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Role of Water Saving Technologies

Categories Environment, Nature, Pollution, Water, Water Pollution

Essay, Pages 12 (2761 words)

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Essay, Pages 12 (2761 words)

Role of Water Saving Technologies as a Measure to Avert Fluoride Contamination in Tamil Nadu

Introduction

In India most of the people are directly or indirectly related with agriculture sectors for their livelihoods. Groundwater is one of the major source of irrigation for agriculture activities. However, the availability of groundwater (433 billion cubic meter) is less compared to surface water (690 billion cubic meter). Groundwater is used almost everywhere through borewells and about 89 per cent of abstracted water its used for the irrigation purpose (CGWB 2016).

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Due to over extraction of groundwater, about one billion people in India live in areas of physical water scarcity, of which 600 million are in areas of high to extreme water stress and India is currently ranked 120 among 122 countries in the water quality index. India uses the largest amount of groundwater about 24 percent of the global total, more than that of China and the US combined and it is the third largest exporter of groundwater contributing 12 percent of the global total (India Today, 2019).

Over abstraction of groundwater and over use of phosphatic fertilizer is one of the cause of fluoride contamination in groundwater. The occurrence of the high fluoride concentrations in ground water is a problem faced by many countries, notably India, Srilanka and China, the rift valley countries in East Africa, Turkey and parts of South Africa (Ali et al. 2016). In India, many states are endemic fluorosis viz., Andhra Pradesh, Tamil Nadu, Karnataka, Gujarat, Rajasthan, Punjab, Haryana, Bihar and Kerala are the endemic fluorosis states (CGWB, 2016). In Tamil Nadu, 23 out of 33 district were affected by fluoride contamination in water.

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Salem, Erode, Dharmapuri, Coimbatore, Thiruchirapalli, Dindugal, Theni, Perambalur, Vellore, Madurai, Virudhunagar and Krishnagiri are having fluoride contamination and people risk with dental and skeletal fluorosis (CGWB 2016). Tamil Nadu is traditionally an agricultural state. Major crops cultivated is Paddy, Maize, sorghum, cotton, Sugarcane are being cultivated in the study area. Over exploitation of groundwater leads to contamination of fluoride in the study area. Farmers are irrigating the crops through groundwater for high water consumption crops such as paddy, sugarcane. In order to control over exploitation of groundwater, farmers need to save each drop of water by implementing water saving technologies, so that water wastage can be avoided and contamination of groundwater could be reduced. Agricultural technology helps to shifts the production function up, enabling higher quantity and better quality of output from a given set of inputs. At the prevailing prices, it turns into higher income. In this context, it is generally accepted that there is scope for averting fluoride contamination in groundwater in the study area. A serious examination is important to provide right signals to the farmers so the research work is carried out.

Objectives

  • To study the level of adoption of agricultural technologies among fluoride affected farmers for major crops in the study area.
  • To analyse factor influencing water saving technologies in fluoride affected locales.

Methodology

Study area

Multistage random sampling method was used for the selection of study area. At first stage, District wise fluoride affected locales of Tamil Nadu with the permissible limit of above 1.5 mg/L collected from central ground water board, 2016-17. In second stage, district has been segregated into different agro climatic zones based on fluoride content and finally, western zone was selected. At third stage, it was classified into affected locale (highly, moderately and less fluoride affected locale) and non affected locale. From this two blocks from each of the locales, then three villages of each block were selected based on secondary data. Finally, 248 samples was selected based on sample size methodology given by Slovin formula

Slovin Formula (n) = N/(N*(d)^2+1)

Where: n = sample size; N = total number of farmers population (6021618); d= error limit of 5 % (0.05).

Application of the sampling formula with the values specified which in fact maximizes the sample size; yielded a total required sample of 399, out of 399 samples considering time constraint along with convenience about 45 per cent i.e. 182 samples from less fluoride affected locale, moderate fluoride affected locale, high fluoride affected locale and interviewed through pre tested schedule.

Analytical Tools Employed

Adoption Index

There are several strategies to reduce impact of fluoride contamination in groundwater and technology adoption is one of the important strategies to reduce the impact of fluoride contamination. But, one of the important issues with regard to technologies is adoption of technologies generated by the State Agricultural Universities (SAUs).

An adoption index was constructed to quantify the adoption of technologies by Kiresur et al (1996) :

Adoption Index = [a?p]*100

Where, a = Number of practices adopted by respondents, and p = Total number of practices recommended. The respondents were classified as five categories viz., Non-adopters (0), low adopters (1-25), medium adopters (25-50), high adopters (50-75) and very high adopters (75-100) on the basis of their level of adoption measured in terms of TAI. The recommended practices for crop production are given in the ‘Package of Practices’ approved by the State Department of Agriculture in consultation with the Tamil Nadu Agricultural University. From this package of practices technologies recommended for water stress were identified to quantify adoption. The recommended technologies considered in the present study were System of Rice Intensification for paddy, Hybrid variety, Alternate wetting and drying, herbicide application and line planting. For maize crop package of practices recommended were hybrids, following spacing, herbicide application, soil test crop response, integrated pest management and inter cropping, mulching, summer ploughing and agricultural allied activities like animal husbandry.

