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European Journal of Soil Biology - Soil enzymes activity

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European Journal of Soil Biology 103 (2021) 103292

Available online 15 February 2021

(http://creativecommons.org/licenses/by/4.0/).

Soil enzymes activity: Effect of climate smart agriculture on rhizosphere

and bulk soil under cereal based systems of north-west India

H.S. Jat

, Ashim Datta

, Madhu Choudhary

, P.C. Sharma

***

, Bharti Dixit

, M.L. Jat

ICAR-Central Soil Salinity Research Institute (CSSRI), Karnal, Haryana, India

International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India

Choudhary Charan Singh Haryana Agricultural University, Hisar, India

ARTICLE INFO

Handling editorYakov Kuzyakov

Keywords:

Conservation agriculture

Climate smart agriculture

Rhizosphere

Rice-wheat system

Maize-wheat system

Long term managements

ABSTRACT

In agriculture production system, soil enzymes are important indicators of soil quality. Measurements of soil

quality parameter changes are essential for assessing the impact of soil and crop management practices. Keeping

this in view, an experiment was conducted to evaluate the enzyme activities namely dehydrogenase (DHA),

β-glucosidase, acid and alkaline phosphatase (AcP & AlP), uorescein diacetate hydrolases (FDH), cellulase,

urease and aryl sulphatase in rhizosphere and bulk soil after 8 years of different management regimes. Soil

organic carbon (SOC), moisture content and few enzyme indices such as enzymatic pH indicator (AcP/AlP),

alteration index three (Al3) and geometric mean (GMea) were also measured. The treatments were conventional

rice-wheat system (termed as scenario (Sc1), CT system), partial conservation agriculture (CA)-based rice-wheat-

mungbean system (Sc2, PCA-RW), partial climate smart agriculture (CSA)-based rice-wheat-mungbean system

(Sc3), partial CSA-based maize-wheat-mungbean system (Sc4), full CSA-based rice-wheat-mungbean system

(Sc5), and full CSA-based maize-wheat-mungbean system (Sc6). Soil samples were collected from rhizosphere

and away from roots (bulk soil) at 0–15 cm soil depth before sowing (from rhizosphere of previous crops), at

maximum tillering, owering, and after harvesting of wheat crop. Results showed that DHA activity was higher

before sowing (59.8%), at maximum tillering (48.4%), owering (8.6%) and after harvesting (19.1%) in rice

based CSA systems (mean of Sc3 and Sc5) over maize based CSA systems (mean of Sc4 and Sc6) in rhizospheric

soil. On average, β-glucosidase activity was signicantly higher in rhizospheric soils of rice based system over

maize based CSA system. Before sowing of wheat, signicantly higher (21.4%) acid phosphatase activity was

observed in rhizosphere over bulk soils of maize based CSA system. Signicantly higher alkaline phosphatase

activity was observed before sowing of wheat in bulk soils of rice (25.3%) and maize (38.5%) based CSA systems

over rhizospheric soils. Rice based CSA systems showed 27% higher FDH activity than maize based systems.

Signicant interaction effect was observed between the managements and enzymes. SOC played an important

role in regulating the enzymes activity both in rhizosphere and bulk soil. Signicant variation in AcP/AlP, Al3

and GMea was observed among the managements. Therefore, CSA managements are benecial in improving

enzyme activities not only in rhizosphere but also in bulk soil where residues are retained thereby may help in

improving nutrient cycling.

1. Introduction

Soil enzymes are the key players in biochemical processes of organic

matter recycling in the soil system and their activities are closely related

to soil organic matter (SOM), soil physical properties, and microbial

activity [1]. During decomposition of SOM and nutrient cycling, soil

enzymes act as necessary catalysts and strongly inuence energy

transformation, environmental quality, and agronomic productivity.

Soil enzymes provide early detection of changes in soil health because

they respond to soil management changes and environmental factors

much sooner than other soil quality parameters. Qualitative and quan-

titative changes in soil enzymes determine the availability of nutrients

* Corresponding author. ICAR-CSSRI, Karnal.

** Corresponding author. ICAR-CSSRI, Karnal.

*** Corresponding author. ICAR-CSSRI, Karnal.

E-mail addresses: ashimdatta2007@gmail.com (A. Datta), madhucssri@gmail.com (M. Choudhary), pcsharma.knl@gmail.com (P.C. Sharma).

Contents lists available at ScienceDirect

European Journal of Soil Biology

journal homepage: www.elsevier.com/locate/ejsobi

https://doi.org/10.1016/j.ejsobi.2021.103292

Received 25 November 2019; Received in revised form 13 August 2020; Accepted 4 February 2021

European Journal of Soil Biology 103 (2021) 103292

and crop productivity [2]. Different agricultural practices like tillage,

cropping systems, irrigation and nutrient management inuence soil

enzyme activities, thereby inuencing yield sustainability [3]. Adverse

impacts of mechanical tillage, cropping systems, and residues removal

have been observed in soil enzymatic activities and availability of plant

nutrients [4]. Application of organic and inorganic fertilization exerts a

strong inuence on soil quality. In any agriculture production system,

amending with organic matter and application of balanced fertilizers

improve soil organic carbon and biological properties including micro-

bial biomass and enzymatic activities [5,6]. Dehydrogenase enzyme

activity (DHA) is considered as the indicator of oxidative activity of soil

microorganisms and increases signicantly upon application of

balanced fertilization [6].

Climate Smart Agriculture (CSA) practices are based on conservation

agriculture (CA) principles of zero tillage (ZT), residue management and

sustainable crop rotation along with precision irrigation and N-man-

agement using sub-surface drip irrigation (SDI) system. There are

number of advantages of CSA practices such as higher soil organic car-

bon, improved soil physical properties, nutrient availability, and crop

productivity as reported by researchers all over the world [7–10].

Several studies have been done on the effect of different agriculture

management practices on soil enzyme activities in cereal based systems

[11–13]. While studying the effect of series of CA based managements

on soil enzymes, Choudhary et al. [14] reported 210% and 49% higher

DHA and alkaline phosphatase activity (AlP), respectively in soils under

maize-wheat-mungbean with residue retention and 140% and 42%

under rice-wheat-mungbean system with residue retention over con-

ventional rice-wheat system, respectively in NW India. Bergstrom et al.

[12] compared six enzymes namely urease, glutaminase, phosphatase,

arylsulfatase, β-glucosidase and dehydrogenase under no tillage along a

topographic and soil textural gradient and observed higher β-glucosi-

dase, glutaminase, phosphatase and aryl sulfatase activity in

coarse-textured soils at a lower slope than in ne-textured soil at an

upper slope. No-tilled soil showed higher dehydrogenase, urease, pro-

tease, phosphatase and β-glucosidase activities over conventional tillage

system in sorghum cultivation under subtropical conditions [15].

The rhizosphere zone of the plants acts as hotspot of enzyme activ-

ities. Recently, it was stated that the rhizosphere activity should be

extended from mm scale to cm scale due to H

fertilization effect and

volatile organic compounds released by roots [16]. The quantity and

quality of root exudates depends on plant type and growth stages of

plants [ 17] which also inuence the diversity and activity of microbes,

biochemical processes and enzyme activities [17]. ‘Rhizosphere priming

effect’ is also an important factor playing an important role in SOM

decomposition [17].

In most of the studies so far, soil samples were collected between

rows of the crops after harvesting (called bulk soil sample). In conven-

tional agricultural practices, tillage mixes the stubbles/roots of previous

crop with soil before sowing of next crop and after planking stubbles are

accumulated and removed from the eld. But in CSA practices, stubbles

and roots of the crops are undisturbed with zero tillage condition and

loose crop residues are retained over the soil surface instead of burning

or removal as practiced in conventional agriculture. Generally rhizo-

sphere soil is characterized by higher microbial activity due to rhizo-

deposition, root secretion etc. than bulk soil (away from roots) [17].

Therefore, in CSA, we may expect higher enzymes activity in bulk soil

compared to conventional practices due to residue retention and also

enzymes activity in rhizosphere and bulk soil need to be investigated to

capture whether there is any synergistic effect exists. There is hardly any

study to unveil this aspect. The objectives of this study are to assess the

enzyme activities and SOC concentration in rhizosphere and bulk soils

and their interactions after 8 years of continuous smart crop manage-

ment practices. We hypothesize that soil enzyme activities in bulk soil

with CSA based management practices will be improved at different

growth stages compared to those of conventional/tillage based man-

agement practices.

2. Materials and methods

2.1. Field experimental design

The experiment was established in 2009 at the research farm of In-

dian Council of Agricultural Research (ICAR) - Central Soil Salinity

Research Institute (CSSRI) (29

◦

′

N, 76

◦

′

E), Karnal, India. Soil type is

loam in texture with 34% sand, 46.1% silt and 19.9% clay. It falls under

Typic Natrustalf category. Climate is extreme hot and dry (April–June) to

wet summers (July–September) and cold dry winters (October–March).

Average annual temperature is 26

◦

C with maximum and minimum of

◦

and 18

◦

C, respectively with annual precipitation of 650 mm.

Initially, the experiment comprised of four cereal-based scenarios

varying in cropping system, tillage, crop establishment methods, and

residue management practices (Sc1, Sc2, Sc3 and Sc4). Treatments were

replicated thrice in 20 m × 100 m plot size in randomized complete

block design. In May 2016, precise water management practice (sub-

surface drip irrigation; SDI) was included in subdivided plots (20 m ×

50 m) of Sc3 and Sc4, respectively. Briey, six treatments termed as

scenarios (Sc) were: i) conventional-till (CT) rice-CT wheat (Sc1;

farmers’ practice; CT); ii) CT rice-Zero tillage (ZT) wheat-ZT mungbean

with ood irrigation (Sc2; partial CA); iii) ZT rice-ZT wheat-ZT mung-

bean with ood irrigation (Sc3; rice based partial CSA); iv) ZT maize-ZT

wheat-ZT mungbean with ood irrigation (Sc4; maize based partial

CSA); v) ZT rice-ZT wheat-ZT mungbean with SDI (Sc5; rice based full

CSA); and vi) ZT maize-ZT wheat-ZT mungbean with SDI (Sc6; maize

based full CSA). Sc3 and Sc4 were based on principles of CA practices

where irrigation water and N application were not precisely managed

and called it partial climate smart agriculture (CSA). However, in Sc5

and Sc6, irrigation water and N in the form of urea was precisely applied

using subsurface drip irrigation (SDI) and called full CSA. Best crop

management practices were followed in all the treatments except Sc1,

where farmer’s traditional practices were followed (Supp. Table 1, Supp.

Fig. 1). We used four systems for convenience, conventional tillage

based rice-wheat system (CT-RW), partial CA based rice-wheat mung-

bean system (PCA-RW) (Sc2), rice based CSA system (mean of Sc3 and

Sc5) and maize based CSA system (mean of Sc4 and Sc6).

2.2. Soil sampling, processing and analysis

Wheat is common crop among all the scenarios so soil samples were

collected from wheat season in order to explore the effect of manage-

ment practices (CT v/s PCA and/CSA) on different enzyme activities like

dehydrogenase (DHA), acid phosphatase (AcP), alkaline phosphatase

(AlP), beta-glucosidase (β-glu), uorescein diacetate hydrolases (FDH),

aryl sulphatase (ArS), urease (Ur) and cellulose (CeL) activity and SOC

changes. From each plot, soil samples were collected at 0–15 cm soil

depth by an auger from nine locations from each rhizosphere and non-

rhizosphere zones (bulk soils) and composite samples were prepared

separately before sowing, maximum tillering, owering, and after har-

vesting in the year 2017–18 (wheat was sown in November 2017 and

harvested in April 2018). For rhizosphere zone samples, wheat plants

were uprooted and soil adhered to roots was collected. Bulk soil samples

were collected from the mid-point of the two rows. Row to row distance

in wheat crop was 22.5 cm. Rhizosphere soil before sowing indicates

rhizosphere of previous crops. As after harvesting of previous crop (rice

and maize), the stubbles remain intact in soil, we collected soil samples

from the rhizosphere of those crop stubbles and designated as rhizo-

sphere soil before sowing. Fresh soil samples were immediately stored in

a refrigerator at 4

◦

C till analysis of different enzymes. DHA, AcP and AlP

activities were estimated as described by Dick et al. [18]. β-glucosidase

activity was determined by the method of Eivazi and Tabatabai [19],

urease was by the method of Tabatabai [20] and aryl sulfatase was by

the method of Tabatabai and Bremner [21]. Cellulase activity was

measured by the method of Hope and Burns [22] and FDH assay by the

method of Green et al. [23].

