Influence of landholding size on the adoption of climate resilient technologies and agro-advisory services

Introduction

The problem of human induced climate change caught the attention of the scientists and policy makers for the first time when Inter Governmental Panel on Climate Change (IPCC) was established in 1988 [8] . IPCC defined climate in a narrow sense as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period ranging from months to thousands or millions of years. The classical period is 30 years, as defined by the World Meteorological Organization (WMO). Climate change is any change in climate over time that is attributed directly or indirectly to human activity that alters the composition of the global atmosphere in addition to natural climate variability observed over comparable time periods (IPCC, 2007) [8] . Since climatic factors serve as direct inputs to agriculture, any change in climatic factors is bound to have a significant impact on crop yields. Studies have shown a significant effect of climate change on the average crop yield (Dinar et al. 1998) [7] . According to the IPCC, the greenhouse gas emission could cause an average global temperature to rise by another 1. [2,3] , economic decline, and threats to human health, often leading to grave consequences in urban areas (Debone et al. 2020) [6] . It is one of the most significant contemporary challenges faced by ecosystems and urban societies (Cortekar et al. 2016; Pachauri and Meyer, 2014) [5,11] . Any shift in agriculture can affect the food system to some extent, and thus lead to the vulnerability of resource-poor farmers. There is a need to understand the possible coping strategies adopted by different sections and categories of farmers to deal with global climatic change. Nyong et al (2019) [10] revealed that small-scale farmers in Cameroon faced mostly negative impacts of climate variability and change. Saddam (2019) [12] opined that the perception of most of the farmers is associated with gender, size of the land holding, education, occupation, extension contacts and farming experience in the Barak valley zone of Assam. Latha et al. (2012) [9] reported that small and medium rainfed farmers were highly vulnerable to climate change www.extensionjournal.com and therefore adopted to a large extent the coping mechanisms for climate change when compared to large farmers. The present study was taken with an objective to evaluate the adoption of climate resilient practices and adoption of weather based agro advisory services by different category of farmers based on the land holding size.

Methodology

The erstwhile Kurnool and Anantapur districts were selected for the study as the NICRA project was being implemented since 2010-11. The NICRA project was implemented in six villages from the erstwhile Kurnool and Anantapur districts of Andhra Pradesh. All these six project villages were purposively chosen as the sample for the study, viz., Yagantipalle, Meerapuram and Cherlokothuru, Chamalur, Chakaraipeta and Peravali. These villages fall under the purview of three revenue mandals namely Banaganapalle, Singanamalla and Narpala, which belonged to two revenue divisions called Nandyal and Anantapur. The list of all farmers from the above six villages who have participated in the implementation of the NICRA project was prepared and following the n th number random sampling technique method, 30 farmers were selected as the sample from each village for the study. The sample size was fixed at 30 from each project village because 30 is considered a large sample in Statistics, and the findings based on a large sample would be valid and reliable and facilitate inter-district comparison of specific parameters. Thus, 90 sample farmers each were chosen from the erstwhile Kurnool and Anantapur districts, together constituting a total of 180 sample farmers for the study. The sampling framework adopted for the study is shown in table 1. Both Primary and Secondary data constitute the sources of data for the study. Primary data was collected from the sample farmers. Secondary data was collected from the annual reports of the NICRA project from Anantapur and Kurnool KVKs, the ATARI zone 10 website, the NICRA website, the ICAR website, various journals in the field of agricultural sciences, social sciences, books, magazines, and other published articles in leading newspapers etc. Based on the objectives of the study, a semi-structured interview schedule was prepared for the study. The interview schedule was pretested with 30 samples and made modifications for more accurate data. With the finalized interview schedule the data was personally collected by interviewing the sample farmers who acted as the respondents for the study. The farmers were contacted at their residences or field. The data collected from the respondents was scored, tabulated, and analysed using statistical tools and techniques such as percentage mean, standard deviation, chi-square test, correlation, ANOVA and multiple regression with the help of a computer using SPSS (statistical package for social sciences). Based on the objectives of the study, variables were identified with the help of experts and based on the available literature. Independent variables like Age, Education, Farming experience, and Farm size were classified into certain categories. Dependent variables like Perception, Adoption, Timeliness of messages, Ease of understanding of the message, Extent of adoption of WBAAS were measured on a three-point continuum scale (0-2). Dependent variables such as Quality of information, Utility of information and Satisfaction with WBAAS were measured on a five-point continuum Scale (0-4).

Land holing size

The distribution of sample farmers by landholding size group is presented in table 2. It may be seen that in both the study districts taken together, about 34 per cent were small farmers and 15 per cent marginal farmers. The medium farmers constituted about 36 per cent and big farmers 16 per cent. Thus, about 49 per cent of the sample farmers belonged to the category of small and marginal farmers, while 51 per cent belonged to the category of medium and big farmers. The proportion of small and marginal farmers was substantially higher in Kurnool district (71 per cent) as compared to Anantapur district (27 per cent). On the other hand, the proportion of medium and big farmers was substantially higher in Anantapur district (73 per cent) when compared to Kurnool district (29 per cent). The chi-square value indicates a strong association between landholding size group and study district among the sample farmers, significant at 1 per cent probability level. The above results could be perceived that majority of overall project farmers are medium and small. Majority of Kurnool farmers are small and majority of the Anantapur farmers are medium. Due to sub-division of land holdings from generation to another generation lead to declining the land holding size of individual farmer in the country. The same results were given by Archana (2017) [1] and Chinnam Naidu (2012) [4] .

