Bridging knowledge to fields: Impact of Krishi Vigyan Kendra on farm practices, productivity and farmers’ income in Uttarakhand

Introduction

India is predominantly a rural country, and agriculture forms the backbone of its economy, serving as the main source of livelihood for the rural population. The overall economic growth of the nation largely hinges on the development and performance of agriculture and allied sectors, which contributed 20.19% to the Gross Domestic Product (GDP) in 2020-21 (Ministry of Statistics and Programme Implementation, 2021). In states like Uttarakhand, agriculture remains the primary livelihood activity, especially in the hill regions where over 74% of landholdings are marginal. Most farmers in these areas still practice traditional, low-input subsistence farming. In districts such as Nainital, agricultural productivity and income remain stagnant due to challenges like fragmented landholdings, limited irrigation (only 10.79% of net sown area), and lack of awareness about modern agricultural technologies. Despite the availability of innovative farming solutions developed by research institutions, their adoption among hill farmers remains low. To bridge this gap, Krishi Vigyan Kendras (KVKs) were established as district-level farm science centres to promote improved technologies through Frontline Demonstrations (FLDs), On-Farm Trials (OFTs), and farmer training programs. KVK Jeolikote, located in Nainital district, plays a crucial role in transferring these technologies to the hill farmers of Kumaon region. However, empirical evidence on its effectiveness in improving farmers' knowledge, practices, productivity, and income is limited. Although anecdotal reports and national assessments suggest positive outcomes from KVKs, region-specific evaluations are lackingparticularly in the unique agro-climatic and socio-economic setting of Uttarakhand. The absence of such data-driven assessments limits the ability to design need-based extension strategies. The study was undertaken with the primary objective of assessing the role of KVK Jeolikote in the transfer and adoption of agricultural technologies at the farm level. It aimed to evaluate how effectively the KVK had contributed to improving farmers' knowledge, management practices, and overall productivity through its various interventions such as training programs, Frontline Demonstrations (FLDs), and On-Farm Trials (OFTs). Another key objective was to identify and analyze the major constraints faced by farmers in the adoption of technologies disseminated by the KVK. These objectives were framed to generate empirical evidence on the impact of KVK activities in the region and to provide insights for strengthening future extension strategies, particularly in the context of hill agriculture in Uttarakhand. www.extensionjournal.com KVK was purposively selected to assess its impact on the management practices, crops productivity and enhancement of farmers' income. Both primary and secondary data were utilized. The sample included 60 farmers-30 beneficiaries and 30 non-beneficiaries. Secondary data were sourced from KVK records, government publications, and relevant statistical reports. A two-stage random sampling method was adopted. In the first stage, three adopted villages (Amritpur, Dehra, Amiya) and three non-adopted villages (Nathupur Chamwal, Himmatpur Motahaldu, Lachampur) were randomly chosen. In the second stage, 10 beneficiary and 10 non-beneficiary farmers were randomly selected from each group of villages. This sampling design allowed for a comparative analysis of the KVK's influence across differently exposed farming communities. To evaluate the impact of KVK Jeolikote on improved management practices, crop productivity, and farm income, regression analysis was employed. The Ordinary Least Squares (OLS) method was used to estimate the parameters associated with multiple independent variables. OLS is widely recognized for estimating coefficients in linear regression models that relate one or more independent variables to a dependent variable. The coefficient of multiple determination (R²) was calculated to assess the overall fit of the regression models. To account for the influence of the number of explanatory variables, the adjusted R² was also computed. The significance of the adjusted R² was tested using the F-test:

Results and Conclusion Impact of KVK on Management Practices, Productivity, and Farm Income

Regression analysis was employed to analyze factors influencing the adoption of improved management practices, productivity of crops, and farm income, with a focus on quantifying the impact of KVK interventions. www.extensionjournal.com

Factors Affecting Management Practices and Impact of KVK Cereals

The regression results summarized in Table -1 indicate that among the explanatory variables-farming experience, age of the respondent, education level of the household head, and a dummy variable representing KVK involvementonly education and KVK dummy were statistically significant in influencing the adoption of improved management practices for cereal crops. The dummy variable was assigned a value of 1 for farmers who had received training from KVK (beneficiaries) and 0 for those who had not (non-beneficiaries).