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

Root mass after harvesting of rice, wheat and maize was measured by

using standard procedure. Soil blocks up to 40 cm depth with plant roots

were taken out from four random places in each scenario. Roots were

washed carefully, detached from the main stem at the rst node. Roots

were dried at 65 ± 5

◦

C at oven and dry weight was calculated and then

converted to t/ha.

Soil moisture content was determined by drying the fresh soil sam-

ples at 105

◦

C for 24 h in a hot air oven until a constant weight and

calculated by following formula.

Moisture content (%) = (Moist soil wt-dry soil wt)/dry soil wt × 100.

One part of the fresh samples collected from both rhizosphere and

bulk soil was dried in shade, ground and sieved and stored in plastic

container for chemical analysis. Oxidizable organic carbon (SOC) was

determined by following Walkley and Black method [24].

The enzymatic pH indicator was calculated using the results of

alkaline and acid phosphatase activity [25]:

Enzymatic pH indicator = AlP/AcP

The alteration index three (Al3) was also calculated using the results

of β-glucosidase, phosphatase and urease [26]:

Alteration index three (Al3) = 7.87 β-glucosidase− 8.22 acid phos-

phatase− 0.49 urease

For each scenario, the geometric mean (GMea) was calculated as the

mean for the assayed enzymes activities [27]. It is a general index to

consolidate information from variables with different units and range of

variation:

GMea = (DHA * GLU * AlP *AcP * FDH* Ur*CeL*ArS)

1/8

Where DHA, GLU, AlP, AcP, FDH, Ur, CeL, ArS are dehydrogenase,

β-glucosidase, alkaline phosphatase, acid phosphatase, uorescein

diacetate hydrolases, urease, cellulase and aryl sulphatase, respectively.

2.3. Residue load

Crop residues recycled in each year under different scenarios are

presented at Supp. Table 2. Signicantly higher residues amount (129

Mg ha

− 1

) were recycled in maize based system (mean of Sc4 and Sc6)

over others, followed by PCA-RW (Sc2) (115.5 Mg ha

− 1

). About 111 Mg

− 1

residues were added in rice based CSA systems (mean of Sc3 and

Sc5) during the last 8 years.

2.4. Statistical analysis

The data were subjected to analysis of variance (ANOVA) and using

the general linear model procedure of the SPSS window version 17.0

(SPSS Inc., Chicago, USA). Treatment means were separated by Duncan

Multiple Range Test (DMRT) at 5% level of signicance (P < 0.05).

Correlation study was performed among the enzymes, indices calculated

from different enzymes, residue load, SOC and soil moisture content. To

determine the effect of scenarios, rhizosphere/bulk soils and stages

(xed factors) and their interaction effect on the different enzyme ac-

tivities (random variable), three-way ANOVA was carried out. Linear

contrasts were used to compare single or multiple treatments against

one another.

3. Results

3.1. Dehydrogenase (DHA) and β-glucosidase activity as inuenced by

management practices

Signicant variation in DHA was observed both in rhizosphere and

bulk soils of different scenarios (Fig. 1). Before sowing of the crop, DHA

activity was signicantly higher in rhizospheric soils over bulk soils

irrespective of cropping system. In rhizospheric soil, DHA activity was

signicantly higher before sowing (59.6%) and owering stage (18.7%)

in rice based CSA systems (mean of Sc3 and Sc5) over maize based CSA

systems (mean of Sc4 and Sc6) (Fig. 1a and 1c). Before sowing in maize

based system, about 35% higher DHA activity was observed at rhizo-

spheric soil over bulk soil. At maximum tillering stage, DHA activity was

signicantly higher in partial CA based rice system (PCA-RW, Sc2) (145

g TPF g

− 1

soil hr

− 1

) over others irrespective of sampling location

(Fig. 1b). At owering, DHA activity was 12% higher in rhizosphere of

PCA-RW (Sc2) over bulk soil (Fig. 1c). After harvesting of the crop,

signicantly higher (21%) DHA activity was observed in bulk soils (98

g TPF g

− 1

soil hr

− 1

) over rhizospheric soils (81

g TPF g

− 1

soil hr

− 1

) in

rice based CSA system (Fig. 1d). Being the responsible enzyme for car-

bon cycle in soil, on average β-glucosidase activity was signicantly (p

< 0.05) higher in rhizospheric soils of rice based CSA systems (108

g p-

NP g

− 1

soil hr

− 1

) over bulk soil (92

g p-NP g

− 1

soil hr

− 1

) whereas under

maize based systems similar values were observed (Suppl. Fig. 2) irre-

spective of crop growth stages. In bulk soil under maize systems

signicantly higher β-glucosidase activity was observed before sowing

(Suppl. Fig. 2a) and at maximum tillering stage (Suppl. Fig. 2b)

compared to rice based CSA system. Whereas at harvesting stage, rhi-

zospheric soils under maize systems (112

g p-NP g

− 1

soil hr

− 1

) showed

signicantly (p < 0.05) higher β-glucosidase activity over rhizospheric

soil under rice based CSA system (94

g p-NP g

− 1

soil hr

− 1

) (Suppl.

Fig. 2d). At maximum tillering stage, signicantly higher β-glucosidase

activity was observed at rhizosphere soil of rice based CSA system (93

p-NP g

− 1

soil hr

− 1

) over bulk soil (72

g p-NP g

− 1

soil hr

− 1

) and also

maize based system (78

g p-NP g

− 1

soil hr

− 1

) (Suppl. Fig. 2b). In rice

based CSA system, after harvesting of the crop signicantly (p < 0.05)

higher β-glucosidase activity was observed in bulk soils (12.8%) over

rhizospheric soils (Suppl. Fig. 2d). On average β-glucosidase activity was

20% higher (p < 0.05) in CA based scenarios (Sc2-Sc6) over conven-

tional tillage scenario (Sc1) irrespective of crop growth stages and

sampling location.

3.2. Acid and alkaline phosphatase activity under different managements

Signicant variation in acid and alkaline phosphatase activity was

observed irrespective of scenarios and sampling location (Supp. Table 3

and 4). Before sowing of wheat, signicantly higher (21.4%) acid

phosphatase activity was observed in rhizosphere over bulk soils of

maize based systems (Supp. Table 3). But at maximum tillering, about

8.2% higher acid phosphatase activity was observed in bulk soils over

rhizosphere soil of rice based CSA systems. In maize based systems, at

owering stage 6% higher (p < 0.05) acid phosphatase activity was

recorded at bulk soil over rhizospheric soil. At harvesting, bulk soils

recorded signicantly higher acid phosphatase activity in both rice

(9.5%) and maize (7.4%) based CSA systems over rhizospheric soils

(Supp. Table 3). Signicantly higher alkaline phosphatase activity was

observed before sowing of wheat in bulk soils of rice (25.3%) and maize

(38.5%) based CSA systems over rhizospheric soils (Supp. Table 4).

3.3. Fluorescein diacetate hydrolases (FDH) and aryl sulphatase (ArS)

activity under different managements

On average rice based CSA system showed 27% higher FDH activity

than maize based system (Supp. Table 5). In rhizosphere, on average it

was 18% and in bulk soils it was 38% higher in rice based CSA systems

than maize based systems. At maximum tillering stage, about 29%

higher FDH activity was observed in bulk soils of rice based CSA system

over rhizosphere soil whereas rhizospheric soils of partial CA based

system recorded 11.5% higher FDH activity over bulk soils (Fig. 2).

Rhizosphere soils under maize based systems showed about 39% higher

FDH activity over bulk soils at owering stage (Fig. 3). Similar FDH

activity was observed under partial CA and rice based CSA systems in

rhizosphere and bulk soils (Fig. 3). After harvesting, bulk soils under

maize based CSA and partial CA based systems recorded 29% and 69%

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

higher FDH activity over rhizosphere soils (Suppl. Fig. 3). In rhizosphere

zone an increase of 14% FDH activity can be seen in CA based systems

(mean of Sc2 to Sc6) over CT system (Sc1) but simultaneously 6%

decrease was noticed in CA based systems over CT system in bulk soils

(Supp. Table 5).

At different growth stages of wheat crop, variation in activities of ArS

(17.63–117.58

g p-NP g

− 1

soil hr

− 1

) was recorded for rhizosphere and

bulk soils (Supp. Table 6). In both rhizosphere and bulk soil, ArS activity

was higher by 12% and 15%, respectively before the sowing of wheat

than the tillering stage. It was rst decreased from sowing to tillering

and then increased from tillering to owering in both soils. At maximum

tillering stage, about 32% higher aryl sulphatase activity was observed

in rhizosphere soils of partial CA based system (Sc2) over bulk soil

(Fig. 2). Highest activity of ArS was recorded at owering stage with few

exceptions (Fig. 3). It was noticed that lowest activities of ArS was found

in conventional till scenario (CT system, Sc1) irrespective of growth

stages and sampling location. In both the zones, on average, 28% higher

ArS activity was recorded in PCA and CSA based scenarios (mean of Sc2

to Sc6) over CT system (Sc1). Overall activities of ArS were found similar

in all scenarios in both rhizosphere and bulk soils, with a mean value of

g p-NP g

− 1

soil hr

− 1

in both the zones. Rice based partial CSA system

Fig. 1. Dehydrogenase activity (

g TPF g

− 1

soil hr

− 1

) in rhizosphere and bulk soils a) before sowing of crop, b) at maximum tillering c) owering stage of crop and d)

after harvesting of crop under different tillage, residue and crop rotations.

Same upper case letters among the CSA systems and same lower case letters between rhizosphere and bulk soils in each system are not signicantly different at P <

0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.

Fig. 2. Fluorescein diacetate hydrolases (FDH) (

g uorescein g

− 1

soil hr

− 1

)

and Aryal sulphatase activity (

g p-NP g

− 1

soil hr

− 1

) in rhizosphere and bulk

soils at maximum tillering stage of crop under different tillage, residue and crop

rotations.

Where, R: rhizosphere; ARS: aryal sulphatase activity; FDH: Fluorescein diac-

etate hydrolases activity.

Same upper case letters among the CSA systems and same lower case letters

between rhizosphere and bulk soils in each system are not signicantly different

at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation

of mean.

Fig. 3. Fluorescein diacetate hydrolases (

g uorescein g

− 1

soil hr

− 1

) and Aryal

Sulphatase activity (

g p-NP g

− 1

soil hr

− 1

) in rhizosphere and bulk soils at

owering stage of crop under different tillage, residue and crop rotations.

Where, R: rhizosphere; ARS: aryal sulphatase activity; FDH: Fluorescein diac-

etate hydrolases activity.

Same upper case letters among the CSA systems and same lower case letters

between rhizosphere and bulk soils in each system are not signicantly different

at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation

of mean.

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

(Sc3) has more ArS activities than maize based partial CSA (Sc4) except

in rhizosphere soil of maximum tillering stage. In rhizosphere and bulk

soil, it was 13% and 12% higher in rice based CSA system than maize

based system, respectively (Supp. Table 6).

