SSSAJ: Volume 73: Number 1 • January–February 2009 317

Soil Sci. Soc. Am. J. 73:317–327doi:10.2136/sssaj2007.0374Received 15 Oct. 2007.*Corresponding author (judithnyiraneza@yahoo.fr).© Soil Science Society of America677 S. Segoe Rd. Madison WI 53711 USAAll rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Crop yield and N uptake are infl uenced by soil properties, yet the identifi cation of soil attributes or indices most de-

terminant to crop yield and N nutrition is still a matter of debate. Nitrogen is often the most limiting nutrient to plant growth. Studies using N-labeled fertilizers showed that current year fertilization contributes to only 10 to 50% of the total N uptake by corn, whereas the rest comes from the soil N reserve (Omay et al., 1998; Stevens et al., 2005).

Net mineralization of soil organic N is infl uenced by cli-matic factors, such as temperature and precipitation regimes

(McDonald et al., 1995), but also by soil properties such a moisture, total N and C content, pH, clay content, pore-size distribution, pore volume, and microbial biomass (Breland and Hansen, 1996; Scott et al., 1996). Soil physical properties are also important to N mineralization because they control water movement, heat transfer, and aeration. For instance, Breland and Hansen (1996) showed that the mineralization rate of soil organic N was negatively correlated with soil pore size. Other studies reported that the mineralization rate of soil organic N was higher in macroaggregates than in microaggregates (Aoyama et al., 1999; Manna et al., 2006).

Some studies have shown that it is possible to explain part of the variability in crop yield by considering a set of soil attri-butes at the same time. Kravchenko and Bullock (2000) mea-sured a set of chemical properties and topographic variables in eight soils of Midwestern United States cropped to corn and soybean. They observed that 6 to 54% of the variability in crop yields was explained by the topographic variables, and the proportion of explained variability increased from 10 to 78% when the measured soil properties were included in the analysis. In contrast, Pierce et al. (1995) did not fi nd a signifi cant corre-lation between soil chemical properties and corn grain yield in

Judith Nyiraneza*Université LavalPavillon Paul Comtois, QuébecDépartement des Sols et Génie AgroalimentaireG1K 7P4, Canada

Adrien N’DayegamiyeInstitut de Recherche et de Développement en Agroenvironnement (IRDA)2700 Rue Einstein, QuébecG1P 3W8, Canada

Martin H. ChantignyAgriculture et Agroalimentaire Canada2560 Boul. Hochelaga, QuébecG1V 2J3, Canada

Marc R. LaverdièreInstitut de Recherche et de Développement en Agroenvironnement (IRDA)2700 Rue Einstein, QuébecG1P 3W8, Canada

NU

TRIEN

T MA

NA

GEM

ENT &

SOIL &

PLAN

T AN

ALY

SIS

Variations in Corn Yield and Nitrogen Uptake in Relation to Soil Attributes and Nitrogen Availability Indices

Identifi cation of soil attributes most determinant to crop yield is still a matter of debate. The main objective of the present study was to relate the variations in corn (Zea mays L.) yield and N uptake to 16 soil attributes. Samples were collected in 2005 and 2006 from a long-term experiment. Soil organic C (SOC), total N (TN), potential mineralizable N (PMN), NO3 extractable with KCl (NO3–KCl) and CaCl2 (NO3–CaCl2), NO3 adsorbed on anion exchange membranes (NO3–AEM), N extracted with NaHCO3 read at 205 nm (N-NaHCO3–205) and 220 nm (N-NaHCO3–220), N present in fulvic acid (FA-N), humic acid (HA-N) and non-humifi ed fractions (NHF-N), mean weight diameter of aggregates (MWD), total, macro- and microporosity, and bulk density (Db) were measured. Principal component analysis (PCA) was conducted with the measured soil attributes, and the principal components (PCs) were used in a stepwise regression with corn yield and N uptake. In both years, a maximum of 88% of the total variance was explained. The stepwise regression analysis indicated that the fi rst two PCs explained 78 to 91% of the variability in corn yield and N uptake. Based on the PCA, TN, HA-N, NO3–KCl, NO3–CaCl2, NO3–AEM, and PMN appeared as primary indicators of corn yield and N uptake, whereas MWD, FA-N, and NHF-N appeared as secondary indicators. When the variability in corn yield and N uptake explained by each N availability index was assessed, NO3–KCl and NO3–CaCl2 appeared as the best predictors of corn yield because of their ease of measurement and reliability across years.

Abbreviations: CV, coeffi cient of variation; Db, bulk density; FA-N, N in fulvic acids; HA-N, N in humic acids; MSA, measure of sampling adequacy; MWD, Mean weight diameter of water-stable soil aggregates; NHF-N, N in the non-humifi ed fraction; N-NaHCO3–205, N extracted by 0.01 M NaHCO3 and absorbance read at 205 nm; N-NaHCO3–220, the absorbance of a 0.01 M NaHCO3 extract at 220 nm; NO3–AEM, cumulative NO3–N adsorbed on anion exchange membranes; NO3–CaCl2, NO3–N extracted by 0.01M CaCl2; NO3–KCl, NO3–N extracted by 2 M KCl; PCA, Principal Component Analysis; PCs, Principle Components; PMN, potentially mineralizable nitrogen; SOC, soil organic carbon; TN, total Nitrogen; ***, signifi cant at the 0.001 probability level.

318 SSSAJ: Volume 73: Number 1 • January–February 2009

three Michigan soils, and concluded that soil physical proper-ties might be better indices. Shukla et al. (2004) indicated that an improvement in soil structure (e.g., increased water-stable aggregation, water retention, water infi ltration, and decreased soil Db) increased corn yield.

Reports by Giroux and Tran (1987) and Appel and Mengel (1993) showed that quick N availability indices tests such as the absorbance in ultraviolet of a soil extract by 0.01 M NaHCO3, or the NO3 extracted by KCl or CaCl2, could explain a sig-nifi cant proportion of the variability in crop yield. However, Walley et al. (2002) reported that basic soil properties, such as soil total C and total N, were more powerful in explaining the variability in wheat (Triticum aestivum L.) yield and N up-take than N availability indices, such as the N released during a 2 wk aerobic incubation, NO3 sorbed on anion exchange membranes, and N extracted with hot KCl.

Combining basic soil properties with N availability indi-ces could help to explain a greater proportion of the variability in crop yield and N uptake. One of the common problems encountered when using multiple regression analysis is mul-ticollinearity. This occurs when two or more parameters are highly correlated and provide redundant information to the regression model (Bowerman and O’Connell, 1990). Principal component analysis helps to avoid these problems by grouping highly correlated parameters into PCs. Those PCs can then be used as a new set of independent variables for regression analy-sis (Shukla et al., 2004).

