Contrasting land use systems and soil organic matter quality and temperature sensitivity in North Eastern India

Soil & Tillage Research 199 (2020) 104573

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Contrasting land use systems and soil organic matter quality and temperature sensitivity in North Eastern India

T

Avijit Ghosha,*,1, Anshuman Dasa, Debarup Dasa, Prasenjit Rayb,**, Ranjan Bhattacharyyac, Dipak Ranjan Biswasa,*, Siddhartha Sankar Biswasa,2 a

Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat, 785004, Assam, India c CESCRA, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India b

A R T I C LE I N FO

A B S T R A C T

Keywords: Soil organic carbon pools CO2 emission Q10

Although quality of soil organic matter (SOM) plays an important role in carbon (C) cycling of cropland and natural ecosystems under the changing climatic scenario, its impact on temperature sensitivity (Q10) is seldom studied together in surface and sub-surface soils of natural and managed ecosystems situated at different altitudes. So, a study was conducted by collecting soil samples from 0 to 15 and 15 to 30 cm depths under natural forest (NF), mulberry plantation (MP), rice-mustard (RM) and rice-fallow (RF) systems of north-eastern region of India to find out the quality and Q10 of SOM under varying land uses at different altitudes. Soils were incubated at 15, 25 and 35 °C for 52 days to estimate C decay rates (k), intermediate (after 24 day) Q10(24), final Q10, activation energy (Ea) and SOM quality parameters. Results revealed that cumulative CO2 emission was the lowest under NF at 15 °C, while it was the lowest under RF at 35 °C. The proportion of C-mineralized to total soil organic C (SOC) was the highest under RF (situated at higher altitude) and the lowest under NF system (situated at lower altitude). The SOC of the NF system had the highest Q10 value and higher recalcitrant C. The Q10 values of NF were ∼16 and 44% higher in the 0–15 and 15−30 cm soil layers, respectively than managed ecosystems. The ratios of microbial biomass C (MBC) to SOC and Ea were well correlated (P < 0.05) with Q10. The Q10(24) of surface soil was higher in managed ecosystems, but Q10 was higher for sub-surface C in the natural ecosystem. Hence, protecting natural ecosystems is very important to mitigate climate change. We found MBC/SOC and Ea to be better predictors of SOM quality and these should be included in soil C models for predicting C dynamics. Path analysis and PCA analysis revealed that soil variants, SOM quality and C pools significantly affected Q10 but climatic variables had nonsignificant impact on Q10.

1. Introduction Soil organic carbon (SOC) is crucial for maintaining soil health, performing uninterrupted environmental functions and sustaining agricultural production. Soil management practices like land use changes, frequent tillage, improper fertilization and low or no exogenous C addition may lead to excess emission of CO2 from soils, causing global warming and climate change (Ghosh et al., 2019). Generally, natural systems like forests and grasslands have higher SOC stocks than managed land use systems (Choudhury et al., 2016). More than 70% of total SOC is stored in subsurface layers (below 15 cm) in

soils of natural and managed ecosystems (Jobbágy and Jackson, 2000; Ghosh et al., 2018). Despite this, studies investigating the effects of cropping on SOC dynamics have mostly been restricted to surface soils of managed ecosystems (Poeplau and Don, 2013). The drivers of SOC storage vary widely with climate and soil depth (Jobbágy and Jackson, 2000). Furthermore, natural and managed land use systems largely differ with respect to quantity and quality of C inputs, SOC protection mechanisms and distribution pattern of C pools in soil profile. Hence, investigation on SOC dynamics solely in surface (0−15 cm) layer under managed ecosystems may produce incorrect conclusions on SOC storage behaviour of a particular region. Furthermore, SOC pools of

⁎

Corresponding authors at: Division of Soil Science and Agricultural Chemistry, ICAR-Indian Agricultural Research Institute, New Delhi, 110 012, India. Corresponding author at: ICAR-National Bureau of Soil Survey and Land Use Planning, Regional Centre, Jorhat, 785 004, Assam, India. E-mail addresses: [email protected] (A. Ghosh), [email protected] (P. Ray), [email protected] (D.R. Biswas). 1 Present address: ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284 003, Uttar Pradesh, India. 2 Present address: ICAR-National Research Centre for Orchids, Pakyong 737 106, Sikkim, India. ⁎⁎

https://doi.org/10.1016/j.still.2020.104573 Received 7 December 2018; Received in revised form 22 August 2019; Accepted 10 January 2020 0167-1987/ © 2020 Elsevier B.V. All rights reserved.

