An economic inquiry into adoption of non-conventional bio pesticides and fungicides R.Ravikumar* 1, S. Ramesh Kumar 2 and P.

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1 2015 RELS ISSN: Res. Environ. Life Sci. 8 (1) (2015) An economic inquiry into adoption of non-conventional bio pesticides and fungicides R.Ravikumar* 1, S. Ramesh Kumar 2 and P. Anbarasan 3 1 Department. of Agricultural Economics, 2 Faculty of Horticulture, 3 Department of Social Science, Vanavarayar Institute of Agriculture, Pollachi , Tamil Nadu, India * raviageconomics@gmail.com (Received: August 05, 2014; Revised received: November 26, 2014; Accepted: November 29, 2014) Abstract: Organic farming is a holistic production system of farm management to create an eco-system to achieve sustainable productivity. Due to the external effects of chemicals, farmers recently practiced non-conventional bio pesticides and fungicides in crop protection and management. In this context, the present study had been taken up to study the determinant factors of adoption of bio pesticide and fungicides in Erode district of Tamil Nadu. Logit analysis revealed that education, land holding size and extension agency contact are the most significant factors influencing adoption of bio pesticide and fungicide usage in Erode district. Partial budgeting estimates shows that using of bio pesticides-fungicides in rice; turmeric and sugarcane cultivation is economically viable. From garret rank analysis it could be concluded higher price is the major constraint in adoption of bio pesticides indicated by farmers. Key words: Bio pesticide and Fungicide, Logit model, Adoption partial budgeting Introduction Organic farming production system aims at promoting and enhancing agro-ecosystem health, biodiversity, biological cycles and soil biological activities (Prasad, 2005). Continuous application of chemical pesticides and fungicides creates environmental pollution problems. Residues during harvest reduce the food quality and limit the trade opportunities. Due to these ill effects of chemical, farmers recently practiced non-conventional bio pesticides and fungicides in crop protection and management. Bio-pesticides and fungicides based on pathogenic microorganisms specific to a target pest and disease offer an ecofriendly and effective solution (Kalra and Khanuja, 2007). National Agricultural Policy 2001, has strongly recommended that the promotion of bio inputs for increasing agricultural production, sustaining the health of farmers and environment. Pest and disease problem in rice, sugarcane and turmeric increase over the years and application of bio pesticides and fungicides alternate to chemicals (Zeigler and Savary, 2009). In this context, the present study has been taken up to study the awareness and adoption level of bio pesticide and fungicides in Erode district of Tamil Nadu. Erode district was purposively selected for the study as it has more area under rice, turmeric and sugarcane and usage level of bio pesticides and fungicides increases in the district. The specific objectives of the study are (i) to analyze the farmers awareness and adoption of bio pesticides and fungicides (ii) to study the cost and returns usage of bio pesticides and fungicides (iii) to study the perception and constraints of bio pesticides and fungicide use. Materials and Methods Sampling design: Three blocks namely Gopichetipalayam, Bhavani and Sathyamangalam of Erode district of Tamil Nadu state which have relatively more numbers of bio pesticide and fungicide users; hence the blocks were purposively selected for the study. The study was conducted during the period of From each block 30 respondents those who cultivating rice, turmeric and sugarcane were selected thus, totally 90 respondents of bio pesticide and fungicide were selected at first stage. In order to make comparative analysis 45 farmers who do not using fungicides also been taken in the same locality with same crops like adopters to avoid discrimination. Hence multi stage purposive mixed random sampling method was followed in the selection of sample respondents. Thus, totally 135 farmers were interviewed with tested and pre structured interview schedule on memory basis. Method of data analysis: Data collected from farmers and analyzed using quantitative and qualitative models through SPSS software package version Percentage analysis: Percentage analysis was used to study the general characteristics of farmers which include age, education and occupation. Logit analysis: The logit analyses were carried out to quantify the relative importance of factors influencing farmers decision to bio pesticides and fungicides for crop production. In logit analysis, the farmers were categorized as adopters those who are using bio pesticide and fungicide. In that bio pesticides and fungicides adopters was a dichotomous dependent variable. Its determinants were assessed using logit model based on logistic cumulative distribution function (McFeddan, 1974 and Maddala, 1983). The logit technique allowed examination of the effects of a number of variables on the underlying probability of adopting bio pesticides and fungicides. The behavioral model used to examine the factors influencing using bio pesticide was Research in Environment and Life Sciences 21 February, 2015

