The Soil Management Assessment Framework

指标值 环境科学 集合(抽象数据类型) 功能(生物学) 选择(遗传算法) 统计 土壤水分 计算机科学 环境资源管理 土壤科学 数据挖掘 数学 机器学习 生态学 程序设计语言 生物 进化生物学
作者
Susan S. Andrews,Douglas L. Karlen,Cynthia A. Cambardella
出处
期刊:Soil Science Society of America Journal [Wiley]
卷期号:68 (6): 1945-1962 被引量:1240
标识
DOI:10.2136/sssaj2004.1945
摘要

Erosion rates and annual soil loss tolerance (T) values in evaluations of soil management practices have served as focal points for soil quality (SQ) research and assessment programs for decades. Our objective is to enhance and extend current soil assessment efforts by presenting a framework for assessing the impact of soil management practices on soil function. The tool consists of three steps: indicator selection, indicator interpretation, and integration into an index. The tool's framework design allows researchers to continually update and refine the interpretations for many soils, climates, and land use practices. The tool was demonstrated using data from case studies in Georgia, Iowa, California, and the Pacific Northwest (WA, ID, OR). Using an expert system of decision rules as an indicator selection step successfully identified indicators for the minimum data set (MDS) in the case study data sets. In the indicator interpretation step, observed indicator data were transformed into unitless scores based on site‐specific algorithmic relationships to soil function. The scored data resulted in scientifically defensible and statistically different treatment means in the four case studies. The efficacy of the indicator interpretation step was evaluated with stepwise regressions using scored and observed indicators as independent variables and endpoint data as iterative dependent variables. Scored indicators usually had coefficients of determination ( R 2 ) that were similar or greater than those of the observed indicator values. In some cases, the R 2 values for indicators and endpoint regressions were higher when examined for individual treatments rather than the entire data set. This study demonstrates significant progress toward development of a SQ assessment framework for adaptive soil resource management or monitoring that is transferable to a variety of climates, soil types, and soil management systems.
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