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2. Materials and methods

natural and managed ecosystems respond differently to changes in climatic variables. The reasons behind such differences are still not clear (Schmidt et al., 2011). Hence, for a better understanding, simultaneous comparisons between surface and sub-surface SOC dynamics in natural and managed land uses are required. According to a recent estimate of Intergovernmental Panel on Climate Change (Intergovernmental Panel on Climate Change, 2015), the global average temperature would rise by ∼2 °C (1.1–2.9 °C) by the end of this century. Mimicking this scenario, simulation models have revealed variable predictions on response of soil C pools to climate change (Sakalli et al., 2017). Such variations in prediction exist because of lack of insight into the dynamics and temperature sensitivity (Q10) of SOC in different soil layers across various land uses. The Q10 is governed by soil organic matter (SOM) quality and composition (Liu et al., 2017), vegetation type and distribution (Gutiérrez-Girón et al., 2015), soil minerals (Cotrufo et al., 2013), pH, C to N ratio (Liu et al., 2017), and soil microbes (Fontaine et al., 2003). Out of these, SOM quality has not been included in most of the terrestrial C models (von Lützow and Kögel-Knabner, 2009). Hence, a perception of SOM quality and its impact on Q10 would help in improving the existing terrestrial C models and enable parameterization under tropical conditions for precisely predicting the response of SOC to climate change in specific land uses (Davidson and Janssens, 2006). Recent shreds of evidences on C quality-temperature (CQT) hypothesis state that Q10 of microbial decomposition should increase with increasing activation energy; hence, recalcitrant C compounds would be more temperature sensitive than labile compounds (Davidson and Janssens, 2006; Craine et al., 2010). In CQT hypothesis, both positive (Xu et al., 2014) and negative (Fierer et al., 2006) correlations between Q10 and SOM quality parameters have been observed due to complex interactions among SOC, soil microbes (Fontaine et al., 2003) and soil minerals (Cotrufo et al., 2013). However, very few researchers have tested the CQT hypothesis in natural as well as managed land use systems and in surface and sub-surface soils to better perceive the SOCstability of a particular region (Conant et al., 2008; Li et al., 2017). Due to favourable climate and poor soil management practices, SOC decomposition is more pronounced in the tropical areas. The Brahmaputra valley region of north-eastern India stands as an instance of such scenario. North-eastern region (NER) of India is one of the 12 biodiversity hotspots in the world (Choudhury et al., 2016). So, investigating SOC dynamics in surface and sub-surface layers of natural and managed land use systems in NER is of global relevance. Here, forest covers ∼64% area and rice occupies ∼13% area (Baruah et al., 2014). Around 23% of the country’s silk production comes from NER (Central Silk Board, 2018), and mulberry plantation is very important in this regard. The minimum and maximum temperatures have also shown a rising trend in NER in the last century (Jain et al., 2013). According to Jenny (1941) five major factors influencing soil formation and its properties are parent material (p), climate (c), topography (r), biotic factors (b) and time (t). In this study p, t and c are similar, so the major influence on SOC dynamics is only due to r (i.e altitude) and b (i.e soil microbial activity and above ground crops). On this background, we hypothesized that (i) SOM quality and consequently its Q10 would differ between natural and managed ecosystems due to interferences like fertilization, tillage and irrigation in the latter; and (ii) both of these parameters would change with soil depth under different land use systems. In this study, we specifically aim to (i) identify temporal changes of Q10 in one natural ecosystem (i.e. forest) and three managed ecosystems; and (ii) test the relevance of the CQT hypothesis in the surface and sub-surface soils of natural and managed ecosystems of NER of India.

2.1. Study area and soil sampling Soil samples from surface (0−15 cm) and sub-surface (15−30 cm) layers were collected from four land use systems, namely natural forest (NF), mulberry plantation (MP), rice-mustard rotation (RM) and ricefallow (RF) from Brahmaputra valley of Assam, north-eastern India. For each ecosystem, four sampling locations were randomly chosen. In each location, four sub-plots (30 × 30 m) were selected maintaining at least 100 m distance between each other. All sub-plots within a land use were uniform in terms of soil homogeneity, slope, historical land use, density and tree age. Soil samples were collected from every sub-location using an 8-cm diameter core sampler. For each sampling depth, soils collected from four sub-locations of each location were pooled together. Approximately 200 g soil from each location was separately stored at 4 °C for incubation and soil microbial biomass C (MBC) determination. The stored samples were passed through a 4.75-mm sieve, and stones and plant materials were discarded before using the soils for incubation study. The second set was passed through a 2-mm sieve to avoid interferences of stones and plant residues and used for analysis of chemical parameters. The four locations of each ecosystem were considered as four replications. Climatic variables, management practices and soil properties of these systems are presented in Tables 1 and 2. Location of the study area, land Use system distribution and soil sampling procedure has been illustrated in Fig. S1. 2.2. Soil analysis Soil microbial biomass carbon (MBC) (Jenkinson and Powlson, 1976), permanganate oxidizable C (KMnO4-C) (Tirol-Padre and Ladha, 2004), and labile and recalcitrant C (using 10 mL 36.0 N H2SO4; Chan et al., 2001) were measured. Total SOC and nitrogen (N) concentrations were analysed using an elemental analyser (Owens and Rees, 1989) in a continuous flow mode. Available phosphorus (P), available potassium (K), oxidizable SOC, cation exchange capacity (CEC), pH, electrical conductivity (EC), soil texture (Jackson, 1973), and overall clay mineralogy (Jackson, 1985; Das et al., 2019a) of all soils were determined and are reported in Table 2. 2.3. Soil incubation and temperature sensitivity determination The soil samples (maintained at field capacity (0.33 bar) moisture) were incubated, at three temperatures (15, 25 and 35 °C) in the laboratory incubators for 52 days. For each replication, 50 g air-dried soil was incubated in a 500 mL jar (along with two blanks) with an alkali trap containing 10 mL of 0.5 N NaOH to capture CO2. To measure C mineralization, periodically alkali traps (on 3, 10, 17, 24, 31, 38, 45 and 52 days after incubation) were drawn out of the jars. Amount of CO2 trapped was determined by back titration of the 0.5 N NaOH with 0.5 N HCl at pH 8.3 in presence of saturated BaCl2. The alkali trap was replaced with fresh 0.5 N NaOH at each sampling date, and compressed air was flushed into the flask to facilitate O2 supply. A separate set of soil samples of each eco-system was maintained to identify the water requirement of each soil at each sampling day for adjusting moisture at 0.33 bar. The required amount of water was also added to maintain the moisture level at field capacity. The following equation was used to assess CO2 emission:

CO2 produced = (A − B ) × N × 22

(1)

where, A and B are the volumes (mL) of HCl consumed for titrating 10 mL 0.5 N NaOH of flask without soil and flask with soil, respectively; N is the normality of HCl and 22 is the equivalent weight of CO2. A first-order decay model as given below was used to determine C loss (as CO2) with time (Stanford and Smith, 1972). 2

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Rice (Oryza sativa)-fallow (at Jorhat)

26°34′42.69″ N/94°12′10.41″ E 100 Inceptisol The soil sample was collected from Titabor, Jorhat, Assam. Rice productivity in the area is very high and farmers usually follow standard management practices as recommended by Regional Agricultural Research Station (RARS), Assam Agricultural University, Jorhat. The recommended dose of N: P2O5: K2O is 40:20:30 kg ha−1.

Rice-mustard (Brassica juncea) (at Nagaon)

26°22′9.73″ N/92°36′26.7″ E 61.2 Inceptisol Nagaon district is well known for mustard cultivation in Assam. The recommended dose of N: P2O5: K2O for rice is 60: 20: 40 kg ha−1 and for mustard the dose of N: P2O5: K2O is 60: 30: 30 kg ha−1 is applied.

