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The main raw material for manufacture of paper is wood pulp which are mainly obtained from the pulpwood trees like Casuarina, Eucalyptus and Meliadubia. In Tamil Nadu, the cultivation of these pulpwood trees was promoted by two leading paper manufacturing industries viz., Tamil Nadu Newsprint and Paper Limited (TNPL) and Seshasayee Paper and Boards Limited (SPB).This study aims at estimating the cost, returns and financial feasibility of major pulpwood tree species cultivated in Tamil Nadu. The study found that the cost of cultivation of casuarina, eucalyptus and Meliadubia were Rs.1,76,939, Rs.1,60,593 and Rs.1,52,672 and the net income obtained from these trees were Rs.1,58,473, Rs.1,37,028 and Rs.1,33,831 respectively. Among these trees, Casuarina is more profitable and the results of the study concluded that the cultivation of these three major pulpwood trees are profitable and financially viable.

Cotton is a high commercial profile crop serving as a lifebelt crop for millions. It is a predominant crop under cultivation in a large spectrum since the ancient period. Globally it is a significant income earning crop hence aptly termed as White Gold. India is a leading cotton producer, exporter, and consumer in the world. It has surpassed China’s cotton production in 2021 and stands first in world cotton production. But India’s cotton productivity is 466 kg/ha very low than the global average cotton productivity; ranking 42nd in world productivity. Cotton is cultivated in an area of 12.35 million hectares with a production of 5.79 million metric tonnes. The present study aims in estimating the growth rate and the factors influencing cotton acreage and production in India for the last four decades of 1981-82 to 2020-21. The compound growth rate was examined using exponential growth function and instability by Cuddy Della Valle instability index in three different periods i.e Period I 1981-82 to 2001-02 (Pre introduction of Bt), Period II 2002-02 to 2020-21(Post introduction of Bt) and Overall period (1981-82 to 2020-21).The data used in current study is secondary in nature and collected from sources namely Directorate of Economics and statistics, India stat and from FAO. The acreage and supply response was estimated using the Nerlovian double-lagged adjustment model. The findings revealed that a higher growth rate and instability have been witnessed after the introduction of Bt technology in India which symbolizes the magnanimous role of Bt technology in the Indian cotton sector. The responsiveness of cotton area towards the lag cotton area, cotton MSP, rainfall and lagged export price stands significant whereas cotton supply response is highly triggered by lagged cotton production, rainfall and Bt technology.

Rainfed agriculture covers 80 percent of the World’s cropland and produces about 60 percent of the World’s food grains. In India, 55 percent of gross sown area accounts 40 percent of food production. In Tamil Nadu, nearly 48 per cent of gross sown area is under rainfed cultivation which is highly vulnerable to climate change and therefore it requires appropriate climate adaptation strategies. The present study covering 180 sample households to examine the farmers perception on climate change and various adaptation responses practiced in rainfed agriculture in Tamil Nadu, Southern India. The farmers were requested to choose one or more response among the three options namely, change in rainfall pattern, change in temperature and decrease in ground water availability. The results of the analysis indicated that rainfall and ground water availability are major concerns due to climate change were perceive by 84 and 58 percent of farmers. The adaptation responses to climate change are examined and the farmers were requested to indicate the various adaptation strategies viz., changing the cropping pattern, drilling new bore wells, deepening existing wells, introducing water saving irrigation methods and reducing the number of irrigations etc. The change in rainfall and temperature perceived by farmers were followed the reduction in number of irrigations, change in cropping pattern, advancement or delaying of cropping seasons and growing rain-fed crops are the major adaptation strategies to reduce the impact of climate change.

Dairying has been an important auxiliary enterprise and regular source of income to the farmers. Indian dairy industry has grown commendably with the seven-fold rise in milk output since independence; however, the productivity of the milch animals was relatively low owing to inadequate fodder and inefficient input management. This study investigates the factors influencing milk production under enhanced fodder production in Tamil Nadu using the two-stage least square method. Primary data were obtained from 407 dairy farm households during the year 2021-22. Two models using single-cut and multi-cut fodder were employed to assess the factors determining milk production in dairy farms. The findings of the first stage regression of both models show that fertilizers, seeds, area under fodder, herd size, machinery, farming experience and education were the determinants of the fodder production. In the second stage, factors that influence milk production were fodder, concentrates, herd size, dairy experience, and education. Efficient utilization of feed coupled with concentrates will enhance milk production. The adoption of multi-cut fodder will offer succulent nutrient-rich fodder throughout the year to the cattle, even in the lean season and as a result, it improves the productivity of milk in the dairy farms.

Dairy farming is the subsidiary occupation for millions of farmers in India. Due to risks and uncertainties in rainfed areas, crop production alone was not much remunerative. Diversifying dairy with the crop and allied activities would generate better income, nutritional security, and regular employment to the farming community and ensure risk reduction. This study investigates the extent and determinants of income diversification among dairy farm households in Tamil Nadu using the Simpson Index of Diversity (SID) and the Tobit regression model. Primary data were collected from dairy farm households during the year 2021-22. The results show that two-thirds of the total household income was shared by on-farm income and the remaining one-third by off-farm and non-farm activities to the total household income. Simpson Index of Diversity (0.38) indicated that the households were diversified with milch animals, but the degree of the diversification was low since high degree of diversification requires more labour and high cost. Further, education, family size, landholding size, herd size, proximity to agricultural or allied industry, access to credit, and membership in farmer producer organizations were the important determinants of income diversification. This study indicates that farm households should adopt a concentric approach that requires targeted research, information dissemination, infrastructure development, and agricultural technical institution establishments to boost income diversification and livelihood.