arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.
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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.
The study evaluates crop‑yield forecasting methods for the 2012 Midwestern US drought, comparing non‑deep learning machine learning models with a deep learning model (VITA) using 16 meteorological predictors. It highlights challenges such as distributional dissimilarity between training and test data, spatial and temporal sparsity, and demonstrates that sample weighting and feature selection improve non‑deep learning models but not VITA. The work contrasts deep versus non‑deep learning approaches and shows how modifications can mitigate issues arising from extreme drought conditions.
arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.
arXiv:2506.12809v2 Announce Type: replace Abstract: The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects o...
The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.
arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.