Quantitative predictions of crop yields and prices from satellite-based machine learning Article Swipe
Florian Teste
,
David Makowski
,
Philippe Ciais
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5694134/v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-5694134/v1
Accurate forecasting of agricultural commodity yields and prices is essential for informed decision-making by farmers, traders, and policymakers. This study presents a novel satellite-based framework that integrates satellite-based Gross Primary Production (GPP) data with advanced machine learning (ML) models, including Autoencoders (AE) and Variational Autoencoders (β-VAE), combined with ElasticNet regression. The framework is applied to produce quantitative predictions of US corn and soybean yields and prices throughout the growing season. For yields, the satellite-based ML models achieved high accuracy, with coefficient of determination (R2) values reaching up to 0.90 and 0.75 for corn and soybean, respectively. For prices, the satellite-based ML models significantly outperformed standard statistical time-series models—Exponential Smoothing State Space Model (ETS) and Seasonal Autoregressive Integrated Moving Average (SARIMA)—during early to mid-growing seasons (May to August), achieving R2 values up to 0.48 and 0.46 for corn and soybean, respectively. Conversely, standard statistical models exhibited superior performance during the late-growing season (September), with SARIMA achieving R2 values up to 0.85 and 0.69 for corn and soybean. These findings show that satellite-based ML is highly relevant to forecasting crop yields and prices several months before harvest. However, standard statistical models provide better predictions later in the growing season, a few weeks before harvest. By identifying which models offer the best predictive performance at different crop stages, this study contributes to improving the tools used for crop yield and price forecasting.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-5694134/v1
- https://www.researchsquare.com/article/rs-5694134/latest.pdf
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408679383
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408679383Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-5694134/v1Digital Object Identifier
- Title
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Quantitative predictions of crop yields and prices from satellite-based machine learningWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-20Full publication date if available
- Authors
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Florian Teste, David Makowski, Philippe CiaisList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-5694134/v1Publisher landing page
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https://www.researchsquare.com/article/rs-5694134/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-5694134/latest.pdfDirect OA link when available
- Concepts
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Satellite, Crop, Computer science, Artificial intelligence, Machine learning, Econometrics, Agricultural engineering, Economics, Agronomy, Engineering, Biology, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.contributes | 218 |
| abstract_inverted_index.forecasting | 2, 177 |
| abstract_inverted_index.identifying | 204 |
| abstract_inverted_index.mid-growing | 123 |
| abstract_inverted_index.performance | 147, 211 |
| abstract_inverted_index.predictions | 58, 192 |
| abstract_inverted_index.regression. | 50 |
| abstract_inverted_index.statistical | 106, 143, 188 |
| abstract_inverted_index.time-series | 107 |
| abstract_inverted_index.(September), | 152 |
| abstract_inverted_index.Autoencoders | 41, 45 |
| abstract_inverted_index.agricultural | 4 |
| abstract_inverted_index.forecasting. | 229 |
| abstract_inverted_index.late-growing | 150 |
| abstract_inverted_index.outperformed | 104 |
| abstract_inverted_index.quantitative | 57 |
| abstract_inverted_index.R<sup>2</sup> | 129, 156 |
| abstract_inverted_index.determination | 83 |
| abstract_inverted_index.policymakers. | 18 |
| abstract_inverted_index.respectively. | 96, 140 |
| abstract_inverted_index.significantly | 103 |
| abstract_inverted_index.Autoregressive | 116 |
| abstract_inverted_index.(R<sup>2</sup>) | 84 |
| abstract_inverted_index.decision-making | 13 |
| abstract_inverted_index.satellite-based | 24, 28, 74, 100, 171 |
| abstract_inverted_index.(SARIMA)—during | 120 |
| abstract_inverted_index.models—Exponential | 108 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.11722684 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |