agronomy
Article
A Decade of Climate-Smart Agriculture in Major
Agri-Food Systems: Earthworm Abundance and Soil
Physico-Biochemical Properties
Hanuman S. Jat 1 , Madhu Choudhary 1, Suresh K. Kakraliya 1, Manoj K. Gora 1,2, Manish Kakraliya 1,2 ,
Vikas Kumar 1,2, Priyanka 1, Tanuja Poonia 1, Andrew J. Mcdonald 3 , Mangi L. Jat 4,* , Parbodh C. Sharma 1,*
and Ahmed M. Abdallah 5
1 ICAR-Central Soil Salinity Research Institute (CSSRI), Karnal 132001, India; hsjat_agron@yahoo.com (H.S.J.);
madhucssri@gmail.com (M.C.); kakraliyask@gmail.com (S.K.K.); goramanoj6@gmail.com (M.K.G.);
manishkakraliya719@gmail.com (M.K.); vk437347@gmail.com (V.K.); priyankakaulash.com95@gmail.com (P.);
tannupoonia@gmail.com (T.P.)
2 College of Agriculture, CCS Haryana Agricultural University, Hisar 125004, India
3 College of Agriculture and Life Sciences, Cornell University, Ithaca, NY 14853, USA; ajm9@cornell.edu
4 International Maize & Wheat Improvement Centre (CIMMYT), New Delhi 110012, India
5 Faculty of Agriculture, Damanhour University, Damanhour 22516, Egypt; ph7@damanhour.edu.eg
* Correspondence: m.jat@cgiar.org (M.L.J.); pcsharma.knl@gmail.com (P.C.S.)
Abstract: Earthworms (EWs) could be a viable indicator of soil biology and agri-food system man-
agement. The influence of climate-smart agriculture (CSA)-based sustainable intensification practices





 (zero tillage, crop rotations, crop residue retention, and precision water and nutrients application)
Citation: Jat, H.S.; Choudhary, M.; on earthworms’ (EWs) populations and soil physico-biochemical properties of rice-wheat cropping
Kakraliya, S.K.; Gora, M.K.; system in the Indo-Gangetic plains of South Asia was investigated. This study investigates the effect
Kakraliya, M.; Kumar, V.; Priyanka; of 10-years adoption of various CSA practices on the abundance of earthworms and physical and
Poonia, T.; Mcdonald, A.J.; Jat, M.L.; biochemical properties of the soil and EWs’ casts (EWC). Five scenarios (Sc) were included: con-
et al. A Decade of Climate-Smart ventionally managed rice-wheat system (farmers’ practices, Sc1), CSA-based rice-wheat-mungbean
Agriculture in Major Agri-Food system with flood irrigation (FI) (Sc2) and subsurface drip irrigation (SDI) (Sc3), CSA-based maize-
Systems: Earthworm Abundance and wheat-mungbean system with FI (Sc4), and SDI (Sc5). Results revealed that EWs were absent under
Soil Physico-Biochemical Properties. Sc1, while the 10-year adoption of CSA-based scenarios (mean of Sc2–5) increased EWs’ density
Agronomy 2022, 12, 658. https:// and biomass to be 257.7 no. m−2 and 36.05 g m−2, respectively. CSA-based maize scenarios (Sc4
doi.org/10.3390/agronomy12030658
and Sc5) attained higher EWs’ density and biomass over rice-based CSA scenarios (Sc2 and Sc4).
Academic Editors: Jiangxin Gu, Also, SDI-based scenarios (Sc3 and Sc5) recorded higher EWs’ density and biomass over FI (Sc2
Fengge Zhang and Chao Yan and Sc4). Maize-based CSA with SDI recorded the highest EWs’ density and EWs’ biomass. The
Received: 27 January 2022 higher total organic carbon in EWC (1.91%) than in the bulk soil of CSA-based scenarios (0.98%) and
Accepted: 4 March 2022 farmers’ practices (0.65%) suggests the shift of crop residue to a stable SOC (in EWC). EWC contained
Published: 9 March 2022 significant amounts of C and available NPK under CSA practices, which were nil under Sc1. All
CSA-based scenarios attained higher enzymes activities over Sc1. CSA-based scenarios, in particular,
Publisher’s Note: MDPI stays neutral
maize-based scenarios using SDI, improved EWs’ proliferation, SOC, and nutrients storage (in soil
with regard to jurisdictional claims in
and EWC) and showed a better choice for the IGP farmers with respect to C sequestration, soil quality,
published maps and institutional affil-
iations. and nutrient availability.
Keywords: climate-smart agriculture; earthworms; soil enzymes; subsurface drip irrigation; maize-
based system; rice-wheat system; soil quality
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and 1. Introduction
conditions of the Creative Commons The rice-wheat (RW) cropping system dominates in the Indo-Gangetic Plains (IGP)
Attribution (CC BY) license (https://
of South Asia covering an area of 13.5 Mha [1]. Besides being high water, energy, labor,
creativecommons.org/licenses/by/
and fertilizers demanding cropping system, burning crop residues, and intensive tillage
4.0/).
Agronomy 2022, 12, 658. https://doi.org/10.3390/agronomy12030658 https://www.mdpi.com/journal/agronomy
Agronomy 2022, 12, 658 2 of 24
practices contributed to soil degradation [2–5]. Consequently, the sustainability of the
RW cropping system in the IGP of South Asia is under threat [6], promoting the need
for better agri-food systems management practices [7]. Climate-smart agriculture (CSA)
practices could enhance soil properties through the adoption of zero tillage (ZT), crop
residue retention (CRR), and crop diversification [8–10] with best-fit crop management
technologies [5,11–13]. Despite the positive impacts of CSA practices on cropping system
productivity, profitability, and resource use efficiency in the IGP of India, little information
is available about the combined effects of CSA practices on the abundance of earthworms
(EWs) and soil physico-biochemical properties.
Conventional farming practices not only influence soil physico-biochemical properties
but also intensively reduce the population, diversity, and activity of soil fauna [4,14]. The
protection of soil fauna has a key role in soil management that determines soil quality [15].
Among organisms, soil properties are more amplified by EWs’ abundance and activity
than other soil fauna [16]. EWs’ abundance was found to influence soil biophysical proper-
ties [17], crop productivity [18], and agroecosystem health [19]. EWs’ feeding, burrowing,
and casting activities impact OM distribution and dynamics [20], soil structure formation,
aggregate stability, and soil porosity [21,22], nutrient availability and cycling [18,20,23],
water infiltration [24], and microbial activity [25], thereby crop growth and productiv-
ity [18,23]. Consequently, due to the numerous ecosystem services that EWs provide, it was
identified as ecosystem engineers [26,27] that could act as a bioindicator of land use and
management [20,28].
In general, EWs’ density, diversity, and biomass in arable lands are lesser com-
pared to gardens [14,29], organic farming [30], and integrated farming [28]. Several
meta-analyses [22,30] and reviews [23,31] concluded that tillage reduces EWs’ abundance.
Moreover, EWs’ abundance and diversity are influenced by crop residues amount and
type [15,19,29] and cropping rotation [20]. Indeed, numerous studies have shown that CRR,
ZT, and crop diversity positively affect EWs’ population [24,27,32]. However, the layering
of such practices on EWs’ abundance in intensively irrigated rice-wheat cropping systems
is limited.
Soil biological properties, carbon mineralization, and nutrient release are strongly
related. Earthworms primarily decompose low-quality OM to a nutrient-rich product by
establishing a mutualistic relationship with soil microflora [32]. Accordingly, changes in
EWs’ density and activity could be manifested by increased nutrient levels and enzyme
activities [14]. Enzymes, proteins produced by plant roots, soil microbiota, and fauna to
hydrolyze SOM, are responsive to the soil management practices, e.g., tillage, cropping
diversity, residue, and nutrient management [4,33–35]. Due to their relationship with soil
biology, soil enzymes have been described as “biological fingerprints”, considered key soil
quality indicators [36] that catalyze various reactions for OM decomposition, and OM- and
nutrient cycling [37,38]. For example, phosphatases play roles in the organic Phosphorus
(P) bioavailability [39], while dehydrogenase (DHA) plays a role in the oxidation of SOM by
shifting organic H to inorganic acceptors [40]. In brief, DHA activity reflects a good picture
of the overall soil microbial activities, thus it is a viable soil health indicator [41]. Hence, the
effect of CSA and EWs’ activity might be reflected by nutrient levels and enzyme activities
in both bulk soil and EWs’ casts (EWC).
The nature of retained crop residues and EWs’ abundance has produced contradictory
results. While OM supply usually increases EWs’ populations [42], Bamminger et al., [43]
found no effect. Additionally, not always the adoption of sustainably managed systems
promotes EWs’ abundance [22,44]. More importantly, the effect of EWs on SOC is found
ambiguous [45], as EWs influence SOC in two different directions [46]. EWs could accelerate
crop residues decomposition by promoting microbial activity [33,47]. However, EWs’
activity could increase SOC levels by boosting the formation of stable aggregates that
protect the SOC against microbial attack [48,49] and by stabilizing the newly added OM
in their casts, thus aiding soil C storage in the long term [25,46,48,50]. Therefore, the
impact of retaining various crop residues under conservative managed systems on the EWs’
Agronomy 2022, 12, 658 3 of 24
abundance and their impact on SOC sequestration and soil quality in the IGP is important
in light of global warming.
In northern India, studies revealed the anthropogenic pressure on EWs’ communities
in arable lands [4,28,29]. Despite researchers confirming the ultimate beneficial effects of
CSA in the IGP of India, little information is available about the effects of various CSA
practices on the EWs’ density and biomass. We hypothesize that CSA-based sustainable
intensification practices (zero tillage, crop rotations, crop residue retention, and precision
water and nutrients application) would augment EWs’ populations that could enhance soil
physico-biochemical properties than conventional practices, which could vary as per the
cropping system management practices. In the present study, after a decade of adopting
various CSA practices, we measured EWs’ density and biomass in two soil layers (0–15 and
15–30 cm), as well the soil physical properties and the biochemical properties of both the
bulk soil and EWs’ casts. The CSA practices were compared with farmers’ practices. CSA
practices involved tillage (zero tillage Vs intensive tillage), crop rotations (maize-wheat vs
rice-wheat), crop residue management (retention Vs removal), and water and nutrients
application (precision application via subsurface irrigation vs flood irrigation). The effect
of different management practices and their linear contrast on the above-mentioned param-
eters were analyzed. The results of these studies could help in designing a better cropping
system with the best CSA practices that could show a better choice for the IGP farmers
with respect to EWs’ proliferation, C sequestration, nutrients storage, and availability, and
soil physical and biological properties.
2. Materials and Methods
2.1. Experimental Site Description
The experiment was conducted at the Indian Council of Agricultural Research (ICAR),
Central Soil Salinity Research Institute (CSSRI), Karnal (29◦42′′20.7′ N latitude, 76◦57′′19.79′ E
longitude, 243 m elevation), India. This area is typically known for the rice-wheat (RW)
system. The climate of the region is semi-arid subtropical with an average rainfall of 670 mm
annually, of which ~80% is distributed mainly from June to September (monsoon season).
The climate is characterized by three distinctive seasons, i.e., wet-Kharif (July–October),
dry-Rabi (November–March), and summer-Zaid (April–June). The soil of the experimental
site is silty loam in texture (34.0% sand, 46.0% silt, and 20.0% clay), slightly alkaline (pH of
8.0), and poor in organic carbon content (0.45%).
2.2. Experimental Design and Treatment Details
This study included five treatments (hereinafter named scenarios) consisting of two
cropping systems (rice-wheat and maize-wheat) with combinations of tillage, residue man-
agement, crop establishment, mungbean integration, and water management practices
(Table 1). The treatments were designated as scenarios by keeping in view present man-
agement practices, as well as future drivers of agricultural changes in the region. Before
initiating the experiment, the experimental site was under a conventionally managed rice-
wheat system. The experiment began in 2009 and was laid out in a randomized complete
block design and replicated thrice in 20 m × 50 m plot size. The five scenarios (Sc) were:
conventional tillage (CT)-puddled transplanted rice (PTR) -CT wheat with flood irrigation
(FI) (Sc1; farmers’ practice); ZT direct-seeded rice (DSR) -ZT wheat -ZT mungbean with
FI (Sc2); ZT DSR -ZT wheat -ZT mungbean with subsurface drip irrigation (SDI) (Sc3); ZT
maize -ZT wheat -ZT mungbean with FI (Sc4) and ZT Maize -ZT Wheat -ZT mungbean
with SDI (Sc5). Details of all CSA-based management scenarios comprised of crop rotation,
tillage, crop establishment, and residue and water management are given in Table 1. In
all the scenarios, similar management practices were followed across the years. A sam-
pling of soil and EWs was carried out during the year 2019 (after 10 years of adopting
CSA practices).
Agronomy 2022, 12, 658 4 of 24
Table 1. Climate-smart agriculture (CSA)-based management scenarios comprised of crop rotation, tillage, crop establishment, and residue and water management.
Scenarios Crop Rotations Tillage Crop Establishment Method Residue Management Water Management
Rice: Flood irrigation (FI) of 5-cm depth for
Sc1 Rice-wheat- fallow CT-CT Rice: Transplanting
Wheat: Broadcast All residue removed 1 month, followed by irrigation applied at the
hair-line crack
Wheat: Need-based flood irrigation
Rice: Drill seeding Full (100%) rice and Mungbean; Rice: FI and kept soil wet for first 20 days ‘fb’
Sc2 Rice-wheat-mungbean ZTDSR-ZT-ZT Wheat: Drill seeding anchored wheat residue retained irrigation at −20 to −30 kPa matric potential
Mungbean: Drill/relay on the soil surface Wheat: FI at −40 to−50 kPa matric potential
Sub-surface drip irrigation (SDI) at −20 to
Sc3 Rice-wheat-mungbean ZT-ZT-ZT Same as in scenario 3 Same as in scenario 3 −30 kPa in rice and −40 to−50 kPa matric
potential in wheat
Maize: Drill seeding Maize (65%) and full mungbean;
Sc4 Maize-wheat- mungbean ZT-ZT-ZT Wheat: Drill seeding anchored wheat residue retained FI at −40 to 50 kPa matric potential in both
Mungbean: Drill/relay on the soil surface maize and wheat
Sc5 Maize-wheat- mungbean ZT-ZT-ZT Same as in scenario 5 Same as in scenario 5 SDI at −40 to −50 kPa matric potential in both
maize and wheat
CT—conventional tillage; ZT—zero tillage; DSR—direct-seeded rice; PB—permanent beds; FI—flood irrigation; SDI—subsurface drip irrigation.
Agronomy 2022, 12, 658 5 of 24
2.3. Crop Residue Management and Estimation of Recycled Residues
All crop residues were removed in farmers’ practice (Sc1) from the ground level.
However, in rice-based CSA scenarios (Sc2 and Sc3), all rice and mungbean residues and
anchored wheat residues were retained on the soil surface. In maize-based CAS scenarios
(Sc4 and Sc5), partial (~65%) maize residues and anchored wheat stubbles were retained at
the soil surface. To assess the amount of crop residue recycled in each scenario, five rows
with a length of 1.0 m were sampled from three locations in each plot after the harvest of
each crop. The residues were cut from the soil surface, oven-dried, and expressed on a
dry weight basis per hectare. The average residue load varied from 7.36–7.75 Mg ha−1 for
rice, 9.52–9.88 Mg ha−1 for maize, 1.85–1.97 Mg ha−1 for wheat, and 2.95–3.30 Mg ha−1
for mungbean over the years. Annual average crop residues of 12.29, 12.39, 14.78, and
14.33 Mg ha−1 were retained on the soil surface for Scenario 2, 3, 4, and 5, respectively.
The C/N ratio of rice and maize was 58–65 and 43–50, respectively, and for wheat and
mungbean, 50–56 and 20–30, respectively.
2.4. Irrigation Management
In Sc1 (farmers’ practice), conventional irrigation practices were applied; for CT-PTR,
flood irrigation (FI) of ~5 cm depth for one month followed by irrigation applied at the hair-
line crack, while needs-based FI was applied for wheat (Table 1). In CSA-based scenarios,
irrespective of the irrigation system (e.g., FI for Sc2 and Sc4 and SDI for Sc3 and Sc5),
irrigation was based on a soil matric potential (SMP) of −20 to −30 kPa for rice and −40 to
−50 kPa for maize throughout the season. The only exception is rice in Sc2, in which the soil
was kept wet for the first 20 days to ensure good germination and establishment (Table 1).
However, in the case of mungbean, needs-based irrigation was applied. Within each
plot, a soil tensiometer “gauge-type” (IRROMETER, Riverside, California) was installed
to monitor SMP. The ceramic cups were placed at 15 cm depth, ensuring good contact
between the ceramic tip and the surrounding soil. The subsurface drip irrigation (SDI)
system was installed; polyvinyl chloride pipes having an inside diameter of 90 and 63 mm
for mains and sub mains, respectively. The laterals were placed at depth of 20 cm using
tractor operated drip laying machine. Dripping lines (16 mm in diameter) were laid down
along the rows at 20 cm depth with in-line emitters with a flow rate of 2.0 L h−1 and located
30 cm apart and the distance between dripping lines was 67.5 cm.
2.5. Crop Management
In Sc1 (farmers’ practice), both rice and wheat were cultivated as common farmers’
practices in northwest India. The tillage operations in conventional till puddled trans-
planted rice (CT-PTR) comprised of two passes of harrow, one pass of rotavator, and two
passes of puddle harrow followed by planking. Twenty-five-days-old rice seedlings were
manually transplanted in a random geometry (20 cm × 15 cm) in puddled fields. In CT
wheat, preparatory tillage included two passes each of harrowing, and two cultivators
then wheat seeds were manually broadcasted and followed by planking. In CSA scenarios
(Sc2–5), under ZT, all crops (rice, wheat, mungbean) except maize were planted with a
row spacing of 22.5 cm using Happy Seeder; while maize was seeded at a row spacing
of 67.5 cm. For CT (PTR) and DSR rice, a seed rate of 10 and 20 kg ha−1 were used, re-
spectively. For maize, wheat, and mungbean, a seed rate of 100, 20, and 20 kg ha−1 was
used, respectively. The best management practices were followed for weed, insect, pest,
and disease management as per the recommendations of Punjabi Agricultural University.
2.6. Soil Physical Analysis
Soil bulk density was measured following the core method [51]. The soil penetration
resistance was measured using a manual cone penetrometer (Eijkelkamp Agrisearch Equip-
ment, Germany) at a 5 cm interval to a depth of 0–30 cm. The infiltration rate (IR) was
determined using a double-ring infiltrometer as described by Gathala et al. [52]. The mean
weight diameter (MWD) of water-stable soil aggregates was determined using the wet
Agronomy 2022, 12, 658 6 of 24
sieving method [53]. An air-dry soil sample of 100 g was passed through an 8 mm sieve and
transferred in a set of sieves with diameters of 4, 2, 1, 0.5, 0.25, and 0.125 mm. After which,
sieves (including the soil) were soaked for 10 min for capillary rewetting, followed by
10 min of shaking in a water drum with an oscillation frequency of 30 cycles min−1 and an
amplitude of 3 cm, resulting in distinct aggregate class diameters, i.e., 0–0.125, 0.125–0.25,
0.25–0.5, 0.5–1, 1–2, 2–4 and 4–8 mm. The aggregates that remained in each class were
collected and oven-dried at 65–70 ◦C until a constant weight was recorded. The aggregates
MWD was calculated according to Kemper and Rosenau [54] using the following equation.
MWD (mm ∑n
) i=1(xi wi)
=
2 ∑n
i=1 wi
where, n is the number of fractions (0.1–0.25, 0.25–0.5, 0.5–1.0, 1.0–2.0, 2.0–4 and 4–8 mm),
xi is the mean diameter (mm) of the sieve size class (0.175, 0.375, 0.75, 1.5 and 2.0, 3, and
6 mm), and wi is the weight of soil (g) retained on each sieve.
2.7. Earthworms’ Sampling
Sampling was performed for EWs in the morning (during their active period) [29]
during mid-September 2019, using the digging and hand sorting method for two soil
depths, i.e., 0–15 and 15–30 cm, from each replicate with three grid points. Soil blocks
(0.25 cm × 25 cm × 15 cm) were removed from each plot. Similarly, soil blocks at a depth of
15–30 cm were sampled [55]. Soil blocks were bagged and directly moved to the laboratory
and hand sorted. EWs were sorted into three categories based on their length, e.g., small
(<20 mm), medium (20–40 mm), and large (>40 mm). The fresh biomass (g m−2) and
density (No. m−2) were determined for each size category.
