Effects of tree fostering on soil health and microbial biomass under different land use systems in the Central Himalayas

Abstract

This study evaluated the impact of predominant land uses on the physico‐chemical and biological properties of soils along an altitudinal gradient in Indian Central Himalaya to enhance the scientific knowledge and identify suitable land use pattern. Soil samples were collected from six predominant agricultural land uses including (a) open cropland, (b) cropland with multiple tree species (C + mT), (c) cropland with single tree species, (d) crop near rhizosphere of trees, (e) home gardens (HGs), and (f) agriculturally discarded land (ADL). The physico‐chemical properties showed the significant differences with land use systems and altitude. Soil texture varied from sandy loam to clayey loam with altitude. The minimum bulk density and higher porosity were recorded for the HG system whereas water holding capacity, moisture, pH, C, oil carbon stock, N, soil nitrogen stock, and P in the C + mT system. Soil microbial biomass carbon (16–397 μg g⁻¹) and soil microbial biomass nitrogen (28–68 μg g⁻¹) were significantly higher in C + mT and lowest under open cropland. The highest microbial biomass was recorded in the lower altitudinal region of Tarai, and the lowest was recorded in the higher altitudinal region. Across the seasons, soil microbial biomass was maximum during the rainy season and minimum during the winter season. Interestingly, ADL also showed significant contribution in soil microbial biomass carbon and soil microbial biomass nitrogen and could be used for crop production in the future. This study concludes that good soil health, higher amount of microbial biomass, and better soil qualities occurred in tree planted soils than in open crop lands, mainly attributed to the greater availability of organic matter, litter diversity, and fine roots.

Full text

RESEARCH ARTICLE
Effects of tree fostering on soil health and microbial biomass
under different land use systems in the Central Himalayas
Surendra Singh Bargali |Kirtika Padalia |Kiran Bargali
Department of Botany, D.S.B. Campus,
Kumaun University, Nainital, Uttarakhand
263001, India
Correspondence
S. S. Bargali, Department of Botany, D.S.B.
Campus, Kumaun University, Nainital,
Uttarakhand 263001, India.
Email: surendrakiran@rediffmail.com
Funding information
University Grants Commission, India
Abstract
This study evaluated the impact of predominant land uses on the physico‐chemical
and biological properties of soils along an altitudinal gradient in Indian Central
Himalaya to enhance the scientific knowledge and identify suitable land use pattern.
Soil samples were collected from six predominant agricultural land uses including (a)
open cropland, (b) cropland with multiple tree species (C + mT), (c) cropland with sin-
gle tree species, (d) crop near rhizosphere of trees, (e) home gardens (HGs), and (f)
agriculturally discarded land (ADL). The physico‐chemical properties showed the sig-
nificant differences with land use systems and altitude. Soil texture varied from sandy
loam to clayey loam with altitude. The minimum bulk density and higher porosity
were recorded for the HG system whereas water holding capacity, moisture, pH, C,
oil carbon stock, N, soil nitrogen stock, and P in the C + mT system. Soil microbial bio-
mass carbon (16–397 μgg
−1
) and soil microbial biomass nitrogen (28–68 μgg
−1
) were
significantly higher in C + mT and lowest under open cropland. The highest microbial
biomass was recorded in the lower altitudinal region of Tarai, and the lowest was
recorded in the higher altitudinal region. Across the seasons, soil microbial biomass
was maximum during the rainy season and minimum during the winter season. Inter-
estingly, ADL also showed significant contribution in soil microbial biomass carbon
and soil microbial biomass nitrogen and could be used for crop production in the
future. This study concludes that good soil health, higher amount of microbial bio-
mass, and better soil qualities occurred in tree planted soils than in open crop lands,
mainly attributed to the greater availability of organic matter, litter diversity, and fine
roots.
KEYWORDS
land use systems, microbial biomass, soil, tree plantations, vegetation
1|INTRODUCTION
Trees play an important role in soil formation and nutrient cycling
(Kooch, Hosseini, Zaccone, Jalilvand, & Hojjati, 2012) through their
extensive root network, which potentially accumulates nutrients and
litterfall‐contemplated nutrients near the soil horizon. Litter decompo-
sition influences the nutrient dynamics and serves as one of the inputs
of nutrients in the planted forests (S. S. Bargali, 1996) and in
agroecosystems (Pant, Negi, & Kumar, 2017). Both the leaf quantity
and the nutrient release pattern determine nutrient budget and their
impact on the ecosystems (Gonzalez‐Quinones et al., 2011). The vari-
ous effects of tree fostering on soil properties in agroforestry systems
is briefly described in Figure 1. Some of these impacts are mediated
through impacts on soil biota including soil microbial biomass, which
acts as the agent of biochemical changes and a repository of plant
nutrients that are more labile than the bulk of soil organic matter
Received: 23 May 2018 Revised: 18 April 2019 Accepted: 9 June 2019
DOI: 10.1002/ldr.3394
Land Degrad Dev. 2019;1–15. © 2019 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/ldr 1

(Patra, 1994). In terms of soil productivity, the soil microbial biomass
controls the major processes involved in nutrient transformation and
cycling, soil organic maintenance, and macroaggregation for favorable
water and aeration characteristics. It represents a major labile pool of
nutrients in the soil containing 1–5% of the organic carbon and more
than 5% of total nitrogen (Jenkinson & Ladd, 1981). The soil nutrient
status and its transformation are greatly related to the amount of
microbial biomass present in the soil. These microorganisms play a
critical role in the decomposition of plant and animal residuals and
release of the nutrients (Gonzalez‐Quinones et al., 2011), and their
activities are very sensitive with regard to management practices such
as irrigation, fertilizer application, and conventional tillage
(Arunachalam & Arunachalam, 2002). Therefore, the level of soil
microbial biomass is an important factor in determining the soil health.
In addition to influencing the environment and economy, foresta-
tion with native and non‐native species affects soil physico‐chemical
properties (S. S. Bargali, Singh, & Joshi, 1993). Fisher (1995) suggested
that trees might improve soil quality in several ways. Development of
agroforestry in pure grasslands may help to restore the degraded
lands, maintain soil fertility, control water runoff, and manage soil ero-
sion by water and strong winds (Hairiah et al., 2006). Tree planted soil
positively influences the amount and distribution of macropores,
enhancing vertical hydraulic conductivity (Udawatta, Gantzer,
Anderson, & Garrett, 2008) and reducing surface evaporation
(K. Bargali, Manral, Padalia, Bargali, & Upadhyay, 2018). Tree farm sys-
tem reduced the risk of salinity by lowering the net evaporation of
groundwater (George, Nulsen, Ferdowsian, & Raper, 1999). Mixtures
of annuals and perennials result in better nutrient capture. The exten-
sive, deep root systems and associated mycorrhizal fungi enable
woody plants to enrich the soil by recycling leached or unavailable
nutrients at rhizosphere beyond the reach of annual plants. Similar
to forest systems, the perennial trees in agroforestry ensure perma-
nent soil coverage and lead to an intensified humus formation with
concurrent benefits for soil biota. Many tropical tree species can fix
atmospheric nitrogen and consequently increase soil nitrogen content.
Trees may also control aboveground and belowground microclimate,
namely, mesofauna and microfauna. Both microflora and microfauna,
which are considered as the essential part of the ecosystem, alter soil
chemical, biological, and physical properties near the root region by
building up soil organic matter, increasing plant nutrient availability
and water holding capacity (WHC), and improving soil structure
(Bargali et al., 2018).
Uttarakhand, an agricultural state of the Indian Central Himalayas,
encompasses a geographical area of 53,483 km
2
, which shared about
4.5% of forest cover and 3.1% of agricultural land of the country
(Government of Uttarakhand, 2014). The different land use systems
of the Kumaun Himalaya vary enormously in their structural complex-
ity and species diversity, their productive and protective attributes,
and their socioeconomic dimensions. In the Kumaun Himalayan region,
a total of six land use systems were commonly practiced (Padalia,
Bargali, Bargali, & Parihaar, 2018; Parihaar, 2016), namely, (a) sole
cropping system (herbaceous crop only); (b) agri‐horticulture systems
(herbaceous crops + fruit trees); (c) agri‐silviculture system (herbaceous
crops + fuel/fodder/timber trees); (d) agri‐horti‐silviculture system
(herbaceous crops + fruit trees + fuel/fodder/timber trees); (e)
agri‐silvi‐pastoral system (grasses + trees + shrubs); and (f) home
garden (HG; vegetable crops + fuel or fodder trees + multipurpose
tree + ornamental plants + shrubs).
The major question/problem associated with the farming system
of this region is less availability of agricultural land with low crop
FIGURE 1 Effects of trees in agroforestry system (1) Rowe, Hairiah, Giller, VanNoordwijk, and Cadisch (1998), (2) Hadgu et al. (2009), (3)
Montagnini et al. (1993) [Colour figure can be viewed at wileyonlinelibrary.com]
2BARGALI ET AL.