1245

27.8

8.92

26°44′42.8″ N/93°56′3.2″ E 80.2 Inceptisol Usually fertilizer is not applied for mulberry cultivation in Brahmaputra valley of Assam and no management practices are followed.

1493

30.7

10.6

Ct = Co (1 − e−kt )

(2)

where Co represents the potentially mineralizable C as CO2, and Ct is C (as CO2) lost at time t, with decay rate k. An exponential function was used to fit SOC decomposition rates with incubation temperatures (Eq. 3; R2 > 0.90), and temperature sensitivity parameter (Q10) of SOC decomposition was estimated using Eq. 4 (R2 > 0.90) (Fierer et al., 2006).

kT = Ro e bT

(3)

where kT is respiration rate at T°C temperature, R0 is respiration rate at 0 °C temperature, b is temperature coefficient.

10.8

31.2

1555

Q10 = e10b

(4)

The activation energy of SOC decomposition (Ea) was measured using the Arrhenius equation Ea

k = Ae− RT

(5)

where k is the decay rate at T K temperature, A is a pre-exponential factor and R is the universal gas constant.

2.5. Statistical analysis To determine the effects of land use systems on soil properties, SOM quality, and Q10 value, a one-way analysis of variance (ANOVA) was performed for each soil layer. Paired t-test was used to compare the differences in soil properties, SOM quality, and Q10 between the two soil layers. Pearson’s correlation analysis was performed to show the relationships between Q10, and SOM quality parameters. All figures were drawn using MS Excel 2010. Interaction effect of land use and temperature on cumulative CO2 emission and SOC decay rate (k) was computed following the procedures of Gomez and Gomez (1984). A principal Components Analysis (PCA) was performed with soil properties, C pools, chemical and thermodynamic SOM quality parameters and microbial SOM quality parameters. Further, first three principal components (PC1, PC2 and PC3) were extracted for the ordination of cases. Stepwise regression models were calculated to predict the role of different factors on Q10. We used a path model (i.e., structural equation model) with four latent variables, i.e., climate, soil (i.e. various soil physical and chemical properties), SOM quality and C pools, to assess their direct and indirect effects on Q10. The latent variables were reflected by

8.92

27.8

26°29′42.85″ N/92°56′56.68″ E 70.7 Inceptisol Albizia species, Anthocephalus chinensis, Michelia champaca, Tectona grandis are dominant tree species in forest. Usually in natural forest, no manure or fertilizer is applied. In the present case, the soil sample was collected from a natural forest cum nursery under Forest Department, Govt. of Assam. Workers apply only cow dung for maintaining soil health. 1245 Latitude/Longitude Altitude (m) Soil order Management practices

The SOM quality generally governs the ease of its decomposition under different biotic and abiotic influences. Based on this perception, dissolved organic C and MBC are commonly taken as indicators of SOM quality (Haynes, 2000); but they cannot comprehensively reveal the same in natural or managed ecosystems. The parameter R0 (in Eq. 3), on the contrary, can be defined as the decomposability of SOC, as it quantifies the fraction of SOC that can be decomposed over a given period of time (i.e. SOM quality/lability) at 0 °C. Hence, R0 was used as an indicator of SOM quality in this study (Fierer et al., 2006; Ding et al., 2016). The activation energy of SOC decomposition, which denotes the molecular complexity of substrates, was also used as an indicator of SOM quality (Davidson and Janssens, 2006; Craine et al., 2010). Apart from these, total polyphenol content (measured using Prussian Blue Spectrophotometric method of Prince and Butler, 1977), which signals for the recalcitrant pool of SOC; and total polysaccharide content (analyzed by a technique modified from Whistler and Wolfram, 1962), which indicates labile pool of SOC, were measured. We also assessed SOM quality by a novel parameter, i.e., the proportion (in percentage) of MBC in total SOC as it indicates the amount of SOC available for microbial growth i.e. SOC lability (Fang et al., 2005; Ghosh et al., 2018).

Rainfall (mm, mean of last 5 years) Mean maximum temperature (°C) Mean minimum temperature (°C)

Mulberry (Morus alba) (at Golaghat) Forest (at Nagaon)

2.4. Soil organic matter (SOM) quality

Land use

Table 1 Climatic variables and management practices of the studied land use systems of Assam.

A. Ghosh, et al.

3

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Table 2 Soil properties at the four sites under different land use systems of Assam. Property Surface soil (0−15 cm) Dominant clay minerals Mechanical composition Texture Walkley-Black C (g kg−1) Total N (g kg−1) Available P (kg ha−1) Available K (kg ha−1) CEC [cmol(p+) kg−1] pH EC (dS m−1) Sub-surface soil (15−30 cm) Dominant clay minerals Mechanical composition Texture Walkley-Black C (g kg−1) Total N (g kg−1) Available P (kg ha−1) Available K (kg ha−1) CEC [cmol(p+) kg−1] pH EC (dS m−1)

Forest (at Nagaon)

Mulberry (at Golaghat)

Rice-mustard (at Nagaon)

Rice-fallow (at Jorhat)

Illite, kaolinite, smectite, illite-smectite interstratified minerals, illite-kaolinite interstratified minerals Sand 42.4%, silt 32%, clay 25.6% Sand 32.4%, silt 44%, clay 23.6% Sand 60.4%, silt 20%, clay 19.6% Loam Loam Sandy loam 10.3 7.5 7.7 1.63 1.1 0.87 70.5 16.8 14.2 130 199 41.2 13.7 11.4 8.8 5.3 5.8 5.1 0.18 0.16 0.37

Sand 42.4%, silt 46%, clay 11.6% Loam 5.4 0.44 19 27.1 8.4 5.2 0.71

Illite, kaolinite, smectite, illite-smectite interstratified minerals, illite-kaolinite interstratified minerals Sand 24.4%, silt 52%, clay 23.6% Sand 16.4%, silt 58%, clay 25.6% Sand 58.4%, silt 22%, clay 19.6% Silt loam Loam Sandy loam 9.2 6.4 5.7 1.20 0.80 0.75 52.3 13.6 8.5 74.1 260 58.2 11.7 12.2 10.3 5.4 6.2 5.3 0.47 0.17 0.82

