Chapter 3: EFFECTS OF LAND USE AND MANAGEMENT ON SOIL BACTERIAL
3.3 RESULTS
3.3.1 Analyses of soils at the Baynesfield experimental site
evenness (J') (a measure of the equitability of the relative band intensities across the gels) (Pielou, 1977). Pairwise comparisons were made, using the Ryan/Einot- Gabriel/Welsch multiple range test. For Mount Edgecombe data, a two-way ANOVA of soil variables, and another of the main effects and interaction of the trashing and fertilizer treatments on soil bacterial richness, evenness and diversity were carried out.
The results of ANOVA however, are not unequivocal, because neither the richness nor evenness parameter can be determined with certainty for prokaryotes in soil samples (Tebbe and Schloter, 2007). Least significant differences were calculated at the 5% level.
TABLE 3.2 Means (± sd) for selected physicochemical properties of the study soils collected at 0–5 cm depth from different land uses at Baynesfield Estate
Land use
pH (KCL)
Organic Carbon
Extractable P
Exchangeable cations Exch.
acidity
Total cations (ECEC)
K Ca Mg
% mg kg-1 --- cmolc kg-1 ---
SC 4.3 (0.12) 3.7 (0.26) 235 (53.60) 2.24 (0.555) 6.78 (1.102) 3.04 (0.513) 0.16 (0.027) 12.24 (1.517) M 4.5 (0.12) 4.1 (0.19) 163 (17.70) 1.43 (0.150) 6.91 (0.841) 2.69 (0.435) 0.07 (0.062) 11.11 (1.140) KIK 4.6 (0.17) 9.9 (2.11) 15 (3.38) 0.93 (0.375) 7.22 (0.238) 5.03 (0.531) 0.10 (0.874) 13.30 (1.915) NAT 4.6 (0.05) 5.1 (0.58) 6.0 (1.66) 0.47 (0.119) 6.63 (0.406) 4.19 (0.337) 0.08 (0.055) 11.38 (0.853) PF 4.1 (0.05) 6.5 (0.26) 5.8 (0.49) 0.48 (0.295) 3.95 (0.533) 2.62 (0.311) 1.53 (0.381) 8.59 (0.671) W 5.2 (0.49) 5.1 (0.56) 5.4 (1.55) 0.54 (0.145) 9.08 (1.227) 3.57 (0.608) 0.08 (0.027) 13.28 (1.937)
Key: SC = sugarcane (burnt cane harvested); M = maize (conventional tillage); KIK = permanent kikuyu pasture;
NAT = native grassland; PF = pine plantation; W = wattle plantation.
Overall dissimilarities among the soil samples from the different fields and plantations were calculated by MRPP. A greater difference between the soils under the various land uses than among the three replicates of each soil was shown. However, soils under M and SC could not be differentiated nor those under KIK and NAT. Results are summarised in Table 3.3.
TABLE 3.3 Multi-Response Permutation Procedures (MRPP) of soil physicochemical properties, showing pairwise comparisons of the Baynesfield soils
Land use Test statistic (T) Probability (p)
KIK vs. M -2.655 0.024
KIK vs. NAT -1.510 0.075
KIK vs. PF -2.984 0.021
KIK vs. SC -2.666 0.023
KIK vs. W -2.404 0.027
M vs. NAT -2.920 0.021
M vs. PF -2.905 0.022
M vs. SC -0.991 0.152
M vs. W -2.581 0.025
NAT vs. PF -2.981 0.021
NAT vs. SC -2.851 0.022
NAT vs. W -2.443 0.026
PF vs. SC -2.946 0.021
PF vs. W -2.876 0.022
SC vs. W -2.684 0.023
All land uses -8.035 <0.001
Key: SC = sugarcane (burnt cane harvested); M = maize (conventional tillage); KIK = permanent kikuyu pasture; NAT = native grassland; PF = pine plantation; W = wattle plantation.
Analysis by PCA clustered the replicate soil samples from the different land uses on the basis of the correlation between the samples and soil variables (Figure 3.1). The closer the vector for an individual variable aligns with a principal component axis, the more that particular chemical variable can be used to explain the variation in the data along that axis. ECEC, Ca, pH and exchange acidity were highly correlated with PC1 and P, K and organic C were highly correlated with PC2. PC1 accounted for 45.8%,
PC2 for 32.6% and cumulatively, for 78.4% of the total variance in the soil data. Soils under the two arable crops SC and M were closely associated with high concentrations of P and K whereas PF soils were correlated with high exchange acidity, low pH and low concentrations of ECEC. KIK soils were characterised by high concentrations of organic C and Mg, with those of NAT also associated with these two soil variables, but at lower concentrations than those in KIK soils. Soils under W were correlated with the highest pH of all the land uses at this site.