productivity. Soil erosion, especially in the Hill region, is a common
phenomenon. Other than this, regular crop failure, low crop productiv-
ity, and poor soil health also lowers the economy. In spite of huge
information on this topic globally, the effect of land use patterns,
especially of agroecosystems on physico‐chemical and biological prop-
erties of soil, remains unexplored in the Indian Central Himalaya. In
view of the above‐mentioned problems, we hypothesized that (a)
incorporation of trees in agroecosystem will enhance the soil quality
in a natural way, (b) input of organic resource (through quality, quan-
tity, and diversity of litter and fine roots of tree) will affect the soil
quality and amount of microbial biomass, and (c) altitudinal variation
and seasonality will influence the soil properties. The main objectives
of this study were to investigate (a) the effects of tree fostering on
physico‐chemical and biological properties of the soil under different
land use systems; (b) the effects of land use systems, tree plantation,
and altitudinal ranges on the soil microbial biomass; and (c) the effects
of seasonality on the soil microbial biomass carbon (SMBC) and soil
microbial biomass nitrogen (SMBN). The overall aim of this research
work was to understand the role of tree fostering in different land
use systems to sustain the soil quality and to promote sustainable
farming in the Central Himalayan region in a better manner.
2|MATERIAL AND METHODS
2.1 |Study area and land use systems
The Nainital district of Uttarakhand, where the present study was con-
ducted, forms part of the Indian Central Himalaya. Different land use
systems, representing all the agriculture land utilization patterns and
commonly practiced in the Central Himalayan region, were selected
at three altitudes, namely, (a) Hill (28°43′to 31°27′N latitude and
77°34′to 81°02′E longitude, at elevation of 1,219 m asl), (b) Bhabhar
or foot hill (29°13′to 5.75″N latitude and 79°30′to 46.72″E longi-
tude, at elevation of 424 m asl), and (c) Tarai or plain region (28°59′
to 15.03″N latitude and 79°24′to 50.84 E longitude, at elevation of
284 m asl).
The six different land use systems selected for the study were (a)
open cropland (OC): pure herbaceous cropland, covered at least 80%
of the total landscape or less than 20% tree/shrub cover. (b) Cropland
with multiple tree species (C + mT): It is a multistrata system in which
trees and crops are interplanted with multiple tree species (inconsis-
tently spaced), maturing at different rates and occupying different
canopy covers. (c) Cropland incorporated with single tree species
(C + sT): It is a planned program of treatments during the whole life
of a stand, designed to achieve specific stand structural objectives.
This program of treatments integrates specific harvesting, regenera-
tion, and stand tending methods to achieve a predictable yield of ben-
efits from the stand over time. (d) Crop near tree rhizosphere region
(C + R): The rhizosphere is a narrow region of soil that is directly influ-
enced by root secretions and associated soil microorganisms and
belongs to the C + mT land use system. (e) HG: The dynamic supple-
mentary food production system, characterized by the high diversity
of plant species that can reduce the risk of crop failure and increase
biological stability. (f) Agriculturally discarded land (ADL) left unculti-
vated for more than 8 years (selected as control). We have randomly
selected five plots (10 × 10 m size) for each land use system at all
the three altitudinal regions.
2.2 |Geology
Geologically in the Hill Belt, Lesser Himalayan rocks are brought into
juxtaposition with Lower Siwalik rocks by the Main Boundary Thrust.
The rock of this region belongs to the Mussoorie Group (Proterozoic
age), which comprises sandstone and shale with alternating bands of
mudstone and predominantly sandy in nature. The Bhabhar Belt
belongs to the Siwalik range and is made up of 10‐km‐thick succession
of sandstone and mudstone shed from the Himalayan Mountains with
an elevation range of 250–800 m, carried and deposited by rivers. The
Tarai Belt is characterized by tertiary sediments consisting of lower
sandstones of old Tarai deposits washed from the Himalayan Moun-
tains. The soil is fertile, fine textured, alluvial, and free from boulders
and gravels (Valdiya, 1980). This belt is recharged by water seepage
from the higher altitude; therefore, it has a high water table, which
maintains the moisture and nutrient content in the soil (Bargali &
Singh, 1991).
2.3 |Climate
The climate is monsoonal temperate in the Hill region whereas mon-
soonal subtropical in the Bhabhar and Tarai regions (Figure S1). In
the Hill region, mean annual minimum and maximum temperatures
(4°C [January] to 26°C [June]) recorded were comparatively low than
in the plain regions (8°C [January] to 38°C [June]). Most of the precip-
itation occurred during the months of the rainy season (July to
September). The total monthly rainfall of the Hill Region (2,527 mm)
ranged from zero precipitation (November) to 842 mm (July), and for
the plain regions (1,087 mm), it varied from zero precipitation
(November) to 428 mm (July).
2.4 |Vegetation analysis
The tree composition (cbh ≥30 cm) was analyzed by randomly placing
10 quadrats in the surroundings of the studied plots (to get the aver-
age value of the tree composition) of 10 m × 10 m size, and herb com-
position was analyzed by placing quadrats of 1 m × 1 m size (Misra,
1968; Vibhuti & Bargali, 2018). The vegetational data for tree layer
were quantitatively analyzed by following Curtis and McIntosh
(1950). The species diversity index (H′) was computed by following
Shannon and Wiener (1963). Species richness was simply taken as a
count of the total number of species. Biomass of the tree layer was
determined by using already developed regression equations by
Chaturvedi and Singh (1987), Bargali, Singh, and Singh (1992),
Lodhiyal, Singh, and Singh (1995), and Parihaar (2016).
BARGALI ET AL.3

2.5 |Soil sampling and analysis
The soil samples were collected randomly from each land use system
in different seasons (summers, rainy, and winter) by removing soil
monoliths (10 cm long × 10 cm wide × 15 cm deep) to determine
the physico‐chemical and biological properties (microbial biomass) of
the soil. Only surface soil layer (0–15 cm) was considered for the anal-
ysis because most of the microbial activities remain confined to this
region (Srivastava & Singh, 1989; Adeboye et al., 2011; Pant et al.,
2017; Bargali et al., 2018). We have collected the soil samples from
each plot resulting 20 samples from each land use system (5 plots × 4
places = 20 samples/land use system). The large fragments of plant
material were removed by hand sorting, thoroughly mixed to prepare
a composite sample, and again divided into three replicates for a par-
ticular land use system and analyzed. The samples were taken as close
as possible to planting and the time when the crops need the nutri-
ents. The samples were taken 2–4 weeks before applying fertilizers.
The soil sampling was avoided when the soil was very dry, wet, and
frozen.
The air‐dried samples were mixed together, and three composite
stocks were prepared for each land use system and season for further
analysis. Therefore, a total of 162 soil samples were prepared across
the systems from different altitudes in different seasons. For microbial
biomass carbon (MBC) and microbial biomass nitrogen (MBN), soils
were immediately processed just after collecting from the fields.
The texture was determined by soil sieves having different mesh
sizes. Moisture content, WHC, bulk density (bD), void ratio, and
porosity were calculated following Misra (1968). Soil chemical prop-
erties, that is, pH (1:2 soil–water ratio), carbon (Walkley & Black,
1934), nitrogen (Peach & Tracy, 1956), and phosphorus (Olsen, Cole,
Watanabe, & Dean, 1954), were determined by the standard
methods. The values of total organic carbon (TOC) and soil organic
matter were determined by multiplying the values of carbon (%) with
factors of 1.3 and 1.724 (Van Bemmelen factor), respectively. Soil
carbon stock (SCS) and soil nitrogen stock were calculated by multi-
plying the percentage value of C and N with depth and bD. Chloro-
form fumigation and extraction method (Vance, Brookes, &
Jenkinson, 1987) was used to determine the SMBC and SMBN in
the soil.
SMBC ¼TOC FðÞ−TOC NFðÞ
Kc ;
SMBN ¼TN FðÞ−TN NFðÞ
KN
;
where Fis the fumigated soil, NF is the non fumigated soil,
Kc = 0.45 (Jenkinson & Ladd, 1981), and K
N
= 0.54 (Brookes, Kragt,
Powlson, & Jenkinson, 1985).
The dominance of microorganisms in the soil was estimated by
microbial biomass C:N ratio and categorized according to Campbell,
Biederbeck, Zentner, and Lafond (1991).
2.6 |Statistical treatment
The data collected for the different experiments and field samples
were compiled and processed for statistical treatment by using the
Microsoft EXCEL. SPSS‐16 and xlstat‐win.exe software were used to
TABLE 1 Details of vegetation composition and diversity parameters of tree layer in different land use patterns
Systems
Tree species
H (1,219 m asl) B (424 m asl)
Age D TBA H′SR BM Age D TBA
OC —— — ———— — —
C + mT >12 140 ± 4.94 2.32 ± 0.09 2.41 6 60.01 >20 360 ± 10.56 19.29 ± 1.18
Boehmeria rugulosa,Citrus limetta,Mangifera indica,
Punica granatum,Psidium guajava,Prunus persica
Artocarpus heterophyllus,Eucalyptus tereticornis,Litchi chinensis,Mangifera
indica,Psidium guajava,Shorea robusta,Syzygium cumini
C+sT 8–10 150 ± 0.00 1.73 ± 0.00 0.83 1 31.78 10–12 590 ± 0.00 29.07 ± 0.00
Pyrus communis Mangifera indica
C+R —30 ± 0.00 0.264 ± 0.00 0 1 7.34 —100 ± 0.00 3.011 ± 0.00
Prunus persica Mangifera indica
HG 15 90 ± 2.24 0.96 ± 0.01 2.50 6 26.14 11 290 ± 16.02 3.65 ± 0.27
Citrus limon,Diospyros kaki,Mangifera indica,
Pyrus communis,Prunus persica,Psidium guajava
Carica papaya,Citrus aurantiifolia,Citrus limon,
Litchi chinensis,Mangifera indica,Psidium guajava
ADL > 8 8–9 110 ± 4.29 1.87 ± 0.13 2.55 7 19.14 12 200 ± 4.96 11.09 ± 0.47
Bombax ceiba,Bauhinia variegata,Citrus limon,Pyrus
communis,Psidium guajava,Pyrus pashia,Pinus
roxburghii
Azadirachta indica,Cinnamomum tamala,Melia azedarach,Ficus glomerata,
Mangifera indica,Phyllanthus emblica,Shorea robusta,Tamarindus indica,
Ziziphus jujuba
Abbreviations: ADL > 8 years, agriculturally discarded land more than 8 years; B, Bhabhar region; BM, biomass (t ha
−1
); C + mT, cropland with multiple tree
species; C + R, crop near rhizosphere region; C + sT, cropland with single tree species; D, tree density (ind ha
−1
); H, Hill region; H′, tree diversity; HG, home
garden; m asl, metre above sea level; OC, open cropland; SR, species richness; T, Tarai region; TBA, total basal area (m
2
ha
−1
).
4BARGALI ET AL.

