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Soil Science Society of America Journal 64:300-311 (2000)
© 2000 Soil Science Society of America

DIVISION S-6-SOIL & WATER MANAGEMENT & CONSERVATION

Performance of Soil Condition Indicators Across Taxonomic Groups and Land Uses

L.A. Schippera and G.P. Sparlinga

a Landcare Research, Private Bag 3127, Hamilton, New Zealand

sparlingg{at}landcare.cri.nz


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 Materials and methods
 Results
 Discussion
 REFERENCES
 
Information on soil conditions in New Zealand is needed to assess soil quality at a national scale. We tested a standard set of 16 primary indicators at 29 sites (0–10 cm depth) across nine soil great groups with matched examples of indigenous forest, plantation forest, pastures, and crops. Soils under indigenous forest were acidic (pH 5.5–5.7), low in Olsen P (5–14 µg cm-3), with high microbial C (814–1228 µg cm-3), respiration (1.1–1.4 µg C cm-3 h-1), total C (31.8–52.9 mg cm-3), macroporosity (9.6–11.7% v/v), and total available water (29.2–31.5% v/v). Plantation forest soils had generally similar characteristics. Pasture soils were less acidic (pH 5.3–6.9) than forest soils, but with more available P (5.5–43.0 µg cm-3), higher total C (30.7–141.5 mg cm-3), total N (2.7–9.0 mg cm-3), and mineralizable N (68–175 µg cm-3). The physical condition was similar to forest soils. Cropped soil had low total C (20–34 mg cm-3), microbial C (160–956 µg cm-3), respiration (0.29–1.33 µg C cm-3 h-1), and total available water (6.7–30.1% v/v), but high pH (5.8–7.2), Olsen P (11.2–199 µg cm-3), and bulk density (0.96–1.3 g cm-3). Principal component analysis identified outlier sites and grouped land uses independently of soil great groups. Some indicators were less useful because of high variability (unsaturated hydraulic conductivity), correlation to other indicators (microbial C) or interpretation difficulties (respiration). Overall, the standardized approach provided useful information about soil conditions on a national scale.

Abbreviations: ANOVA, analysis of variance • CEC, cation-exchange capacity • CV, coefficient of variance • K-40, unsaturated conductivity at -40 kPa • PCA, principal components analysis


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
Materials and methods
 Results
 Discussion
 REFERENCES
 
INDICATORS OF SOIL QUALITY are required for environmental reporting; they help us to assess human and natural impacts on soils and to identify sustainable land-management practices (Doran and Parkin, 1994; Sims et al., 1997). Numerous soil chemical, biological, and physical characteristics have been suggested as suitable indicators for these purposes (Garlynd et al., 1994; Harris and Bezdicek, 1994; Jordan et al., 1995). Land administrators involved in regional and national reporting would prefer a standard set of indicators so that trends in soil condition across regions, soils, time, and land use can be readily discerned and analyzed. Various minimum data sets have been proposed (Harris and Bezdicek, 1994; Doran and Parkin, 1994; Pankhurst et al., 1994) but have been the subject of ongoing discussion. Currently, there is no consensus on a definitive data set for soil-quality monitoring, nor consensus on how the indicators should be interpreted. Part of this lack of consensus about soil-quality monitoring arises because different soil conditions are desirable, depending on the land use. There are various lengthy definitions of soil quality (e.g., see Doran and Parkin, 1994; Carter et al., 1997). To interpret soil condition in terms of soil quality we have used the concise fitness for use criteria suggested by Larson and Pierce (1994). In this definition, soil quality is defined in the context of matching the soil condition to those characteristics suitable (fit) for a particular land use. Also implicit in this brief definition is the capacity for the soil to maintain its fitness into the future.

There have been many studies on specific aspects of soil condition under different land uses, and investigators have used many different indicators and differing sampling strategies (e.g., Reganold et al., 1993; Schacht et al., 1996; Werner, 1997; Boehm and Anderson, 1997; Robertson et al., 1997; Staben et al., 1997). These studies provide good information for those particular soils and land-management practices, but comparison between these studies and others is difficult because of the variety of indicators and methodologies used. A standardized methodology may to be too broad when applied across contrasting soils and land uses. However, it is not practical to optimize sampling and analytical techniques for each soil and land use for extensive sampling on a national scale. Standardized methods are common in large-scale soil fertility studies where soils are usually sampled with a fixed-depth coring device and the same analyses and extraction methods are applied to all samples (e.g., the Olsen P test). Although the methods are standardized, the results are interpreted differently, depending on the soil group and land use (Saunders et al., 1987).