3.4. Urease (ur) and cellulase (CeL) activity under different

managements

Higher activity of urease was noticed in maize based full CSA system

(Sc6) compared to other scenarios (Supp. Table 7). There was hardly any

effect of growth stages on Ur in both rhizosphere and bulk soils. Effect of

CSA practices has not been observed on Ur activities as it was found

similar in CA based scenarios (mean of Sc2 to Sc6) (319.9

g urea g

− 1

soil hr

− 1

) and CT system (319.6

g urea g

− 1

soil hr

− 1

) (Supp. Table 7).

Cellulase activity was found to increase from before sowing to

maximum tillering and owering stage with some exceptions and

decreased towards harvesting stage (Supp. Table 8). In CA based sce-

narios (mean of Sc2 to Sc6) signicantly higher CeL activities (15.54

glucose g

− 1

soil hr

− 1

) were noticed over CT system (8.03

g glucose g

− 1

soil hr

− 1

). Partial CA system (Sc2) showed 52% increase in CeL activities

over CT system. Integration of mungbean (mean of Sc3 to Sc6) showed

34% increase in CeL activity over partial CA.

3.5. Interactions effect of managements, sampling locations and crop

growth stages on soil enzymes

The analysis of variance showed that all the enzymes in rhizosphere

and bulk soils were signicantly inuenced by scenarios, rhizosphere

(R)/bulk soil (B), crop growth stages and their interactions i.e. scenario

× stage, scenario × R/B, stage × R/B and scenario × stage × R/B except

few instances (Table 1). The interaction effect between rhizosphere ×

bulk soils was not signicant for acid and alkaline phosphatase activity

whereas crop growth stage × R/B was also not signicant for acid

phosphatase activity. Contrast analysis showed that there was signi-

cant difference between rice and maize based cropping systems

(Table 1).

3.6. Soil organic carbon and soil moisture under different managements

and crop growth stages

Signicant variation in SOC was observed at different crop growth

stages and sampling locations. On average irrespective of crop growth

stages and sampling locations, rice and maize based CSA (45%) and

PCA-RW (37%) recorded signicantly higher SOC over conventional

practices (Fig. 4). Before sowing, rhizosphere soils of CSA based rice and

maize systems recorded 90% (in rice rhizosphere) and 63% (in maize

rhizosphere) higher SOC over their respective bulk soils. Whereas

18–24% higher SOC was observed in bulk soils under CT (rice crop) and

PCA-RW system (rice crop) over rhizosphere soils (Fig. 4a). At maximum

tillering stage, signicantly higher SOC was observed in bulk soils under

CSA based rice (36%) and maize (44%) based system over rhizosphere

soils but about 21.4% lower SOC was observed in bulk soils over

rhizosphere soil under PCA-RW system (Fig. 4b). At owering stage,

higher SOC concentration was observed in rhizosphere soils compared

to maximum tillering stage irrespective of scenarios (Fig. 4c). But in bulk

soils, signicantly lower SOC were observed in rice (11%) and maize

(18%) based system whereas PCA-RW system recorded 66% higher SOC

compared to the bulk soil at maximum tillering stage (Fig. 4c). At har-

vesting stage, higher SOC was observed in all the scenarios irrespective

of sampling locations except the bulk soil under CT system which

registered 14% lower SOC compared to the bulk soil under owering

stage (Fig. 4d).

Residue retention exhibited strong inuence on soil moisture content

in all the scenarios (Supp. Table 9). At maximum tillering stage, similar

moisture content was observed irrespective of scenarios. CSA based rice

system recorded 34% higher moisture content at rhizosphere soil over

bulk soil whereas 51% higher moisture was observed at bulk soils under

maize based system over rhizosphere soil at owering stage. On average

64% higher moisture content was observed at bulk soils under CSA

based rice and maize based system over rhizosphere soil (Supp. Table 9).

3.7. Enzymatic pH indicator, Al3 and GMea and their relationship with

enzymes, residue load, SOC and soil moisture

The enzymatic pH indicator was calculated using the values of the

alkaline and acid phosphatase activities under different managements.

The value of this indicator varied from 0.71 to 1.34 irrespective of

sampling location and crop growth stages (Fig. 5). Enzymatic pH indi-

cator was signicantly negatively correlated with β-glucosidase activity

(r = − 0.95, p < 0.05), Al3 (r = − 0.97, p < 0.05), SOC (r = − 0.79, p <

0.05) and residue load (r = − 0.82, p < 0.05) irrespective of sampling

location and crop growth stages (Table 2). Alteration index three varied

signicantly among the crop growth stages and rhizosphere and bulk

soil under different managements (Fig. 6). Lower values of Al3 indicated

better soil quality. In rhizosphere soil, lowest Al3 (− 516) was observed

at owering stage of partial CA based system whereas in bulk soil rice

based CSA system recorded lowest Al3 (− 567) at maximum tillering

stage. Signicantly higher Al3 values were recorded before sowing of

crop irrespective of sampling location (Fig. 6). Signicant negative

correlation was observed between Al3 and β-glucosidase (r = − 0.94, p

< 0.05), and also negatively correlated with residue load (r = − 0.84, p

< 0.05) and SOC (r = − 0.73, p < 0.05) (Table 2). Signicant variation in

GMea index values was recorded among the managements and sampling

locations. In rhizosphere and bulk soil, highest GMea index was

observed in maximum tillering (90) and owering stage (85) of partial

CA based system (Fig. 7). Lower values of GMea index was recorded in

conventional system and before sowing of wheat irrespective of sam-

pling location. Signicant positive correlation was observed between

GMea and β-glucosidase (r = 0.73, p < 0.05), acid phosphatase (r = 0.99,

p < 0.05), aryl sulphatase (r = 0.95, p < 0.05), SOC (r = 0.93, p < 0.05)

Table 1

Interactions among the scenarios, crop growth stages and rhizosphere and bulk soil and contrast effect between rice-wheat and maize - wheat systems.

Statistical signicance (P value)

Source of variation DHA AcP AlP ArS Ur β-Glu FDH CeL

Scenario <0.0001 0.0174 <0.0001 <0.0001 0.0001 <0.0001 0.0105 <0.0001

Stage <0.0243 <0.0001 <0.0001 <0.0001 <0.0003 <0.0001 <0.0001 <0.0001

Scenario*stage <0.0001 <0.0001 0.0201 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Rhizosphere/bulk soil <0.0001 0.4524 0.4524 <0.0001 <0.0001 <0.0001 <0.0002 <0.0001

Scenario*R/B <0.0001 0.0324 0.0008 <0.0001 0.0020 <0.0001 0.0112 <0.0004

Stage*R/B <0.0001 0.2097 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Scenario*stage*R/B <0.0001 0.0297 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

Contrast – Rice-wheat: Maize-Wheat 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

Where R: rhizosphere; B: bulk soil.

DHA: Dehydrogenase, AcP: Acid Phosphatase, AlP: Alkaline Phosphatase, ArS: Arylsulfatase.

Ur: Urease, β-Glu: β-glucosidase, FDH: Fluorescein diacetate hydrolases, CeL: Cellulase.

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

and residue load (r = 0.84, p < 0.05) (Table 2). CA based management

practices recorded signicantly higher soil moisture over conventional

system and signicantly positively correlated with acid phosphatase (r

= 0.84, p < 0.05), aryl sulphatase (r = 0.70, p < 0.05), cellulase (r =

0.86, p < 0.05), GMea (r = 0.88, p < 0.05), residue load (r = 0.84, p <

0.05) and SOC (r = 0.80, p < 0.05) irrespective of managements and

sampling location (Table 2).

4. Discussion

Climate smart agriculture management inuences soil enzyme ac-

tivities at different extents. Signicant variations were observed for

activities of the enzymes studied under partial CA, CSA and CT practices.

Signicant variation among the enzymes in rhizosphere and bulk soils

among the scenarios at different crop growth stages might be due to the

crop and soil management practices followed. Generally the activity of

the microorganisms are higher at rhizosphere zone of the crop because it

has been found that the proportions of rhizodeposition carbon (C) of

below ground carbon inputs through roots, rhizodeposition etc. aver-

aged 54–63% for the cereals [28]. Because of this labile carbon, the

activity of the microorganisms at rhizosphere is higher compared to bulk

soil resulting in higher enzymes activity in rhizosphere. Moreover, zero

tillage, resource (irrigation water and nutrients) management and suit-

able crop rotation with mungbean integration facilitated congenial

environment for the microorganisms.

Higher DHA activity in bulk soils after harvest of the rice crop might

be due to the availability of very labile carbon originated from decom-

position of previous year’s wheat and mungbean residues. Decomposi-

tion of earlier surface retained maize residues releases labile carbon

which was available to microbes and resulted in higher DHA activity in

bulk soils under maize based system than rice based CSA system at

owering stage. Lower C: N ratio of maize roots (35:1) and stover (57:1)

Fig. 4. Soil organic carbon (g/kg) in rhizosphere and bulk soils a) before sowing of crop, b) at maximum tillering c) owering stage of crop and d) after harvesting of

crop under different tillage, residue and crop rotations.

Same upper case letters among the CSA systems and same lower case letters between rhizosphere and bulk soils in each system are not signicantly different at P <

0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.

Fig. 5. Enzymatic pH indicator (AlP/AcP) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, owering stage and after harvesting of crop

under different tillage, residue and crop rotations.

Same lower case letters among the management system are not signicantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation

of mean.

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

over rice (root: 58.8 and straw: 67:1) facilitated faster decomposition of

maize residues. Higher root biomass of maize (3.8 t ha

− 1

) over rice root

mass (2.26 t ha

− 1

) had resulted higher root carbon input to soil under

maize system. Bergstrom et al. [12] reported higher DHA activity under

zero tillage conditions. In a lab experiment Datta et al. [29] also showed

faster decomposition of maize residues placed at surface soil in respect

to maize as well as rice and wheat residues and their mixtures incor-

porated and placed at soil surface. Similar or higher activities of en-

zymes in bulk soil over rhizosphere soil was attributed to the production

of volatile organic compounds released by roots which can be carried far

away from rhizosphere in dry soil due to higher air lled porosity

resulting in higher microbial activity [16]. ‘Rhizosphere priming effect’

Table 2

Pearson’s bivariate correlations among different enzymes and enzyme indices, SOC, residue load, moisture content irrespective of scenarios, crop growth stages and

sampling locations.

Correlations

β-Glu AcP AlP DHA ArS Ur FDH CeL AL EP GM SOC RL MC

β-Glu 1

AcP 0.81*

AlP − 0.09 0.47

DHA − 0.19 0.35 0.98*

ArS 0.69 0.96* 0.66 0.56

Ur 0.38 − 0.13 − 0.56 − 0.48 − 0.13

FDH − 0.09 0.50 0.97* 0.93* 0.64 − 0.71

Cl 0.77 0.64 − 0.26 − 0.42 0.40 − 0.07 − 0.12

AL ¡0.94* 0.56 − 0.42 − 0.49 0.40 0.61 − 0.43 0.72

EP ¡0.95* − 0.62 0.26 0.31 − 0.52 − 0.65 0.31 − 0.59 ¡0.97*

GM 0.73* 0.99* 0.53 0.41 0.95* − 0.27 0.59 0.63 0.45 − 0.51

SOC 0.92* 0.97* 0.27 0.16 0.90* 0.07 0.29 0.71* ¡0.73* ¡0.78* 0.93*

RL 0.96* 0.88* 0.06 − 0.13 0.74* 0.11 0.06 0.88* ¡0.84* ¡0.82* 0.84* 0.95*

MC 0.66 0.84* 0.26 0.09 0.70* − 0.44 0.40 0.86* 0.44 − 0.38 0.88* 0.80* 0.84* 1

*. Correlation is signicant at the 0.05 level (2-tailed).

