盐碱地花生高产栽培研究进展及面临的挑战
Research Progress and Challenge of High-Yield Cultivation of Peanut in Saline-Alkali Lands
高华鑫 1张佳蕾 1王建国 1慈敦伟 2杨莎 1唐朝辉 1郭峰 1刘珂珂 1万书波3
作者信息
- 1. 山东省农业科学院农作物种质资源研究所,山东济南 250100
- 2. 山东省花生研究所,山东青岛 266100
- 3. 山东省农业科学院,山东济南 250100
- 折叠
摘要
盐碱地是我国宝贵的后备耕地资源,进一步挖掘盐碱地作物产量是我国农业生产的重要任务.花生是我国主要的经济和油料作物,开发利用盐碱化耕地进行花生栽培促高产,对于盐碱地农业增效、农民增收和保障食用油安全意义重大.本文简述了盐碱地改良的科学原理,结合山东省农业科学院花生课题组开展的一系列试验,从花生抗盐胁迫分子调控机理、物理和化学改良措施及农艺治理措施等方面概述了盐碱地条件下缓解花生盐碱胁迫及促进高产的研究进展,并进一步指出提升盐碱地花生绿色生产所面临的挑战.本研究可为盐碱地花生绿色高效生产提供有力的理论依据与技术支撑,对于保障国家粮油安全和改善盐碱区域农业生态环境具有重要的参考意义.
Abstract
Saline-alkali lands are valuable reserve resources of cultivated land,further improving yield of crops in these lands is a priority task in agricultural production in China.Peanut is the main cash and oil crop in China,exploring and utilizing saline-alkali lands for high-yield cultivation of peanut is of great significance to increasing agricultural efficiency and farmers'income and guaranteeing edible oil security.This paper brief-ly described the scientific principle of saline-alkali land improvement,summarized the research progress on al-leviating salt and alkali stresses and promoting high yield of peanut in saline-alkali lands from molecular regu-lation mechanisms of peanut resistant to salt stress,physical and chemical improvement measures and agro-nomic management measures based on some field experiments conducted by the peanut research team in Shan-dong Academy of Agricultural Sciences,and pointed out the challenges of further improving peanut green pro-duction.This study could provide strong theoretical bases and technical supports for green and efficient produc-tion of peanut in saline-alkali lands,which have key reference significance for guaranteeing national food and oil security and improving regional agroecological environment.
关键词
花生/盐碱地/高产栽培/产量/挑战Key words
Peanut/Saline-alkali lands/High-yield cultivation/Yield/Challenge引用本文复制引用
基金项目
山东省农业科学院农业科技创新工程项目(CXGC2023F13)
山东省自然科学基金青年科学基金项目(ZR2021QC162)
山东省重点研发计划盐碱地草牧业科技示范工程项目(2021SFGC0302)
出版年
2024
国家科技期刊平台
NSTL
万方数据