New Zealand has diverse soils and multiple land uses (Molloy, 1988). The responsibility for soil condition reporting lies with 12 Regional Councils who desired standardized methods for measurements. It was important that the methods could be applied to the diversity of soils and land uses in the different regions, were affordable, and internationally acceptable (Hortensius and Welling, 1996). We selected a set of soil condition indicators based on current international literature (e.g., Doran et al., 1994; Doran and Jones, 1996) and applied these indicators across 9 different soil great groups and multiple land uses. Our objective was to test whether a standard set of indicators could discriminate between land-management practices and provide soil condition information at a national scale.


   Materials and methods
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
Results
 Discussion
 REFERENCES
 
Soils and Land Use
The selection of sites and land uses was made after consulting Regional Council staff responsible for soil monitoring and reporting. Excluding contaminated or eroding soils, principal soil condition concerns raised by New Zealand land managers and regulators were those resulting from intensification or change in land use. Examples were impacts of long-term market gardening and continuous arable cropping, conversion of sheep pasture to intensive dairying, irrigation onto peat, and establishment of pine plantations on former indigenous forest or pastures. The priorities of the Regional Councils were to sample soils where a particular land use might be unsuitable for that great group, or where lack of information made it questionable whether the land use was sustainable. To examine effects of land use on soil condition, soils under more intensive land use, involving tillage and agrochemical use for cropping, were compared with those under less intensive use, such as long-term permanent pasture, plantation forests or indigenous forest vegetation. A consequence of sampling only a minimum number of at risk soils was an unbalanced experimental design; however, effects of different land uses on soil condition were assessed by selecting matched sites as described below.

Site Selection and Sampling
Soils were sampled from the Auckland, Waikato (North Island), and Canterbury (South Island) Regions, a geographical spread of more than 1000 km comprising nine soil great groups under different land uses (Table 1) . Matched sites on the same great group were selected that were in close (0.1–1 km) proximity and differed only in their land use. Each site was surveyed by hand augering to determine the uniformity of soil across the landscape. A pit was excavated and the soil profile examined (Milne et al., 1995). For each great group, matched sites with comparable profiles were selected and characterized. Site information included location, map reference, soil series and soil classification, land use, vegetation, slope, elevation, landform, annual precipitation, parent material, and soil drainage class (Milne et al., 1995). Not all land uses occurred on all nine soil great groups; land uses on each great group are shown in Table 1. At each site, five replicate plots, 5 by 5 m with 1-m buffer strips, were laid out along a 30-m transect. From within each plot, 25 cores were taken to a 10-cm depth across the sampling area at about 1-m spacing using a tube auger (2.5-cm diam.). Individual cores from each plot were bulked and mixed before analysis for chemical and biological characteristics. For physical analyses, undisturbed soil cores were obtained from each plot by pressing 75-mm-deep by 100-mm-diam. greased steel liners into the top 75 mm of soil. The cores were then excavated by digging around the liner (Cook et al., 1993).


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  		Table 1 Site identifier code, land use, soil texture, drainage class, altitude, and rainfall for 29 sites used to trial soil quality indicators. Within the dashed lines, sites on the same soil group were matched

 
Soil Condition Indicators
Indicators were selected to provide information on the chemical, physical, and biological condition of the soils (Table 2) . Also listed is the specific information provided by the indicators. This list is similar to those proposed by other researchers (Doran and Parkin, 1994; Larson and Pierce, 1994). From this data set it is also possible to derive further indicators, such as base saturation, readily available water, total available water, macroporosity, microbial quotient, and respiratory quotient, which have also been suggested as useful for soil-quality monitoring (Doran and Parkin, 1994; Larson and Pierce, 1994; Garlynd et al., 1994; Harris and Bezdicek, 1994; Jordan et al., 1995).


