Organic farming has emerged as a sustainable alternative to conventional farming, providing potential benefits in environmental conservation, crop quality, and market access. This study measures the impact of organic farming on agricultural productivity, resource utilisation, environmental sustainability, and trade competitiveness in Punjab, India. A comparative analysis of organic and conventional farming was conducted, focusing on key metrics such as crop yield per hectare, soil health, biological diversity, carbon footprint, and market access. As indicated by the research results, while conventional farming generated higher crop yield (e.g., the wheat crop: 4.47 vs. 3.82 t/ha, p = 0.034), organic farming improved in terms of resource utilisation, using less water (130.4 vs. 160.2 m3/t, p = 0.03) and energy consumption (225.6 vs. 270.8 kWh/t, p = 0.02). Organic farming was associated with better soil health (organic farming: 3.8% vs. 2.9%, p = 0.005) and superior biological diversity (species richness: 25 vs. 15, p = 0.008). Both findings were considered statistically significant. In terms of the marketplace, 68.2% of organic farming were able to access top marketplaces, whereas only 40.5% of conventional farming were (p = 0.01). By doing this, these individuals were able to negotiate higher costs (₹75.6 vs. ₹63.4/kg, p = 0.02). There is a clear result that organic farming products can be successful globally, as shown by the revealed comparative advantage (1.53).
Hayder M. Ali, G. Ananthakrishnan, Anusha Papasani et al.· Research on World Agricultur...· 0 citations
Accurate wind power forecasting is essential for the reliable operation and large-scale integration of renewable energy into modern power grids. This study develops and systematically evaluates a hybrid computational intelligence framework that integrates advanced machine learning models with nature-inspired optimization algorithms for wind power prediction. CatBoost (CAT), Long Short-Term Memory (LSTM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were optimized using Cuckoo Search Optimization (CSO) and the Stochastic Paint Optimizer (SPO) to determine the most effective model–optimizer configuration under variable wind conditions. A comparative analysis demonstrates that the CAT–SPO hybrid model achieved the best predictive performance, yielding a test RMSE of 0.0338 and an R² of 0.984, outperforming alternative configurations. Feature relevance analysis and multicollinearity assessment using the Variance Inflation Factor (VIF) identified hub-height wind speed (100 m) as the dominant predictor (32.5% relative importance; VIF ≈ 3.96), while lower-height wind speed (10 m) was excluded due to high collinearity. Wind gust measurements at 10 m retained substantial explanatory contribution (≈ 19.2% importance; VIF ≈ 4.34), highlighting the role of short-term atmospheric variability in power modeling. The proposed framework enhances forecasting reliability and supports improved grid stability, reserve allocation, renewable energy integration, and data-driven operational planning. These findings advance intelligent energy management systems and sustainable power grid engineering.
Tariq Alkhrissat, Fıras Abed, S. Aldulaimi et al.· Scientific Reports· 0 citations
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