Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub
Pastures are a primary food source for livestock in Australia, with various pasture species grown in rotations. Accurate prediction of pasture availability is critical for effective farm management, livestock growth, and maintaining the supply chain. Environmental factors, particularly climate, heav...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000202 |
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| author | Thirunavukarasu Balasubramaniam Wathsala Anupama Mohotti Kenneth Sabir Richi Nayak |
| author_facet | Thirunavukarasu Balasubramaniam Wathsala Anupama Mohotti Kenneth Sabir Richi Nayak |
| author_sort | Thirunavukarasu Balasubramaniam |
| collection | DOAJ |
| description | Pastures are a primary food source for livestock in Australia, with various pasture species grown in rotations. Accurate prediction of pasture availability is critical for effective farm management, livestock growth, and maintaining the supply chain. Environmental factors, particularly climate, heavily influence pasture yield. However, different pasture species respond to climate attributes with varying time lags; for example, one species might be more influenced by last week’s weather while another by the previous month’s highlighting the nuanced temporal dependencies. This time-lagging effect complicates the development of machine-learning models that can learn the temporal dependencies to predict pasture yield. To address this, our study proposes an averaging-based feature engineering approach, effectively capturing the varying temporal dependencies across pasture species and also allowing interpretation of the dependencies. Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. This approach also enhances interpretability, revealing diverse time-lagging effects on different pasture species. XGBoost-based feature importance analysis further unveils insights into the influence of each climate attribute and its temporal dependencies on pasture yield. |
| format | Article |
| id | doaj-art-ab8351c602ab4d9c87b66da9bd5c5b62 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-ab8351c602ab4d9c87b66da9bd5c5b622025-01-31T05:10:57ZengElsevierEcological Informatics1574-95412025-05-0186103011Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHubThirunavukarasu Balasubramaniam0Wathsala Anupama Mohotti1Kenneth Sabir2Richi Nayak3School of Computer Science, Queensland University of Technology, Brisbane, 4000, Queensland, Australia; Corresponding author.School of Computer Science, Queensland University of Technology, Brisbane, 4000, Queensland, AustraliaAgriWebb, Sydney, 2010, New South Wales, AustraliaSchool of Computer Science, Queensland University of Technology, Brisbane, 4000, Queensland, AustraliaPastures are a primary food source for livestock in Australia, with various pasture species grown in rotations. Accurate prediction of pasture availability is critical for effective farm management, livestock growth, and maintaining the supply chain. Environmental factors, particularly climate, heavily influence pasture yield. However, different pasture species respond to climate attributes with varying time lags; for example, one species might be more influenced by last week’s weather while another by the previous month’s highlighting the nuanced temporal dependencies. This time-lagging effect complicates the development of machine-learning models that can learn the temporal dependencies to predict pasture yield. To address this, our study proposes an averaging-based feature engineering approach, effectively capturing the varying temporal dependencies across pasture species and also allowing interpretation of the dependencies. Utilizing remote sensing and climate data, covering 196 farms (and 6885 paddocks) across Australia, we applied several machine learning techniques, including XGBoost, random forest, linear regression, deep neural networks, stacking, and bootstrapping. Our results show that incorporating averaging-based feature-engineered climate attributes significantly improves pasture yield predictions, with enhancements of up to 20.28%, 31.81%, and 31.11% across the three evaluation measures, RMSE, MAE, and R2, respectively. This approach also enhances interpretability, revealing diverse time-lagging effects on different pasture species. XGBoost-based feature importance analysis further unveils insights into the influence of each climate attribute and its temporal dependencies on pasture yield.http://www.sciencedirect.com/science/article/pii/S1574954125000202Pasture growthTime-lagging effectClimate attributesPasture speciesFeature engineeringMachine learning |
| spellingShingle | Thirunavukarasu Balasubramaniam Wathsala Anupama Mohotti Kenneth Sabir Richi Nayak Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub Ecological Informatics Pasture growth Time-lagging effect Climate attributes Pasture species Feature engineering Machine learning |
| title | Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub |
| title_full | Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub |
| title_fullStr | Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub |
| title_full_unstemmed | Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub |
| title_short | Feature engineering on climate data with machine learning to understand time-lagging effects in pasture yield predictionGitHub |
| title_sort | feature engineering on climate data with machine learning to understand time lagging effects in pasture yield predictiongithub |
| topic | Pasture growth Time-lagging effect Climate attributes Pasture species Feature engineering Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000202 |
| work_keys_str_mv | AT thirunavukarasubalasubramaniam featureengineeringonclimatedatawithmachinelearningtounderstandtimelaggingeffectsinpastureyieldpredictiongithub AT wathsalaanupamamohotti featureengineeringonclimatedatawithmachinelearningtounderstandtimelaggingeffectsinpastureyieldpredictiongithub AT kennethsabir featureengineeringonclimatedatawithmachinelearningtounderstandtimelaggingeffectsinpastureyieldpredictiongithub AT richinayak featureengineeringonclimatedatawithmachinelearningtounderstandtimelaggingeffectsinpastureyieldpredictiongithub |