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  		Table 2 Soil indicators selected for assessing soil condition

 
Soils used to measure respiration (CO2 production) and microbial biomass were sieved <4 mm and equilibrated at a -5 kPa moisture content for 7 d at 25°C before measurement, to allow any effects of disturbance to subside. Respiration was measured by the accumulation of CO2 in the head space gases of 1-L sealed glass jars containing 25 g equivalent dry weight of soil (Sparling and Zhu, 1993). Incubation was for 7 d at 25°C. Microbial biomass was estimated by the fumigation–extraction method (Vance et al., 1987), with soluble C in 0.5 M K2SO4 measured by automated TOC Analyses (Wu et al., 1990), and microbial C calculated using a kEC of 0.41 (Sparling et al., 1998). Potentially mineralizable N was estimated by the anaerobic (waterlogged) incubation method; the increase in NH+4 concentration was measured after incubation for 7 d at 40°C and extraction in 2 M KCl (Keeney and Bremner, 1966).

Total C and N were determined by dry combustion on air-dried, finely ground soils using a Leco 2000 CNS analyzer (St. Joseph, MI). The cation exchange capacity was determined after leaching of <2 mm air-dry soil with 1 M CH3COONH4 at pH 7.0. Exchangeable cations Ca2+, Mg2+, K+, and Na+ were determined by flame spectrophotometer, and NH+4–N in KCl extracts was determined colorimetrically using standard autoanalyzer methods (Blakemore et al., 1987). Olsen P was determined by extracting <2-mm air-dried soils for 30 min with 0.5 M NaHCO3 at pH 8.5 (Olsen et al., 1954) and measuring the PO4 concentration by the molybdenum blue method. Soil pH was measured in water using glass electrodes and a 2.5:1 water-to-soil ratio (Blakemore et al., 1987).

Unsaturated hydraulic conductivity (-40-mm potential) was determined on soil cores by disk permeameter (Reynolds, 1993) and water release by drainage on pressure plates at 5, 10, 100, and 1500 kPa (Klute, 1986). Dry bulk density was measured on a subsampled core dried at 105°C (Klute, 1986) and the remaining soil was analyzed for particle size and density by the pipette method (Gee and Bauder, 1986). Readily available water, total available water, macroporosity, and total porosity were calculated as described by Klute (1986).

Statistical Analysis
For each soil property, one-way analysis of variance and Bonferroni pair-wise comparisons were used to determine the minimum significant difference (P < 0.05) between sites (SYSTAT, 1992). Some data items (Olsen P, respiratory quotient, unsaturated conductivity, and readily available water) did not fit a normal distribution and were log transformed for statistical comparisons. Both transformed and nontransformed data are presented.

The multivariate data set was examined using principal component analysis (PCA) to identify major patterns of variation (SYSTAT, 1992). All the soil condition indicators were included except the sand, silt, and clay contents, and the particle density, because comparing matched sites showed these inherent soil properties to be little affected by land-management practices. ANOVA of Factor 1 and Factor 2 scores was used to determine significant separation between land uses.


   Results
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
Discussion
 REFERENCES
 
General Soil Characteristics
The 29 soils in the trial fell into nine soil taxonomic great groups (Soil Survey Staff, 1992) and comprised four textural classes. They ranged from poorly drained to well-drained soils, at altitudes of 10 to 450 m, with annual rainfall between 625 to 1750 mm (Table 1). The proportions of sand, silt, and clay showed marked differences between soils (Table 5) , reflecting the widely differing mineralogy and texture of the range of soils examined. In general, particle size and particle density were consistent across land uses within each soil group. An exception was Soil 1, where the forestry and logging operations had mixed sandy subsoil material into the 0- to 10-cm layer.


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  		Table 5 Indicators of soil physical condition across a range of soils and land uses. Within the dashed lines, sites on the same soil group were matched

 
Land uses were grouped into four major categories: pastoral farming (including sheep, dairy, and beef stock), radiata pine (Pinus radiata D. Don) plantation, arable cropping (cereal and seed crops including market gardening for onions and brassica), and indigenous forest. Not all land uses occurred on all great groups.

