Modeling the Adaptation of Agricultural Production to Climate Change

Xiao, Dengpan; Shi, Wenjiao

doi:10.3390/agriculture13020414

Cited by 2 publications

(1 citation statement)

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“…Numerous studies offer various methods to tackle the chal-lenges of fluctuating weather conditions in crops. The author of this research [35] critically examines various approaches for evaluating the influence of climate change on agricultural yield and suggests potential adaptation measures through crop models. The article encompasses various farm products, geographical areas, and situations while delving into the difficulties and constraints associated with modeling methodologies.…”

Section: Related Workmentioning

confidence: 99%

Incorporating Meteorological Data and Pesticide Information to Forecast Crop Yields Using Machine Learning

Hoque,

Islam,

Uddin

et al. 2024

IEEE Access

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The agricultural sector is more vulnerable to the adverse effects of climate change and excessive pesticide application, posing a significant risk to global food security. Accurately predicting crop yields is essential for mitigating these risks and providing information for sustainable agricultural practices. This research presents a novel crop yield prediction system utilizing a year's worth of meteorological data, pesticide records, crop yield data, and machine learning techniques. We employed rigorous methods to gather, clean, and enhance data and then trained and evaluated three machine learning models: Gradient Boosting, K-Nearest Neighbors, and Multivariate Logistic Regression. We utilized the GridSearchCV method for hyper-parameter tweaking to identify the most suitable hyper-parameter throughout K-Fold cross-validation, aiming to improve the model's performance by avoiding overfitting. The remarkable performance of the Gradient Boosting model, with an almost flawless coefficient of determination (R 2 ) of 99.99%, demonstrates its promise for precise yield prediction. This research also examined the correlation between projected and actual crop yields and identified the ideal meteorological conditions. It paves the way for data-driven methods in sustainable agriculture and resource distribution, ultimately leading to a more secure future regarding food availability and robustness to climate change.INDEX TERMS Agriculture; crop yield prediction; machine learning; deep learning

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Section: Related Workmentioning

confidence: 99%