2.8. Sampling and Analysis of Soil Bulk and Earthworms’ Casts (EWC)
Soil samples were collected (mid-September 2019) from each plot with three grid
points from 0–15 cm depth using an auger with an internal diameter of 5 cm from each
replicate. The activity of EWs is usually higher at night and therefore EWC was collected
from the upper soil surface within a sampling area of 25 cm × 25 cm early in the morning
using a brush and a small spoon [14]. Part of the fresh soil and EWC samples were kept
(separately) in a refrigerator at 4 ◦C till the analysis of enzyme activities, i.e., dehydrogenase
activity (DHA), alkaline phosphatase activity (ALP), and acid phosphatase activity (ACP).
The other part of soil and EWC samples were air-dried, ground, sieved through a 2 mm
sieve, and stored in a plastic jar for laboratory analysis of selected chemical properties.
Soil and EWC were analyzed for electrical conductivity (EC), pH, total organic carbon
(TOC), available N, P, and K, and enzymes (DHA, ALP, and ACP). EC and pH were
determined in soil water extract (1 soil: 2 water) following the methods of Jakson [56].
The TOC content was determined using the wet oxidation method [57]. Available N was
determined by the alkaline permanganate method [58]. Available P was determined by the
ascorbic acid reductant method of Olsen et al. [59]. Available K was measured by flame
photometer using neutral 1 N ammonium acetate extractant as described by Jackson [56].
Enzymes (DHA, ALP, and ACP) activities were estimated as described by Dick et al. [37].
2.9. Data Analysis
Data on earthworm population density, biomass weight, chemical properties of EWC,
and soil had a greater√coefficient of variation than 20%, and hence were transformed
through a square-root ( x + 0.5) method [60].
The data were subjected to ANOVA (analysis of variance) using a one-way completely
randomized design in the Glmmix procedure in SAS 9.1. The significant effect was de-
termined at a significance level of 5% (p ≤ 0.05) and was compared according to Fisher’s
least significant difference (LSD). Principal component analysis (PCA) and correlation
matrix were carried out using the R package GEA-R. The results were submitted to PCA to
determine the common relationships between parameters.
Agronomy 2022, 12, 658 7 of 24
3. Results
3.1. Earthworm Density and Biomass
For All CSA-based scenarios (Sc2–5), EWs’ density and biomass of the three size
categories (small, medium, and large) were higher in the surface soil layer (0–15 cm),
compared to the subsurface soil layer (15–30 cm) (Table 2). However, for farmers’ practices
(Sc1), EWs’ density and biomass were nil across the tested soil depths. For the two tested
soil layers, all CSA-based scenarios had a significantly (p ≤ 0.05) higher total EWs’ density
and biomass compared with Sc1 (farmers’ practices) and the increase varied with different
crop-based scenarios. Across the two soil layers, CSA-based maize scenarios (Sc4 and
Sc5) did not significantly (p ≤ 0.05) differ in EWs’ density and biomass (across all size
categories) (Table 2). Similarly, CSA rice-based scenarios (Sc2 and Sc3) did not significantly
(p ≤ 0.05) differ in EWs’ density and biomass across all size categories. The only exception
is the medium size (20–40 mm), only in 15–30 cm soil depth, in which an EW density of
Sc3 was significantly (p ≤ 0.05) higher than Sc2. In the top 15 cm soil depth, Sc5 recorded
maximum EWs’ density and biomass across all size categories, in which total EWs’ density
and biomass were 257.7 indv. m−2 and 36.05 g m−2, respectively, instead of nil under Sc1.
However, in sub-surface soil depth (15–30 cm), maximum EWs’ density and biomass were
recorded for Sc4 that was at par with Sc5.
Total EWs’ density and biomass were significantly (p ≤ 0.01) higher in maize-based
CSA scenarios (Sc4 and Sc5) than rice-based CSA scenarios (Sc2 and Sc3), in particular for
the surface soil layer (Table 2). Maize-based CSA scenarios (Sc4 and Sc5) recorded higher
total EWs’ density and biomass by 76.3, 240.0% and 56.0 and 52.0%, respectively, over rice-
based CSA scenarios (Sc2 and Sc3), for the surface and subsurface soil layers, respectively,
irrespective of system management (tillage, irrigation, and CRR). Similarly, precise water
and N management through SDI (Sc3 and Sc5) recorded significantly (p ≤ 0.05) higher
EWs’ density (p ≤ 0.05) and biomass (p ≤ 0.01) by 25.0 and 39.12%, respectively, relative
to flood irrigation (FI)-based scenarios (Sc2 and Sc4) for the surface soil layer, irrespective
of the cropping system. In the sub-surface soil depth, no differences were observed due
to the irrigation system. Within the CSA rice-based scenarios, the SDI-irrigated rice (Sc3)
recorded a significantly (p ≤ 0.05) higher EWs’ density and biomass by 5.86% and 48.48%,
respectively over FI-irrigated rice (Sc2) for the 0–15 cm soil depth. Similarly, within maize-
based scenarios, the SDI-irrigated maize (Sc5) recorded significantly (p ≤ 0.01) higher EWs’
density and biomass by 37.5 and 36.5%, respectively, over FI-irrigated maize (Sc4) for the
0–15 cm depth.
3.2. Soil Physical Properties
3.2.1. Bulk Density (BD)
The CSA practices significantly (p ≤ 0.05) affected soil BD, which ranged from
1.4 Mg m−3 (Sc5) to 1.54 Mg m−3 (Sc1) (Figure 1). Soil BD of farmers’ practices scenario
was 7.8% higher than the CSA-based scenarios (Sc2–5). Full CSA-based scenarios did not
significantly (p ≤ 0.05) differ in BD (Figure 1). However, maize-based CSA scenarios (Sc4
and Sc5) had significantly (p ≤ 0.05) lower BD compared to Sc1; while rice-based CSA
scenarios (Sc2 and Sc3), did not significantly (p ≤ 0.05) differ from Sc1. Compared to Sc1,
CSA-based maize and rice scenarios significantly (p ≤ 0.05) decreased BD by 9.7 and 5.2%,
respectively, irrespective of crop management practices. Interestingly, the effect of the
irrigation system on soil BD was minimal, in which both SDI-based scenarios (Sc3 and Sc5)
and FI-based scenarios (Sc2 and Sc4) showed similar BD.
Agronomy 2022, 12, 658 8 of 24
Table 2. Earthworm density (individual m−2) and biomass weight (g m−2) under long-term CSA-based management practices (data transformed through
square-root method).
Depth 0–15 cm Depth 15–30 cm
Scenarios † Small (<20 mm) Medium (20–40 mm) Large (>40 mm) Total Small (<20 mm) Medium (20–40 mm) Large (>40 mm) Total
D W D W D W D W D W D W D W D W
Sc1 0.71 Bb (0) ‡ 0.71 B (0) 0.71 A (0) 0.71 A (0) 0.71 B (0) 0.71 C (0) 2.12 C (0) 0.71 C (0) 0.71 B (0) 0.71 B (0) 0.71 C (0) 0.71 D (0) 0.71 B (0) 0.71 B (0) 2.12 D (0) 2.12 C (0)
Sc2 11.92 A 2.79 A 4.97 A 2.04 A C 18.34 B 3.66 B 8.30 A 2.01 A
(144) (7.38) (35) (4.71) 1.44 B (3) 1.12 B
(1.09) (181) (13.18) (69) (3.58) 1.44 C (3) 0.91 CD
(0.41) 0.71 B (0) 0.71 B (0) 10.45 C 3.63 BC
(72) (9.80)
Sc3 11.91 A 2.91 A 5.41 A 2.12 A 3.98 B 2.70 B 21.30 B 4.47 B 8.33 A 2.05 A 3.98 B 1.59 BC
(144) (8.12) (32) (4.38) (16) (7.07) (192) (19.57) (69) (3.75) (16) (2.11) 0.71 B (0) 0.71 B (0) 13.02 BC 4.36 B
(85) (14.48)
A
Sc4 12.87 A 3.37 A 6.18 A 2.43 A 8.15 A 5.42 A 27.30 AB 6.80 A 9.05 A 2.20 A 6.36 A 2.43 3.24 A 2.38 A 18.65 A 7.01 A
(168) (11.34) (40) (5.60) (69) (30.16) (277) (47.10) (83) (4.41) (40) (5.39) (13) (6.67) (136) (20.84)
Sc5 15.05 A 3.76 A 8.36 A 3.23 A 8.32 A 6.26 A 31.73 A 7.95 A 7.73 A 2.00 A 5.13 AB 2.06 AB 4.36 A 2.95 A 17.21 AB 7.01 A
(229) (13.81) (77) (10.91) (75) (39.63) (381) (64.35) (61) (3.65) (29) (4.15) (19) (8.29) (109) (16.10)
D is earthworm density and W is the biomass weight of earthworms, † refer to Table 1 for scenario description, ‡ data in parenthesis represent the original values, b means followed by a
similar uppercase letter (s) within a column in a size category are not significantly different at 0.05 level of probability using Tukey’s HSD test.
Agronomy 2022, 12, x FOR PEER REVIEW 9 of 22 
 
3.2. Soil Physical Properties 
3.2.1. Bulk Density (BD) 
The CSA practices significantly (p ≤ 0.05) affected soil BD, which ranged from 1.4 Mg 
m−3 (Sc5) to 1.54 Mg m−3 (Sc1) (Figure 1). Soil BD of farmers’ practices scenario was 7.8% 
higher than the CSA-based scenarios (Sc2–5). Full CSA-based scenarios did not signifi-
cantly (p ≤ 0.05) differ in BD (Figure 1). However, maize-based CSA scenarios (Sc4 and 
Sc5) had significantly (p ≤ 0.05) lower BD compared to Sc1; while rice-based CSA scenarios 
(Sc2 and Sc3), did not significantly (p ≤ 0.05) differ from Sc1. Compared to Sc1, CSA-based 
maize and rice scenarios significantly (p ≤ 0.05) decreased BD by 9.7 and 5.2%, respec-
tively, irrespective of crop management practices. Interestingly, the effect of the irrigation 
Agronomy 2022, 12, 658 system on soil BD was minimal, in which both SDI-based scenarios (Sc3 and Sc5) an9do fF2I4-
based scenarios (Sc2 and Sc4) showed similar BD. 
