DEVELOPMENT AND VALIDATION OF AN ATTITUDE SCALE FOR FARMERS TOWARDS WEATHER BASED AGRO-ADVISORY SERVICES

INTRODUCTION

 In developing countries, agriculture needs to change in order to address the issues of food insecurity and climate change (FAO, 2010). Climate change has significantly impacted biological, ecological and geological systems and has become a serious threat to humankind. Agriculture is inherently sensitive to weather variability making timely and accurate weather information crucial for enhancing farm productivity, sustainability and resilience (Harrison et al., 2016). The agriculture sector remains highly vulnerable to fluctuations in temperature, rainfall, humidity and extreme weather events such as droughts, floods and cyclones (Kumar et al., 2018; Vijai et al., 2023). It was expected that variations in weather parameters such as temperature and precipitation in marginal areas would cause an unpredictable decline within the region's production and productivity.  In recent years, the increasing frequency and intensity of such climatic extremes largely attributed to climate change have posed significant challenges to agricultural systems worldwide (Hatfield et al., 2020). In this context, the need for reliable location-specific and timely agro-advisory services has become more critical than ever before. Weather Based Agro-Advisory Services have emerged as an important tool to support farmers in adapting to climatic uncertainties (Dheebakaran et al., 2019; Rahman et al., 2021). These services provide actionable and need-based information including short and medium-range weather forecasts, crop management practices, pest and disease advisories, irrigation scheduling and contingency planning measures (Sarkar et al., 2023; Hussain et al., 2025). By integrating meteorological data with agronomic recommendations WBAAS enable farmers to make informed decisions regarding sowing, input application, harvesting and risk mitigation. Thus, they help in improving the efficiency of agricultural production, reducing cost of inputs, preventing loss and increasing security of livelihood (Gangopadhyay et al., 2019).

 Although WBAAS are delivered through multiple channels including mobile applications, SMS services, extension systems and digital platforms their utilization among farmers remains inconsistent and varies considerably. There are many factors that affect the adoption and usage of these services such as socio-economic characteristics, information availability, technological awareness and institutional support. Among all, one of the important factors influencing the adoption of WBAAS is the attitude of farmers towards such services (Doss and Asokhan, 2024). Attitude being a psychological variable denotes an individual's inclination towards responding positively or negatively towards innovation (Peterson and Thurstone, 1932; Fishman et al., 2021). This is an important determinant affecting the perception, acceptance and behavioral intention of an individual which ultimately helps in adoption. The farmers attitudes towards WBAAS depend on their perceptions about the credibility, relevance, timeliness, accessibility and usefulness of the information available through it (Srishailam et al., 2023). Therefore, understanding and accurately measuring farmers attitudes is essential for designing effective extension strategies and improving service delivery mechanisms.

 Although numerous studies have explored the adoption behavior and impact of agro advisory services there exists a notable gap in terms of standardized and validated instruments to measure farmers attitudes specifically towards WBAAS (Kumar et al., 2025; Mallick et al., 2025; Paliwal et al., 2026). The absence of a scientifically developed scale limits the ability of researchers and policymakers to capture the multidimensional nature of attitude, assess variations across different contexts and identify critical constraints affecting utilization. In this context, the present study aims to develop and standardize a reliable and valid attitude scale to measure farmers attitudes towards WBAAS. Despite the growing interest in WBAAS as a climate-smart agriculture approach, the existing literature mostly revolves around farmers adoption behavior while no attempt has been made to devise a context-specific scale to measure farmers attitudes toward WBAAS. Lack of a well-established measurement technique makes it impossible to compare and assess the attitude level of farmers under different farming situations.

 The current study attempts to fill this gap by devising a scale for measuring the attitude of farmers towards WBAAS. A key strength of this study is that the attitude scale was developed using established psychometric procedures ensuring its validity and reliability. The study contributes to the field of agricultural extension by employing a rigorous methodological approach and provides a scientifically validated tool for assessing farmer attitudes towards WBAAS. The findings can support policymakers, extension agencies and other stakeholders in designing effective advisory strategies and interventions to enhance the adoption of WBAAS and strengthen farmers adaptive capacity to climate variability.

MATERIALS AND METHODS

Construction of Attitude Scale: According to Robbins and Judge (2009), attitude is defined as an evaluative statement either favourable or unfavourable about objects, individuals or events representing a person’s feelings or predisposition. Understanding attitudes is particularly important for researchers and scientists as it helps reveal how individuals perceive and react to new technologies.

