Next Article in Journal
Understanding the Ecosystem Services of Riparian Forests: Patterns, Gaps, and Global Trends
Previous Article in Journal
Heat Transmittance and Weathering Performance of Thermally Modified Fir Wood Exposed Outdoors
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 6
10.3390/f16060946
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of Long-Term Agroforestry Systems on Carbon Pools and Sequestration in Top and Deep Soil Layers of Semi-Arid Region of Western India

by
Mahesh Sirimalle
1,
Chiranjeev Kumawat
1,*,
Raimundo Jiménez-Ballesta
2,*,
Ramu Meena
1,
Kamlesh Kumar Sharma
1,
Abhik Patra
3,
Kiran Kumar Mohapatra
4 and
Arvind Kumawat
5
1
Department of Soil Science and Agricultural Chemistry, Sri Karan Narendra Agriculture University, Jobner 303329, Rajasthan, India
2
Department of Geology and Geochemistry, Autónoma University of Madrid, 28049 Madrid, Spain
3
Krishi Vigyan Kendra, Narkatiaganj, West Champaran, Dr. Rajendra Prasad Central Agricultural University, Narkatiaganj 845455, Bihar, India
4
AICRP for dryland Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar 751003, Odisha, India
5
College of Dairy Science and Technology, Sri Karan Narendra Agriculture University, Jobner 303329, Rajasthan, India
*
Authors to whom correspondence should be addressed.
Forests 2025, 16(6), 946; https://doi.org/10.3390/f16060946
Submission received: 11 April 2025 / Revised: 15 May 2025 / Accepted: 28 May 2025 / Published: 4 June 2025
(This article belongs to the Section Forest Soil)
Browse Figures
Versions Notes

Abstract

:
To explore the impact of different agroforestry systems on carbon sequestration, the carbon management index, and carbon fractions, a long-term (37 years) field trial was conducted using three tree-based agroforestry systems consisting of tree species, namely Acacia tortilis, Hardwickia binata, and Tecomella undulata, along with fallow land in a semi-arid region of India. The soil samples were taken at four distinct depths (0–15, 15–30, 30–60, and 60–90 cm) with eight replications and analyzed for soil total organic carbon (TOC), soil organic carbon fractions, soil carbon stocks, and the carbon management index (CMI). In the topsoil layer (0–30 cm), the Acacia tortilis-based agroforestry system recorded a total organic carbon (TOC) content of 4.09%, which was 42.5% higher than that of fallow land. In this layer, the active carbon pool (ACP) was more prominent than the passive carbon pool (PCP). Compared to fallow land, the ACP increased by 68.3%, 59%, and 53.6% for the Acacia tortilis-, Hardwickia binata-, and Tecomella undulata-based systems, respectively. Similarly, the PCP increased by 18.4%, 11.8%, and 8.2% for the same respective systems in the topsoil layer. For the 0–90 cm soil layer, the Acacia tortilis-based agroforestry system sequestered the highest amount of total organic carbon (39.34 Mg C ha−1), followed by agroforestry systems based on Hardwickia binata (37.86 Mg C ha−1), Tecomella undulata (36.99 Mg C ha−1), and fallow land (30.65 Mg C ha−1). Carbon sequestration is higher in the subsurface soil layers (30–90 cm) than in the surface layers. This trend is observed across all agroforestry systems. The carbon management index registered higher for the Acacia tortilis-based agroforestry system (166.58) at the top soil layer than others. Hence, long-term agroforestry systems could improve soil carbon storage and the carbon management index as compared to fallow land. A 37-year field study in a semi-arid region of India revealed that Acacia tortilis-based agroforestry significantly enhances soil carbon sequestration, active carbon pools, and the carbon management index, especially in deeper soil layers, compared to fallow land.

1. Introduction

Global anthropogenic carbon dioxide emissions into the atmosphere are gradually increasing [1]. The global energy-related carbon dioxide emissions in 2021 increased by 4.8% as the demand for coal and oil gas expanded in step with the economy. In addition to exacerbating the effects of climate change, this increased atmospheric concentration of carbon dioxide creates a major environmental risk in the form of land degradation [2]. By 2030, India aims to decrease its GDP per unit GHG emissions by 33 to 35 percent compared to 2005 levels. We must investigate viable options for reducing atmospheric stresses in order to maintain environmental integrity and natural biogeochemical cycling. To accomplish this, we must expand the amount of forest and tree cover so that, by 2030, there will be a 2.5–3 billion tonnes carbon dioxide equivalent carbon sink. Deeper soil layers play a critical role in long-term carbon sequestration due to their greater stability, reduced microbial activity, and lower disturbance, which slow down organic matter decomposition. Carbon stored at greater depths is less prone to oxidation and loss, enhancing its residence time in the soil. Additionally, deep-rooted agroforestry species contribute organic inputs below the surface, promoting carbon accumulation in subsurface horizons. This makes deep soil carbon a more secure and durable sink for atmospheric CO2.
Agroforestry is the most reliable method for reducing climate change through carbon sequestration. As part of climate-smart agriculture, agroforestry has been acknowledged and is often highlighted for its significant potential to adapt to and mitigate the effects of climate change [3,4]. This potential has been investigated in great detail under a number of international initiatives, including the Paris Agreement [5], the Kyoto Protocol of 2001 [6], the REDD + mechanism, and the Sustainable Development Goals. Numerous experts estimate that India’s agroforestry has a 0.29 to 15.21 Mg C ha−1 year−1 potential for sequestering carbon [7,8]. Soil carbon sequestration in agroforestry systems occurs through multiple interconnected mechanisms. Trees capture atmospheric CO2 via photosynthesis and transfer a portion of this carbon below ground through root biomass, exudates, and litterfall. These organic inputs enhance soil organic matter, which stabilizes as humus or binds with soil minerals, reducing decomposition rates. The diverse root systems of agroforestry species improve soil structure, increase microbial activity, and promote carbon retention in microaggregates. Shade and reduced soil disturbance under tree cover further slow organic matter oxidation. Collectively, these processes enhance both the active and passive carbon pools, contributing to long-term carbon storage.
Trees act as a CO2 sink by fixing carbon via photosynthesis and storing excess carbon pools as biomass. The overall CO2 sink/source relationship in the forest changes over time as trees grow, die, and degrade. Reforestation, agroforestry, and natural reforestation are the most promising CO2 mitigation strategies. Developing trees in urban areas can be a latent contributor to lowering CO2 concentrations in the atmosphere through biomass accumulation. It is well-established that soil organic matter contributes nitrogen and soil organic carbon (SOC). Soil organic carbon (SOC) and soil inorganic carbon (SIC) comprise the two main groups of the soil carbon reservoir. Based on the decomposition rate, SOC is separated into two main pools: the active C pool, which includes extremely labile and labile carbon, and the passive carbon pool, which includes less labile and refractory carbon [9]. Microbial activity slowly modifies the passive carbon pool, whereas the active carbon pool has a fast turnover rate and provides energy to the soil fauna. SOC, as a terrestrial carbon reservoir, accumulates carbon over millennia and can be enhanced through agroforestry practices. In contrast, the majority of carbonates in soil inorganic carbon (SIC) primarily consist of both inherited and deposited carbonates [10]. Carbon sequestration is defined by the United Nations Framework Convention on Climate Change (UNFCCC) as “the process of removing carbon from the atmosphere and depositing it in a reservoir”. It describes the process of taking carbon dioxide out of the atmosphere and storing it for a long time in pools with a long lifespan [11]. Carbon sequestration in agroforestry is primarily concerned with capturing atmospheric CO2 during photosynthesis and transferring the fixed carbon into vegetation and soil pools for “long-term” storage [12]. Soil organic carbon sequestration is the process of transporting carbon dioxide from the atmosphere into the soil of a land unit using crop leftovers and other organic solids, which are stored or kept in the unit as part of soil organic matter [13].
The ability of land use to improve soil quality is assessed using the Carbon Management Index (CMI), which can be determined to show changes in the carbon dynamics of each system and indicates the ecosystem’s response relative to a paired reference soil [14,15].
According to Wang et al. and Ghosh et al., the CMI can be a useful indicator for evaluating the possibility of long-term manure addition, straw integration, or conservation agriculture to enhance soil quality and prevent soil deterioration [16,17]. Kumar et al. stated that land use systems have a significant impact on the various carbon pools and CMI [18]. Therefore, the investigation of SOM dynamics is essential by analyzing its different fractions, each of which has a unique residence duration, soil function, and set of controlling factors as suggested by Solomon et al. [19]. The present study aims to investigate specific objectives pertaining to the impact of tree-based agroforestry systems on soil organic carbon (SOC) stocks and fractions. The study focused on three key research questions: (1) How do SOC fractions and stocks respond to different tree-based agroforestry systems? (2) Do SOC fractions and stocks in the surface and subsurface layers exhibit similar responses across different tree-based agroforestry systems? (3) How do different agroforestry systems affect soil carbon sequestration and the carbon management index? These research objectives are of utmost significance, as they will help us understand the potential of agroforestry systems in mitigating climate change and promoting sustainable agriculture.