In this study, PCA coupled with stepwise regression was used with a set of 16 soil attributes to identify soil properties that better explain the variations in corn yield and N uptake. The study used data measured on the 27th and 28th year of a long-term experi-ment with contrasting mineral and organic fertilizations.

MATERIALS AND METHODSSite Description and Experimental Design

The present study (2005–2006) was conducted on two long-term trials established in 1977 at the IRDA Experiment Station of Saint Lambert de Lauzon (46°35′ N; 72°12′ W), in the province of Quebec, Canada. The climate is temperate cold with a growing season varying between 111 and 144 d, mean annual temperature of 4°C, and mean annual precipitation of 1200 mm. The long-term trials are located on a clay loam (sandy over clayey, mixed, nonacid, frigid, Typic Humaquept). The initial soil pH (water) was 6.2, and the soil total C and N contents were 28 g C kg−1 and 2.2 g N kg−1, respectively.

One of the long-term trials was arranged in a split-plot design with annual dairy cattle (Bos taurus) manure application (0 and 20 Mg ha−1 on a wet weight basis) as the main factor, and mineral fertilizer treat-

ments (PK, NP, NK, NPK, NPKMg, and a control without fertilizer) in subplots. All experimental treatments were replicated three times and the soil was cropped to a corn–cereal rotation (Table 1). The subplot size was 8 × 10 m.

The second long-term trial was a randomized complete block de-sign with six fertilizer treatments (PK, NP, NK, NPK, NPKMg, and a control without fertilizer) replicated three times. The plot size was 8 × 10 m. Dairy cattle manure was applied at a rate of 20 Mg ha−1 yr−1 to all plots, and the soil was cropped to a corn–perennial grass rota-tion (Table 1).

In both trials, dairy cattle manure was applied in the fall. The manure contained on average 94 g C and 4.4 g N kg−1 dry matter, and each application provided on average 90 kg N ha−1 yr−1. Fertilizers were surface applied in spring before seeding. Nitrogen fertilizer was applied at a rate of 160 kg N ha−1 as NH4NO3. The fertilizer rates for P2O5 and K2O differed in plots with and without manure ap-plication. With manure, P was applied as super-phosphate at a rate of 40 kg P2O5 ha−1, and K was applied at a rate of 60 kg K2O ha−1 as potassium chloride. Without manure, P2O5 and K2O rates were 60 and 120 kg ha−1, respectively.

The present study was conducted on only six treatments of the fi rst long-term trial (Control, PK, and NPKMg, with and without manure), and three treatments of the second trial (Control, PK, and NPKMg). Manure was applied from 1977 to 2003, and mineral N was not applied to the NPKMg treatment to assess soil N availability as a function of cropping history. In all, 27 plots with 9 different cropping histories were sampled. During 2005 and 2006, all plots were cropped to silage corn, var. Hyland (HL-S009; 2250 Corn Heat Units).

Soil Sampling and PreparationSoil samples were collected in spring 2005 and 2006 for analy-

ses. In each experimental plot, six soil cores were taken randomly to a depth of 20 cm with a 2-cm diameter auger (Oakfi eld model B, Oakfi eld Apparatus Co., Oakfi eld, WI) and mixed to make a compos-ite sample. Each composite sample was sieved at 2 mm. Field-moist soil was used to assess NO3–KCl and NO3–CaCl2, whereas air-dried soil was used to assess PMN, N-NaHCO3–205, and N-NaHCO3–220. A subsample of air-dried soil was ground to pass a 0.15-mm sieve for SOC and TN analysis.

Additional soil samples were collected in late summer 2005 for phys-ical analyses. Two intact cores per plot (6.5 cm height × 6.5 cm diam.) were collected to determine soil Db and porosity, whereas one intact soil block of about 500 g was taken in each plot with a spade to a depth of 20 cm to assess water stable aggregation. The soil blocks were sieved at 8 mm and kept at 4°C until analysis.

Total N, SOC, MWD, Db, FA-N, HA-N and NHF-N were measured only in 2005, assuming that these soil attributes were stable during the period of study. On the contrary, all N availability indi-

Table 1. Crop rotations in the two long-term fi eld trials.

Year Crop sequenceThe fi rst experiment

1977–1985 i) Corn (Zea mays L.); ii) corn; iii) wheat (Triticum aestivum L.); iv) beetroot (Beta vulgaris).

1986–1989 i) Corn; ii) corn; iii) wheat; iv) Canola (Brassica campestris)

1990-Present i) Corn; ii) corn; iii) wheat; iv) barley (Hordeum Vulgaris L.)

The second experiment

1978–1994 i) Corn; ii) corn; iii) corn; iv) corn

1995-Present i) Corn; ii) barley underseeded with red clover (Trifolium pretense L., timothy (Phleum pratense L.), millet (Pennisetum glaucum). The prairie phase is kept for 3 yr before cultivating to corn.

SSSAJ: Volume 73: Number 1 • January –February 2009 319

ces (NO3–KCl, NO3–CaCl2, NO3–AEM, PMN, N-NaHCO3–205, and N-NaHCO3–220), corn yield and N uptake were measured in each year of the study because they vary among years (Table 2).

Soil Physical AnalysisBulk density was measured using the soil cores described above.

The same cores were used to assess total, macro-, and microporos-ity following the method of Klute (1986) with slight modifi cations. Briefl y, the cores were saturated by total immersion in water for 24 h. The cores were weighed and then fi tted to a tension table placed at 50 cm (draining pores > 60 μm) for 24 h. The cores were again weighed and returned to the tension table placed at 100 cm (draining pores > 30 μm) for 24 h. The cores were weighed, and fi nally dried at 105°C for 24 h and weighed again. Calculations were made as follows:

total porosity = EDA − [1]

macroporosity (>60 μm) = EBA − [2]

mesoporosity (30–60 μm) = ECA − [3]

microporosity (<30 μm) = total porosity – (macro- + mesoporosity) [4]

where A is the weight (g) of water-saturated soil core, B is the weight (g) of soil core after equilibration on the tension table at 50 cm, C is the weight (g) of soil core after equilibration on the tension table at 100 cm, D is the weight (g) of soil core dried at 105°C, and E is the total volume of a soil core.

Water-stable aggregation was determined by the wet-sieving method (Kemper and Rosenau, 1986). Briefl y, 40 g (wet weight basis) of soil sieved at 8 mm were put on the top of a series of sieves (5, 2, 1, and 0.25 mm) and the sieves were immersed in water and gently shaken for 10 min. Soil remaining on the four different sieves was col-lected in pre-weighed beakers, and oven dried at 65°C until constant weight. The aggregate weight on each sieve was corrected for sand content and expressed as a percentage of the total dry soil. The MWD was calculated according to Kemper and Rosenau (1986).

