Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework
Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather pattern...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-12-01
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| Series: | Modelling |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-3951/2/4/40 |
| _version_ | 1797502283300208640 |
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| author | John C. Chrispell Eleanor W. Jenkins Kathleen R. Kavanagh Matthew D. Parno |
| author_facet | John C. Chrispell Eleanor W. Jenkins Kathleen R. Kavanagh Matthew D. Parno |
| author_sort | John C. Chrispell |
| collection | DOAJ |
| description | Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions. |
| first_indexed | 2024-03-10T03:30:51Z |
| format | Article |
| id | doaj.art-ecc634e7e80d4cb98e517c8394f0f4c1 |
| institution | Directory Open Access Journal |
| issn | 2673-3951 |
| language | English |
| last_indexed | 2024-03-10T03:30:51Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Modelling |
| spelling | doaj.art-ecc634e7e80d4cb98e517c8394f0f4c12023-11-23T09:43:15ZengMDPI AGModelling2673-39512021-12-012475377510.3390/modelling2040040Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical FrameworkJohn C. Chrispell0Eleanor W. Jenkins1Kathleen R. Kavanagh2Matthew D. Parno3Department of Mathematical and Computer Sciences, Indiana University of Pennsylvania, Indiana, PA 15705, USASchool of Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634, USADepartment of Mathematics, Clarkson University, Potsdam, NY 13699, USADepartment of Mathematics, Dartmouth College, Hanover, NH 05755, USAMultiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.https://www.mdpi.com/2673-3951/2/4/40stochastic modelingGaussian processesagricultural modelingcrop yieldwater managementglobal sensitivity analysis |
| spellingShingle | John C. Chrispell Eleanor W. Jenkins Kathleen R. Kavanagh Matthew D. Parno Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework Modelling stochastic modeling Gaussian processes agricultural modeling crop yield water management global sensitivity analysis |
| title | Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework |
| title_full | Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework |
| title_fullStr | Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework |
| title_full_unstemmed | Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework |
| title_short | Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework |
| title_sort | characterizing prediction uncertainty in agricultural modeling via a coupled statistical physical framework |
| topic | stochastic modeling Gaussian processes agricultural modeling crop yield water management global sensitivity analysis |
| url | https://www.mdpi.com/2673-3951/2/4/40 |
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