Assessing soil-quality indices for subtropical rice-based cropping systems in India.
Globally, the area of rice (Oryza sativa L.) production has increased from 148 Mha in 2002 to 164Mha in 2011 (FAOSTAT 2013). Asia is the main continent where this expansion has been reported. Food and nutritional security in Asian countries depend largely upon rice, because it is the source of 15% of protein and 21% of energy intake for the population (Depa et al. 2011). However, productivity of rice in lowland cultivated areas is low because of declining soil fertility (Haefele et al. 2014), degradation of soil structure (Das et al. 2014a), and unreliable water resources, lack of resources and widespread poverty (Das et al. 2014b).
Management practices such as pudding, excessive and repeated tillage, planting and leveling on lowland rice soils can affect soil properties differently from management practices used in other agricultural systems. Intensity of cultural practices deficient in drainage can lead to breakdown of stable soil structure and disturbance to habitat of soil biota. Following rice, potato (Solarium tuberosum L.) is a profitable crop for marginal landholders and for large-scale growers.
For growing potatoes, intensive ploughing is practiced and heavy doses of nitrogen (N), phosphorus (P) and potassium (K) are generally applied. At maturity, potatoes are harvested by uprooting tubers from soil. Above- and belowground crop residue is kept away from the field for early preparation of land to grow sesame (Sesamum indicum L.) with residual soil moisture and nutrition. These intensive and cyclic cultural practices may lead to degradation of soil health. Monocropping, exclusion of legumes from rotation, no or low use of organic manure and green manure, and imbalanced and injudicious application of N, P, and K fertiliser may accelerate these problems.
Several authors have reported assessment of soil quality for non-rice systems (Karlen et al. 2008; Wienhold et al. 2006; Armenise et al. 2013; Stott et al. 2013). However, such studies are rare for soils under rice-based cropping systems in lowland areas. Attempts have been made in Brazil (Lima et al. 2008, 2013) and China (Li et al. 2013; Yao et al. 2013, 2014; Liu et al. 2014) to address soil quality issues in lowland areas under rice crops followed by arable crops. In India, such studies are few and have been carried out on experimental sites with controlled treatments (Chaudhury et al. 2005; Mandal et al. 2005; Mohanty et al. 2007). Sometimes, these studies may mimic farmers' fields for various Soil Orders. Selecting quality indicators of soils in farmers' fields and assessing soil health provides a measure of its functioning ability and its limitations and deficiencies. These indicators are important keys for expressing soil as a natural resource, an ultimate source for nourishment, ecosystem functions and sustainability.
India has a variety of soils, each of which presents its own opportunity and limitation. Among these, based on area, distribution and supply of agricultural products, Inceptisols, Entisols and Alfisols are dominant Soil Orders (Sarkar et al. 2001). Management practices have significant influence on the performances of these soils. Several attempts have been made to assess soil quality at regional and farm scale (Karlen et al. 2008; Wienhold et al. 2006; Armenise et al. 2013) by using various indexing techniques (Andrews et al. 2002a; Masto et al. 2007) based on statistical and mathematical formulations. These attempts have had mixed success because soil quality is very complex.
There has been little assessment of quality for soils collected from fanners' fields belonging to the Soil Orders Inceptisols, Entisols and Alfisols carrying a common rice-based cropping system. Our hypothesis was that a rice management system with puddling, intensive tillage and mud-water practices, followed by potato with heavy fertiliser and excessive tillage and sesame with zero-input cultivation, might have different effects on different soils in terms of physical, chemical and biological properties. As such, the study was initiated with the following objectives: (i) to identify soil-quality indicators prepared with a minimum dataset; and (ii) to evaluate whether this minimum dataset is correlated with system yield for the Inceptisols, Entisols and Alfisols under rice-potato-sesame production system.
Materials and methods
Soil sampling and crop yield assessment
Sixty geo-referenced composite soil samples (0-0.2 m), 20 each from Inceptisols, Entisols and Alfisols supporting a long-term (>25 years) rice-potato-sesame cropping system under subtropical climate in India, were collected 7-10 days after harvesting of kharif rice (Fig. 1). Data on biomass yield for the individual crops (rice, potato and sesame) and the amount of NPK fertiliser used at each of the 60 sites were recorded for three consecutive years (2006,2007 and 2008) to validate the evaluation of soil quality (Fig. 2). To express yield in a common unit, an equivalent rice yield (ERY) was calculated for all sites for the production system, taking the average yield of the individual crop (rice, potato and sesame) for the last 3 years, according to the equation:
ERY = Tuber yield of potato x unit price of potato/Unit price of rice + Grain yield of sesame x unit price of sesame/Unit price of rice + rice yield
Soil samples collected from each of the sites were divided into three portions. One portion of the samples was processed and analysed immediately for biological attributes: microbial biomass carbon ([C.sub.mic]) and N ([N.sub.mic]), mineralisable C ([C.sub.min]) and N ([N.sub.min]), dehydrogenase activity, fluorescein diacetate hydrolysing activity (FDHA), [beta]-glucosidase activity, aryl sulfatase activity, acid and alkaline phosphatase activity, and urease activity. Soil samples were kept in refrigerated conditions at 4[degrees]C until required.