Soil Nitrogen Availability IndicesSoil NO3 was extracted with either 2 M KCl (Bremner, 1965),

or 0.01 M CaCl2 (Houba et al., 1986). Labile N was extracted with 0.01 M NaHCO3 (MacLean, 1964), and the UV absorbance of the extracts was read at 205 and 220 nm with a UV spectrometer (Model Spectronic 1201, Milton Roy, Ivyland, PA). The PMN of soil was as-sessed by a 7-d anaerobic incubation as described by Keeney (1982). Briefl y, 10 g of air-dried soil sieved at 2 mm was mixed with 25 mL of distilled water and incubated using plastic bottles with airtight closure (Desi-Vac, Cole-Parmer, Vernon Hills, IL) at 40°C for 7 d. A particular attention was paid to ensure that air was pumped out before closing

Table 2. The descriptive statistics including mean, standard deviation (SD), coeffi cient of variation (CV), kurtosis, skewness, minimum and maximum values for corn (Zea mays L.) yield, N uptake, and soil physical and chemical properties in 2005 and 2006.

Variables Mean Median SD CV Kurtosis Skewness Minimum Maximum

2005Yield, kg ha−1 9035 8235 4563 50 −1.1 0.36 2789 17752

N uptake, kg ha−1 77 62 56 72 −1.1 0.66 14 186

Db, g cm–3 1.3 1.3 0.10 7.8 −0.2 −0.09 1.1 1.5

Total porosity, % 55 53 5.1 9.1 0.1 0.77 47 68

Macroporosity, % 15 12 6.5 42 1.02 15.3 8.1 33

Microporosity, % 24 26 7.8 32 −0.6 −0.55 6.2 36

MWD, mm 3.3 3.5 0.5 15 0.59 -.055 1.9 4.3

TN, % 0.19 0.18 0.04 21 −0.62 0.16 0.11 0.26

SOC, % 2.6 2.5 0.6 22 −0.21 0.0015 1.30 3.7

NHF-N, mg kg−1 3796 3845 55 14 8.1 −2.02 165 470

FA-N, mg kg−1 135 137 24 17 2.6 −1.06 58 173

HA-N, mg kg−1 364 330 138 37 −0.43 0.43 101 622

NO3–KCl, mg kg−1 8.6 8.03 4.1 47 −0.89 0.43 2.6 17

NO3–CaCl2, mg kg−1 8.00 8.03 4.1 47 −0.89 0.43 2.6 17

PMN, mg kg−1 37 38 11.2 29 −0.99 −0.11 16 54

N-NaH,O3–205 1.2 1.2 0.2 18 0.83 0.37 0.88 1.62

N-NaHCO3–220 0.8 0.8 0.11 13 −0.68 0.34 0.65 1.05

NO3-AEM, g cm−2 80 d−1 62 48 33 54 −1.02 0.74 22 125

2006

Yield, kg ha−1 4316 3214 3469 80 0.79 1.3 1049 13298

N uptake, kg ha−1 34 22 28 84 1.02 1.4 8 110

NO3–KCl, mg kg−1 6. 6 6.1 2.8 43 −0.38 0.54 2.3 1310

NO3–CaCl2, mg kg−1 6.7 6.3 2.8 40 −0.57 0.53 2.7 12

PMN, mg kg−1 41 42 11 28 −0.19 0.03 17 66

N-NaHCO3–205 1.003 0.98 0.13 13 −0.23 0.25 0.71 1.28

N-NaHCO3–220 0.71 0.71 0.08 11 0.10 0.07 0.53 0.89NO3-AEM, μg cm−2 90 d−1 104 94 49 47 0.21 0.74 26 233

320 SSSAJ: Volume 73: Number 1 • January–February 2009

the bottles. Soil KCl-extractable NH4–N concentration was measured before incubation and subtracted from the fi nal NH4–N concentration measured at the end of the 7-d incubation to estimate PMN.

Anion exchange membranes fi xed into plastic probes (Plant Root Simulator, Western Ag. Innovations, SK, Canada) were used to moni-tor soil NO3 availability over the growing season (NO3–AEM). Two membranes were inserted in the central row of each plot. The mem-branes were attached to a wire to facilitate retrieval and to minimize soil disturbance. The membranes were changed every 10 d and re-placed with new ones. Preparation and extraction of the membranes were performed following the supplier protocol (http://www.wester-nag.ca/innov/prsprobe.php). Confi rmed 10 Nov. 2008). Membranes were charged by soaking in 0.5 M NaHCO3 for four consecutive 1-h intervals while being shaken at 100 rpm. Membranes were rinsed thoroughly with deionized water before installation in the fi eld. After removal from the soil, the membranes were cleaned with distilled wa-ter to remove attached soil particles. Each membrane was placed in a resealable plastic bag with 17.5 mL of 0.5 M HCl and extracted on a reciprocal shaker at 100 rpm for 1 h. The NO3–N concentration of extract was measured.

Determination of NO3–N and NH4–N in the various soil extracts was performed on an automated colorimeter (Model AAII, Technicon Instruments, Tarrytown, NY).

Soil Organic Carbon, Total Nitrogen, and Humic Fractions

Soil organic C content was determined using the Walkley–Black method (Allison et al., 1965), and total N by the microKjeldhal method (Bremner, 1965). Humic substances were extracted accord-ing to the method described by Schnitzer et al. (1981). Ten grams of air-dried soil ground to pass a 0.15-mm sieve was placed in polyeth-ylene 250-mL centrifuge tubes containing 100 mL of 0.1 M NaOH and 0.1 M Na4P2O7 10H2O. The tubes were shaken for 24 h on a reciprocating shaker and centrifuged for 20 min at 3000 × g. The supernatant was decanted and centrifuged for 15 min at 1500 × g. To precipitate humic acids, a 25-mL aliquot of the supernatant was acidifi ed to pH 2 with H2SO4 50%, and humic acids were allowed to precipitate for 24 h at 4°C. The humic acids were then separated from fulvic acids by centrifugation for 15 min at 1500 × g. The precipitated humic acid fraction was oven-dried at 45°C and redissolved in 25 mL of 0.5 M NaOH.

The fulvic acids in the supernatant were then separated from the nonhumifi ed fraction by adsorption onto polyvinylpyrolidone (Sequi et al., 1986). A column containing 12 g of polyvinylpyrolidone resin (Aldrich, Germany) was equilibrated with 0.005 M H2SO4. A 25-mL aliquot of extract was passed through the column and then rinsed with 0.005 M H2SO4 to remove all nonhumifi ed materials. The sorbed fulvic acids were eluted with 0.5 M NaOH. The three frac-tions obtained were stored at 4°C until analysis. The N concentration in the HA-N, FA-N, and in the NHF-N fractions was determined using alkaline persulfate oxidation (Cabrera and Beare, 1993).