2 Y i = g(z i ).... (1) (Z i ) = a + Σ bk X ki..... (2) Where: Y i = The observed response of the i th respondent (i.e. the binary variable Y 1 = 1 adopter and Y 2 = 0 for non-adopter); Z i = An underlying and unobserved index for the i th respondent (when Z exceeded some threshold Z*, the farmer was observed to be adopter otherwise non-adopter); X ki = The k th explanatory variable of i th respondent, i = 1, 2 N, where, N was the number of respondent s k = 1, 2 M; M was the total number of explanatory variables a = Constant, and b = Vector of coefficients The logit model postulated that P i, the probability that i th respondent selling of, was a function of an index variable Z i summarizing a set of the explanatory variables. In fact, Z i was equal to the logarithm of the odds ratio, i.e. the ratio of probability that the respondent selling of farmland to the probability that he do not sell and it could be estimated as a linear function of explanatory variable (Xki). This could be mathematically expressed as,......(3) Equation (3) is the logit model and once this equation is estimated, P i could be calculated (4) The goodness of fit of the model was tested by three approaches. Firstly, predictions were compared with the observed outcomes and expressed in percentage of correctly predicted. Secondly, 2-times the log of the likelihood (-2LL) estimate was used as a measure of how well the estimated model fitted the data. A good model was one that resulted in a high likelihood of the observed results. Empirical Model: Y= b 0+ b 1 AGE+ b 2 EDU+b 3 EXP+b 4 PNFINC +b 5 LHS + b 6 EAC + b 7 LQI+ U i (Where: Ui is the disturbance-term) Age: This is a continuous independent variable indicating the age of the respondents in years. Basically young respondents have more interest in adoption of new products. Therefore, a-priori expectation was that the probability of adoption was indirectly related to age of the respondents. EDU (Year of schooling): Education increased the ability of respondent to interpret, understand and modify new information. Thus, it was treated as a proxy for farmer s managerial ability. It was therefore, hypothesized that the probability of adoption of bio inputs was directly related to the farmer s education. EXP (Experience of the farmer): Experience of farming increased the ability of respondent to interpret, understand and modify new information. It was therefore, hypothesized that the probability of adoption of bio inputs was directly related to the farmer s experience. PNFINC: (Proportion of non-farm income): The total nonfarm income included income was hypothesized that the probability of purchasing of bio inputs was directly related to the percentage of non-farm income of the farmer. LHS (land holding size): Farm-size was one of the important factors adopting new methods. Therefore, a-priori expectation was that the probability of adoption was directly related to the size of farm. EAC (Extension agency contact): (Contact -1, No Contact -0 ) Extension agency contact also the important factor in adopting modern techniques. Therefore, a-priori expectation was that the probability of adoption was directly related to the contact of extension agency contact. LQI (Land quality index) (Poor-1, Average-2, Good-3): Land quality index has been used by Peterson s (1987) as a land quality indicator. Peterson s index is based on the three indicators. Land with well drained facilities, good precipitation rate and without the problem sodicity and salinity categorized as good quality index. Lacking in one of the indicator quoted as an average quality and lacking in two indicators quoted as poor quality. Higher land quality index assured the more profitability of the farm and low productivity index leads to lower level of farm profitability. Generally farmers wishes to invest more on quality land. Therefore, a-priori expectation was that the probability of adoption was directly related to the land productivity index. Partial Budgeting (Blagburn, 1961): To measure the profitability of using bio pesticide and fungicide a partial budgeting analysis was done to find out whether using bio inputs is economically profitable to farmers. Likert Scaling technique: In this approach, the sample respondents were asked to indicate on a five point scale (Rensis Likert, 1932) whether they were highly satisfied, satisfied, neutral, dissatisfied, highly dissatisfied with the various attributes of bio pesticides and fungicides. The responses were recorded and the scores were added to obtain the mean score towards the satisfaction level of the sample respondents. The score for each factor responses is given in the table 1. Garrett s ranking technique (Henry and Woodworth, 1971) was adopted to find the relative importance of various factors as revealed by the respondents in use of bio pesticides and bio fungicides. The conversion method used was as follows. As a first step, the per cent position of each rank was found out by the following formula: [ 100 ( Rij 0.5 )] Percent position = Nj Where: R ij = Rank given for i th item by the j th individual; N j = Number of items ranked by j th individual The per cent position of each rank, thus, obtained was then converted into scores by referring to the table given by Garrett and Wood Worth (1971). The respondents were requested to rank the opinions/reasons relevant to them according to the degree of importance. The ranks given by each of the respondents was converted into scores. Then for each reason, the scores of individual respondents were added together and divided by the total number of respondents. These mean scores for all the reasons were arranged in the descending order and ranks were given. By this method, the accuracy in determining the preference was obtained. Research in Environment and Life Sciences 22 February, 2015