 
FFiigguurree 11.. BBuullkk ddeennssiittyy ((BBDD,, Mgg mm−3−),3 i)n, fiinltfirlattriaotnio rnatrea (tme (m hm−1)h, −an1)d,  amnedanm weaenigwhte digiahmt deitaemr (eMteWr (DM, WmmD,) 
munmd)eur nddifeferrdeinffte CreSnAt-CbaSsAed-b carsoepd mcraonpamgeamneangte smceennatrsicoesn (aforiro as d(feotraialedde dtaeislcerdipdteiosncr oipf tsicoennoarfisocse rneaferiro tso 
rTeafebrlet o1)T. aPbalres1 w).iPtha ras swimitihlaar sloimwielarcralosew leertctears e(sl)e attreer n(so)t asriegnniofitcsaingtnlyifi dcaifnfetlryendti fafte r0e.0n5t aletv0e.0l 5ofl epvreolboaf-
bility using Tukey’s HSD test. Letters apply only within each parameter. 
probability using Tukey’s HSD test. Letters apply only within each parameter.
33..22..22.. IInnfifillttrraattiioonn Raattee 
TThheei innfifillttrraattioionnr raattee( (IIRR))o offa allll CSSAA--bbaasseedds scceennaarrioioss( S(Scc22––55))w waasss sigignnifiifciacanntltyly( p(p≤ ≤ 00..0055)) 
hhiigghheerrt thhaannS Scc11( (FFiigguurree1 1).). Noos siiggnniififcicaanntt( p(p≤ ≤ 0..05)) diifffferrencess iin IIR werre obsserrved amongg 
CSSA--bassed sscceenarriios ((Scc2–5)).. IIrrespecttiive off crop ttype aand maanaageementt,, IIR ooff CSSA waass 
33..55 aanndd 33..00 ttiimeess oovveerr SScc11 ffoorr rriiccee--bbaasseedd CSSA sscceennaarriioss aannd maaiizzee--bassed CSA ssccenariios,, 
rreessppeeccttiivveelyly. .O Onna as syysstetemmb baassisis, ,r ricicee-b-baasseeddC CSSAAs scceennaariroiosss isgignnifiifciacnantltyly( p(p≤ ≤ 0..05)) iincrreeaasseedd 
tthhee IIR rrattee by 15..4% comparreed tto maiize--basseed CSA ssccenaarriiooss,, iirrrreessppeeccttiivveeo off tthee iirrrriigaattiioonn 
ssyysstteem.. The IIR ooff SSDII--bbaasseed sscceennaarriiooss ((SScc33 aanndd SScc55))w waasss sigignnifiifcicaanntltyly( p(p≤ ≤ 00..0055)) hhiigghheerr 
tthhaann FFII--bbaasseedd sscceennaarriiooss ((SScc22a annddS Scc44))b byy3 3.8.8%%.. 
33..22..33.. Meeaann WeeiigghhttD Diiaameetteerr( (MWDD))o offS SooililA Aggggrreeggaatetess 
TThhee CCSSAA--bbaasseedd sscceennaarriioo ddiidd nnoott ssiiggnniifificcaannttllyy ((pp≤ ≤ 00..0055)) ddiiffffeerr iinn tthhee MWDD aanndd wwaass 
ssiiggnniifificcaannttllyyh higighheerrt hthaannf afarmrmeerrp prarcatcitciece( S(cS1c)1.)T. TheheC CSASA-b-absaesdedsc secneanraiorio(a (vaevreargaegoef oSfc S2–c25–)
i5n)c irneacrseeadsMedW MDWsiDgn siifigcnainfitclayn(tply≤ (p0 .≤0 50).0b5y) 1b5y9 %15,9i%rre, sirpreecstpiveectoivf ec roofp ctryoppe taynpde manadn amgaenmaegnet-
pmraecntti cpersa(cFtiicgeusr e(F1ig).uSreu m1)m. Sinugmtmhiendg atthaeo dvaetrat hoevecrr othpep icnrgopspysintegm s,ytshteemre, stuhlet sreresuvletasl erde-
tvheaatlmeda itzhea-tb masaeidzeC-bSaAsesdce CnaSrAio sscsehnoawrioeds sshtaotwisetidca sltlyatsisimticialallryM siWmDilatro MthWosDe rteoc othrdoseed rfeocr-
roicred-ebda sfeodr rCicSeA-bsacseenda rCioSsA,  isrcreenspareicotsiv, eirorefscpreocptimvea noaf gcerompe mnta(ntiallgaegme,einrtr i(gtailtliaogne,, ainrrdigCatRioRn)., 
Wanhdil eCRSDRI)-. bWasheidle CSDSAI-bscaesendar CioSsA( Sscc3enaanrdioSsc (5S)ci3n acnreda sSecd5)t ihnecrMeaWseDd sthigen MifiWcaDnt lsyig(npif≤ica0n.0t5ly)  
b(py ≤6 .05.%05o) vbyer 6S.5D%I- obvaseerd SDCIS-Abassceedn CarSiAos s(cSecn2aarniods S(Scc42), ainrrde sSpce4c),t iivrreeospf ethcetivcreo opfp thineg csryosptpeimng.  
3s.y2s.4te. mSo. il Penetration Resistance (SPR)
  CSA-based scenarios significantly (p ≤ 0.05) influenced SPR at all measured depths,
except at 0–5 cm and 15–20 cm, in which no significant differences (p ≤ 0.05) were observed
 among all scenarios (Figure 2). In general, in the top 15 cm, minimum SPR was recorded for
Sc1 (0.64–0.91 MPa), while the highest values were recorded for Sc4 (0.87–1.83 MPa). Below
15 cm, an opposite trend was observed, in which CSA-based scenarios had significantly
(p ≤ 0.05) lower SPR relative to Sc1. The effect of CSA was minimal in the upper 5 cm
surface, in which no significant (p ≤ 0.05) differences were observed among all scenarios
(Figure 2). In the 5–10 and 10–15 cm soil depths, all CSA-based scenarios significantly
increased SPR except Sc3 which was at par with Sc1 (Figure 2). The threshold point was
at a soil depth of 15–20 cm, wherein CSA practices did not significantly affect SPR. In the
20–25 and 25–30 cm soil depths, all CSA scenarios significantly (p ≤ 0.05) increased SPR
(Figure 2), with Sc3 (rice-based CSA with SDI) having the lowest SPR (0.97–1.2 MPa). The
average SPR of maize-based CSA scenarios (Sc4 and Sc5) was 5.0 and 39.6%, over rice-based
CSA scenarios (Sc4 and Sc5), for the 0–15 and 15–30 cm soil depth, respectively. Moreover,
Agronomy 2022, 12, x FOR PEER REVIEW 10 of 22 
 
3.2.4. Soil Penetration Resistance (SPR) 
CSA-based scenarios significantly (p ≤ 0.05) influenced SPR at all measured depths, 
except at 0–5 cm and 15–20 cm, in which no significant differences (p ≤ 0.05) were observed 
among all scenarios (Figure 2). In general, in the top 15 cm, minimum SPR was recorded 
for Sc1 (0.64–0.91 MPa), while the highest values were recorded for Sc4 (0.87–1.83 MPa). 
Below 15 cm, an opposite trend was observed, in which CSA-based scenarios had signifi-
cantly (p ≤ 0.05) lower SPR relative to Sc1. The effect of CSA was minimal in the upper 5 
cm surface, in which no significant (p ≤ 0.05) differences were observed among all scenar-
ios (Figure 2). In the 5–10 and 10–15 cm soil depths, all CSA-based scenarios significantly 
increased SPR except Sc3 which was at par with Sc1 (Figure 2). The threshold point was 
at a soil depth of 15–20 cm, wherein CSA practices did not significantly affect SPR. In the 
20–25 and 25–30 cm soil depths, all CSA scenarios significantly (p ≤ 0.05) increased SPR 
Agronomy 2022, 12, 658 (Figure 2), with Sc3 (rice-based CSA with SDI) having the lowest SPR (0.97–1.2 MPa1)0. Tofh2e4 
average SPR of maize-based CSA scenarios (Sc4 and Sc5) was 5.0 and 39.6%, over rice-
based CSA scenarios (Sc4 and Sc5), for the 0–15 and 15–30 cm soil depth, respectively. 
MSDoIr-eboavseerd, CSDSAI-bsacseenda rCioSsA(S scc3enanardioSsc 5(S)cd3e carneda sSecd5)S PdRecsriegansiefidc aSnPtRly s(ipgn≤ifi0c.a0n5)tlbyy (p1 8≤.5 0a.0n5d) 
b15y. 51%8.,5r ealnadti v1e5.t5o%F,I -rbealasetidveC StoA FsIc-ebnaasreido sC(SSAc2 sacnednaSrci4o)sf o(Srct2h ean0–d1 5Sca4n)d fo1r5 –th30e c0m–1s5o ailndde p15th–,
3re0s pcmec tsioviell yd.eSpPthR, orfesmpaeiczteiv-bealyse. dSPCRS Aofs mceaniazrei-obsawseads ChiSgAhe srctehnaanriroisc ew-baass ehdigChSeAr tshcaen arricioes-
bfoarseadll CteSsAte dscdeenpatrhiosse xfocre patll0 t–e5stcemd sdoeipl tdhesp ethxc. eSpPtR 0o–5f  mcmai zseo-ibl adsepdthC.S SAPRsc eonf amriaoiszew-baass5e.d8, 
C19S.A0, 3sc3e.0n,a3r3io.5s, wanads 650.8.0, %19.o0v, e3r3r.0ic, e3-3b.a5s, eadndC S6A0.0s%ce noavreiro rsifcoer-b5a–s1e0d,  1C0S–A15 s,c1e5n–a2r0i,o2s0 f–o2r5 5,–a1n0d, 
1205–1350,c 1m5–s2o0il, d2e0p–t2h5,, raesnpde c2t5iv–e3l0y .cImnc osorpilo draetpiothn, orfeSspDeIc-btiavseeldy.s yInstceomrpdoeracrteioanse odfp SeDneI-tbratsieodn 
sreysitsetman cdeescirgenaisfiecda nptelyne(tpra≤ti0o.n0 5r)ebsiyst2a3n.4c,e1 s6i.g0n, 1if7i.c6a,n2t3ly.0 ,(1p4 ≤.0 ,0a.0n5d) 5b.y7 %23f.o4r, 01–65.0, ,5 –1170.6, ,1 02–31.05,, 
154–.02,0 a,n2d0 –52.75%, a nfodr 205––53, 05–c1m0,s 1o0il–d15e,p 1th5–, 2re0s, p2e0c–t2iv5,e alyn.d 25–30 cm soil depth, respectively. 