2. Materials and Methods

2.1. Site and Climate

The present study was carried out at the Agriculture Research Station, Fatehpur, Sri Karan Narendra Agriculture University Agriculture University, Jobner, situated in village Harsava of Fatehpur Tehsil in Sikar district of Rajasthan (27°56′11.2′′ N latitude and 74°58′50.0′′ E longitude) (Figure 1). The study was conducted as part of the All India Coordinated Research Project (AICRP) on Agroforestry, featuring different tree-based agroforestry systems arranged in a randomized block design (RBD) block plantation. The research station is situated in the IIA agro-climatic zone of Rajasthan, known as the “Transitional Plain of Inland Drainage”, which receives an average annual rainfall of 300–500 mm. The temperature in this region is highly variable, dipping below the freezing point (up to −2.5 °C) during the winter and soaring to as high as 52 °C during the summer.

2.2. Experimental Details

After a thorough survey of the area, three tree-based agroforestry systems, viz., Acacia tortilis (Israeli babool), Hardwickia binata (Anjan), and Tecomella undulata (Rohida), were selected as treatments along with a control (Table 1, Figure 2). The selected tree species are native or well-adapted to the semi-arid region of western India and are commonly used in regional agroforestry practices. Among the different tree-based agroforestry systems assessed in the present investigation, Acacia tortilis was maintained as farm forestry because it was unsuitable for an agri-silvicultural system. Hardwickia binata-based agroforestry systems were maintained as agri-silvicultural systems where they grew major pulse crops as intercrops, and sometimes, they did not raise any intercrop. Tecomella undulata-based agroforestry systems were also maintained as agri-silvicultural systems. Thus, there were a total of four treatments, which included three tree-based agroforestry systems along with a control (fallow land). Each treatment was divided into three replications. The soil samples taken from various depths (0–15, 15–30, 30–60, and 60–90 cm) were analyzed to determine different soil carbon parameters.

2.3. Soil Sampling and Processing

Soil samples were taken from all the treatments following standard procedures during October 2021. The area of each treatment was divided approximately into eight equal parts. At each part, nine sampling points were selected following a zigzag pattern and combined into three composite samples from each part. Soil samples were collected from four depths (0–15, 15–30, 30–60, and 60–90 cm) at each sampling point after scraping off the surface litter without removing the top soil. Soil sampling was carried out using two standard techniques. A total of 384 collected composite soil samples were brought to the laboratory, air-dried in the shade, and spread uniformly over a separate piece of polythene paper. These samples were then crushed on a hard wooden slab with the help of a wooden roller, passing it through 2 mm and subsequently 0.5 mm sieves. The samples were kept for chemical analysis. Bulk density measurements were also taken using a soil core of a known volume from the undisturbed plots. For bulk density, undisturbed soil cores were collected using a stainless steel core sampler of known volume from each treatment and depth (0–15, 15–30, 30–60, and 60–90 cm). The collected cores were oven-dried at 105 °C to a constant weight, and bulk density (g cm−3) was calculated by dividing the dry mass by the core volume.

2.4. Methods of Soil Analysis

The initial characteristics of the soil samples were determined by following standard procedures [20]. Total organic carbon was determined using the wet oxidation method [21]. The procedure for determining soil organic carbon fractions is a modified version of the procedure by Walkley and Black [22], employing 5, 10, and 20 mL of concentrated sulfuric acid, corresponding to 12, 18, and 24 N H2SO4 [23] as follows. For Fraction 1 (CVL–very labile carbon), this fraction represents the organic carbon (OC) oxidized by 5 mL of concentrated H2SO4 (12 N H2SO4). For Fraction 2 (CL–labile carbon), the difference in OC oxidized by 5 mL and 10 mL of concentrated H2SO4 (18N-12N H2SO4) represents this fraction. For Fraction 3 (CLL–less labile carbon), this fraction represents the difference in OC oxidized by 10 mL and 20 mL of concentrated H2SO4 (24N-18N H2SO4). For Fraction 4 (CR–recalcitrant carbon), this fraction is the difference in OC oxidized by 20 mL of concentrated H2SO4 and the total organic carbon content of the soil.

2.5. Soil Organic Carbon Storage/Stock (Mg C Ha−1)

The soil’s organic carbon stock represents the quantity of organic carbon content stored within it. This stock was determined using the following formula [24]:
Soil organic carbon stock (measured in t ha−1 or Mg C ha−1) = [SOC (%) × Soil depth (m) × BD (Mg m−3) × Area (104 m2) × 10−2]
In this equation, SOC stands for the total organic carbon content (%), and BD denotes the bulk density of the respective soil depth. Using this approach, the storage of SOC in various soil layers (0–15, 15–30, 30–60, and 60–90 cm) was computed.

2.6. Carbon Management Index (CMI)

The carbon management index (CMI) is calculated as the product of the carbon pool index (CPI) and the lability index (LI) [14]. This index serves as a measure of the rate at which soil organic matter changes in response to alterations in land management practices, compared to a reference soil with higher stability.
The CMI is computed as follows:
Carbon management index (CMI) = CPI × LI × 100.
Carbon pool index (CPI) = Sample total organic carbon (g kg−1)/Reference total organic carbon (g kg−1)
Lability of carbon = [(Cfrac1/TOC) × 3 + (Cfrac2/TOC) × 2 + (Cfrac3/TOC) × 1]
Carbon Lability index (LI) = lability of sample carbon/lability of reference carbon
In CMI and LI, the fallow land is used as reference carbon.