prove the statistical significance of the results. The analysis of variance
(ANOVA; two way) was used to test the effect of altitude and land use
patterns on microbial biomass and other soil characteristics. Pearson's
correlation analysis was used to determine the significant interrela-
tionship levels among measured properties of the soils. All the data
in the table were expressed as the average of the three replicates ± SE.
The principal component analysis (PCA) was carried out with the help
of xlstat‐win.exe software. The sampling adequacy of individual and
set variables in the PCA was tested by the Kaiser–Meyer–Olkin mea-
sure (0.518), which was computed to be greater than 0.50. The
Bartlett test of sphericity (χ
2
= 490.236, df = 120) was used to test
the significance level (P< .001) in the PCA.
3|RESULTS
3.1 |Vegetation
The tree layer composition of the Bhabhar and Tarai regions was more
or less similar but sharply distinct from that of the Hill region because
of altitude, topography, and climatic conditions that regulate the com-
position of vegetation significantly. Mangifera indica was frequently
present in the Bhabhar and Tarai regions, whereas Psidium guajava
was present in the Hill region. Glycine max was the commonly culti-
vated crop during the rainy season, whereas Triticum aestivum was
considered as the winter crop at different altitudes. The croplands
were left fallow during the summer just after harvesting the wheat
crop. In ADL, only wild herbs were recorded. HGs were maintained
throughout the year by seasonal vegetables (Table 1).
The tree density and diversity among different land use systems
varied significantly (P< .05; Table S1a) from 90 (HG‐H) to
590 ind ha
−1
(C + sT‐B), 0.96 (HG‐H) to 29.07 m
2
ha
−1
(C + sT‐B),
and 0.72 (ADL‐T) to 2.62 (C + mT‐B), respectively. The tree layer bio-
mass showed significant difference along with altitude and ranged
from 7.34 t ha
−1
(C + R‐H) to 640 t ha
−1
(C + sT‐B) under different land
use systems. Tukey's honestly significant difference (HSD) post hoc
tests for various vegetation parameters under different altitudes and
land use patterns to permit pair‐wise comparisons of means are given
in Table S1b.
3.2 |Effects of altitude and land use on
physico‐chemical properties of soil
Sand/silt/clay content, bD, porosity, soil moisture, and temperature
showed significant differences (P< .01 and P< .05) with altitudinal
TABLE 1 Continued.
Systems
Tree species
Herbaceous species
B (424 m asl) T (284 m asl)
H’SR BM Age D TBA H′SR BM
OC ——— —————Glycine max,Triticum aestivum,
Cynodon dactylon,Parthenium
sp., etc.
C + mT 2.62 7 371.39 >15 270 ± 17.78 11.24 ± 0.80 2.04 5 324.30 Cynodon dactylon,Glycine max,
Triticum aestivum,Zingiber
officinale,Cannabis sativa,Poa
annua, etc.
Artocarpus heterophyllus,Eucalyptus tereticornis,
Litchi chinensis,Mangifera indica,Psidium
guajava,Shorea robusta,Syzygium cumini
Artocarpus heterophyllus,Citrus
pseudolimon,Eucalyptus tereticornis,
Mangifera indica,Psidium guajava
C + sT 0.81 1 640.28 6 530 ± 0.00 19.31 ± 0.00 1.01 1 147.9 Glycine max,Triticum aestivum,
Cannabis sativa,Curcuma
longa Cynodon dactylon,
Parthenium sp., etc.
Mangifera indica Populus deltoides
C + R 0 1 105.14 —30 ± 0.00 0.294 ± 0.00 0 1 8.23 Glycine max,Triticum aestivum,
Cynodon dactylon, etc.Mangifera indica Psidium guajava
HG 2.17 6 131.63 8 110 ± 15.10 1.90 ± 0.37 0.94 2 57.60 Cynodon dactylon,Solanum
melongena,Glycine max,Oxalis
corniculata,Stellaria media,
Arthraxon lancifolius,Capsicum
annuum,Colocasia esculenta,
Commelina benghalensis,
Curcuma longa,Cyperus
rotundus, etc.
Carica papaya,Citrus aurantiifolia,
Citrus limon,Litchi chinensis,Mangifera
indica,Psidium guajava
Carica papaya,Mangifera indica
ADL > 8 2.90 9 172.99 10 200 ± 16.00 9.73 ± 1.12 0.72 2 59.35 Cyperus rotundus,Arthraxon
lancifolius,Ageratum conyzoides,
Cannabis sativa,Commelina
benghalensis,Cynodon dactylon,
Parthenium sp,Poa annua,Sida
cordifolia, etc.
Azadirachta indica,Cinnamomum tamala,Melia
azedarach,Ficus glomerata,Mangifera indica,
Phyllanthus emblica,Shorea robusta,Tamarindus
indica,Ziziphus jujuba
Eucalyptus tereticornis,Mangifera
indica
BARGALI ET AL.5

variation and land use systems (Table 2). The soil was sandy loam in
the Hill region, loam in the Bhabhar region, and clay loam in the Tarai
region (Table 3). Sand varied from 41.47% (OC) to 50.40% (C + R)
under different land use systems. The highest value of sand was
observed at the Hill region, without taking into account the land use
systems. At the C + mT, C + sT, and HG systems, the lowest values
were recorded at the Tarai region. In contrast, at the C + R and ADL
land use systems, the lowest values were observed at the Bhabhar
region, whereas at the OC system, no differences of values were
observed between the Hill and Bhabhar regions. The constitution of
silt was quite similar under different land use systems, which ranged
from 30.76% (C + mT) to 34.48% (OC), whereas a clear demarcation
was recorded in clay content, which varied from 16.63% (C + R) to
24.23% (C + mT). Among the different altitudes, the lowest value of
silt percentage was recorded in the Bhabhar region as compared with
the Tarai and Hill regions. The clay content of the soil was highest
recorded for the Tarai region, whereas under all the land use systems,
the lowest values of clay content were observed in the Hill region. bD
ranged from 1.09 g cm
−3
(ADL) to 1.26 g cm
−3
(OC), porosity ranged
from 52.68% (OC) to 58.98% (ADL), and WHC fluctuated between
32.25% (HG) and 40.89% (C + mT). Among the different altitudes,
the Hill region showed the lowest soil bD. At the OC system, the
highest value was recorded for the Bhabhar region, whereas the
C + sT system showed the highest value for the Tarai region; however,
in the C + mT, C + R, and HG land use systems, the values between the
Bhabhar and Tarai regions were quite similar. The lowest WHC was
recorded in the Hill region under the C + mT, C + sT, C + R, and ADL
land use systems, whereas at the OC (between the Hill and Tarai
regions) and ADL systems (between the Hill and Bhabhar regions),
no differences of values were observed. Soil temperature oscillated
from 19.83°C (C + mT) to 21.54°C (OC), and moisture content was
maximum (15.21%) under C + sT and minimum (10.71%) in the OC sys-
tem. The lowest values of soil temperature and moisture were
observed in the Hill region and highest in the Tarai region among all
the land use systems.
The soil nutrients were influenced by the land use systems and alti-
tude (Table 4). Remarkable differences (P< .01 and P< .05) were
observed in the chemical properties of the soil with the altitudinal var-
iations and land use systems. Soil pH varied from 7.20 (C + mT) to 7.67
(C + R). The pH value of the soil did not show a regular pattern among
different altitudes. The highest value of pH was recorded for the
C + mT, HG, and ADL land use systems in the Hill region, the OC sys-
tem in the Bhabhar region, and the C + sT and C + R systems in the
Tarai region. The overall organic carbon ranged from 1.52% (OC) to
2.24% (C + mT) and SCS from 27.85 t C ha
−1
(OC) to 38.64 t C ha
−1
(C + mT). The highest percentage of carbon, total organic carbon, soil
organic matter, and SCS was observed at the Hill region, without tak-
ing into account the land use systems. At the OC, C + mT, and C + R
land use systems, the lowest values were observed in the Bhabhar
region. In contrast, at the HG and ADL systems, the lowest values
were observed in the Tarai region, whereas at the C + sT system, no
differences were observed between the Bhabhar and Tarai regions.
The soil nitrogen varied from 0.177% (C + sT) to 0.224% (C + mT),
and soil nitrogen stock from 3.06 t N ha
−1
(ADL) to 4.09 t N ha
−1
(OC) under different systems. The highest values of nitrogen and soil
nitrogen stock were recorded for the Bhabhar region among different
altitudes. In the OC, HG, and ADL systems, the lowest value of nitro-
gen was recorded for the Tarai region; however, in the C + mT, C + sT,
and C + R systems, no significant differences were observed between
the Hill and Tarai regions. The lowest values of soil nitrogen stock
were observed at the Hill region at the OC, C + sT, C + R, and ADL
land use systems, whereas at the C + mT and HG systems, the lowest
values were observed in the Tarai region. The available phosphorus
ranged between 31.87 kg ha
−1
(ADL) and 49.02 kg ha
−1
(C + mT). It
was highest in the Tarai region as compared with the Hill and Bhabhar
regions. In the C + mT, C + R, HG, and ADL systems, the lowest values
were observed in the Hill region, whereas at the OC system, no differ-
ences between the Hill and Bhabhar regions were observed. The C:N
ratio varied from 7.24 (OC) to 10.18 (C + mT). Among the different
altitudes, the lowest ratio was recorded for the Bhabhar region and
TABLE 2 Effect of altitude and land uses patterns (two‐way
ANOVA) on soil properties (0‐to 15‐cm soil depth)
Soil properties Altitude
Land use
system
Altitude × Land use
system
df 2510
Sand 1,054.37*** 46.26*** 43.57***
Silt 45.93** 6.97** 13.80**
Clay 700.43*** 39.56*** 54.54***
bD 233.97*** 27.37*** 18.22***
VR 1.18*** 131.88*** 186.65***
Po 1.21*** 158.81*** 158.48***
WHC 75.47*** 56.54*** 33.89***
SM 8.31*** 3.36* 0.56*
pH 20.05*** 90.26*** 23.75***
C 255.94*** 39.06*** 18.71***
N 7,212.82*** 222.67*** 184.45***
C:N 2.23*** 36.22*** 53.77***
P 1,019.80*** 126.50*** 39.80***
MBC 217.14*** 91.08*** 7.88***
MBN 466.52** 41.39** 11.91**
Microbial C:N 136.11*** 4.69*** 3.45***
N in biomass 160.89*** 5.44*** 2.58**
Biomass C/total C 1.17*** 14.39*** 25.75***
Biomass N/total N 419.00*** 11.56*** 4.23***
Note. Values in columns and rows represent Fvalues.
Abbreviations: ANOVA, analysis of variance; bD, bulk density; biomass N/
total N, microbial biomass nitrogen/total soil nitrogen; C:N, soil carbon
nitrogen ratio; C, carbon; df, degrees of freedom; MBC, microbial biomass
carbon; MBN, microbial biomass nitrogen; microbial C:N, microbial carbon
nitrogen ratio; N, nitrogen; P, phosphorus; Po, porosity; SM, soil moisture;
VR, void ratio; WHC, water holding capacity.
*P< .05. **P< .01. ***P< .001.
6BARGALI ET AL.