The coefficient for education (0.5313) was significant at the 5% level, suggesting that each additional year of schooling for the household head is associated with a 0.5313% increase in the adoption of improved practices. The coefficient for the KVK dummy variable was 37.3232, highly significant at the 1% level, indicating that beneficiary farmers adopted improved practices by 37.32% more than non-beneficiaries.

Although farming experience and age were positively associated with improved practices, their effects were statistically non-significant. The model accounted for approximately 59.55% of the variation in the dependent variable, with the adjusted R² significant at the 1% level, implying a good fit of the model.

Major Vegetables

Similarly for vegetables the results are presented in Table-2. From the table, it is evident that farming experience and the KVK dummy variable significantly influenced the adoption of improved management practices in vegetable cultivation. Specifically, each additional year of farming experience led to a 1.2826% increase in the adoption of such practices, and this relationship was statistically significant at the 1% level. The KVK dummy variable, with a coefficient of 30.8711, was also highly significant, indicating that farmers who received training from KVK adopted improved management practices 30.87% more than those who did not. This highlights the substantial positive effect of KVK training programs on the management practices of beneficiary farmers. On the other hand, the variables age and education of the respondents, while positively associated with improved practices, were found to be statistically nonsignificant. The model explained about 41.22% of the total variation in the adoption of management practices, with the adjusted R² being statistically significant at the 1% level, suggesting a reasonably good fit for the data.

Factors Influencing Crop Productivity and the Impact of KVK Interventions

It is a well-established fact that the adoption of advanced technologies and improved farming practices contributes to enhanced crop productivity. One of the primary objectives of extension services, such as those offered by Krishi Vigyan Kendras (KVKs), is to raise agricultural productivity at the farm level. To evaluate the influence of various factors on crop productivity for different crops and assess the impact of KVK, regression analysis was employed.

Crop Productivity

The data presented in Table -3 and Figure-1 depict the average yield levels achieved by beneficiary and nonbeneficiary farmers. The findings clearly indicate that the average yield of most crops was consistently higher on farms where farmers had availed KVK support and training, compared to those who had not.

Productivity of Cereal Crops

The regression analysis from the Table-4 revealed that in the case of wheat, the area under cultivation, NPK fertilizer use, and participation in KVK training significantly influenced productivity. A 1% increase in the area under wheat led to a 0.2094 q/ha increase in yield, while each additional kilogram per hectare of NPK fertilizer contributed to a 0.0535 q/ha gain. Farmers who had received KVK training achieved, on average, 9.0937 q/ha higher yields than nonbeneficiaries. The model demonstrated a strong fit, explaining approximately 59% of the variation in wheat productivity and was statistically significant at the 1% level. For paddy, seed quantity, FYM application, and the KVK dummy variable emerged as significant factors. A 1 kg/ha increase in seed use resulted in a yield improvement of 0.1368 q/ha, and each additional quintal of FYM increased productivity by 0.0228 q/ha. Beneficiary farmers recorded a yield advantage of 10.3359 q/ha over non-beneficiaries. The model accounted for about 62% of the variation in paddy productivity, with a high level of statistical significance. In maize cultivation, productivity was significantly influenced by NPK fertilizer use and KVK participation. A 1 kg/ha increase in NPK application led to a yield increase of 0.1049 q/ha, while KVK-trained farmers produced 4.0512 q/ha more than their counterparts. The model explained 37% of the variability in maize yield and was significant at the 5% level. Overall, the findings underscore the positive and significant role of KVK interventions in enhancing cereal crop productivity, particularly in wheat and paddy, validating the importance of targeted agricultural extension services.