The second portion was dried at room temperature, ground and sieved (2.0-mm nylon sieve), and stored for future analysis of chemical attributes: pH in 0.02 m Ca[Cl.sub.2] ([pH.sub.Ca]) or water ([pH.sub.w]); cation exchange capacity (CEC); pools of soil organic C (SOC) and total C; available N, P and K; exchangeable calcium (Ca) and magnesium (Mg); available iron (Fe), manganese (Mn), zinc (Zn), and copper (Cu) and boron (B).
The third portion was used for analysis of physical attributes: bulk density; water-stable aggregates; and clay, silt and sand. Saturated hydraulic conductivity was computed by using a pedotransfer function as proposed by Naskar et al. (2010). All of the analyses were performed in triplicate using standard protocols (Table 1).
Computation of soil quality index
Multivariate statistical analyses (principal component analysis (PCA) and discriminate analysis (DA)) were employed to screen the attributes and form a minimum dataset (MDS) from the 27 analysed parameters (Masto et al. 2007; Armenise et al. 2013). The general rules of principal component and factor analyses were followed; that is, principal components (PC) receiving high eigenvalues (>1.00) and variables with high factor loadings with such components are the best representatives and thus retained for screening of MDS (Armenise et al. 2013). In each PC, only highly weighted factors, i.e. those with absolute values within 10% of the highest weight, were screened for the MDS (Andrews et al. 2002b). To reduce redundancy, correlation analysis was performed amongst the highly weighted variables. Further, DA was employed with the screened MDS to choose the best discriminated variables (Lima et al. 2008; Yao et al. 2014). Finally, for validation of results, a regression analysis was computed using ERY as dependent variable and the screened soil properties as independent variables to investigate whether the MDS are truly correlated with system yield (Yao et al. 2013). For all statistical analyses of data (PCA, DA, regression equations), Microsoft Excel and SPSS (version 16.0, SPSS Inc., Chicago, 1L, USA) packages were used.
Soil physical and chemical properties
As expected, the clay content of Inceptisols (257.4 g [kg.sup.-1]) and Entisols (266.2g [kg.sup.-1]) was significantly higher than that of Alfisols (138.4g [kg.sup.-1]). Alfisols were more compacted with higher bulk density (1.5 Mg [m.sup.-3]) than Inceptisols (1.3 Mg [M.sup.-3]) and Entisols (1.2Mg [m.sup.-3]). As such, Inceptisols and Entisols had more total water-stable aggregates than Alfisols (Table 2). Similarly, the mean weight diameter (0.89 and 0.87 mm) and aggregate stability (36.9% and 32.6%) were higher in Inceptisols and Entisols than in Alfisols. The experimental soils were acidic, and their [pH.sub.Ca] values were always lower than [pH.sub.w] values. The CEC were higher for Inceptisols (14.1 [cmol.sub.(+)] [kg.sup.-1]) and Entisols (14.1 [cmol.sub.(+)] [kg.sup.-1]) than for Alfisols (8.9 [cmol.sub.(+)] [kg.sup.-1]). The values of all of the three pools of organic C (OC), that is, very labile, Walkley-Black oxidisable (WBOC) and total OC (TOC), followed the trend Inceptisols > Entisols > Alfisols. A similar trend was evident for available N. However, C: N ratios were narrower for Inceptisols (10.9) followed by Entisols (11.2) and Alfisols (12.4). By contrast, the available K content was higher in Alfisols, followed by Entisols and Inceptisols. The available P and DTPA-extractable cationic micronutrient contents, on average, followed the trend: Inceptisols > Entisols > Alfisols. However, the available B content was in the deficient to medium range, particularly in Inceptisols and Alfisols.
Soil microbiological properties
The [C.sub.mic] of the soils was significantly higher in Inceptisols, followed by Entisols and Alfisols with average values of 524.3, 483.4 and 379.6 [micro]g C[g.sup.-1] soil, respectively (Table 2). [C.sub.mic] showed significant positive correlation with very labile C (r=0.62; P< 0.01), WBOC (r=0.81; P< 0.01) and TOC (r=0.74; Z3< 0.01) contents of the soils. Microbial quotient (MQ), however, did not show significant differences among the soils. On the other hand, the values of [N.sub.mic] were similar in Entisols (54.4 [micro]g N [g.sup.-1]) and Inceptisols (47.8 [micro]g N [g.sup.-1]), which were higher than Alfisols(36.1 [micro]g N [g.sup.-1]). The ratios of [C.sub.mic]: [N.sub.mic] were thus narrower for Entisols (8.9) and wider for Alfisols and Inceptisols (10.5 and 11.0). [N.sub.mic] showed significant positive correlations with available and total N (r=0.42 and 0.82; P < 0.01) contents of the soils. Except for urease, activities of the other six enzymes estimated (dehydrogenase, acid phosphatase, alkaline phosphatase, aryl sulfatase, [beta]-glucosidase and FDHA) were, on average, higher in Inceptisols, followed by Entisols and Alfisols. There were significant positive correlations among the enzymes (Table 3); additionally, alkaline phosphatase had a positive correlation with soil pH, whereas [beta]-glucosidase had positive correlations with all of the pools of organic C in soils.