Sand 30.4%, silt 36%, clay 33.6% Silt loam 3.6 0.30 10.8 51.1 14.1 5.4 0.26

indicators. For ‘climate’, we considered two indicators: temperature and precipitation. All measurements of soil geochemical properties including pH, clay percentage, EC, CEC, available N, and available P were considered as potential indicators of the latent variable ‘soil’. For ‘SOM quality’, we considered two indicators i.e. activation energy and MBC/ SOC (based on partial least square regression). For ‘C pools’, we considered LC and RC as indicators. We considered the following potential paths in a hypothesis-oriented path model. First, we hypothesized that all the four latent variables have direct effect on Q10. Second, climate may also indirectly affect Q10 through its effect on soil geochemical properties, SOM quality, and C pools. Third, soil may indirectly affect Q10 through its effect on SOM quality and C pools. At last, SOM quality may indirectly affect Q10 through its effect on soil C pools. By the stepwise removal of nonsignificant paths in the initial model, we selected a final model that best fit our data. The adequacy of the model was determined by χ2-test, goodness of fit (GIF) index, and root mean squared error of approximation (RMSEA) index. The χ2 was used to test whether the model reasonably explained the patterns of the data. Favourable model fits were suggested by no significant difference on the χ2-test (P > 0.05), high GIF (> 0.9), and low RMSEA (< 0.08).

Table 3 Total soil organic carbon (SOC), labile C (LC), recalcitrant C (RC), KMnO4 oxidizable C (KMnO4-C) and microbial biomass C (MBC) in the surface and subsurface soil layers under different land use systems. Land use

Total SOC (g kg−1)

Surface soil (0−15 cm) Forest 15.75a Mulberry 10.47b Rice-mustard 9.83b Rice-fallow 7.94c Mean 10.9A Sub-surface soil (15−30 cm) Forest 11.67a Mulberry 8.19b Rice-mustard 7.27c Rice-fallow 4.60d Mean 7.9B

LC (g kg−1)

RC (g kg−1)

KMnO4-C (g kg−1)

MBC (mg kg−1)

7.20a 5.38b 5.76b 4.43c 5.69A

8.55a 5.09b 4.07c 3.51d 5.30A

2.29a 1.72b 1.26c 0.83d 1.52A

260a 220b 195c 160d 209A

5.19a 3.96b 3.84c 2.42d 3.85B

6.47a 4.23b 3.44c 2.18d 4.08A

1.66a 1.26b 0.84c 0.45d 1.05B

220a 180b 176b 133c 177A

Means with similar lower-case letters are not significantly different within a column according to Tukey’s HSD test at p < 0.05. Means with upper-case letters in the last row indicate the effect of soil depth on the parameters according to paired t-test.

3. Results

use systems, total SOC of surface layer was ∼38% higher than that of sub-surface layer. Among the C pools, labile SOC and KMnO4-C concentrations were significantly higher in surface than sub-surface layer; but recalcitrant C and MBC concentrations were similar in both layers (Table 3).

3.1. Total SOC and C pools Generally, total SOC and its pools showed significant differences over land uses in both soil layers (Table 3). In the surface layer (0−15 cm), total SOC concentration under NF was ∼50 and 61% higher than MP and RM systems, respectively. Total SOC under RF was ∼19% lower than the RM system. Labile C closely followed the trend of SOC in the surface layer. The NF system had ∼34, 25 and 63% higher labile C than MP, RM and RF systems, respectively. Interestingly, NF had the lowest proportion of labile SOC (∼45% of total SOC), but other three systems had more than 50% of total SOC stored as labile C. The KMnO4-C followed a trend more or less similar to that of LC. In NF system it was ∼34% higher than MP system. Soil MBC was also the highest under NF system (∼33% higher than RM). Recalcitrant SOC concentration under the NF system was ∼68% higher than the MP. Importantly, in NF system ∼55% of SOC was stored in recalcitrant pool. Variations in SOC pools in sub-surface (15−30 cm) layer were almost similar to that observed in surface layer. Averaged across the land

3.2. Carbon mineralization Carbon di-oxide emissions from 0 to 15 and 15 to 30 cm soil layers under different land use systems showed wide temporal variations at all incubation temperatures (Fig. 1). From surface soil, cumulative CO2 emission was the highest under MP at 15 °C after 52 days. The C mineralization was ∼10, 17 and 16% lower in NF than RM, MP and RF systems, respectively. At 25 °C, cumulative CO2 emission did not vary much among different land uses up to 38 days of incubation. However, beyond 38 days, CO2 emission from MP increased sharply, causing a higher (∼15%) CO2 emission than the NF system. Despite the differences in total SOC, cumulative CO2 emissions from RF, RM and NF systems were similar at 25 °C (Fig. 1). Interestingly at 35 °C, CO2 emission from soils of NF, RM and MP systems increased sharply from 4

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Fig. 1. Land use impacts on cumulative CO2 emission from surface (0−15 cm) soil at 15, 25 and 35 °C temperatures. Error bars indicate LSD values (p < 0.05).

of NF, ranging from ∼5% (as average of three temperatures) in surface soil to 6% in sub-surface soil. The same was the highest under RF, varying from ∼10% in surface soil to 16% in sub-surface soil. Proportion of C mineralization to total SOC was significantly higher in sub-surface as compared to surface soil.

