摘要
研究不同有机无机配施比例对新疆地区棉花产量、养分吸收利用率及土壤肥力的影响,探究适宜的有机无机配施比例,为新疆棉田有机无机肥配施提供理论参考.试验于2018 和 2019 年在新疆昌吉市开展,试验设 6个施肥处理,分别为不施氮、单施化肥、低量有机肥配施化肥、中量有机肥配施化肥、高量有机肥配施化肥、加倍有机肥配施化肥.经 2 年施肥后,2019 年进行取样,分析不同有机无机配施处理对棉花产量、养分吸收、利用率及棉田土壤肥力的影响.低量有机肥配施化肥处理提高了棉花产量、养分吸收.有机肥配施处理与单施化肥相比,能改善棉田土壤理化性质.合理有机无机配施能提高棉花产量和养分吸收、利用率及土壤肥力、减少环境污染,合理有机无机配施是有利于新疆农田可持续利用的施肥技术.
Abstract
The effects of different organic and inorganic application ratios on cotton yield,nutrient absorption and utilization and soil fertility in Xinjiang were studied,and the appropriate organic and inorganic application ratios was explored in order to provide theoretical reference for the combined application of organic and inorganic fertilizer in cotton fields in Xinjiang.The experiment was carried out in Changji city,Xinjiang province in 2018 and 2019.Six fertilization treatments were conducted,including no nitrogen application,single fertilizer application,low amount of organic fertilizer combined with chemical fertilizer,middle amount of organic fertilizer combined with chemical fertilizer,high amount of organic fertilizer combined with chemical fertilizer,double organic fertilizer and fertilizer application.After two years of fertilization,samples were collected in 2019 to analyze the effects of different organic and inorganic application treatments on cotton yield,nutrient absorption,utilization rate and soil fertility in cotton fields.The results showed that low amount of organic fertilizer combined with chemical fertilizer treatment increased cotton yield and nutrient absorption.The combined application of organic fertilizer and chemical fertilizer improved the physical and chemical properties of cotton soil compared with the single application of chemical fertilizer.Reasonable organic and inorganic application could improve cotton yield,nutrient absorption,utilization rate,soil fertility and reduce environmental pollution.Reasonable organic and inorganic combined application is a fertilization technology conducive to sustainable utilization of farmland in Xinjiang.
NETL
NSTL
万方数据