首页> 外文期刊>Journal of Science and Technology of Agriculture and Natural Resources >Spatial Variability of Soil Surface Nutrients Using Principal Component Analysis and Geostatistics: A Case Study of Appaipally Village, Andhra Pradesh, India
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Spatial Variability of Soil Surface Nutrients Using Principal Component Analysis and Geostatistics: A Case Study of Appaipally Village, Andhra Pradesh, India

机译:基于主成分分析和地统计学的土壤表面养分空间变异性研究:以印度安得拉邦阿帕帕利村为例

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Understanding distribution of soil properties at the field scale is important for improving agricultural management practices and for assessing the effects of agriculture on environmental quality. Spatial variability within soil occurs naturally due to pedogenic factors as well as land use and management strategies. The variability of soil properties within fields is often described by classical statistical and geostatistical methods. This research was conducted to study what factors control the spatial variability of soil nutrients using an integration of principal component analysis and geostatistics in Appaipally Village, Andra Pradesh, India. 110 soil samples were randomly collected from 0-30 cm and prepared for laboratory analyses. Total N, available P, Ca, K, Na, Mg, S, B, Mn, Fe, Zn were measured using standard methods. Statistical and geostatistical analysis were then performed on raw data. The results of PCA analysis showed that 4 PC's had Eigen-value of more than 1 and explained 71.64 % of total variance. The results of geostatistical analysis revealed that three PC's had isotropic distribution based on surface variogram. Spherical model was fitted to all PC's. Ranges of model were 288 and 393 m for PC1 and PC3 respectively. On the other hand the range for PC2 was significantly different (877m). The most important elements in PC2 such as Fe, Mn, and Zn probably had similar range of effectiveness (700-900m). The comparison of PC's distributions indicated that PC1 and PC3 including total N, available Mg, K, Cu, Ca and P, were in accordance with farming plots dimensions and management practices. Therefore, it is necessary to improve the appropriate fertilizers used by farmers. The pattern of PC2 distribution was not consistent with farmer's plots, but had the best concordance with soil acidity. Therefore, the most correlated elements with this PC including Fe, Mn, and Zn are mainly controlled by soil acidity and not affected by management practices. However, spatial variability of these elements in areas lower than critical values should be considered for site-specific management. Keywords: Spatial variability, Principal component analysis, Geostatistics, Soil nutrients, Appaipally village, India Full-Text Type of Study: Research | Subject: Ggeneral Received: 2010/06/16 Related Websites Scientific Publications Commission - Health Ministry Scientific Publications Commission - Science Ministry Yektaweb Company Site Keywords ?????, Academic Journal, Scientific Article, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ????? ??, ???? ?? Vote ? 2015 All Rights Reserved | JWSS - Isfahan University of Technology
机译:了解田间规模的土壤特性分布对于改善农业管理实践和评估农业对环境质量的影响非常重要。土壤内部的空间变异性自然是受成岩因素以及土地利用和管理策略的影响。田间土壤特性的变化通常通过经典的统计和地统计学方法来描述。这项研究的目的是结合主成分分析和地统计学在印度安德拉邦阿帕帕里村(Appaipally Village)中研究哪些因素控制土壤养分的空间变异性。从0-30 cm处随机收集110个土壤样品,并准备进行实验室分析。使用标准方法测量总氮,有效磷,钙,钾,钠,镁,硫,硼,锰,铁,锌。然后对原始数据进行统计和地统计分析。 PCA分析的结果表明,四台PC的特征值大于1,解释了总方差的71.64%。地统计分析的结果表明,基于表面变异函数,三台PC具有各向同性的分布。球形模型适用于所有PC。 PC1和PC3的模型范围分别为288和393 m。另一方面,PC2的范围明显不同(877m)。 PC2中最重要的元素(如铁,锰和锌)可能具有相似的有效范围(700-900m)。 PC分布的比较表明,PC1和PC3包括总氮,有效的Mg,K,Cu,Ca和P,与耕地面积和管理实践一致。因此,有必要改善农民使用的适当肥料。 PC2的分布模式与农民的地块不一致,但与土壤酸度最一致。因此,与此PC最相关的元素包括Fe,Mn和Zn主要受土壤酸度控制,不受管理实践的影响。但是,对于特定地点的管理,应考虑这些元素在低于临界值的区域中的空间变异性。关键词:空间变异性,主成分分析,地统计学,土壤养分,Appaipally村庄,印度全文研究类型:研究主题:一般收稿日期:2010/06/16相关网站科学出版物委员会-卫生部科学出版物委员会-科学部Yektaweb公司网站关键字??????,Academic Journal,Scientific Article,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ?????? ??,???? ??投票吗? 2015版权所有| JWSS-伊斯法罕工业大学

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