3 Table-1: Five point scale used for the satisfaction level Particulars Highly satisfied Satisfied Neutral Dissatisfied Highly dissatisfied Scale Table-2: General characteristics of sample respondents General Particulars Adopters Non adopters Age < 30 years 6 (6.67) 0 (0.00) years 42 (46.66) 2 (4.42) years 31 (34.44) 20 (44.45) >50 years 11 (12.22) 23 (51.11) Educational status Illiterate 13 (14.45) 13 (28.89) Primary 9 (10.00) 9 (20.00) High school 29 (32.22) 10 (22.22) Higher secondary 36 (40.00) 11 (24.44) Graduate 3 ( 3.33) 2 ( 4.45) Total (Figures in parentheses indicates the percent to total) Table-3: Result of logit analysis coefficients of factors determinant in adoption of bio pesticide and fungicides (Adopters=1, and Non adopters =0) Variables Co- efficient t- value Odds ratio Probability Intercept Age ** Education 2.382* Experience in farming Proportion of non-farm income Land holding status 4.43*** Extension agency contact 3.821** 2.26** Land quality index Log Likelihood Sample size 135 * = one percent significance level; ** = five percent significance level; **** = ten percent significance level Results and Discussion General characteristics of farmers: General characteristics of sample farmers such as age, educational status, exposure etc., may have a significant bearing on the awareness, purchase and use of bio pesticides and fungicides. Therefore, details regarding the same were analyzed and the results are presented and discussed. From the above table 2, the adopters are mostly belong to the age group of 31 to 40 years and it account for 42 percent followed by 41 to 50 age group accounted for 34.4 percent. With respect to education status 72 percent of respondents in adopter category together completed high school and higher secondary. Factors influencing adoption of bio pesticides and bio fungicides: The factors that influence conversion adoption of bio pesticides and fungicides depend on various socio economic conditions. The relative importance of these factors was quantified by using a logit regression as adoption of bio pesticides was a binary variable. The important variables selected and maximum likelihood estimates of the coefficients of logistic regression analysis are presented in table 3. The model fitted very well to the data as indicated by observed significance of log likelihood ratio test. The estimation yielded expected signs for the independent variables according to a priori expectation. From the results of logistic regression analysis it could be inferred that education status and land holding size of farmer positive influences and increase the probability adoption by percent and percent, respectively. Contact of farmer with extension agency was the important factor positively influences and increases the probability of adoption by percent. Factors like proportion of non-farm income (26.47 %) and land quality index (2.53 %) had positive effects on adoption though these coefficients turned out to be non-significant. Age factor (26.53 %) had negative effect (negative sign) on adoption, though these coefficients turned out to be nonsignificant. Economics of crop production: Economics of crop production was separately done for bio pesticide adopters and non- adopters for all the three crops. A close look at the Table 4 reveals that land preparation cost, seedling and transplanting, manures, fertilizers, weeding, post-harvest management is more or less equal to adopters and non- adopters for the crops studied. From the table 4 it was observed that the adopter categories for rice growers additionally Research in Environment and Life Sciences 23 February, 2015

4 Table-4: Economics of Crop Production (Rupees per hectare) Cost particulars Paddy Turmeric Sugarcane Adopters Non-Adopters Adopters Non-Adopters Adopters Non-Adopters Land Preparation Seed and Transplanting Manures and manuring Fertilizers Special Practices Cost Bio pesticides and fungicides Plant protection chemicals Weeding Harvest and post-harvest Total Yield (tonnes/ha) Average Price (Rs/Kg) Total Returns Net Income Table-5: Partial budgeting of bio pesticide and fungicide users and non-users Credit (A) (in Rs ) Debit (B) (in Rs) Partial budgeting for Paddy growers Total Added Cost Total Reduced Cost Land Preparation 190 Seed and Transplanting Cost 300 Manures and manuring 160 Fertilizer 200 Bio-Fungicides and pesticides 1100 Plant Protection chemicals 835 Weeding after cultivation 120 Harvest and post-harvest 90 Total Added Cost 1850 Total Reduced Cost 1335 Reduced Return 0 Added Return 2820 Total credit 1850 Total Debit 4155 Incremental Income (B-A) Rs 2305 Partial