Douglas L. Karlen

Soil quality: why and how?

The soil quality concept evolved throughout the 1990s in response to increased global emphasis on sustainable land use and with a holistic focus emphasizing that sustainable soil management requires more than soil erosion control. The concept includes two areas of emphasis—education and assessment—both based soundly on principles of soil science. Soil quality test kits, farmer-based scorecards, visual assessment procedures, fact sheets, and video presentations were developed as educational materials because many people have no basis to recognize, understand or appreciate the complexity of soil resources. Assessment tools for indexing soil quality at various scales were pursued to show the multiple functions (e.g. nutrient and water cycling, filtering and buffering of contaminants, decomposition of crop residues and other organic matter sources, and recycling of essential plant nutrients) that soils provide as the foundation for sustainable land management. Worldwide research and technology transfer efforts have increased awareness that soil resources have both inherent characteristics determined by their basic soil formation factors and dynamic characteristics influenced by human decisions and management practices. Soil quality assessment and education are intended to provide a better understanding and awareness that soil resources are truly living bodies with biological, chemical, and physical properties and processes performing essential ecosystem services. D 2003 Elsevier Science B.V. All rights reserved.

Soil quality: Current concepts and applications

Soil quality has evolved as an educational and assessment tool for evaluating relative sustainability of soil resource management practices and guiding land-use decisions.This review discusses the rapid development of the soil quality concept throughout the decade of the 1990s,addresses misconceptions regarding soil quality efforts,and presents examples to illustrate how soil quality research,education,and technology-transfer activities are being used to help solve various soil resource and agroecosystemproblems.This review stresses that soil quality assessment re .ects biological,chemical,and physical properties,processes,and their interactions within each soil resource unit.By using examples from throughout the United States and around the world,we demonstrate the importance of using soil quality concepts to integrate both inherent and dynamic properties and processes occurring within a living,dynamic medium.We also emphasize that there is no ideal or magic soil quality index value by illustrating a framework for indexing that can be adapted to local conditions.The framework requires identifying critical soil functions,selecting meaningful indicators for those functions,developing appropriate scoring functions to interpret the indicators for various soil resources, and combining the information into values that can be tracked over time to determine if the soil resources are being sustained,degraded,or aggraded.This review is intended to provide a reference and background for land managers,resource conservationists,ecologists,soil scientists,and others seeking tools to help ensure that land-use decisions and practices are sustainable

Crop rotation effects on soil quality at three northern corn/soybean belt locations

This paper examines how three different rotations effect on soil quality and profitability.

Spatial Analysis of Soil Fertility Parameters

Databases identifying spatial distributions of soil properties are needed to implement site-specific management practices. This study examined spatial patterns for nine soil chemical properties in two adjacent fields, one in a corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation with inorganic fertilizer and the other in a 5-yr corn-soybean-corn-oat (Avena sativa L.)-meadow rotation with organic nutrient sources. We established sampling grids in both fields and collected soil cores to a depth of 30 cm. Soil properties with strong spatial correlations (low nugget variance/total variance ratio) and the maximum distance to which those properties were correlated (range) differed for the two fields. Soil pH, exchangeable Ca, total organic C, and total N were strongly correlated and had range values greater than 182 m in the conventional field. Bray P and exchangeable Mg were strongly correlated with range values of less than 100 m within the other. Low nugget/total variance ratios and small range values for P and Mg suggest patchy distributions, probably from long-term animal manure and municipal sludge application. Since most variance was structural in the organic field, placing sampling points closer together would improve data precision. In contrast, a relatively coarse sampling grid with fewer sampling points spaced further apart appears adequate for the conventional field. To develop accurate sampling strategies for precision agriculture, long-term field management histories should be documented since the practices appear to affect both the properties that are strongly correlated and the range to which the correlation exists.

Twelve-year tillage and crop rotation effects on yields and soil chemical properties in northeast Iowa

Abstract Long‐term tillage and crop management studies may be useful for determining crop production practices that are conducive to securing a sustainable agriculture. Objectives of this field study were to evaluate the combined effects of crop rotation and tillage practices on yield and changes in soil chemical properties after 12 years of research on the Clyde‐Kenyon‐Floyd soil association in northeastern Iowa. Continuous corn (Zea mays L.) and a corn‐soybean [Glycine max L. (Herr.)] rotation were grown using moldboard plowing, chisel plowing, ridge‐tillage, or no‐tillage methods. Tillage and crop rotation effects on soil pH, Bray P1, 1M NH4OAc exchangeable K, Ca, and Mg, total C, and total N in the top 200 mm were evaluated. Profile NO3‐N concentrations were also measured in spring and autumn of 1988. Crop yields and N use efficiencies were used to assess sustainability. Bray P1 levels increased, but exchangeable K decreased for all cropping and tillage methods. Nutrient stratification was evident for...

Soil and crop management effects on soil quality indicators

People are becoming more aware that our soil resources are as vulnerable to degradation as air or water, but criteria are needed to learn how soil quality is changing. Our objectives in this review are: (1) to illustrate that interactions between human and natural factors determine soil quality; (2) to identify indicators that can be used to evaluate human-induced effects on soil quality; and (3) to suggest soil and crop management strategies that will sustain or improve soil quality. The physical, chemical, and biological processes and interactions within the soil are critical factors affecting all indicators of soil quality. The biological processes are especially important because they provide much of the resiliency or buffering capacity to ameliorate stress. Presumably, no single soil or crop management practice will guarantee improved soil quality, but conservation tillage, cover crops, and crop rotations are practices that may be effective. Alley or narrow-strip cropping may facilitate adoption of several of those agronomic practices and increase temporal and spatial diversity across the landscape. To maintain or possibly improve soil quality and simultaneously address a growing waste disposal problem, we suggest that urban lawn and newspaper waste be evaluated as carbon sources. We conclude that the most critical factor, regardless of the soil and crop management strategy, is to recognize that carbon is an essential element for improving soil quality in the U.S. and around the world.

