The study highlights the importance of integrating organic resources, such as vermicompost and biofertilizers with inorganic fertilizers to sustain coconut yields and manage costs. The experimental trial was conducted from 2016 to 2020 in a 47-year-old East Coast Tall coconut garden at Coconut Research Station in South India. The research evaluated four nutrient management treatments viz., T₁ (75% Recommended Dose of Fertilizer (RDF) + 25% N organic), T₂ (50% RDF + 50% N organic), T₃ (100% N organic) and a control (100% RDF). Intercrops included black pepper (Piper nigrum), banana (Musa acuminata) and cocoa (Theobroma cacao). Organic manure significantly improved soil physical properties, water retention and overall soil health. The T₂ treatment achieved the highest yields for coconut, cocoa, banana and black pepper. The added soil health parameters supported to these findings, with T₂ showing the highest fungal (15.27 × 10³ cfu/g of soil) and bacterial populations (17.25 × 10⁵ cfu/g of soil), along with a significant earthworm population (26/m²), indicating enhanced soil ecosystem health. Additionally, soil moisture content was highest under T₃ (100% organic) across various depths, followed by T₂, highlighting the critical role of organic matter in improving soil moisture conservation. The economic feasibility analysis, including a net present value (NPV), benefit/cost ratio (B/C ratio) and an internal rate of return (IRR), revealed T₂ to be the most economically viable nutrient management strategy. This study highlights the economic benefits of intercropping coconuts using an integrated nutrient management (INM) approach, demonstrating its superiority over traditional monocropping practices.

The paper has examined the nature of conservation technologies adopted by the farmers and has identified the factors affecting the investment on these technologies by selecting 50 farmers from each priority category of five watersheds (very high, high, medium, low and very low). The technical feasibility and economic viability of different conservation technologies have been studied using NPV, BCR and IRR. The conservation structures were constructed at two levels, viz. farm and community. Farm level adoption of conservation structures like staggered trench has accounted for 72 per cent. Carrot, potato and beans and perennial crops like tea have shown positive additional net returns for adopters compared with nonadopters of conservation technologies. In tea plantation, stone wall had the highest NPV, and in carrot, bench terrace had the highest IRR. The BCR, NPV and IRR for both annual and perennial crops have been found encouraging. Multinomial logit model has been used for identification of factors influencing the choice for soil conservation technologies by the farmers. The study has found that bench terrace was being adopted for annual crops and the variables, number of land parcels, educational level and on-farm income, significantly influence the adoption rate.

The study was conducted in the Nilgiris district of Tamil Nadu, where the problems of soil erosion were extensive. Multistage purposive sampling method was used to examine the financial feasibility and the factors influencing the investment of soil conservation technologies adopted by the farmers. In tea plantation, stone wall had the highest NPV of Rs.74335. The bench terrace had the highest Internal Rate of Return IRR of 34 % for carrot. The Net Present Value (NPV), Benefit Cost Ratio (BCR) and Internal Rate of Return (IRR) for both annual and perennial crops were found to be encouraging. In farmer’s investment analysis, change in one standard deviation in farm size leads to change in investment of Rs.9022 on soil conservation investment. Farmers’ education increasing the likelihood of investment in soil conservation. High cost of conservation technologies was the important constraints to farmers to adopt the soil conservation technologies.

This study explores the use of Artificial Neural Networks (ANN) and Markov Chain models to forecast India’s black tea exports to major international markets. While the Markov Chain approach provided a simplified, state-based view of export behaviour, the ANN models were designed to capture continuous patterns and subtle market dynamics. Forecasts were generated for the next five years across ten key importing countries. Model performance was assessed using standard evaluation metrics- Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and R-squared (R²). The results clearly favoured the ANN model, which consistently delivered more accurate and reliable forecasts. Notably, the United Arab Emirates (UAE) emerged as the top growth market, with predicted export values from India reaching ₹1.23 lakhs by the fifth year. Russia and the USA also showed strong forecasted demand, with expected values of ₹64767 lakhs and ₹45238 lakhs, respectively. These insights offer practical value for exporters, traders and policymakers by highlighting priority markets and supporting more informed decision-making. Overall, this research reinforces the importance of intelligent forecasting systems in managing the complexity of international tea trade.