TABLE 3 Soil physical properties in different land use patterns (0‐to 15‐cm soil depth)
Systems Altitude Sand (%) Silt (%) Clay (%) bD (g cm
−3
) VR Po (%) WHC (%) ST (°C) SM (%)
OC H 44.43 ± 1.30
b
34.73 ± 1.14
b
20.94 ± 0.39
a
1.08 ± 0.01
a
1.46 ± 0.01
c
59.40 ± 0.22
c
35.78 ± 2.41
b
19.87 ± 0.01
a
7.78 ± 0.10
a
B 44.33 ± 0.90
b
31.36 ± 0.44
a
24.21 ± 0.28
b
1.39 ± 0.03
c
0.93 ± 0.04
a
48.25 ± 0.98
a
30.42 ± 0.14
a
22.06 ± 0.52
b
11.03 ± 0.27
b
T 35.64 ± 1.57
a
37.34 ± 0.91
c
27.02 ± 0.56
c
1.32 ± 0.01
b
1.02 ± 0.01
b
50.38 ± 0.22
b
36.23 ± 0.72
b
22.68 ± 0.31
b
13.33 ± 0.18
c
AV ± SE 41.47 ± 2.91
a
34.48 ± 1.73
d
24.06 ± 1.76
e
1.26 ± 0.09
c
1.14 ± 0.16
a
52.68 ± 3.42
a
34.14 ± 1.87
c
21.54 ± 0.85
c
10.71 ± 1.61
a
C + mT H 55.24 ± 1.21
c
30.44 ± 2.30
b
14.33 ± 0.67
a
1.12 ± 0.02
a
1.37 ± 0.04
c
57.77 ± 0.76
b
36.34 ± 0.70
a
18.29 ± 0.16
a
12.30 ± 0.19
a
B 42.89 ± 0.73
b
24.98 ± 1.67
a
32.13 ± 1.30
c
1.16 ± 0.01
b
1.28 ± 0.02
a
56.14 ± 0.45
a
47.67 ± 0.17
c
20.10 ± 0.48
b
14.87 ± 0.18
b
T 36.89 ± 1.27
a
36.87 ± 1.06
c
26.24 ± 1.12
b
1.17 ± 0.00
b
1.29 ± 0.00
b
56.39 ± 0.00
a
38.67 ± 0.04
b
21.10 ± 0.01
c
17.45 ± 0.09
c
AV ± SE 45.01 ± 5.40
b
30.76 ± 3.44
a
24.23 ± 5.24
e
1.15 ± 0.02
b
1.31 ± 0.03
b
56.77 ± 0.51
b
40.89 ± 3.45
e
19.83 ± 0.82
a
14.87 ± 1.49
b
C + sT H 58.23 ± 1.34
c
34.13 ± 0.56
c
7.64 ± 0.89
a
0.90 ± 0.02
a
1.95 ± 0.07
b
66.04 ± 0.76
b
30.78 ± 1.52
a
18.69 ± 0.41
a
12.56 ± 0.24
a
B 44.45 ± 0.90
b
28.67 ± 0.86
a
26.88 ± 1.34
b
1.23 ± 0.01
b
1.16 ± 0.02
a
53.63 ± 0.45
a
42.92 ± 2.36
c
20.33 ± 0.08
b
16.82 ± 0.11
b
T 38.27 ± 0.56
a
32.94 ± 0.92
b
28.79 ± 0.10
c
1.29 ± 0.03
c
1.07 ± 0.04
a
51.63 ± 0.98
a
40.71 ± 1.83
b
21.78 ± 0.06
c
16.24 ± 0.10
b
AV ± SE 46.98 ± 5.90
c
31.91 ± 1.66
b
21.10 ± 6.75
c
1.14 ± 0.12
b
1.39 ± 0.28
c
57.10 ± 4.51
b
38.14 ± 3.73
d
20.27 ± 0.89
b
15.21 ± 1.33
b
C + R H 59.65 ± 0.67
c
34.02 ± 1.20
b
6.33 ± 0.89
a
0.84 ± 0.01
a
2.18 ± 0.05
c
68.55 ± 0.45
b
28.78 ± 2.13
a
19.30 ± 0.37
a
10.45 ± 0.11
a
B 44.64 ± 0.74
a
37.85 ± 2.45
c
17.51 ± 0.83
b
1.28 ± 0.02
b
1.07 ± 0.03
a
51.75 ± 0.76
a
38.37 ± 1.46
c
21.50 ± 0.55
b
11.94 ± 0.17
b
T 46.92 ± 1.37
b
27.03 ± 2.36
a
26.05 ± 2.31
c
1.26 ± 0.01
b
1.12 ± 0.02
b
52.76 ± 0.50
a
32.76 ± 0.24
b
21.89 ± 0.56
b
15.27 ± 0.34
c
AV ± SE 50.40 ± 4.67
d
32.97 ± 3.17
c
16.63 ± 5.71
a
1.13 ± 0.14
b
1.46 ± 0.36
c
57.69 ± 5.44
b
33.30 ± 2.78
bc
20.90 ± 0.81
b
12.55 ± 1.42
ab
HG H 57.22 ± 2.23
c
26.96 ± 1.05
a
15.82 ± 0.67
a
1.06 ± 0.01
a
1.51 ± 0.03
a
60.15 ± 0.43
a
32.05 ± 0.53
b
19.45 ± 0.37
a
9.48 ± 0.08
a
B 45.02 ± 0.89
b
34.14 ± 1.13
b
20.84 ± 1.06
b
1.11 ± 0.01
b
1.38 ± 0.01
a
58.02 ± 0.25
a
33.78 ± 0.49
c
22.16 ± 0.45
b
9.76 ± 0.02
a
T 37.10 ± 0.67
a
33.89 ± 0.92
b
29.01 ± 0.65
c
1.12 ± 0.01
b
1.38 ± 0.03
a
58.02 ± 0.45
a
30.92 ± 0.14
a
22.56 ± 0.56
b
14.65 ± 0.13
b
AV ± SE 46.45 ± 5.85
c
31.66 ± 2.35
b
21.89 ± 3.84
d
1.10 ± 0.02
a
1.42 ± 0.04
c
58.73 ± 0.71
c
32.25 ± 0.83
a
21.39 ± 0.98
c
11.30 ± 1.68
ab
ADL
>8 years
H 59.82 ± 3.07
c
28.30 ± 1.54
b
11.88 ± 0.50
a
1.02 ± 0.02
a
1.54 ± 0.06
b
60.53 ± 0.87
b
31.61 ± 0.71
a
19.36 ± 0.09
a
6.89 ± 0.14
a
B 42.34 ± 1.24
a
27.23 ± 1.06
a
30.43 ± 0.67
c
1.07 ± 0.02
b
1.61 ± 0.04
b
61.65 ± 0.57
b
32.36 ± 0.90
a
21.45 ± 0.28
b
11.80 ± 0.15
b
T 48.00 ± 1.45
b
36.98 ± 2.03
c
15.02 ± 0.43
b
1.18 ± 0.01
c
1.21 ± 0.03
a
54.76 ± 0.55
a
34.89 ± 0.22
b
21.66 ± 0.16
b
13.76 ± 0.29
c
AV ± SE 50.05 ± 5.15
d
30.84 ± 3.09
a
19.11 ± 5.73
b
1.09 ± 0.06
a
1.45 ± 0.12
c
58.98 ± 2.13
c
32.95 ± 0.99
ab
20.82 ± 0.73
b
10.82 ± 2.04
a
Abbreviations: ADL > 8 years, agriculturally discarded land more than 8 years; Av + SE, average with standard error; B, Bhabhar region; bD, bulk density; C + mT, cropland with multiple tree species; C + R, crop
near rhizosphere region; C + sT, cropland with single tree species; H, Hill region; HG, home garden; OC, open cropland; Po, porosity; SM, soil moisture; ST, soil temperature; T, Tarai region; VR, void ratio; WHC,
water holding capacity.
The subscript letters define the relation of data to other land use systems and altitudes.
BARGALI ET AL.7

