Leaf area index (LAI) of Teak (Tectona grandis) and Bamboo (Dendrocalamus strictus) grown in Shoolpaneshwar Wildlife Sanctuary of Narmada District, Gujarat, India was obtained by destructive sampling, photo-grid method and by litter trap method. An allometric equation (between leaf area by litter trap method and canopy spread area) was developed for the determination of LAI. Results show that LAI value calculated by the developed allometric equation was similar to that estimated by destructive sampling and photo-grid method, with Root Mean Square Error (RMSE) of 0.90 and 1.15 for Teak, and 0.38 and 0.46 for Bamboo, respectively. There was a perfect match in both the LAI values (estimated and calculated), indicating the accuracy of the developed equations for both the species. In conclusion, canopy spread is a better and sensitive parameter to estimate leaf area of trees. The developed equations can be used for estimating LAI of Teak and Bamboo in tropics.

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Background: Assessment of carbon pools in semi-arid forests of India is crucial in order to develop a better action plan for management of such ecosystems under global climate change and rapid urbanization. This study, therefore, aims to assess the above-and belowground carbon storage potential of a semi-arid forest ecosystem of Delhi. Methods: For the study, two forest sites were selected, i.e., north ridge (NRF) and central ridge (CRF). Aboveground tree biomass was estimated by using growing stock volume equations developed by Forest Survey of India and specific wood density. Understory biomass was determined by harvest sampling method. Belowground (root) biomass was determined by using a developed equation. For soil organic carbon (SOC), soil samples were collected at 0-10-cm and 10-20-cm depth and carbon content was estimated. Results: The present study estimated 90.51 Mg ha −1 biomass and 63.49 Mg C ha −1 carbon in the semi-arid forest of Delhi, India. The lower diameter classes showed highest tree density, i.e., 240 and 328 individuals ha −1 (11-20 cm), basal area, i.e., 8.7 (31-40 cm) and 6.08 m 2 ha −1 (11-20 cm), and biomass, i.e., 24.25 and 23.57 Mg ha −1 (11-20 cm) in NRF and CRF, respectively. Furthermore, a significant contribution of biomass (7.8 Mg ha −1 ) in DBH class 81-90 cm in NRF suggested the importance of mature trees in biomass and carbon storage. The forests were predominantly occupied by Prosopis juliflora (Sw.) DC which also showed the highest contribution to the (approximately 40%) tree biomass. Carbon allocation was maximum in aboveground (40-49%), followed by soil (29.93-37.7%), belowground or root (20-22%), and litter (0.27-0.59%). Conclusion: Our study suggested plant biomass and soils are the potential pools of carbon storage in these forests. Furthermore, carbon storage in tree biomass was found to be mainly influenced by tree density, basal area, and species diversity. Trees belonging to lower DBH classes are the major carbon sinks in these forests. In the study, native trees contributed to the significant amount of carbon stored in their biomass and soils. The estimated data is important in framing forest management plans and strategies aimed at enhancing carbon sequestration potential of semi-arid forest ecosystems of India.

Background: Soil respiration (S R) is a critical process for understanding the impact of climatic conditions and land degradation on the carbon cycle in terrestrial ecosystems. We measured the S R and soil environmental factors over 1 year in four land uses with varying levels of disturbance and different vegetation types viz., mixed forest cover (MFC), Prosopis juliflora (Sw.) forest cover (PFC), agricultural field (AF), and vegetable field (VF), in a semi-arid area of Delhi, India. Our primary aim was to assess the effects of soil moisture (S M), soil temperature (S T), and soil microbial activity (S MA) on the S R. Methods: The S R was measured monthly using an LI-6400 with an infrared gas analyser and a soil chamber. The S M was measured using the gravimetric method. The S T (10 cm) was measured with a probe attached to the LI-6400. The S MA was determined by fluorescein diacetate hydrolysis. Results: The S R showed seasonal variations, with the mean annual S R ranging from 3.22 to 5.78 μmol m −2 s −1 and higher S R rates of~15-55% in the cultivated fields (AF, VF) than in the forest sites (MFC, PFC). The VF had significantly higher S R (P < 0.05) than the other land uses (AF, PFC, MFC), which did not vary significantly from one another in S R (P < 0.05). The repeated measures ANOVA evaluated the significant differences (P < 0.05) in the S R for high precipitation months (July, August, September, February). The S M as a single factor showed a strong significant relationship in all the land uses (R 2 = 0.67-0.91, P < 0.001). The effect of the S T on the S R was found to be weak and non-significant in the PFC, MFC, and AF (R 2 = 0.14-0.31; P > 0.05). Contrasting results were observed in the VF, which showed high S R during summer (May; 11.21 μmol m −2 s −1) and a significant exponential relationship with the S T (R 2 = 0.52; P < 0.05). The S R was positively related to the S MA (R 2 = 0.44-0.5; P < 0.001). The interactive equations based on the independent variables S M , S T , and S MA explained 91-95% of the seasonal variation in S R with better model performance in the cultivated land use sites (AF, VF). Conclusion: S M was the key determining factor of the S R in semi-arid ecosystems and explained~90% of the variation. Precipitation increased S R by optimizing the S M and microbial activity. The S MA , along with the other soil factors S M and S T , improved the correlation with S R. Furthermore, the degraded land uses will be more susceptible to temporal variations in S R under changing climatic scenarios, which may influence the carbon balance of these ecosystems.