 Assessing the farmers’ attitude is crucial for understanding their acceptance of WBAAS and identifying potential resistance, thereby enabling necessary refinements to improve adoption. The attitude scale used to evaluate farmers’ perceptions of WBAAS was developed using the summated rating method proposed by (Likert, 1932). The summated rating method was employed to develop the attitude scale as it is a commonly used technique in scale development (Desselle, 2005). This method captures different dimensions of a belief through a series of statements rather than relying on a single item thereby providing a more accurate reflection of actual behaviour (Thurstone, 1929; Reid, 2006). According to Kothari (2004), in a summated rating or Likert scale respondents are asked to indicate their level of agreement or disagreement with each statement along a five-point or seven-point continuum. To construct the attitude, scale the following procedures were followed:

Collection of statements: A statement is defined as anything that is said about a psychological object (Bardhan et al., 2023; Mohanraj et al., 2024). Initially a total of 60 items or statements were developed to assess farmers attitude towards WBAAS on an extensive review of relevant literature and expert opinions from the concerned discipline. These statements included a balanced combination of both positive and negative expressions. Using the criteria outlined by Edwards (1957), each item was carefully evaluated for clarity, relevance and distinctiveness. Based on this assessment redundant, vague and overlapping statements were either removed or revised. Consequently, a final set of 48 well-structured attitude statements including 30 positive and 18 negative statements was developed for the study.

Judges rating and item selection’: When evaluating the attitude toward WBAAS, not all of the statements collected may be equally pertinent. The initial set of 48 attitude statements was evaluated through item analysis using the judgments of 64 experts to determine their relevance and appropriateness. A panel of experts in the field of WBAAS was requested to assess each statement in terms of the clarity, relevance and ability to capture farmers attitudes regarding WBAAS.  The judges were asked to rate each statement on a five-point continuum from "Strongly Agree" to "Strongly Disagree" in order to determine its relevance and screen for inclusion in the final scale. The judges were asked to score 5, 4, 3, 2 and 1 for each of the statements: Strongly Agree (SA), Agree (A), Undecided (UD), Disagree (DA) and Strongly Disagree (SDA), while the scoring was reversed for negatively worded items. As a result, a respondent could receive a maximum score of 240 and a minimum score of 48.

 Based on the judge’s evaluation, the total scores were arranged in descending order and two criterion groups were formed: the top 25% (high group) and the bottom 25% (low group). The t-value of each statement was determined using these two groups as criterion groups.

Calculation of 't' value: By calculating the t-value (critical ratio) between the mean scores of the high and low groups, each statement's discriminating power was evaluated using the method suggested by Edwards (1957). Higher t-value items were considered to be more effective in distinguishing favourable from unfavourable attitudes. The final t value calculation was calculated by using the following formula:

Where,

 = Mean score on given statement of the high group

 = Mean score on given statement of the low group

 = Variance of the high group

Variance of the low group

Number of respondents in high group

 = Number of respondents in low group

 = The degree to which a specific statement differentiates between high and low groups

After calculating the t-value for all the statements using the specified formula, those statements with a t-value < 1.75 were selected, while those with a t-value ≤ 1.75 were discarded, following the guideline recommended by Edwards in 1957. The degree to which each attitude statement distinguishes between respondents with high and low scores on the WBAAS attitude scale is indicated by the "t" value.

Standardization of the scale: To ensure that the developed scale accurately measures farmers attitudes towards WBAAS and yields consistent and dependable results, the process of standardization was undertaken. Standardization refers to establishing the reliability and validity of the scale, thereby confirming its scientific soundness and applicability.

Reliability of scale: According to Ray and Mondal (2011), reliability as the accuracy or precision of a measurement or score. As stated by Kumar et al. (2015), a test is considered reliable if it consistently gives the same results when applied on the same sample. The finalized set of 26 statements designed to assess farmers attitudes towards WBAAS was administered to a non-sample group of 30 farmers using a five-point continuum scale. To evaluate the reliability of the attitude scale, the same group of farmers were re-tested after an interval of 15 days using the same instrument. The test-retest method yielded a correlation coefficient (r) of 0.85, which was significant (p ≤ 0.01) suggesting. The scale is highly reliable and consistently measures attitudes of the farmers towards WBAAS.

