气候变化领域集成服务门户(CCPortal): Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops

CCPortal

DOI	10.1016/j.compag.2024.108941
	Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops
	Guo, Wei; Huang, Yimei; Huang, Yudan; Li, Yilun; Song, Xiaoxiang; Shen, Jikai; Qi, Xiping; Zhang, Bicheng; Zhu, Zhaolong; Peng, Shouzhang; An, Shaoshan
发表日期	2024
ISSN	0168-1699
EISSN	1872-7107
起始页码	221
卷号	221
英文摘要	Understanding the driving mechanisms and future trends of crop planting structures was crucial for ensuring food security, but research addressing this issue was lacking. This study focused on the crop planting structure in Qinghai Province. The crop structure from 2000 to 2020 was clarified through remote sensing interpretation, and data on farmers and the environment were collected using remote sensing images and 2000 questionnaires. Based on machine learning and the HLM model, the driving mechanism of the current crop planting structure and the future change trend under the background of population aging were discussed. From 2000 to 2020, the proportion of corn and greenhouses in valley agriculture increased, while the proportion of food crops in oasis agricultural areas decreased, and both were negatively correlated with labor. The results of the HLM model showed that only 7% and 28% of the planting structures of valley agriculture and oasis agriculture were directly affected by environmental factors (economy, population, and climate). In the influence path of planting structure, farmers themselves (Family condition, basic condition of cultivated land, mode of agricultural production) and the interaction between farmers and the environment (precipitation and crop yield, precipitation days and pesticide dosage) were the main ways of influence. The direct effect of the environment on crop planting structure was small. The random forest model accurately predicted the future crop planting structure with a prediction accuracy of 74%. The region was facing a serious issue of population aging. By 2020, the proportion of people aged 60 and above reached 12.15%, showing an aging trend. In the three future development scenarios (SSP1, SSP4, and SSP5) from 2020 to 2100, the population decreased by 21.41%, 23.83%, and 19.94%, respectively. Under this influence, the proportion of food crop planting in valley agriculture and oasis agriculture increased by (2.73%, 2.93%, and 3.07%) and (0.59%, 1.47%, and 2.36%), respectively. This study challenged the traditional notion that the environment directly dictates the structure of crop planting and introduced a new model for studying its driving mechanisms and making future predictions. To safeguard global food quantity and diversity, it was critical to consider the impact of population aging on crop planting structures.
英文关键词	Planting structure; Aging population; Qinghai -Tibet Plateau; Machine learning; Hierarchical linear model
语种	英语
WOS研究方向	Agriculture ; Computer Science
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001230741900001
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/305755
作者单位	Northwest A&F University - China; Ministry of Agriculture & Rural Affairs; Northwest A&F University - China
推荐引用方式 GB/T 7714	Guo, Wei,Huang, Yimei,Huang, Yudan,et al. Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops[J],2024,221.
APA	Guo, Wei.,Huang, Yimei.,Huang, Yudan.,Li, Yilun.,Song, Xiaoxiang.,...&An, Shaoshan.(2024).Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops.COMPUTERS AND ELECTRONICS IN AGRICULTURE,221.
MLA	Guo, Wei,et al."Develop agricultural planting structure prediction model based on machine learning: The aging of the population has prompted a shift in the planting structure toward food crops".COMPUTERS AND ELECTRONICS IN AGRICULTURE 221(2024).