 
FFiigguurree 22.. SSooiill ppeenneettrraattiioonn rreessiissttaannccee ((MMPPaa)) aass iinnfflluueenncceedd bbyy CCSSAA--bbaasseedd ccrroopp mmaannaaggeemmeenntt pprraaccttiicceess.. 
VVaalluueess wwiitthh aas simimilialarrl olowwerecracsaesele ltetettre(rs )(sw) iwthitihn iena cehacsho islodiel pdtehpathre anroet nsoigt nsiifigcnainfitclayndtliyff edriefnfetraetn0t. 0a5t 
0le.0v5e lleovfepl roofb parboibliatybiulistyin ugsTinugk eTyu’kseHyS’sD HtSeDst .te(fsot.r (afodr eat adieletadildeeds dcreispctriiopntiofns ocfe nscaernioasriroesf errefteor Ttoab Tlaeb1l)e. 
1). 
3.3. Soil and Earthworms’ Casts (EWC) Biochemical Properties
33..33.. 1S.oSilo ailnSda Elianritthywaonrmd sp’ HCasts (EWC) Biochemical Properties 
3.3.1.T Shoeile Sffaelcint oitfyC aSnAd- bpaHse d scenarios on the soil salinity and pH was minimal, in which no
significant (p ≤ 0.05) differences in soil salinity and pH were observed owing to CSA-based
manaTgheem eeffnetcpt roaf cCtiScAes-b(Taasbedle s3c)e.nIarrreiosps eocnt itvheeo sfoCil SsAal-ibnaitsye dansdce pnHar iwosa,ss moiilnsiamlianli,t yinr wanhgiechd 
nfroo msig0n.2if2ictaont0 .(2p8 ≤d 0S.0m5)− d1,ifwfehreilnectehse ins osiol iwl saaslianlimtyo asnt dn epuHtr awl,earne dobthseervpeHd voawriiendg ftroo CmS7A.1-
btoas7e.d8. mHaonwageevmere,nbt optrhacEtCiceasn (dTapbHle i3n).E IWrreCspwecetrieves iogfn CifiScAan-btlayse(dp s≤ce0n.a0r5i)osh,i gsohiel rsainlinriictye 
CSA-based scenarios over maize CSA-based and farmers’ practices (Sc1) (Table 3). The
rice-based CSA scenarios (Sc2 and Sc3) showed similar EC in EWC to those observed for
 maize-based CSA scenarios (Sc4 and Sc5), irrespective of crop management. However, the
pH of EWC in rice-based CSA scenarios (Sc2 and Sc3) was significantly (p ≤ 0.01) higher
than maize-based scenarios (Sc4 and Sc5) by 7.4%, irrespective of management systems.
Agronomy 2022, 12, 658 11 of 24
Table 3. Chemical properties of soil and earthworms cast (EWC) as influenced by mid-term CSA based management practices (data transformed through
square-root method).
EC (dS m−1) pH TOC N P K
Scenarios a (%) (kg ha−1) (kg ha−1) (kg ha−1)
Soil EWC Soil EWC Soil EWC Soil EWC Soil EWC Soil EWC
Sc1 0.86 Ab (0.23) * 0.71 C 2.83 A (7.49) 0.71 D 1.07 B (0.65) 0.71 B 11.73 C (137) 0.71 D 5.26 A (27) 0.71 D 13.18 A (173) 0.71 B
Sc2 0.87 A (0.25) 1.11 A (0.74) 2.82 A (7.44) 2.80 A (7.34) 1.20 A (0.95) 1.53 A (1.84) 12.40 ABC (154) 13.07 AB (170) 5.39 A (29) 7.17 AB (51) 14.45 A (209) 23.99 A (578)
Sc3 0.85 A (0.22) 1.11 A (0.74) 2.79 A (7.30) 2.75 B (7.05) 1.21 A (0.97) 1.53 A (1.83) 12.73 AB (162) 12.74 B (162) 5.52 A (30) 7.57 A (57) 14.22 A (202) 22.88 A (524)
Sc4 0.86 A (0.24) 0.94 B (0.40) 2.81 A (7.38) 2.69 C (6.75) 1.20 A (0.93) 1.59 A (2.04) 12.22 BC (149) 12.57 B (158) 5.75 A (33) 6.82 B (46) 14.39 A (207) 23.33 A (545)
Sc5 0.89 A (0.28) 0.96 B (0.42) 2.75 A (7.08) 2.68 C (6.67) 1.26 A (1.08) 1.57 A (1.95) 13.02 A (169) 13.38 A (179) 5.94 A (35) 7.01 B (49) 15.10 A (228) 23.30 A (544)
noindentEC—electrical conductivity; TOC—total organic carbon; N—nitrogen; P—phosphorus, K—potassium; EWC—earthworms cast; a refer to Table 1 for scenario description;
* data in parenthesis represent the original values; b means followed by a similar uppercase letter (s) within a column in a size category are not significantly different at 0.05 level of
probability using Tukey’s HSD test.
Agronomy 2022, 12, 658 12 of 24
3.3.2. Total Organic Carbon (TOC)
All CSA-based scenarios (Sc2–5) showed higher TOC in both bulk soil and EWC than
farmers’ practice (Sc1) but did not significantly (p ≤ 0.05) differ from each other (Table 3).
Interestingly, under CSA-based scenarios (Sc2–5), the average TOC in EWC was two times
higher than the average TOC in bulk soil. In farmers’ practices, OC was found only in
the bulk soil (0.65%); however, under CSA-based scenarios (Sc2–5), OC was found in the
bulk soil (0.98%) and in EWC (1.91%). TOC in bulk soil under CSA-based scenarios was
significantly (p ≤ 0.01) higher (50.0%) over Sc1. Maize-based CSA scenarios (Sc4 and Sc5)
significantly (p ≤ 0.01) increased TOC in bulk soil by 4.7% and in EWC by 8.7% over rice-
based CSA scenarios (Sc2 and Sc3), irrespective of crop management practices. Similarly,
SDI-based CSA scenarios (Sc3 and Sc5) significantly (p ≤ 0.01) increased TOC in bulk soil
by 9.0% over FI-based CAS scenarios (Sc2 and Sc4), irrespective of the cropping system.
3.3.3. Macronutrients (N, P, and K)
All CSA-based scenarios significantly (p ≤ 0.05) increased available N in bulk soil
and EWC over Sc1, irrespective of cropping systems, with Sc5 having the maximum effect
(Table 3). The exception is FI-based CAS scenarios (Sc2 and Sc4), which were on par with
Sc1 for available N in bulk soil. Maize-based CSA scenarios (Sc4 and Sc5) and rice-based
scenarios (Sc2 and Sc3) significantly (p ≤ 0.05) increased available N in bulk soil by 14.8%
and 15.8%, respectively, over Sc1, irrespective of crop management practices. The effect of
the cropping system on available N in bulk soil was minimal, where both cropping systems
had similar values. However, implementing SDI significantly (p ≤ 0.01) increased available
N in bulk soil by 9.3% compared to FI, irrespective of the cropping system.
Available P and K in bulk soil were not significantly (p ≤ 0.05) affected by CSA
scenarios, while in EWC, all CSA scenarios significantly (p ≤ 0.05) increased available
P and K over Sc1 (Table 3). Full CSA scenarios (Sc2–5) did not significantly (p ≤ 0.05)
differ in P and K in EWC, except for Sc3 that recorded a significantly higher available P
relative to Sc4 and Sc5. Rice-based scenarios (Sc2 and Sc3) significantly (p ≤ 0.01) increased
available P in EWC by 14.0% over maize-based scenarios (Sc4 and S5). However, in bulk
soil, maize-based scenarios (Sc4 and Sc5) showed statistically similar available P to those
obtained from rice-based scenarios (Sc2 and Sc3).
Interestingly, similar to C, in farmers’ practices, available N, P, and K were found only
in the bulk soil. However, under CSA-based scenarios (Sc2–5), available N, P, and K were
found in the bulk soil and in EWC. Furthermore, the available N, P, and K in EWC were
always higher under all CSA scenarios than in bulk soil. Irrespective of the CSA scenario,
available N, P, and K in EWC were higher than in bulk soil by 5.6, 60.0, and 160.0% for N, P,
and K, respectively. Moreover, amounts of available N, P, and K were highly affected by
CSA practices. On average, CSA scenarios increased available N, P, and K levels (in EWC)
from 0.0 kg ha−1 in Sc1 to 167.0, 50.8, and 547.8 kg ha−1, respectively.
3.3.4. Enzymes in Bulk Soil and Earthworms’ Casts
In the EWC, DHA, acid phosphates (ACP) and alkaline phosphates (ALP) were
significantly (p ≤ 0.05) higher under all CSA-based scenarios with respect to Sc1 (Figure 3).
CSA-based scenarios did not significantly (p ≤ 0.05) differ in DHA and ALP activities,
only DHA under Sc4 was significantly (p ≤ 0.05) lower than other CSA-based scenarios.
As for ACP, the highest activity was recorded for Sc3 which was on par with Sc4 and
Sc5 (Figure 3). In EWC, rice-based CSA scenarios significantly (p ≤ 0.01) increased DHA,
ACP, and ALP activities by 9.4, 5.4, and 14.0%, respectively, over maize-based scenarios.
Moreover, SDI-based CSA scenarios (Sc3 and Sc5) significantly (p ≤ 0.01) increased DHA
and ACP activities in EWC over FI-based CSA scenarios (Sc2 and Sc4) by 46.6 and 11.5%,
respectively, while ALP was not affected by the irrigation system. As for the bulk soil,
CSA-based practices did not significantly (p ≤ 0.05) differ in ALP and ACP activity and
were on par with Sc1 (Figure 3). The exception is ACP in Sc5, in which a significantly lower
Agronomy 2022, 12, x FOR PEER REVIEW 12 of 22 
 
K over Sc1 (Table 3). Full CSA scenarios (Sc2–5) did not significantly (p ≤ 0.05) differ in P 
and K in EWC, except for Sc3 that recorded a significantly higher available P relative to 
Sc4 and Sc5. Rice-based scenarios (Sc2 and Sc3) significantly (p ≤ 0.01) increased available 
P in EWC by 14.0% over maize-based scenarios (Sc4 and S5). However, in bulk soil, maize-
based scenarios (Sc4 and Sc5) showed statistically similar available P to those obtained 
from rice-based scenarios (Sc2 and Sc3). 
