the farmers of different occupation (developed by the authors).

Figure 6.

Constrains among the farmers of aspirational districts of West Bengal (developed by the authors).

4.5 Constraints perceived by the farmers in aspirational districts of West Bengal

In the aspirational districts of West Bengal, the farmers who raise livestock face a number of challenges, which have been evaluated and are shown in Table 12. Overall, constraints show that non-availability of feed (82.22%), epidemic of animal diseases (73.54%), lack of credit facility (68.28%), lack of green fodder (72.30%), and high cost of feed ingredients (67.89%) were the major constraints, while lack of input (22.68%), lack of Panchayat assistant (20.89%), lack of remunerative price (20.12%), lack of marketing facility (18.60%), lack of training facility (38.83%), and lack of proper information (31.48%) were the minor constraint.

Factors	Birbhum	Nadia	Maldah	South Dinajpur	Murshidabad	Overall
	%	%	%	%	%	%
1. High cost of feed	57.94	64.02	67.89	80	71.97	67.89
2. Non-availability of feed	80	88.05	81.98	72.02	89.9	82.22
3. Lack of transportation facility	24.02	75.98	66.5	36.67	44.07	48.26
4. Lack of marketing facility	16.08	15.98	28.05	15.95	18.06	18.6
5. Lack of credit facility	55.98	42.01	77.66	87.98	81.94	68.28
6. High cost of veterinary medicines	81.37	35.98	55.96	64.05	85.98	65.2
7. Non-availability of veterinary Services	50	40	47.97	47.98	30.05	43.57
8. Lack of training facilities	41.96	30.06	39.97	37.98	43.94	38.83
9. Lack of green fodder	84.02	57.99	76.02	76.79	64.02	72.3
10. Lack of remunerative price	13.73	18.11	21.45	32.62	15.91	20.12
11. Lack of input (live animal)	22.06	20	26.02	22.14	23.61	22.68
12. Lack of Panchayet assistance	18.53	16.33	25.25	20	25.38	20.89
13. Epidemic of animal disease	71.96	66.04	71.95	84.05	73.99	73.54
14. Lack of proper information	28.04	36.45	39.21	24.17	30.68	31.48

Table 12.

Constraints among the farmers of aspirational districts of West Bengal (N = 5000).

The constraints in the various aspirational districts varied accordingly. In the case of Murshidabad districts, the highest constraints were observed to be the lack of feed availability (89.90%), followed by the high cost of veterinary medicine (85.98%) and a lack of credit facilities (81.94%), while a lack of marketing facilities (18%) and the lack of veterinary services (30%) were found to be less of a constraining factor. However, a similar observation has not been recorded in South Dinajpur. Lack of credit facilities (87.98%) and an animal disease epidemic (84%) were reported to be important obstacles in South Dinajpur, while a lack of green fodder was found to be a big obstacle for farmers in Birbhum district.

It was clear that there was not enough land in Birbhum district to grow fodder, and there were a lot of farmers who needed finance in order to expand their farming operations. For improved livelihood, emphasis may be placed on the production of feed and fodder for animal farming. The study closely resembles an observation made by other study [12] who identified a similar set of constraints among goat farmers in six different agro-climatic zones in West Bengal. Since lack of quality input, lack of marketing facilities were considered to be an area-specific constraints faced by the goat farmers under their studies which is not to be a problem in West Bengal.

5. Conclusions

In conclusions, the livestock rearing is a significant contributor in rural economy and plays vital role to raise socio-economic status of the rural people. According to the observations, the majority of livestock farmers in aspirational districts are from the most active age group, have less education, and are of lower socio-economic level. The majority of them live in mixed dwellings and have up to one hectare of land. For most farmers to maintain their security of livelihood, cultivation was their primary occupation. According to the findings, most farmers were married and involved in livestock rearing.

It had been observed that education, training received, occupation and landholding had highly significant effect on adoption index. It had been appeared that adoption index among the farmers of South Dinajpur district was significantly higher than the other four districts namely Maldah, Murshidabad, Birbhum, and Nadia. The result indicated that the farmers of South Dinajpur district were more adapted to improved animal husbandry and fishery practices. It had been observed that majority of the farmers of Birbhum district had low adoption level in comparison to other four districts.

Hence, a comprehensive human resource development (HRD) programme should be conducted for better livelihood generation. Adoption index among the farmers needs to be assessed for sustainable livelihood driven work plan as promising endeavour in future. Considering this scenario, a suitable plan needs to be promoted for better livelihood of the farmers in various aspirational districts of West Bengal with a linkage of available science and technology. In future, a comprehensive farmers’ training and demonstration programmes are needed to be conducted in this area for livelihood improvement of the farmers. The adoption level of the farmers about livestock farming needs to be assessed for formulation of proper policies to enhance sustainable livelihood in these districts of West Bengal.

The study on knowledge index of farmers in various aspirational districts of West Bengal explore that, improved technology for scientific livestock rearing is essential for economic gain of stakeholders and farmers. Results of the study revealed that knowledge score among the farmers of Nadia district was highest and in South Dinajpur was lowest than the other three districts namely Birbhum, Maldah, and Murshidabad. Study also revealed that the farmers of Nadia district were having more knowledge about improved animal husbandry practices. Therefore, special attention must be given before implementing any livestock-related entrepreneurial programme among them.

Finally, it also depicted various constraints like lack of training facilities and education, etc. These were the major drawbacks for the upliftment of the socio-economic status of the farmers in the selected districts of West Bengal. It is clear from the findings of the study that farmers use livestock as a secondary job to supplement their income. Therefore, it is observed that small ruminants play an essential role in generating jobs, revenue, capital, and assets. It is crucial to adopt advanced technology and to provide the facilities required for better practices in order to increase the breed’s capacity for production.

The rural poor can use livestock raising as an alternative source of income and as a significant way to meet their financial needs. In order to improve the socio-economic situation of these districts, the Government should take steps to provide appropriate training and assistance, and region-specific policies should be created to support this livelihood sector.

Acknowledgments

We are thankful to the Department of Biotechnology (DBT), Government of India, for funding the “Establishment of Biotech KISAN Hub” initiative as well as for their prompt assistance and collaboration throughout the study process. We also extend our profound gratitude to the Directorate of Research, Extension and Farms, West Bengal University of Animal and Fishery Sciences for their advice, assistance, and ongoing encouragement.