The paper proposes an event-based approach to predict transitions into vineyard disease‑risk periods within a 3–7 day window, rather than daily disease status. It defines events only after a minimum disease‑free gap to reduce label fragmentation and uses multi‑year agro‑meteorological data to capture humidity, rainfall, temperature, and seasonal patterns. Experiments with XGBoost, LSTM, and TCN models show that this formulation improves short‑horizon warning, highlighting trade‑offs among recall, lead time, and false alerts.
By Ivica Dimitrovski, Ivan Kitanovski, Danco Davcev, Slobodan Kalajdziski, Kosta Mitreski
arXiv:2607. 07759v1 Announce Type: new Abstract: Agricultural supply chains are vulnerable to disruptions through linked biophysical and economic systems.
By Joshua R. Waite, Dana Golden, Brett Indelicato, Kevin Camp, Mojdeh Saadati, Shannon Regan, Patrick Schnable, Baskar Ganapathysubramanian, Carlos Messina, Suzanne Thornsbury, Soumik Sarkar
arXiv:2603. 04818v3 Announce Type: replace Abstract: Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritize forecasting accuracy without providing operationally interpretable early warnings.
By Zhiming Xue, Yujue Wang, Menghao Huo
The article introduces the TAISE framework, which uses AI weather forecasting models to generate coherent extreme weather sequences at a fraction of the cost of traditional catastrophe risk models. By self‑iteratively producing continuous global atmospheric fields, TAISE captures temporal continuity and cross‑regional correlations that snapshot‑based methods miss. A proof‑of‑concept experiment shows an order‑of‑magnitude reduction in computational cost while maintaining key statistical properties of extreme events.
By Hang Gao
arXiv:2509.18123v2 Announce Type: replace
Abstract: Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moist...
By Yeonju Lee, Rui Qi Chen, Joseph Oboamah, Po Nien Su, Wei-zhen Liang, Yeyin Shi, Lu Gan, Yongsheng Chen, Xin Qiao, Jing Li
arXiv:2607. 21785v1 Announce Type: cross Abstract: Roadblocks in Bolivia are a social conflict phenomenon with devastating economic impacts, estimated at losses equivalent to 4% of the national Gross Domestic Product.
By Rodrigo Vargas Sainz, Christian Ber\'on Curti
arXiv:2609.24394v1 Announce Type: new
Abstract: Climate variability influences whether a market disruption escalates into a food crisis, yet broad climate patterns like El Ni\~no, tracked months befo...
By Jordi Cerd\`a-Bautista, Vasileios Sitokonstantinou, Homer Durand, Gherardo Varando, Michele Ronco, Gustau Camps-Valls
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
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.
By Artur Sokolovsky, Bhavik Merai, Moe Jafari, Muen Chen
arXiv:2602. 17683v3 Announce Type: replace Abstract: Short-term forecasting of vegetation dynamics is a key enabler for data-driven decision support in precision agriculture.
By Irene Iele, Giulia Romoli, Daniele Molino, Elena Mulero Ayll\'on, Filippo Ruffini, Paolo Soda, Matteo Tortora
arXiv:2608.30392v1 Announce Type: new
Abstract: Global food security and sustainable climate action increasingly rely on robust, scalable agricultural monitoring. Earth observation foundation models...
By Vishal Nedungadi, Xingguo Xiong, Marc Ru{\ss}wurm, Ioannis N. Athanasiadis
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.
By Julian F. Schmitt, Bertrand Delorme, Robert C. King, Yashica Patodia, Tapio Schneider, Aditi Sheshadri, Ravi Jain