Comparative livelihood vulnerability to climate change: Tribal and non-tribal farmers in Surajpur district of Chhattisgarh

Introduction

India has the largest proportion of tribals in the world, next to Africa. India is home to a wide variety of distinct tribal communities. The year 2011 Census found that 10.45 crore people, or 8.6% of the overall population, are Scheduled Tribes (ST) and Over 705 different groups make up these communities, whereby about 90 percent of the communities that live in and around forest areas, occupying about 19 percent of the land of India (A. K. Singh, 2008). In Chhattisgarh, tribal populations are concentrated in the northern hills and Bastar plateau, where forests cover over 44% of the state. The population includes STs (37%), SCs (22%), and OBCs (14%), with a density below 132 persons/km². Agriculture is the main livelihood for over 85% of people, though productivity is low, irrigation covers less than 30% of cropped area, and rice dominates 75% of cultivation. While both tribal and non-tribal communities depend on agriculture, tribes also rely heavily on forest produce, whereas non-tribes engage in wage labour. Despite numerous development programs, outcomes for tribal communities remain unsatisfactory, highlighting the need to assess gaps in livelihoods and promote equity and sustainable development (Khan and Chauhan 2020) [10] . The Many tribe members work as cultivators or agricultural labourers, making up a sizable share of the workforce in the primary sector, which is mainly agriculture. Tribes are now more represented in regular government occupations because to reservation rules, although they are still less prevalent than other groups in high-production industries like manufacturing and services. In order to augment their income, some tribal communities also work in traditional activities including hunting, gathering, and hill agriculture. Tribal employment in non-farm industries, such as lowpaying temporary positions in the public sector and construction, is on the rise. However, this trend is frequently associated with reservation policies, implying that, in the absence of affirmative action, access to higher-paying positions may be restricted (Subhi). The entrepreneurship skills of tribal women in Bastar, Chhattisgarh, where forests cover 75.54% of the area. Women, who form 80% of NTFP gatherers, contribute the largest share (65.62%) of household income from NTFPs through collection and processing, though men dominate marketing. Processing adds higher profits (53.79%) than selling raw produce, with Kalmegh yielding the highest margin (65.40%). However, women's share in the consumer rupee remains low (53-54%), which could be enhanced to 85-95% through training, market access, and sustainable harvesting practices (Acharya et. al., 2017) [2] . The tribal and non-tribal districts of Chhattisgarh showsed higher diversification in tribal areas compared to non-tribal, though Surguja and Balrampur exhibited greater specialization. Overall, diversification indices across most crop groups remained low. That despite abundant natural www.extensionjournal.com resources, farmers' economic progress is limited due to poor infrastructure, erratic rainfall, and lack of technological support. Suggested that improving irrigation, quality inputs, and technology dissemination to promote diversification into high-value crops and enhance farm incomes (Kaushal and Jain 2023) [9] .

Objectives

Methodology

Analysis

The following tools and techniques were employed, and an analysis has been carried out in simple average, probability packages, and MS Excel sheet for the study. Socioeconomics and demographical feature was calculated by using simple arithmetic averages and percentages. Income, employment and consumption were examined through regression analysis and using simple arithmetic averages and percentages.

Income

The analysis of data is done keeping in view the objectives of the study. It is proposed to use both tabular and statistical methods for analysis. The following functional analyses were carried out to know the factors influencing income of selected samples by using the following multiple linear regression equations. Garretts ranking method was used to investigate advantages, preferences, and changes of constraint orders into numerical scores. The constraints are arranged according to their severity from the respondent's point of view, which is the primary advantage of this method over conventional frequency distribution.

Percent position = 100 (Rij -0.5)/ Nj)

• Rij = Rank given for ith constraint by jth respondents • Nj = Number of constraints ranked by jth respondents

Results and Discussion

An overview of the socioeconomic standing of selected respondents, land holdings, cropping methods, irrigation, and family size was provided by the results. It also looked at the cost and returns, resource use efficiency, and major obstacles.

Comparative employment pattern under tribal and nontribal farmers by using model

The regression analysis of employment functions shows that for tribal farmers; the model has very high explanatory power with an R-square of 0.96. Non-farm employment emerges as the strongest positive determinant with a coefficient followed by livestock, service and business activities. In contrast, working expenditure shows a significant negative effect on employment, while MANREGA participation also exerts a small but significant negative influence. Gross cropped area and family workers are not statistically significant in the tribal model. For non-tribal farmers, the model also fits well with an Rsquare of 0.89. Livestock was the most important factor while non-farm employment contributes positively but moderately. Service and business activities remain highly significant, confirming their role in generating employment. Similar to the tribal case, gross cropped area, working expenditure, and family workers are not significant. MANREGA participation has a negative coefficient with a marginal p-value, suggesting a near-significant substitution effect. www.extensionjournal.com The income pattern of tribal and non-tribal respondents by using model Table 5, presents the regression analysis of income determinants for tribal and non-tribal farmers, both models showed a strong fit with R-square values of 0.94 and 0.93 respectively. For tribal farmers, the intercept was significant, indicating a substantial base income. Gross cropped area (GCA) was the most important determinant, with a strong positive effect, showed that larger landholdings directly raise incomes. Family workers also contribute positively, while livestock adds further support. In contrast, working expenditure reduces income slightly but significantly, and employment (on-farm and non-farm) has a negative effect, suggesting that such work may be low-paying or substitute more productive sources. Participation in MANREGA and income from service and business are statistically insignificant, meaning they do not play a meaningful role in tribal household income.