10th day onwards; but the soil under RF system showed a steady path of CO2 release (Fig. 1). After 52 days of incubation, total CO2 emission from soils of NF, RM and MP was ∼14, 11 and 12% greater than the soil of RF system, respectively. Cumulative CO2 emission (mean of four land uses) at 35 °C was ∼17 and 40% higher than that at 25 and 15 °C, respectively. In sub-surface layer at 15 °C, soil under NF had the lowest cumulative CO2 emission (∼28% lower than soil under MP) (Fig. 2). At 25 °C, total CO2 emission from NF was similar to RM. Soil under RF showed ∼12% higher CO2 emission than soil under RM (Fig. 2). Interestingly, at 35 °C, CO2 emission from RF system was the lowest (∼14% lower than NF) (Fig. 2). Mean (of four land use systems) CO2 emissions at 15, 25 and 35 °C temperatures of two soil layers were at par. Irrespective of land uses and incubation temperatures, ∼50% of total CO2 emission was completed within 17–24 days of incubation in both soil layers. The proportion of C mineralized to total SOC was the lowest in case

3.3. Decay rates In surface soil, SOC decay rate (k) at all incubation temperatures was the highest under RF (Table 4). At 15 °C, k of surface soil under RF was ∼16 and 36% higher than that under MP and NF, respectively. At same temperature and soil depth, k under NF was ∼14% lower than that under MP. At 25 and 35 °C, k in surface soil of NF was at par to that of RF, but ∼19 (at 25 °C) and 35% (at 35 °C) higher than that of MP. The k averaged over land uses for surface soil was ∼13 and 46% higher at 35 °C than that at 25 and 15 °C, respectively. Irrespective of temperatures, k values of sub-surface soil were higher under MP than the 5

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Fig. 2. Land use impacts on cumulative CO2 emission from sub-surface (15−30 cm) soil at 15, 25 and 35 °C temperatures. Error bars indicate LSD values (p < 0.05).

Table 4 Land use impacts on soil organic carbon decay rates (k) at different incubation temperatures in surface and sub-surface soil. Land use

Decay rate (mg CO2 evolved 100 g−1 soil day−1)(×10-2) 15 °C

Forest Mulberry Rice-mustard Rice-fallow Mean

25 °C

35 °C

0−15 cm

15−30 cm

0−15 cm

15−30 cm

0−15 cm

15−30 cm

1.06c(c) 1.24b(c) 1.00c(b) 1.44a(c) 1.19A

0.90c(c) 1.67a(c) 1.48b(c) 1.38b(b) 1.36A

1.65a(b) 1.39b(b) 1.39b(a) 1.74a(b) 1.54A

1.73b(b) 2.09a(b) 1.80b(b) 1.43c(b) 1.76A

1.94a(a) 1.51b(a) 1.46b(a) 2.05a(a) 1.74A

2.53b(a) 2.81a(a) 1.96c(a) 1.56d(a) 2.21B

Lower-case letters indicate the impact of land use, while lower-case letters within parentheses indicate the effect of incubation temperature on decay rate. Means with similar lower-case letters are not significantly different within a column according to Tukey’s HSD test at p < 0.05. Means with similar upper-case letters (indicate effect of soil depth) are not significantly different in last row according to paired t-test. 6

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Table 5 Temperature sensitivity (Q10) of soil organic carbon decay in surface and subsurface soil under different land use systems. Land use

Forest Mulberry Rice-mustard Rice-fallow Mean

Q10

Table 6 Quality parameters of soil organic carbon in surface and sub-surface soil under different land use systems.

Q10(24)

Land use

0–15 cm

15–30 cm

0–15 cm

15–30 cm

1.35a(B) 1.10c(B) 1.21b(A) 1.19bc(A) 1.21A

1.67a(A) 1.29b(A) 1.15c(A) 1.06c(A) 1.30A

3.47a(A) 1.08c(A) 1.43b(A) 1.44b(A) 1.86A

3.89a(A) 1.09c(A) 1.21b(B) 1.19b(B) 1.85A

TPS (g kg−1)

Surface soil (0−15 cm) Forest 4.31a Mulberry 2.86b Rice-mustard 2.69b Rice-fallow 2.37c Mean 3.06A Sub-surface soil (15−30 cm) Forest 3.19a Mulberry 2.24b Rice-mustard 1.99c Rice-fallow 1.26d Mean 2.17B

Q10: temperature sensitivity of SOC decomposition for 52 days of incubation. Q10(24) : temperature sensitivity of SOC decomposition for 24 days of incubation. Means with similar lower-case letters are not significantly different within a column according to Tukey’s HSD test at p < 0.05. Means with upper-case letters indicate the effect of soil depth on Q10 and Q10(24) according to paired ttest at p < 0.05.

TPP (g kg−1)

MBC/SOC

Ea (kJ mol−1)

R0

11.25a 5.25b 3.14c 2.54d 5.54B

1.65b 2.10a 1.99a 2.02a 1.94B

22.19a 7.31c 14.16b 13.07b 14.18B

2.00E-06d 7.22E-04a 4.33E-05c 9.00E-05b 1.83E-05B

13.75a 8.97b 4.49c 2.84d 7.51A

1.89c 2.20b 2.42b 2.90a 2.35A

38.08a 19.01b 10.52c 4.31d 17.98A

3.00E-09d 1.00E-05c 2.51E-04b 2.52E-03a 6.95E-04A

Means with similar lower-case letters are not significantly different within a column according to Tukey’s HSD test at p < 0.05. Means with upper-case letters indicate the effect of soil depth on these parameters according to paired t-test. Means with similar upper-case letters (indicate effect of soil depth) are not significantly different according to paired t-test. TPS: Total polysaccharide, TPP: Total polyphenol, MBC/SOC: Per cent of MBC to total SOC, Ea: Activation energy, R0: Respiration rate at 0 °C temperature.

rest of the land uses. The k in sub-surface soil of NF was ∼46 and 27% lower than that of MP system at 15 and 25 °C, respectively. Between the two rice-based systems, decay rates in 15–30 cm soil layer were higher in RM than RF at all three incubation temperatures. While SOC decay rates (averaged over land uses) at 15 and 25 °C were at par in both soil layers, the same was ∼27% higher in sub-surface than surface layer at 35 °C (Table 4).

NF in both soil layers (∼3 and 2 times higher than MP systems in 0–15 and 15−30 cm soil layers, respectively) (Table 6). In NF and MP systems, respective values of Ea were ∼69 and 160% higher in sub-surface than surface soil. However, in RF and RM systems, the Ea was ∼67 and 26 lower in sub-surface than surface soil layer, respectively. Hence, degree of recalcitrance was more in sub-surface than surface soils of NF and MP, and vice-versa for RF and RM systems. However, the overall degree of recalcitrance was higher under NF than other land uses. These findings were supported by another thermodynamic parameter, i.e. R0 (Table 6). It was the lowest under NF in both soil layers, signalling towards higher stability of SOC. Under MP and NF systems, R0 values were significantly higher in surface soil than their respective values in sub-surface soil. Hence, SOM was more stable in sub-surface than surface layer for MP and NF systems. Altogether, SOM under NF had lower quality i.e. higher stability in both soil layers. All quality parameters, except polyphenol content in surface soil, were well fitted to Q10 (Figs. 3 and 4).