Where β-Glu: β-glucosidase, AcP: Acid Phosphatase, AlP: Alkaline Phosphatase, DHA: Dehydrogenase, ArS: Arylsulfatase, Ur: Urease, FDH: Fluorescein diacetate

hydrolases, CeL: Cellulase, Al: alteration index three, EP: enzymatic pH indicator, GM: geometric mean, SOC: soil organic carbon, RL: residue load, MC: moisture

content.

Fig. 6. Alteration index three (Al3) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, owering stage and after harvesting of crop under

different tillage, residue and crop rotations.

Same lower case letters among the management system are not signicantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation

of mean.

Fig. 7. Geometric mean (GMea) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, owering stage and after harvesting of crop under

different tillage, residue and crop rotations.

Same lower case letters among the management system are not signicantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation

of mean.

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

mediated by microorganisms also plays an important role in increasing

and decreasing enzymes activity in rhizosphere and bulk soil [17].

Higher β-glucosidase activity in rhizospheric soils over bulk soil under

rice based CSA systems might be due to higher carbon input from brous

root mass of rice in previous year (Suppl. Fig. 2a). But higher β-gluco-

sidase activity in bulk soils under maize based systems at maximum

tillering stage might be due to the higher residue load from maize res-

idue (Suppl. Fig. 2b) leading to more carbon input to soil. This is also

supported by the higher soil carbon concentration under maize based

systems over rice based CSA systems (Fig. 3) and signicant positive

correlations between β-glucosidase activity with residue load and SOC

(Table 2). Moreover, during owering stage the decomposition of the

previous year’s residue were at peak as revealed from temperature rise

at the end of February which might facilitate higher β-glucosidase ac-

tivity at maize based systems. The owering in wheat starts around

mid-February and that time the temperature (22.8

◦

C) and relative hu-

midity (73%) is conducive for microbial growth compared to January

(18.4

◦

C and 79.5%). CA based scenarios received higher carbon input in

the form of residue (Table 2) and zero tillage practice which resulted in

the higher activity of β-glucosidase in soil. Pausch and Kuzyakov [30]

reported that the highly dynamic nature of rhizodeposition and rhizo-

deposits are rapidly incorporated into microorganisms, soil organic

matter, and decomposed to CO

which explains the higher β-glucosidase

activity in rhizospheric soil under rice. Before sowing of wheat, higher

root biomass of maize (3.8 t ha

− 1

) in addition of previous years residues

left in the soil which is decomposing and supplying labile carbon to

microbes, led to higher acid phosphatase activity. Hirte et al. (2018)

[31] showed higher root biomass of maize (186 ± 15 g m

− 2

) over wheat

root biomass (137 ± 6 g m

− 2

) after harvesting in long term eld trial of

DOK (bio-Dynamic, bio-Organic, Conventional site in Switzerland).

Higher rhizodeposition in root zone led to the acidity which further

accentuates the acid phosphatase activity [30]. Higher acid phosphatase

activity in bulk soils at maximum tillering stage of rice and owering

stage of maize system is attributed to differential residue decomposition

leading to varying rates of labile carbon release in these systems. After

harvesting in both the system, the residues placed away from root zone

were completely decomposed whereas the roots just start decaying

which explains higher acid phosphatase activity in bulk soils. Similar

observations were also reported in alkaline phosphatase activity. Crop

rotation particularly inclusion of legume has played an important role in

enzyme activities observed in those CSA based scenarios. In our case,

legume integration in rice-wheat and maize-wheat systems has facili-

tated higher microbial activity leading to release of both acid and

alkaline phosphatase activity in soil. Plant roots release a wide range of

compounds that may differ between plant species [30] which leads to

the difference between FDH activity in rice and maize based systems.

Different crop rotation is also a factor in variation of FDH activities [32].

Different plant species results distinct microbial communities with

different activity [33]. CSA practices have positive effect on soil enzy-

matic activities [14] and this can be seen in the overall FDH activity in

CSA based scenarios and particularly in rhizosphere zone. Sulphatases

are reported to be also found as exoenzymes in the soil and are closely

linked to organic matter [34] applied in the form of heavy load of res-

idues. Higher SOC in PCA and CSA based scenarios (Sc2 to Sc6) (Fig. 4)

resulted in higher activities of ArS as evidenced from signicant positive

correlations between SOC and ArS (Table 2). Kotkova et al. [34] re-

ported that in wheat ArS activity was higher in rhizosphere as compared

to bulk soil and vice versa was reported in lupine but in our study such

type of trend was not reported. In this study both the zones have similar

activities of ArS. Higher ArS activity in rice over maize based CSA can be

linked to differences in crop rotation and their residues as rice and maize

were grown before wheat in those scenarios. Activity of cellulase at

different growth stages can be regulated by the available moisture

content due to crop residue mulch and irrigation water given to crops as

evidenced from Supp. Table 9.

Higher β-glucosidase and acid phosphatase activity in no till soil over

conventional agriculture in varying textured soils were also reported by

other researchers [12,35,36]. Choudhary et al. [11,14] also reported

higher DHA and alkaline phosphatase activity under zero till based

conservation agriculture practices. Residue retention and zero tillage are

the main reasons behind the higher CeL activity under CSA based sce-

narios. Residues provide favourable conditions to the microbes and

microbial transformation of crop residue and soil organic matter en-

hances enzyme activities under zero tillage systems [8]. Higher enzyme

activities in CSA based scenarios (both rice and maize based systems) as

compared to CT and partial CA was mainly due to the integration of

legume [14].

Higher SOC under CSA and PCA based rice and maize systems was

due to higher residue load (112–129 Mg ha

− 1

) (Suppl. Table 2) which

supplies organic carbon to soils in addition to carbon input from plants

through roots, rhizodepositions, secretions etc. We do not have mea-

surements on plant and root derived carbons. Generally rhizosphere soil

is characterized by higher amount of very labile carbon and lower

contents of mineral nitrogen as well as other nutrients with 19–32 times

higher number of microorganisms compared to bulk soil [17]. In bulk

soil (away from roots), all the nutrients are mostly available with

limiting easily available carbon for microbial growth [17]. Priming ef-

fect plays an important role in increasing or decreasing soil organic

matter decomposition in rhizosphere as well as bulk soil with crop res-

idue retention at soil surface under CSA [17,37]. Before sowing of

wheat, lower SOC in maize rhizosphere soil compared to rice might be

due to faster decomposition of maize roots with lower C:N ratio (35:1)

over rice roots (C:N ratio of 58.8:1) thereby facilitating positive priming

effect. Also maize being a C4 plant releases less organic compounds to

soil through roots due to lesser investment of C in the below ground

processes over C3 rice crop [17]. As the stubbles of rice and wheat were

kept and mixed properly with soil during puddling under CT and PCA

system resulting higher SOC in bulk soil compared to rhizospheric soil of

wheat which might have experienced positive priming leading to lower

SOC. At maximum tillering stage, lower SOC in rhizosphere soil

compared to bulk soil in CSA based rice and maize systems might be due

to higher rhizosphere priming effect caused by root secretions, rhizo-

deposition with vigorous microbial activity. In bulk soil at owering

stage, lower SOC was observed under rice and maize based CSA systems

which might be due to higher priming effect mediated by greater activity

of microorganisms caused by decomposing residues retained at soil

surface. Whereas at rhizosphere soil, there might be higher rhizodepo-

sition which results in higher SOC but with limiting mineral nitrogen

leads to lower decomposition [17]. Signicantly higher SOC in bulk soil

at owering under PCA system might be due to higher humication of

crop residue carbon to SOC caused by proper mixing of crop residues

with soil during previous puddled rice crop. At harvesting stage except

CT system, higher SOC both at rhizosphere and bulk soil might be due to

higher carbon released by roots and decomposing surface retained crop

residues of previous crops with lower mineral nitrogen leading to lower

priming effect. Whereas in CT system, due to availability of mineral

nitrogen and other nutrients, microbial activity was higher in bulk soil

and thereby facilitating more oxidation of SOM [17].

Higher soil moisture in bulk soils under CSA based systems irre-

spective of crop growth stages was due to crop residue retentions at soil

surface (Suppl. Table 2). Crop residue mulch enhances soil water storage

by regulating soil temperature, reducing evaporation and increasing

inltration and SOM concentration and thereby increasing water

retention capacity of soil [9]. In bulk soils, SOC derived from crop res-

idues interacts with soil matrix and enhances the specic surface area of

soil which facilitates higher adsorption and retention of water molecules

under CSA based systems [9].

For optimum plant growth and development, soil pH at which the

ratio of enzymatic indicator (AlP/AcP) is about 0.5, can be considered as

optimum [38]. In our experiment, both rhizosphere and bulk soil sam-

ples, the AlP/AcP value exceeded 0.5. These results were conrmed by

soil pH measurement in soil:water 1:2 ratio (data not shown). Negative

H.S. Jat et al.

European Journal of Soil Biology 103 (2021) 103292

correlations between AlP/AcP and residue load, SOC and few enzymes

might be due to the decline in soil pH because of release of organic acids

upon decomposition of crop residues retained at soil surface [25]. The

balance between three soil enzymes β-glucosidase, urease and phos-

phatase is quantied by alteration index three (Al3) which is sensitive to

soil characteristics alterations. The low values of Al3 indicated the better

soil [26]. Higher values before sowing of crop irrespective of scenarios

and sampling locations manifested poor soil quality which improved

signicantly upon growth of the plants as observed at maximum tillering

and owering stages. Importantly at harvest, signicantly lower values

of Al3 were observed at bulk soils under CSA based systems which might

be due to the higher SOC derived from crop residues decomposition.

Higher SOC improves soil quality in CSA based systems and explains

lower Al3 values as evidenced from negative correlations between them

(Table 2) [38]. Soil physico-chemical and biological properties are

related to GMea of the studied enzymes and therefore, is used as an index

of soil quality. Higher values of GMea designate better soil quality and

can describe qualitative changes in soil without considering physico-

chemical properties [38]. Higher SOC in CSA based systems enhanced

soil enzymes activities and subsequently GMea as evidenced from sig-

nicant positive correlations between GMea with residue load, SOC and

soil moisture content (Table 2). Lemanowicz et al. [38] also observed

signicant positive correlations between GMea and SOC while studying

enzyme activities under different tree species in Poland.

Signicant interactions among the scenarios, crop growth stages and

rhizosphere and bulk soils on the soil enzymes might be due to the effect

of Climate Smart Agriculture practices followed. Residue retention in-

creases microbial population [39] by providing a stimulating substrate

for their growth resulting in higher enzyme activities. Higher population

counts of total bacteria, uorescent Pseudomonas, and actinomycetes

were observed under residue retention with ZT over residue removal

under conventional tillage [40].

5. Conclusions

The enzyme activities are strongly inuenced by tillage and crop

establishment, crop rotation, and crop residues and water management

practices. In bulk soil, enzymes activities were higher under CSA prac-

tices over conventional managements and activities of some of the en-

zymes were similar or comparable to rhizospheric soils. Rice based CSA

systems showed higher enzyme activities over maize based systems. The

CSA based systems has shown higher DHA and alkaline phosphatase

activities before sowing rather than at maximum tillering, owering and

after harvesting of wheat. FDH activity in rice based CSA systems was

27% higher than maize based systems. Higher SOC was observed under

CSA based systems which also inuenced soil moisture availability due

to crop residue retention. Crop management practices under a specic

agro-ecosystem has important implications in nutrient availability to

plants because upon decomposition, crop residues release nutrients

which could help in savings of precious nutrients applied externally

besides improving overall soil quality and carbon enrichment. There-

fore, future studies should consider nutrients availability and priming

effect at different crop growth stages in rhizosphere and bulk soils under

CSA based cereal systems.

