摘要
【目的】通过调整种植结构,达到水资源利用与配置更加合理化,以有限的水资源促进当地农业可持续发展。【方法】根据玛纳斯县农业结构种植特点,建立了基于遗传算法的多目标作物种植结构优化模型,并结合"三条红线"相关指标,运用MATLAB软件进行求解,提出了2020、2030年玛纳斯县主要农作物种植结构优化方案。【结果】现状年小麦种植面积可以维持现状,适当增加玉米、葵花和甜菜的种植面积,减少棉花种植面积,优化后灌溉节水量为137.01万m3,2020、2030年优先增加玉米和葵花的种植面积,当种植面积一定时,随着灌溉水利用系数的提高,可以增加棉花的种植面积。【结论】通过种植结构的调整,可以在兼顾经济、社会和节水效益的基础上缓解农业用水供需矛盾。
【Objective】Adjusting crops cultivation is one way to develop sustainable agriculture for areas where available water resources are limited. The objective of this paper is to show how to achieve this using multi-objective optimization methods.【Method】We took Manasi County in Xinjiang as an example and developed a multiobjective optimization model to calculate the optimal cultivation areas of main crops using the genetic algorithm with the＂three red lines＂proposed for economic development in the region in consideration. The model was solved using MATLAB, which was then applied to calculate the optimal cultivation areas of the main crops for2020 and 2030 in this county.【Result】The calculated results showed that the cultivation area of the wheat can remain as what it is, yet the areas for corns, helianthus and beetroots should be increased and the areas of cotton decreased.. These could save the water used for irrigation by 1.370 1 million m3. As for 2020 and 2030, the local government could give priority to increasing the cultivation areas of corns and helianthus. When the cultivated areas for each crop was established, the area of cotton could be increased if the irrigation water use coefficient can be improved.【Conclusion】Adjusting the current cultivation areas of the main crops can deliver economic and social benefits to the county.
作者
马林潇
何英
林丽
彭亮
MA Linxiao;HE Ying;LIN Li;PENG Liang(College of Water Conservancy and Civil Engineering,Xinjiang Agricultural University,Urumqi 830052,China)
出处
《灌溉排水学报》
CSCD
北大核心
2018年第9期123-128,共6页
Journal of Irrigation and Drainage
基金
国家自然科学基金地区基金项目(51569031)
新疆水利水电工程重点学科基金项目(xjzdxk-2010-02-12)
关键词
种植结构
优化方案
三条红线
模型
多目标优化
planting structure
optimal scheme
three red lines
model
multi-objective optimization