Relationship Between Six Years of Corn Yields and Terrain Attributes

Crop yield, soil properties, and erosion are strongly related to terrain attributes. The objectives of our study were to examine the relationship between six years of corn (Zea mays L.) yield data and relative elevation, slope, and curvature, and to develop a linear regression model to describe the spatial patterns of corn yield for a 16 ha field in central Iowa, USA. Corn grain yield was measured in six crop years, and relative elevation was measured using a kinematic global positioning system. Slope and curvature were then determined using digital terrain analysis. Our data showed that in the four years with less than normal growing season precipitation, corn yield was negatively correlated with relative elevation, slope, and curvature. In the two years with greater than normal precipitation, yield was positively correlated with relative elevation and slope. A multiple linear regression model based on relative elevation, slope, and curvature was developed that predicted 78% of the spatial variability of the average yield of the transect plots for the four dry years. This model also adequately identified the spatial patterns within the entire field for yield monitor data from 1997, which was one of the dry years. The relationship between terrain attributes and corn yield spatial patterns may provide opportunities for implementing site-specific management.

ANALYSIS OFWATER STRESS EFFECTS CAUSING SPATIAL YIELD VARIABILITY IN SOYBEANS

Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability has had limited success. In this article, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16-ha field in Iowa using three years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over three years. The root mean square error was 286 kg ha–1 representing approximately 12% of the three-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water run on into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.

Tillage and crop effects on ponded and tension infiltration rates

Abstract Tillage and residue management practices influence soil surface compaction and sealing, but the effects vary with time and weather history. The objective of this study was to compare surface ponded and tension infiltration rates for different tillages and crop rotations at various dates in a Kenyon loam. Four tillage systems (minimum tillage (no-till system with two cultivations), chisel, moldboard plow, and ridge-till) and two rotations (continuous corn and soybean-corn rotation with corn in 1991) were examined. Ponded and tension infiltration rates were measured in the row at four dates: 18 June 1991, 9 July 1991, 23 September 1991, and 11 May 1992. Soil was collected for measurement of aggregate stability and undisturbed soil cores were collected for measurements of saturated hydraulic conductivity and bulk density on the first measurement date to relate the infiltration measurements. Minimum tillage had significantly faster ponded infiltration than chisel or moldboard, as well as greater aggregate stability and less bulk density at that date because of reduced surface sealing. Tillage and crop rotation effects on infiltration for the other measurement dates were inconsistent. When the measurement dates were compared, ponded and tension infiltration rates for the first measurement date were less than for measurements at later dates because of a surface seal which was not present at the later dates. Temporal changes in infiltration were greater than tillage or rotation differences. To quantify management effects on surface soil properties we conclude that several well documented measurements are required. If possible, the measurements should be taken soon after rainfall events.

CROPPING SYSTEM EFFECTS ON NO3–N LOSS WITH SUBSURFACE DRAINAGE WATER

An appropriate combination of tillage and nitrogen management practices will be necessary to develop sustainable farming practices. A six–year (1993–1998) field study was conducted on subsurface–drained Clyde–Kenyon–Floyd soils to quantify the impact of two tillage systems (chisel plow vs. no tillage) and two N fertilizer management practices (preplant single application vs. late–spring soil test based application) on nitrate–nitrogen (NO3–N) leaching loss with subsurface drain discharge from corn (Zea mays L.) soybean (Glycine max L.) rotation plots. Preplant injected urea ammonium nitrate solution (UAN) fertilizer was applied at the rate of 110 kg ha–1 to chisel plow and no–till corn plots, while the late–spring N application rate averaged 179 and 156 kg ha–1 for the no–till and chisel plow corn plots, respectively. Data on subsurface drainage flow volume, NO3–N concentrations in subsurface drainage water, NO3–N loss with subsurface drainage flow, and crop yield were collected and analyzed using a randomized complete block design. Differences in subsurface drainage flow volume due to annual variations in rainfall significantly (P = 0.05) affected the NO3–N loss with subsurface drainage flows. High correlation (R2 = 0.89) between annual subsurface drainage flow volume and the annual NO3–N leaching loss with subsurface drainage water was observed. The flow–weighted average annual NO3–N concentrations varied from a low of 6.8 mg L–1 in 1994 to a high of 13.9 mg L–1 in 1996. Results of this study indicated that NO3–N losses from the chisel plow plots were 16% (16 vs. 19 kg–N ha–1) lower in comparison with no–till plots, while corn grain yield was 11% higher in the chisel plow plots (8.3 vs. 7.5 Mg ha–1). Late–spring N application applied as a sidedress resulted in 25% lower NO3–N leaching losses with subsurface drainage water in comparison with preplant single N application and also significantly (P = 0.5) higher corn grain yield by 13% (8.4 vs. 7.4 Mg ha–1). These results clearly demonstrate that chisel plow tillage with late–spring soil test based N application for corn after soybean can be a sustainable farming practice for the northeast part of Iowa.