Soil Condition on Different Great Groups
The soil condition characteristics differed depending on the great group (Tables 3, 4, and 5) . All units were corrected to a volumetric basis (Reganold and Palmer, 1995) to permit comparison between soils differing in bulk density, particularly the peat soils and the mineral soils. Some indicators showed little difference across great groups, whereas others, such as potentially mineralizable N, showed a 15-fold range (Table 3). Organic C, total N, and cation-exchange capacity (CEC) were high in the Medisaprist soils, and much lower in the Dystrochrepts (Table 4). Readily available water was greater in the Medisaprists, Udivitrands, and Hapludands than in the Fragiochrepts and Dystrochrepts (Table 5). Indicators generally showed greater differences between land uses than between great groups, the exceptions being particle size and particle density.


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  		Table 3 Indicators of soil biological condition across a range of soils and land uses. For site identifiers, see Table 1; within the dashed lines, sites on the same soil group were matched

 

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  		Table 4 Indicators of soil chemical condition across a range of soils and land uses. See Table 1 for site identifier codes. Within the dashed lines, sites on the same soil group were matched

 
Effect of Land Use on Soil Condition
The comparison of matched sites on a single great group allowed the effects of land use on soil condition characteristics to be measured. Except for Soil 1, where soil horizons had been mixed by the logging operations, the consistent particle sizes within a single great group confirmed that matched sites under different land uses had similar basic characteristics. Soil biological and chemical indicators generally showed greater change in response to land use than did the soil physical indicators. Differences between land uses are presented as box-and-whisker plots.

Cropped soils generally had less microbial respiration, biomass C, and potentially mineralizable N than the equivalent soil under pasture, radiata pine, or indigenous forest (Fig. 1) . Soil 8 under long-term market gardening had the lowest microbial biomass C (160 µg cm-3) and respiration (0.29 µg C cm-3 h-1) of all the soils tested.



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  		Fig. 1 Box-and-whisker plots of microbial respiration (µg CO2–C cm-3 h-1), microbial biomass (µg C cm-3 soil), and potentially mineralizable N (µg N cm-3 soil) under pasture, crop, indigenous forest, and radiata pine plantation sites on nine soil great groups in New Zealand. The median of each category is shown by the center line; the box hinges represent the interquartile range. The whiskers show the range of values that fall within 1.5 times the interquartile range. Values significantly (95%) greater or less than 1.5 or 3.0 times the interquartile range are displayed as asterisks or open circles, respectively

 
The chemical condition of the soils had been much modified by land use (Fig. 2) . Soil pH was greater under pastures, arable cropping, and market gardening than radiata pine plantation or indigenous forest. Total C was greatest under pasture and least under the cropping soils. Olsen P was lowest under the forested soils (<17 µg cm-3), higher under pasture (6–43 µg cm-3), and very high under market gardening (200 µg cm-3). Base saturation was lower on the forested sites than under arable cropping or pasture.



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  		Fig. 2 Box-and-whisker plots of soil pH, total C (µg C cm-3), and Olsen P (µg P cm-3 soil) under pasture, crop, indigenous forest, and radiata pine plantation sites on nine soil great groups in New Zealand. See Fig. 1 legend for explanation of box plots

 
Macroporosity was generally lower under pasture than other land uses, while arable cropping and market garden soils had a wide range in macroporosity (Fig. 3) . Under cropping, total available water was lower and bulk density was greater than under any other land use. There were generally few significant differences in readily available water or unsaturated conductivity that could be attributed to land use.



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  		Fig. 3 Box plots of total available water (% v/v), macroporosity (% v/v), and bulk density (Mg m-3) under pasture, crop, indigenous forest, and radiata pine plantation sites on nine soil great groups in New Zealand. See Fig. 1 legend for explanation of box plots

 
Principal Component Analysis
Principal component analyses confirmed the patterns observed with individual indicators in the matched site comparisons, with the added advantage of being independent of great group. Principal component analysis, using the set of 17 indicators, separated the different land uses into clear groupings (Fig. 4A) . Factors 1 and 2 explained 38 and 23% of the variance, respectively. Analyses of variance of the Factor 1 scores showed significant separation (P < 0.05) of arable cropping sites from pasture and forest sites. Similarly, ANOVA of Factor 2 scores showed significant separation (P < 0.05) of forest sites from cropping and pasture sites.



