The study investigates how local weather conditions influence price dynamics in Sri Lankan tea markets by creating a structured dataset from 105 weekly broker reports and regional weather data. Using Granger causality and tree‑based machine learning models, the authors find that market forces dominate but weather—especially precipitation and sunshine for Low Grown tea, and temperature for Off‑Grade and Dust—significantly affects prices with notable lag effects. Catalogue‑specific models, particularly LightGBM, outperform unified approaches, underscoring the value of tailored forecasting.
By Hesandi Mallawarachchi, Senilka Madurapperumage, Nadil Kulathunge, Thilokya Angeesa, Nethsith Gunaweera, Sandeepa Weerasekara, Patalee Narasinghe, Nisansa de Silva, Sandareka Wickramanayake
The study evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that ML models alone fail to recover the best nitrogen rate within farm tolerance and underperform standard UK advice across price scenarios. However, a simple post‑model correction step significantly reduces profit losses, and a hybrid approach combining standard advice with a damped correction eliminates bias and large losses.
By Xulong Wang, Po Yang
The study evaluates machine learning models for nitrogen recommendations in winter wheat by directly scoring profit loss on 892 yield response curves, rather than relying on prediction accuracy. Results show that none of the models recover the best rate within farm tolerance, and at typical prices all models underperform standard UK advice. A simple post‑model correction step reduces profit losses by up to 43% without retraining, while a hybrid approach further mitigates bias and large losses.
By Xulong Wang, Po Yang
arXiv:2608. 04023v1 Announce Type: cross Abstract: Sri Lanka's fisheries sector is important for jobs and food supply.
By Ruzaini Ahmed, Yohan Jayasinghe, Tharumini Gamage, Ifaz Ikram, Hasini Lawanya, Nirasha Munasinghe, Patalee Narasinghe, Nisansa de Silva, Sandareka Wickramanayake
arXiv:2606. 13959v1 Announce Type: new Abstract: Sierra Leone's agriculture operates with almost no data-driven decision support, and no published machine learning study has examined the country's crop yields.
By Ibrahim Denis Fofanah
The paper introduces a lightweight deep learning framework that forecasts Brazilian soybean yields using only routine weather data and two simple static inputs (crop year and agro-environmental label). Across 20 seasons, transformer-based models achieved the highest accuracy, outperforming traditional ridge regression and a moving‑average baseline by nearly 48%. Ablation studies show that the static inputs and spatial expansion improve performance without adding complexity, and SHAP analysis highlights the importance of crop year and weather variables in driving yield variations.
By Fernando Dupin da Cunha Mello (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil), Prashant Kumar (Global Centre for Clean Air Research), Erick G. Sperandio Nascimento (Stricto Sensu Department, SENAI CIMATEC University, Salvador, Bahia, Brazil)