Soil Health and Carbon Stock Enhancement through Fruit Tree-based Agroforestry in the Degraded Lands of Central Kashmir Himalayas

Materials and methods

The study was carried out in the experimental field of Division of Silviculture and Agroforestry, Faculty of Forestry, Ganderbal, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir (J&K) at 340 16ʹ 46ʹʹ N and 740 46ʹ 18ʹʹ E with an elevation of 1790 m (5872 feet) above mean sea level. The climate varies considerably with the altitude. It is mild and salubrious in the lower elevations but very cold in the higher elevations. Average minimum and maximum temperature varies from -5.4 to 38 °C.

Experimental methodology

Experimental details:

Treatments:

Seed sowing:

Harvesting of the crops

First harvesting of Rajmash was done in June, followed by the second in September, and harvesting of moong was done in the month of October.

Details of observation recorded

Physico-chemical characteristics of soil: Before laying out the experiment, random soil samples were collected from the depth of 0-20 cm from different spots, and the composite sample for each replication was prepared, which was analyzed for various soil characteristics in order to get information about the physico-chemical properties of the soil. After the experiment, the samples from each plot were again drawn and analyzed for various characteristics by the standard methods.

Methods employed to determine physico-chemical properties of soil:

g) Soil moisture (%)

The soil moisture content was determined gravimetrically. Before laying out the experiment, random soil samples were collected up to a depth of 20 cm, using an auger, and the composite sample was prepared. The composite sample was dried at 105 ºC till constant weight, and the soil moisture content was calculated as follows:

$\text{Soil moisture }\%=\frac{\text{Fresh weight }–\text{ Dry weight}}{\text{Fresh weight}}\times \text{ 1}00$

After the experiment, the samples from each plot were again drawn, and soil moisture (%) content was determined.

h) Soil bulk density

Bulk density is the ratio of the oven-dry mass of the solids to the volume (the bulk volume includes the volume of the solids and of the pore space) of the soil. It was analyzed following the weighing bottle method. A measuring cylinder of 100 ml was weighed and filled with soil, with continuous tapping of the bottom of the cylinder until a soil volume of 100ml was obtained. Then the weight of the cylinder containing soil was recorded. The procedure was replicated thrice, and the average weight was taken. The bulk density of the soil samples was calculated by the formula:

$\text{Bulk density }\left(\text{g}/{\text{cm}}^{\text{2}}\right)=\frac{\text{Dry weight of soil sample }\left(\text{g}\right)}{\text{Volume occupied by the same soil sample }\left({\text{cm}}^{\text{2}}\right)}\times \text{ 1}00$

Carbon stock

a. Stem biomass:

Fruit Trees:

Y= exp{-2.4090+0.9522 ln(D2HS)} [15]

Where,

Y= Biomass per tree in kg

D= collar diameter of fruit trees

H= Tree height in cm

S= Wood density (g cm^-3)

b. Wood density (g cm^-3)

The wood density was calculated following the procedure prescribed by Bhatt and Toderia [16].

Wood density = Mass/volume.

c. Estimation of the biomass of the canopy

Canopy Biomass = Crown Volume x Specific gravity

The vol. occupied by the crown was estimated by

d. Crown volume (m3)

$CV=\frac{\Pi D{b}^{2}L}{12}$

The Crown volume was calculated following the procedure prescribed by Avery and Burkhart [17].

Where,

CV= Crown volume (m³)

Db= Diameter (m) at crown base

L= Crown length (m)

e. Specific gravity:

The stem cores were taken to find out specific gravity, which was used further to determine the biomass of the stem using the maximum moisture method [18].

$G_f=\frac{1}{\frac{M_n}{M_0}-\frac{M_{0}1}{M_{s0}}}$

Where,

G_f = specific gravity based on gross volume

M_n = weight of saturated volume sample

M_o = weight of oven-dried sample

G_so = Average density of wood substance equal to 1.53

f. Fruit biomass t ha^-1

The fruit samples (1 kg) were sun-dried for 4 to 5 days, followed by oven drying at 60ºC till constant weight. Dry weight of the fruit samples was recorded in grams and then worked out in t ha^-1.