budgeting for Turmeric growers Total Added Cost Total Reduced Cost Preparatory cultivation 66 Seeds and sowing 96 Bio pesticide and Fungicides 2200 Manures 1204 Fertilizers 610 Plant protection 3256 Harvesting and post-harvest 88 Total Added Cost 2226 Total Reduced Cost 5074 Reduced Return Added Return 6000 Total Credit (A) 2226 Total Debit (B) Incremental income (B-A) Rs 8848 Partial budgeting for sugarcane growers Total Added Cost Total Reduced Cost Basal Manure and Manuring 230 Land Preparation 90 Herbicide 60 Sett, Set Treatment & Planting 365 Bio fungicide 1100 Fertilizer cost 190 Harvest and transport charges 675 Propping 100 Plant protection Chemical 1270 Manual Weeding 35 Total Added Cost 2065 Total Reduced Cost 2050 Reduced Return 0 Added Return 2580 Total Credit (A) 2065 Total Debit 4630 Incremental Income (B-A) Rs 2565 Research in Environment and Life Sciences 24 February, 2015

5 Table-6: Satisfaction level of the farmers towards bio pesticides and fungicides Statements Score Bio pesticide Effectiveness Overall quality Regular usage experience First use experience Availability of packaging Physical appearance of the treated plant Price of product Bio fungicide Table-7: Constraints faced by the adopters Particulars Percentage to total Rank Lack of technical support III High price of the products I Adequate unavailability II Lack of technical labour IV spent Rs. 1100, turmeric growers Rs and sugarcane growers Rs for the usage of bio pesticides and fungicides. While, compared with non-adopters the bio-pesticide adopters cost on plant protection chemicals was very low in turmeric and low. With respect to yield compared to non-adopters adopter categories recorded higher yield in all the three crops. Partial budgeting for bio pesticide users and non-users: Partial budgeting was done to assess the incremental income in the use of bio pesticide and bio fungicide and presented in table 5. Partial budgeting was worked the entire crop and presented in below tables. Results from the Table 5 showed that Rs net increment in income with the usage of the bio pesticides and fungicides indicated the profitability of the technology. The results of partial budgeting revealed that there was an added return of Rs.2820 with added cost of Rs by using bio pesticides and fungicides in rice cultivation. The net change in income (Rs. 2305) showed that using bio pesticide and fungicides in rice cultivation is economically viable. It could be seen from table 5 showed a positive (Rs. 8848) net increment in income with the usage of the bio inputs. The results of partial budgeting revealed that there was an added return of Rs.6000 with added cost of Rs by using bio-inputs in turmeric cultivation. The net change in income (Rs. 8,848) showed that in turmeric cultivation is economically viable. It could be inferred from the table 5 that positive (Rs. 2565) net increment in income with the usage of the bio inputs indicated the profitability of the technology. There were also was an added return of Rs.2, 580 with added cost of Rs. 2,065 by using bio inputs in sugarcane cultivation. The net change in income (Rs. 2,065) showed that sugarcane cultivation is economically viable. Satisfaction level of the farmers towards bio pesticides and fungicides: Likert Scaling approach was used to study the satisfaction level of farmers using bio pesticides and bio fungicides. It is evident from the table 6 major share of farmers in the study area were highly satisfied with the effectiveness of bio pesticide and bio fungicide followed by overall quality and regular usage experience. Conversely equal percentages of the farmers were not satisfied with the price of the products. The result infers that the price of the product is not in line with its performance. Constraints faced by the farmers: The constraints faced by farmers in usage of bio pesticides and bio fungicides were studied and are presented in Table 7. It was noticed from the Table 7 from that high price was the major constraints faced by the farmers was ranked first. Adequate unavailability of bio pesticide and fungicide and lack of technical support were the other constrains and they were ranked second and third respectively. Salient findings of the study: The present study clearly indicates that Education, land holding size and extension agency contact were most significant factors influencing adoption of bio pesticide usage. Partial budgeting estimates shows using bio pesticidesfungicides in rice; turmeric and sugarcane cultivation is economically viable. High price is the major constraint in adoption of bio products quoted by farmers. The usage of bio pesticide residual free and ensure safety of food production. The study also reveals that that usage of bio pesticide and fungicide are economically viable to farmers. Institutional measures and suitable promotion measures need to be taken to wide spread the adoption of bio pesticides and fungicide use increase food safety and ecofriendly to environment. Acknowledgments The author wishes to thank the field officer and sample respondents for providing the informations to carry out the present investigation. Research in Environment and Life Sciences 25 February, 2015

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