2.7. Statistical Analysis

The data generated underwent statistical analysis [25], and the research data were analyzed using statistical software SPSS 16.0. Soil properties across different agroforestry systems were tested for significant differences using a two-way analysis of variance (ANOVA), with the agroforestry system as the first factor and the depths at which soil samples were collected as the second factor. Subsequently, Duncan’s Multiple Range Test (DMRT) at a p ≤ 0.05 level of significance was used to evaluate the significant differences among mean values. Additionally, correlation studies, as described by Panse and Sukhatme, were conducted to explore the interrelationships among various parameters [26].

3. Results

3.1. Total Soil Organic Carbon (TOC) Distribution

The distribution of TOC content across various agroforestry systems and fallow land (0–90 cm) is summarized in Figure 3. Results indicate that all tree-based agroforestry systems contained higher TOC accumulation in surface layers, with concentration decreasing with increasing soil depth. In the top soil layers (0–30 cm depth), the Acacia tortilis-based agroforestry system demonstrated the highest TOC concentration (4.09 g kg−1), followed by the Hardwickia binata-based agroforestry system (3.85 g kg−1), the Tecomella undulata-based agroforestry system (3.75 g kg−1), and fallow land (2.87 g kg−1). Therefore, the Acacia tortilis-based system had 5.6% and 29.8% higher TOC than the Hardwickia binata-based agroforestry system and fallow land, respectively. In deep layers (30–90 cm), similar trends were observed.

3.2. Organic Carbon Fractions

3.2.1. Very Labile Carbon Fraction (VLC)

The VLC fraction ranged from 0.32 g kg−1 to 1.72 g kg−1 for different tree-based agroforestry systems and soil depths (Table 2 and Table 3). Further, in all the systems, the content of VLC decreased with increase in soil depth. All the tree-based agroforestry systems showed a higher VLC content as compared to fallow land in all soil depths. Among the agroforestry systems, the VLC followed the trend of Acacia tortilis > Hardwickia binate > Tecomella undulata across all depths. The Acacia tortilis-based agroforestry system had the highest VLC content (1.72 g kg−1, 1.24 g kg−1, 0.77 g kg−1, and 0.54 g kg−1 at 0–15 cm, 15–30 cm, 30–60 cm, and 60–90 cm soil depths, respectively), which was significantly higher as compared to fallow land (54%, 47.6%, 42.8%, and 40.7% higher at 0–15 cm, 15–30 cm, 30–60 cm, and 60–90 cm, respectively). The VLC fraction constituted higher portion of TOC, ranging from 32.8% to33.6% with an average of 31.2% in different tree-based agroforestry systems.

3.2.2. Labile Carbon Fraction (LCF)

Irrespective of different agroforestry systems and soil depths, the labile carbon (LC) fraction ranged from 0.24 g kg−1 to 0.81 g kg−1 and showed a decreasing trend with increase in soil depths (Table 2 and Table 3). Similar to VLC, all the tree-based agroforestry systems improved LC content as compared to fallow land in all soil depths. The Acacia tortilis-based agroforestry system had a significantly higher LC fraction content at 0 to 60 cm soil depths compared to fallow land (22%, 24.6%, and 32.5% higher at 0–15 cm, 15–30 cm, and 30–60 cm soil depth, respectively). However, at a depth of 60–90 cm, LC variation was non-significant.

3.2.3. Less Labile Carbon Fraction (LCF)

Similar to VLC and LC, the CLL decreased with soil depth, and the tree-based agroforestry systems had higher CLL fractions as compared to fallow land at all soil depths (Table 2 and Table 3). The Acacia tortilis-based agroforestry system accumulated the maximum CLL at all soil depths. Compared to fallow land, the CLL content was 7.1%, 15.7%, 18.4%, and 15.7% higher at 0–15 cm,15–30 cm, 30–60 cm, and 60–90 cm, respectively.

3.2.4. Recalcitrant Carbon Fraction (RCF)

The CR fraction followed the trend of Acacia tortilis-based system > Hardwickia binata-based system > Tecomella undulata-based system > fallow land at all soil depths (Table 2 and Table 3). The CR fraction was significantly high in the Acacia tortilis-based system compared to fallow land at soil depths of 0 to 60 cm. However, at greater depths, the treatment effect is non-significant.

3.3. Active Carbon Pools (ACPs) and Passive Carbon Pools (PCPs) of Soil Organic Carbon

The data pertaining to active carbon pool and passive carbon pool of different agroforestry systems and fallow land in different soil depths are presented in Figure 4. Among agroforestry systems, the maximum active carbon pool at the top soil layer (0–30 cm) was observed for the Acacia tortilis-based agroforestry system (2.21 g kg−1) followed by the Hardwickia binata-based agroforestry system (2.08 g kg−1), Tecomella undulata-based agroforestry system (2.02 g kg−1), and fallow land (1.28 g kg−1). Similarly, the highest passive carbon pool was observed for the Acacia tortilis-based agroforestry system, and the lowest was observed for fallow land. The ACP was increased by 68.3%, 59%, and 53.6% for Acacia tortilis-, Hardwickia binata-, and Tecomella undulata-based agroforestry systems, respectively, compared with fallow land (Table 3). The PCP was increased by 18.4%, 11.8%, and 8.2% for Acacia tortilis-, Hardwickia binata-, and Tecomella undulata-based agroforestry systems, respectively, compared with fallow land at the top soil layer (0–30 cm). At the deep soil layers (30–90 cm), a similar trend was also observed for both the ACP and PCP (Figure 4).

3.4. Soil Organic Carbon Stock (Sequestrated Organic Carbon)

Soil organic carbon stock in the soil profile (up to 90 cm depth) was highest in the Acacia tortilis-based agroforestry system (39.34 Mg C ha−1) and the least in fallow land (30.65 Mg C ha−1) (Figure 5). The SOC stock was significantly higher in deeper layers and across all the agroforestry systems compared to fallow land. At the top soil layer (0–30 cm), the Acacia tortilis-based agroforestry system registered an increase of 31.7%, the Hardwickia binata-based agroforestry system registered an increase of 26.5%, and the Tecomella undulata-based agroforestry system registered an increase of 25.7% compared with fallow land. However, at the deeper soil layer (30–90 cm), the Acacia tortilis-based agroforestry system registered an increase of 67.7%, the Hardwickia binata-based agroforestry system registered an increase of 61.6%, and the Tecomella undulata-based agroforestry system registered an increase of 55.8% over fallow land. Thus, agroforestry systems accumulated higher SOC stock at the deeper layers compared to top layers. The soil organic carbon stock distribution in the different tree-based agroforestry systems was of the following order: Acacia tortilis-based agroforestry system > Hardwickia binata-based agroforestry system > Tecomella undulata-based agroforestry system > fallow land.

