Knowledge Level and Adoption Pattern of Climate Smart Technologies in NICRA Adopted Village in the Western Agro-climatic Zone of Punjab, India
Manpreet Jaidka
*
Punjab Agricultural University, KVK, Moga, Punjab-142002, India.
Harkanwaljot Singh Sekhon
Punjab Agricultural University, Regional Research Station, Faridkot, Punjab-151203, India.
*Author to whom correspondence should be addressed.
Abstract
Background: Climate smart agriculture technologies including the management of crop residues without resorting to burning, foliar application of potassium nitrate, the adoption of direct-seeded rice (DSR), and the cultivation of short-duration paddy varieties were systematically demonstrated as part of the initiative. These interventions were complemented by a series of structured awareness campaigns and capacity-building programmes designed to enhance farmers’ knowledge, promote sustainable agronomic practices, and encourage the adoption of environmentally resilient farming systems. Collectively, these efforts aimed to mitigate adverse environmental impacts while improving productivity and resource-use efficiency within the agricultural sector.
Aims: The present survey-based investigation was carried out with an objective to have an idea about the impact of various extension activities carried out under NICRA project in the context of the knowledge level and the adoption pattern of climate smart technologies (CSTs).
Study Design: The study was carried out during the year 2024-25 using a pre-designed interview cum questionnaire schedule by randomly contacting the beneficiaries (70) and non-beneficiaries (40) of NICRA project in the adopted village.
Methodology: The test to check the knowledge level constituted 35 queries which included a list of questions pertaining to CSTs. The answers to the question were quantified by giving 2 score to full knowledge, 1 score for partial knowledge and zero score for no knowledge. The correlation matrix between knowledge level and independent variables was used to identify the deriving factors behind the preference of a given technology by the beneficiaries.
Results: The data showed that beneficiary farmers reported highest percentage of farmers in the category of high knowledge level (44.3%) i.e., 31 followed by medium knowledge level category (32.9%) i.e., 23. On the contrary, non-beneficiary farmers recorded highest percentage in low knowledge level (17) followed by medium category (12). Among beneficiaries recorded 50, 47.1 and 71.4% in high category in terms of technology wise understanding, respectively. Adoption pattern depicted that 32.9% (23) beneficiaries follow alternate wetting and drying in comparison to 22.5% (09) among non-beneficiaries. Understanding about the CSTs depicted highly significant positive correlation with education (0.817), farming experience (0.359) and attending the training programmes (0.519) and significantly positive correlation with age of the respondents (0.262).
Conclusion: Education level, training component, land holding and access to the credit depicted positive relation with the adoption of technologies making these factors as best fitting determinants.
Keywords: NICRA, adoption pattern, climate smart technologies, correlation matrix, knowledge level