TABLE 4 Soil chemical properties in different land use patterns (0‐to 15‐cm soil depth)
Systems Altitude pH C (%) TOC (%) SOM (%) SCS (t C ha
−1
) N (%) SNS (t N ha
−1
) P (kg ha
−1
) C:N
OC H 7.50 ± 0.02
a
2.05 ± 0.04
c
2.66 ± 0.06
c
3.54 ± 0.08
c
33.16 ± 0.72
c
0.146 ± 0.01
b
2.37 ± 0.01
a
31.97 ± 0.52
a
14.02 ± 0.30
c
B 7.60 ± 0.16
b
1.20 ± 0.02
a
1.56 ± 0.02
a
2.07 ± 0.03
a
24.71 ± 0.43
a
0.349 ± 0.01
c
7.20 ± 0.18
c
34.78 ± 0.02
a
3.43 ± 0.06
a
T 7.50 ± 0.06
a
1.30 ± 0.04
b
1.69 ± 0.05
b
2.25 ± 0.03
b
25.68 ± 0.43
b
0.137 ± 0.01
a
2.71 ± 0.01
b
51.13 ± 0.27
b
9.46 ± 0.12
b
AV ± SE 7.53 ± 0.03
b
1.52 ± 0.27
a
1.97 ± 0.35
a
2.62 ± 0.46
a
27.85 ± 2.67
a
0.211 ± 0.07
c
4.09 ± 1.56
d
39.29 ± 5.97
c
8.97 ± 3.07
a
C + mT H 7.30 ± 0.04
b
2.29 ± 0.06
b
2.98 ± 0.07
b
3.97 ± 0.08
b
38.58 ± 0.64
b
0.162 ± 0.01
a
2.73 ± 0.05
a
42.06 ± 0.77
a
14.14 ± 0.07
b
B 7.10 ± 0.02
a
2.04 ± 0.08
a
2.65 ± 0.09
a
3.53 ± 0.07
a
35.63 ± 0.31
a
0.354 ± 0.01
b
6.19 ± 0.10
b
49.78 ± 0.98
b
5.76 ± 0.09
a
T 7.20 ± 0.03
a
2.40 ± 0.08
b
3.12 ± 0.09
b
4.15 ± 0.02
b
41.70 ± 0.21
c
0.155 ± 0.01
a
2.70 ± 0.12
a
55.23 ± 1.06
c
15.51 ± 0.61
c
AV ± SE 7.20 ± 0.06
a
2.24 ± 0.11
e
2.92 ± 0.14
e
3.88 ± 0.18
e
38.64 ± 1.75
d
0.224 ± 0.07
d
3.87 ± 1.16
b
49.02 ± 3.82
e
11.80 ± 3.05
c
C + sT H 7.30 ± 0.09
a
2.28 ± 0.06
b
2.97 ± 0.07
b
3.95 ± 0.04
b
30.93 ± 0.61
c
0.149 ± 0.01
a
2.02 ± 0.05
a
41.04 ± 0.04
b
15.32 ± 0.14
c
B 7.20 ± 0.18
a
1.48 ± 0.07
a
1.92 ± 0.09
a
2.56 ± 0.02
a
27.38 ± 0.38
b
0.240 ± 0.01
b
4.43 ± 0.07
c
30.12 ± 0.60
a
6.18 ± 0.18
a
T 8.10 ± 0.11
b
1.33 ± 0.07
a
1.73 ± 0.08
a
2.30 ± 0.05
a
25.66 ± 0.36
a
0.143 ± 0.01
a
2.76 ± 0.06
b
51.47 ± 1.13
c
9.30 ± 0.07
b
AV ± SE 7.53 ± 0.28
b
1.70 ± 0.29
b
2.21 ± 0.39
b
2.94 ± 0.51
b
27.99 ± 1.55
a
0.177 ± 0.03
a
3.07 ± 0.71
a
40.88 ± 6.16
d
10.27 ± 2.68
ab
C + R H 7.70 ± 0.06
b
2.59 ± 0.05
c
3.37 ± 0.06
c
4.49 ± 0.07
c
32.53 ± 0.11
c
0.156 ± 0.01
a
1.96 ± 0.03
a
27.02 ± 0.49
a
16.62 ± 0.27
c
B 7.30 ± 0.04
a
1.23 ± 0.07
a
1.59 ± 0.08
a
2.12 ± 0.05
a
23.63 ± 0.86
a
0.361 ± 0.01
b
6.95 ± 0.22
c
29.78 ± 0.50
b
3.40 ± 0.05
a
T 8.00 ± 0.07
c
1.65 ± 0.07
b
2.14 ± 0.09
b
2.85 ± 0.08
b
31.03 ± 0.68
b
0.149 ± 0.01
a
2.81 ± 0.03
b
54.54 ± 0.20
c
11.05 ± 0.29
b
AV ± SE 7.67 ± 0.20
c
1.82 ± 0.40
cd
2.37 ± 0.53
cd
3.15 ± 0.70
cd
29.06 ± 2.75
ab
0.222 ± 0.07
d
3.91 ± 1.54
c
37.11 ± 8.75
b
10.36 ± 3.83
ab
HG H 7.90 ± 0.19
c
2.43 ± 0.04
c
3.16 ± 0.05
c
4.21 ± 0.06
c
38.69 ± 0.72
c
0.159 ± 0.01
b
2.52 ± 0.11
b
28.21 ± 0.47
a
15.36 ± 0.38
c
B 6.90 ± 0.13
a
1.60 ± 0.05
b
2.08 ± 0.05
b
2.78 ± 0.02
b
26.86 ± 0.30
b
0.255 ± 0.01
c
4.27 ± 0.03
c
38.67 ± 0.83
b
6.29 ± 0.03
a
T 7.10 ± 0.09
b
1.36 ± 0.06
a
1.76 ± 0.07
a
2.35 ± 0.02
a
22.72 ± 0.13
a
0.144 ± 0.01
a
2.41 ± 0.03
a
53.86 ± 0.11
c
9.42 ± 0.08
b
AV ± SE 7.30 ± 0.31
ab
1.80 ± 0.32
c
2.33 ± 0.42
c
3.11 ± 0.56
c
29.42 ± 4.78
ab
0.186 ± 0.03
b
3.07 ± 0.60
a
40.25 ± 7.45
d
10.36 ± 2.66
ab
ADL
>8 years
H 7.50 ± 0.07
b
2.58 ± 0.07
c
3.35 ± 0.09
c
4.46 ± 0.10
c
40.62 ± 1.80
c
0.162 ± 0.02
b
2.55 ± 0.06
a
19.35 ± 0.29
a
15.91 ± 0.36
c
B 7.40 ± 0.17
a
1.72 ± 0.08
b
2.24 ± 0.09
b
2.98 ± 0.09
b
26.32 ± 0.42
b
0.254 ± 0.01
c
3.89 ± 0.06
c
27.18 ± 0.48
b
6.78 ± 0.01
a
T 7.40 ± 0.07
a
1.36 ± 0.02
a
1.76 ± 0.03
a
2.35 ± 0.04
a
24.50 ± 0.67
a
0.153 ± 0.01
a
2.75 ± 0.08
b
49.07 ± 0.33
c
8.91 ± 0.32
b
AV ± SE 7.43 ± 0.03
a
1.89 ± 0.36
d
2.45 ± 0.47
d
3.26 ± 0.63
d
30.48 ± 5.10
c
0.190 ± 0.03
b
3.06 ± 0.42
a
31.87 ± 8.89
a
10.53 ± 2.76
ab
Abbreviations: ADL > 8 years, agriculturally discarded land more than 8 years; Av ± SE, average with standard error; B, Bhabhar region; C + mT, cropland with multiple tree species; C + R, crop near rhizosphere
region; C + sT, cropland with single tree species; C, carbon; H, Hill region; HG, home garden; N, nitrogen; OC, open cropland; P, phosphorus; SCS, soil carbon stock; SNS, soil nitrogen stock; SOM, soil organic
matters; T, Tarai region; TOC, total organic carbon.
The subscript letters define the relation of data to other land use systems and altitudes.
8BARGALI ET AL.