Productivity of Vegetable Crops

Regression analysis was conducted to evaluate the factors affecting productivity in major vegetable crops-potato, tomato, cabbage, and okra-and to assess the impact of KVK interventions. The variables considered included seed quantity (kg/ha), percentage area under the crop, NPK fertilizer use (kg/ha), FYM application (q/ha), and a dummy variable for KVK participation. The table-5 shows that in the case of potato, NPK fertilizer use, FYM application, and the KVK dummy were statistically significant. A one kg/ha increase in NPK use resulted in a 0.0618 q/ha rise in yield, while 1 q/ha more FYM contributed 0.0986 q/ha to productivity. KVK beneficiary farmers recorded a yield advantage of 23.36 q/ha. The model explained 50.55% of the variation, significant at the 1% level. For tomato, FYM application and KVK participation were the key determinants of yield. Each 1 q/ha increase in FYM raised productivity by 1.6508 q/ha, and KVK beneficiaries achieved an average yield of 18.01 q/ha more than non-beneficiaries. The model explained 39.31% of the variation, also significant at the 1% level. In cabbage, seed quantity, FYM application, and the KVK dummy were found to be significant. A 1 kg/ha increase in seed use raised yield by 0.0331 q/ha, and FYM application improved productivity by 1.3324 q/ha. KVK-trained farmers had a yield advantage of 17.88 q/ha, with the model explaining 44.10% of the variation at the 1% significance level. For okra, only NPK fertilizer use and the KVK dummy were statistically significant. A 1 kg/ha increase in NPK usage resulted in a 0.0307 q/ha gain, while KVKtrained farmers produced 22.25 q/ha more than nonbeneficiaries. The model accounted for 53.51% of the variation, significant at the 5% level. These findings underscore the strong and positive influence of KVK interventions on vegetable crop productivity, particularly through the promotion of balanced fertilizer use and organic inputs such as FYM.

Composition of Farm Income and Role of KVK

The primary goal of every farmer is to increase income and ensure a stable and sustainable livelihood. In this context, the role of extension services such as Krishi Vigyan Kendras (KVKs) becomes critical in strengthening farmlevel income sources.

Composition of Farm Income

Farm income in the study was classified into three major components: income from field crops, vegetable crops, and dairy (milch animals). As presented in Table-6 and illustrated in Figure-2, it was observed that income from field crops was higher on beneficiary farms (₹58,300/farm) compared to non-beneficiary farms (₹43,066/farm). A major difference was seen in vegetable cultivation, which contributed a substantial 57.89% to the total farm income of beneficiary farmers, in contrast to only 16.81% on nonbeneficiary farms. This reflects the significant impact of KVK support in promoting high-value horticulture. On the other hand, dairy income accounted for 46.44% of the income on non-beneficiary farms, compared to 30.01% on beneficiary farms. However, in absolute terms, even dairy income was higher on beneficiary farms by ₹4,933.34 annually.

Factors Affecting Farm Income and Impact of KVK

Regression results in Table-6 show that all selected variables significantly influenced farm income. An increase of 1 hectare in area under field crops was associated with an income rise of ₹29,904.39, while a 1-hectare increase in vegetable cultivation contributed an additional ₹38,346.89. Moreover, each additional milch animal increased farm income by ₹22,843.57. The KVK dummy variable was highly significant, demonstrating that beneficiary farmers earned ₹54,440 more annually than non-beneficiaries. The model explained 81% of the variation in farm income and was statistically significant at the 1% level, confirming the strong positive impact of KVK interventions on farm-level income generation. These results emphasize the vital role of KVKs in promoting diversified farming, enhancing income through high-value crops, and improving dairy productivity, thereby contributing substantially to the economic wellbeing of farmers.

Conclusion

The study clearly highlights the significant impact of Krishi Vigyan Kendras (KVKs) interventions on management practices, crops productivity and farm income. The results revealed that KVK beneficiary farmers adopted better management practices across cereal and vegetable crops, resulting in higher productivity and income compared to non-beneficiaries. In cereal crops such as wheat, paddy, and maize, variables like area under cultivation, fertilizer use, and KVK participation had a statistically significant impact on yield. Beneficiaries reported yield gains of 9.09 q/ha in wheat, 10.33 q/ha in paddy, and 4.05 q/ha in maize over non-beneficiaries. Similarly, in vegetable crops such as potato, tomato, cabbage, and okra, KVK interventions significantly improved productivity. Yield gains on beneficiary farms ranged from 17.88 q/ha (cabbage) to 23.36 q/ha (potato). The role of farmyard manure and NPK fertilizer was also evident in improving yields, reaffirming earlier findings by researchers such as Lal et al. (2013) [5] , who demonstrated the positive effects of technological interventions on cumin yield, and Chauhan et al. (2013) [3] , who showed increased chickpea productivity through frontline demonstrations. In terms of income, vegetable crops contributed the highest share (40.52%) to total income on beneficiary farms, while dairy income was more dominant on non-beneficiary farms. Overall, KVK beneficiary farmers earned ₹54,440 more per year than non-beneficiaries, and regression results explained 81% of the variation in farm income, reaffirming the effectiveness of KVK services. These results are consistent with prior studies such as Bairwa et al. (2012) [1] and Kumar et al. (2014) [4] , which also reported significant yield and income improvements through frontline demonstrations and extension activities.

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