Declaration of competing interest

The authors declare no competing interests.

Acknowledgements

Collaborations and support from ICAR-Central Soil Salinity Research

Institute (CSSRI) and CIMMYT and funding from U.S. Agency for In-

ternational Development (USAID) and the Bill and Melinda Gates

Foundation (BMGF) through CSISA (Cereal Systems Initiative for South

Asia) project are duly acknowledged. We also acknowledge the technical

support from GCIAR Research Programs on Climate Change, Agriculture

and Food Security (CCAFS). We are grateful to the editor and two

anonymous reviewers for their constructive comments which improved

the quality of the manuscript a lot.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.

org/10.1016/j.ejsobi.2021.103292.

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European Journal of Soil Biology 103 (2021) 103292
Contents lists available at ScienceDirect
European Journal of Soil Biology
journal homepage: www.elsevier.com/locate/ejsobi
Soil enzymes activity: Effect of climate smart agriculture on rhizosphere
and bulk soil under cereal based systems of north-west India
H.S. Jat a,b, Ashim Datta a,*, Madhu Choudhary a,**, P.C. Sharma a,***, Bharti Dixit a,c, M.L. Jat b
a ICAR-Central Soil Salinity Research Institute (CSSRI), Karnal, Haryana, India
b International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
c Choudhary Charan Singh Haryana Agricultural University, Hisar, India A R T I C L E I N F O A B S T R A C T
Handling editorYakov Kuzyakov
In agriculture production system, soil enzymes are important indicators of soil quality. Measurements of soil
quality parameter changes are essential for assessing the impact of soil and crop management practices. Keeping Keywords:
this in view, an experiment was conducted to evaluate the enzyme activities namely dehydrogenase (DHA), Conservation agriculture
β-glucosidase, acid and alkaline phosphatase (AcP & AlP), fluorescein diacetate hydrolases (FDH), cellulase, Climate smart agriculture
urease and aryl sulphatase in rhizosphere and bulk soil after 8 years of different management regimes. Soil Rhizosphere Rice-wheat system
organic carbon (SOC), moisture content and few enzyme indices such as enzymatic pH indicator (AcP/AlP), Maize-wheat system
alteration index three (Al3) and geometric mean (GMea) were also measured. The treatments were conventional Long term managements
rice-wheat system (termed as scenario (Sc1), CT system), partial conservation agriculture (CA)-based rice-wheat-
mungbean system (Sc2, PCA-RW), partial climate smart agriculture (CSA)-based rice-wheat-mungbean system
(Sc3), partial CSA-based maize-wheat-mungbean system (Sc4), full CSA-based rice-wheat-mungbean system
(Sc5), and full CSA-based maize-wheat-mungbean system (Sc6). Soil samples were collected from rhizosphere
and away from roots (bulk soil) at 0–15 cm soil depth before sowing (from rhizosphere of previous crops), at
maximum tillering, flowering, and after harvesting of wheat crop. Results showed that DHA activity was higher
before sowing (59.8%), at maximum tillering (48.4%), flowering (8.6%) and after harvesting (19.1%) in rice
based CSA systems (mean of Sc3 and Sc5) over maize based CSA systems (mean of Sc4 and Sc6) in rhizospheric
soil. On average, β-glucosidase activity was significantly higher in rhizospheric soils of rice based system over
maize based CSA system. Before sowing of wheat, significantly higher (21.4%) acid phosphatase activity was
observed in rhizosphere over bulk soils of maize based CSA system. Significantly higher alkaline phosphatase
activity was observed before sowing of wheat in bulk soils of rice (25.3%) and maize (38.5%) based CSA systems
over rhizospheric soils. Rice based CSA systems showed 27% higher FDH activity than maize based systems.
Significant interaction effect was observed between the managements and enzymes. SOC played an important
role in regulating the enzymes activity both in rhizosphere and bulk soil. Significant variation in AcP/AlP, Al3
and GMea was observed among the managements. Therefore, CSA managements are beneficial in improving
enzyme activities not only in rhizosphere but also in bulk soil where residues are retained thereby may help in improving nutrient cycling. 1. Introduction
enzymes act as necessary catalysts and strongly influence energy
transformation, environmental quality, and agronomic productivity.
Soil enzymes are the key players in biochemical processes of organic
Soil enzymes provide early detection of changes in soil health because
matter recycling in the soil system and their activities are closely related
they respond to soil management changes and environmental factors
to soil organic matter (SOM), soil physical properties, and microbial
much sooner than other soil quality parameters. Qualitative and quan-
activity [1]. During decomposition of SOM and nutrient cycling, soil
titative changes in soil enzymes determine the availability of nutrients
* Corresponding author. ICAR-CSSRI, Karnal.
** Corresponding author. ICAR-CSSRI, Karnal.
*** Corresponding author. ICAR-CSSRI, Karnal.
E-mail addresses: ashimdatta2007@gmail.com (A. Datta), madhucssri@gmail.com (M. Choudhary), pcsharma.knl@gmail.com (P.C. Sharma).
https://doi.org/10.1016/j.ejsobi.2021.103292
Received 25 November 2019; Received in revised form 13 August 2020; Accepted 4 February 2021
Available online 15 February 2021
1164-5563/© 2021 The Author(s).
Published by Elsevier Masson SAS. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/). H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
and crop productivity [2]. Different agricultural practices like tillage,
2. Materials and methods
cropping systems, irrigation and nutrient management influence soil
enzyme activities, thereby influencing yield sustainability [3]. Adverse
2.1. Field experimental design
impacts of mechanical tillage, cropping systems, and residues removal
have been observed in soil enzymatic activities and availability of plant
The experiment was established in 2009 at the research farm of In-
nutrients [4]. Application of organic and inorganic fertilization exerts a
dian Council of Agricultural Research (ICAR) - Central Soil Salinity
strong influence on soil quality. In any agriculture production system,
Research Institute (CSSRI) (29◦70′N, 76◦95′E), Karnal, India. Soil type is
amending with organic matter and application of balanced fertilizers
loam in texture with 34% sand, 46.1% silt and 19.9% clay. It falls under
improve soil organic carbon and biological properties including micro-
Typic Natrustalf category. Climate is extreme hot and dry (April–June) to
bial biomass and enzymatic activities [5,6]. Dehydrogenase enzyme
wet summers (July–September) and cold dry winters (October–March).
activity (DHA) is considered as the indicator of oxidative activity of soil
Average annual temperature is 26 ◦C with maximum and minimum of
microorganisms and increases significantly upon application of
34◦ and 18 ◦C, respectively with annual precipitation of 650 mm. balanced fertilization [6].
Initially, the experiment comprised of four cereal-based scenarios
Climate Smart Agriculture (CSA) practices are based on conservation
varying in cropping system, tillage, crop establishment methods, and
agriculture (CA) principles of zero tillage (ZT), residue management and
residue management practices (Sc1, Sc2, Sc3 and Sc4). Treatments were
sustainable crop rotation along with precision irrigation and N-man-
replicated thrice in 20 m × 100 m plot size in randomized complete
agement using sub-surface drip irrigation (SDI) system. There are
block design. In May 2016, precise water management practice (sub-
number of advantages of CSA practices such as higher soil organic car-
surface drip irrigation; SDI) was included in subdivided plots (20 m ×
bon, improved soil physical properties, nutrient availability, and crop
50 m) of Sc3 and Sc4, respectively. Briefly, six treatments termed as
productivity as reported by researchers all over the world [7–10].
scenarios (Sc) were: i) conventional-till (CT) rice-CT wheat (Sc1;
Several studies have been done on the effect of different agriculture
farmers’ practice; CT); ii) CT rice-Zero tillage (ZT) wheat-ZT mungbean
management practices on soil enzyme activities in cereal based systems
with flood irrigation (Sc2; partial CA); iii) ZT rice-ZT wheat-ZT mung-
[11–13]. While studying the effect of series of CA based managements
bean with flood irrigation (Sc3; rice based partial CSA); iv) ZT maize-ZT
on soil enzymes, Choudhary et al. [14] reported 210% and 49% higher
wheat-ZT mungbean with flood irrigation (Sc4; maize based partial
DHA and alkaline phosphatase activity (AlP), respectively in soils under
CSA); v) ZT rice-ZT wheat-ZT mungbean with SDI (Sc5; rice based full
maize-wheat-mungbean with residue retention and 140% and 42%
CSA); and vi) ZT maize-ZT wheat-ZT mungbean with SDI (Sc6; maize
under rice-wheat-mungbean system with residue retention over con-
based full CSA). Sc3 and Sc4 were based on principles of CA practices
ventional rice-wheat system, respectively in NW India. Bergstrom et al.
where irrigation water and N application were not precisely managed
[12] compared six enzymes namely urease, glutaminase, phosphatase,
and called it partial climate smart agriculture (CSA). However, in Sc5
arylsulfatase, β-glucosidase and dehydrogenase under no tillage along a
and Sc6, irrigation water and N in the form of urea was precisely applied
topographic and soil textural gradient and observed higher β-glucosi-
using subsurface drip irrigation (SDI) and called full CSA. Best crop
dase, glutaminase, phosphatase and aryl sulfatase activity in
management practices were followed in all the treatments except Sc1,
coarse-textured soils at a lower slope than in fine-textured soil at an
where farmer’s traditional practices were followed (Supp. Table 1, Supp.
upper slope. No-tilled soil showed higher dehydrogenase, urease, pro-
Fig. 1). We used four systems for convenience, conventional tillage
tease, phosphatase and β-glucosidase activities over conventional tillage
based rice-wheat system (CT-RW), partial CA based rice-wheat mung-
system in sorghum cultivation under subtropical conditions [15].
bean system (PCA-RW) (Sc2), rice based CSA system (mean of Sc3 and
The rhizosphere zone of the plants acts as hotspot of enzyme activ-
Sc5) and maize based CSA system (mean of Sc4 and Sc6).
ities. Recently, it was stated that the rhizosphere activity should be
extended from mm scale to cm scale due to H2 fertilization effect and
2.2. Soil sampling, processing and analysis
volatile organic compounds released by roots [16]. The quantity and
quality of root exudates depends on plant type and growth stages of
Wheat is common crop among all the scenarios so soil samples were
plants [ 17] which also influence the diversity and activity of microbes,
collected from wheat season in order to explore the effect of manage-
biochemical processes and enzyme activities [17]. ‘Rhizosphere priming
ment practices (CT v/s PCA and/CSA) on different enzyme activities like
effect’ is also an important factor playing an important role in SOM
dehydrogenase (DHA), acid phosphatase (AcP), alkaline phosphatase decomposition [17].
(AlP), beta-glucosidase (β-glu), fluorescein diacetate hydrolases (FDH),
In most of the studies so far, soil samples were collected between
aryl sulphatase (ArS), urease (Ur) and cellulose (CeL) activity and SOC
rows of the crops after harvesting (called bulk soil sample). In conven-
changes. From each plot, soil samples were collected at 0–15 cm soil
tional agricultural practices, tillage mixes the stubbles/roots of previous
depth by an auger from nine locations from each rhizosphere and non-
crop with soil before sowing of next crop and after planking stubbles are
rhizosphere zones (bulk soils) and composite samples were prepared
accumulated and removed from the field. But in CSA practices, stubbles
separately before sowing, maximum tillering, flowering, and after har-
and roots of the crops are undisturbed with zero tillage condition and
vesting in the year 2017–18 (wheat was sown in November 2017 and
loose crop residues are retained over the soil surface instead of burning
harvested in April 2018). For rhizosphere zone samples, wheat plants