Validity of scale: The responses were obtained from 30 expert judges to assess the content validity of the attitude scale pertaining to WBAAS. The minimum score of 2.5 on the average was set for accepting each item in the in a 5-point Likert scale. Those items having a score higher than 2.5 on the average were accepted as valid items and used in the scale. The average score of the whole scale turned out to be 3.42, which suggests that the constructed attitude scale is content valid. Exploratory factor analysis (EFA) has been carried out in order to find the factor structure of the attitude scale comprising 26 items. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett's Test of Sphericity were used to evaluate the suitability of the data for factor analysis. The KMO value of 0.71 showed the adequate sampling adequacy, whereas Bartlett's Test of Sphericity has shown the statistical significance (χ² = 1458.32, p ≤ 0.001). Thus, the data satisfied the assumptions required for exploratory factor analysis. Confirmatory Factor Analysis (CFA) and Validity Testing: CFA was carried out on the 26-item attitude scale to validate its measurement model. The measurement model fit the data well based on the following model fit statistics (χ²/df = 3.90, Comparative Fit Index (CFI) = 0.92, Tucker-Lewis Index (TLI) = 0.94, Root Mean Square Error of Approximation (RMSEA) = 0.06 and Standardized Root Mean Square Residual (SRMR) = 0.07). Construct validity was proved by standardized factor loadings that were higher than 0.50. Convergent validity was achieved through Composite Reliability (CR) values more than 0.70 and Average Variance Extracted (AVE) values of more than 0.50 for the selected constructs. Discriminant validity was proven since the square root of the AVE of each construct was higher than the correlation among constructs as per Fornell-Larcker criterion and all the HTMT ratios were less than 0.85.

Scoring procedure: The attitude scale items were constructed on a psychological five-point continuum, i.e. Strongly Agree (SA), Agree (A), Undecided (UD), Disagree (DA) and Strongly Disagree (SDA), each item having its own numerical score of 5, 4, 3, 2 and 1 respectively, to measure farmers attitude towards WBAAS. The overall attitude score of each respondent was computed by summing the scores obtained for all the items in the scale (Mukherjee et al., 2018).

Research location: The study was carried out in the Western Zone of Tamil Nadu. The study area is purposively selected because the Western Zone of Tamil Nadu is characterized by agricultural prominence and climatic diversity. This region plays a crucial role in the state's agricultural output and cultivating a variety of crops. With its varying climatic conditions, including hot and dry weather during certain seasons, the Western Zone faces unique challenges that impact crop growth and productivity. The Western Zone comprises of revenue districts of Erode, Coimbatore, Tiruppur, Theni, Karur (part), Namakkal (part), Dindigul, Perambalur and Ariyalur (part). Erode, Coimbatore, Tiruppur districts are purposively selected for the study (Fig. 1).

Fig. 1 Map depicting area selected for the present study

Research design: The study was conducted using an ex post facto research design, which is appropriate for examining existing phenomena where the variables are already established and cannot be manipulated by the researcher (Rohwer, 2022). This design was considered suitable as the primary objective was to assess farmers attitudes towards WBAAS on their prior exposure, experience and adoption levels. No direct control over the variables was exercised in the study.

Sampling procedure: The present study was conducted in the Western Zone of Tamil Nadu, where WBAAS are widely disseminated. Three districts from this zone were selected for the study based on the prevalence and accessibility of WBAAS among farmers. A list of farmers receiving or exposed to WBAAS in the selected districts was obtained from relevant sources such as the Agro Climate and Research Centre, Tamil Nadu Agricultural University. From this sampling frame, respondents were selected using the simple random sampling technique to ensure equal chance of selection for all individuals. A total sample size of 300 farmers was fixed for the study. Accordingly, 100 farmers were randomly selected from each of the three selected districts, making the sample representative of the study. Therefore, the final sample consisted of 300 farmers selected through simple random sampling. Simple random sampling technique was used in order to reduce any selection bias from the targeted beneficiaries of WBAAS services. But the sampling frame comprised only those farmers who had been using WBAAS services; hence, the results may apply only to this population and not to all the farmers in the study area.

Data collection: Data were collected from 300 farmers across three selected districts in the Western Zone of Tamil Nadu using a structured interview schedule. The interview schedule was specifically designed to assess farmers attitudes towards WBAAS. Responses were recorded on a five-point Likert scale ranging from strongly disagree (1) to strongly agree (5). Prior to the main survey, the schedule was pre-tested in a non-sample area to ensure clarity, content validity and reliability.

