|
|
|
|
|
Dania Tamayo-Vera, Xiuquan Wang and Morteza Mesbah
The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current ut...
ver más
|
|
|
|
|
|
|
|
|
Bakry A. Bakry, Mervat Sh. Sadak, Nagla M. Al Ashkar, Omar M. Ibrahim, Mohammad K. Okla and Amira M. El-Tahan
Drought stress is an important challenge to global food security and agricultural output, and dramatic and rapid climate change has made the problem worse, causing unexpected impacts on the growth, development, and yield of different plants. Understandin...
ver más
|
|
|
|
|
|
|
|
|
Laura Lisso, John B. Lindsay and Aaron Berg
Climate change research identifies risks to agriculture that will impact agricultural land suitability. To mitigate these impacts, agricultural growing regions will need to adapt, diversify, or shift in location. Various machine learning algorithms have ...
ver más
|
|
|
|
|
|
|
|
|
Liquan Jing, Xunkang Wang, Yihan Zhao, Fan Li, Yu Su, Yang Cai, Fucheng Zhao, Guichun Dong, Lianxin Yang and Yunxia Wang
Duckweed growing in paddy fields (DGP) has substantially increased because of the effects of climate warming and/or eutrophication in irrigated water. Previous studies have primarily focused on investigating the effects of DGP as a nonchemical agent for ...
ver más
|
|
|
|
|
|
|
|
|
Cori Salinas, Edward Osei, Mark Yu, Selin Guney, Ashley Lovell and Eunsung Kan
Wheat offers winter forage for cattle grazing and is one of the most valuable cash crops in Texas. In this study, we evaluate the impacts of climate change projections on winter wheat grain yields in five major wheat producing counties in Texas (Deaf Smi...
ver más
|
|
|
|