INTRODUCTION Now-a-days, the agriculture sector has emerged as an important enterprise in the world. Earlier, it was the process of producing food but, now it is an act that requires greater investment in every aspect of production practices. Since the Green revolution, agricultural inputs have been regarded as the important input of production and it is price intensive. But the prolonged and over usage of agricultural inputs has resulted in deteriorating human and environmental health. Now, the concern is to continue the usage of agricultural inputs or to shift to eco-friendly inputs; to conserve the environment and human health. Green technologies represents green pesticides which were environmentally safe and does not cause any harmful side effects to human and the environment. Meanwhile, it ensures food security and safeguards the environment by employing environment-safe practices. Adnan et al. (2017) reported that in Malaysia, usage of green fertilizer technology in paddy production has increased the yield. Meanwhile, Suji and Sathish et al. (2020) mentioned that most of the farmers had medium level of utilization behaviour towards eco-friendly agricultural practices. Suji and Sathish (2020) commented that education and farming experience of the farmers had resulted in positive and significant relationship with the adoption level of the farmers regarding eco-friendly technologies. Naher et al. (2021) concluded that bio-organic fertilizer (green fertilizer technology) in paddy production reduce synthetic nitrogen and triple super phosphate content in soil; thereby improving the soil health. Since, green technology is an emerging technology, there arises a need to develop the certain package of practices according to its objectives. The development and transfer of eco-friendly technologies require government extension agencies. Hence, this study was proposed to develop an utilization behaviour index to assess the utilization pattern of green technology among the paddy farmers. METHODOLOGY To measure the utilization behaviour of green technologies among the beneficiaries in a rice-based ecosystem, a scale was developed as suggested by Likert (1932); Edwards (1957). The methodology used in the development of the utilization behaviour index was given as follows. Collection and editing of items. Various practices followed in green technology were stated and discussed with the experts of Agronomy, Entomology, and Pathology. A set of 100 hundred practices were stated and revised according to fourteen criteria given by Thrustone & Chave (1938); Likert (1932); Edwards (1957). After revision, 95 statements were retained and sent to the judges opinion. Relevancy test. The revised 95 statements/ practices were sent to judges opinion to 120 experts in the field of Agronomy, Entomology, Pathology, and senior faculty members of State Agricultural Universities, Programme co-ordinator, and Subject Matter Specialists of KVK, ICAR Scientists, and Scientists related to this domain. They were asked to indicate their for each statement as ‘Most Relevant’, ‘Relevant’, and ‘Not relevant’ with the scores of 3, 2, and 1 respectively. They were also requested to include statements if it was left. Hence, a total of 60 members responded to the index. Based on the responses received, for each statement, the relevancy weightage, relevancy percentage, and mean relevancy score was calculated by using the following formula; Relevancy weightage Indicates the relevancy of the statement to the impact index. RW= (MRR*3+ RR*2+ NRR*1)/(MOS (3*55=165)) Where, RW = Relevancy Weightage MRR = Most Relevant Response RR = Relevant Response NRR = Not Relevant Response MOS = Maximum Obtainable Score Relevancy percentage Indicates the relevant percentage of the statement to the impact index. RP= OS/(MOS (3*55=165))× 100 Where, RP = Relevancy Percentage OS = Obtained Score MOS = Maximum Obtainable Score Mean relevancy score Indicates the mean relevancy score of each statement to the impact index. MRS= (MRR*3+ RR*2+ NRR*1)/(No.of Judges (55)) Where, MRS = Mean Relevancy Score MRR = Most Relevant Response RR = Relevant Response NRR = Not Relevant Response Based on the relevancy percentage (>66%), relevancy weightage (0.66) and mean relevancy score (>2); the final statements were selected. Calculation of ‘t’ value (Item analysis). The relevant 95 statements were subjected to item analysis to assess the statements based on their ability to differentiate the respondent with high impact and low impact (extent to differentiate) towards green technology beneficiaries. For this purpose, the selected 95 statements were sent to 60 farmers in non-sample area. The farmers were requested to indicate their response on a five point continuum ranging from ‘strongly agree’, ‘agree’, ‘undecided’, ‘disagree’ and ‘strongly disagree’ with the scores of 5, 4, 3, 2 and 1 respectively for positive statements and vice versa for negative statements. Based on the responses obtained from the farmers, they were arranged in descending order according to their total scores. As suggested by Edwards (1957), the high group (top 25 per cent of farmers) and the low group (lowest 25 per cent of farmers) were identified to evaluate the individual statements. Finally, out of 60 farmers, the 20 farmers with highest and lowest scores were used as criterion groups to evaluate the individual statements. As suggested by Edwards (1957), the ‘t’ value is calculated by using the following formula, t= ((X_H ) ̅-(X_L ) ̅)/√((∑▒〖〖(X_H- X ̅_H)〗^2+〖(X_L- X ̅_L)〗^2 〗)/(n (n-1))) Where, 〖(X_H- X ̅_H)〗^2= 〖X_H〗^2-〖(X_X)〗^2 〖(X_L- X ̅_L)〗^2= 〖X_L〗^2-〖(X_L)〗^2 XH = The mean score on given statement of the high group XL = The mean score on given statement of the low group XH2 = Sum of square of the individual score on a given statement for high group XL2 = Sum of square of the individual score on a given statement for low group XH = Summation of scores on given statement for high group XL = Summation of scores on given statement for low group n = Number of respondents in each group ∑ = Summation Selection of statements for final scale. According to the calculated ‘t’ value, for the 90 statements, the statements with highest ‘t’ value were selection for inclusion in scale. Thus, a total of 87 practices or statement were selected to develop the index; in order to assess the utilization behaviour of green technology among the paddy farmers. The relevancy percentage, relevancy weightage and mean relevancy score along with the t-value of the selected statements were presented in Table 1. Thus, a total of 87 statements with highest ‘t’ values were selected for the construction of final scale which differentiate between highest and lowest groups. The statements with low ‘t’ value were deleted. The index procedure developed by Asokhan and Ganapathy Ramu (2021) was followed in the present study. Reliability Test-retest method. The final 87 statements which represents the utilization behaviour of green technology beneficiaries in rice based ecosystem were administered on a three point continuum scale to a 30 farmers in non-sample area. These 87 statement were identified based on many reviews consulted with experts and scientists. After a time period of 15 days, the scale was again administered to the same respondents and thus there were two set of scores obtained. For both sets of scores, the correlation co-efficient was calculated and the ‘r’ value was 0.869 which represents significant at 1 per cent level of probability. Thus, it indicates the impact index was highly suitable to assess the utilization behaviour of green technology among the beneficiaries in the rice based ecosystem. The index was stable and dependable in its measurement. Validity Content validity. Content validity refers to the sampling adequacy of the content, the substance, the matter and the topics of a measuring instrument. This method was adopted to determine the content validity of the developed index. As the content of the index examines the utilization behaviour of green technology beneficiaries in rice-based ecosystem, it was assumed that the present scale satisfies the content validity. As the scale value differs for each of the statement with a high discriminating value, this scale is said to be a valid measure of the impact.