Data analysis: The value of CR (critical ratio or t-value) was obtained for each statement about farmers attitudes. Critical ratio method was used to find out discriminating power of each statement in respect of mean score of respondents belonging to high attitudinal and low attitudinal groups. Respondents were arranged in descending order according to their attitudes and the top and bottom groups were selected for analysis. Critical ratio value of each statement was obtained to find out its significance in discriminating between the two groups. Statements whose values of t-statistics were high and statistically significant had good discriminating power; otherwise, those were discarded from further analysis. Thus, reliable and valid statements were selected for measuring farmers attitudes towards WBAAS.

Construction of Attitude Scale: After completing item collection, item analysis and relevancy testing (including relevance percentage, relevancy weightage and mean relevancy score), the ‘t’ values were calculated for all the statements to finalize the attitude scale (Bhati et al., 2023; Akour et al., 2024). The calculated ‘t’ values for each item are presented in Table 1.  The statements which possess the highest ‘t’ value are selected. Based on this analysis, the final attitude scale was constructed by selecting 26 statements with ‘t’ values ≥ 1.75 from the 48 statements subjected to item analysis.

 Table 1. Calculated t-values of the statements on the Attitude of farmers towards Weather based agro advisory services

Statement-wise distribution of farmer’s responses towards attitude on Weather Based Agro Advisory Services: The statement-wise analysis of farmers attitudes towards WBAAS was carried out using a five-point continuum scale comprising strongly agree, agree, undecided, disagree and strongly disagree. This section presents the frequency and percentage distribution of responses for each of the 26 selected statements. In this regard, the data provide a complete insight into farmers attitudes on how useful, advantageous, difficult and applicable on WBAAS. This information can be very helpful for recognizing the advantages and weaknesses of farmers attitudes, which will help to develop relevant strategies and policies for better use of WBAAS. From the distribution of responses to 26 attitudinal questions, one can see that farmers hold positive attitudes toward WBAAS. A number of farmers strongly agreed or agreed with questions regarding the importance and effectiveness of WBAAS.

Overall distribution of farmers based on their attitude towards Weather Based Agro Advisory Services: To gain a broader understanding of farmers attitude levels the respondents were classified into three categories namely highly favorable, favorable and unfavorable based on their total attitude scores. The classification was carried out using the mean and standard deviation (mean ± SD) values. The results of the overall attitude of the farmers towards WBAAS are given in Table 2, Fig. 2.

 Table 2. Overall attitude of farmers on Weather Based Agro Advisory Services

Fig. 2 Overall attitude of farmers on Weather Based Agro Advisory Services

RESULTS

 From the Table 3, it is evident that 57.33% of the farmers strongly agreed and 32.33% agreed to the timely and accessible nature of WBAAS. More than half 55.00% of the respondents strongly agreed while 26.33% agreed that scientific WBAAS were more accurate than traditional methods. The findings also indicated that 52.67% of the farmers strongly agreed while 20.67% agreed that the weather forecast information was location-specific rather than general in nature. 

Furthermore, 44.67% of the farmers strongly agreed and 24.00% agreed that WBAAS enabled them to select suitable crop cultivars for particular sowing season. Only, 6.67% of the farmers strongly agreed while only 10.67% of the farmers agreed that procurement of the inputs and pesticides recommended in the agricultural advisory circulars was tough or not easily available in the market. In addition, the majority 33.67% of the farmers strongly agreed and 31.33% agreed on how clear, adequate and useful weather based advisory messages affected the adoption of these messages. The results indicated that 38.33% of the farmers strongly agreed and 32.67% agreed that they frequently recommended WBAAS to their peers.

Table 3. Statement wise distribution of farmers on attitude towards Weather Based Agro Advisory Services

SA - Strongly Agree; A - Agree; UD - Undecided; DA - Disagree; SDA - Strongly Disagree

Values given in parenthesis () indicates percentage of total

 Additionally, 34.00% of the farmers agreed strongly and 32.33% agreed that the use of WBAAS is linked to higher yields of crops, while 36.67% strongly agreed and 28.33% agreed that it enhances crop productivity. Apart from this, 39.67% strongly agree that advisories help in ensuring proper use of fertilizers, whereas 62.00% strongly agree that advisories could help reduce the number of pests and diseases. The results indicated that 34.33% of the farmers strongly agreed and 28.67% agreed that social interactions among farmers improved as they shared knowledge gained through WBAAS. Approximately, 37.67% of the farmers strongly agreed and 28.33% agreed that there is recognition of the importance of weather advisory services in addressing the impact of climate change. The results revealed that 38.33% of the farmers strongly agreed and 25.00% agreed that the adoption of WBAAS has reduced their reliance on extension agents for weather-related information.

