摘要
以阴山北麓柠条锦鸡儿(Caragana korshinskii)、垫状锦鸡儿(Caragana tibetica)、狭叶锦鸡儿(Caragana stenophylla)和刺叶柄棘豆(Oxytropis aciphylla)4种豆科荒漠植被群系及其环境因子为研究对象,采用样方调查法,揭示不同荒漠灌丛生物量变化规律及其与群落特征及土壤因子之间关系。结果表明:不同灌木群系中,建群种地上生物量受群落内植物种种间竞争以及土壤钙积层和湿土层厚度的影响,主要表现为伴生种长势越好,建群种生物量越低,并且随着湿土层厚度的增加,钙积层厚度的减少,建群种地上生物量增加。同时利用拟合函数求得柠条锦鸡儿和刺叶柄棘豆群系建群种地上生物量最佳预测模型为幂函数模型,垫状锦鸡儿、狭叶锦鸡儿群系建群种地上生物量最佳预测模型为多项式函数模型。
Caragana korshinskii,Caragana tibetica,Caragana stenophylla and Oxytropis aciphylla were studied in the northern foothills of Yinshan Mountains,and the environmental factors of four leguminous desert vegetation groups were investigated.The quadrata survey method was used to reveal the biomass changes of different desert shrubs and their relationships with community characteristics and soil factors.The results showed that in different shrub communities,the aboveground biomass of constructive species is influenced by inter plant competition within the community,as well as the thickness of soil calcium and wet soil layers,the main manifestation was that the better the growth of the accompanying species,the lower the biomass of the constructive species,and as the thickness of the wet soil layer increased and the thickness of the calcium layer decreased,the aboveground biomass of the constructive species increased.At the same time,the power function model was used to determine the optimal prediction model for aboveground biomass of the constructed species in the Caragana koraiensis and Oxytropis aciphylla group,while the polynomial function model was used to determine the optimal prediction model for aboveground biomass of the Caragana tibetica,Caragana stenophylla group.
作者
赵宏胜
冯霜
徐金娥
代素敏
杨泽宇
李燕南
兰登明
ZHAO Hong-sheng;FENG Shuang;XU Jin-e(Inner Mongolia Agricultural University,Hohhot,Inner Mongolia 010010;Bayannaoer Forestry and Grassland Development Center,Bayannaoer,Inner Mongolia 015000)
出处
《安徽农业科学》
CAS
2024年第10期95-102,共8页
Journal of Anhui Agricultural Sciences
基金
国家科技部基础资源调查专项(2017FY100204)。
关键词
阴山北麓
豆科
荒漠灌木群系
地上生物量
环境因子
预测模型
North foot of Yinshan Mountain
Legumes
Desert shrub group
Aboveground biomass
Environmental factors
Prediction model