Grouping of soil-quality indicators based on principal component analysis
The effect of long-term cultivation of similar cropping systems with similar management practices on the quality of soils of three Soil Orders was evaluated by screening soil quality indicators. For this purpose, separate PCA was performed for each of the Soils Orders with 27 soil attributes analysed (Fig. 3 and Table 4). Each PCA analysis generated seven PCs. According to the criteria proposed by Kaiser (1960), the first six PCs were kept in the scree plot because they had eigenvalues >1.00 (Norman and Streiner 2008) and explained >5% of the variance in total dataset. PCI explained 37%, 37% and 34% of the total variance for Inceptisols, Entisols and Alfisols, respectively. Under PC1 for Inceptisols, CEC was the highest weighted variable with component loading weight of 0.92. The chemical attributes [pH.sub.Ca] and available Zn were screened from PC2 and PC3, respectively. In PC4, dehydrogenase showed highest component loading, explaining 7.8% of the total variance. Available K and aryl sulfatase were the highest weighted variables in PC5 but they were highly correlated (r=0.51; P< 0.05). To reduce redundancy, only available K was retained because of its higher weighted loading in PC5 and ease of estimation. Available B was selected from PC6. Many auto-correlated variables appeared in PCI for both Entisols and Alfisols (data not presented). Among these, WBOC and very labile C were selected because they were higher weighted variables. Several soil chemical properties were screened. Besides these, [pH.sub.Ca] was screened from PC2 and available P from PC6 because they had higher factor loading for Entisols. Again, [N.sub.min] from PC4 and [beta]-glucosidase from PC5 with higher factor loading were also screened in Entisols. Among the physical attributes, bulk density was screened from PC3. For Alfisols, under PC2 dehydrogenase and aryl sulfatase were highly weighted variables but they were significantly correlated (r=0.28); consequently aryl sulfatase was selected for further analysis. The attributes [C.sub.min], [C.sub.mic] and [beta]-glucosidase with higher factor loading were retained from PC3, PC5 and PC6, respectively.
Selecting soil quality indicators
Discriminate analyses were performed on the basis of results from factor analysis (PCs) for individual Soil Orders. For Inceptisols, DA revealed that only first two discriminate functions (DFs) were significant and explained 73.6% and 18.3% of the total variance with canonical correlation of 0.95 and 0.84 (Table 5); [pH.sub.Ca] (0.97), CEC (0.83) and available K. (0.56) were found to be powerful discriminators with high positive coefficient under the component DF1. Dehydrogenase with high discriminate coefficient (1.08) was a clear choice from DF2 for inclusion in MDS. Available Zn and B were dropped from the MDS because they carried equal and minimum values of discriminate coefficient in DF1. Two separate DAs, one for selected soil attributes from factor analysis and another for highly weighted attributes in PCI, were also performed for selecting MDS for both Entisols and Alfisols (Tables 6, 7). Two segregated DFs explaining ~78.7% and 14.8% of the total variance with canonical correlation of 0.95 and 0.8, respectively, were generated for Entisols. In DF1, [pH.sub.Ca] was discriminated (negative coefficient) from WBOC and [N.sub.min] (positive coefficient). The second DF was also significant with bulk density carrying the highest coefficient loading for inclusion. For Entisols, the first two DFs explained 61.6% and 28.6% of the total variance, with canonical correlation of 0.95 and 0.8. The WBOC and [pH.sub.Ca] were included in MDS, but very labile C was excluded because it is a highly oxidative form of WBOC (Mandal et al. 2008) and showed positive correlation with WBOC (data not presented, P<0.05). The next highly weighted attribute, [C.sub.mic], with canonical factor load of -0.86 was also screened for inclusion in MDS. For Alfisols, only one DF appeared explaining 98.5% and 99.5% of the total variance for both DAs irrespective of all selected attributes in PCs and highly weighted attributes in PCI (Table 7). Dehydrogenase was selected but both [C.sub.min] and aryl sulfatase were dropped from MDS because they yielded a minimum and similar discriminate coefficient. Further, [N.sub.min], very labile C and [C.sub.mic] with highly weighted discriminate coefficients were retained in MDS.
Minimum dataset validation
Results of the validation of the MDS (Table 8) showed that dehydrogenase activity (P<0.05) and CEC (P<0.05) for Inceptisols ([R.sup.2] = 0.45), WBOC (P< 0.01) for Entisols ([R.sup.2] = 0.77), and Nmin (P< 0.01) and very labile C (P<0.05) for Alfisols ([R.sup.2] = 0.78) were identified as main attributes for soil quality influencing significantly the variability of ERY of the systems studied.
Most of the soil quality attributes analysed varied significantly among the three Soil Orders, Inceptisols, Entisols and Alfisols. Of the 27 attributes analysed, only four, CEC, dehydrogenase activity, available K and [pH.sub.Ca], were screened out for their overriding influence in maintaining the productivity of the soils under Inceptisols. However, among the four attributes, only dehydrogenase activity and CEC showed significant correlations with ERY. Results thus indicated that for maintaining soil microbial activity, dehydrogenase appeared to be a good indicator of soil quality for sustaining soil systems. The importance of dehydrogenase activity as an indicator of soil microbiological quality in the initial oxidation of organic matter has been emphasised by others (Chaudhury et al. 2005). Dehydrogenase representation as a microbial attribute for assessment of soil quality was reinforced by its significant positive correlation with soil microbial abundance (r=0.59 and 0.47; P<0.01). Chaudhury et al. (2005) also observed that dehydrogenase was a good indicator in their minimum dataset, contributing 19.9% of variability in soil quality index, when assessing soil quality under long-term rice-based cropping system in Gangetic Inceptisols. CEC represents the capacity of a soil to store nutrients and the content of organic C (Masto et al. 2007). CEC values ranged between low and medium in magnitude considering the values found in soils of tropical and subtropical countries. Basak (2011) established that a CEC value of 14.9 [cmol.sub.(+)] [kg.sup.-1] was 'optimum' for Inceptisols for harvesting 80% yield potential of a rice-based cropping system. The mean value of CEC of the studied soil was less than this 'optimum' value. This justified its occurrence as one of the main parameters for soil quality in Inceptisols. The available K content of the experimental soils was also in the range low-medium and its deficiency was encountered in some pockets in Inceptisols, justifying its representation in the identified key indicators. There are reports of K. deficiency playing an important role for maintaining system productivity in rice-growing regions of the world (Li et al. 2013). The other indicator screened out was [pH.sub.Ca] because of its obvious importance in controlling availability of essential nutrients and activities of soil microbes. Rehabilitation of acid soil through low-cost liming materials has been well researched (Bhat et al. 2010; Badole et al. 2015).