Corn Yield and Nitrogen UptakeThe two innermost rows of corn of each experimental plot were

harvested in late autumn. Entire corn plants were passed in a one-furrow chopper and the fresh material was weighed. Eight subsamples of the chopped material were taken to make one composite sample per plot. The samples were dried at 60°C until constant weight to de-termine dry matter content. Tissue N was extracted by microKjeldhal

digestion (Bremner, 1965) and concentration determined on an auto-mated colorimeter (Model AAII, Technicon Instruments, Tarrytown, NY). Corn N uptake was calculated as the product of tissue N con-centration and dry matter yield.

Statistical AnalysisThe mean and the median of each variable were used as primary

estimates of the central tendency. Standard deviation, coeffi cient of variation (CV), kurtosis and skewness were used to describe the de-gree of variability in the measured soil attributes, and in corn N up-take and grain yield using PROC UNIVARIATE of SAS (SAS, 1999). The Kaiser’s measure of sampling adequacy (MSA) was estimated (Kaiser, 1974). The PCA is not suitable when the overall MSA is <0.6 (Kaiser, 1974) as smaller values indicate that the correlation between each pair of measured parameters is unique, that is, not related to the remaining variables outside each single correlation (Wuensch, 2004). The calculated Kaiser’s MSA was 0.79 in 2005 and 0.77 in 2006. The PCA could thus be performed for both years.

A PCA was performed using the PROC FACTOR of SAS with the 16 measured soil attributes. Those attributes were grouped into PCs based on their correlation matrix. Principal component analy-sis method was used to extract PCs because it does not require pri-or estimates of the variability of each soil attribute included in the procedure (Kaspar et al., 2004). Its main purpose is to derive linear combinations (PCs) of a set of variables that retain most of the infor-mation contained in these variables (Kaspar et al., 2004). Only PCs with eigenvalues >1 were retained for the regression analysis because PCs with eigenvalues <1 explain less variance than a single measured attribute. The retained PCs were rotated orthogonally with the vari-max option of SAS to maximize the correlations between PCs and the measured soil attributes. A measured soil attribute was assigned to a PC for which it had the highest loading value. All data were com-bined to remove stochastic effects of treatments and cropping history (Shukla et al., 2004) and Pearson’s correlation coeffi cients between corn yield and N uptake and the soil attributes were calculated using the CORR procedure of SAS.

Scoring coeffi cients were obtained by using the SCORE proce-dure of SAS and a stepwise regression was conducted using corn grain yield or nitrogen uptake as the dependent variables and scoring coef-fi cients for the retained PCs as independent variables.

To assess the proportion of variability in corn yield and N uptake that could be explained by each of N availability index, single linear regressions were performed using the PROC REG of SAS.

RESULTS AND DISCUSSIONCorrelations between Corn Yield and Nitrogen Uptake and Measured Soil Properties

The mean and the median were close to each other for most of the measured parameters (Table 2), meaning that the outliers did not dominate the measures of central tendency (Cambardella et al., 1994). The measured soil attributes had a CV varying from 8 to 72%, with corn yield, N uptake and N availability indices showing the highest CVs.

Corn yield and N uptake were positively correlated with each other (Table 3). Corn yield and N uptake were negatively correlated to Db and positively related to total porosity and MWD. Even though the Db values measured in this study did not appear unusually high, those fi ndings suggest that even a slight increase in Db was enough to signifi cantly reduce the soil

SSSAJ: Volume 73: Number 1 • January –February 2009 321

total porosity and MWD, which negatively affected crop growth. Previous studies have demonstrated the negative impact of compaction on crop yield and N uptake (Castillo et al., 1982; Bakken et al., 1987; Rachman et al., 2003).

Corn yield and N uptake were positively correlated to TN and SOC (Table 3). Soil organic C and TN may not be sensitive indicators of short-term changes in soil qual-ity (Gregorich et al., 1994). However, the close correlations (0.74 < r < 0.82) found between SOC and TN, and corn yield and N uptake in this study (Table 3) suggest that on the long-term basis, these two parameters can represent good indicators of the overall soil fertility.

Corn yield and N uptake were highly correlated (r > 0.80***) with all N availability indices (Table 3). This is in agreement with Giroux and Tran (1987) and Smith and Shengxiu (1993) who also found that different N availability indices could explain a large proportion of the variability in corn yield and N uptake. In the present study, all N avail-ability indices tended to be correlated with TN (0.71 < r < 0.88), SOC (0.68 < r < 0.87), MWD (0.52 < r < 0.69), and total porosity (0.60 < r < 0.71), suggesting that more N is available in soils with a higher quality. Previous studies have also reported a positive relationship between soil N avail-ability and other soil attributes, such as texture and structure (Breland and Hansen, 1996; Scott et al., 1996), TN, and SOC (Sahrawat, 1980; Beauchamp et al., 2003).

Corn yield and N uptake were also positively correlated with HA-N, FA-N and NHF-N, and the strength of the re-lationship decreased in the order: HA-N > FA-N > NHF-N (Table 3). According to Stout et al. (1981), the nonhumi-fi ed fraction accounts for 15 to 25% of SOC and includes living and dead roots, decomposing organic residues, and microbial metabolites. In contrast, humic acids have long been defi ned as recalcitrant macromolecules (Swift, 1999). The fact that HA-N was more strongly correlated to corn yield than NHF-N was unexpected. However, recent studies suggested that the humic acid may be aggregates of smaller heterogeneous organic molecules including sugars, organic acids and others aliphatic and aromatic components where small molecules are loosely held together by H-bonding and hydrophobic forces, instead of covalent bonded cross linkages (Piccolo et al., 2002). In agreement, the positive relationship between HA-N and corn yield and N uptake suggests that the humic acid fraction may include a reservoir of relatively labile organic matter which may contribute to plant nutrition.

Grouping of Soil PropertiesIn 2005, the PCA showed eigenvalues >1 for the fi rst

three PCs, which accounted for 87% of the variance (Table 4). In 2006, four PCs were retained accounting for 88% of the total variability (Table 5). In 2005 and 2006, the rotated factor pattern and the communality estimates (CE, portion of the variance explained by PCs for each measured parameter) were high and showed that the retained PCs explained >80% of most measured parameters except Db, MWD and FA-N in 2005(Table 4), and MWD in 2006 (Table 5).

In 2005, the fi rst PC explained 65% of the total variance and had high positive loadings for TN, HA-N, NO3–KCl, NO3–

CaCl2, NO3–AEM, N-NaHCO3–205, N-NaHCO3–220, and PMN (Table 4). The second PC explained 13% of the variance and had high loadings for MWD, SOC, NHF-N, FA-N and a negative loading for Db. The third PC explained 9% of the variance and had positive loadings for total porosity and macroporosity, and a negative loading on microporosity.

Tabl

e 3.

Pea

rson

’s c

orre

lati

on c

oeffi

cie

nts

amon

g so

il ph

ysic

al, c

hem

ical

pro

pert

ies

and

corn

yie

ld a

nd N

upt

ake

in 2

005.