3.5. Soil Carbon Management Indices of Different Agroforestry Systems

3.5.1. Carbon Lability Index

The carbon lability index (CLI) value was higher in the Acacia tortilis-based agroforestry system among all soil profile depths (0–90 cm) followed by the Hardwickia binata-based agroforestry system and the Tecomella undulata-based agroforestry system, and it was the least in fallow land (Table 4). The CLI values in the three agroforestry systems were significantly higher than fallow land, but did not significantly differ from one another. At the top soil layer (0–30 cm), the Acacia tortilis-based agroforestry system had a maximum CLI value of 1.18. Moreover, irrespective of treatment, the CLI decreased with the soil profile depth.

3.5.2. Carbon Pool Index (CPI)

A perusal of the data presented in Table 4 reveals that the carbon pool index (CPI) value was significantly higher in the Acacia tortilis-based agroforestry system in both soil layers, followed by the Hardwickia binata-based agroforestry system, the Tecomella undulata-based agroforestry system, and fallow land.
A CPI value of 1.43 was recorded for the Acacia tortilis-based agroforestry system at the top soil layer. Similar to the CLI, the CPI was increased in all the agroforestry systems compared with fallow land. High CPI values for the Acacia tortilis-based agroforestry system throughout the soil profile indicate the high potential of the Acacia tortilis-based agroforestry system in restoring the original soil organic carbon stocks. On an average, the CPI was increased to 37%, 30%, and 26% for the Acacia tortilis-based agroforestry system, Hardwickia binata-based agroforestry system, and Tecomella undulata-based agroforestry system, respectively, over fallow land. For all the agroforestry systems and fallow land, the CPI was decreased as the depth increased.

3.5.3. Carbon Management Index (CMI)

The CMI value was significantly higher in the Acacia tortilis-based agroforestry system across all depths of the soil profile (0–90 cm). A high CMI value of 166.6 was recorded in the Acacia tortilis-based agroforestry system at the top soil layer (0–30 cm) followed by the Hardwickia binata-based agroforestry system (157.6) and the Tecomella undulata-based agroforestry system (153.4). As a whole, CMI increased in all the agroforestry systems over fallow land. Taking depth as a whole (0–90 cm), the CMI increased to 58%, 50%, and 45% for the Acacia tortilis-based agroforestry system, Hardwickia binata-based agroforestry system, and Tecomella undulata-based agroforestry system, respectively, compared with fallow land (Table 4). Similar to the CPI and CLI, there was a reduction in the CMI with increasing depths for all of the tree-based agroforestry systems.

3.6. Relationship Among Different Soil Organic Carbon Fractions

The correlation analysis among different soil organic carbon fractions showed that all the carbon fractions were significantly and positively correlated with each other (Table 5). CVL showed positive and highly significant correlations with TOC (r= 0.983), CL (r= 0.936), CLL (r= 0.882), and CR (r= 0.936). The CL is also positively and significantly correlated with TOC (r = 0.979), CVL (r = 0.936), CLL (r = 0.975), and CR (r = 0.936). CLL showed positive and highly significant correlations with TOC, CVL, CL, and CR with r values of 0.95, 0.88, 0.975 and 0.94, respectively. The CR also showed positive and highly significant correlations with TOC (r = 0.970), CVL (r = 0.936), CL (r = 0.936), and CLL (r = 0.944).

4. Discussion

4.1. Total Organic Carbon

There was a notable difference (p < 0.05) in TOC between the various tree-based agroforestry systems. This difference could potentially be attributed to the inclusion of the litter fall, the yearly recycling of fine root biomass and root exudates, and reduced oxidation of organic matter under tree shade. The research showed that changes in TOC are mainly affected by several factors, such as organic matter, favorable temperature and moisture conditions, quantity of litter fall, chemical composition of tree roots, and litter fall under varying climates and soils. These results are in accordance with the findings of Benbi et al. and Kaushal et al. [2,27]. The results also revealed that the TOC decreased with an increasing soil depth. However, the decrease in TOC content with increasing soil depth is comparatively low for agroforestry systems, which may be due to a noticeable rise in root development in the upper layers and the lack of mechanical soil disturbance. On the other hand, the TOC concentration of fallow land decreased sharply in the deeper layers, probably due to greater intensity of soil erosion in the absence of perennial trees [28]. Decreasing TOC values in deeper soil layers have also been reported by many researchers [29,30]. The long-term improvements in soil carbon stocks and quality for agroforestry systems, particularly with Acacia tortilis, highlight the critical role of tree-based land uses in climate change mitigation. Enhanced total organic carbon, active and passive carbon pools, and a high carbon management index (CMI) demonstrate not only better soil health but also a substantial long-term carbon sink. Importantly, the increased sequestration in deeper soil layers ensures carbon stability and longevity, aligning with global efforts to reduce atmospheric CO2 under the Paris Agreement and India’s carbon sink targets. These findings underscore the potential of agroforestry as a nature-based solution to combat land degradation and achieve national and international climate goals. Promoting such systems in semi-arid regions can thus serve dual purposes: restoring degraded lands and supporting climate resilience.

4.2. Soil Organic Carbon Fractions

4.2.1. Very Labile Carbon Fraction

The proportion of very labile carbon in total organic carbon (TOC) was significantly higher in tree-based agroforestry systems than in fallow land, likely due to the continuous input of easily decomposable organic matter. All agroforestry systems exhibited elevated levels of very labile carbon compared to fallow land. This aligns with Thangavel et al. [31], who reported up to a 46% increase in readily oxidizable carbon following conversion of fallow land to fruit tree plantations of C. reticulata, which showed the highest increase (51%), and P. persica, which showed the lowest (38%). Regardless of the system type, very labile carbon declined with increasing soil depth, consistent with Maia et al. [32], who found greater stocks of this fraction in surface layers, reflecting the predominance of easily mineralizable organic compounds in upper soil horizons under agroforestry.

4.2.2. Labile Carbon Fraction

Labile organic carbon, though less readily decomposable than the very labile fraction, remains a dynamic component of soil organic carbon due to its microbial accessibility. Across all soil depths, its concentration followed the order of Acacia tortilis > Hardwickia binata > Tecomella undulata > fallow land. This pattern likely reflects the continuous input and turnover of litter, roots, and stump material in agroforestry systems. The year-round deposition of decomposable leaf litter may have contributed to the elevated labile carbon levels observed. Similar trends were reported by earlier studies [18,30], which found higher labile carbon fractions under agroforestry compared to fallow land.

4.2.3. Less Labile Carbon Fraction

The less labile organic carbon fraction represents a more stable component of soil organic carbon, with limited microbial decomposition. All agroforestry systems showed higher levels of this fraction compared to fallow land, consistent with previous findings [18,33]. Its accumulation is primarily attributed to soil aggregation and the strong chemical binding of microbial decomposition products to the mineral matrix [34].

4.2.4. Recalcitrant Carbon Fraction

Higher levels of recalcitrant carbon in tree-based systems suggest reduced TOC oxidation, likely due to the input of lignin and other resistant organic compounds from roots and leaf litter [24]. This increase enhances the physical and chemical stability of carbon pools, reflecting sustained plant-derived carbon inputs for long-term soil organic matter formation [35]. Thangavel et al. similarly reported a 13.1% increase in non-oxidizable carbon for C. reticulata compared to control [31]. Woody species, especially in acidic soils typical of tree-based agroecosystems, are key sources of recalcitrant biomolecules [36]. Singh et al. noted that high-molecular-weight compounds such as lignin, suberin, cutins, and tannins may progressively transform labile carbon into more stable recalcitrant forms, thereby enhancing carbon pool stability under agroforestry systems [37]. Regardless of tree species, recalcitrant carbon declined with increasing soil depth, consistent with previous findings [28,38].

