Development of a knowledge assessment tool for evaluating dairy farmers' understanding of dairy waste management in India

Introduction

According to the 20th Livestock Census, the livestock population in India is 536.76 million, which produces approximately 1655 million tons of dung per year. In urban and peri-urban areas, a large volume of dairy animal waste is flushed with water, leading to significant effluent discharge into nearby drainage systems. The most common problem with livestock waste is that it contributes to large-scale emissions of Carbon Dioxide, thereby contributing to global warming. It further pollutes water sources, which play a key role in the spread of infectious diseases (Singh et al., 2019) [5] .

Knowledge

Knowledge is often defined as a belief that is true and justified. The state of knowing about a particular fact or situation. Knowledge tests are a mechanism to measure farmers' knowledge and abilities and provide a snapshot of farmers' performance in a particular context. Therefore, to determine respondents' knowledge level regarding Dairy waste management. The present study was conducted to provide a clear understanding of dairy owners' existing knowledge and practices regarding waste handling. A knowledge test on dairy waste management was developed and standardized.

Materials and Methods

The knowledge test was developed according to Edwards's (1957) [1] method. Item Collection: The knowledge test consists of different types of questions, called items, and scores were assigned to them. Items for the test were compiled through literature, discussions with field extension personnel, subject matter specialists, academicians, and the researcher's own experience. According to the criteria laid by Edwards (1957) [1] ,

• Table no. 1 shows that the 97 items initially selected encompass major areas of dairy waste management. • These 97 Items/questions were administered to 36 dairy farmers from the villages of Bhir, Devtalab, and Bahuti in the Mauganj district of Madhya Pradesh, in the non-sample area. • The responses were quantified by giving a score of one (1) to the correct response and zero (0) to the incorrect response, or in case farmers do not know the answer. • Thus, the total number of correct answers rendered by an individual was the knowledge score secured by him. Thus, the range of obtainable scores was 0-97.

Item analysis

The item analysis of a test usually yields two kinds of information: item difficulty and item discrimination.

• The index of the item difficulty reveals how difficult the item is, whereas • The item discrimination indicates the extent to which an item discriminates between the well-informed farmer and the poorly informed ones. • After calculating the scores for 36 respondents, they were arranged in descending order. • These 36 respondents were then divided into six equal groups, each having six respondents. Respondents in each group have been arranged in descending order by the score each obtained. • These groups were named as G1, G2, G3, G4, G5 and G6 respectively. • For item analysis, the middle two groups, i.e., G3 and G4, were eliminated. • Only four extreme groups with high (G1, G2) and low scores (G5, G6) have been considered for the computation of item difficulty and their discrimination indices.

Difficulty index

Difficulty index (D.I.) = R/T × 100

Where, R = Number of respondents who got the item right. T = Total number of respondents

Discrimination index

The discrimination index was calculated by using the method suggested by Mehta (1958) [4] . The formula by which the item discrimination index was calculated is specified:

Where, S1, S2, S5 and S6 = Frequencies of incorrect answers in G1, G2, G5 and G6 groups respectively N = Total number of respondents in the sample of item analysis Based on the results of the difficulty and discrimination index Final selection of items was done. Two criteria, viz., item difficulty and item discrimination index, were used to select items for the final format of the knowledge test. Of 97 items, only 34 were included in the final format of the knowledge test.

Reliability and validity

Further, all the knowledge items were subjected to reliability and validity.

Reliability in the present study was measured using the split-half method, in which the 34-item format was split into two halves of 17 each, and the instrument was administered to 60 farmers. Two sets of scores were obtained and were correlated using the correlation between forms (r).

Reliability of full test

Spearman-Brown Prophecy Formula (also known as Spearman-Brown Prediction Formula) was used to calculate the reliability of the full test and is given as follows:

Reliability of test = 2r/1+r

Where, R = Reliability of full test r = Correlation between two half tests

Results and Discussion

Table no. 2 shows the final selection of knowledge items based on item difficulty and discrimination index, and the details are explained below. Selection of items for the test • Responses were collected from 36 respondents on 97 items related to dairy waste management. • For the selection of items in the final format of the knowledge test, two criteria, viz., item difficulty and item discrimination index, were considered. • In the present study, items with difficulty indices ranging from 25 to 75 and discrimination indices ranging from 0.2 to 0.55 were included in the final format of the knowledge test, as described by Mehta (1958) [4] .

According to this procedure, 34 items were selected for the final format of the knowledge test on dairy waste management and are given in Table 1. Is there any chance of transmitting pathogens through using the digested slurry from the biogas plant? 50.00 0.67 47.

Is there any waste treatment to be followed for slurry disposal? 50.00 0.00 48.

Who developed NADEP method of organic composting? 11.11 0.33 49.

The recommended size of the tank in NADEP composting is? 13.89 0.17 50.

Conclusion

• As long as there is life on earth, dairy waste will be produced, and managing that waste will be a real challenge. • The test can serve as a tool to ascertain the knowledge of stakeholders regarding the management of dairy waste. • The test can be used by researchers, institutes, and organizations to ascertain the knowledge of respondents under study. • The test can be used for estimating the net knowledge deficit regarding dairy waste management that will thrust policy-making in this direction. • Assessment of knowledge gain and knowledge-enhancing interventions can be made using the test. • The test items presented in the test are simple in nature and easily understandable, making the test handy to use. • It also promotes the dairy industry as a profitable one, along with milk and dairy products. Dung and urine act as an extra income source via various types of organic manures such as compost, vermin compost, bio-enriched compost, etc.

Application of research

The test can be used for estimating the net knowledge deficit regarding dairy waste management that will thrust policymaking in this direction.

Research Category

Acknowledgement

AcknowledgementAuthor thankful to Major advisor, Dr. Bhabesh Chandra Das, Professor and Head, Department of Veterinary Science and Animal Husbandry, Rewa, NDVSU, Jabalpur (M.P.). for Ph.D. work entitle "Assessment of dairy waste management practices followed by urban and peri-urban dairy farmers of Vindhya region of Madhya Pradesh"

References

1. Edwards AL. Techniques of attitude scale construction. New York: Appleton-Century-Crofts; 1957.
2. Guilford JP. New standards for test evaluation. Educ Psychol Meas. 1946;6:427-439.
3. Kerlinger FN. Foundations of behavioural research. New York: Holt; 1964.
4. Mehta P. A study of communication of agricultural information and extent of distortion occurring from district to village level workers in selected IADP of Udaipur, Rajasthan. Udaipur: Rajasthan Agricultural University; 1958.
5. Singh A, Tiwari R, Panda P, Joshi P, Dutt T. Development and standardization of knowledge test for organic waste management. Int J Curr Microbiol Appl Sci. 2019;8(8):1443-1449. https://doi.org/10.20546/ijcmas.2019.808.168