Yie

ldN

upta

keD

bTo

tal

por

osit

yM

acro

poro

sity

Mic

ropo

rosi

tyM

WD

TNSO

CN

HF-

NFA

-NH

A-N

NO

3–K

Cl

NO

3–C

aCl2

PMN

N-N

aHC

O3–

205

N-N

aHC

O3–

220

Nup

take

0.97

***

Bul

k de

nsity

−0.5

1**

−0.4

3*

Tota

l por

osity

0.66

***

0.63

***

−0.6

1***

Mac

ro-p

oros

ity0.

300.

30−0

.39*

0.88

***

Mic

ropo

rosi

ty−0

.05

−0.0

60.

20−0

.67*

**−0

.89*

**

MW

D0.

65**

*0.

58**

−0.5

0**

0.54

**0.

27−0

.10

TN0.

82**

*0.

78**

*−0

.66*

**0.

61**

*0.

250.

060.

66**

*

SOC

0.80

***

0.74

***

−0.7

7***

0.66

***

0.31

−0.0

20.

68**

*0.

95**

*

NH

F-N

0.46

*0.

38*

−0.6

7***

0.44

*0.

20−0

.05

0.57

**0.

68**

*0.

76**

*

FA-N

0.55

**0.

50**

−0.5

1**

0.52

**0.

32−0

.19

0.56

**0.

59**

*0.

66**

*0.

81**

*

HA

-N0.

92**

*0.

91**

*−0

.59*

*0.

69**

*0.

36−0

.05

0.67

***

0.91

***

0.90

***

0.65

***

0.69

***

NO

3–K

Cl

0.92

***

0.92

***

−0.3

8*0.

64**

*0.

32−0

.07

0.57

**0.

73**

*0.

70**

*0.

40*

0.53

**0.

87**

*

NO

3–C

aCl 2

0.92

***

0.92

***

−0.3

70.

62**

*0.

29−0

.04

0.53

**0.

74**

*0.

72**

*0.

40*

0.52

**0.

87**

*0.

99**

*

PMN

0.85

***

0.79

***

−0.7

2***

0.67

***

0.33

−0.0

10.

69**

*0.

88**

*0.

87**

*0.

59**

0.55

**0.

88**

*0.

82**

*0.

82**

*

N-N

aHC

O3–

205

0.91

***

0.90

***

−0.5

9**

0.70

***

0.37

−0.1

00.

58**

0.84

***

0.83

***

0.48

**0.

49**

0.91

***

0.91

***

0.92

***

0.89

***

N-N

aHC

O3–

220

0.85

***

0.83

***

−0.6

7***

0.71

***

0.40

*−0

.13

0.56

**0.

85**

*0.

84**

*0.

49**

0.45

*0.

88**

*0.

81**

*0.

83**

*0.

89**

*0.

97**

*

NO

3–A

EM0.

91**

*0.

95**

*−0

.36

0.60

***

0.30

−0.0

70.

52**

0.71

***

0.68

***

0.36

0.53

**0.

86**

*0.

90**

*0.

90**

*0.

72**

*0.

87**

*0.

80**

*

*Sig

nifi c

ant a

t 0.0

5 pr

obab

ility

leve

l.

**Si

gnifi

cant

at 0

.01

prob

abili

ty le

vel.

***S

igni

fi can

t at 0

.001

pro

babi

lity

leve

l.

322 SSSAJ: Volume 73: Number 1 • January–February 2009

In 2006, the fi rst PC explained 59% of the total variance and had positive loadings for TN, SOC, HA-N, NO3–KCl, NO3–CaCl2, NO3–AEM, and PMN (Table 5). The second PC explained 15% of the variance and had positive loadings for MWD, NHF-N, and FA-N. The third PC explained 8% of the variance and had positive loadings for N-NaHCO3–205, N-NaHCO3–220, and a negative loading for Db. The fourth PC explained 6% of the variance and had positive loadings for total porosity and macropo-rosity, and a negative loading on microporosity.

Variation in Corn Yield and Nitrogen Uptake Explained by Retained PCs

In 2005, the stepwise regression analysis with corn yield (Y ) and N uptake (N) as dependent variables and PCs as inde-pendent variables indicated the following relationships:

Y = 9035 + 4025 × PC1 (r2 = 0.78; P < 0.001) [5]

Y = 9035 + 4025 × PC1 + 1643 × PC2 (r2 = 0.91; P < 0.001) [6]

N = 77 + 51 × PC1 (r2 = 0.84; P < 0.001) [7]

N = 77 + 51 × PC1 +14 × PC2 (r2 = 0.90; P < 0.001) [8]

The regression model including only the fi rst PC (Eq. [5] and [7]) explained 78% of the variability in corn grain yield and 84% for N uptake. Adding the second PC to the model (Eq. [6] and [8]) raised the proportion of explained variability to 91 and 90%, respectively.

In 2006, the stepwise regression analysis resulted in the following relationships:

Y = 4317 + 3014 × PC1 (r2 = 0.75; P < 0.001) [9]

Y = 4317 + 3014 × PC1 + 851 × PC2 (r2 = 0.81; P = 0.01) [10]

N = 34 + 24 × PC1 (r2 = 0.73; P < 0.001) [11]

N = 34 + 24 × PC1 + 6.6 × PC2 (r2 = 0.78; P = 0.02) [12]

Overall, for 2005 and 2006, the regression equations with the fi rst PC explained 73 to 84% of the annual variability in corn yield and N uptake (Eq. [5], [7], [9], [11]). The fi rst PC was as-sociated in both years with soil TN, HA-N, NO3–KCl, NO3–CaCl2, NO3–AEM, and PMN (Tables 4, 5), which could be considered as primary indicators of soil N availability and corn yield. The addition of the second PC in the model explained up to 91% of the variability in corn yield and N uptake (Eq. [6], [8], [10], [12]). The second PC was consistently associated with MWD, FA-N and NH-N (Tables 4, 5), which could then be considered as secondary indicators of soil N availability and corn yield.

The proportion of variability in corn yield and N uptake that could be explained by the retained PCs in this study was higher than what was reported in previous studies. Cox et al. (2003) used PCA to predict soybean yield considering soil fer-tility parameters (Ca, Mg, K, P, pH), clay content, and the topographic features of three different fi elds. The total variance explained by all measured parameters varied from 70 to 79%, and only 20 to 54% of the variability in soybean yield was explained by a regressive model based on retained PCs. Shukla et al. (2004) investigated the relationships between corn yields

Table 4. Rotated principal components (PCs) and total com-munality estimates (CE) in 2005.

