Multivariate logarithmic modeling of grain production in the Yangtze River Basin: incorporating extreme weather factors.

Multivariate logarithmic modeling of grain production in the Yangtze River Basin: incorporating extreme weather factors. Abstract Whilst essential to the nutrition of societies, grain crops are demonstrated to be largely susceptible to the influence of anthropological climate change and extreme weather. However, few previous attempts at modeling grain yield took thorough consideration about the potential impact of extreme temperature events (ETEs) on average (or per-hectare) grain yield. From historical data in a Chinese agriculture hub, namely the Middle-Lower Yangtze Plains (MLYP) region, through a 2-step, nested OLS-FGLS multivariate log-log regression model, this study underscored the strong, sustained and significant negative influence ETEs had on grain production in the last 32 years in MLYP provinces of Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, and Hunan; supported the literature with further evidence of global warming reducing crop productivity; and corroborated previous studies highlighting a reduction in crop productivity sourced from inefficient distribution and management of labor in the context of technological advancements. Climate-model-based provincial predictions through Shared Socioeconomic Pathways (SSPs) indicate a strong need for agricultural workers and scientists to address the increasing threat of future heat and cold stress through both micro-level (such as genomics-assisted breeding) and macro-level (such as AI-mediated farm management tools), in order for them to be prepared for a wide range of climate change scenarios. Formats available You can view the full content in the following formats: Indexing Terms Descriptors Identifiers Geographical Locations Broader Terms Information & Authors Information Published In 2024 Applicable geographic locations Anhui, Asia, China, Hubei, Hunan, Jiangsu, Jiangxi, Zhejiang Copyright Open Access This preprint is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. History Issue publication date: 2024 Submitted: 17 January 2024 Published online: 17 January 2024 Language English Authors Metrics & Citations Metrics SCITE_ Citations Export citation Select the format you want to export the citations of this publication. EXPORT CITATIONSExport Citation Citing Literature - Zijun Mu, Junfei Xia, Predicting the influence of extreme temperatures on grain production in the Middle-Lower Yangtze Plains using a spatially-aware deep learning model, PeerJ, 10.7717/peerj.18234, 12, (e18234), (2024). View Options View options Login Options Check if you access through your login credentials or your institution to get full access on this article.