4.3. Active Carbon Pool and Passive Carbon Pool

The active carbon pool (CVL + CL) is a key nutrient source that significantly influences soil quality and productivity [23]; however, due to its short residence time, it has limited carbon sink potential [39]. In agroforestry systems, organic inputs from litterfall likely contribute to the active carbon pool, serving as substrates for microbial activity and increasing the very labile carbon fraction. Active carbon levels were consistently higher under agroforestry systems than fallow land, likely due to the greater availability of fresh organic matter and litter. A general decline in the active carbon pool was observed with increasing soil depth, reflecting the concentration of very labile and labile fractions in surface layers. These findings align with previous studies [28,40].
In contrast, the passive carbon pool (CLL + CR), characterized by a longer residence time, plays a central role in long-term carbon sequestration [41]. It was more prominent at lower soil depths, coinciding with the dominance of less labile and recalcitrant carbon fractions. Although passive carbon also decreased gradually with depth, its presence remained relatively stable, consistent with observations by Nath et al. [38] and others [28,37,40].

4.4. Soil Organic Carbon Stock

The soil organic carbon (SOC) stock was calculated by multiplying TOC content, bulk density (BD), and soil depth. Significant variations in SOC stock were observed, largely driven by differences in BD and profile depth. Consistent with TOC trends, total SOC stock (0–90 cm) followed the order of Acacia tortilis (39.07 Mg C ha−1) > Hardwickia binata (38.19 Mg C ha−1) > Tecomella undulata (37.37 Mg C ha−1) > fallow land (30.83 Mg C ha−1), indicating the influence of tree species on SOC accumulation. These results align with findings by Tanwar et al. [42], who also reported higher SOC values for A. tortilis systems. The quantity and composition of root exudates and litter, particularly their lignin and polyphenol content, likely influence decomposition rates and SOC buildup across depths. All agroforestry systems stored more SOC than fallow land, attributed to greater organic matter inputs—consistent with previous studies [42,43].

4.5. Carbon Pool Index

The carbon pool index (CPI) increased by 38%, 28%, and 19% in Acacia tortilis-, Hardwickia binata-, and Tecomella undulata-based agroforestry systems, respectively, compared to fallow land. This enhancement is attributed to greater availability of organic carbon, litterfall, plant biomass, and root biomass under agroforestry. Similar trends were reported by Kumar et al. [18]. All agroforestry systems exhibited a CPI > 1, indicating enhanced decomposition of organic residues (e.g., litter and root inputs), which promotes soil organic matter (SOM) accumulation and improves soil quality. In the present study, the A. tortilis system recorded the highest CPI (1.43) in the surface layer (0–30 cm), driven by higher concentrations of total organic carbon, very labile carbon, and organic matter. These findings are consistent with previous studies [2,18].
In this study, the carbon lability index (CLI) followed the order of Acacia tortilis (1.18) > Hardwickia binata (1.17) > Tecomella undulata (1.16). Higher CLI values reflect greater concentrations of easily decomposable compounds in the leaf litter, particularly for A. tortilis-based systems [36]. These findings align with earlier studies [2,18,24,40]. Across all agroforestry systems, CLI declined with increasing soil depth, likely due to a reduction in labile carbon and a corresponding increase in recalcitrant fractions—consistent with previous observations [2,18].

4.6. Carbon Management Index

The carbon management index (CMI) ranged from 136.8 to 166.6, with the highest value observed for the Acacia tortilis system at the surface layer (0–30 cm) and the lowest for Tecomella undulata at the deeper layer (30–90 cm). Higher CMI values indicate a greater capacity for carbon storage and improved soil quality [44]. Agroforestry systems exhibited elevated CMI due to improvements in both the quantity and quality of organic matter. Enhanced CMI reflects increased annual carbon inputs and changes in organic matter composition—such as the C/N ratio and levels of lignin, cellulose, hemicellulose, proteins, and carbohydrates—which influence carbon lability and oxidation [45]. Regardless of species, CMI declined with depth, attributable to higher CPI and CLI values in surface soils compared to subsurface layers. These findings are consistent with previous studies [2,18,30].

5. Conclusions

The tree-based agroforestry systems enhance the proportions of very labile C fractions in soil, thereby promoting nutrient availability and better soil quality. Higher recalcitrant C in these systems indicates greater well protected C and C sequestration. Further, among the different tree-based agroforestry systems, the Acacia tortilis-based agroforestry system is very promising in terms of soil total organic carbon, active and passive carbon pools, CMI, CPI, CLI, and C sequestration potential. Thus, conversion of fallow land into these tree-based agroforestry systems can play a tremendous role in mitigating GHS emissions through sequestering atmospheric CO2 in the soil as high amounts of well protected soil organic carbon. In hilly terrain, promoting agroforestry in fallow lands can mitigate huge soil loss, increase a farmer’s income, and promote C sequestration. The absence of continuous crop yield and biomass data also limits the understanding of the aboveground contributions to carbon pools. Furthermore, microbial and biochemical drivers of carbon stabilization were not explored, warranting further investigation. Future research should investigate the seasonal dynamics and long-term stability of different carbon fractions in agroforestry systems under varying climate scenarios. Exploring the microbial mechanisms driving carbon stabilization in deeper soil layers and evaluating above- and below-ground biomass contributions across tree species would provide deeper insights. Additionally, integrating remote sensing for spatial monitoring of carbon sequestration could enhance large-scale agroforestry assessments.

Author Contributions

Conceptualization: M.S., C.K., R.M. and K.K.S.; Soil sampling and analysis: M.S., R.M. and C.K.; Data analysis: M.S., A.P., K.K.M. and A.K.; Writing-original draft preparation: M.S., C.K., R.J.-B., A.P. and K.K.M.; writing—review and editing: C.K., R.J.-B., K.K.S., A.P., K.K.M. and A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All the data are contained in the article.

Acknowledgments

The authors sincerely thanks Dharmendra Tripathi, Assistant Professor, Agriculture Research Station, Fatehpur-Sekhawati, Sri Karan Narendra Agriculture University, Jobner, Rajasthan 303329, India and ICAR-AICRP Unit on Agroforestry project, Jhansi for their support and motivation behind the research.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MDPIMultidisciplinary Digital Publishing Institute
DOAJDirectory of open access journals
TLAThree letter acronym
LDLinear dichroism