Tree Biomass = Stem Biomass + Canopy Biomass + fruit biomass

g. Estimation of below-ground biomass

Below ground biomass = Above ground biomass x 0.33 [19]

Total biomass = Above-ground biomass + below-ground biomass.

h. Estimation of Carbon Density

C (t ha^-1) = Total biomass (t ha^-1) x 0.5 (IPCC, 2006)

i. Soil carbon (t ha^-1) = [(soil bulk density (g cm^-3) x (soil depth (cm) x C (%)] x100 [20].

j. Total carbon pool of system (t ha^-1) = crop carbon density + tree carbon density + soil carbon.

Statistical analysis

All recorded data on soil physico-chemical properties (pH, electrical conductivity, soil organic carbon, available N, P, and K, soil moisture, and bulk density), biomass components, and carbon stock parameters were subjected to statistical analysis following the procedures described by Gomez and Gomez [21]. The experiment was laid out in a Randomized Block Design (RBD) with eight treatments and four replications.

Analysis of variance (ANOVA) was performed to test the significance of treatment effects on the measured variables. Treatment means were compared using the Least Significant Difference (LSD) test at the 5% level of significance (p ≤ 0.05). Standard error of mean (SEm) and critical difference (CD) values were calculated wherever treatment effects were found significant. All statistical analyses were carried out using R statistical software.

The experimental data about soil health parameters, biomass accumulation, and carbon stocks were analyzed statistically using analysis of variance (ANOVA) appropriate for a Randomized Block Design (RBD). The significance of differences among treatment means was tested at the 5% probability level. Wherever significant differences occurred, treatment means were separated using the Least Significant Difference (LSD) test. Statistical computations were performed using R software as outlined by Gomez and Gomez [21].

Results

Effect of agroforestry systems on physico-chemical properties of soil

The results indicated that fruit tree–based agroforestry systems significantly influenced soil physico-chemical properties compared to sole cropping systems (Tables 1-3). Parameters such as bulk density, soil pH, electrical conductivity, soil organic carbon, available nitrogen, phosphorus, potassium, and soil moisture showed marked improvement under agroforestry treatments.

Soil bulk density

Agroforestry plots recorded significantly lower soil bulk density at harvest (1.29–1.31 g cm⁻³) compared to sole cropping systems (1.33–1.34 g cm⁻³) (Table 1). Among treatments, the lowest bulk density was observed under Apricot + Rajmash (T3), while the highest was recorded in sole Moong and Rajmash plots (T7 and T8).

Soil pH and electrical conductivity

Soil pH and EC values decreased under agroforestry systems compared to open field conditions. The minimum soil pH (6.61) and EC (0.21 dS m⁻¹) were recorded under Apricot-based agroforestry treatments, whereas sole cropping treatments showed relatively higher values (Table 1).

Available nitrogen, phosphorus, and potassium

Available N, P, and K increased significantly under agroforestry systems at the time of harvesting. Maximum available nitrogen (361.23 kg ha⁻¹), phosphorus (17.43 kg ha⁻¹), and potassium (224.58 kg ha⁻¹) were recorded under Apricot + Moong bean (T4), while minimum values were observed in sole cropping treatments (Table 2).

Soil organic carbon and soil moisture

Soil organic carbon content increased significantly under agroforestry treatments, with the highest value (0.68%) recorded under Apricot + Rajmash (T3) (Table 3). Soil moisture content was also significantly higher in agroforestry plots, with a maximum of 10.01% observed in Apricot + Moong bean (T4).