the highest for the Hill region except in the C + mT system, which
showed the highest value for the Tarai region.
3.3 |SMBC and SMBN
The highest microbial biomass was recorded in the Tarai region and
the lowest in the Hill region (Table 5). It reached a maximum during
the rainy season and minimum during the summer season. The OC
system showed the least microbial biomass in all the altitudinal ranges,
whereas the C + mT system shared the maximum values (Figure S2).
The SMBC ranged from 163 μgg
−1
(OC) to 397 μgg
−1
(C + mT).
SMBN followed the same trend, minimum under OC (28 μgg
−1
) and
maximum under C + mT (68 μgg
−1
). Microbial C:N ratio varied from
5.61 (HG) to 8.49 (C + mT). The SMBC constituted about 1.18%
(OC) to 1.94% (ADL) of the total soil carbon, whereas SMBN shared
1.55% (OC) to 3.19% (C + mT) of the total soil nitrogen.
The Leven test for homogeneity of variances was satisfied for all
soil parameters and did not show a significant effect leading to the
conclusion that homogeneity of variances holds. Two‐way ANOVA
showed that various soil properties were significantly affected by alti-
tude and land use patterns (Table 2). Tukey's HSD post hoc tests for
various soil parameters under different altitudes and land use patterns
to permit pair‐wise comparisons of means are given in Table S2.
Almost all soil properties showed significant interactions between sys-
tems and altitude; therefore, a one‐way ANOVA was used to analyze
the differences in soil properties across systems at each altitude
(Table S3a), followed by Tukey's HSD post hoc tests (Table S3b). The
biological properties of the soil were also significantly affected by sys-
tems and the seasonal variation; however, the interaction between
system and season was found to be nonsignificant (Table S4a–d).
The significant positive correlations (P< .01) were observed between
soil MBC‐C and MBC‐MBN (Figure S3a,d).
The PCA was carried out with 16 soil quality indicators. It was then
reduced to few indicators, which explains a minimum relation using
eigenvalue (sum of squared values of factor loading). In the selected
principal components (PCs), the measured indicators with maximum
loading or relation were selected. There were four PCs with an eigen-
value of more than one, which construct about 83.98% of total cumu-
lative percentage (Figure 2).
Ypc1¼Sand 0:935ðÞ;Clay −0:853ðÞ;MBN −0:836ðÞ;
Ypc2¼SCS 0:596ðÞ;WHC −0592ðÞ;Silt −0:571ðÞ:
The components contributing to maximum variance always
became the first PC (Ypc
1
), and hence, more quality indicators were
selected from this component. In combination, both principal compo-
nents (Ypc
1
and Ypc
2
) are responsible for 59.92% of the total varia-
tion. The PCA axis F
1
(sites) accounted for 43.85% variation in
quality indicator composition and was found to be most reliable
(Ypc
1
), in which the highest loading value (which explains how closely
the variables are related to each other) was recorded with sand. PCA
axis F
2
(variables) accounted for 16.07% variation and was found to
be the second most reliable (Ypc
2
). The remaining components, that
is, Ypc
3
and Ypc
4
, shared about 14.86% and 9.21%, respectively, of
the total variations and collectively constituted 24.06%. In the PCA
plot, the OC system does not show any relation with other parameters
of F
1
and F
2
factors, as represented in the Supporting Information.
3.4 |Relationship between physico‐chemical,
microbial biomass, and vegetation structure
Soil microbial biomass showed a negative correlation with altitude,
sand, and pH whereas a positive correlation with different land use
systems, clay content, bD, WHC, soil moisture, temperature, C, N, P,
tree density, basal area of the tree species with their diversity, and
tree biomass (Table S5).
The correlation interpreted that the vegetation cover influenced
physico‐chemical properties of the soil. Tree density was positively
correlated with bD, WHC, moisture, pH, and C. Tree diversity was
negatively correlated with silt, bD, and pH whereas positively corre-
lated with sand, WHC, C, and P. The altitude showed a positive corre-
lation with sand, pH, C, and N whereas a negative correlation with silt,
clay, bD, WHC, temperature, moisture, phosphorus, and microbial bio-
mass (Table S5).
4|DISCUSSION
The incorporation of tree species along with the crop significantly
improved the soil quality (P< .01) under all the altitudes and land
use systems. Trees along with the cultural practices may alter the soil
environment by influencing the microclimate and detritus production
(Montagnini, Ramstad, & Sancho, 1993; Bargali et al., 1993),
reallocating nutrients (Hadgu, Kooistra, Rossing, & Van Bruggen,
2009), and promoting N
2
fixation (Gonzalez‐Quinones et al., 2011)
and soil invertebrate populations (Pant et al., 2017) and by physico‐
chemical properties (Lal, 2004). Many soil characteristics alter with
land use patterns (Bangrooa, Najara, & Rasoolb, 2017; Oyedele,
Olayungbo, Denton, Ogunrewo, & Momodu, 2015; Qi et al., 2018;
Townsend, Vitousek, & Trumbore, 1995) and management regimes.
The moisture and WHC mainly depend on the soil texture (Chau,
Bagtzoglou, & Willig, 2011). In our study, OC contains the low sand
and silt content, which is parallel to the findings of Fadl and Sheikh
(2010) where sand and silt contents were significantly higher
(P< .05) under intercropping with trees than the sole cropping. This
could be attributed to the tree cover and the shelter they provided
to soil from wind erosion (Fadl & Sheikh, 2010). Low proportion of
clay in the Hill region could be due to the steep slopes of the Kumaun
Himalayan region governed by subsurface flow system where fine par-
ticles get involved and deposited in the Bhabhar and Tarai Belts. The
impact of the surface flow could affect the physico‐chemical proper-
ties of the soil (S. S. Bargali et al., 1993; Ojeda et al., 2006). The OC
system showed 5–14% more bD than did the other systems. Aweto
and Dikinya (2003) also reported low bD and high porosity in the tree
canopies than in the open lands. Moreover, frequent use of heavy
BARGALI ET AL.9

machinery to plow croplands especially in the Bhabhar and Tarai
regions may be the major cause of higher bD in the OC systems.
The highest bD in the OC system could be attributed to the corre-
sponding low organic matter contents as evidence by the low soil
organic values in the soil (Table 4). According to Gindaba, Rozanov,
& Negash (2005), bD is highly influenced by tree‐based systems and
management practices that enhanced the accumulation of organic
matter to modify the soil properties, such as bD. Several studies have
also reported the effect of matured trees on reduction on soil bD
(Mishra & Saha, 2003; Seobi, Anderson, Udawatta, & Gantzer, 2005).
The relative high soil bD under the OC system indicates the occur-
rence of soil compactness, which directly impels decreased soil poros-
ity and reduced permeability.
The better soil quality in the C + mT and ADL systems may be due
to the presence of more tree vegetation. The ADL system was not
used for cultivation for a long time; therefore, the nutrient released
from the decomposing litter accumulated in the soil. Other than this,
no‐tillage in this system may also promote the soil quality because
excessive tillage can demolish the habitat of microorganisms, affect
soil structure and organic matter, and endorse soil compaction. Similar
results were also observed by Marchão et al. (2009) where increase in
crop remains resulted in protecting the soil, reducing the tillage,
diminishing the risk of organic matter loss, and enhancing the
diversity/density of microorganisms. The rhizosphere region along all
the altitudes showed better physico‐chemical and biological properties
of the soil as compared with the OC, C + sT, and HG systems because
near the rhizosphere, the developed microclimate could enhance the
decomposition of organic matter and release of the nutrients. Due to
this, soil chemical and biological properties might be improved and
also could affect the physical properties of soil. Vegetation cover along
with topography, climate, weathering processes, and several other
biotic and abiotic factors affects the physico‐chemical properties of
the soils, which vary in space and time (S. S. Bargali et al., 1993; Kooch
et al., 2012). Soil properties, therefore, changed within short distances
according to present rocks, vegetation cover, and land use systems.
The absence of tree canopy exposed the crop field to sunlight,
which enhances soil temperature consequently in OC. Reduced water
loss by canopy shading might contribute to higher soil WHC, moisture,
and microbial biomass under tree plantations, which aligned with the
findings of Lal (2004). Low organic matter and carbon stock in the
OC system may be due to the improper management practices
adopted by farmers, although the OC system showed a good amount
of the soil nitrogen, soil nitrogen stock, and phosphorus than did other
land use systems except the C + mT system. The higher percentage of
soil nitrogen and phosphorus content in OCs may be due to the regu-
lar application of fertilizers, which artificially fulfill the nutrient loss.
The C + mT system was represented by the sustained soil pH, higher
soil carbon, soil organic matter, SCS, nitrogen, and phosphorus. Similar
results were also reported by Hadgu et al. (2009) where fertility
increased near trees and declined with increasing distances from the
tree trunk. Similarly, Aweto and Dikinya (2003) reported that soil
nutrients were higher (47–55%) under the tree canopies, primarily
TABLE 5 Soil microbial biomass carbon and nitrogen under different land uses patterns (0‐to 15‐cm soil depth)
System Se
MBC (μgg
−1
) MBN (μgg
−1
) Microbial C:N ratio
H B T Av ± SE H B T Av ± SE H B T
OC S 114 ± 1.20
a
166 ± 1.76
b
210 ± 1.67
c
163 ± 28
a
14 ± 0.33
a
32 ± 3.53
b
38 ± 0.33
c
28 ± 07
ab
8.14
c
5.19
a
5.53
b
R 182 ± 3.28
a
213 ± 4.16
b
301 ± 4.73
c
232 ± 36
c
19 ± 0.58
a
43 ± 3.79
b
53 ± 1.53
c
38 ± 10
c
9.58
c
4.95
a
5.68
b
W 145 ± 2.08
a
196 ± 1.20
b
234 ± 4.16
c
192 ± 26
b
15 ± 0.10
a
31 ± 2.60
b
42 ± 1.15
c
29 ± 08
b
9.67
c
6.32
b
5.57
a
C + mT S 194 ± 1.53
a
365 ± 7.77
c
267 ± 2.65
b
275 ± 50
b
12 ± 0.58
a
71 ± 3.06
c
64 ± 1.15
b
49 ± 19
d
16.17
c
5.14
b
4.17
a
R 291 ± 2.08
a
430 ± 3.28
b
469 ± 5.36
c
397 ± 54
d
29 ± 0.58
a
97 ± 4.18
c
78 ± 3.06
b
68 ± 20
d
10.03
c
4.43
a
6.01
b
W 213 ± 5.04
a
380 ± 5.69
b
395 ± 12.90
c
329 ± 58
c
23 ± 0.58
a
71 ± 3.38
c
56 ± 3.06
b
50 ± 14
c
9.26
c
5.35
a
7.05
b
C + sT S 151 ± 2.08
a
211 ± 1.76
b
260 ± 6.24
c
207 ± 32
b
15 ± 0.33
a
34 ± 3.06
b
43 ± 1.76
c
31 ± 08
b
10.07
c
6.21
b
6.05
a
R 207 ± 41.6
a
267 ± 6.81
b
299 ± 0.88
c
258 ± 27
b
26 ± 0.58
a
56 ± 3.46
b
60 ± 2.73
c
47 ± 11
d
7.96
c
4.77
a
4.98
b
W 166 ± 4.16
a
234 ± 5.04
b
309 ± 2.65
c
236 ± 41
b
22 ± 1.15
a
48 ± 1.15
b
50 ± 1.86
b
40 ± 09
c
7.55
c
4.88
a
6.18
b
C + R S 160 ± 3.28
a
227 ± 3.84
b
241 ± 1.20
c
209 ± 25
b
22 ± 1.45
a
38 ± 1.15
b
51 ± 1.73
c
37 ± 08
c
7.27
c
5.97
b
4.73
a
R 249 ± 3.28
a
265 ± 0.58
b
315 ± 2.96
c
276 ± 20
b
29 ± 0.58
a
61 ± 3.18
b
67 ± 2.89
b
52 ± 12
d
8.59
c
4.34
a
4.70
b
W 210 ± 0.58
a
228 ± 2.65
b
279 ± 7.13
c
239 ± 21
b
30 ± 0.58
a
51 ± 1.76
c
44 ± 2.31
b
42 ± 06
b
7.00
c
4.47
a
6.34
b
HG S 153 ± 1.53
a
244 ± 2.96
b
242 ± 4.73
b
213 ± 30
b
12 ± 1.15
a
41 ± 1.73
b
57 ± 3.21
c
37 ± 13
d
12.75
c
5.95
b
4.25
a
R 210 ± 1.20
a
306 ± 3.61
b
318 ± 5.69
c
278 ± 34
b
27 ± 0.58
a
78 ± 2.60
c
62 ± 2.31
b
56 ± 15
d
7.78
c
3.92
a
5.13
b
W 267 ± 2.65
b
263 ± 1.33
b
251 ± 0.58
a
260 ± 05
b
28 ± 2.60
a
51 ± 2.31
b
49 ± 1.76
b
43 ± 07
c
9.54
c
5.16
a
5.12
a
ADL >8 years S 180 ± 3.28
a
276 ± 0.58
b
289 ± 6.24
c
248 ± 34
b
18 ± 2.85
a
44 ± 0.88
b
47 ± 2.03
b
36 ± 09
c
10.00
c
6.27
a
6.15
a
R 280 ± 1.20
a
363 ± 8.33
b
356 ± 3.28
b
333 ± 27
c
31 ± 2.40
a
58 ± 1.53
b
67 ± 2.91
c
52 ± 11
d
9.03
c
6.26
b
5.31
a
W 230 ± 1.20
a
303 ± 0.58
b
306 ± 2.65
b
280 ± 25
d
24 ± 2.03
a
54 ± 0.88
b
46 ± 1.45
b
41 ± 09
c
9.58
c
5.61
a
6.65
b
Abbreviations: ADL > 8 years, agriculturally discarded land more than 8 years; Av ± SE, average with standard error; B, Bhabhar region; biomass N/totalN,
microbial biomass nitrogen/total soil nitrogen; C + mT, cropland with multiple tree species; C + R, crop near rhizosphere region; C + sT, cropland with single
tree species; H, Hill region; HG, home garden; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen; microbial C:N, microbial carbon nitrogen
ratio; OC, open cropland; R, rainy; S, summer; Se, seasons; T, Tarai region; W, winter.
a
Assuming that dry biomass contains 50% C (Brookes et al., 1985).
The subscript letters define the relation of data to other land use systems and altitudes.
10 BARGALI ET AL.