Interestingly, similar to C, in farmers’ practices, available N, P, and K were found 
only in the bulk soil. However, under CSA-based scenarios (Sc2–5), available N, P, and K 
were found in the bulk soil and in EWC. Furthermore, the available N, P, and K in EWC 
were always higher under all CSA scenarios than in bulk soil. Irrespective of the CSA 
scenario, available N, P, and K in EWC were higher than in bulk soil by 5.6, 60.0, and 
160.0% for N, P, and K, respectively. Moreover, amounts of available N, P, and K were 
highly affected by CSA practices. On average, CSA scenarios increased available N, P, and 
K levels (in EWC) from 0.0 kg ha−1 in Sc1 to 167.0, 50.8, and 547.8 kg ha−1, respectively. 
3.3.4. Enzymes in Bulk Soil and Earthworms’ Casts 
In the EWC, DHA, acid phosphates (ACP) and alkaline phosphates (ALP) were sig-
nificantly (p ≤ 0.05) higher under all CSA-based scenarios with respect to Sc1 (Figure 3). 
CSA-based scenarios did not significantly (p ≤ 0.05) differ in DHA and ALP activities, only 
DHA under Sc4 was significantly (p ≤ 0.05) lower than other CSA-based scenarios. As for 
ACP, the highest activity was recorded for Sc3 which was on par with Sc4 and Sc5 (Figure 
3). In EWC, rice-based CSA scenarios significantly (p ≤ 0.01) increased DHA, ACP, and 
ALP activities by 9.4, 5.4, and 14.0%, respectively, over maize-based scenarios. Moreover, 
SDI-based CSA scenarios (Sc3 and Sc5) significantly (p ≤ 0.01) increased DHA and ACP 
activities in EWC over FI-based CSA scenarios (Sc2 and Sc4) by 46.6 and 11.5%, respec-
Agronomy 2022, 12, 658 tively, while ALP was not affected by the irrigation system. As for the bulk soil,1 3CoSfA24-
based practices did not significantly (p ≤ 0.05) differ in ALP and ACP activity and were on 
par with Sc1 (Figure 3). The exception is ACP in Sc5, in which a significantly lower value 
v(raelulaeti(vree ltaot itvhee tcoonthtreolc)o wntarso ol)bwsearsveodb.s DerHveAd a. cDtivHitAy wacatsiv siitgynwifiacsansitglyn (ifip c≤a 0n.t0l5y) (dpe≤cre0a.s0e5d)  
dine carlela CseSdAi-nbaaslel dC SpAra-cbtaicseesd, pexracectpitc efos,re Sxcc5e pwthfiocrhS wc5asw ohnic phawr wasitohn Spca1r (wFiigthurSec 31)(. Figure 3).
 
FFiigguurree3 3..D Deehhyyddrrooggeennaasese( µ(μggT PTFPFg −g1−1s sooililh h−−11)),, aacciidd aanndda alklkaalilnineep phhoospsphhataatsaese(µ (gμgp -pN-NP gP− g1−1s osoilihl −h−11
)) 
aaccttiivviittyy iinn ssooiill aanndd eeaarrtthhwwoorrmm ccaasstt ((EEWWCC)) aass iinnflfluueenncceedd bbyy ddiiffffeerreennttC CSSAA--bbaasseedd ccrrooppm maannaaggeemmeenntt 
scenarios (data transformed through square-root method). Pars with a similar lowercase letter (s) 
sacreen naorito ssig(dnaiftiacatnratlnys dfoirffmereedntt harto 0u.0g5h lesvqeula oref -prrooobtambieltithyo uds).inPga Trsuwkeiyth’sa HsSimD itleasrtl. oLwetetrecrass aeplpetlyte orn(sly)  
awreitnhoint  seiagcnhi fipcaarnatmlyetdeirf.f e(rfoenr taa dte0t.a0i5leldev deelsocfripprtoiobna boifl istcyeunsairniogsT ruekfeery ’tso HTaSbDlet e1s)t. . Letters apply only
within each parameter. (for a detailed description of scenarios refer to Table 1).
 3.4.  Principal Component Analysis (PCA)
In the PCA of 25 variables, three PCs were extracted with eigen values > 0.9 and
explained 75.8% of the variance (Figure 4); first and second components explained 59.9%
and 13.9%, respectively, of variation. Based on PCA, about 59.9% loadings were provided
 by the parameters in PC1 that can be used as key indicators for assessing soil quality. The
results showed a strong and positive correlation between EW abundance (density and
biomass for the 0–15 and 15–30 cm soil depths) and parameters located on the upper right-
hand side (Figure 4), i.e., IR, MWD, TOC in bulk soil and EWC, available N, P, and K in
the bulk soil and EWC, enzymes’ activities (DHA, ALP, and ACP) only in EWC. However,
EW density and biomass for the 0–15 and 15–30 cm soil depths were negatively correlated
with enzyme activity (DHA, ALP, and ACP) in the bulk soil and BD. In the top-soil layer, a
significant positive correlation was observed between EWs’ density and TOC in bulk soil
(r = 0.84), TOC in EWC (r = 0.87), IR (r = 0.68), MWD (r = 0.94), P in bulk soil (r = 0.95), P
in EWC (r = 0.74), K in soil (r = 0.96), and K in EWC (r = 0.86) (Table S1). Similarly, EWs’
biomass significantly correlated with TOC in bulk soil (r = 0.66), TOC in EWC (r = 0.92), IR
(r = 0.44), MWD (r = 0.81), P in bulk soil (r = 0.83), and P in EWC (r = 0.81). Similar trends
were observed for the sub-surface soil layer (Table S1).
Agronomy 2022, 12, x FOR PEER REVIEW 13 of 22 
 
3.4. Principal Component Analysis (PCA) 
In the PCA of 25 variables, three PCs were extracted with eigen values> 0.9 and ex-
plained 75.8% of the variance (Figure 4); first and second components explained 59.9% 
and 13.9%, respectively, of variation. Based on PCA, about 59.9% loadings were provided 
by the parameters in PC1 that can be used as key indicators for assessing soil quality. The 
results showed a strong and positive correlation between EW abundance (density and 
biomass for the 0–15 and 15–30 cm soil depths) and parameters located on the upper right-
hand side (Figure 4), i.e., IR, MWD, TOC in bulk soil and EWC, available N, P, and K in 
the bulk soil and EWC, enzymes’ activities (DHA, ALP, and ACP) only in EWC. However, 
EW density and biomass for the 0–15 and 15–30 cm soil depths were negatively correlated 
with enzyme activity (DHA, ALP, and ACP) in the bulk soil and BD. In the top-soil layer, 
a significant positive correlation was observed between EWs’ density and TOC in bulk 
soil (r = 0.84), TOC in EWC (r = 0.87), IR (r = 0.68), MWD (r = 0.94), P in bulk soil (r = 0.95), 
P in EWC (r = 0.74), K in soil (r = 0.96), and K in EWC (r = 0.86) (Table S1). Similarly, EWs’ 
biomass significantly correlated with TOC in bulk soil (r = 0.66), TOC in EWC (r = 0.92), 
Agronomy 2022, 12, 658 IR (r = 0.44), MWD (r = 0.81), P in bulk soil (r = 0.83), and P in EWC (r = 0.81). Similar t1r4eonfd24s 
were observed for the sub-surface soil layer (Table S1). 
 
Figure 4. PCA bettweeeen eeaarrtthwoorrm ddeennssiittyy ((EEW.. D)),, earrtthworrm biiomaassss ((EW.. W),, and physiio--
cchheemiiccaall ssooiill ppaarraameetteerrss aafftteerr 1100 yyeeaarrss ooff aaddooppttiinngg CSSA--bbaasseedd maannaaggeemeenntt pprraaccttiicceess.. TTOC—ttoottaall 
oorrggaanniicc ccaarrbboonn, ,N N——nnitirtorgoegne,nP, —P—phpohsopshpohroursu, sK, —Kp—optaostsaiussmiu,mBD, B—Db—ulbkudlekn dsietyn,sIiRty—, IinRfi—ltirnaftiilotrnartaioten, 
MratWe, DM—WmDe—anmweaenig whteidgihatm deitaemr,eDteHr,A D—HdAe—hydderhoygdernoagseen, aAseC, PA—CaPc—idacpihdo psphohsaptahsaet,aAseL, PA—LPa—lkaalliknae-
line phosphatase, SPR—soil Penetration resistance, EC—electrical conductivity. 
phosphatase, SPR—soil Penetration resistance, EC—electrical conductivity.
3..5.. Effffeecctt off Diiffffeerreentt Manageemeentt Prraccttiicceess and Theeiirr Liineearr Conttrrasstt 
The effffeecctt ooffd dififfefreernent tm manaangaegmemenetnpt rparcaticcteisceasn adntdh etihreliirn elianrecaorn ctroansttranstd aindte rianctetiroancs-
otinonEsW osn’ EdWenss’i tdy,enEsWitys’, bEiWoms’a bssio, mphayss,i cpoh-cyhsiecmo-ichael,maincdal,e nanzydm eensz’yamcteisv’i taiectsivuintidese ruCnSdAer- 
bCaSsAed-bmasaenda mgeamnaengtempreanct ipcerascatriceegsi vareen ginivTeanb ilne T4a. bTlhee 4e. fTfehcet eofffescte nofa rsicoesn,adreiopsth, d/espoitlh-c/saositls-,
acansdtst,h aenirdi nttheeriarc tiinotnesra(cStcieonasr i(oScxeDnaerpitoh /x sDoiel-pctahs/tso) iol-ncaEsWtss) ’odne nEsWitys’a dndenbsiiotym anssd,  TbOioCm, apsHs,, 
NTO, PC, ,a npdHK, Nw,a Ps ,s iagndifi Kca nwta(Ts asbiglen4if)i.cTahnet e(Tffaebctleo f4s)c. eTnhaer ioefsfaenctd oSfc esnceanriaori×osD aenpdth S/cseonila-criaos t×s 
iDneteprtahc/tsioinl-scawstass isnitgenriaficctiaonntsf owraasl lsimgneaifsiucarnedt fpoar ralml metearsuerxecde ptaEraCmaentedrsD eHxAce.pHt oEwCe avnedr, 
the effect of depth/soil-casts interaction was significant for all parameters except N, P, and
enzymes’ activities.
