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The Soil Management Assessment Framework

A Quantitative Soil Quality Evaluation Method

^a USDA-NRCS, Soil Quality Institute, 2150 Pammel Dr., Ames, IA 50011-4420
^b USDA-ARS, National Soil Tilth Lab., 2150 Pammel Dr., Ames, IA 50011-4420

View larger version (42K): [in a new window]   Fig. 1. Conceptual framework for the soil management assessment tool (after Andrews, 1998).

View larger version (20K): [in a new window]   Fig. 2. Scoring functions for soil test P, showing differences based solely on slope: for sites with 0–2% slopes, for 4–8% slopes, and >16% slopes. Other assumptions made to generate this example were: P was determined using Mehlich III, soils were planted to fescue, and inherent soil characteristics include medium high organic matter (approximately 3.5–5% total organic C), silt or silt loam texture, and only slight weathering. In this example, the inflection points for the ascending portion of the curve depend on primarily crop requirements while the descending portion inflection points are largely dictated by slope.

View larger version (35K): [in a new window]   Fig. 3. Scoring algorithms (multiplied by 10) for all indicators and case studies, illustrating the site-specific shifts in scores. AGG, water-stable aggregates; Db, bulk density; MBC, microbial biomass C; PMN, potentially mineralizable N; AWC, plant-available water-holding capacity; EC, electrical conductivity; SAR, sodium adsorption ratio; TOC, total organic C.

View larger version (49K): [in a new window]   Fig. 4. Examples of observed and scored indicator results, illustrating the four general relationships that occurred: (a) observed and scored results were equivalent (example is potentially mineralizable N from Natural Resource Inventory [NRI] cropped Xerolls data), (b) observed and scored results were opposite (example is soil test P from GA data), (c) observed results had significant differences but scored results did not (example is sodium adsorption ratio [SAR] from California data (org = organic; low = low input; conv 4 = 4-yr conventional rotation; conv 2 = 2-yr conventional rotation), and (d) observed results showed no significant differences among treatments but scored results were significantly different (example is soil pH from IA data). Treatments labeled with different letters are significantly different at = 0.05. Error bars represent one standard deviation from the mean. WS represents watershed.

View larger version (12K): [in a new window]   Fig. 5. Detailed example of observed and scored indicator results for soil pH in 1994 from the IA case study data. WS represents watershed.

View larger version (39K): [in a new window]   Fig. 6. Soil management assessment framework (SMAF) outcomes for the four case studies including ANOVA and Student's t results: (a) Natural Resources Inventory (NRI) data for all land uses and soil types; (b) NRI cropped Xerolls only, grouped by tillage type and cropping system; (c) Iowa (IA) data grouped by watershed (WS) or tillage type and sampling year; (d) California (CA) data grouped by vegetable production system (org = organic; low = low input; conv 4 = 4-yr conventional rotation; conv 2 = 2-yr conventional rotation); and (e) Georgia (GA) data grouped by site (or soil order) and amendment treatment (trt). Treatments or land uses labeled with different letters are significantly different at = 0.05. Error bars represent one standard deviation from the mean.