References

  1. Lorenz, K.; Lal, R. Soil Organic Carbon Sequestration in Agroforestry Systems—A Review. Agron. Sustain. Dev. 2014, 34, 443–454. [Google Scholar] [CrossRef]
  2. Kaushal, R.; Tewari, S.; Thapliyal, S.D.; Kumar, A.; Roy, T.; Islam, S.; Durai, J. Build-Up of Labile, Non-Labile Carbon Fractions under Fourteen-Year-Old Bamboo Plantations in the Himalayan Foothills. Heliyon 2021, 7, e07850. [Google Scholar] [CrossRef]
  3. Ajit; Dhyani, S.K.; Handa, A.K.; Newaj, R.; Chavan, S.B.; Alam, B. Estimating Carbon Sequestration Potential of Existing Agroforestry Systems in India. Agrofor. Syst. 2017, 90, 1101–1118. [Google Scholar] [CrossRef]
  4. UNFCCC. Adoption of the Paris Agreement. Conference of the Parties, Twenty-First Session, Paris, 30 November to 11 December 2015; United Nations Framework Convention on Climate Change: Geneva, Switzerland, 2015. [Google Scholar]
  5. Rizvi, R.H.; Newaj, R.; Chaturvedi, O.P.; Saxena, A.; Karmakar, P.S.; Maurya, A. State Level Assessment of Area and Carbon Sequestration under Agroforestry Systems for Karnataka, India. In Proceedings of the Conference on Promotion of Agroforestry for Rural Income Generation, Climate Change Mitigation and Adaptation, Kathmandu, Nepal, 27–29 April 2018; Central Agroforestry Research Institute: Jhansi, India, 2018. [Google Scholar]
  6. Nair, P.K.; Mohan Kumar, B.; Nair, V. Agroforestry as a Strategy for Carbon Sequestration. J. Plant Nutr. Soil Sci. 2009, 172, 10–23. [Google Scholar] [CrossRef]
  7. Newaj, R.; Chaturvedi, O.P.; Handa, A.K. Recent Development in Agroforestry Research and Its Role in Climate Change Adaptation and Mitigation. Indian J. Agrofor. 2016, 18, 1–9. [Google Scholar]
  8. Chavan, S.B.; Newaj, R.; Rizvi, R.H.; Prasad, R.; Alam, B.; Handa, A.K.; Tripathi, D. Reduction of Global Warming Potential vis-à-vis Greenhouse Gases through Traditional Agroforestry Systems in Rajasthan, India. Environ. Dev. Sustain. 2021, 23, 4573–4593. [Google Scholar] [CrossRef]
  9. Ramesh, T.; Manjaiah, K.M.; Mohapatra, K.P.; Rajasekar, K.; Ngachan, S.V. Assessment of Soil Organic Carbon Stocks and Fractions under Different Agroforestry Systems in Subtropical Hill Agroecosystems of North-East India. Agrofor. Syst. 2015, 89, 677–690. [Google Scholar] [CrossRef]
  10. Tan, L.P.; He, X.D.; Wang, H.T.; Zhang, N.; Gao, Y.B. Analysis of Soil Water Content in Relation to Accumulation of Pedogenic Calcium Carbonate of Artemisia ordosica Community in Tengger Desert. J. Desert Res. 2008, 28, 701–705. [Google Scholar]
  11. UNFCCC. Report of the Conference of Parties on Its Thirteenth Session, Bali, Indonesia; United Nations Framework Convention on Climate Change: Geneva, Switzerland, 2007. [Google Scholar]
  12. Nair, P.R.; Nair, V.D.; Kumar, B.M.; Showalter, J.M. Carbon Sequestration in Agroforestry Systems. Adv. Agron. 2010, 108, 237–307. [Google Scholar] [CrossRef]
  13. Lal, R. World Crop Residues Production and Implications of Its Use as a Biofuel. Environ. Int. 2005, 31, 575–584. [Google Scholar] [CrossRef]
  14. Blair, G.J.; Lefroy, R.D.; Lisle, L. Soil Carbon Fractions Based on Their Degree of Oxidation and the Development of a Carbon Management Index for Agricultural Systems. Aust. J. Agric. Res. 1995, 46, 1459–1466. [Google Scholar] [CrossRef]
  15. Blair, N.; Faulkner, R.D.; Till, A.R.; Poulton, P.R. Long-Term Management Impacts on Soil C, N and Physical Fertility: Part I: Broad Balk Experiment. Soil Tillage Res. 2006, 91, 30–38. [Google Scholar] [CrossRef]
  16. Wang, Z.; Guo, S.; Sun, Q.; Li, N.; Jiang, J.; Wang, R.; Zhao, M. Soil Organic Carbon Sequestration Potential of Artificial and Natural Vegetation in the Hilly Regions of Loess Plateau. Ecol. Eng. 2015, 82, 547–554. [Google Scholar] [CrossRef]
  17. Ghosh, B.N.; Meena, V.S.; Alam, N.M.; Dogra, P.; Bhattacharyya, R.; Sharma, N.K.; Mishra, P.K. Impact of Conservation Practices on Soil Aggregation and the Carbon Management Index after Seven Years of Maize-Wheat Cropping System in the Indian Himalayas. Agric. Ecosyst. Environ. 2016, 216, 247–257. [Google Scholar] [CrossRef]
  18. Kumar, M.; Panwar, N.R.; Kumawat, R.N.; Santra, P. Influence of Different Land Use Systems on Soil Properties in Hot Arid Rajasthan. J. Indian Soc. Soil Sci. 2020, 68, 121–127. [Google Scholar] [CrossRef]
  19. Solomon, D.; Lehmann, J.; Zech, W. Land Use Effects on Soil Organic Matter Properties of Chromic Luvisols in Semi-Arid Northern Tanzania: Carbon, Nitrogen, Lignin and Carbohydrates. Agric. Ecosyst. Environ. 2000, 78, 203–213. [Google Scholar] [CrossRef]
  20. Jackson, M.L. Soil Chemical Analysis; Prentice-Hall of India Pvt. Ltd.: New Delhi, India, 1973. [Google Scholar]
  21. Schollenberger, C.J. A Rapid Approximate Method for Determining Soil Organic Matter. Soil Sci. 1927, 24, 65–68. [Google Scholar] [CrossRef]
  22. Walkley, A.; Black, I.A. Rapid Titration Method of Organic Carbon of Soils. Soil Sci. 1934, 37, 29–33. [Google Scholar] [CrossRef]
  23. Chan, K.Y.; Bowman, A.; Oates, A. Oxidizable Organic Carbon Fractions and Soil Quality Changes in an Oxic Paleustalf under Different Pasture Leys. Soil Sci. 2001, 166, 61–67. [Google Scholar] [CrossRef]
  24. Datta, A.; Basak, N.; Chaudhari, S.K.; Sharma, D.K. Soil Properties and Organic Carbon Distribution under Different Land Uses in Reclaimed Sodic Soils of North-West India. Geoderma Reg. 2015, 4, 134–146. [Google Scholar] [CrossRef]
  25. Gomez, K.A.; Gomez, A.A. Statistical Procedures for Agricultural Research, 2nd ed.; John Wiley & Sons: New York, NY, USA, 1984; p. 680. [Google Scholar]
  26. Panse, V.G.; Sukhatme, P.V. Statistical Methods for Agricultural Workers, 3rd ed.; ICAR Publications: New Delhi, India, 1978; p. 347. [Google Scholar]
  27. Benbi, D.K.; Brar, K.; Toor, A.S.; Singh, P. Total and Labile Pools of Soil Organic Carbon in Cultivated and Undisturbed Soils in Northern India. Geoderma 2015, 237, 149–158. [Google Scholar] [CrossRef]
  28. Sahoo, U.K.; Singh, S.L.; Gogoi, A.; Kenye, A.; Sahoo, S.S. Active and Passive Soil Organic Carbon Pools as Affected by Different Land Use Types in Mizoram, Northeast India. PLoS ONE 2019, 14, e0219969. [Google Scholar] [CrossRef] [PubMed]
  29. Singh, I.; Rawat, P.; Kumar, A.; Bhatt, P. Soil Physico-Bio-Chemical Properties under Different Agroforestry Systems in Terai Region of the Garhwal Himalayas. J. Pharmacogn. Phytochem. 2018, 7, 2813–2821. [Google Scholar]
  30. Maini, A.; Sharma, V.; Sharma, S. Assessment of Soil Carbon and Biochemical Indicators of Soil Quality under Rainfed Land Use Systems in North Eastern Region of Punjab, India. Carbon Manag. 2020, 11, 169–182. [Google Scholar] [CrossRef]
  31. Thangavel, R.; Kanchikerimath, M.; Sudharsanam, A.; Ayyanadar, A.; Karunanithi, R.; Deshmukh, N.A.; Vanao, N.S. Evaluating Organic Carbon Fractions, Temperature Sensitivity and Artificial Neural Network Modeling of CO2 Efflux in Soils: Impact of Land Use Change in Subtropical India (Meghalaya). Ecol. Indic. 2018, 93, 129–141. [Google Scholar] [CrossRef]
  32. Maia, S.M.F.; Otutumi, A.T.; de Sá Mendonça, E.; Neves, J.C.L.; de Oliveira, T.S. Combined Effect of Intercropping and Minimum Tillage on Soil Carbon Sequestration and Organic Matter Pools in the Semiarid Region of Brazil. Soil Res. 2019, 57, 266–275. [Google Scholar] [CrossRef]
  33. Santos, U.J.; Duda, G.P.; Marques, M.C.; Valente de Medeiros, E.; de Sousa Lima, J.R.; Soares de Souza, E.; Hammecker, C. Soil Organic Carbon Fractions and Humic Substances Are Affected by Land Uses of Caatinga Forest in Brazil. Arid Land Res. Manag. 2019, 33, 255–273. [Google Scholar] [CrossRef]
  34. Cotrufo, M.F.; Wallenstein, M.D.; Boot, C.M.; Denef, K.; Paul, E. The Microbial Efficiency–Matrix Stabilization (MEMS) Framework Integrates Plant Litter Decomposition with Soil Organic Matter Stabilization: Do Labile Plant Inputs Form Stable Soil Organic Matter? Glob. Change Biol. 2013, 19, 988–995. [Google Scholar] [CrossRef]
  35. Liu, X.; Herbert, S.J.; Hashemi, A.M.; Zhang, X.F.; Ding, G. Effects of Agricultural Management on Soil Organic Matter and Carbon Transformation—A Review. Plant Soil Environ. 2006, 52, 531. [Google Scholar] [CrossRef]
  36. Lorenz, K.; Lal, R.; Preston, C.M.; Nierop, K.G. Strengthening the Soil Organic Carbon Pool by Increasing Contributions from Recalcitrant Aliphatic Bio(Macro)Molecules. Geoderma 2007, 142, 1–10. [Google Scholar] [CrossRef]