or removal as practiced in conventional agriculture. Generally rhizo-
were uprooted and soil adhered to roots was collected. Bulk soil samples
sphere soil is characterized by higher microbial activity due to rhizo-
were collected from the mid-point of the two rows. Row to row distance
deposition, root secretion etc. than bulk soil (away from roots) [17].
in wheat crop was 22.5 cm. Rhizosphere soil before sowing indicates
Therefore, in CSA, we may expect higher enzymes activity in bulk soil
rhizosphere of previous crops. As after harvesting of previous crop (rice
compared to conventional practices due to residue retention and also
and maize), the stubbles remain intact in soil, we collected soil samples
enzymes activity in rhizosphere and bulk soil need to be investigated to
from the rhizosphere of those crop stubbles and designated as rhizo-
capture whether there is any synergistic effect exists. There is hardly any
sphere soil before sowing. Fresh soil samples were immediately stored in
study to unveil this aspect. The objectives of this study are to assess the
a refrigerator at 4 ◦C till analysis of different enzymes. DHA, AcP and AlP
enzyme activities and SOC concentration in rhizosphere and bulk soils
activities were estimated as described by Dick et al. [18]. β-glucosidase
and their interactions after 8 years of continuous smart crop manage-
activity was determined by the method of Eivazi and Tabatabai [19],
ment practices. We hypothesize that soil enzyme activities in bulk soil
urease was by the method of Tabatabai [20] and aryl sulfatase was by
with CSA based management practices will be improved at different
the method of Tabatabai and Bremner [21]. Cellulase activity was
growth stages compared to those of conventional/tillage based man-
measured by the method of Hope and Burns [22] and FDH assay by the agement practices. method of Green et al. [23]. 2 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
Root mass after harvesting of rice, wheat and maize was measured by
significantly higher before sowing (59.6%) and flowering stage (18.7%)
using standard procedure. Soil blocks up to 40 cm depth with plant roots
in rice based CSA systems (mean of Sc3 and Sc5) over maize based CSA
were taken out from four random places in each scenario. Roots were
systems (mean of Sc4 and Sc6) (Fig. 1a and 1c). Before sowing in maize
washed carefully, detached from the main stem at the first node. Roots
based system, about 35% higher DHA activity was observed at rhizo-
were dried at 65 ± 5 ◦C at oven and dry weight was calculated and then
spheric soil over bulk soil. At maximum tillering stage, DHA activity was converted to t/ha.
significantly higher in partial CA based rice system (PCA-RW, Sc2) (145
Soil moisture content was determined by drying the fresh soil sam-
μg TPF g− 1 soil hr− 1) over others irrespective of sampling location
ples at 105 ◦C for 24 h in a hot air oven until a constant weight and
(Fig. 1b). At flowering, DHA activity was 12% higher in rhizosphere of
calculated by following formula.
PCA-RW (Sc2) over bulk soil (Fig. 1c). After harvesting of the crop,
Moisture content (%) = (Moist soil wt-dry soil wt)/dry soil wt × 100.
significantly higher (21%) DHA activity was observed in bulk soils (98
One part of the fresh samples collected from both rhizosphere and
μg TPF g− 1 soil hr− 1) over rhizospheric soils (81 μg TPF g− 1 soil hr− 1) in
bulk soil was dried in shade, ground and sieved and stored in plastic
rice based CSA system (Fig. 1d). Being the responsible enzyme for car-
container for chemical analysis. Oxidizable organic carbon (SOC) was
bon cycle in soil, on average β-glucosidase activity was significantly (p
determined by following Walkley and Black method [24].
< 0.05) higher in rhizospheric soils of rice based CSA systems (108 μg p-
The enzymatic pH indicator was calculated using the results of
NP g− 1 soil hr− 1) over bulk soil (92 μg p-NP g− 1 soil hr− 1) whereas under
alkaline and acid phosphatase activity [25]:
maize based systems similar values were observed (Suppl. Fig. 2) irre-
spective of crop growth stages. In bulk soil under maize systems
Enzymatic pH indicator = AlP/AcP
significantly higher β-glucosidase activity was observed before sowing
The alteration index three (Al3) was also calculated using the results
(Suppl. Fig. 2a) and at maximum tillering stage (Suppl. Fig. 2b)
of β-glucosidase, phosphatase and urease [26]:
compared to rice based CSA system. Whereas at harvesting stage, rhi-
zospheric soils under maize systems (112 μg p-NP g− 1 soil hr− 1) showed
Alteration index three (Al3) = 7.87 β-glucosidase− 8.22 acid phos-
significantly (p < 0.05) higher β-glucosidase activity over rhizospheric phatase− 0.49 urease
soil under rice based CSA system (94 μg p-NP g− 1 soil hr− 1) (Suppl.
For each scenario, the geometric mean (GMea) was calculated as the
Fig. 2d). At maximum tillering stage, significantly higher β-glucosidase
mean for the assayed enzymes activities [27]. It is a general index to
activity was observed at rhizosphere soil of rice based CSA system (93 μg
consolidate information from variables with different units and range of
p-NP g− 1 soil hr− 1) over bulk soil (72 μg p-NP g− 1 soil hr− 1) and also variation:
maize based system (78 μg p-NP g− 1 soil hr− 1) (Suppl. Fig. 2b). In rice
based CSA system, after harvesting of the crop significantly (p < 0.05)
GMea = (DHA * GLU * AlP *AcP * FDH* Ur*CeL*ArS) 1/8
higher β-glucosidase activity was observed in bulk soils (12.8%) over
rhizospheric soils (Suppl. Fig. 2d). On average β-glucosidase activity was
Where DHA, GLU, AlP, AcP, FDH, Ur, CeL, ArS are dehydrogenase,
20% higher (p < 0.05) in CA based scenarios (Sc2-Sc6) over conven-
β-glucosidase, alkaline phosphatase, acid phosphatase, fluorescein
tional tillage scenario (Sc1) irrespective of crop growth stages and
diacetate hydrolases, urease, cellulase and aryl sulphatase, respectively. sampling location. 2.3. Residue load
3.2. Acid and alkaline phosphatase activity under different managements
Crop residues recycled in each year under different scenarios are
Significant variation in acid and alkaline phosphatase activity was
presented at Supp. Table 2. Significantly higher residues amount (129
observed irrespective of scenarios and sampling location (Supp. Table 3
Mg ha− 1) were recycled in maize based system (mean of Sc4 and Sc6)
and 4). Before sowing of wheat, significantly higher (21.4%) acid
over others, followed by PCA-RW (Sc2) (115.5 Mg ha− 1). About 111 Mg
phosphatase activity was observed in rhizosphere over bulk soils of
ha− 1residues were added in rice based CSA systems (mean of Sc3 and
maize based systems (Supp. Table 3). But at maximum tillering, about Sc5) during the last 8 years.
8.2% higher acid phosphatase activity was observed in bulk soils over
rhizosphere soil of rice based CSA systems. In maize based systems, at
2.4. Statistical analysis
flowering stage 6% higher (p < 0.05) acid phosphatase activity was
recorded at bulk soil over rhizospheric soil. At harvesting, bulk soils
The data were subjected to analysis of variance (ANOVA) and using
recorded significantly higher acid phosphatase activity in both rice
the general linear model procedure of the SPSS window version 17.0
(9.5%) and maize (7.4%) based CSA systems over rhizospheric soils
(SPSS Inc., Chicago, USA). Treatment means were separated by Duncan
(Supp. Table 3). Significantly higher alkaline phosphatase activity was
Multiple Range Test (DMRT) at 5% level of significance (P < 0.05).
observed before sowing of wheat in bulk soils of rice (25.3%) and maize
Correlation study was performed among the enzymes, indices calculated
(38.5%) based CSA systems over rhizospheric soils (Supp. Table 4).
from different enzymes, residue load, SOC and soil moisture content. To
determine the effect of scenarios, rhizosphere/bulk soils and stages
3.3. Fluorescein diacetate hydrolases (FDH) and aryl sulphatase (ArS)
(fixed factors) and their interaction effect on the different enzyme ac-
activity under different managements
tivities (random variable), three-way ANOVA was carried out. Linear
contrasts were used to compare single or multiple treatments against
On average rice based CSA system showed 27% higher FDH activity one another.
than maize based system (Supp. Table 5). In rhizosphere, on average it
was 18% and in bulk soils it was 38% higher in rice based CSA systems 3. Results
than maize based systems. At maximum tillering stage, about 29%
higher FDH activity was observed in bulk soils of rice based CSA system
3.1. Dehydrogenase (DHA) and β-glucosidase activity as influenced by
over rhizosphere soil whereas rhizospheric soils of partial CA based management practices
system recorded 11.5% higher FDH activity over bulk soils (Fig. 2).
Rhizosphere soils under maize based systems showed about 39% higher
Significant variation in DHA was observed both in rhizosphere and
FDH activity over bulk soils at flowering stage (Fig. 3). Similar FDH
bulk soils of different scenarios (Fig. 1). Before sowing of the crop, DHA
activity was observed under partial CA and rice based CSA systems in
activity was significantly higher in rhizospheric soils over bulk soils
rhizosphere and bulk soils (Fig. 3). After harvesting, bulk soils under
irrespective of cropping system. In rhizospheric soil, DHA activity was
maize based CSA and partial CA based systems recorded 29% and 69% 3 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
Fig. 1. Dehydrogenase activity (μg TPF g− 1 soil hr− 1) in rhizosphere and bulk soils a) before sowing of crop, b) at maximum tillering c) flowering stage of crop and d)
after harvesting of crop under different tillage, residue and crop rotations.
Same upper case letters among the CSA systems and same lower case letters between rhizosphere and bulk soils in each system are not significantly different at P <
0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.
Fig. 2. Fluorescein diacetate hydrolases (FDH) (μg fluorescein g− 1 soil hr− 1)
Fig. 3. Fluorescein diacetate hydrolases (μg fluorescein g− 1 soil hr− 1) and Aryal
and Aryal sulphatase activity (μg p-NP g− 1 soil hr− 1) in rhizosphere and bulk
Sulphatase activity (μg p-NP g− 1 soil hr− 1) in rhizosphere and bulk soils at
soils at maximum tillering stage of crop under different tillage, residue and crop
flowering stage of crop under different tillage, residue and crop rotations. rotations.
Where, R: rhizosphere; ARS: aryal sulphatase activity; FDH: Fluorescein diac-
Where, R: rhizosphere; ARS: aryal sulphatase activity; FDH: Fluorescein diac- etate hydrolases activity. etate hydrolases activity.
Same upper case letters among the CSA systems and same lower case letters
Same upper case letters among the CSA systems and same lower case letters
between rhizosphere and bulk soils in each system are not significantly different
between rhizosphere and bulk soils in each system are not significantly different
at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation
at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean. of mean.
and then increased from tillering to flowering in both soils. At maximum
higher FDH activity over rhizosphere soils (Suppl. Fig. 3). In rhizosphere
tillering stage, about 32% higher aryl sulphatase activity was observed
zone an increase of 14% FDH activity can be seen in CA based systems
in rhizosphere soils of partial CA based system (Sc2) over bulk soil
(mean of Sc2 to Sc6) over CT system (Sc1) but simultaneously 6%
(Fig. 2). Highest activity of ArS was recorded at flowering stage with few
decrease was noticed in CA based systems over CT system in bulk soils
exceptions (Fig. 3). It was noticed that lowest activities of ArS was found (Supp. Table 5).
in conventional till scenario (CT system, Sc1) irrespective of growth
At different growth stages of wheat crop, variation in activities of ArS
stages and sampling location. In both the zones, on average, 28% higher
(17.63–117.58 μg p-NP g− 1soil hr− 1) was recorded for rhizosphere and
ArS activity was recorded in PCA and CSA based scenarios (mean of Sc2
bulk soils (Supp. Table 6). In both rhizosphere and bulk soil, ArS activity
to Sc6) over CT system (Sc1). Overall activities of ArS were found similar
was higher by 12% and 15%, respectively before the sowing of wheat