-1.0 -0.5 0.0 0.5 1.0
-1.0 -0.5 0.0 0.5
1.0 P
K
Ca
Mg
Acidity Cations
pH
OrgC SC1 SC2
SC3 M1 M3 M2
PF1
PF2
PF3 Kik1
Kik2 Kik3
W1
W2
W3 Nat1
Nat3 Nat2
SPECIES
SAMPLES
SC M PF Kik W Nat
FIGURE 3.1 A PCA biplot (standardised and centred data) of sites and soil variables for subsamples of fields with various land uses at Baynesfield Estate.
The PC1 (horizontal) and PC2 (vertical) components accounted for 45.8% and 32.6%, respectively, and, cumulatively for 78.4% of the total variance in the soil data.
Key: SC = sugarcane; M = maize; KIK = kikuyu pasture; NAT = native grassland; PF = pine plantation; W = wattle plantation.
CCA was used to show the effects of selected soil physicochemical variables on the bacterial community structure at site 1 (Figure 3.2). Eigenvalues for CCA 1 and CCA 2 were 0.234 and 0.161, accounting respectively, for 22.8% and 15.7% of the total variability (therefore these results should be interpreted with caution), and 33.3% and 22.9% (cumulatively 56.2%) of the variability in community composition related to soil. The soils were related to variation on axis 1 (p = 0.002) and along all canonical axes (p = 0.002). Bacterial community structure in soils under SC and M were closely associated with exchangeable K, extractable P and ECEC. Soil bacterial communities under KIK were associated with high concentrations of organic C (9.9%) and Mg, whereas lower concentrations of organic C (5.1%) and Mg were associated with bacteria in NAT soils. High acidity was correlated with the PF soil bacterial community structure.
FIGURE 3.2 Plot of samples (classified by land use) and selected soil variables along the first two axes of a CCA of the effect of soils on bacterial composition (band presence) at Baynesfield.
Key: SC = sugarcane; M = maize; KIK = kikuyu pasture; NAT = native grassland; PF = pine plantation; W = wattle plantation.
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
-1.0 -0.5 0.0 0.5 1.0 1.5
P K Mg
Acidity
ECEC Organic C
KIK1
KIK2 KIK3
M1 M M3 NAT1
NAT2 NAT3
PF1
PF2
PF3 SC
SC3 SC2
W1 W2 W3
ENV. VARIABLES
SAMPLES
SC M PF KiK W NAT
CCA1 (22.8%)
CCA2 (15.7%)
The statistical significance of the relationship between environmental variables and variation in DGGE profiles was tested by the Monte Carlo Permutation test, which showed that P was the most significant variable affecting bacterial community composition, followed by acidity, Mg, ECEC, K, and organic C. (Table 3.4).
TABLE 3.4 Conditional effects of variables in a stepwise selection test in the CCA of soils on bacterial communities at Baynesfield
Variable Eigenvalue P F
P 0.22 0.002 4.25
Acidity 0.14 0.006 3.36
Mg 0.11 0.008 2.70
ECEC 0.09 0.012 2.58
K 0.08 0.036 2.29
Organic C 0.06 0.042 2.24
pH 0.03 0.498 0.89
Ca 0.00 1.000 0.08
Note: All variables (in bold) were used in the CCA (significant effects on composition). In the CCA, the various inflation factors of the selected soil variables were acceptably low (< 10) indicating no severe problem of multicollinearity (i.e. the selected variables had relatively independent effects on composition).
The correlations between the selected soil variables used in the CCA and the ordination axes are shown in Table 3.5.
TABLE 3.5 Correlations between soil variables used in the CCA and compositional gradients (ordination axes)
Variable CCA1 CCA2
P 0.8874 -0.2020
K 0.8700 0.0099
Mg 0.0848 0.7079
Acidity -0.5805 -0.2774
ECEC 0.4788 0.1332
Organic C -0.3724 0.6146
The results of the physicochemical analyses of Baynesfield soils showed that soil composition differed markedly under the various land uses, while CCA indicated which soil properties had affected bacterial community structure. These findings
concur with those of other workers at this site (Graham, 2003; Graham and Haynes, 2005).