Learning-based agricultural management in partially observable environments subject to climate variability
Department of Mechanical Engineering, Iowa Technology Institute, University of Iowa（机械工程系、爱荷华技术研究所、爱荷华大学）；Department of Chemical and Biochemical Engineering, Iowa Technology Institute, University of Iowa（化学与生物化学工程系、爱荷华技术研究所、爱荷华大学）
Agricultural management, with a particular focus on fertilization strategies, holds a central role in shaping crop yield, economic profitability, and environmental sustainability. While conventional guidelines offer valuable insights, their efficacy diminishes when confronted with extreme weather conditions, such as heatwaves and droughts. In this study, we introduce an innovative framework that integrates Deep Reinforcement Learning (DRL) with Recurrent Neural Networks (RNNs). Leveraging the Gym-DSSAT simulator, we train an intelligent agent to master optimal nitrogen fertilization management. Through a series of simulation experiments conducted on corn crops in Iowa, we compare Partially Observable Markov Decision Process (POMDP) models with Markov Decision Process (MDP) models. Our research underscores the advantages of utilizing sequential observations in developing more efficient nitrogen input policies. Additionally, we explore the impact of climate variability, particularly during extreme weather events, on agricultural outcomes and management. Our findings demonstrate the adaptability of fertilization policies to varying climate conditions. Notably, a fixed policy exhibits resilience in the face of minor climate fluctuations, leading to commendable corn yields, cost-effectiveness, and environmental conservation. However, our study illuminates the need for agent retraining to acquire new optimal policies under extreme weather events. This research charts a promising course toward adaptable fertilization strategies that can seamlessly align with dynamic climate scenarios, ultimately contributing to the optimization of crop management practices.
Deutsches Zentrum für Luft- und Raumfahrt (DLR)（德国航空航天中心）；Ludwig-Maximilians-Universität München（慕尼黑大学）；Columbia University（哥伦比亚大学）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿迈克尔·斯蒂费尔数据驱动与模拟科学中心）；University of Bremen（不来梅大学）
Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from…
National Observatory of Athens（雅典国家天文台）；National Technical University of Athens（雅典国家技术大学）
Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate change. Recent developments in the field of causal machine learning enable the estimation of intervention impacts on an outcome of interest, for samples described by a set of observed characteristics. We introduce an extensible data-driven framework that leverages earth observations and frames agricultural land suitability as a geospatial impact assessment problem, where the estimated effects of agricultural practices on …
To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agr…
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach
National Observatory of Athens（雅典国家天文台）；ERATOSTHENES Centre of Excellence（埃拉托色尼卓越中心）；Cyprus University of Technology（塞浦路斯理工大学）；Wageningen University & Research（瓦赫宁根大学及研究中心）
Climate-Driven Doubling of U.S. Maize Loss Probability: Interactive Simulation with Neural Network Monte Carlo
Eric and Wendy Schmidt Center for Data Science and Environment, University of California Berkeley（埃里克和文迪施密特数据科学与环境中心，加州大学伯克利分校）；Department of Agricultural Economics and Agribusiness, University of Arkansas（农业经济学与农业商业系，阿肯色大学）；Department of Environmental Science, Policy & Management, University of California Berkeley（环境科学、政策与管理系，加州大学伯克利分校）
University of Minnesota, Twin Cities（明尼苏达大学双城分校）；George Mason University（乔治梅森大学）；Carnegie Mellon University（卡内基梅隆大学）
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate variables such as temperature and precipitation in the 2-week to 2-month time scales. Skillful SSF would have immense societal value, in areas such as agricultural productivity, water resource management, transportation and aviation systems, and emergency planning for extreme weather events. However, SSF is considered more challenging than either weather prediction or even seasonal prediction. In this paper, we carefully study a variety of machine learning (ML) approaches for SSF over the US mainland. While atmosphere-lan…
University of Minnesota, Twin Cities（明尼苏达大学双城校区）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；George Mason University（乔治梅森大学）
Sub-seasonal climate forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to ad…
Designing probabilistic AI monsoon forecasts to inform agricultural decision-making
Development Innovation Lab, University of Chicago（芝加哥大学发展创新实验室）；University of Chicago（芝加哥大学）；Department of the Geophysical Sciences, University of Chicago（芝加哥大学地球科学系）；Data Science Institute, University of Chicago（芝加哥大学数据科学研究所）；Development Innovation Lab India, University of Chicago Trust（芝加哥大学印度发展创新实验室）；University of Chicago Trust（芝加哥大学信托）；Department of Earth and Planetary Science, University of California, Berkeley（加州大学伯克利分校地球与行星科学系）；University of California, Berkeley（加州大学伯克利分校）；Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory（伯克利国家实验室气候与生态系统科学部）；Department of Economics, University of Chicago（芝加哥大学经济学系）
Nowadays, agricultural field is experiencing problems related to climate change that result in the changing patterns in cropping season, especially for paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The cropping patterns of rice and CGPRT crops highly depend on the availability of rainfall throughout the year. The changing and shifting of the rainy season result in the changing cropping seasons. It is important to find out the cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in every area. The Oldeman's method which is usually used in the cl…
Climate change impacts could cause progressive decrease of crop quality and yield, up to harvest failures. In particular, heat waves and other climate extremes can lead to localized food shortages and even threaten food security of communities worldwide. In this study, we apply a deep learning architecture for high resolution forecasting (300 m, 10 days) of the Leaf Area Index (LAI), whose dynamics has been widely used to model the growth phase of crops and impact of heat waves. LAI models can be computed at 0.1 degree spatial resolution with an auto regressive component adjusted with weather …
Artificial intelligence pathways from weather to climate
University of California, Los Angeles（加州大学洛杉矶分校）；Hong Kong University of Science and Technology（香港科技大学）；University of Lausanne（洛桑大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Colorado Boulder（科罗拉多大学博尔德分校）；California Institute of Technology（加州理工学院）；NOAA/Geophysical Fluid Dynamics Laboratory（NOAA地球物理流体动力学实验室）；Google Research（谷歌研究院）；New York University（纽约大学）