Biomass production of fruit trees

Agroforestry treatments significantly influenced stem biomass, canopy biomass, fruit biomass, above-ground biomass, below-ground biomass, and total biomass of fruit trees (Table 4)(Figure 1). The highest stem biomass (8.22 t ha⁻¹), canopy biomass (3.36 t ha⁻¹), fruit biomass (1.52 t ha⁻¹), and total tree biomass (17.42 t ha⁻¹) were recorded under Apricot + Rajmash (T3), followed closely by Apricot + Moong bean (T4). The lowest biomass values were observed under sole Peach plantations (T6).

Carbon density and carbon stock

Tree carbon density, soil carbon stock, and total ecosystem carbon pool varied significantly among treatments (Table 5). The highest tree carbon density (8.71 t ha⁻¹) and total carbon pool (52.88 t ha⁻¹) were recorded under Apricot + Rajmash (T3), whereas the lowest total carbon pool (34.13 t ha⁻¹) was observed under sole Moong bean (T7). Soil carbon stock ranged from 33.50 to 43.75 t ha⁻¹, with agroforestry treatments consistently outperforming sole cropping systems.

Discussion

Soil health improvement under agroforestry systems

The reduction in soil bulk density under agroforestry systems can be attributed to increased organic matter inputs from tree litter and root turnover, which improve soil aggregation and porosity. Similar reductions in bulk density under tree-based systems have been reported by Bargali, et al. [22] and Kumar, et al. [23] in Himalayan agroforestry systems. Lower soil pH and EC observed under agroforestry systems are likely due to organic acid production during litter decomposition and enhanced leaching of soluble salts. Comparable trends have been reported by Pandey, et al. [24] in fruit-based agroforestry systems.

Enhancement of soil nutrient status

The significant increase in available N, P, and K under agroforestry systems highlights the role of trees in nutrient cycling through litterfall, root exudation, and microbial activity. Recent studies by Tamang, et al. [25] and Dhyani, et al. [26] also reported higher soil fertility in agroforestry compared to monocropping systems.

Soil organic carbon and moisture dynamics

Higher soil organic carbon and moisture content under agroforestry systems confirm their potential in restoring degraded soils. Increased carbon inputs from perennial tree components and reduced evaporation losses due to canopy cover explain these improvements. Similar findings were reported by Nair, et al. [27], Rao, et al. [28], and Chaudhary, et al. [29].

Biomass accumulation and carbon sequestration

Greater biomass accumulation under Apricot-based agroforestry systems reflects species-specific growth characteristics and positive tree–crop interactions. Trees contribute significantly to above- and below-ground carbon pools, enhancing overall carbon sequestration. Studies by Jose, et al. [30], Yadav et al. [31], and Mandal, et al. [32] support the superior carbon sequestration potential of agroforestry systems over sole cropping.

Ecosystem carbon stock and climate mitigation potential

The higher total ecosystem carbon stock recorded under agroforestry treatments underscores their importance in climate change mitigation. Integration of fruit trees with annual crops offers a sustainable land-use option for degraded Himalayan landscapes, as also reported by Benbi, et al. [33], Kumar, et al. [34], and FAO [35,36].

Conclusion

The findings of this study clearly demonstrate that agroforestry systems significantly improve soil physico-chemical properties and enhance carbon sequestration compared to sole cropping systems. Tree-based intercropping practices led to reduced soil bulk density, pH, and electrical conductivity, while boosting organic carbon, available macronutrients (N, P, K), and soil moisture content. These improvements are attributed to continuous organic inputs through litterfall, root turnover, and enhanced microbial activity under diversified vegetation cover. Furthermore, agroforestry systems exhibited higher biomass and soil carbon stocks, with the combination of fruit trees and legumes (e.g., Apricot + Rajmash) showing the greatest potential for carbon accumulation. The results confirm that agroforestry serves a dual function: improving soil health and acting as an effective carbon sink. Thus, integrating fruit trees with crops is a promising climate-smart land use strategy that supports both environmental sustainability and long-term soil productivity, particularly in fragile ecosystems like the Indian Himalayas.