Factors influencing Water Saving Technology Adoption: Logistic model

Binary choice models are appropriate when the decision making in choice between two alternatives depends on the characteristics of the problem.

Following Maddala (1992), the logistic distribution for the involving decision in adopters of respondent can be specified as:

P_i=1/(1+e^(-z(i)) ) ····(1)

Where, Pi is a function of an index variable Z, summarizing a set of the explanatory variables (Xi). In fact, Z is equal to the logarithm of the odds ratio, i.e. ratio of probability of non-adoption and it can be estimated as linear function of explanatory variables (Gujarati (1995)). The functional form logistic model may be given by the equation as follows:

z_i=?_0+ ????_i X_i ? ····(2)

Where ?0 is the intercept and ?i is a vector of unknown slope co-efficients.

The relationship between i and , which is non-linear, can be written as follows:

p_i= 1/(1+e^(?_0+?_1+x_i+?+?_n x_n ) ) ····(3)

The slopes tell how the log-odds in favour of adoption as independent variables change. If i is the probability of Adoption, then 1- i represents the probability of non- Adoption which can be written as:

1-p_i=1/((1+e^(-zi) ) )=e^(-zi)/((1+e^(-zi) ) )=1/((1+e^(-zi) ) ) ··(4)

Dividing equation (3) by equation (4) and simplifying gives:

p_i/(1-p_i )=(1+e^zi)/((1+e^(-zi)))=e^(-zi) ····(5)

It is the ratio of the probability of the respondent involved in adoption. Finally, the logit model is obtained by taking the logarithm of equation (5) as follows.

L_i=L_n (p_i/(1-p_i ))=z_i=?_0+?_1 x_1+?_2 x_2+?+?_n x_n ···(6)

Where is log of the odds ratio, which is not only linear in independent variables, but also linear in the parameters.

This econometric model is estimated using the iterative Maximum Likelihood Estimation (MLE) procedure due to the nonlinearity of the logistic regression model.

The MLE procedure yields unbiased, asymptotically efficient, and normally distributed regression coefficients (parameters). The model was estimated using Stata software.

The empirical model used in the study is

Yi = ?0 + ?1(AGE) + ?2 (EDU) +?3 (FI) + ?4 (EXP) + ?5(DOW) + ?6 (AFC) + ?7 (CEA) + e

Where,

Y =1, for the water saving technology adopter

Y=0, for the water saving technology non-adopter

?0 – Intercept

?1 to ?7 – Co-efficients of independent variables

AGE – Age of the farmer (in years)

EDN – Educational status of the farmers (in years)

FI – Farm Income (Rs./annum).

EXP – Experience in farming (years)

DOW – Depth of Well (in feet)

AFC – Awareness about Fluoride Contamination in Water (dummy, 1 if farmers aware; 0, otherwise)

CEA – Contact with extension personnel, (dummy, 1if contact with extension personnel; 0, otherwise)

Results and Discussion

Technology adoption index for fluoride affected locales

Level of adoption of technologies in fluoride affected locales were analysed through technology adoption index (TAI) and the results are furnished in the table 1. In less fluoride affected locale, revealed that 61.29 per cent farmers were non adopters and 38.71 per cent farmers were adopters.

In case of moderately fluoride affected locale, it showed that 51.61 per cent for non adopters and 48.39 per cent farmers were adopters. In highly fluoride affected locale, it revealed that 67.74 per cent farmers were non adopters and 32.26 per cent farmers were adopters. It clearly shows that farmers in fluoride affected locales were less aware about fluoride pollution.

Factor influencing water saving technologies in fluoride affected locales using logit model

The results of factors influencing water saving technologies in less fluoride affected locale using logit model are presented in Table.1. Age of the farmers, educational status of the farmers, farm income, experience in farming, depth of the well, awareness about fluoride contamination in groundwater and contact with extension agency are the independent variables included in these model.

