Development and standardisation of knowledge test on entrepreneurial dairy farming
Abstract
This study focuses on developing and standardising a reliable and valid knowledge test to assess dairy farmers' knowledge regarding entrepreneurial Dairy farming in the sub-tropics of Jammu Region. A sample size of 120 dairy farmers, comprising 60 entrepreneurial dairy farmers and 60 non-entrepreneurial dairy farmers, was used for the study, employing a multi-stage random sampling technique. A total of 104 statements covering the main facets of entrepreneurial dairy farming were initially chosen based on the criteria established by Edwards (1957). The choices were made based on the dairy producers' knowledge and comprehension levels. These 104 statements underwent a relevance check. Utilising the difficulty and discrimination index, item analysis was performed. The knowledge test's final format, as described by Mehta (1958), included statements with difficulty indices ranging from 30 to 80 and discrimination indices ranging from 0.30 to 0.55 in the current study. This process resulted in the selection of 57 statements for the final knowledge test on entrepreneurial dairy farming. The reliability of the test was assessed using the split-half approach. The correlation coefficient between forms was (r) 0.87. The test's internal consistency was calculated using Cronbach's alpha in this study, and the value was 0.83, indicating that the knowledge test was internally consistent. Therefore, the final knowledge test containing statements related to different aspects of entrepreneurial dairy farming such as Breeds and breeding management, Feeding, Housing, Health and General Management, Calf management, Government Schemes, Clean Milk Production, etc. were standardized. All the coefficients were found to be significant at a 1% level of significance.
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Introduction
Livestock is an important source of income and plays an important role in the national economy. The livestock sector has continuously been growing at a Compound Annual Growth Rate (CAGR) of 7.93% (at constant price) from 2014-15 to 2020-21, which is comparable to the CAGR of manufacturing at 4.93% (at constant price) and Services at 4.82% (at constant price) and in contrast to Agriculture (crop sector) CAGR of 2.05% (at constant price). The current situation calls for entrepreneur-driven, sustainable and profitable dairy farming that can produce income (Patel et al., 2014) [10] . Dairying in India accounts for more than two-thirds of the total livestock output and is principally responsible for the rising importance of the livestock sector in India (Kumar et al., 2011) [8] . Dairy farming has good potential for employment generation both in rural and periurban areas besides being a source of liquidity and insurance against crop failure. In other words, dairy provides employment throughout the year. Even more, profits can be earned through dairying, depending upon the breeds of animal, managerial skills and marketing (Raut and Sankhala 2014). About 80 per cent of the population in Jammu and Kashmir lives in rural areas and about 60 per cent of the UT's revenue is generated by the agriculture and animal husbandry sector (Kumari et al., 2020) [9] . Given the diversity of rural vocations in India, there is a lot of room for entrepreneurship development. It is crucial to research the level of information held by dairy farmers about entrepreneurial dairy production to support entrepreneurial potential. To measure the knowledge level of dairy farmers toward entrepreneurial dairy farming practices, a tool in the form of a knowledge test was designed and standardised. The test is suitable to determine whether dairy farmers' understanding of entrepreneurial dairy farming procedures is lacking and whether appropriate interventions can be undertaken to fill that gap.
Methodology
The methods outlined by Edwards (1957) were used to develop the knowledge test. A total of 104 statements covering the main facets of commercial dairy farming, including Breeds and breeding management, Feeding, Housing, Health and General Management, Calf management, Government Schemes, Clean Milk Production, etc., were initially chosen based on the criteria established by Edwards (1957). The choices were made based on the dairy producers' knowledge and comprehension levels. These 104 statements underwent a relevance check. Finally, 81 statements that the experts deemed to be "most relevant" were chosen for their answers from the Jammu region. 30 dairy farmers from the village of Suchetgarh in the Jammu district (non-sample area) were given these 81 chosen statements to consider. The answers were scored, with one (1) being given for the right answer and zero (0) being given if the farmers didn't know the answer. The total number of right answers a person provided, then, was his knowledge score.
Item analysis
Utilising the difficulty and discrimination index, item analysis was performed. Two types of information, namely the item difficulty and item discrimination, are often obtained from an item analysis of a test. The index of item difficulty discloses how challenging the item is, and the index of discrimination shows how much an item separates farmers with good information from those with poor information. The scores were calculated for each of the 30 respondents, and then they were ordered in descending order. Then, six equal groups of five respondents each were created from the 30 respondents. G1, G2, G3, G4, G5 and G6 were the respective group names. The middle two groups, G3 and G4, were excluded to calculate the discrimination index. For the computation of the discrimination indices, only four extreme groups with high and low scores were taken into consideration.
Calculation of difficulty index
The percentage of right responses received for a specific question served as the difficulty index measurement. This was calculated by using the formula:
Where, P i = Difficulty index in percentage of the i th item n i = Number of respondents giving correct answer, to i th item N i = Total number of respondents to whom i th item was administered i.e. 30 in the present study.
Calculation of discrimination index
The discrimination index was calculated by using the method suggested by Mehta (1958). The formula by which the item discrimination index was calculated by using the given formula:
Where, S1, S2, S5 and S6 = Frequencies of correct answers in G1, G2, G5 and G6 groups respectively. N = Total number of respondents in the sample of item analysis Final statement selection was carried out using the difficulty and discrimination index results. When a respondent provided the right response, it was assumed that the statement was easier for him to handle than it was. The knowledge test's final format, as described by Mehta (1958), included statements with difficulty indices ranging from 30 to 80 and discrimination indices ranging from 0.30 to 0.55 in the current study. This process resulted in the selection of 57 statements for the final knowledge test on entrepreneurial dairy farming. (Table 1).
Results and Discussion
Selection of Respondents
For the selection of entrepreneurial dairy farmers, a list of dairy farmers having more than 10 dairy animals was prepared with the help of Veterinary Assistant Surgeons (VASs), and Livestock Assistants (LAs) and also known as progressive dairy farmers, present in the study areas. Sixty progressive dairy farmers having more than 10 dairy animals were selected purposively from the prepared list, and an equal number of non-entrepreneurial dairy farmers (mainly resource-poor farmers) having 1-5 dairy animals were selected randomly from the same area. Thus, a total sample size of 120, comprising 60 entrepreneurial and 60 non-entrepreneurial dairy farmers, was selected for the study.
Validity and reliability
All knowledge items were also tested for validity and reliability. To improve the accuracy of their assessment, measurement instruments are evaluated for their validity and reliability. Instruments for measuring should be valid and dependable to be effective. The degree to which an instrument consistently measures what it is supposed to measure is referred to as reliability. While reliability necessitates validity, the opposite is not true because an instrument can be reliable without being valid.
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