in all scenarios in both rhizosphere and bulk soils, with a mean value of
than the tillering stage. It was first decreased from sowing to tillering
60 μg p-NP g− 1soil hr− 1 in both the zones. Rice based partial CSA system 4 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
(Sc3) has more ArS activities than maize based partial CSA (Sc4) except
CSA based rice (36%) and maize (44%) based system over rhizosphere
in rhizosphere soil of maximum tillering stage. In rhizosphere and bulk
soils but about 21.4% lower SOC was observed in bulk soils over
soil, it was 13% and 12% higher in rice based CSA system than maize
rhizosphere soil under PCA-RW system (Fig. 4b). At flowering stage,
based system, respectively (Supp. Table 6).
higher SOC concentration was observed in rhizosphere soils compared
to maximum tillering stage irrespective of scenarios (Fig. 4c). But in bulk
3.4. Urease (ur) and cellulase (CeL) activity under different
soils, significantly lower SOC were observed in rice (11%) and maize managements
(18%) based system whereas PCA-RW system recorded 66% higher SOC
compared to the bulk soil at maximum tillering stage (Fig. 4c). At har-
Higher activity of urease was noticed in maize based full CSA system
vesting stage, higher SOC was observed in all the scenarios irrespective
(Sc6) compared to other scenarios (Supp. Table 7). There was hardly any
of sampling locations except the bulk soil under CT system which
effect of growth stages on Ur in both rhizosphere and bulk soils. Effect of
registered 14% lower SOC compared to the bulk soil under flowering
CSA practices has not been observed on Ur activities as it was found stage (Fig. 4d).
similar in CA based scenarios (mean of Sc2 to Sc6) (319.9 μg urea g− 1
Residue retention exhibited strong influence on soil moisture content
soil hr− 1) and CT system (319.6 μg urea g− 1 soil hr− 1) (Supp. Table 7).
in all the scenarios (Supp. Table 9). At maximum tillering stage, similar
Cellulase activity was found to increase from before sowing to
moisture content was observed irrespective of scenarios. CSA based rice
maximum tillering and flowering stage with some exceptions and
system recorded 34% higher moisture content at rhizosphere soil over
decreased towards harvesting stage (Supp. Table 8). In CA based sce-
bulk soil whereas 51% higher moisture was observed at bulk soils under
narios (mean of Sc2 to Sc6) significantly higher CeL activities (15.54 μg
maize based system over rhizosphere soil at flowering stage. On average
glucose g− 1 soil hr− 1) were noticed over CT system (8.03 μg glucose g− 1
64% higher moisture content was observed at bulk soils under CSA
soil hr− 1). Partial CA system (Sc2) showed 52% increase in CeL activities
based rice and maize based system over rhizosphere soil (Supp. Table 9).
over CT system. Integration of mungbean (mean of Sc3 to Sc6) showed
34% increase in CeL activity over partial CA.
3.7. Enzymatic pH indicator, Al3 and GMea and their relationship with
enzymes, residue load, SOC and soil moisture
3.5. Interactions effect of managements, sampling locations and crop
growth stages on soil enzymes
The enzymatic pH indicator was calculated using the values of the
alkaline and acid phosphatase activities under different managements.
The analysis of variance showed that all the enzymes in rhizosphere
The value of this indicator varied from 0.71 to 1.34 irrespective of
and bulk soils were significantly influenced by scenarios, rhizosphere
sampling location and crop growth stages (Fig. 5). Enzymatic pH indi-
(R)/bulk soil (B), crop growth stages and their interactions i.e. scenario
cator was significantly negatively correlated with β-glucosidase activity
× stage, scenario × R/B, stage × R/B and scenario × stage × R/B except
(r = − 0.95, p < 0.05), Al3 (r = − 0.97, p < 0.05), SOC (r = − 0.79, p <
few instances (Table 1). The interaction effect between rhizosphere ×
0.05) and residue load (r = − 0.82, p < 0.05) irrespective of sampling
bulk soils was not significant for acid and alkaline phosphatase activity
location and crop growth stages (Table 2). Alteration index three varied
whereas crop growth stage × R/B was also not significant for acid
significantly among the crop growth stages and rhizosphere and bulk
phosphatase activity. Contrast analysis showed that there was signifi-
soil under different managements (Fig. 6). Lower values of Al3 indicated
cant difference between rice and maize based cropping systems
better soil quality. In rhizosphere soil, lowest Al3 (− 516) was observed (Table 1).
at flowering stage of partial CA based system whereas in bulk soil rice
based CSA system recorded lowest Al3 (− 567) at maximum tillering
3.6. Soil organic carbon and soil moisture under different managements
stage. Significantly higher Al3 values were recorded before sowing of and crop growth stages
crop irrespective of sampling location (Fig. 6). Significant negative
correlation was observed between Al3 and β-glucosidase (r = − 0.94, p
Significant variation in SOC was observed at different crop growth
< 0.05), and also negatively correlated with residue load (r = − 0.84, p
stages and sampling locations. On average irrespective of crop growth
< 0.05) and SOC (r = − 0.73, p < 0.05) (Table 2). Significant variation in
stages and sampling locations, rice and maize based CSA (45%) and
GMea index values was recorded among the managements and sampling
PCA-RW (37%) recorded significantly higher SOC over conventional
locations. In rhizosphere and bulk soil, highest GMea index was
practices (Fig. 4). Before sowing, rhizosphere soils of CSA based rice and
observed in maximum tillering (90) and flowering stage (85) of partial
maize systems recorded 90% (in rice rhizosphere) and 63% (in maize
CA based system (Fig. 7). Lower values of GMea index was recorded in
rhizosphere) higher SOC over their respective bulk soils. Whereas
conventional system and before sowing of wheat irrespective of sam-
18–24% higher SOC was observed in bulk soils under CT (rice crop) and
pling location. Significant positive correlation was observed between
PCA-RW system (rice crop) over rhizosphere soils (Fig. 4a). At maximum
GMea and β-glucosidase (r = 0.73, p < 0.05), acid phosphatase (r = 0.99,
tillering stage, significantly higher SOC was observed in bulk soils under
p < 0.05), aryl sulphatase (r = 0.95, p < 0.05), SOC (r = 0.93, p < 0.05) Table 1
Interactions among the scenarios, crop growth stages and rhizosphere and bulk soil and contrast effect between rice-wheat and maize - wheat systems.
Statistical significance (P value) Source of variation DHA AcP AlP ArS Ur β-Glu FDH CeL Scenario <0.0001 0.0174 <0.0001 <0.0001 0.0001 <0.0001 0.0105 <0.0001 Stage <0.0243 <0.0001 <0.0001 <0.0001 <0.0003 <0.0001 <0.0001 <0.0001 Scenario*stage <0.0001 <0.0001 0.0201 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Rhizosphere/bulk soil <0.0001 0.4524 0.4524 <0.0001 <0.0001 <0.0001 <0.0002 <0.0001 Scenario*R/B <0.0001 0.0324 0.0008 <0.0001 0.0020 <0.0001 0.0112 <0.0004 Stage*R/B <0.0001 0.2097 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 Scenario*stage*R/B <0.0001 0.0297 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001
Contrast – Rice-wheat: Maize-Wheat 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001
Where R: rhizosphere; B: bulk soil.
DHA: Dehydrogenase, AcP: Acid Phosphatase, AlP: Alkaline Phosphatase, ArS: Arylsulfatase.
Ur: Urease, β-Glu: β-glucosidase, FDH: Fluorescein diacetate hydrolases, CeL: Cellulase. 5 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
Fig. 4. Soil organic carbon (g/kg) in rhizosphere and bulk soils a) before sowing of crop, b) at maximum tillering c) flowering stage of crop and d) after harvesting of
crop under different tillage, residue and crop rotations.
Same upper case letters among the CSA systems and same lower case letters between rhizosphere and bulk soils in each system are not significantly different at P <
0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.
Fig. 5. Enzymatic pH indicator (AlP/AcP) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, flowering stage and after harvesting of crop
under different tillage, residue and crop rotations.
Same lower case letters among the management system are not significantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.
and residue load (r = 0.84, p < 0.05) (Table 2). CA based management
the microorganisms are higher at rhizosphere zone of the crop because it
practices recorded significantly higher soil moisture over conventional
has been found that the proportions of rhizodeposition carbon (C) of
system and significantly positively correlated with acid phosphatase (r
below ground carbon inputs through roots, rhizodeposition etc. aver-
= 0.84, p < 0.05), aryl sulphatase (r = 0.70, p < 0.05), cellulase (r =
aged 54–63% for the cereals [28]. Because of this labile carbon, the
0.86, p < 0.05), GMea (r = 0.88, p < 0.05), residue load (r = 0.84, p <
activity of the microorganisms at rhizosphere is higher compared to bulk
0.05) and SOC (r = 0.80, p < 0.05) irrespective of managements and
soil resulting in higher enzymes activity in rhizosphere. Moreover, zero sampling location (Table 2).
tillage, resource (irrigation water and nutrients) management and suit-
able crop rotation with mungbean integration facilitated congenial 4. Discussion
environment for the microorganisms.
Higher DHA activity in bulk soils after harvest of the rice crop might
Climate smart agriculture management influences soil enzyme ac-
be due to the availability of very labile carbon originated from decom-
tivities at different extents. Significant variations were observed for
position of previous year’s wheat and mungbean residues. Decomposi-
activities of the enzymes studied under partial CA, CSA and CT practices.
tion of earlier surface retained maize residues releases labile carbon
Significant variation among the enzymes in rhizosphere and bulk soils
which was available to microbes and resulted in higher DHA activity in
among the scenarios at different crop growth stages might be due to the
bulk soils under maize based system than rice based CSA system at
crop and soil management practices followed. Generally the activity of
flowering stage. Lower C: N ratio of maize roots (35:1) and stover (57:1) 6 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292 Table 2
Pearson’s bivariate correlations among different enzymes and enzyme indices, SOC, residue load, moisture content irrespective of scenarios, crop growth stages and sampling locations. Correlations β-Glu AcP AlP DHA ArS Ur FDH CeL AL EP GM SOC RL MC β-Glu 1 AcP 0.81* AlP − 0.09 0.47 DHA − 0.19 0.35 0.98* ArS 0.69 0.96* 0.66 0.56 Ur 0.38 − 0.13 − 0.56 − 0.48 − 0.13 FDH − 0.09 0.50 0.97* 0.93* 0.64 − 0.71 Cl 0.77 0.64 − 0.26 − 0.42 0.40 − 0.07 − 0.12 AL ¡0.94* 0.56 − 0.42 − 0.49 0.40 0.61 − 0.43 0.72 EP ¡0.95* − 0.62 0.26 0.31 − 0.52 − 0.65 0.31 − 0.59 ¡0.97* GM 0.73* 0.99* 0.53 0.41 0.95* − 0.27 0.59 0.63 0.45 − 0.51 SOC 0.92* 0.97* 0.27 0.16 0.90* 0.07 0.29 0.71* ¡0.73* ¡0.78* 0.93* RL 0.96* 0.88* 0.06 − 0.13 0.74* 0.11 0.06 0.88* ¡0.84* ¡0.82* 0.84* 0.95* MC 0.66 0.84* 0.26 0.09 0.70* − 0.44 0.40 0.86* 0.44 − 0.38 0.88* 0.80* 0.84* 1
*. Correlation is significant at the 0.05 level (2-tailed).
Where β-Glu: β-glucosidase, AcP: Acid Phosphatase, AlP: Alkaline Phosphatase, DHA: Dehydrogenase, ArS: Arylsulfatase, Ur: Urease, FDH: Fluorescein diacetate
hydrolases, CeL: Cellulase, Al: alteration index three, EP: enzymatic pH indicator, GM: geometric mean, SOC: soil organic carbon, RL: residue load, MC: moisture content.
Fig. 6. Alteration index three (Al3) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, flowering stage and after harvesting of crop under
different tillage, residue and crop rotations.
Same lower case letters among the management system are not significantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.
Fig. 7. Geometric mean (GMea) in A) rhizosphere and B) bulk soils before sowing of crop, maximum tillering, flowering stage and after harvesting of crop under
different tillage, residue and crop rotations.
Same lower case letters among the management system are not significantly different at P < 0.05 according to Duncan Multiple Range Test (DMRT) for separation of mean.
over rice (root: 58.8 and straw: 67:1) facilitated faster decomposition of