 The effect of tillage (ZT vs CT), irrigation management (SDI vs FI), and cropping system
(RW vs MW) was evident on EWs’ density and biomass, most soil physical properties,
and all biochemical properties of EWs’ casts. However, except for (TOC, N, and K), the
biochemical properties of bulk soil were not significantly affected by tillage, irrigation
system, and cropping system (Table 4). Moreover, all soil physical properties (except SPR)
and biochemical properties of EWC were significantly (p≤ 0.01) affected by tillage (Table 4).
However, in the bulk soil, only TOC, N, K, and DHA were significantly affected by tillage.
The effect of the cropping system (RW vs. MW) was significant (p ≤ 0.05) on all soil
physical properties (except MWD) and biochemical properties of EWC (p ≤ 0.01), while
the biochemical properties of soil bulk were not affected by the cropping system (except,
TOC, ALP, and DHA) (Table 4). Furthermore, the effect of the irrigation system (SDI vs FI)
was significant (p ≤ 0.05) on all soil physical properties (except BD), and all biochemical
properties of EWC. As for the soil-bulk biochemical properties, only TOC, pH, N, and K
were affected by the irrigation system.
Agronomy 2022, 12, 658 15 of 24
Table 4. Significance effect of different management practices and their linear contrast and interactions on earthworm density, earthworm biomass weight,
physio-chemical, and enzymes’ activities under long-term CSA-based management practices.
Linear Contrast Interactions
Parameters Depth or Soil-Cast
CT vs. ZT FI vs. SDI RW vs. MW Scenario Depth/Soil-Cast Scenario * Depth/Soil-Cast
0–15 *** * ***
Earthworm density (No. m−2) *** *** *
15–30 *** * ***
0–15 *** *** ***
Earthworm biomass weight (g m−2) *** *** ***
15–30 *** ** ***
Soil NS NS NS
EC (dS m−1) NS *** NS
Cast *** ** NS
Soil NS ** NS
pH * * **
Cast *** *** ***
Soil *** *** ***
TOC (%) *** *** **
Cast *** *** ***
Soil * ** NS
N (kg ha−1) *** NS **
Cast *** *** ***
Soil NS NS NS
P (kg ha−1) **** NS ***
Cast *** *** ***
Soil ** * NS
K (kg ha−1) *** ** **
Cast *** *** ***
Agronomy 2022, 12, 658 16 of 24
Table 4. Cont.
Linear Contrast Interactions
Parameters Depth or Soil-Cast
CT vs. ZT FI vs. SDI RW vs. MW Scenario Depth/Soil-Cast Scenario * Depth/Soil-Cast
Soil *** NS **
DHA (µg TPF g−1 soil h−1 NS NS **
Cast *** *** ***
Soil NS NS **
ACP (µg p-NP g−1 soil h−1) * NS ***
Cast *** *** ***
Soil NS NS NS
ALP (µg p-NP g−1 soil h−1) * NS *
Cast *** *** ***
BD (Mg m−3) - * NS ** - - -
IR (cm h−1) - *** * * - - -
MWD (mm) - ** * NS - - -
SPR (MPa) - NS ** * - - -
CT—conventional tillage, ZT—zero tillage, PB—permanent bed, FI—flood irrigation, SDI—sub-surface drip irrigation, RW—rice-wheat, MW—maize-wheat, EC—electrical conductivity,
TOC—total organic carbon, N—nitrogen, P—phosphorus, K—potassium, DHA—dehydrogenase, ACP—acid phosphatase, ALP—alkaline phosphatase, BD—bulk density, IR—infiltration
rate, MWD—mean weight diameter, SPR—soil penetration resistance. NS = Not Significant. * Significant at p < 0.05, ** significant at p < 0.05, *** significant at p < 0.01.
Agronomy 2022, 12, 658 17 of 24
4. Discussion
4.1. Earthworms’ Density and Biomass
The absence of EWs in farmers’ practice (Sc1) indicates the intensive soil biolog-
ical degradation in the IGP of India. The limited EWs’ abundance in the convention-
ally managed agroecosystems has been reported previously in India [4,28,29] and world-
wide [15,20,47,61]. Intensive tillage lessens the EWs’ abundance by killing and injuring
them, exposing them to predators by bringing them closer to the soil surface, decreasing
their food source through the acceleration of OM decomposition [22,26], and creating unfa-
vorable soil physical conditions, e.g., temperature, moisture, and soil structure [62]. During
tillage, about 68–70% of the EWs’ biomass can be lost [44,63]. The very low abundance of
EWs under farmers’ practices in rice fields was confirmed by Singh et al. [29] in which they
outlined that EWs were more abundant at the margins of the paddy fields in India, but no
EWs were observed inside the fields. Our results were also confirmed by the findings of
Giannitsopoulos et al. [64] who found that the highest EWs’ density (181–228 m−2) was
achieved under the least destructive tillage, while the most disruptive tillage yielded the
lowest densities (75–98 m−2).
The burning or removal of crop residue directly influenced EWs by reducing their
food source. In contrast, the input of OM by crop residue retention or organic amendments
promotes EWs’ density and abundance [20,24,30]. In the present study, TOC strongly
correlated with EWs’ density and EWs’ biomass for the two soil depths (Figure 4). Several
studies reported a significant correlation between soil OC and EWs’ abundance [26,42,61,65].
Giannitsopoulos et al. [64] estimated that for every 10% increase in crop residue retention
on the soil surface, EWs’ density could increase by 15 indv. m−2.
The effect of the cropping system on EW density and biomass was significant. The
higher EWs’ density and biomass of CSA maize-based scenarios (Sc4 and 5) over CSA
rice-based scenarios (Sc2 and Sc3) could be attributed to the increased crop residues amount
in which 14.5 Mg ha−1 of crop residues was retained annually on the soil surface compared
to 12.34 Mg ha−1 for rice-based CSA scenarios. The higher input of crop residue in maize-
based CSA systems has been translated to a 4.7% higher TOC in bulk soil by over rice-based
CSA scenarios that could contribute to better EWs’ proliferation under maize-based CSA
scenarios. Similar results were reported by other researchers [15,19,20] in which they
reported that EWs’ abundance is affected by crop residues amount and TOC in the soil.
Irrespective of food “OM” quantity, the quality of crop residues also plays an important
role whereas the C/N ratio of maize residues (43–50) is lower than rice residue (58–65),
implying that maize residue is a favorable food source of EWs relative to rice residues.
Abail and Whalen [55] reported that EWs’ biomass and density were higher under crop
residues with a lower C/N ratio. In the present study, maize was irrigated at SMP of −40
to −50 kPa, while the rice was irrigated at −20 to −30 kPa, indicating better aeration under
maize cultivation which could be another compelling reason for the enhanced EWs’ density
and biomass under maize-based CSA scenarios. Dhar and Chaudhuri [66] attributed the
low EWs’ density in paddy soils to inadequate drainage and anaerobic conditions. This
explanation was validated by the higher EWs’ density and biomass for SDI-based scenarios
compared to the FI-based scenarios. Interestingly, SDI-based CSA scenarios (Sc3 and Sc5)
could promote the EWs’ population not only by providing better soil anaerobic conditions
but also SDI-based CSA scenarios increased TOC in bulk soil by 9.0% over FI-based CAS
scenarios, irrespective of the cropping system.
4.2. Total Organic Carbon
The observed increase in TOC in bulk soil under CSA-based scenarios could be ex-
plained by the greater residue input and the less soil disturbance over ten years. Several
studies reported higher SOC under conservation agriculture practices than conventional
practices [11,67–69]. The low SOC under Sc1 might be due to the combination of crop
residue removal and intensive tillage that increases SOC losses by increasing its exposure
to environmental fluctuations [35]. Furthermore, tillage (puddling/ploughing) breaks
Agronomy 2022, 12, 658 18 of 24
stable aggregates and exposes the protected OM to microbial decay [61,64,68]. Subse-
quently, lesser soil disturbance provides potential protection of SOC inside the macro-
aggregates [68,70,71].
The amount of TOC in bulk soil is determined by the OM input and soil fauna
activities, in particular by activities of EWs. EWs’ feeding, burrowing, and casting activities
impact OM distribution and dynamics [18,20,21,23,71,72]. Besides the higher TOC in the
bulk soil of CAS-based scenarios (0.98%), relative to Sc1 (0.65), the presence of the EWC
is another significant source for the TOC (1.98%), which was absent under Sc1. Angest
et al. [46] demonstrated that EWs function similar to biochemical reactors that assist in
converting plant compounds into microbial necromass in stabilized carbon pools “in their
casts” without altering the TOC. In the present study, EWs’ activity promoted the formation
of stable aggregates that protected the SOC against microbial attack [48,49], in which the
MWD was positively correlated with EWs’ density (r = 0.94) (Figure 4 and Table S1). This
suggestion is corroborated by the observed significantly higher MWD under CSA-based
scenarios (Figure 1), and TOC in bulk soil, in which MWD was significantly correlated
with TOC in bulk soil (r = 0.89) (Figure 4 and Table S1). EWs’ activity might have also
stabilized SOM through its incorporation and protection in their casts [48]. The higher
average TOC in EWC (1.91%) relative to the bulk soil in CSA (0.98%) and the bulk soil of
Sc1 (0.65) supports this scenario (Table 3). In short, EWs can promote C sequestration by
increasing the decomposition of newly added C into stable C either inside stable aggregates
or inside their casts, thus aiding soil C storage in the long term [25,46,48].
4.3. Soil Physical Properties
CSA practices increase the mean weight diameter (MWD) of water-stable aggregates
through increasing TOC, decreasing soil disturbance, increasing EWs’ population, and
reducing SOC loss [64,68,70,71]. The MWD was positively correlated with TOC (r = 0.89)
and EWs’ density (r = 0.94) and EWs’ biomass (r = 0.81). Under farmers’ practices, intensive
tillage breaks the stable aggregates and exposes protected OM to microbial decay and kills
the EWs [9,24,68]. Similar to our results, the water-stable aggregates under ZT and residue
retention were higher in the studies of Li et al. [73] and Song et al. [9] over conventional
practices. EWs’ activity not only promotes humus formation but also mixes such compo-
nents to create stable aggregates [74]. Upon cast deposition, microbial products together
with EWs’ mucilages, bind soil particles and form very stable aggregates [75].