Although differences between Inceptisols and Entisols were small, a different set of key indicators (WBOC, [pH.sub.Ca], [N.sub.min], BD and [C.sub.mic]) was found to govern soil quality of Entisols for its improved productivity. Research on assessment of soil quality for typical Entisols in India and elsewhere is rare, even though Entisols support production of a huge amount of food grains in Southeast Asian countries. Basak (2011) in a preliminary experiment showed that the optimum value of WBOC for maintaining good productivity of rice-based cropping system for Entisols was 7.6 g [kg.sup.-1]. The average WBOC content of the studied Entisols was 7.2 g [kg.sup.-1]. Considering the importance of WBOC in productivity, its occurrence as one of the key indicators for Entisols was not surprising. WBOC influences biological, chemical and physical properties of soil by regulating microbial activity, soil pH, response to fertiliser application, nutrient availability and structural stability (Rahmanipour et al. 2014). Here, [N.sub.min] and [C.sub.mic] provide less significant influence on ERY. Although a part of WBOC, [C.sub.mic] was found to be one of the screened indicators. Likewise, despite a large amount of [N.sub.min] in Entisols (49.2 [micro]g N[H.sub.4]-N g 1 [day.sup.-1]) compared with Inceptisols (38.8 pg N[H.sub.4]-N g 1 day ') and Alfisols (35.8 pg N[H.sub.4]-N [g.sup.-1] [day.sup.-1]), [N.sub.min] also constituted one of the screened key indicators of Entisols. From relationships with ERY, it was found that the WBOC had the highest correlation value, indicating its overall influence on all other screened indicators, as expected.
The major constraints for increasing productivity of Alfisols are its low OC and poor microbiological activity. As such, a large number of microbial attributes, dehydrogenase activity, [N.sub.min], [C.sub.mic] and very labile C, appeared as key indicators for soil quality for Alfisols. Nitrogen mineralisation in soils plays an important role in N nutrition of wetland rice because half to two-thirds of total N taken up by rice crops, even in N-fertilised paddies, comes from the soil N pool (Sahrawat 1983; Rader et al. 2013). It also helps to predict system yield and N extraction from soils and assess environmental pollution risk (Hirzcl et al. 2012). Occurrence of [N.sub.min] as one of the key indicators was thus justified. However, the method for estimation of [N.sub.min] is tedious and time-consuming. In our soil-testing laboratory, available N is determined by distillation with KMn[O.sub.4] as an oxidising agent. Because routine estimation of [N.sub.min] in such a laboratory may be difficult, development of a robust relationship between [N.sub.min] and available N in Alfisols may be useful for estimating N-supplying capacity and yield sustainability of such soil. This is reinforced by the existence of significant positive correlation between ERY and [N.sub.min] of the soil.
In a semi-arid tropical Alfisols, Sharma et al. (2008), however, observed mean weight diameter (MWD), available N and [C.sub.min] as the key indicators for soil quality and crop productivity. Usefulness of MWD, even in Inceptisols, was also shown by Chaudhury et al. (2005). Our statistical approaches failed to screen MWD as a main soil-quality indicator, possibly due to its far greater values than those reported by Chaudhury et al. (2005) and Sharma et al. (2008).
Occurrence of the very labile pool of soil C as one of the key indicator was expected because it fuels the soil food web, and therefore greatly influences nutrient cycles for maintaining soil quality (Zou et al. 2005; Majumder et al. 2008). This is more so in Alfisols. Further, the method for estimation of very labile C is analytically attractive and least expensive (Majumder et al. 2008).
Of the 27 soil attributes, dehydrogenase activity and CEC for Inceptisols, organic C for Entisols and [N.sub.min] and very labile C for Alfisols were screened as key indicators for assessment of soil quality under a rice-based cropping system (rice-potatosesame). Biological and chemical attributes were more sensitive than physical attributes in indicating differences in soil quality and influences on system yield in all three Soil Orders.
Received 3 September 2014, accepted 29 May 2015, published online 12 October 2015
Authors are extremely thankful to the Indian Council of Agricultural Research (ICAR), New Delhi, for funding the work through the World Bank assisted multi-institutional collaborative National Agricultural Innovation Project (NAIP: Sub-project C-2060) entitled 'Assessment of quality and resilience of soils under diverse Agro-ecosystems'.
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Nirmalendu Basak (A,B,C), Ashim Datta (A), Tarik Mitran (A), Satadeep Singha Roy (A), Bholanath Saha (A), Sunanda Biswas (A), and Biswapati Mandal (A)
(A) Directorate of Research, Bidhan Chandra Krishi Viswavidyalaya, Kalyani 741 235, West Bengal, India.