 The results also revealed that 41.67% of the farmers strongly agreed and 29.67% agreed that their income improved through the use of weather advisory information. Similarly, 40.67% strongly agreed that advisory services promote the use of advanced agricultural technologies through innovation.  Awareness of WBAAS among farmers had significantly increased with 35.00% of the farmers strongly agreed and 23.33% agreed. The findings showed that 36.00% strongly agreed while 30.00% agreed that WBAAS contributed towards sustainable agricultural activities. Furthermore, 40.67% of the farmers strongly agreed and 26.00% agreed that the introduction of WBAAS has contributed to the increase in adoption of innovative agricultural technology. Additionally, 37.00% strongly agreed that the forecasting of temperature aided them in taking necessary precautions for extreme weather occurrences. The results revealed that 52.67% of the farmers strongly agreed and 22.33% agreed that they modified their farming practices based on weather advisory information.

 A majority of farmers 59.00% were found to have a favorable attitude, followed by 27.00% exhibiting a highly favorable attitude while only 14.00% of the respondents fell under the unfavorable category. The high proportion of farmers in the favorable and highly favorable categories (a combined 86.00%) clearly indicates a strong acceptance and positive perception of WBAAS.

DISCUSSION

 These findings may be due to the respondent’s positive perception of WBAAS and their notion that such advisories are beneficial for decision-making related to farming. Farmers seemed to believe that these weather-based advisories could assist them in mitigating risks associated with weather, making optimal utilization of farm inputs and crop management practices. Such positive perceptions may be because of the increasing understanding about the usefulness of such weather-based advisory services in agriculture (Suckall and Soares, 2022; Nepal et al., 2024). The positive attitude is also because of the farmers increased faith in the value and significance of weather based advisory systems amid the rising unpredictability of the climatic conditions (Elias et al., 2019). Regular exposure to such WBAAS through extension agencies and digital communication channels may be another factor for developing positive perceptions about such services. On the contrary, the relatively low number of respondents with negative attitudes indicates that there were still some farmers who expressed their concerns about the accuracy, reliability and comprehensibility of the weather advisory service (Guido et al., 2021). Only a small proportion of farmers expressed doubts about the usefulness of the service and also few farmers considered advisories as being hard to match with the farming schedule and unsuitable recommendations (Raja and Naika, 2023).

 The predominance of favorable and highly favorable attitudes indicates that WBAAS have gained considerable acceptance among farmers as a reliable source of information for farm decision-making. This positive attitude may be attributed to the increasing awareness of the usefulness of weather advisories in minimizing weather-related production risks, improving resource-use efficiency, enhancing crop productivity and supporting climate-resilient agriculture (Zuma-Netshiukhwi et al., 2016; Singh et al., 2018; Kumar et al., 2022; Lakshmi et al., 2023). Regular dissemination of timely, location-specific advisories through extension agencies, mobile applications and other digital communication platforms may have further strengthened farmers trust in WBAAS (Wehde and Nowlin, 2023). Although only a small proportion of respondents exhibited an unfavorable attitude their concerns may be associated with perceived limitations in the accuracy, reliability and practical applicability of the advisory information. Furthermore, the higher proportion of farmers with favorable rather than highly favorable attitudes suggests that there is scope to enhance farmers confidence and sustained utilization of WBAAS through improved advisory quality, targeted awareness campaigns, capacity-building programmes and strengthened extension support (Vedeld et al., 2020; Rohit et al., 2024; Ojo et al., 2025). The educational status influenced farmers attitudes with better-educated farmers exhibiting a more favorable attitude towards WBAAS than less-educated farmers.

 Overall, the WBAAS has achieved a considerable level of acceptance among farmers but targeted interventions are required to enhance usability and inclusiveness thereby promoting wider adoption and maximizing its impact on agricultural sustainability and climate resilience.

Conclusion: This study has evaluated the farmers attitudes towards WBAAS through a standardized and validated scale among the farming community in the Western Zone of Tamil Nadu. According to the findings obtained from this evaluation, it was observed that most of the farmers had very favourable attitudes towards the WBAAS, which signifies that they recognize the utility of these services in farm management and risk mitigation due to adverse weather conditions. However, a few of the farmers had unfavourable attitudes towards the services, mainly because of forecast errors and inaccessibility of relevant information about them.