Soil attributes† PC1 PC2 PC3 CE

Db −0.78 0.72

Total porosity 0.74 0.96

Macroporosity 0.95 0.97

Microporosity −0.97 0.95

MWD 0.61 0.57

TN 0.68 0.89

SOC 0.74 0.93

NHF-N 0.92 0.87

FA-N 0.74 0.66

HA-N 0.79 0.94

NO3–KCl 0.93 0.93

NO3–CaCl2 0.94 0.94

PMN 0.75 0.88

N-NaHCO3-205 0.89 0.96

N-NaHCO3-220 0.82 0.88

NO3–AEM 0.90 0.87

Eigenvalues 10.34 2.18 1.46

Variance proportion 0.65 0.13 0.09

Cumulative variance 0.65 0.78 0.87†A measured soil attribute was assigned to a PC for which it had the highest loading value.

Table 5. Rotated principal components (PCs) and total com-munality estimates (CE) in 2006.

Soil attributes† PC1 PC2 PC3 PC4 CE

Db −0.66 0.83Total porosity 0.78 0.95

Macroporosity 0.96 0.97

Microporosity −0.96 0.95

MWD 0.55 0.59

TN 0.71 0.89

SOC 0.63 0.94

NH-N 0.87 0.89

FA-N 0.83 0.83

HA-N 0.80 0.94

NO3–KCl 0.92 0.96

NO3–CaCl2 0.91 0.96

PMN 0.72 0.93

N-NaHCO3–205 0.84 0.96

N-NaHCO3–220 0.95 0.96

NO3–AEM 0.76 0.96

Eigenvalues 9.46 2.37 1.30 1.05

Variance proportion 0.59 0.15 0.08 0.06Cumulative variance 0.59 0.74 0.82 0.88

†A measured soil attribute was assigned to a PC for which it had the highest loading value.

SSSAJ: Volume 73: Number 1 • January –February 2009 323

and various soil chemical and physical properties using PCA, and reported that 24 to 42% of the variability in yield could be explained. Using various soil properties and topographic data, Jiang and Thelen (2004) were able to explain 28 to 85% of corn and soybean yields, depending on the growing season. The higher proportion of variability explained by selected soil attributes in our study might be because the studied plots were located close to each other on a site with relatively uniform topographic characteristics.

A slightly larger proportion of the variability in corn yield and N uptake could be explained in 2005 than in 2006 (Eq. [5–12]). Corn growth and productivity may be affected by several external fac-tors (Beauchamp et al., 2004). The soil attributes measured in our study were not the sole limiting factors, and annual variations in precipita-tion, soil temperature and moisture may have impacted corn yield and N uptake (Soon et al., 2006). Variation in climatic conditions is most likely to explain the slightly different re-sults obtained in 2005 and 2006.

This study showed that combin-ing basic soil properties explained 81 to 91% of the variability in corn yield and 78 to 90% for N uptake, depending on the year. Nevertheless, the measurement of several soil properties may be tedious and is not always possible. Indices that are easy to measure, such as N availabil-ity indices, could be useful tools for predicting corn yield and N uptake.

Yield and Nitrogen Uptake Variation Explained by Individual Nitrogen Availability Indices

When the proportion of vari-ability in corn yield and N uptake explained by each N availability in-dex was assessed, NO3–KCl, NO3–CaCl2, NO3–AEM, and PMN were the most signifi cant indices in both years (Fig. 1–4). The NO3–KCl and NO3–CaCl2 indices performed well in both years, explaining 66 to 87% of the variability in corn yield and N uptake (Fig. 1 and 3). This is in line with Appel and Mengel (1993) who reported a signifi cant correlation between NO3–CaCl2 and ryegrass (Lolium perenne L.) N uptake (r2 = 0.66) in a pot experiment. In anoth-er controlled experiment, Jalil et al. (1996) reported signifi cant correla-tion (r2 = 0.79) between NO3–KCl

measured in 38 different soils and N uptake by canola (Brassica campestris L.). Finally, Smith and Shengxiu (1993) reported signifi cant correlations (r2 = 0.64–0.85) between NO3–KCl and N uptake by ryegrass, barley (Hordeum vulgaris L.) and oats (Avena sativa L.) in pot experiments.

Besides indices based on the extractability of soil mineral N with various aqueous solutions, we considered two indices inte-grating N availability over a longer period of time by measuring NO3–AEM over the growing season and PMN. The NO3–AEM explained 63 to 83% of the variability in corn yield and

Fig. 1. Relationships between corn yield and N availability indices (NO3–KCl, NO3–CaCl2, NO3–AEM) in 2005 and 2006.

324 SSSAJ: Volume 73: Number 1 • January–February 2009

65 to 91% of the variability in N uptake (Fig. 1 and 3). Anion exchange membranes have been used to measure soil N supply rates and to assess the effect of management practices on soil N availability (Qian and Schoenau, 1995; Nyiraneza and Snapp, 2007). Qian and Schoenau (1994) assessed in 74 different soils the relationship between NO3–AEM and crop N uptake and reported coeffi cients of variations (r2 = 0.57 to 0.86) similar to those found in the present study.

The PMN test explained 55 to 74% of the variability in corn yield and N uptake depending on year (Fig. 2 and 4).

These values are comparable with those obtained by Hansen et al. (2005) who found a signifi cant cor-relation (r2 = 0.55) between PMN and N uptake by barley in a pot experiment. Similar relationships (r2 = 0.60 to 0.69) were reported for corn grown in pots (Cornforth and Walmsley, 1971; Cantarella et al., 1994). Ammonium released during a 7-d anaerobic incubation at 40°C is considered to refl ect soil’s N supplying capacity (Keeney and Bremner, 1966; Narteh and Sahrawat, 1997), therefore it should be a good predictor of crop yield or N uptake. Russell et al. (2006) reported that net N mineralization measured after anaerobic incubation at 40°C for 21 d was closely related to crop productivity. However, the same authors argued that this in-cubation technique may underesti-mate NH4–N production in some soils due to either suppression of mineralization from high NH4–N concentration, or NH4–N fi xa-tion in clay minerals. In the present study, PMN explained less of the variability in corn yield and N up-take than NO3–KCl, NO3–CaCl2, or NO3–AEM.

The N-NaHCO3–205 and N-NaHCO3–220 were well corre-lated to corn yield and N uptake in 2005, but not in 2006 (Fig. 2 and 4). The absorbance of N-NaHCO3–205 and N-NaHCO3–220 is propor-tional to its N content (Giroux and Tran, 1987) and refl ects both organic and mineral N forms (Sharifi et al., 2007). Good relationships have been reported between N-NaHCO3–205 and ryegrass yield (r2 = 0.78) and N uptake (r2 = 0.66) in a pot experi-ment (Giroux and Tran, 1987). In contrast, Hong et al. (1990) report-ed only moderate correlations (r2 = 0.54–0.56) between N-NaHCO3

read at 200 nm and the N supplying capability (NSC) of 49 soils cropped to corn. In the present study, N-NaHCO3–205 and N-NaHCO3–220 explained only 10 to 73% of the vari-ability in corn yield and N uptake, and were not as consistent across years as the other N indices tested.