For non-tribal farmers, the results are broadly similar but with some important differences. GCA has an even stronger impact on income, underlining the role of land in agricultural returns. Family workers remain significant with the same coefficient, while livestock also contributes positively p-value. Like in the tribal case, the employment variable shows a negative and significant effect on income, again pointing to low returns from such activities. Working expenditure does not significantly affect income for nontribal households. However, unlike tribal farmers, service and business income was statistically significant, reflecting its growing importance as a diversification source. MANREGA participation was again insignificant, showing little contribution to household income. The consumption patterns of tribal and non-tribal farmers Table 6, presented compares the consumption expenditure pattern of tribal and non-tribal farmers. It reveals that the total consumption expenditure of non-tribal farmers Rs. 2,34,782.85 which was higher than tribal farmers Rs. 1,88,556.33, indicating that non-tribal households enjoy a relatively higher standard of living and purchasing power. Tribal farmers spend 41.25 percent of their total expenditure on food items, whereas non-tribal farmers spend 34.39 percent, showing that tribal households allocate a larger share of income to food, reflecting their lower income levels and limited capacity for non-food expenditure. In terms of non-food items, non-tribal farmers spend Rs. 1,03,521.59 compared to Rs. 68,433.86 among tribal farmers. That was higher non-food expenditure among nontribal farmers suggests better access to education, healthcare, communication, and modern facilities. The share of other miscellaneous expenses was also similar for both groups 22.43% for tribal and 21.50% for non-tribal farmers respectively. Looking at specific food items, cereals account for the largest share among both groups, accounted with 9.63% of total spending for tribal and 8.69% for non-tribal farmers. Milk and meat consumption are relatively higher among tribal farmers 5.14% and 3.08% than among non-tribal farmers 3.23% and 1.92% respectively, likely due to their dependence on livestock and self-production. On the other hand, non-tribal farmers spend more on oil, ghee, and vegetables, reflecting greater dietary diversity and higher purchasing power. Overall, the data indicates a clear economic disparity between the both groups. Tribal farmers spend more on basic food necessities due to limited income, while nontribal farmers demonstrate a more diversified spending pattern, with higher expenditure on non-food and lifestylerelated items, reflecting better socio-economic conditions and living standards. 7, presents the regression results of consumption patterns for tribal and non-tribal farmers, both models showing an excellent fit with R-square values of 0.98. For tribal farmers, the intercept was statistically significant, indicating a strong base level of consumption. Food expenditure contributes positively, while non-food items also have a positive and significant effect. Input materials emerge as one of the most influential factors, and very high significance, highlighting their centrality in household consumption. Livestock expenditure makes a smaller but significant contribution. The strongest positive determinant, however, it was essential living expenses such as healthcare, education, and social obligations. By contrast, capital asset expenditure shows a negative and significant relationship, suggesting that investment in durable goods reduces regular household consumption in the short term.

For non-tribal farmers, the regression also explains 98 percent of the variation, but the pattern of significance differs. The intercept was not statistically significant, implying less importance of baseline consumption once variables are controlled. Food expenditure has a higher coefficient but it was statistically insignificant, indicating that food consumption levels were relatively stable and not a major driver of variation in total spending. Non-food items contribute only marginally but were statistically significant. Input materials remain a crucial determinant, while essential living expenses again shows the strongest effect, confirming their dominance in overall household consumption. Unlike in the tribal model, livestock and capital assets do not have a significant effect on non-tribal consumption, showing that these categories are less relevant for explaining differences in non-tribal household expenditure. 8, identifies the major constraints faced by respondents using Garrett ranking techniques, economic, social, and institutional challenges. The most severe issue was the erratic rainfall and changing climatic conditions, which received the highest average score followed by insecure land tenure and ownership issue as the second most critical problem, while limited access to education facility and inadequate health care facility at infrastructure ranked third, reflecting serious gaps in basic services. Limited economic opportunities and resource scored fourth. Seasonal employment opportunities and income source ranking sixth. The seventh constraint was weak implementation of government policies and programs, highlighting governance gaps. Interestingly, unpredictable weather patterns, though important, ranked last showing that respondents perceive economic hardship, institutional inefficiencies, and social disadvantages as more pressing constraints than climate variability. www.extensionjournal.com

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