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)
The paper evaluates machine learning for nitrogen recommendations in winter wheat by directly scoring the profit lost on measured yield response curves, rather than relying on prediction accuracy. Using 892 yield curves from UK experiments, the authors find that machine learning models fail to recover the best nitrogen rate within farm tolerance and generally underperform standard UK advice in terms of profit. However, a simple post‑model correction step can reduce profit losses by up to 43% without retraining, suggesting that machine learning can enhance standard advice rather than replace it.
arXiv:2608. 06001v1 Announce Type: new Abstract: Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting.
By Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath, Md Zahidul Islam, David W. Lamb
Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially. A single forecasting model rarely performs well across all regimes, while dense ensembles increase inference cost and provide limited insight into expert suitability.
arXiv:2510. 16898v2 Announce Type: replace-cross Abstract: Accurate prediction of electricity prices is crucial for stakeholders in the energy market, particularly for grid operators, energy producers, and consumers.
By Salih Salihoglu, Ibrahim Ahmed, Afshin Asadi
arXiv:2606. 08896v1 Announce Type: new Abstract: Large-scale retail and industrial forecasting systems contain many heterogeneous time series whose lifecycle, sparsity, volatility, seasonality, spectral patterns, and contextual sensitivity differ substantially.
By Qianyang Li, Xingjun Zhang, Shaoxun Wang, Tao Peng, Jia Wei
arXiv:2606. 15058v1 Announce Type: new Abstract: This study examines whether machine learning (ML) models can outperform the naive random walk benchmark in forecasting the monthly USD/CAD exchange rate.
By Louis Agyekum, Edmund Fosu Agyemang, Obu-Amoah Ampomah, Kofi Acheampong, Emmanuel Boadi, Priscilla Yaa Amakye, Fafa Shalom Tchorly, Enock Adu Bonsu, Eric Nyarko