(B) Present address: Division of Soil & Crop Management, ICAR--Central Soil Salinity Research Institute, Karnal 132 001, Haryana, India.
(C) Corresponding author. Email: email@example.com
Table 1. Parameters analysed Physical attributes Chemical attributes Biological attributes * Bulk density * Soil pH: in water * Microbial biomass C ([pH.sub.w]) and 0.02 using the * Clay by hydrometer m Ca[Cl.sub.2] relationship: method solution ([pH.sub.Ca]) [C.sub.mic] = ((1- 0.38) x C-flush) * Water-stable * Cation exchange (Voroney and Paul aggregates by wet- capacity following 1984) and microbial sieving technique Jackson (1973) biomass N by Brookes (Yoder 1936); mean et al. (1985) weight diameter, * Total, oxidisable geometric mean and pools of soil * Mineralisable C by diameter, aggregate organic carbon (Mandal alkali trap method ratio, and aggregate et al. 2008) (Anderson 1982) and stability were mineralisable N by computed from there * Available nitrogen Bremner and Keeney following Subbiah and (1965) * Saturated hydraulic Asija (1956), conductivity phosphorus and * Dehydrogenase estimated by potassium by Jackson activity by triphenyl pedotransfer (1973) formazan production function, K.s = (Dick et al 1996) (0.174 + 0.144 x * Exchangeable calcium organic C (%) x day and magnesium * Fluorescein (%)) proposed by following diacetate hydrolysing Naskar et al. (2010) Schwarzenbach et al. activity by measuring (1946) extractable fluorescein (Dick et al. 1996) * Available iron, manganese, zinc and * [beta]-glucosidase, copper and boron by aryl sulfatase, and following Lindsay and acid and alkaline Norvell (1978) and phosphatase, activity Sarkar et al. (2015) by determination of p- nitrophenol released on incubation of soil with respective substrate: [beta]- glucopyranoside, p- nitrophenyl sulfate and p-nitrophenyl phosphate (Dick et al. 1996) * Urease activity determined by quantifying the N[H.sub.4] released on incubation (Dick et al. 1996) Table 2. Some important physical, chemical and microbial attributes of the soils of three Soil Orders Within rows, values followed by the same letter are not significantly different at P=0.05 by Duncan's multiple-range test Inceptisols (n = 20) Range Mean [+ or -] s.e.m. Clay (g [kg.sup.-1]) 81.7-144.3 257.4 [+ or -] 19.2a Bulk density (Mg [m.sup.-3]) 1.2-1.5 1.3 [+ or -] 0.02b Mean weight diameter (mm) 0.55-1.11 0.89 [+ or -] 0.03a Aggregate stability (%) 24.3-62.3 36.9 [+ or -] 2.3a Total water-stable aggregates 31.4-56.5 44.1 [+ or -] 1.4a (%) [pH.sub.w] 4.5-6.6 5.6 [+ or -] 0.1 [pH.sub.ca] 3.9-6.0 5.1 [+ or -] 0.1 Cation exchange capacity 8.0-19.9 14.1 [+ or -] 0.7a ([cmol.sub.(+)][kg.sup.-1]) Walkley-Black organic C 4.8-11.3 7.6 [+ or -] 0.4a (g [kg.sup.-1]) Very labile C (g [kg.sup.-1]) 1.4-5.0 3.5 [+ or -] 0.2ab C: N 9.5-13.4 10.9 [+ or -] 0.3a Available N (kg [ha.sup.-1]) 90.6-283.5 175.1 [+ or -] 8.9a Available P (kg [ha.sup.-1]) 54.7-183.4 105.5 [+ or -] 18.6a Available K (kg [ha.sup.-1]) 76.5-300.7 166.3 [+ or -] 18.2bc Available Zn (mg [kg.sup.-1]) 0.5-1.5 1.8 [+ or -] 0.3a Available B (mg [kg.sup.-1]) 0.4-1.7 0.7 [+ or -] 0.1b Microbial biomass C 258.1-1086.2 524.3 [+ or -] 44.3a ([C.sub.mic], [micro]g C [g.sup.-1] soil) Mineralisable C ([micro]g C 108.9-354.1 239.6 [+ or -] 13.6 [g.sup.-1] [day.sup.-1] soil) Metabolic quotient ([micro]g 2.5-9.2 5.8 [+ or -] 0.4 [C.sub.mic] [g.sup.-1] soil TOC x 100) Microbial biomass N ([micro]g 22.4-96.3 47.8 [+ or -] 4.3a N [g.sup.-1] soil) Mineralisable N ([micro]g 18.8-71.7 38.8 [+ or -] 3.0ab N[H.sub.4]-N [g.sup.-1] day 1 soil) Dehydrogenase ([micro]g TPF 38.4-158.7 64.4 [+ or -] 7.5a [g.sup.-1] [day.sup.-1] soil) Fluorescein diacetate 31.2-83.5 53.8 [+ or -] 3.8 hydrolysing activity ([micro]g fluorescein [g.sup.-1] soil [h.sup.-1]) Urease activity ([micro]g 11.5-95.7 38.8 [+ or -] 4.5 N[H.sub.4]-N [g.sup.