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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2411.05219 |
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| _version_ | 1866913575859126272 |
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| author | Willmes, David Krall, Nick Tanis, James Terner, Zachary Tavares, Fernando Miller, Chris Haberlin III, Joe Crichton, Matt Schlichting, Alexander |
| author_facet | Willmes, David Krall, Nick Tanis, James Terner, Zachary Tavares, Fernando Miller, Chris Haberlin III, Joe Crichton, Matt Schlichting, Alexander |
| contents | With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_05219 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anticipatory Understanding of Resilient Agriculture to Climate Willmes, David Krall, Nick Tanis, James Terner, Zachary Tavares, Fernando Miller, Chris Haberlin III, Joe Crichton, Matt Schlichting, Alexander Computer Vision and Pattern Recognition Machine Learning Applications With billions of people facing moderate or severe food insecurity, the resilience of the global food supply will be of increasing concern due to the effects of climate change and geopolitical events. In this paper we describe a framework to better identify food security hotspots using a combination of remote sensing, deep learning, crop yield modeling, and causal modeling of the food distribution system. While we feel that the methods are adaptable to other regions of the world, we focus our analysis on the wheat breadbasket of northern India, which supplies a large percentage of the world's population. We present a quantitative analysis of deep learning domain adaptation methods for wheat farm identification based on curated remote sensing data from France. We model climate change impacts on crop yields using the existing crop yield modeling tool WOFOST and we identify key drivers of crop simulation error using a longitudinal penalized functional regression. A description of a system dynamics model of the food distribution system in India is also presented, along with results of food insecurity identification based on seeding this model with the predicted crop yields. |
| title | Anticipatory Understanding of Resilient Agriculture to Climate |
| topic | Computer Vision and Pattern Recognition Machine Learning Applications |
| url | https://arxiv.org/abs/2411.05219 |