尼日利亚大学南苏卡区ST分类群土壤性质变化

Emeka P. Ukaegbu, F. Akamigbo
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摘要

研究评估了美国农业部土壤分类系统对尼日利亚大学恩苏卡土壤的预测准确性。9个剖面的0 - 20cm和30 - 60cm深度数据,每个剖面代表一个地图单元,用于确定整个采样区域(对照)的土壤性质变异系数(CV),在Great类群和系列中。从高到低类别的cv逐渐减少,属性的变化是不规则的。表层土壤各水平的平均cv值分别为59.58%(全区)、56.97%(大组)、50.77%(串联),下层土壤各水平cv值分别为38.15%(全区)、31.53%(大组)、25.19%(串联)。在表层土壤上,Great组对K、OC的预测平均提高36.16%,Clay、K、OC的预测平均提高43.71%。在粉土下,Mg、CEC、OC、TN在Great组平均提高34.17%,而粘土、粉土、Mg、CEC、OC、TN、avp在序列上平均提高47.49%。预测的性质与其他性质相关,影响土壤生产力。砂和pH值几乎不受分类的影响。研究强调了利用小样本评估土壤分类预测准确性的技术以及土壤详细表征的本质。
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Soil Property Variation within Taxa of ST at University of Nigeria, Nsukka
Study evaluated predictive accuracy of USDA Soil Taxonomy Classifications of Soils of University of Nigeria, Nsukka. Data from 0 – 20cm and 30 – 60cm depths of 9 profiles, each representing a map unit, were used to determine coefficients of variation (CV) of soil properties over whole area sampled (control), within Great group class and series. There was progressive reduction in CVs from high to low categories, with the properties doing so irregularly. Average CVs for the various levels were 59.58% (over whole area), 56.97% (Great group), 50.77% (series) at topsoil, while at subsoil they were 38.15% (whole area), 31.53% (Great group), 25.19% (series). At topsoil, predictions of K & OC improved by 36.16% on the average at Great group, while it did for Clay, K, OC by 43.71% at series. At subsoil Silt, Mg, CEC, OC, TN improved by 34.17% at Great group on the average, while Clay, Silt, Mg, CEC, OC, TN, av.P did by 47.49% at series. Predicted properties, which were found to correlate with others, influence soil productivity. Sand and pH were virtually unaffected by classification. Study highlights a technique for evaluating predictive accuracy of soil classification using small sample size as well as the essence of detailed characterization of the soils.
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