-1] soil 2 [h.sup.-1]) [beta]-glucosidase ([micro]g 30.0-138.9 66.4 [+ or -] 7.2a p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Acid phosphatase ([micro]g 436.1-1132.4 860.9 [+ or -] 45.3a p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Alkaline phosphatase ([micro]g 39.2-224.4 119.8 [+ or -] 12.2a p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Aryl sulfatase ([micro]g 15.0-102.4 52.3 [+ or -] 5.8a p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Entisols (n = 20) Range Mean [+ or -] s.e.m. Clay (g [kg.sup.-1]) 65.5-103.4 266.2 [+ or -] 18.5a Bulk density (Mg [m.sup.-3]) 1.1-1.3 1.2 [+ or -] 0.01c Mean weight diameter (mm) 0.62-1.03 0.87 [+ or -] 0.03a Aggregate stability (%) 21.9-42.7 32.6 [+ or -] 1.3a Total water-stable aggregates 30.3-52.2 39.8 [+ or -] 1.5b (%) [pH.sub.w] 5.1-6.9 5.7 [+ or -] 0.1 [pH.sub.ca] 4.1-6.4 5.1 [+ or -] 0.1 Cation exchange capacity 7.5-21.2 14.1 [+ or -] 0.8a ([cmol.sub.(+)][kg.sup.-1]) Walkley-Black organic C 5.6-9.0 7.2 [+ or -] 0.3a (g [kg.sup.-1]) Very labile C (g [kg.sup.-1]) 2.9-5.1 3.7 [+ or -] 0.2a C: N 10.3-13.4 11.2 [+ or -] 0.2a Available N (kg [ha.sup.-1]) 115.0-195.8 160.7 [+ or -] 5.3ab Available P (kg [ha.sup.-1]) 56.8-148.1 55.8 [+ or -] 5.5b Available K (kg [ha.sup.-1]) 75.4-319.3 192.2 [+ or -] 30.2b Available Zn (mg [kg.sup.-1]) 0.1-3.9 1.5 [+ or -] 0.2ab Available B (mg [kg.sup.-1]) 0.4-1.7 1.1 [+ or -] 0.1 a Microbial biomass C 326.0-697.9 483.4 [+ or -] 25.9a ([C.sub.mic], [micro]g C [g.sup.-1] soil) Mineralisable C ([micro]g C 153.1-374.5 249.7 [+ or -] 43.6 [g.sup.-1] [day.sup.-1] soil) Metabolic quotient ([micro]g 4.4-7.0 5.8 [+ or -] 0.2 [C.sub.mic] [g.sup.-1] soil TOC x 100) Microbial biomass N ([micro]g 30.4-91.2 54.4 [+ or -] 3.3a N [g.sup.-1] soil) Mineralisable N ([micro]g 16.5-76.1 49.2 [+ or -] 4. la N[H.sub.4]-N [g.sup.-1] day 1 soil) Dehydrogenase ([micro]g TPF 31.3-76.8 50.3 [+ or -] 2.9ab [g.sup.-1] [day.sup.-1] soil) Fluorescein diacetate 31.3-75.4 45.3 [+ or -] 2.9 hydrolysing activity ([micro]g fluorescein [g.sup.-1] soil [h.sup.-1]) Urease activity ([micro]g 10.6-134.0 30.5 [+ or -] 5.9 N[H.sub.4]-N [g.sup.-1] soil 2 [h.sup.-1]) [beta]-glucosidase ([micro]g 44.0-92.8 65.1 [+ or -] 2.9a p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Acid phosphatase ([micro]g 224.0-955.8 586.1 [+ or -] 42.8b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Alkaline phosphatase ([micro]g 30.1-123.3 67.2 [+ or -] 6.8b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Aryl sulfatase ([micro]g 10.6-51.5 32.2 [+ or -] 2.2b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Alfisols (n = 20) Range Mean [+ or -] s.e.m. Clay (g [kg.sup.-1]) 90.7-171.2 138.4 [+ or -] 18.3b Bulk density (Mg [m.sup.-3]) 1.3-1.7 1.5 [+ or -] 0.02a Mean weight diameter (mm) 0.42-1.13 0.76 [+ or -] 0.04b Aggregate stability (%) 15.1-37.2 22.8 [+ or -] 1.2b Total water-stable aggregates 14.9-37.5 24.3 [+ or -] 1.5c (%) [pH.sub.w] 4.8-6.3 5.5 [+ or -] 0.1 [pH.sub.ca] 4.3-5.6 4.8 [+ or -] 0.1 Cation exchange capacity 4.2-13.8 8.9 [+ or -] 0.1b ([cmol.sub.(+)][kg.sup.-1]) Walkley-Black organic C 2.9-8.3 5.7 [+ or -] 0.3b (g [kg.sup.-1]) Very labile C (g [kg.sup.-1]) 1.5-1.7 3.1 [+ or -] 0.2b C: N 10.1-13.9 12.4 [+ or -] 0.4b Available N (kg [ha.sup.-1]) 105.0-213.3 142.2 [+ or -] 5.8b Available P (kg [ha.sup.-1]) 21.8-80.0 65.0 [+ or -] 10.4b Available K (kg [ha.sup.-1]) 77.0-326.0 257.3 [+ or -] 84.6a Available Zn (mg [kg.sup.-1]) 0.6-2.0 1.2 [+ or -] 0.1c Available B (mg [kg.sup.-1]) 0.1-1.1 0.8 [+ or -] 0.1b Microbial biomass C 230.2-521.0 379.6 [+ or -] 22.7b ([C.sub.mic], [micro]g C [g.sup.-1] soil) Mineralisable C ([micro]g C 153.3-270.0 220.4 [+ or -] 8.2 [g.sup.-1] [day.sup.-1] soil) Metabolic quotient ([micro]g 3.0-6.7 5.2 [+ or -] 0.2 [C.sub.mic] [g.sup.-1] soil TOC x 100) Microbial biomass N ([micro]g 21.2-52.3 36.1 [+ or -] 2.lb N [g.sup.