This study showed that quick N availability tests, such as NO3–KCl, NO3–CaCl2 NO3–AEM, and PMN may explain a high proportion of the variability in corn yield and N uptake. The proportion of the variability in corn yield and N uptake explained by those simple N availability tests was generally high

Fig. 2. Relationships between corn yield and N availability indices (PMN, N-NaHCO3–205, N-NaHCO3–220) in 2005 and 2006.

SSSAJ: Volume 73: Number 1 • January –February 2009 325

in our fi eld study and comparable to values reported in studies conducted under controlled environment. This may be partly because the 9 crop-ping systems investigated in our study were infl uenced by similar soil texture, topographical and cli-matic conditions. The relationships between N availability indices and corn yield and N uptake could have been different if the study had in-cluded varied environmental condi-tions and soil types. Nevertheless, our results provide evidence that simple soil N availability indices can explain a high variability in corn yield and N uptake for clay loam soils under cool and wet climatic conditions.

CONCLUSIONSThis study is indicative of

which soil properties or N availabil-ity indices can mostly affect corn yield and N uptake. It can therefore help as a basis in soil management or yield prediction. A combination of a set of 16 soil attributes explained from 81 to 91% of the variability in corn yield and from 78 to 90% of the variability in N uptake. Based on PCA combined with stepwise regression, TN, HA-N, NO3–KCl, NO3–CaCl2, NO3–AEM, and PMN were primary indicators of corn productivity, whereas MWD, FA-N and NHF-N could be con-sidered as secondary indicators. Among the six soil N availability indices tested, NO3–KCl, NO3–CaCl2 were the most promising to explain corn yield variability be-cause of their ease of measurement and reliability across years.

ACKNOWLEDGMENTSThe senior author gratefully acknowledges the fi nancial support provided by the Institut de Recherche et Développement en Agroenvironnement (IRDA) and the technical assistance received from Anne Drapeau, Michel Noël and Benoît Bolduc.

REFERENCESAllison, L.E., W.B. Bollen, and C.D. Moodie. 1965. Total Carbon. p. 1346–

1366. In Black et al. (ed.) Methods of soil analysis. Agron. Monogr. 9. ASA, Madison, WI.

Aoyama, M., D.A. Angers, A. N’Dayegamiye, and N. Bissonnette. 1999. Protected organic matter in water stable aggregates as affected by mineral fertilizer and manure applications. Can. J. Soil Sci. 79:419–425.

Appel, T., and K. Mengel. 1993. Nitrogen fractions in sandy soils in relation

to plant nitrogen uptake and organic matter incorporation. Soil Biol. Biochem. 25:685–691.

Bakken, L.R., T. Børresen, and A. Njøs. 1987. Effect of soil compaction by traffi c on soil structure, denitrifi cation, and yield of wheat. J. Soil Sci. 38:541–552.

Beauchamp, E.G., R. Pararajasingham, and B.D. Kay. 2003. Relationships of total soil nitrogen to several soil nitrogen indices. Commun. Soil Sci. Plant Anal. 34:505–518.

Beauchamp, E.G., B.D. Kay, and R. Pararajasingham. 2004. Soil tests for predicting the N requirement of corn. Can. J. Soil Sci. 84:103–113.

Bowerman, B.L., and R.T. O’Connell. 1990. Linear statistical models: An

Fig. 3. Relationships between corn N uptake and N availability indices (NO3–KCl, NO3–CaCl2, NO3–AEM) in 2005 and 2006.

326 SSSAJ: Volume 73: Number 1 • January–February 2009

applied aproach. 2nd ed. Duxbury Press, Belmont, CA.Breland, T.A., and S. Hansen. 1996. Nitrogen mineralization and microbial

biomass as affected by soil compaction. Soil Biol. Biochem. 28:655–663.Bremner, J.M. 1965. Total nitrogen, inorganic forms of nitrogen. p. 1149–

1255. In Black et al. (ed.) Methods of soil analysis. Agron. Monogr. 9. Part 2. ASA, Madison, WI.

Cabrera, M.L., and M.H. Beare. 1993. Alkaline persulfate oxidation for determining total nitrogen in microbial biomass extracts. Soil Sci. Soc. Am. J. 57:1007–1012.

Cambardella, C.A., T.B. Moorman, J.M. Novak, T.B. Parkin, D.L. Karlan, R.F. Turco, and A.E. Konopka. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J. 58:1501–1511.

Cantarella, H., D. Mattos, Jr., and B. Van. Raij. 1994. Lime effect on soil N availability indices as measured by plant uptake. Commun. Soil Sci. Plant Anal. 25:989–1006.Castillo, R.S., R.H. Dowdy, J.M. Bradford, and W.E. Larson. 1982. Effects of applied mechanical stress on plant growth and nutrient uptake. Agron. J. 74:526–530.Cornforth, I.S., and D. Walmsley. 1971. Methods of measuring available nutrients in western Indian soils. Plant Soil 35:389–399.Cox, M.S., P.D. Gerald, and D.B. Reynolds. 2003. Selected soil property variability and their relationships with cotton yield. Soil Sci. 170:928–937.Giroux, M., and T.S. Tran. 1987. Comparaisons des différentes méthodes d´analyse de l´azote du sol en relation avec sa disponibilité pour les plantes. Can. J. Soil Sci. 67:521–531.Gregorich, E.G., M.R. Carter, D.A. Angers, C.M. Monreal, and B.H. Ellert. 1994. Towards a minimum data set to assess soil organic matter quality in agricultural soils. Can J. Soil Sci. 75:367–385.Hansen, J.P., J. Eriksen, and I.S. Jensen. 2005. Residual nitrogen effect of a dairy crop rotation as infl uenced by grass-clover ley management, manure type and age. Soil Use Manage. 21:278–286.Hong, S.D., R.H. Fox, and W.P. Piekielek. 1990. Field evaluation of several chemical indices of soil nitrogen availability. Plant Soil 123:83–88.Houba, V.G., I. Novozamsky, I. Hubregts, A.W.M. Vander, and J.J. Lee. 1986. Comparison of soil extractions by 0.01 M CaCl2, EUF and by some conventional extraction procedures. Plant Soil 96:433–437.Jalil, A., C.A. Campbell, J. Schoenau, J.L. Henry, Y.W. Jame, and G.P. Lafond. 1996. Assessment of two chemical extraction methods as indices of available nitrogen. Soil Sci. Soc. Am. J. 60:1954–1960.Jiang, P., and K.D. Thelen. 2004. Effect of soil and topographic properties on crop yield in a North-central corn-soybean cropping system. Agron. J. 96:252–258.Kaiser, H.F. 1974. An index of factorial simplicity. Psychometrika 39:31–36.Kaspar, T.C., D.J. Pulido, T.E. Fenton, T.S. Colvin, D.K. Karlen, D.B. Jaynes, and D.W. Meek. 2004. Relationship of corn and soybean yield and terrain properties. Agron. J. 96:700–709.Keeney, D.R. 1982. Nitrogen availability indices. p. 711–733. In A.L. Page et al. (ed.) Methods of soil analysis. Part 2. 2nd ed.