3.4. Temperature sensitivity (Q10) of SOC The Q10 of SOC varied significantly over land uses; and also soil depths under NF and MP (Table 5). In both soil layers, Q10 was the highest under NF. The Q10 of MP was ∼19 and 23% lower than NF in the 0–15 and 15−30 cm soil layers, respectively. The Q10 of NF was ∼12 and 45% higher than RM system in those layers, respectively. Soil depth had no significant impact on Q10 under the rice-based systems (i.e. RM and RF); but Q10 values in sub-surface soil under NF and MP were ∼24 and 17% higher than corresponding surface soil. As ∼50% of total C mineralization occurred within 24 days of incubation, Q10 for 24 days of incubation (Q10(24)) was also calculated (Table 5). The Q10(24) values of MP, RM and RF were similar to the respective final Q10 values (i.e. Q10 observed after 52 days). However, for NF system, Q10(24) was ∼2.57 and 2.33 times higher than the final Q10 in surface and sub-surface soil, respectively. Interestingly, the effect of soil depth on Q10(24) was opposite to that on Q10. Under NF and MP, Q10(24) values in surface layer were similar to corresponding values in sub-surface layer. However, under rice-based systems, Q10(24) values of the surface layer were ∼18 and 21% higher than their respective Q10(24) values of the sub-surface layer.

4. Discussion 4.1. SOC pools Land uses and associated vegetation have noticeable influence on SOC concentrations (Barua and Haque, 2013). In accordance with other studies, SOC, labile C and KMnO4-C were higher in soils of NF system due to higher litter fall and regular addition of cow dung manure than other systems (Saha et al., 2007, 2012; Choudhury et al., 2016). Readily metabolizable C and N in manure, apart from higher litter fall and root exudates, might be the reasons for higher soil MBC in NF system. The greater labile C concentration under MP and NF systems might be due to the priming effect of freshly added organic materials from leaf depositions in these soils (Yagi et al., 2005). Greater canopy cover, litter fall and finer root biomass might have increased SOC accumulation in NF (Saha et al., 2007, 2012) over the managed ecosystems. The rise in recalcitrant C under NF system could be related to higher lignin content of plant materials (McLauchlan et al., 2006; Yan et al., 2012). The mulberry leaves contain more carbohydrates than protein and lipid (Purusothaman et al., 2012). Hence, labile C in MP system was higher than RM and RF systems. Decreased SOC with increased soil depth might be due to slow and low translocation of leaf litter and applied cow dung. RF system situated at higher altitude had the lowest concentrations of all C pools which might be attributed to higher

3.5. SOM quality parameters As mentioned earlier, six parameters were used to predict SOM quality in this study. Among them, four are chemical and two are thermodynamic in nature. All parameters changed significantly with soil depth (Table 6). Total polysaccharide content in surface soil under NF was ∼51, 60 and 82% higher than MP, RM and RF systems, respectively. The trend was similar in the sub-surface soil too. Total polyphenol content under NF was ∼114 and 53% higher than MP at 0–15 and 15−30 cm depths, respectively. Polyphenol content, averaged over land uses, was ∼36% higher in sub-surface than surface soil (Table 6). The MBC/SOC was the lowest under NF in both soil layers (∼19 and 25% lower than the managed land uses in surface and subsurface soil layers, respectively) (Table 6). Interestingly, in NF system, MBC/SOC ratio was the same in both soil layers but in managed ecosystems it was higher in the sub-surface soil. The activation energy of SOC decomposition was the highest under 7

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Fig. 3. Relationships of Q10 with quality parameters of organic C in the surface (0−15 cm) soil layer. NS: R2 is non-significant; * indicates R2 is significant at p < 0.05; ** indicates R2 is significant at p < 0.01; TPP: Total polyphenol, MBC/SOC: Percent MBC to total SOC, Ea: Activation energy, R0: Respiration rate at 0 °C temperature.

4.2. Carbon mineralization and decay rates

mineralization rate (Table 3). As microbial population is low in acidic soils, the persistent C compounds form stable soluble complexes with the Fe/Al-oxides. High rainfall in this region allows leaching of these complexes, followed by their precipitation due to reduction of Fe compounds leading to higher amount of polyphenol content in sub-surface soil (Deb and Sarkar, 2017).

Land utilization types significantly influenced the C mineralization kinetics due to their impact on diversity and composition of microbial communities (Tardy et al., 2015). The lowest C mineralization from the NF system at 15 °C might be related to higher recalcitrant C content than other land uses in both soil layers. As lignin is one of the largest components of forest ecosystem, it has obvious impacts on SOM quality and microbial decomposition (Rey et al., 2008). Lignin is recalcitrant and complex in nature; hence, it requires higher energy for Fig. 4. Relationships of Q10 with quality parameters of organic C in the sub-surface (15−30 cm) soil layer. NS: R2 is non-significant; * indicates R2 is significant at p < 0.05; ** indicates R2 is significant at p < 0.01; TPP: Total polyphenol, MBC/SOC: Percent MBC to total SOC, Ea: Activation energy, R0: Respiration rate at 0 °C temperature.