The minimum soil disturbance (due to adoption of ZT), increased MWD, and higher
SOC content led to a reduction in soil BD of CSA-based scenarios. Our results corroborated
the previous findings of [35] under similar ecologies in which they reported that ZT and
CRR reduced soil BD. In the present study, a significant negative correlation between soil
BD and TOC (r = −0.54) was observed (Figure 4). The higher soil BD under Sc1 could be
due to the compaction induced by tillage operations that were followed by puddling [4].
Puddling destructs soil aggregates and fills the macro-pores with finer soil particles [76].
The reduced BD could also be due to the increased large bio-bores due to EWs’ browning
(vertically and horizontally) and to the enhanced soil structure (increased MWD). Gathala
et al. [52] found that the increase in MWD increases soil macro-porosity and reduces soil BD.
A significant negative correlation was found between soil BD and EWs’ density (r = −72),
EWs’ biomass (r = −0.66), and MWD (r = −0.65) (Figure 4 and Table S1). Similar to our
results, Kumar et al. [77] found a strong negative correlation (r = −0.78) between BD
and MWD.
The observed increase in infiltration rate (IR) in the present study owing to CSA
practices could be attributed to the direct and indirect influences of CRR, ZT, and EWs’
activity. Under CSA practices, Ews’ activity might enhance the MWD which could lead
to an increase in the IR. This is evident from the observed positive correlation between IR
and EWs’ density (r = 0.68) and MWD (r = 0.88) (Figure 4 and Table S1). Under CSA-based
scenarios, the highly stable aggregates and EWs’ activities not only reduce the chances for
the formation of surface crust but also increase the portion of macro- and bio-porosity [78],
Agronomy 2022, 12, 658 19 of 24
and pores connectivity [68,78]. Under ZT, the burrowing activity of the undisturbed EWs
leads to an increase in the number of bio-pores, thus IR [23]. EWs vertically dig and create
large bio-pores (higher than one mm in diameter) that could extend deeper than one meter
in the soil profile [23].
The peak SPR (3.03 MPa) observed for Sc1 was at 20 cm which could be due to the
creation of a hardpan by tillage operations [79]. As for the CSA scenarios, despite the
significantly higher SPR in the 5–15 cm soil depth, (Figure 2) the maximum SPR (1.83 MPa)
was below the critical value of 2–3 MPa that could affect the growth of wheat roots [80].
However, the observed increased SPR in the CSA-based scenarios in the 5–10 and 10–15 cm
depths differs from the results of Jat et al. [3] in which they found lower SPR in ZT than
in CT. Our results corroborate the findings of Dekemati et al. [61] in which they reported
higher SPR under ZT for the top 20 cm. Interestingly, despite high SPR (which might affect
EWs’ abundance), in the present study, the maximum EWs’ density and biomass were
observed under CSA-based scenarios where significantly higher SPR values were observed,
implying that EWs’ abundance is a function of various interacting factors (tillage, amount
and type of crop residue, irrigation management, cropping system, and soil type) and not
only one factor [42,61].
4.4. Chemical Analysis of Bulk Soil and Earthworms’ Casts
Crop residue decay leads to nutrient release that could be available to plants [81]. Soil
biological properties, OM amount, and nutrient availability are strongly correlated [10]. In
the present study, under CSA-based scenarios (Sc2–5), available N, P, and K were higher in
both the bulk soil and in EWC, compared to farmers’ practices in which due to the absence
of the EWs (thus, EWC), available N, P, and K were found only in the bulk soil. This increase
might be due to the integration between CRR, EWs “feeding and casting”, and microbial
activity. This is evident from the strong and positive correlation between available N, P,
and K in the bulk soil and EWs’ density on one side and TOC in the soil on the other side
(Figure 4 and Table S1). The adoption of ZT and continuous CRR for 10 years coupled
with precision water management promoted EWs’ proliferation, which enhanced and
transformed soil microbial structure in a way that boosts SOM mineralization [4,18,20,48],
thus releasing available N, P, and K in the soil while maintaining a higher amount inside the
EWC. As for available N, P, and K in EWC, the EWs ingest litter on the soil surface [15,19].
This digested litter is converted to a nutrient-rich product by establishing a mutualistic
relationship with soil microflora [32]. Once ingested, the unassimilated and undigested
litter portions are returned to the soil as EWC. The fresh EWC depositions lead to a release
of large quantities of nutrients (N, P, and K) that are easily assimilable by plants [82]. In this
way, changes in EWs’ density and activity could be manifested by increased nutrient levels
(both in bulk soil and EWC) [14]. Bertrand et al. [23] reported that high EWs’ abundance
in the 0–15 cm could have more available N than low EWs’ abundance. Our results were
supported by a recent metanalysis that concluded that EWC is highly fertile and contains
40–48% more total P, N, and OC than bulk soil [83]. In the current study, significant amounts
of available N, P, and K were accumulated in the EWC. On average, CSA scenarios (Sc2–5)
increased available N, P, and K from 0.0 kg ha−1 in Sc1 to 167.0, 50.8, and 547.8 kg ha−1,
respectively.
Soil enzymes are responsive to soil management practices, e.g., tillage, cropping
diversity, crop residue, and nutrient management [4,34,35]. However, lower enzymes’
activities in bulk soil were found under CSA-based scenarios than the EWC. In contrast,
CSA-based management scenarios influenced EWs, thus microbial activities, as evidenced
from extracellular enzymes’ activities, i.e., ALP, ACP, and DHA in EWC [10]. EWs establish
a mutualistic relationship with soil microflora [32], converting crop residues to EWC
containing enzymes and microorganisms [4,48]. Earthworms affect enzyme activities
by stimulating microbial communities [46,47] and enhancing enzyme activities [84]. For
example, Hoang et al. [33] found higher enzymes’ activities in the topsoil in bio-pores
compared to rhizosphere and bulk soil. The higher ALP and ACP in EWC might explain
Agronomy 2022, 12, 658 20 of 24
the higher levels for P in bulk soil and EWC [39]. In the present study, we investigated
the impact of different CSA practices that promote EWs’ activity, and how EWs influence
the soil physico-biochemical properties of the soil, irrespective of EWs’ species (ecological
groups). Future studies must include the classification of EWs to their ecological groups as
affected by various CSA practices.
5. Conclusions
Earthworms (EWs) were absent under farmers’ practices, reflecting the high pressure
on EWs’ communities due to intensive tillage and crop residue burning/removal in the
rice-wheat system. Long-term adoption of CSA-based practices markedly boosted EWs’
abundance, positively affected soil structure, increased TOC, nutrient availability, and soil
biological activity, in particular, the CSA maize-based cropping system. The high TOC
in EWC suggests the potential transformation of the retained crop residue to stable SOC
inside the EWC, thus increasing SOC sequestration. Similarly, EWC contained a significant
amount of available N, P, and K under CSA practices. Maize-based CSA scenarios recorded
higher EWs’ density, EWs’ biomass, TOC, and MWD over rice-based CSA scenarios. In-
terestingly, SDI-based CSA scenarios yielded higher EWs’ density and biomass, TOC, and
available NPK, relative to FI-based CSA scenarios. Therefore, long-term adoption of CSA-
based maize-based scenarios using SDI improved EWs’ proliferation, TOC, and nutrient
storage (in the soil and EWC) and showed a better choice for the IGP farmers’ w.r.t. soil
physical and biological properties. Future studies must include the classification of EWs to
their ecological groups as affected by various CSA practices.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/agronomy12030658/s1, Table S1: Correlation values (r) resulted
from the principal component analysis (PCA) between earthworm density (EW.D), earthworm
biomass weight (EW.W), and physio-biochemical soil parameters under mid-term (10 years) CSA-
based management practices. Where, BD- Bulk density, IR- Infiltration rate, MWD- Mean weight
diameter, DHA- Dehydrogenase, ACP- Acid phosphatase, ALP- Alkaline phosphatase, SPR- Soil Pene-
tration resistance, EC -Electrical conductivity, TOC -Total organic carbon, N- Nitrogen, P- Phosphorus,
K- Potassium, and EWC- Earthworm.
Author Contributions: Conceptualization, H.S.J., P.C.S., A.J.M. and M.L.J.; methodology, M.C.,
S.K.K., M.K., M.K.G., V.K., P. and T.P.; software, M.C., S.K.K., M.K., M.K.G., A.M.A. and V.K.;
validation, M.K., V.K., P. and A.M.A.; formal analysis, M.C., S.K.K., M.K. and V.K.; investigation,
M.K., M.K.G., V.K., P. and T.P.; resources, H.S.J., P.C.S., A.J.M. and M.L.J.; data curation, M.C., S.K.K.,
M.K., M.K.G., A.M.A. and V.K.; writing—original draft preparation, A.M.A. and S.K.K.; writing—
review and editing, H.S.J., P.C.S., A.J.M. and M.L.J.; supervision, H.S.J., P.C.S. and M.L.J.; project
administration, H.S.J., P.C.S. and M.L.J.; funding acquisition, H.S.J., P.C.S. and M.L.J. All authors have
read and agreed to the published version of the manuscript.
Funding: This research was funded by and funding from the Bill & Melinda Gates Foundation
[OPP1052535] through Cereal System Initiative for South Asia (CSISA) project. This research was also
funded by ICAR-DARE-W3-CA-2020-CIMMYT, CGIAR-WHEAT-FP4/W1/2 and CGIAR-CCAFS-
FP2/P25).
Data Availability Statement: Data are contained within the article.
Acknowledgments: The authors acknowledge the support received from the ICAR-CSSRI, Karnal,
and CIMMYT. We also acknowledge the financial and technical support from CGIAR research
program on climate change, agriculture, and food security (CCAFS) to undertake this study. The
research was financed and supported by CGIAR Research Program (CRPs) on Climate Change,
Agriculture and Food Security (CCAFS) and Wheat Agri-Food Systems (WHEAT). We acknowledge
the CGIAR Fund Council, Australia (ACIAR), Irish Aid, European Union, and International Fund
for Agricultural Development, Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand
for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security
(CCAFS). Ahmed M. Abdallah was supported by the Department of Science and Technology (DST)
Agronomy 2022, 12, 658 21 of 24
for funding under Research Training Fellowship (RTF) -Developing Countries Scheme, and The
Federation of Indian Chambers of Commerce and Industry (FICCI).
Conflicts of Interest: The authors declare no conflict of interest.
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