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  		Fig. 4 (A) Principal component plots of Factor 1 (38% of the variation) and Factor 2 (23% of the variation), showing grouping of sites under pasture, crop, indigenous forest, and radiata pine plantation, using all soil quality indicators. (B) A loading diagram showing how the various indicators influenced the separation and grouping: +, indigenous forest; {square}, pine forest; {triangleup}, pastures; {circ}, cropping and market gardening

 
The vector loading plot (Fig. 4B) showed those indicators with the greatest influence on the grouping and separation. The loadings that separated arable cropping from pastures were microbial biomass, microbial respiration, potentially mineralizable N, and total C, showing that, overall, arable cropping had decreased biological resources. The loading of Olsen P also contributed to the separation of the arable cropping soils from the pastures. The separation of forests from arable cropping and pastures along Factor 2 was influenced by loadings for macroporosity, total porosity, unsaturated hydraulic conductivity, pH, and base saturation. The separations along these vectors are consistent with the physical, chemical, and biological condition of the soils, as shown by the paired site comparisons.

Variability of Indicators
The overall variability of each indicator was obtained by calculating the coefficient of variation (CV) at each site and then averaging these across all sites. The greatest CVs were obtained with some of the soil physical measurements made on individual cores (Table 6) . Unsaturated hydraulic conductivity had a CV of 48% and macroporosity had a CV of 29%. In contrast, particle density and total porosity had low CVs of 1.2 and 3.5%, respectively. There was generally less variation in chemical and biochemical measurements made on bulked core samples: microbial respiration had the greatest CV (16%); total C and N were midrange (with CVs of 9.4 and 8.6%, respectively); and pH had the lowest CV (2.3%).


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  		Table 6 Coefficients of variation of physical, chemical, and biochemical indices used to measure soil quality

 

   Discussion
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
 Discussion
REFERENCES
 
Indicator Performance
The standard sampling strategy and indicator set clearly revealed the effects of land use on soil condition, both within and across great groups. Consistent trends in soil condition in relation to land use were detectable despite the wide geographic spread of the sampling sites. We suggest a standardized approach to soil sampling, and data analyses can form a useful set of core information for comparisons across soils and land uses at a national scale. The suggested approach is not intended to be exclusive, but it has been proven useful in showing consistent trends resulting from particular land uses and in identifying sites that were behaving in an unusual manner. For specific site investigations, we would anticipate that further add-on measurements would be included to provide more detailed information about the specific topic of concern. For example, it may be appropriate to take additional physical measurements from deeper parts of the soil profile to characterize soil compaction (Baldock and Kay, 1987; Veenhof and McBride, 1996).

Principal component analysis was a useful tool for examining information from the full set of indicators, and allowed data from multiple great groups and land uses to be condensed into two factors. We were able to group similar land uses and identify outlier sites, despite wide differences in soil groups and geographic location. Consequently, the need to identify carefully matched sites was reduced and allows much greater flexibility in site selection. The factor scores obtained by PCA are not unique soil-quality ratings because the scores will differ for different data sets and methods of analysis. Within a set of data, the factor scores provide a useful method for combining information from multiple indicators and identifying sites that differ from others under similar use.

There were several anomalous sites in our own set. One of the pasture sites grouped more closely with the arable cropping sites ( and ). The reason for this grouping was that the site was sampled soon after it had been trampled by cattle, which had resulted in lowered macroporosity and total porosity, and increased bulk density. These characteristics gave this particular pasture greater affinity with the arable cropping sites than other pastures. Another exceptional site was the market garden site ( , ), which showed clear separation from other arable cropping sites. The separation was caused by the very low scores for total C, total N, potentially mineralizable N, microbial biomass C, and microbial respiration, combined with high scores for chemical fertility (Olsen P) and greater bulk density. The ability to identify outlier sites allows monitoring efforts to be targeted to determine the reason for the anomaly.