-1] soil) Mineralisable N ([micro]g 15.6-80.5 35.8 [+ or -] 4.3b N[H.sub.4]-N [g.sup.-1] day 1 soil) Dehydrogenase ([micro]g TPF 13.9-63.1 36.9 [+ or -] 3.2b [g.sup.-1] [day.sup.-1] soil) Fluorescein diacetate 25.8-79.7 45.5 [+ or -] 3.5 hydrolysing activity ([micro]g fluorescein [g.sup.-1] soil [h.sup.-1]) Urease activity ([micro]g 24.1-58.9 41.9 [+ or -] 2.3 N[H.sub.4]-N [g.sup.-1] soil 2 [h.sup.-1]) [beta]-glucosidase ([micro]g 18.0-62.8 40.1 [+ or -] 2.5b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Acid phosphatase ([micro]g 219.3-1090.0 509.7 [+ or -] 51.9b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Alkaline phosphatase ([micro]g 29.7-262.2 67.6 [+ or -] 11.1b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Aryl sulfatase ([micro]g 17.3-51.7 32.0 [+ or -] 2.2b p-nitrophenol [g.sup.-1] soil [h.sup.-1]) Table 3. Correlation among the estimated soil enzyme activities DHA, Dehydrogenase activity; FDHA, fluorescein diacetate hydrolysing activity; URE, urease; [beta]-glu, [beta]-glucosidase ; AcP, acid phosphatase; AlkP, alkaline phosphatase; ArS, aryl sulfatase. * P < 0.05; ** P < 0.01 DHA FDHA URE [beta]-glu AcP AlkP FDHA 0.58 ** 1.00 URE -0.02 0.37 ** 1.00 [beta]-glu 0.32 * 0.43 ** 0.15 1.00 AcP 0.29 * 0.23 0.14 0.32 * 1.00 AlkP 0.03 * 0.42 ** 0.34 ** 0.12 0.22 1.00 ArS 0.34 ** 0.43 ** 0.30 * 0.34 ** 0.43 ** 0.39 ** Table 4. Results of principal component analysis of the soil attributes of soil belonging to three Soil Orders DHA, Dehydrogenase activity; CEC, cation exchange capacity; WBOC, Walkley-Black organic C. Bold values indicate highly weighted variables for the respective principal components (PC) PC1 PC2 PC3 Inceptisols Avail. B 0.77 0.32 0.09 DHA 0.26 -0.39 -0.71 CEC 0.92# -0.02 0.72 [pH.sub.Ca] 0.09 0.83# 0.21 [pH.sub.w] 0.16 0.82# -0.13 Avail. K 0.68 -0.10 0.09 Avail. Zn 0.40 0.20 0.66# Aryl sulfatase 0.79 0.14 -0.28 Entisols [pH.sub.w] 0.82# -0.36 -0.12 [beta]-glucosidase 0.34 0.39 -0.54 [pH.sub.ca] 0.82# 0.84# 0.23 Microbial biomass C 0.89# 0.38 0.04 Microbial biomass N 0.86# 0.48 0.43 WBOC 0.91# -0.10 0.20 Mineralisable N 0.75 0.08 0.24 Bulk density -0.09 0.52 0.73# Very labile C 0.86# 0.85# -0.20 Avail. P -0.63 0.18 -0.46 Alfisols Very labile C 0.904# 0.083 0.255 WBOC 0.869# 0.188 -0.098 Mineralisable N 0.852# 0.735# 0.066 Microbial biomass C 0.829# 0.302 -0.170 CEC 0.824# -0.594 -0.040 Mineralisable C 0.768 -0.396 0.351# DHA 0.636 -0.082# -0.086 Aryl sulfatase 0.569 0.266# 0.140 [beta]-glucosidase 0.426 0.251 -0.117 PC4 PC5 PC6 Inceptisols Avail. B 0.12 -0.05 0.50# DHA -0.58# -0.07 0.35 CEC 0.62 -0.12 0.06 [pH.sub.Ca] 0.31 -0.12 0.03 [pH.sub.w] -0.09 -0.13 -0.04 Avail. K 0.12 0.60# -0.06 Avail. Zn 0.53 -0.20 -0.18 Aryl sulfatase -0.22 0.55# -0.25 Entisols [pH.sub.w] -0.12 -0.20 0.55 [beta]-glucosidase -0.08 0.61# 0.37 [pH.sub.ca] 0.10 0.06 0.13 Microbial biomass C 0.34 -0.20 0.07 Microbial biomass N 0.38 0.20 0.05 WBOC 0.04 -0.21 -0.01 Mineralisable N -0.72# -0.02 -0.02 Bulk density -0.14 -0.28 -0.17 Very labile C 0.12 0.24 -0.22 Avail. P 0.13 0.39 -0.51# Alfisols Very labile C 0.164 0.099 0.545 WBOC -0.364 0.246 0.460 Mineralisable N -0.010 0.084 0.444 Microbial biomass C 0.622 0.243# 0.389 CEC 0.304 -0.234 0.260 Mineralisable C -0.287 -0.387 0.243 DHA 0.124# 0.049 0.004 Aryl sulfatase -0.079 0.325 -0.015 [beta]-glucosidase 0.029 0.303 -0.052# Note: Highly weighted variables for the respective principal components (PC) are indicated with #. Table 5. Results of discriminate analysis of the attributes of soils belonging to the Soil Order Inceptisols Bold values indicate highly weighted variables for the respective discriminate function (DF) DF1 DF2 From selected properties Eigenvalue 9.44 2.35 % of Variance 73.63 18.31 Canonical correlation 0.95 0.84 Variables and discriminant coefficient [pH.sub.Ca] 