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Article summary:
1. Increasing global long-term temperature and frequency of extreme climate and weather events threaten the rain-fed corn production.
2. Estimating corn yields under spatiotemporal heterogeneity of climate variability is important for food security.
3. Deep learning techniques such as the long short-term memory (LSTM) model can be used to better understand how crop yields respond to subseasonal changes of environmental factors throughout the growing season.
Article analysis:
The article provides a comprehensive overview of the current state of research on corn yield estimation, with a focus on deep learning approaches. The authors provide an in-depth analysis of existing methods, including biophysical and statistical models, remote sensing techniques, and data-driven models, as well as their limitations. They then propose a deep learning approach that incorporates phenology dynamics to improve yield estimation accuracy.
The article is generally reliable and trustworthy, as it provides evidence for its claims in the form of citations from other studies in the field. However, there are some potential biases that should be noted. For example, the authors focus primarily on US Corn Belt counties when discussing their proposed approach; while this may be appropriate given the scope of their study, it could lead to results that are not applicable to other regions or countries with different climates or agricultural practices. Additionally, while they discuss potential risks associated with their approach (e.g., overfitting), they do not explore these risks in depth or provide any suggestions for mitigating them.
In conclusion, this article provides a thorough overview of current research on corn yield estimation and proposes a promising new approach using deep learning techniques; however, further exploration into potential risks associated with this approach is needed before it can be fully implemented in practice.