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substrate affinity rises with temperature, thus nullifying the effect of increasing temperature on reaction rate (known as cancelling effect according to Davidson et al., 2006). This ultimately lowers the Q10 (Davidson et al., 2006) as observed under C-depleted RM and RF systems in this study. In all four systems, substrates became progressively limited with the advent of time due to microbial assimilation, which magnified the cancelling effect and led to stabilization of reaction rates at a given temperature. Besides, shifting of microbial population from R- to K- strategists might have reduced the respiration rate at all temperatures (Fontaine et al., 2003). These factors might have resulted in lower values of Q10 at the end of incubation than Q10(24) in most cases.

decomposition. But at higher temperature (35 °C), greater C mineralization from NF system might be due to increased thermal energy of the molecules along with increased rate of reaction and higher solubility of lignin and tannin like substances (Davidson et al., 2012). Cumulative C mineralization was the lowest under RF system in both soil layers due to low SOC concentration causing substrate limitation. Higher amounts of recalcitrant compounds in forest system also led to the lowest proportion of C mineralization to total SOC. On the contrary, lower recalcitrant compounds and higher MBC/SOC resulted in highest proportion of C mineralization under RF system. The highest C decay rate in surface soil of RF system (which was at higher altitude than rest three systems) might be due to low recalcitrant C content, higher MBC/SOC, poor soil structure (due to regular tillage) and regular supply of N and P. Under elevated temperature, SOC decomposition was stimulated by cellulose and chitin degradations (Nie et al., 2013), conformational changes of C compounds, increased solubility of C compounds (Davidson and Janssens, 2006), and/or alteration in microbial community composition (Waldrop and Firestone, 2004). Higher temperature also facilitated migration of C substrates to enzymes’ active sites (Davidson et al., 2012), supported desorption of SOM-humate complexes (Davidson and Janssens, 2006), and increased SOM decomposition rates (Conant et al., 2011; Schmidt et al., 2011). Likewise, we found significant interaction effects of land uses and temperatures on cumulative CO2 emission and SOC decay rate (Table S1, Electronic supplementary material).

4.4. Surface and sub-surface soil Q10 In general, surface soil is dominated by more labile C substrates and sub-surface soil by relatively recalcitrant compounds (Kim et al., 2012; Ghosh et al., 2018). Higher chemical complexity results in higher temperature dependency (Nottingham et al., 2015). The SOC concentrations of NF and MP systems were higher than RM and RF systems in both soil layers. In NF and MP systems, respective Q10(24) values were similar in both layers. In these two systems, soils had high total N and P concentrations in both soil layers (Table 2) hence, microbial nutrient mining might not have occurred (Poeplau et al., 2016; Meyer et al., 2018). Hence, mining of N and P was not controlling microbial soil respiration in any soil layer of NF and MP systems for first 24 days; rather supply of substrate was the major governing factor during that time. With the progress in incubation time, nutrient deficiency accelerates exo-enzymes production and intensifies the decomposition of resistant SOM for mining nutrients (Moorhead and Sinsabaugh, 2006). Additionally, available P concentrations were also high in sub-surface soil of NF and MP systems. As under N limitation, P may accelerate heterotrophic respiration (Poeplau et al., 2016), higher Q10 values were observed in sub-surface soil of NF and MP systems at the end of incubation. With the advent of time, substrate limitation and shifting of microbial population from R- to K- strategists might have overruled the effect of soil depth on Q10 in RM and RF systems, resulting Q10 to be similar in both soil layers (Fontaine et al., 2003). Nevertheless, mechanisms determining the influence of soil depth on decay rate of C mineralization and associated Q10 value need further investigation.

4.3. Incubation period and Q10 We observed that in C depleted soils of RM and RF, intermediate Q10 (i.e. Q10(24)) and final Q10 did not differ significantly in any of the soil layers. Contrarily, in C rich natural land use system i.e. NF, Q10(24) was significantly higher than the final Q10. In C rich soils, CO2 emission during initial periods depends on biologically active pools of SOM i.e. MBC (Franzluebbers et al., 2000). Additionally, the SOM acts as a buffer for microbes against adverse environment, like exposure to high temperature, by its thermal insulation property. Therefore, (a) the extracellular enzymes can perform better in thermally insulated soils of NF system, and (b) higher SOC content offers more food and energy for functioning of microbes, leading to higher heterotrophic respiration resulting greater Q10(24) than C depleted systems. Moreover, in short-term incubation, with ample SOC supply, enzyme-to-substrate affinity becomes irrelevant (Blagodatskaya et al., 2016). In that situation, Q10 is controlled only by the reaction rate, which increases rapidly with increasing temperature up to an optimal level and increases Q10. Such was the case for NF system. On the other hand, when SOC is low, enzyme-to-substrate affinity plays an important role in controlling Q10. Because enzyme-to-

4.5. Factors controlling temperature response of SOC Q10 was influenced by a combination of climate (MAT and MAP), soil chemical properties (labile C, clay content, polyphenol content), SOM quality (R0 and Ea), and soil microbial properties (microbial biomass) (Figs. 3, 4 and 5 ). Principal components analysis resulted in three major components (PC) accounting for 88.9% of the variance of Fig. 5. Path analysis to depict impact of climatic variables, soil properties C quality and C pools on temperature sensitivity (Q10). Models satisfactorily fitted to data based on χ2 and RMSEA analyses [χ2 = 1.21, df = 4, P = 0.87, GFI = 0.99, RMSEA < 0.001]. Solid arrows represent the significant effects whereas dashed arrows indicate nonsignificant impact in a fitted structural equation model, respectively. Widths of the arrows indicate the strength of the casual relationship.

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although correlation coefficient values were not significant (Table 7). This suggested that the soils with more recalcitrant C, as indexed by relatively lower R0, are more temperature sensitive than those with more labile C. Overall, we can infer that MBC/SOC could be a better predictor of SOM quality than R0 for humid tropical soils of northeastern India. Based on the fundamental principles of enzyme kinetics and the Arrhenius equation, the CQT hypothesis suggests that Q10 should increase with increasing activation energy of the reaction (Davidson and Janssens, 2006; Craine et al., 2010). Therefore, the decomposition of bio-geochemically recalcitrant organic matter (those requiring higher activation energy to degrade) should generally be more sensitive to changes in temperature than the decomposition of more labile organic matter (Craine et al., 2010). Overall, the dominant factors regulating Q10 across different ecosystems are different. Thus, future models predicting soil C dynamics and C cycle-climate change feedback should account for this variation across different ecosystems.