0.97# 0.81 Cation exchange capacity 0.83# -0.39 Available Zn 0.35 0.12 Available B 0.35 -0.13 Dehydrogenase activity -0.14# 1.08 Available K 0.56# 0.33 Note: Highly weighted variables for the respective discriminate function (DF) are indicated with #. Table 6. Results of discriminate analysis of the attributes of soils belonging to the Soil Order Entisols WBOC, Walkley-Black organic C. Bold values indicate highly weighted variables for the respective discriminate function (DF) DF1 DF2 From selected properties Eigenvalue 9.28 1.75 % of Variance 78.71 14.82 Canonical correlation 0.95 0.80 Variables and discriminant coefficient WBOC 0.97# -0.09 [pH.sub.Ca] -0.64# 0.43 Bulk density 0.09 -0.95# Mineralisable N 0.91# -0.18 [beta]-glucosidase 0.54 0.46 Available P 0.24 0.34 From selected principal component 1 Eigenvalue 7.11 3.30 % of Variance 61.61 28.56 Canonical correlation 0.95 0.80 Variables and discriminant coefficient WBOC -0.14 -1.37 Microbial biomass C 0.65 -0.86 Very labile C 0.62 1.58# Microbial biomass N 0.27 0.25 [pH.sub.Ca] 0.67 -0.54 [pH.sub.w] -0.94 1.12 Note: Highly weighted variables for the respective discriminate function (DF) are indicated with #. Table 7. Results of discriminate analysis of the attributes of soils belonging to the Soil Order Alfisols Bold values indicate highly weighted variables for the respective discriminate function (DF) DF1 From selected soil properties Eigenvalue 983.61 % of Variance 98.43 Canonical correlation 1.00 Variables and discriminant coefficient Very labile C 2.91 Aryl sulfatase 3.54 Dehydrogenase activity -5.72# Mineralisable C 3.98 DF1 From selected principal component Eigenvalue 0.94 % of Variance 99.5 Canonical correlation 1.00 Variables and discriminant coefficient Very labile C 7.06# Walkley-Black organic C 2.62 Mineralisable N 8.05# Mineralisable C 4.05 Note: Highly weighted variables for the respective discriminate function (DF) are indicated with #. Table 8. Results of the regression between the indicators retained in the minimum dataset (MDS) and equivalent rice yield DHA, Dehydrogenase activity; CEC, cation exchange capacity; WBOC, Walkley--Black organic C; [C.sub.mic], microbial biomass carbon. Bold soil quality indicators have a strong correlation with equivalent rice yield Indicators Unstandardised t (d.f. = 20) for Significance coefficients each Soil Order level [beta] Std. error Inceptisols ([R.sup.2] = 0.45) Constant 218.46 61.55 3.55 0.003 [pH.sub.Ca] -7.32 10.21 -0.72 0.49 DHA# 0.38 0.18 2.06 0.05 Available K 0.00 0.09 -0.05 0.96 CEC -4.95 2.11 -2.34 0.03 Entisols ([R.sup.2] = 0.77) Constant 66.398 59.130 1.123 0.280 WBOC# 13.751 4.693 2.930 0.011 [pH.sub.Ca] -4.732 5.065 -0.934 0.366 Mineralisable N 0.221 0.314 0.703 0.493 BD 19.859 45.238 -0.439 0.667 [C.sub.mic] 0.019 0.070 0.274 0.788 Alfisols ([R.sup.2] = 0.78) Constant 51.84 10.52 4.93 0.0001 DHA 0.15 0.21 0.73 0.475 Mineralisable N# 0.48 0.12 3.91 0.0014 Very labile C# 10.51 4.08 2.58 0.021 [C.sub.mic] 0.05 0.03 1.63 0.124 Note: Soil quality indicators have a strong correlation with equivalent rice yield are indicated with #. Fig. 2. Gross imposed inorganic fertilisation for rice and potato, and rice, potato, sesame and equivalent rice yield (ERY) of three Soil Orders. For each attribute across Soil Orders, means followed by the same letter are not significantly different at P=0.05). Capped vertical lines are [+ or -] standard error. Inceptisols Entisols Alfisols Rice 42.2 43.7 43.2 Potato 237.3 295.5 211.4 Sesame 11.2 15.5 7.6 ERY 134.5 138.9 125.8 N 303.0 295.8 275.6 P205 297.4 245.6 200.0 K20 287.3 256.3 197.2
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|Author:||Basak, Nirmalendu; Datta, Ashim; Mitran, Tarik; Roy, Satadeep Singha; Saha, Bholanath; Biswas, Sunan|
|Date:||Feb 1, 2016|
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