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首页> 外文期刊>Catena: An Interdisciplinary Journal of Soil Science Hydrology-Geomorphology Focusing on Geoecology and Landscape Evolution >The use of pedo-transfer functions for estimating soil organic carbon contents in maize cropland ecosystem in the Coastal Plains of Tanzania
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The use of pedo-transfer functions for estimating soil organic carbon contents in maize cropland ecosystem in the Coastal Plains of Tanzania

机译:在坦桑尼亚沿海平原中估算玉米农田生态系统土壤有机碳含量的进具功能的使用

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摘要

Soil organic carbon (OC) plays a vital role on physico-chemical and biological properties of soils and on climate change regulation. The use of pedo-transfer functions from easily available soil properties for estimating soil OC could be fast and cheap when considering field and laboratory work implications especially in Sub Saharan Africa including Tanzania. This paper attempts to develop a model for estimating soil OC contents under maize croplands ecosystem using pedo-transfer functions from soil texture. A total of 100 epipedon data entries were randomly collected from the previous soil sampling works that were conducted under maize croplands in coastal plains of Tanzania. Eighty percent of the collected data were used for training the model by using multiple regression analysis while the remained 20% were used to validate the model. The results indicated that, clay and silt had significant (p 0.001) positive correlation with soil OC while sand contents in soils had negative (p 0.001) correlation with soil OC. All together clay, sand and silt were revealed powerful predictors (p 0.001, R-2 = 0.82) of OC content in soils. On validation, the soil OC predicted agreed by 81.2% with the soil OC determined by laboratory test. These results imply that pedo-transfer functions for predicting soil OC based on soil texture is not only fast and cheap but also is an effective option for estimating OC content in soils.
机译:土壤有机碳(OC)对土壤的物理化学和生物学性质和气候变化调节起着至关重要的作用。在考虑领域和实验室工作暗示在包括坦桑尼亚的撒哈拉以南非洲的情况下,在易于使用的土壤特性中,可以快速且便宜地利用易于使用的土壤性质。本文试图利用来自土壤纹理的Pedo-Transion功能,开发玉米农田生态系统下的土壤OC内容的模型。总共有100个截二性数据条目从坦桑尼亚沿海平原的玉米农田下进行的之前的土壤采样作品中随机收集。百分之八十的收集数据用于通过使用多元回归分析来训练模型,而剩余的20%用于验证模型。结果表明,粘土和淤泥具有显着的(P <0.001)与土壤α的正相关(P <0.001),而土壤中的砂内容物与土壤OC的相关性(P <0.001)。所有粘土,沙子和淤泥都揭示了土壤中的强大的预测因子(P <0.001,R-2 = 0.82)。在验证时,土壤OC预测81.2%与实验室试验决定的土壤OC同意。这些结果暗示,用于预测基于土壤质地的土壤OC的进具功能不仅快速且便宜,而且还是估计土壤中OC含量的有效选择。

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