Variations in the Adoption of Climate Resilient

Technologies and WBAAS across the Landholding size groups It is also important to examine whether there were any significant variations across the landholding size groups in the adoption of CRTs and WBAAS by the sample farmers. The results of ANOVA F test are presented in table 3. It could be noted that the variation across the landholding size groups was significant at 1 per cent and 5 per cent probability level, respectively, in respect of mass media exposure and capacity building programmes attended by the sample farmers; the medium and big farmers had a higher mean score than the small and marginal farmers in these two parameters. On the other hand, there was no significant variation in the extension contact of sample farmers across the landholding size groups. Further, the variation in the perception of sample farmers on climate change was significant at 5 per cent probability level across the landholding size groups, with the medium and big farmers faring better than the small and marginal farmers, whereas there was no significant variation in the perception of sample farmers on CRTs across the landholding size groups. Also, the variation in the adoption of CRTs by the sample farmers across landholding size groups was significant at 1 per cent probability level, with the small farmers faring better than the medium, big, and marginal farmers. In respect of different dimensions of WBAAS, the variations across the landholding size groups were significant at 1 per cent probability level in the case of opinion of sample farmers on quality of information and advisory services and www.extensionjournal.com at 5 per cent probability level in the case of ease of understanding of information. On the other hand, there was no significant variation in the opinion of sample farmers on timeliness and utility of information across the landholding size groups. Finally, there was a significant variation at 10 per cent probability level in the adoption of WBAAS by the sample farmers across the landholding size groups, with the medium and big farmers faring better than the small and marginal farmers.

Conclusion

Thus, it becomes clear from the above analysis that there were significant variations in the adoption of CRTs and WBAAS by the sample farmers across the landholding size groups. While small farmers fared better than the medium, big and marginal farmers in the adoption of CRTs, the medium and big farmers fared better than the small and marginal farmers in the adoption of WBAAS. As such, there is a need to focus on small and marginal farmers to improve their levels of adoption of WBAAS. farmers. 4. Institutional mechanism should be strongly established at the village level like seed bank, fodder bank, custom hiring centers, purchasing centers etc so that the technologies will be adopted by all categories of farmers. 5. Promotion of small and marginal farmers with incentives and support system of government for the produce by way of exports and value addition. All the farmers should be digitally connected and agroadvisory services should be regional specific and should reach every farmer.

Suggestions for reaching the CRTs and WBAAS to all

References

1. Rajendranagar (IN): PJTSAU; 2017.
2. Behailu G, Ayal DY, Zeleke TT, Ture K, Bantider A. Comparative analysis of meteorological records of climate variability and farmers' perceptions in Sekota Woreda, Ethiopia. Clim Serv. 2021;23:100239. https://doi.org/10.1016/j.cliser.2021.100239
3. Change AC, Views P, Empirical C. Urban climate: Are climate change, urbanisation and political views correlated? Empirical evidence from South-East Queensland. Urban Clim. 2022;41:101061. https://doi.org/10.1016/j.uclim.2021.101061
4. Hyderabad (IN): Acharya N.G. Ranga Agricultural University; 2012.
5. Cortekar J, Bender S, Brune M, Groth M. Why climate change adaptation in cities needs customised and flexible climate services. Clim Serv. 2016;4:42-51. https://doi.org/10.1016/j.cliser.2016.11.002
6. Debone D, Leiriao LFL, Miraglia SGEK. Air quality and health impact assessment of a truckers' strike in São Paulo state, Brazil: A case study. Urban Clim. 2020;34:100687. https://doi.org/10.1016/j.uclim.2020.100687
7. Dinar A, Mendelson R, Evenson J, Parikh A, Sanghi A, Kumar K, et al. Measuring the impact of climate change on Indian agriculture. World Bank Technical Paper. 1998;402. Washington (DC): World Bank.
8. Intergovernmental Panel on Climate Change (IPCC). Climate change 2007: Synthesis report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the IPCC. Geneva (CH): IPCC; 2008.
9. Latha Asha KV, Gopinath M, Bhat ARS. Impact of climate change on rainfed agriculture in India: A case study of Dharwad. Int J Environ Sci Dev. 2012;3(4):366-70.
10. Awazi NP, Tchamba MN, Avana TML. Climate change resiliency choices of small-scale farmers in Cameroon: Determinants and policy implications. J Environ Manag. 2019. https://doi.org/10.1016/j.jenvman.2019.109560
11. Pachauri K, Meyer A. Climate change 2014: Synthesis report. Environ Policy Collect. 2014;27(2):408. https://doi.org/10.1111/j.1728-4457.2001.00203
12. Majumder SH, Deka N, Mondal B, Bisen J, Barman U. Farmers' perception to climate change in Barak Valley Zone of Assam: An empirical study. Indian J Ext Educ. 2019;55(4):97-101.