生产力指数模型PI在北方土壤生产力评价中的应用
Applied Productivity Index Model (PI) in Soil Productivity Assessment of Northern China
查看参考文献33篇
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文摘
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土壤生产力评价是土地生产力评价的一个重要方法,对土壤生产力进行科学合理的定量评价具有重要意义.论文以中国北方4个耕地类型区为研究区域,选用全国第二次土壤普查成果中44个典型剖面的土层厚度、容重、机械组成、有机质含量、pH值和作物产量数据,建立了新的PI模型并对PI值与产量进行回归分析,以证明PI模型是较好的土壤生产力评价模型.研究结果表明:①东北黑土区、北方平原区、北方山地丘陵区、黄土高原区的PI均值分别为0.377、0.198、0.202和0.172,说明东北黑土区土壤生产力最高,黄土高原区最低;②在各区内部,Pl值与作物产量之间均呈十分显著的线性关系,其回归方程的决定系数变化范围在0.589～0.743,均能通过显著性水平为0.005的检验,表明PI模型可用于区内土壤生产力评价;③回归直线的重合性检验表明,不同类型区PI值与产量的回归方程有显著差异,但北方平原区和北方山地丘陵区在显著性水平为0.05时可将PI值与产量进行统一回归.土地生产力区间差异主要由气候因素造成,而区内差异主要是由土壤因素造成.该模型的不足之处就在于未考虑不同耕地类型区之间不同的气候因素,这一点有待于继续探讨.但是对于耕地类型区内部来说,PI模型不失为一个比较好的土壤生产力评价模型. |
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其他语种文摘
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The soil productivity assessment is an important method of the land productivity assessment , and it is significant to make a quantitative evaluation of soil productivity assessment scientifically and reasonably. In order to prove the Productivity Index (PI) model available in four different cultivated land type regions in northern China, i. e. , the black soil region of northeast Chi-na(Type Ⅰ) , the plain region of northern China(Type Ⅱ) , the mountain/hilly region of northern China (Type Ⅲ) and the Loess Plateau region (Type Ⅳ) , the data of soil thickness, bulk density, mechanical composition, organic matter content and crop yields of 44 soil profiles(12 soil profiles in Type Ⅰ region, 10 in Type Ⅱ region, 12 in Type Ⅲ region and 10 in Type Ⅳ region) in the second nationwide general soil survey was used to establish a new PI model and analyze the linear regression relations between PI values and crop yields in this study. The results demonstrated that: (1) the average PI values in Type Ⅰ to Type IV region were 0. 377, 0. 198, 0. 202 and 0. 172 respectively. It is indicated that the Type Ⅰ region was the best land productivity region whereas the Type Ⅳ region was the worst. The variances of the four regions were 0. 028, 0. 009, 0.003 and 0. 002. The larger variance of the Type Ⅰ region is due to the greater change in its organic matter. (2 ) The PI values and crop yields were in significant linear correlation in all these regions and the determination coefficients of their regression equations were 0. 684, 0. 743, 0. 589 and 0. 703, which can pass the significant test ( P < 0. 005 ). It is illustrated that the new PI model is valid in soil productivity assessment in each region. (3 ) Regression lines from various regions differ obviously in the regression lines coincidence test while the Type Ⅰ and Type HI regions share similar regression lines at 0. 05 significant level. This is because the climates are very different among the four cultivated land type regions, but the climates between the Type Ⅱ and the Type Ⅲ regions are similar, which lead to the similar physio-chemical properties of soils in the two regions. So, the climate diversities determine different soil productivities among the four regions and the soil factor affects the land productivity in each region. This model did not take the differences of climates in the four regions into consideration, which was the deficiency of this model, and further studies need to be carried out. For all that, the brief PI model is practicable in assessing soil productivity in each region. |
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来源
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自然资源学报
,2009,24(4):708-717 【核心库】
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关键词
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土壤生产力
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土壤生产力评价
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PI模型
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中国北方耕地类型区
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土壤理化性质
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地址
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北京师范大学地理学与遥感科学学院, 地表过程与资源生态国家重点实验室, 北京, 100875
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3037 |
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学科
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农业基础科学 |
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基金
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国家科技支撑计划项目
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国家自然科学基金
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文献收藏号
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CSCD:3515834
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