Table 7 Correlation (Pearson’s correlation coefficient) of Q10 with quality parameters of soil organic carbon (n = 16). Soil depth (cm)

Variables

TPS

TPP

MBC/SOC

Ea

R0

0–15 15–30

Q10 Q10

0.791NS 0.968*

0.722NS 0.979*

−0.970* −0.908*

0.999** 0.998**

−0.787NS −0.631NS

RC/SOC: Ratio of recalcitrant C and total SOC, TPS: Total polysaccharide, TPP: Total polyphenol, MBC/SOC: Per cent of MBC to total SOC, Ea: Activation energy, R0: Respiration rate at 0 °C temperature. * NS: Non significant; * Significant at p < 0.05; ** Significant at p < 0.01 according to Pearson correlation test.

data. First component (60.6% variance) represented the soil properties and C pools. Second component (18.3% variance) was largely occupied by chemical and thermodynamic SOM quality parameters. Microbial SOM quality parameters dominated the third component (10% variance) (Fig.S2). Hence, SOC pools, quality and microbial response all are governing the Q10. The dominant factors regulating variations in Q10 differed across land use systems. Overall, clay content, activation energy and polyphenol content had positive role in variation of Q10 across all ecosystems. In contrast, MBC/SOC and R0 negatively affected Q10 across all land uses (Figs. 3 and 4). Step-wise regression analysis revealed that Q10 in forest soil was mainly determined by the total polyphenol, RC and Ea (R2 = 0.73, P < 0.05). In comparison, soil clay and MBC strongly regulated Q10 in managed ecosystems (R2 = 0.77, P < 0.05). Of note, the dominant factors affecting Q10(24) was labile C and total polysaccharides for all land uses. The MBC/SOC significantly affected Q10 because it directly influenced the composition of the microbial community and enzyme activity, along with substrate availability (Fontaine et al., 2003). We also found that Q10 was significantly affected by soil substrate quality across the ecosystems, based on the negatively exponential relationships between Q10 and the substrate quality index (Craine et al., 2010). In surface soil, Q10 was significantly correlated only with Ea (positively) and MBC/SOC (negatively); while in sub-surface soil, Q10 was significantly correlated with all the SOM quality parameters except R0 (Table 7). Chemical recalcitrance, physical protection, and biological accessibility of C determine its quality (McLauchlan and Hobbie, 2004) and control the temperature sensitivity of SOC decomposition. Recalcitrance may arise from any of these sources and contribute to higher Q10. We observed significant negative correlation between MBC/ SOC and Q10 in both soil layers. This may be due to soil acidity (pH ≈5.5) induced hindrance to microbes for SOC decomposition; but they certainly possess the potential to decompose SOC (Fierer et al., 2006; Ghosh et al., 2016). Thus, soil acidity might have imposed a stress on microbes and limited the SOC decomposition. For this reason, MBC/ SOC showed a significantly negative correlation with Q10. In these acid soils with high clay content, the SOC-matrix interactions take place involving the expandable and non-expandable phyllosilicates and Fe-, Al- and Mn-oxides (Cotrufo et al., 2013; Ghosh et al., 2016; Das et al., 2019b), which protects SOM against microbial decomposition. Thus, in-situ chemical recalcitrance and substrate inaccessibility might induce higher Ea in surface than sub-surface layer. Additionally, in sub-surface soil, a) microbial population is limited, b) C supply is restricted, c) air and water supplies are confined, and d) proportion of recalcitrant C and clay content are more. All these factors together played an important role in raising the Ea of SOC. Consequently, the parameters like polyphenol content, and Ea were correlated negatively and MBC/SOC was correlated positively with Q10 in sub-surface soil (Table 7). The parameter R0 is an index of overall quality (availability and lability) of C substrates that are being metabolized by decomposers at a given point of time (Fierer et al., 2006). In this study, negative correlations were found between Q10 value and R0 across soil layers,

5. Conclusions Current findings highlighted that SOM quality significantly influenced the Q10 in major land uses of north-eastern India. The systems having low quality SOM had higher Q10. Thus, our first hypothesis was accepted. We observed that SOC in natural land use system (i.e. forest) has low quality, hence higher stability than the managed ecosystems. From Q10 values we may conclude that soil depth significantly influenced intermediate Q10 in the managed ecosystems, and final Q10 only in the natural ecosystem. From quality parameters of SOM, it can be concluded that managed land use systems are susceptible to immediate loss of C at a higher temperature, but a natural forest system is more invulnerable to sudden temperature rise and could protect and sequester SOC. Hence, protecting these natural ecosystems is very important to mitigate climate change and sustain C balance. In all land use systems, SOC of sub-surface soil had higher Ea values, and greater stability than surface soils. In tropical humid climate, MBC/SOC ratio influenced Q10 > to a greater extent than R0; so, it could be used as an indicator of Q10 in similar kinds of natural and managed ecosystems. Acknowledgments The authors thank the Director, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India for providing financial support during the research work. Authors are thankful to the Head, Regional Centre (Jorhat) of ICAR-National Bureau of Soil Science and Land Use Planning (ICAR-NBSS & LUP) for the assistance extended during soil sample collection. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.still.2020.104573. References Barua, S.K., Haque, S.M.S., 2013. Soil characteristics and carbon sequestration potentials of vegetation in degraded hills of Chittagong, Bangladesh. Land Degrad. Develop. 24, 63–71. https://doi.org/10.1002/ldr.1107. Baruah, U., Bandyopadhyay, S., Reza, S.K., 2014. Land use planning and its strategic measures in the context of North Eastern Regions of India. Agropedol. J 24, 292–303. Blagodatskaya, Е., Blagodatsky, S., Khomyakov, N., Myachina, O., Kuzyakov, Y., 2016. Temperature sensitivity and enzymatic mechanisms of soil organic matter decomposition along an altitudinal gradient on Mount Kilimanjaro. Sci. Rep. 6, 22240. https://doi.org/10.1038/srep22240. Central Silk Board, 2018. Functioning of Central Silk Board and Performance of Indian Silk Industry. (Accessed on 29-05-2018). http://www.csb.gov.in/assets/Uploads/ documents/note-on-sericulture.pdf. Chan, K.Y., Bowman, A., Oates, A., 2001. Oxidizible organic carbon fractions and soil quality changes in an OxicPaleustalf under different pasture leys. Soil Sci. 166, 61–67. https://doi.org/10.1097/00010694-200101000-00009.

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