Acknowledgements: The authors express their deepest gratitude for the contributing authors in the research and faculties from the Department of Agricultural Extension and Rural Sociology for their direct and indirect guidance and support throughout this research and special thanks to the Chairman and advisory members for their valuable feedback and constructive suggestions on the improvement of the manuscript.

Authors’ contributions: JV was responsible for the conceptualization and formation of the original manuscript. AM contributed through conceptualization and supervision. NDM focused on idea development and reviewing. PSR was involved in the collection of the data. AM and KS provided handled writing and editing tasks.

Funding source: There was no financial support for this work from any funding agency in the public, commercial or non-profit sectors. No financial support was sought in carrying out the research.

Conflict of interest: Authors do not have any conflict of interests to declare.

Ethical issues: None

REFERENCES

Akour, M.M., J.K. Damra, T.M. Al Ali, S.M. Ghaith, T.A. Ghbari and N.A. Shammout (2024). Validation of the revised scale of students’ attitudes towards research. Stud. Higher Educ. 49(1): 33–46. https://doi.org/10.1080/03075079.2023.2220700

Bardhan, T., N. Bhardwaj, S.K. Kashyap, V.L.V. Kameswari, G.S. Kuswaha and A. Dey (2023). Development of multi-dimensional scale to measure attitude of farmers towards conservation agricultural practices. Indian J. Ext. Educ. 59(1): 127–130. http://doi.org/10.48165/IJEE.2023.59126

Bhati, K., M. Patel and R.D. Pandya (2023). Attitude of farm families towards gender equity: Development and validation of a scale. Indian J. Ext. Educ. 59(2): 121–123. http://doi.org/10.48165/IJEE.2023.59226

Desselle, S.P. (2005). Construction, implementation and analysis of summated rating attitude scales. Am. J. Pharm. Educ. 69(5): 1–11.

Dheebakaran, G., S. Panneerselvam, V. Geethalakshmi and S. Kokilavani (2019). Weather based automated agro advisories: An option to improve sustainability in farming under climate and weather vagaries. Global Climate Change and Environmental Policy: Agriculture Perspectives. Springer, Singapore. 329–349. https://doi.org/10.1007/978-981-13-9570-3_11

Doss, D.A. and M. Asokhan (2024). Awareness on weather based agro-advisory services among farmers of Tamil Nadu, India. Int. J. Environ. Clim. Change. 14(3): 177–182. https://doi.org/10.9734/IJECC/2024/v14i34030

Edwards, A.L. (1957). Techniques of Attitude Scale Construction. Appleton-Century-Crofts; New York. 149–160 p.

Elias, E.H., R. Flynn, O.J. Idowu, J. Reyes, S. Sanogo, B.J. Schutte, R. Smith, C. Steele and C. Sutherland (2019). Crop vulnerability to weather and climate risk: Analysis of interacting systems and adaptation efficacy for sustainable crop production. Sustainability. 11(23): 1-25. https://doi.org/10.3390/su11236619

FAO (2010). Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). Food and Agriculture Organization of the United Nations, Rome, Italy.

Fishman, J., C. Yang and D. Mandell (2021). Attitude theory and measurement in implementation science: A secondary review of empirical studies and opportunities for advancement. Implement. Sci. 16(87): 1-11. https://doi.org/10.1186/s13012-021-01153-9

Gangopadhyay, P.K., A. Khatri-Chhetri, P.B. Shirsath and P.K. Aggarwal (2019). Spatial targeting of ICT-based weather and agro-advisory services for climate risk management in agriculture. Clim. Change. 154(1): 241–256. https://doi.org/10.1007/s10584-019-02426-5

Guido, Z., S. Lopus, K. Waldman, C. Hannah, A. Zimmer, N. Krell, C. Knudson, L. Estes, K. Caylor and T. Evans (2021). Perceived links between climate change and weather forecast accuracy: New barriers to tools for agricultural decision-making. Clim. Change. 168(1): 1-20. https://doi.org/10.1007/s10584-021-03207-9

Harrison, M.T., B.R. Cullen and R.P. Rawnsley (2016). Modelling the sensitivity of agricultural systems to climate change and extreme climatic events. Agric. Syst. 148: 135–148. https://doi.org/10.1016/j.agsy.2016.07.006