Rationalizing a Minimum Data Set
Indicators are not useful if they have insufficient precision, can be estimated from other indicators, or cannot be readily interpreted (Doran et al., 1994; Doran and Jones, 1996). There were several items in our current data set in these categories. Regarding precision, we suggest that a suitable precision level for soil-condition indicators is to be able to detect a 10% change at the 90% confidence level. We consider this level of precision adequate for most agroecosystems because our responses are also relatively crude and with current methodologies we have no means to take advantage of greater precision. Several of our indicators showed too great a variability to meet even this 10% criterion. Unsaturated hydraulic conductivity had a CV of 48% and, on average, would need 147 samples from each site to be confident of detecting a 10% change in the mean value (Doran and Jones, 1996). A similar argument can be raised against macroporosity with a CV of 29%. On the basis of high variability and the impracticality of collecting many hundreds of intact core samples, it would seem logical to drop these measures from the indicator set. However, indicators with large variability may still be useful, provided the differences in the means are also large. Both unsaturated hydraulic conductivity and macroporosity showed nearly 500% changes in response to land use. On that basis, macroporosity is justified in being retained as an indicator because, despite the high variability, the differences between means were often significant (Table 5). In contrast, the variability of unsaturated hydraulic conductivity was so great that even very large differences between land uses were not significant.

Some indicators showed strong correlations with others and it may not be necessary to measure all of them. There was a strong linear correlation , P < 0.001) between microbial biomass C and potentially mineralizable N, which was also observed elsewhere (Hart et al., 1986; Myrold, 1987). The correlation suggests that the more readily determined measure of potentially mineralizable N could serve as a satisfactory surrogate for microbial biomass. Regression analyses suggest that CEC could be predicted from the clay content and total C. However, this latter relationship proved nonsignificant when tested on other data sets. Consequently, we advise caution in adopting surrogate indicators too freely, and suggest that multiple-regression techniques to reduce the number of measured soil-condition indicators should be applied only to well-proven relationships.

Further indicators can be rejected on the basis that they are not readily interpreted. We consider soil respiration and the derived respiratory quotient to fall into this category. It is by no means clear whether a high rate of respiration is a desirable characteristic. Low respiration could suggest low organic matter status, low microbial activity, or a toxic effect, all of which can be considered undesirable (Sparling, 1997). Conversely, high respiration may indicate rapid utilization of organic resources or a stress response, both of which are undesirable. Wardle and Ghani (1995) point out that a high respiratory quotient indicative of a stress response could also reflect an early stage of ecosystem development. We consider soil respiration will only be a useful indicator once defined thresholds and standards have been established.

To investigate whether a reduced set of indicators could still provide an adequate characterization of soil condition, we repeated PCA using only six indicators. These were selected on the basis that they provided information on the chemical, physical, and biological condition of soil, were readily measured, and had contributed to the vector plots in the PCA using the full set of data. Those selected were potentially mineralizable N, pH, bulk density, total C, Olsen P, and macroporosity. The reduced set still provided distinct groupings (Fig. 5A) . The factor loading (Fig. 5B) using the abbreviated indicator set gave similar reasons for separation between land uses, as did the full indicator set. For example, pasture sites had greater biological resources (greater potentially mineralizable N and total C) than arable cropping, and forestry sites had a more open soil structure (greater macroporosity and lower bulk density) than soils under pasture and arable cropping.



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  		Fig. 5 (A). Principal component plots of Factor 1 (32% of the variation) and Factor 2 (26% of the variation), showing grouping of sites under pasture, crop, indigenous forest, and radiata pine plantation, using a reduced set of six soil quality indicators. (B) A loading diagram showing how the individual indicators contributed to separation and grouping: +, indigenous forest; {square}, pine forest; {triangleup}, pastures; {circ}, cropping and market gardening

 
We advise caution in deleting too many indicators from the data set suggested in Table 2. While some reduction in indicators is clearly possible, there is a need to balance the cost of measuring individual indicators against the value of the information obtained. In our study, the majority of the costs were associated with site identification, description, and sampling rather than with the number and range of laboratory analyses. Streamlining the site identification and sampling strategy would be a more effective way of minimizing costs rather than reducing the number of analyses.

Soil Condition and Soil Quality
Our criterion for soil quality was to match the condition of a soil to the suitability or fitness for a particular use. For a given soil condition, the quality rating will vary depending on the land use. Quality rating will also change if the soil condition changes with time. In their natural, unmodified state, most soils in New Zealand are acidic, have low nutrient status, and have high organic-matter contents (N.Z. Soil Bureau, 1968). However, indigenous forest vegetation is adapted to low nutrient conditions (Wardle, 1991), and where this is the desired land use, the soil condition is fit for use. Soils under radiata plantation forest were also acidic, but were slightly greater in their total N status than were the indigenous forest sites. Plantation-forest management in New Zealand usually includes some N addition (Gadgill et al., 1984; Beets and Madgwick, 1988). The soil conditions were therefore suitable for the growth of plantation forest and the soil quality rating meets the fitness for use criterion.

The soil condition under pasture reflected the historical and maintenance inputs of fertilizer and lime from the time the forests were cleared and clover legumes (necessary for productive pastures on New Zealand soils) were established. Soil chemical status was increased, compared with the indigenous and plantation forests (which was also noted by Giddens et al., 1997). The biological condition was generally greater under pastures than any other land uses, but soil physical condition was variable, this being attributed to treading by livestock. Nongrazed pastures, for example those recently converted to pine plantation, had a physical condition similar to that of mature forests. The physical condition of the compacted pastures had not deteriorated to a point where the root environment was at risk (Scott-Russell, 1977; Webb and Wilson, 1994) and soil quality was again judged suitable for that land use.

Compared with pastures and forests, the overall trend was for long-term arable cropping and market garden soils to have less microbial biomass, respiration, and mineralizable N; have greater bulk density; be less porous; and have less readily available water. These characteristics are commonly found under cropping regimes (Ayanaba et al., 1976; Dalal and Mayer, 1986; Schimel, 1986; Havlin et al., 1990), but there are too few matched site comparisons in the present data set to attribute the differences solely to land use rather than characteristics of cropping soils. Where matched site comparisons were available (Soils 8 and 9), biological and physical condition under cropping followed the trends given above. Trends with time are an important aspect of sustainability and soil quality. In Soil 11, except for microbial biomass and mineralizable N, soil cropped for 30 yr had similar characteristics to that cropped for 10 yr, suggesting that the condition for this soil had stabilized at a new equilibrium level.

The fitness for use criteria are less readily determined for arable cropping and market garden soils. Compared with pastures and forest soils, the organic-matter status and soil physical condition have declined. Once depleted, the organic-matter content can take decades to be restored (Jenkinson et al., 1987; Parshotam and Hewitt, 1995), but these cropping soils may have achieved a new, albeit lower, equilibrium. The soils are being farmed profitably, and productivity has been maintained by better management, new cultivars, increased agrochemical inputs, irrigation, and mechanical cultivation. The risks to a sustainable cropping regime are that these greater inputs depend on farm profitability, and that increased agrochemical use may lead to more nutrient leaching and contamination of receiving waters. Off-site contamination of receiving waters is also a concern for intensively farmed pastures (Francis et al., 1992; Unwin and Smith, 1995). Whether these soils can be regarded as fit for use depends on the acceptance of these risks and the depletion of the organic matter resource.Gregorich Carter 1997; New Zealand Soil Bureau 1968


   ACKNOWLEDGMENTS
 
We thank Auckland, Canterbury, and Waikato Regional Councils who provided financial support and staff time, and the landowners who allowed us access to their properties. Landcare Research staff Malcolm McLeod, Les Basher, and Wim Rijkse provided the site descriptions, and laboratory staff at Hamilton and Palmerston North analyzed the soils. The trial was partially funded by the New Zealand Ministry for the Environment Sustainable Management Fund, and the Foundation for Research, Science, and Technology C09629.

Received for publication June 10, 1998.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 Materials and methods
 Results
 Discussion
 REFERENCES
 





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HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
The SCI Journals Agronomy Journal Crop Science
Journal of Natural Resources
and Life Sciences Education		 Vadose Zone Journal
Journal of Plant Registrations Journal of
Environmental Quality		 The Plant Genome