Agron. Monogr. 9. ASA and SSSA, Madison, WI.Keeney, D.R., and J.M. Bremner. 1966. Comparison and evaluation of

laboratory methods of obtaining an index of soil nitrogen availability. Agron. J. 58:498–503.

Kemper, W.D., and R.C. Rosenau. 1986. Aggregate stability and size distribution. p. 425–442. In A. Klute (ed.) Methods of soil analysis. Part 1. 2nd ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI.

Klute, A. 1986. Water retention: Laboratory methods of soil analysis. p. 635–662. In A. Klute (ed.) Methods of soil analysis. Part 1. Agron. Monogr. 9. ASA and SSSA, Madison, WI.

Kravchenko, A.N., and D.G. Bullock. 2000. Correlation of corn and soybean

Fig. 4. Relationships between corn N uptake and N availability indices (PMN, N-NaHCO3–205, N-NaHCO3–220) in 2005 and 2006.

SSSAJ: Volume 73: Number 1 • January –February 2009 327

grain yield with topography and soil properties. Agron. J. 92:75–83.McDonald, N.W., D.R. Zak, and K.S. Pregtizer. 1995. Temperature effects

on kinetics of microbial respiration and net nitrogen and sulfur mineralization. Soil Sci. Soc. Am. J. 59:233–240.

MacLean, A.A. 1964. Measurement of nitrogen supplying power of soils by extraction with sodium bicarbonate. Nature 203:1307–1309.

Manna, M.C., A. Swarup, R.H. Wanjari, Y.V. Singh, P.K. Ghosh, A.K. Tripathi, and M.N. Saha. 2006. Soil organic matter in a west bengal inceptisol after 30 years of multiple cropping and fertilization. Soil Sci. Soc. Am. J. 70:121–129.

Narteh, L.T., and K.L. Sahrawat. 1997. Potentially mineralizable nitrogen in west African lowland rice soils. Geoderma 76:145–154.

Nyiraneza, J., and S. Snapp. 2007. Integrated management of inorganic and organic nitrogen and effi ciency in potato systems. Soil Sci. Soc. Am. J. 71:1508–1515.

Omay, A., C. Rice, D. Maddux, and W. Cordon. 1998. Corn yield and nitrogen uptake in monoculture and in rotation with soybean. Soil Sci. Soc. Am. J. 62:1596–1603.

Piccolo, A.P., P. Conte, E. Trivellone, B. Van Lagen, and P. Buurman. 2002. Reduced heterogeneity of a lignite humic acid by preparative HPSEC following interaction with an organic acid. Characterization of size-separates by Pyr-GC-Ms and 1H-NMR spectroscopy. Environ. Sci. Technol. 36:76–84.

Pierce, F.S., D. Warncke, and M.W. Everett. 1995. Yield and nutrient variability in glacial soils of Michigan. p. 133–150. In P.C. Robert et al. (ed.) Proc. 2nd Int. Conf. On site-specifi c management for agricultural systems. 27–30 Mar. 1994, Minneapolis, MN.

Qian, P., and J.J. Schoenau. 1994. Assessing nitrogen mineralization from soil organic matter using anion exchange membranes. Nutr. Cycl. Agroecosyst. 40:1385–1314.

Qian, P., and J.J. Schoenau. 1995. Assessing nitrogen mineralization from organic matter using anion exchange membranes. Fert. Res. 40:143–148.

Rachman, A., S.H. Anderson, C.J. Ganzter, and A.L. Thompson. 2003. Infl uence of long-term cropping systems on soil physical properties related to soil erodibility. Soil Sci. Soc. Am. J. 67:637–644.

Russell, C.A., B.W. Dunn, G.D. Batten, R.L. Williams, and J.F. Angus. 2006.

Soil tests to predict optimum fertilizer nitrogen rate for rice. Field Crop Res. 97:286–301.

Sahrawat, K.L. 1980. Nitrogen supplying ability of some Philippine rice soils. Plant Soil 55:181–187.

SAS Institute. 1999. SAS user’s guide. Statistics. Version 8.0. SAS Inst., Cary, NC. Schnitzer, M., L.E. Lowe, J.F. Dormaar, and V. Martel. 1981. Chemical

parameters for the characterization of soil organic matter. Can. J. Soil Sci. 61:517–519.

Scott, N.A., C.V. Cole, E.T. Elliott, and S.A. Huffman. 1996. Soil textural control on decomposition and soil organic matter dynamics. Soil Sci. Soc. Am. J. 60:1102–1109.

Sequi, P., M. De Nobili, L. Leita, and G. Cercignani. 1986. A new index of humifi cation. Agrochimica 30:175–179.

Sharifi , M., B.J. Zebarth, D.L. Burton, C.A. Grant, and J.M. Cooper. 2007. Evaluation of some indices of potentially mineralizable nitrogen in soil. Soil Sci. Soc. Am. J. 71:1233–1239.

Shukla, M.K., R. Lal, and M. Ebinger. 2004. Principal component analysis for predicting corn biomass and grain yields. Soil Sci. 169:215–224.

Smith, K.A., and L. Shengxiu. 1993. Estimation of potentially mineralizable nitrogen in soil by KCl extraction. Plant Soil 157:167–174.

Soon, Y.K., S.A. Brandt, and S.S. Malhi. 2006. Nitrogen supply of a dark brown chernozen soil and its utilization by wheat. Can. J. Soil Sci. 86:483–491.

Stevens, W.B., R.G. Hoeft, and R.L. Mulvaney. 2005. Fate of nitrogen 15 in a long-term nitrogen rate study: II. Nitrogen uptake effi ciency. Agron. J. 97:1046–1053.

Stout, J.D., K.M. Goh, and T.A. Rafter. 1981. Chemistry and turnover of naturally occuring resistant organic compounds in soil. p. 1–73. In E.A. Paul et al. (ed.) Soil biochemistry. Marcel Dekker, New York.

Swift, R.S. 1999. Macromolecular properties of soil humic substances: Fact, fi ction and opinion. Soil Sci. 164:790–802.

Walley, F., T. Yates, J.W. Van Groenigen, and C.V. Kissel. 2002. Relationships between soil nitrogen availability indices, yield and nitrogen accumulation of wheat. Soil Sci. Soc. Am. J. 66:1549–1561.

Wuensch, K.L. 2004. Principal component analysis: http://core.ecu.edu/psyc/wuenschk/MV/FA/FA.doc. Confi rmed 10 Nov. 2008.