Hatfield, J.L., J. Antle, K.A. Garrett, R.C. Izaurralde, T. Mader, E. Marshall, M. Nearing, G.P. Robertson and L. Ziska (2020). Indicators of climate change in agricultural systems. Clim. Change. 163(4): 1719–1732. https://doi.org/10.1007/s10584-018-2222-2

Hussain, S., S. Tehrem and A. Tanveer (2025). Climate change adaptation and farmers' resilience in Pakistan: Adaptation and resilience in agriculture. J. Agric. Policy Transform. 1(1): 10-18. https://doi.org/10.66529/agripat.2025.1.1.10

Kothari, C.R. (2004). Research Methodology: Methods and Techniques. 2^nd Ed. New Age International Publishers; New Delhi. 401 p.

Kumar, A., J. Malik and M. Kamboj (2022). Relevance of extension advisory services in climate smart agriculture: A review. Mausam. 73(3): 695–704. https://doi.org/10.54302/mausam.v73i3.5937

Kumar, A., J.S. Manhas and B. Lal (2025). A scale to measure perception of farmers towards weather based agro-advisory services. Guj. J. Ext. Educ. 39(2): 151–156. https://doi.org/10.56572/gjoee.2025.39.2.0025

Kumar, P., J. Tokas, N. Kumar, M. Lal and H.R. Singal (2018). Climate change consequences and its impact on agriculture and food security. Int. J. Chem. Stud. 6(6): 124–133.

Kumar, R., P.S. Slathia, R. Peshin and M.S. Nain (2015). Development of scale to measure attitude of farmers towards rapeseed mustard crop. J. Community Mobil. Sustain. Dev. 10(2): 221–224.

Lakshmi, G.D., G. Sreedhar and M. Muninarayanappa (2023). Perception and adoption of weather based agro advisory services by farmers in scarce rainfall zone of Andhra Pradesh. Pharma Innov. J. 12(2): 1808–1814.

Likert, R. (1932). A technique for the measurement of attitudes. Arch. Psychol. 22(140): 1–55.

Mallick, S., R.R. Burman, R.N. Padaria, G.S. Mahra, K. Aditya, K. Shekhawat, Satyapriya, S. Paul, S. Sahu, S. Bishnoi, R. Singh, K.K. Manjunath, S. Saini and S. Mukherjee (2025). Exploring farmers' psychological perspectives on multimedia-based agro-advisory services. Sci. Rep. 15(1): 1-14. https://doi.org/10.1038/s41598-025-92936-3

Mohanraj, K., R. Rajasekaran, A. Jeevapriya, T. Vinotha, K. Prakash, M.N. Kumar and P. Anbarasan (2024). Attitude of farmers towards in-situ water conservation in arid regions of Tamil Nadu. Indian J. Ext. Educ. 60(3): 65–71. https://doi.org/10.48165/IJEE.2024.60313

Mukherjee, A., P. Singh, S.S. Rakshit and R.R. Burman (2018). Development and standardization of scale to measure farmer's attitude towards farmers' producer company. Indian J. Ext. Educ. 54(4): 84–90.

Nepal, M., M. Ashfaq, B.R. Sharma, M.S. Shrestha, V.R. Khadgi and M. Bruno Soares (2024). Impact of weather and climate advisories on agricultural outcomes in Pakistan. Sci. Rep. 14(1): 1036. https://doi.org/10.1038/s41598-023-51066-4

Ojo, I.E., A.E. Kolawole, A.G. Adeyonu, A.O. Owolabi, D. Akerele, T.E. Awe, I. Chike, D.P. Ogunsuyi and A.A. Ogundele (2025). Extension agents' attitudes and participation in disseminating climate-smart agricultural practices in North-Central, Nigeria. Front. Nutr. 12: 1-9. https://doi.org/10.3389/fnut.2025.1663720

Paliwal, H., A.K. Singh, S. Kumar, P. Singh and J. Kumari (2026). Scale to measure farmers' attitude towards ICT-based agro-advisory services. Indian J. Ext. Educ. 62(1): 166–170. https://doi.org/10.48165/IJEE.2026.621RT05

Peterson, R.C. and L.L. Thurstone (1932). The Effect of Motion Pictures on the Social Attitudes of High School Children. Edwards Brothers; Ann Arbor. Michigan, USA. 69 p.