In less fluoride affected locale, it could be seen from the table that the chi-square was 48.32 and highly significant. It indicated that the logit model was good fit for the observed data. The coefficient of age of the farmer is negative and statistically significant at ten per cent level. The odds ratio indicated that as the age of farmers increases, the logs of odds ratio in favor of adoption of water saving technologies decrease by 0.901. It implied that young age farmers are risk takers and also they have a higher tendency to adopt technologies than the old age farmers. Education status of the farmers appears to be positive and statistically significant at five per cent level. The odds ratio in favor of water saving technologies, holding other factors constant, increases by a factor of 15.768 for the farmers who are educated than that who did not. Education is thought to improve the farmer’s ability to better process the information provided about new technologies and to increase their allocative and technical efficiency. It was inferred that the educational level would be positively related to technology adoption and when education increases technology adoption also increases. Depth of the well shows negative and significant influence on water saving technologies. The odds ratio, holding other things constant, implies that the probability of adoption of water saving technologies decrease by a factor of 0.816 as the depth of the well increase by one unit. The coefficient of awareness about fluoride contamination in groundwater is positive and significantly influenced the probability of adoption of water saving technologies at ten per cent significance level. From this result, it can be stated that those farmers who have aware of fluoride contamination in groundwater were more likely to adopt water saving technologies than those who have not aware. The odds ratio indicated in the model with regard to awareness about fluoride contamination in groundwater implies that keeping other factors constant, the odds ratio in favor of adopting water saving technologies increases by a factor of 15.549 as farmer aware about fluoride contamination. It also implied that educated farmers were aware about environmental problems compare to uneducated farmers. Contact with extension agency was also found to be significant and positively associated with the adoption of water saving technologies at 10 per cent level of significance. If the farmers are having contact with extension agency, the logs of odds ratio in favor of farmers adoption of water saving technologies increased by a factor of 2.469. This implies that adoption increases when farmers have regular contact with extension agency. This results also indicate that major role of extension service on spreading of information about water saving technologies in the study area.

The results of factors influencing water saving technologies in moderately fluoride affected locales are furnished in Table.2. It could be seen from the table that the chi-square was 53.06 and highly significant. It indicated that the logit model was good fit for the observed data. The coefficient of age of the farmer is negative and statistically significant at five per cent level. The odds ratio indicated that as the age of farmers increases, the logs of odds ratio in favor of adoption of water saving technologies decrease by 0.854. It implied that young age farmers have higher tendency to adopt water saving technologies than old age farmers. The coefficient of education is positive and significant at five per cent level. The odds ratio in favor of water saving technologies, holding other factors constant, increases by a factor of 1.883 for the farmers who are educated than that who did not. Farmers with a well knowledge have a greater tendency to gather information about water pollution that would lead them to find suitable water saving technologies. Farming experience was found to influence adoption water saving technologies positively and significantly at five per cent level.

Source: Primary household survey (2017-2018) Note: Figures in parentheses indicates the z value; *** Significant at 1% per cent level; ** significant at 5% per cent level; * significant at 10% per cent level.

The odds ratio indicated that as the farming experience increases, the logs of odds ratio in favor of adoption of water saving technologies increase by 1.317. The result indicated that famers with more farming experience are less likely to adopt water saving technologies. Depth of the well shows negative and significant influence on water saving technologies. The odds ratio, holding other things constant, implies that the probability of adoption of water saving technologies decrease by a factor of 0.979 as the depth of the well increase by one unit. The coefficient of awareness about fluoride contamination in groundwater is positive and significantly influenced the probability of adoption of water saving technologies at ten per cent significance level. From this result, it can be stated that those farmers who have aware of fluoride contamination in groundwater were more likely to adopt water saving technologies than those who have not aware. The odds ratio indicated in the model with regard to awareness about fluoride contamination in groundwater implies that keeping other factors constant, the odds ratio in favor of adopting water saving technologies increases by a factor of 9.143 as farmer aware about fluoride contamination. It also implied that educated farmers were aware about environmental problems compare to uneducated farmers.

Source: Primary household survey (2017-2018) Note: Figures in parentheses indicates the z value; *** Significant at 1% per cent level; ** significant at 5% per cent level; * significant at 10% per cent level.

The results of factors influencing water saving technologies in highly fluoride affected locales are furnished in Table.3. chi-square was 59.03 and highly significant. The coefficient of age of the farmer is negative and statistically significant at ten per cent level. The odds ratio indicated that as the age of farmers increases, the logs of odds ratio in favor of adoption of water saving technologies decrease by 0.125. It implied that young age farmers have higher tendency to adopt water saving technologies than old age farmers. Depth of the well shows negative and significant influence on water saving technologies. The odds ratio, holding other things constant, implies that the probability of adoption of water saving technologies decrease by a factor of 0.984 as the depth of the well increase by one unit. The coefficient of awareness about fluoride contamination in groundwater is positive and significantly influenced the probability of adoption of water saving technologies at ten per cent significance level. From this result, it can be stated that those farmers who have aware of fluoride contamination in groundwater were more likely to adopt water saving technologies than those who have not aware. The odds ratio indicated in the model with regard to awareness about fluoride contamination in groundwater implies that keeping other factors constant, the odds ratio in favor of adopting water saving technologies increases by a factor of 4.112.

Conclusion

The study has revealed that adoption of technologies in the study area has declined due to less aware about fluoride contamination of groundwater. Awareness is a major variable that influence on adoption of water saving technologies. The water used for irrigation is drawn from higher depths which is contaminated by fluoride, so government may give awareness and encourage the fluoride contamination averting measures that will improve the water table in the study area

Cite this essay

Role of Water Saving Technologies. (2019, Nov 21). Retrieved from https://studymoose.com/role-of-water-saving-technologies-essay

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