due to the accumulation of litter and debris than the open grassland.
Therefore, it is ecologically unwise to eliminate trees from
agroecosystems, as they help to maintain soil fertility and improve
the nutrient pool and microbial activities, as well as long‐term sustain-
ability, which does not depend on the fertilizer inputs from outside.
Similar findings were also reported by Yadav, Yadav, Chhipa, Dhyani,
and Ram (2011) in which significant/substantial improvement in
physico‐chemical and biological properties was observed under agro-
forestry systems as compared with sole tree/sole crop. Besides all
these, in the highly dissected landscape of Himalaya, bioclimatic con-
ditions change rapidly and could vary within short distances, resulting
in a pronounced heterogeneity of soils and their chemical and physical
characteristics (Bäumler, 2015; Bargali et al., 2018).
The plot of PCA supported the analyzed soil factors as predictors
of quality. The absence of tree and clearing of crop residual by burning
after harvesting resulted in relatively low soil organic carbon and soil
microbial biomass in OC, also supported by Collins, Rasmussen, and
Douglas (1992). Tree‐based land use systems, especially in the
C + mT, ADL, and HG systems, have diverse litter inputs than has
the C + sT system. This might be a strong reason to influence soil qual-
ity and a better soil fertility. A large quantity of plant litter that accu-
mulated in the tree planted cropping system is believed to play a
vital role in maintaining nutrient cycling, moisture, and microbial activ-
ity through the decomposition and nutrient release. The process of
decomposition is not only prejudiced by its quality; it can also be
affected by the soil biota and environmental conditions. Therefore,
the association between soil, nutrients, and tree species indirectly
affected soil microbial biomass and other soil properties through
litterfall and decomposition.
Our study revealed that the microbial biomass was also higher
under tree planted soil and greatly influenced by the altitudinal
variation and seasonality. The microbial biomass was decreased with
increasing altitude from the Tarai region to the Hill region, indicating
that high clay content resulted in higher soil microbial biomass
(Srivastava & Singh, 1989). On the one hand, in the Hill region, soil
organic C increased with increasing sand content (r= .670), whereas
it decreased with decreasing clay content (r=−.574), which might be
due the regular mulching of organic manure in the crop fields. On‐
the‐other‐hand, the overall results indicated that soil microbial bio-
mass not only related with C but also depended on other factors also,
that is, soil moisture, nutrients, soil temperature, and vegetation cover,
as well as clay content, also supported by K. Bargali et al. (2018). The
MBC negatively correlated with sand because the WHC of sandy soil
is comparatively low than that of the clayey soil, as most of the micro-
bial activities are promoted with the combined effect of soil moisture
and temperature. Microbial activity is limited in extremely dry or
waterlogged environments and by chilling temperatures (Gonzalez‐
Quinones et al., 2011). In the present study, the highest MBC and
MBN were reported during the rainy season and the lowest during
the summer season, indicating that the drought‐limited microbial
activity takes place during the summer season, similar with the find-
ings of Devi and Yadava (2006). The different land use systems had
a significant effect on SMBC and SMBN (Table 2). The observed dif-
ferences in MBC and nitrogen under the different cropping systems
could be attributed to variable microclimates resulting from the differ-
ences in vegetation cover, topography, and actively growing vegeta-
tion, especially in the systems involving tree plantation (Tetteh et al.,
2019). Our results showed that vegetation cover through increased
tree population influenced both MBC and microbial biomass nitrogen
in the soils. The C + mT systems in all the altitudes had the highest
microbial biomass nitrogen, which implies a corresponding higher nitri-
fication rate than the other systems.
TABLE 5 Continued.
System
Microbial C:N ratio N in biomass
a
(%) Biomass C/total C (%) Biomass N/total N (%)
Av H B T Av H B T Av H B T Av
OC 6.29
ab
6.14
a
9.64
b
9.05
b
8.28
ab
0.56
a
1.38
b
1.62
c
1.18
a
0.96
a
0.92
a
2.77
b
1.55
a
6.74
ab
5.22
a
10.09
c
8.80
b
8.04
ab
0.89
a
1.78
b
2.32
c
1.66
c
1.30
a
1.23
a
3.87
b
2.13
b
7.19
ab
5.17
a
7.91
b
8.97
c
7.35
a
0.71
a
1.63
b
1.80
c
1.38
b
1.03
a
0.89
a
3.07
b
1.66
a
C + mT 8.49
c
3.09
a
9.73
b
11.99
c
8.27
ab
0.85
a
1.79
c
1.11
b
1.25
a
0.74
a
2.01
b
4.13
c
2.29
a
6.83
ab
4.98
a
11.28
c
8.32
b
8.19
ab
1.27
a
2.11
c
1.95
b
1.78
c
1.79
a
2.74
b
5.03
c
3.19
b
7.22
ab
5.40
a
9.34
c
7.09
b
7.28
a
0.93
a
1.86
c
1.65
b
1.48
b
1.42
a
2.01
b
3.61
c
2.35
a
C + sT 7.44
b
4.97
a
8.06
b
8.27
b
7.10
a
0.66
a
1.43
b
1.95
c
1.35
a
1.01
a
1.42
b
3.01
c
1.81
a
5.90
a
6.28
a
10.49
b
10.03
b
8.93
b
0.91
a
1.80
b
2.25
c
1.65
b
1.74
a
2.33
b
4.20
c
2.76
c
6.20
a
6.63
a
10.26
c
8.09
b
8.32
b
0.73
a
1.58
b
2.32
c
1.54
b
1.48
a
2.00
b
3.50
c
2.32
b
C + R 5.99
a
6.88
a
8.37
b
10.58
c
8.61
b
0.62
a
1.85
c
1.46
b
1.31
a
1.41
b
1.05
a
3.42
c
1.96
a
5.88
a
5.82
a
11.51
c
10.63
b
9.32
c
0.96
a
2.15
c
1.91
b
1.67
b
1.86
b
1.69
a
4.50
c
2.68
b
5.94
a
7.14
a
11.18
c
7.89
b
8.74
b
0.81
a
1.85
c
1.69
b
1.45
a
1.92
b
1.41
a
2.95
c
2.10
c
HG 7.65
b
3.92
a
8.40
b
11.78
c
8.03
ab
0.63
a
1.53
b
1.78
c
1.31
a
0.75
a
1.61
b
3.96
c
2.11
a
5.61
a
6.43
a
12.75
c
9.75
b
9.64
c
0.86
a
1.91
b
2.34
c
1.70
c
1.70
a
3.06
b
4.31
c
3.02
c
6.61
ab
5.24
a
9.70
b
9.76
b
8.23
b
1.10
a
1.64
b
1.85
c
1.53
b
1.76
a
2.00
b
3.40
c
2.39
b
ADL >8 years 7.47
b
5.00
a
7.97
b
8.13
c
7.03
a
0.70
a
1.60
b
2.13
c
1.48
a
1.11
a
1.73
b
3.07
c
1.97
a
6.87
ab
5.54
a
7.99
b
9.41
b
7.64
b
1.09
a
2.11
b
2.62
c
1.94
c
1.91
a
2.28
b
4.38
c
2.86
c
7.28
ab
5.22
a
8.91
c
7.52
b
7.21
a
0.89
a
1.76
b
2.25
c
1.63
b
1.48
a
2.13
b
3.01
c
2.20
a
BARGALI ET AL.11

The microbial biomass C:N ratio has often been used to describe
the structure of the microbial community (Moor et al., 2000). This ratio
estimated was used as an indicator of nitrogen supply ability and also
to describe the structure and state of the microbial community under
the different land use patterns (Tetteh et al., 2019). The low ratio indi-
cates a higher proportion of bacteria, whereas high values suggest the
predominance of fungi in microbial population (Campbell et al., 1991).
According to Jenkinson and Ladd (1981), C:N ratio of fungal hyphae is
often 10–12, and that of bacteria is usually between 3 and 5. The
microbial biomass C:N ratio obtained in this study was relatively high
(5.88–8.49), indicating the predominance of fungi in the soils and is
consistent with the values reported (10–12) for most tropical
(Srivastava & Singh, 1989) and temperate forest soils (K. Bargali
et al., 2018).
Soil microbial quotient, a sensitive indicator, represents the
amount of metabolic active carbon in total soil organic matter
(Srivastava & Singh, 1989). Overall, altitudinal variation had significant
effects (P< .01) on soil microbial quotient carbon and nitrogen, which
was the highest in the Tarai region followed by the Bhabhar region.
Brookes et al. (1985) observed that the high values (2–4%) of micro-
bial quotient C for the soil containing more clay content are similar
to the present study, as the Tarai region contains more clay (<25%).
Variation in the seasons also affected the soil microbial quotient car-
bon and nitrogen significantly (P< .01). Under different systems,
microbial biomass constitutes 1.41% (OC) to 1.68% (ADL) of the total
soil carbon and 1.78% (OC) to 2.61% (C + mT) of the total soil
nitrogen. It highlights that soil microbial quotient ratio showed higher
values having trees as compared with OC. The amount of metabolic
active carbon in the total soil organic matter, as a result of various
cropping systems, was described using the microbial quotient. Thus,
the MBC/TC ratio was used in this study as a soil health indicator of
the efficiency in the utilization of organic substrates by the microbes
(Djagbletey, 2017). Thus, MBC/TC ratio could be significantly
enhanced by the soil organic management, improving soil microbial
characteristics and slowly increasing soil organic carbon. Among the
various cropping systems, the lowest value of microbial quotient cal-
culated under sole cropping at all the altitudes, indicating that the size
of MBC and nitrogen as a proportion of the total soil organic carbon
and nitrogen are greater under the tree planted systems, with the
C + mT system having the highest value. In this study, MBC/TC
(1.18–1.94%) falls within the range (1.2–2.7%) reported by Yadava
and Devi (2006) for forest stand and by Vance et al. (1987) for tem-
perate forests (1.8–2.9%). The values of MBN/TN (1.55–3.19%) are
in the lower range compared with agricultural soils (2–6%) reported
by Brookes et al. (1985) and similar for temperate forest soils (1.6–
3.0%) reported by Zhong and Makeschin (2006). According to
Srivastava and Singh (1989), the major cause of the decline in SMBC
is due to the lower input of organic carbon into the soil. As the
results indicated, soil microbial biomass nitrogen showed significant
positive correlation with SMBC (Figure S3a–d) in agreement with
Kujur and Pater (2012) and Arunachalam and Arunachalam (2002).
The detailed correlations, ANOVA, model summary, normality,
FIGURE 2 Principal component analysis
(PCA) of soil properties (0‐to 15‐cm soil
depth) under different land use patterns. PCA
axes 1 (43.85%) and 2 (16.07%) represent first
and second coordinates (scores) of sites,
respectively. BADL, Bhabhar agriculturally
discarded land; BC + mT, Bhabhar crop with
multiple tree species; BC + R, Bhabhar crop
near rhizosphere; BC + sT, Bhabhar crop with
single tree species; bD, bulk density; BHG,
Bhabhar home garden; BOC, Bhabhar open
crop land; C, soil organic carbon; HADL, Hill
agriculturally discarded land; HC + mT, Hill
crop with multiple tree species; HC + R, Hill
crop near rhizosphere; HC + sT, Hill crop with
single tree species; HHG, Hill home garden;
HOC, Hill home garden; MBC, microbial
biomass carbon; MBN, microbial biomass
nitrogen; N, nitrogen; P, phosphorus; Po,
porosity; SCS, soil carbon stock; SM, soil
moisture; SNS, soil nitrogen stock; ST, soil
temperature; TADL, Tarai agriculturally
discarded land; TC + mT, Tarai crop with
multiple tree species; TC + R, Tarai crop near
rhizosphere; TC + sT, Tarai crop with single
tree species; THG, Tarai home garden; TOC,
Tarai open crop land; WHC, water holding
capacity [Colour figure can be viewed at
wileyonlinelibrary.com]
12 BARGALI ET AL.

residual statistics, and coefficient of the regression equations are
given in Table S6a–e.
Tree‐based land use systems are close to natural ecosystems as
they provide ecosystem services similar to the forest such as the biodi-
versity, provision of food stuff, water resource, climate regulation, car-
bon sequestration, nutrient cycling, primary production of oxygen, soil
formation, recreation, and culture (Sharma, Chauhan, & Tripathi, 2016).
It can be represented as an alternative land use management system
that addresses many of the global challenges, including deforestation,
unsustainable cropping practices, loss of biodiversity, and increased
risk of climate change, as well as rising hunger, poverty, and malnutri-
tion (Garrity, 2004). Tree‐based system is suited uniquely to provide
eco‐agriculture solutions that successfully combine objectives for
increased biodiversity conservation gains and food security, especially
by promoting greater use of native/local tree species in agroforestry
systems. It has the potential to improve source of revenue, as it offers
multiple alternatives and opportunities. The introduction of trees on
farm provides numerous benefits, including increased biological pro-
duction, diversified income sources, better water quality, increased
ratio of the soil nutrient, and improved habitat for both humans and
wildlife. Agroforestry plays the most important role in increasing agri-
cultural productivity by nutrient recycling, improving soil fertility,
increasing the percentage of the soil nutrient, reducing soil erosion,
and enhancing farm income than does conventional crop production
(S. S. Bargali et al., 1992). Furthermore, it has promising potentials for
reducing deforestation while increasing food, fodder, and fuel wood
production (Padalia et al., 2018). Agroforestry can be a better climate
change mitigation option than ocean, and other terrestrial option,
because of the secondary environmental benefits such as food security
and secured land tenure, increasing farm income and restoring and
maintaining watershed hydrology and soil conservation (Kürsten &
Burschel, 1993). Tree fostering in pure cropland areas may help to
restore the degraded lands, maintain soil fertility, control water runoff,
and manage soil erosion by water and strong winds. Therefore, pro-
moting tree fostering and their diversity in agroecosystems signifi-
cantly restores the soil quality by improving physico‐chemical
properties and enhances microbial activities in the soil.
5|CONCLUSIONS
The present study concluded that tree planted agroecosystems are
better in sustaining soil health and maintaining the higher amount of
microbial biomass and better soil quality than the OC. This is mainly
attributed to the greater availability of organic matter, litter diversity,
and fine roots of trees. The soil microbial biomass exhibited strong
seasonality and is highly influenced by altitudinal variations. The study
also demonstrated that polyculturing of tree species is more beneficial
than monoculturing to build and sustain the fertility of the soil in the
different land use patterns of the Central Himalayan region. The
degraded lands (ADL) that are left uncultivated should be kept under
cultivation with suitable agroforestry systems.
ACKNOWLEDGMENTS
The second author acknowledges the BSR (University Grants Commis-
sion), New Delhi, for financial support. Thanks to the Head of the
Department of Botany, DSB Campus, Nainital, for providing conve-
niences. We are thankful to Dr. C. J. Barrow and Xingliang Xu for their
critical look and Dr. Suchitra Awasthi, UOU, Haldwani, for the English
review. Our special thanks to Dr. Gerardo Ojeda, Section Editor, for
his critical and specific attention and comments that improved the
quality of the paper. We thank all the four reviewers and concerned
editors for valuable comments and suggestions. We are also thankful
to Dr. Sparsh Bhatt, Department of Statistics, Kumaun University,
Nainital, for detailed statistical analysis.
CONFLICT OF INTEREST
None.
ORCID
Surendra Singh Bargali https://orcid.org/0000-0001-6341-0945
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SUPPORTING INFORMATION
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How to cite this article: Bargali SS, Padalia K, Bargali K.
Effects of tree fostering on soil health and microbial biomass
under different land use systems in the Central Himalayas.
Land Degrad Dev. 2019;1–15. https://doi.org/10.1002/
ldr.3394
BARGALI ET AL.15