Rahman, N.M.F., M.C. Rahman, M.A. Baten, M.I. Hossain, S.M.Q. Hassan, R. Ahmed, M.M. Hossain, A.B.M.Z. Hossain, M.A. Aziz, M.M. Haque, T. Halder, M.A.A. Mamun, M.K.A. Bhuiyan, M.A.I. Khan, A. Chowdhury, M.A. Qayum, M.A.R. Sarkar, M.U. Salam and M.S. Kabir (2021). Weather forecast based rice advisory services in Bangladesh. Bangladesh Rice J. 25(1): 51–74. https://doi.org/10.3329/brj.v25i1.55179

Raja, R.P. and K.V. Naika (2023). Scale development to measure attitude of farmers towards Kisan Call Centre advisory services. Mysore J. Agric. Sci. 57(1): 280–286.

Ray, G.L. and S. Mondal (2011). Research Methods in Social Sciences and Extension Education. 3rd Rev. and Enlarged Ed. Kalyani Publishers; New Delhi. 330 p.

Reid, N. (2006). Thoughts on attitude measurement. Res. Sci. Technol. Educ. 24(1): 3–27. https://doi.org/10.1080/02635140500485332

Robbins, S.P. and T. Judge (2009). Organizational Behavior. 2nd Ed. Pearson Education; South Africa. 152 p.

Rohit, J., S.K. Bal, C.N. Anshida Beevi, K. Nagasree, K. Ravi Shankar, G. Nirmala, Pushpanjali, J. Samuel, V.G. Veni, R.N. Kumar, P.K. Pankaj, M. Prabhakar and V.K. Singh (2024). Exploring farmers' communication pattern and satisfaction regarding the adoption of Agromet advisory services in semi-arid regions of southern India. Front. Sustain. Food Syst. 7: 1-18. https://doi.org/10.3389/fsufs.2023.1284880

Rohwer, D. (2022). Designing ex post facto and experimental studies. In: Inquiry in Music Education. Routledge. 230–252. https://doi.org/10.4324/9781003057703

Sarkar, N.C., K. Mondal, A. Das, A. Mukherjee, S. Mandal, S. Ghosh, B. Bhattacharya, R. Lawes and S. Huda (2023). Enhancing livelihoods in farming communities through super-resolution agromet advisories using advanced digital agriculture technologies. J. Agrometeorol. 25(1): 68–78. https://doi.org/10.54386/jam.v25i1.2080

Singh, K.K., A.K. Baxla, P. Singh and P.K. Singh (2018). Weather based information on risk management in agriculture. In: Climate Change and Agriculture in India: Impact and Adaptation. Springer International Publishing, Cham. 207–216. https://doi.org/10.1007/978-3-319-90086-5_16

Srishailam, B., V. Sailaja, P.G. Kumar, D. Subramanyam, S.N. Umar and A. Sreeniva (2023). A scale to measure the attitude of cotton growers towards weather based agro-advisory services in Telangana and Andhra Pradesh States. Int. J. Environ. Clim. Change 13(6): 454–460. https://doi.org/10.9734/IJECC/2023/v13i61843

Suckall, N. and M.B. Soares (2022). Evaluating the benefits of weather and climate services in South Asia: A systematic review. Reg. Environ. Change 22(3): 104. https://doi.org/10.1007/s10113-022-01947-7

Thurstone, L.L. (1929). Theory of attitude measurement. Psychol. Rev. 36(3): 222–241. https://doi.org/10.1037/h0070922

Vedeld, T., H. Hofstad, M. Mathur, P. Büker and F. Stordal (2020). Reaching out? Governing weather and climate services (WCS) for farmers. Environ. Sci. Policy 104: 208–216. https://doi.org/10.1016/j.envsci.2019.11.010

Vijai, C., W. Worakamol and M. Elayaraja (2023). Climate change and its impact on agriculture. Int. J. Agric. Sci. Vet. Med. 11(4): 1–8. https://doi.org/10.25303/1104ijasvm0108

Wehde, W. and M. Nowlin (2023). Determining patterns of routine weather information usage and their demographic determinants. Weather Clim. Soc. 15(3): 711–725. https://doi.org/10.1175/WCAS-D-22-0106.1

Zuma-Netshiukhwi, G.N., K.C. Stigter and S. Walker (2016). Improving agricultural decision-making using weather and climate information for farmers, south-western Free State, South Africa. Net J. Agric. Sci. 4(4): 67–77.