to maize as well as rice and wheat residues and their mixtures incor-
maize residues. Higher root biomass of maize (3.8 t ha− 1) over rice root
porated and placed at soil surface. Similar or higher activities of en-
mass (2.26 t ha− 1) had resulted higher root carbon input to soil under
zymes in bulk soil over rhizosphere soil was attributed to the production
maize system. Bergstrom et al. [12] reported higher DHA activity under
of volatile organic compounds released by roots which can be carried far
zero tillage conditions. In a lab experiment Datta et al. [29] also showed
away from rhizosphere in dry soil due to higher air filled porosity
faster decomposition of maize residues placed at surface soil in respect
resulting in higher microbial activity [16]. ‘Rhizosphere priming effect’ 7 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
mediated by microorganisms also plays an important role in increasing
conventional agriculture in varying textured soils were also reported by
and decreasing enzymes activity in rhizosphere and bulk soil [17].
other researchers [12,35,36]. Choudhary et al. [11,14] also reported
Higher β-glucosidase activity in rhizospheric soils over bulk soil under
higher DHA and alkaline phosphatase activity under zero till based
rice based CSA systems might be due to higher carbon input from fibrous
conservation agriculture practices. Residue retention and zero tillage are
root mass of rice in previous year (Suppl. Fig. 2a). But higher β-gluco-
the main reasons behind the higher CeL activity under CSA based sce-
sidase activity in bulk soils under maize based systems at maximum
narios. Residues provide favourable conditions to the microbes and
tillering stage might be due to the higher residue load from maize res-
microbial transformation of crop residue and soil organic matter en-
idue (Suppl. Fig. 2b) leading to more carbon input to soil. This is also
hances enzyme activities under zero tillage systems [8]. Higher enzyme
supported by the higher soil carbon concentration under maize based
activities in CSA based scenarios (both rice and maize based systems) as
systems over rice based CSA systems (Fig. 3) and significant positive
compared to CT and partial CA was mainly due to the integration of
correlations between β-glucosidase activity with residue load and SOC legume [14].
(Table 2). Moreover, during flowering stage the decomposition of the
Higher SOC under CSA and PCA based rice and maize systems was
previous year’s residue were at peak as revealed from temperature rise
due to higher residue load (112–129 Mg ha− 1) (Suppl. Table 2) which
at the end of February which might facilitate higher β-glucosidase ac-
supplies organic carbon to soils in addition to carbon input from plants
tivity at maize based systems. The flowering in wheat starts around
through roots, rhizodepositions, secretions etc. We do not have mea-
mid-February and that time the temperature (22.8 ◦C) and relative hu-
surements on plant and root derived carbons. Generally rhizosphere soil
midity (73%) is conducive for microbial growth compared to January
is characterized by higher amount of very labile carbon and lower
(18.4 ◦C and 79.5%). CA based scenarios received higher carbon input in
contents of mineral nitrogen as well as other nutrients with 19–32 times
the form of residue (Table 2) and zero tillage practice which resulted in
higher number of microorganisms compared to bulk soil [17]. In bulk
the higher activity of β-glucosidase in soil. Pausch and Kuzyakov [30]
soil (away from roots), all the nutrients are mostly available with
reported that the highly dynamic nature of rhizodeposition and rhizo-
limiting easily available carbon for microbial growth [17]. Priming ef-
deposits are rapidly incorporated into microorganisms, soil organic
fect plays an important role in increasing or decreasing soil organic
matter, and decomposed to CO2 which explains the higher β-glucosidase
matter decomposition in rhizosphere as well as bulk soil with crop res-
activity in rhizospheric soil under rice. Before sowing of wheat, higher
idue retention at soil surface under CSA [17,37]. Before sowing of
root biomass of maize (3.8 t ha− 1) in addition of previous years residues
wheat, lower SOC in maize rhizosphere soil compared to rice might be
left in the soil which is decomposing and supplying labile carbon to
due to faster decomposition of maize roots with lower C:N ratio (35:1)
microbes, led to higher acid phosphatase activity. Hirte et al. (2018)
over rice roots (C:N ratio of 58.8:1) thereby facilitating positive priming
[31] showed higher root biomass of maize (186 ± 15 g m− 2) over wheat
effect. Also maize being a C4 plant releases less organic compounds to
root biomass (137 ± 6 g m− 2) after harvesting in long term field trial of
soil through roots due to lesser investment of C in the below ground
DOK (bio-Dynamic, bio-Organic, Conventional site in Switzerland).
processes over C3 rice crop [17]. As the stubbles of rice and wheat were
Higher rhizodeposition in root zone led to the acidity which further
kept and mixed properly with soil during puddling under CT and PCA
accentuates the acid phosphatase activity [30]. Higher acid phosphatase
system resulting higher SOC in bulk soil compared to rhizospheric soil of
activity in bulk soils at maximum tillering stage of rice and flowering
wheat which might have experienced positive priming leading to lower
stage of maize system is attributed to differential residue decomposition
SOC. At maximum tillering stage, lower SOC in rhizosphere soil
leading to varying rates of labile carbon release in these systems. After
compared to bulk soil in CSA based rice and maize systems might be due
harvesting in both the system, the residues placed away from root zone
to higher rhizosphere priming effect caused by root secretions, rhizo-
were completely decomposed whereas the roots just start decaying
deposition with vigorous microbial activity. In bulk soil at flowering
which explains higher acid phosphatase activity in bulk soils. Similar
stage, lower SOC was observed under rice and maize based CSA systems
observations were also reported in alkaline phosphatase activity. Crop
which might be due to higher priming effect mediated by greater activity
rotation particularly inclusion of legume has played an important role in
of microorganisms caused by decomposing residues retained at soil
enzyme activities observed in those CSA based scenarios. In our case,
surface. Whereas at rhizosphere soil, there might be higher rhizodepo-
legume integration in rice-wheat and maize-wheat systems has facili-
sition which results in higher SOC but with limiting mineral nitrogen
tated higher microbial activity leading to release of both acid and
leads to lower decomposition [17]. Significantly higher SOC in bulk soil
alkaline phosphatase activity in soil. Plant roots release a wide range of
at flowering under PCA system might be due to higher humification of
compounds that may differ between plant species [30] which leads to
crop residue carbon to SOC caused by proper mixing of crop residues
the difference between FDH activity in rice and maize based systems.
with soil during previous puddled rice crop. At harvesting stage except
Different crop rotation is also a factor in variation of FDH activities [32].
CT system, higher SOC both at rhizosphere and bulk soil might be due to
Different plant species results distinct microbial communities with
higher carbon released by roots and decomposing surface retained crop
different activity [33]. CSA practices have positive effect on soil enzy-
residues of previous crops with lower mineral nitrogen leading to lower
matic activities [14] and this can be seen in the overall FDH activity in
priming effect. Whereas in CT system, due to availability of mineral
CSA based scenarios and particularly in rhizosphere zone. Sulphatases
nitrogen and other nutrients, microbial activity was higher in bulk soil
are reported to be also found as exoenzymes in the soil and are closely
and thereby facilitating more oxidation of SOM [17].
linked to organic matter [34] applied in the form of heavy load of res-
Higher soil moisture in bulk soils under CSA based systems irre-
idues. Higher SOC in PCA and CSA based scenarios (Sc2 to Sc6) (Fig. 4)
spective of crop growth stages was due to crop residue retentions at soil
resulted in higher activities of ArS as evidenced from significant positive
surface (Suppl. Table 2). Crop residue mulch enhances soil water storage
correlations between SOC and ArS (Table 2). Kotkova et al. [34] re-
by regulating soil temperature, reducing evaporation and increasing
ported that in wheat ArS activity was higher in rhizosphere as compared
infiltration and SOM concentration and thereby increasing water
to bulk soil and vice versa was reported in lupine but in our study such
retention capacity of soil [9]. In bulk soils, SOC derived from crop res-
type of trend was not reported. In this study both the zones have similar
idues interacts with soil matrix and enhances the specific surface area of
activities of ArS. Higher ArS activity in rice over maize based CSA can be
soil which facilitates higher adsorption and retention of water molecules
linked to differences in crop rotation and their residues as rice and maize under CSA based systems [9].
were grown before wheat in those scenarios. Activity of cellulase at
For optimum plant growth and development, soil pH at which the
different growth stages can be regulated by the available moisture
ratio of enzymatic indicator (AlP/AcP) is about 0.5, can be considered as
content due to crop residue mulch and irrigation water given to crops as
optimum [38]. In our experiment, both rhizosphere and bulk soil sam- evidenced from Supp. Table 9.
ples, the AlP/AcP value exceeded 0.5. These results were confirmed by
Higher β-glucosidase and acid phosphatase activity in no till soil over
soil pH measurement in soil:water 1:2 ratio (data not shown). Negative 8 H.S. Jat et
European Journal of Soil Biology al. 103 (2021) 103292
correlations between AlP/AcP and residue load, SOC and few enzymes
support from GCIAR Research Programs on Climate Change, Agriculture
might be due to the decline in soil pH because of release of organic acids
and Food Security (CCAFS). We are grateful to the editor and two
upon decomposition of crop residues retained at soil surface [25]. The
anonymous reviewers for their constructive comments which improved
balance between three soil enzymes β-glucosidase, urease and phos-
the quality of the manuscript a lot.
phatase is quantified by alteration index three (Al3) which is sensitive to
soil characteristics alterations. The low values of Al3 indicated the better
Appendix A. Supplementary data
soil [26]. Higher values before sowing of crop irrespective of scenarios
and sampling locations manifested poor soil quality which improved
Supplementary data to this article can be found online at https://doi.
significantly upon growth of the plants as observed at maximum tillering
org/10.1016/j.ejsobi.2021.103292.
and flowering stages. Importantly at harvest, significantly lower values
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Document Outline

Soil enzymes activity: Effect of climate smart agriculture on rhizosphere and bulk soil under cereal based systems of north ...
- 1 Introduction
- 2 Materials and methods
  - 2.1 Field experimental design
  - 2.2 Soil sampling, processing and analysis
  - 2.3 Residue load
  - 2.4 Statistical analysis
- 3 Results
  - 3.1 Dehydrogenase (DHA) and β-glucosidase activity as influenced by management practices
  - 3.2 Acid and alkaline phosphatase activity under different managements
  - 3.3 Fluorescein diacetate hydrolases (FDH) and aryl sulphatase (ArS) activity under different managements
  - 3.4 Urease (ur) and cellulase (CeL) activity under different managements
  - 3.5 Interactions effect of managements, sampling locations and crop growth stages on soil enzymes
  - 3.6 Soil organic carbon and soil moisture under different managements and crop growth stages
  - 3.7 Enzymatic pH indicator, Al3 and GMea and their relationship with enzymes, residue load, SOC and soil moisture
- 4 Discussion
- 5 Conclusions
- Declaration of competing interest
- Acknowledgements
- Appendix A Supplementary data
- References