  37. Singh, J.; Singh, B.; Sharma, S. Comparison of Soil Carbon and Nitrogen Pools among Poplar and Eucalyptus-Based Agroforestry Systems in Punjab, India. Carbon Manag. 2021, 12, 693–708. [Google Scholar] [CrossRef]
  38. Nath, A.J.; Brahma, B.; Sileshi, G.W.; Das, A.K. Impact of Land Use Changes on the Storage of Soil Organic Carbon in Active and Recalcitrant Pools in a Humid Tropical Region of India. Sci. Total Environ. 2018, 624, 908–917. [Google Scholar] [CrossRef]
  39. Didawat, R.K.; Sharma, V.K.; Nath, D.J.; Patra, A.; Kumar, S.; Biswas, D.R.; Anil, A.S. Soil Biochemical Properties and Nutritional Quality of Rice Cultivated in Acidic Inceptisols Using Long-Term Organic Farming Practices. Arch. Agron. Soil Sci. 2023, 69, 1282–1297. [Google Scholar] [CrossRef]
  40. Naik, S.K.; Maurya, S.; Bhatt, B.P. Soil Organic Carbon Stocks and Fractions in Different Orchards of Eastern Plateau and Hill Region of India. Agrofor. Syst. 2017, 91, 541–552. [Google Scholar] [CrossRef]
  41. Paul, E.A.; Collins, H.P.; Leavitt, S.W. Dynamics of Resistant Soil Carbon of Midwestern Agricultural Soils Measured by Naturally Occurring ¹⁴C Abundance. Geoderma 2001, 104, 239–256. [Google Scholar] [CrossRef]
  42. Tanwar, S.P.S.; Kumar, P.; Verma, A.; Bhatt, R.K.; Kumar, A.; Lal, K.; Mathur, B.K. Carbon Sequestration Potential of Agroforestry Systems in the Indian Arid Zone. Curr. Sci. 2019, 117, 2014–2022. [Google Scholar] [CrossRef]
  43. Gupta, D.K.; Bhatt, R.K.; Keerthika, A.; Noor Mohammed, M.B.; Shukla, A.K.; Jangid, B.L. Carbon Sequestration Potential of Hardwickia binata Roxb.-Based Agroforestry in Hot Semi-Arid Environment of India: An Assessment of Tree Density Impact. Curr. Sci. 2019, 116, 112–116. [Google Scholar] [CrossRef]
  44. Kalambukattu, J.G.; Singh, R.; Patra, A.K.; Arunkumar, K. Soil Carbon Pools and Carbon Management Index under Different Land Use Systems in the Central Himalayan Region. Acta Agric. Scand. B Soil Plant Sci. 2013, 63, 200–205. [Google Scholar] [CrossRef]
  45. Tirol-Padre, A.; Ladha, J.K. Assessing the Reliability of Permanganate-Oxidizable Carbon as an Index of Soil Labile Carbon. Soil Sci. Soc. Am. J. 2004, 68, 969–978. [Google Scholar] [CrossRef]
Forests 16 00946 g001
Figure 1. Location map of the experimental site (ARS, Fatehpur).
Figure 1. Location map of the experimental site (ARS, Fatehpur).
Forests 16 00946 g001
Forests 16 00946 g002
Figure 2. Google Earth image of the experimental site (ARS, Fatehpur).
Figure 2. Google Earth image of the experimental site (ARS, Fatehpur).
Forests 16 00946 g002
Forests 16 00946 g003
Figure 3. Total soil organic carbon (TOC) concentrations (g kg−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems in a semi-arid region of western India. Different letters for each parameter show significant difference at p < 0.05 by Duncan’s Multiple Range Test.
Figure 3. Total soil organic carbon (TOC) concentrations (g kg−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems in a semi-arid region of western India. Different letters for each parameter show significant difference at p < 0.05 by Duncan’s Multiple Range Test.
Forests 16 00946 g003
Forests 16 00946 g004
Figure 4. The active carbon pool (g kg−1) and passive carbon pool (g kg−1) in the top and deep soil layers of different tree-based agroforestry systems (37 years old) in a semi-arid region of western India.
Figure 4. The active carbon pool (g kg−1) and passive carbon pool (g kg−1) in the top and deep soil layers of different tree-based agroforestry systems (37 years old) in a semi-arid region of western India.
Forests 16 00946 g004
Forests 16 00946 g005
Figure 5. SOC stock (Mg C ha−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems. Different letters for each parameter show significant difference at p < 0.05 by Duncan’s Multiple Range Test.
Figure 5. SOC stock (Mg C ha−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems. Different letters for each parameter show significant difference at p < 0.05 by Duncan’s Multiple Range Test.
Forests 16 00946 g005
Table 1. The agroforestry systems chosen as treatments in the present study.
Table 1. The agroforestry systems chosen as treatments in the present study.
Sl. No.Agroforestry Tree Species (Botanical Name)Local NameFamilySpacing and Plot Size
1.Acacia tortilisIsraeli baboolFabaceae5 m × 5 m (15 rows)
2.Hardwickia binataAnjanFabaceae5 m × 5 m (15 rows)
3.Tecomella undulataRohidaBignoniaceae5 m × 5 m (15 rows)
Table 2. Soil organic carbon fractions (g kg−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems in a semi-arid region of western India.
Table 2. Soil organic carbon fractions (g kg−1) in the top and deep soil layers of different 37-year-old tree-based agroforestry systems in a semi-arid region of western India.
Very Labile Carbon Fraction (g kg−1)Labile Carbon Fraction (g kg−1)
Agroforestry systems0–1515–3030–6060–90Mean0–1515–3030–6060–90Mean
Acacia tortilis1.72 a1.24 a0.77 a0.54 a1.070.81 a0.65 a0.43 a0.28 a0.54
Hardwickia binata1.58 b1.22 a0.75 ab0.52 a1.020.75 b0.61 a0.40 ab0.26 a0.51
Tecomella undulata1.56 bc1.21 a0.72 b0.51 a1.000.72 bc0.54 b0.37 b0.25 a0.47
Fallow land0.79 d0.65 b0.44 c0.32 b0.550.63 d0.49 c0.29 c0.24 a0.41
Less Labile Carbon Fraction (g kg−1)Recalcitrant Carbon Fraction (g kg−1)
Agroforestry systems0–1515–3030–6060–90Mean0–1515–3030–6060–90Mean
Acacia tortilis0.84 a0.83 a0.65 a0.51 a0.711.07 a1.04 a0.91 a0.73 a0.94
Hardwickia binata0.82 a0.73 b0.62 a0.49 a0.661.01 a0.97 b0.87 a0.71 a0.89
Tecomella undulata0.80 ab0.72 bc0.56 b0.47 ab0.640.99 ab0.96 bc0.81 ab0.69 a0.86
Fallow land0.78 ac0.70 bcd0.53 bc0.43 c0.610.87 c0.82 d0.74 c0.67 a0.78
Within a column, values indicated by different small letters are significantly different at the (p ≤ 0.05) level of probability based on the DMRT method.
Table 3. Soil organic carbon content of varying lability in the top and deep soil layers (0–90 cm) of different tree-based agroforestry systems (37 years old) in a semi-arid region of western India.
Table 3. Soil organic carbon content of varying lability in the top and deep soil layers (0–90 cm) of different tree-based agroforestry systems (37 years old) in a semi-arid region of western India.
Agroforestry SystemsTOCVLCLCLLCRCACPPCP
Acacia tortilis3.25 a1.07 a0.54 a0.71 a0.94 a1.61 a1.64 a
Hardwickia binata3.08 b1.02 b0.51 a0.66 b0.89 a1.52 b1.55 b
Tecomella undulata2.98 bc1.00 bc0.47 ab0.64 bc0.86 ab1.47 bc1.50 bc
Fallow land2.35 d0.55 d0.41 c0.61 cd0.78 c0.96 d1.39 d
Column wise, values indicated by identical small letters are statistically similar at the (p ≤ 0.05) level of probability based on the DMRT method.
Table 4. Carbon pool index, lability index, and carbon management index in the top and deep soil layers for different 37-year-old tree-based agroforestry systems in a semi-arid region of western India.
Table 4. Carbon pool index, lability index, and carbon management index in the top and deep soil layers for different 37-year-old tree-based agroforestry systems in a semi-arid region of western India.
Top Soil Layer (0–30 cm)Deep Soil Layer (30–90 cm)
Agroforestry systemsCPICLICMICPICLICMI
Acacia tortilis1.43 a1.18 a166.58 a1.31 a1.14 a149.13 a
Hardwickia binata1.34 b1.17 a157.68 b1.26 a b1.14 a142.42 b
Tecomella undulata1.31 b1.16 a153.44 c1.21 b1.13 a136.82 c
Fallow
land		1 c1 b100 d1 c1 b100 d
Within a column, values indicated by different small letters are significantly different (p ≤ 0.05) based on the DMRT method.
Table 5. Pearson’s correlation coefficient between various organic carbon fractions in soils (0–90 cm soil depth) for various tree-based agroforestry systems.
Table 5. Pearson’s correlation coefficient between various organic carbon fractions in soils (0–90 cm soil depth) for various tree-based agroforestry systems.
TOCCVLCLCLLCR
TOC1
CVL0.983 **1
CL0.979 **0.936 **1
CLL0.951 **0.882 **0.975 **1
CR0.970 **0.936 **0.936 **0.944 **1
** significant at the 1% level. CVL—Very labile carbon, CL—Labile carbon, CLL—Less labile carbon, CR—Recalcitrant carbon.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sirimalle, M.; Kumawat, C.; Jiménez-Ballesta, R.; Meena, R.; Sharma, K.K.; Patra, A.; Mohapatra, K.K.; Kumawat, A. Impact of Long-Term Agroforestry Systems on Carbon Pools and Sequestration in Top and Deep Soil Layers of Semi-Arid Region of Western India. Forests 2025, 16, 946. https://doi.org/10.3390/f16060946

AMA Style

Sirimalle M, Kumawat C, Jiménez-Ballesta R, Meena R, Sharma KK, Patra A, Mohapatra KK, Kumawat A. Impact of Long-Term Agroforestry Systems on Carbon Pools and Sequestration in Top and Deep Soil Layers of Semi-Arid Region of Western India. Forests. 2025; 16(6):946. https://doi.org/10.3390/f16060946

Chicago/Turabian Style

Sirimalle, Mahesh, Chiranjeev Kumawat, Raimundo Jiménez-Ballesta, Ramu Meena, Kamlesh Kumar Sharma, Abhik Patra, Kiran Kumar Mohapatra, and Arvind Kumawat. 2025. "Impact of Long-Term Agroforestry Systems on Carbon Pools and Sequestration in Top and Deep Soil Layers of Semi-Arid Region of Western India" Forests 16, no. 6: 946. https://doi.org/10.3390/f16060946

APA Style

Sirimalle, M., Kumawat, C., Jiménez-Ballesta, R., Meena, R., Sharma, K. K., Patra, A., Mohapatra, K. K., & Kumawat, A. (2025). Impact of Long-Term Agroforestry Systems on Carbon Pools and Sequestration in Top and Deep Soil Layers of Semi-Arid Region of Western India. Forests, 16(6), 946. https://doi.org/10.3390/f16060946

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop