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Tree diversity and carbon important species vary with traditional agroforestry managers in the Indian Eastern Himalayan region
2022
Nath, Panna Chandra | Thangjam, Uttam | Kalita, Sidhartha Sankar | Sahoo, Uttam Kumar | Giri, Krishna | Nath, Arun Jyoti
Traditional agroforestry systems, one of the time tested and dominant land use from tropical to sub-tropical regions, were recognized for their contributions to food production, biodiversity conservation, and atmospheric carbon sequestration. Their management often varies from region to region. However, these systems frequently mimic economically managed land uses due to increased pressure on the monetary requirement of their managers. The present study aims to evaluate (i) tree density, (ii) tree diversity indices, and (iii) identify the biomass carbon important tree species managed by different communities of the Indian Eastern Himalayan region. We found that the Mizo community harbored the highest number of tree species (35) in the traditional agroforestry system with the highest tree diversity index (3.47). Total biomass carbon of tropical agroforestry systems managed by different communities ranged between 4.72 Mg ha⁻¹ (Meitei) and 29.26 Mg ha⁻¹ (Bengali). Similarly, in the sub-tropical traditional agroforestry system, the highest and the lowest biomass carbon was observed in Mizo- (10.93 Mg ha⁻¹) and Angami- (6.05 Mg ha⁻¹) managed systems. Of the 31 biomass carbon, important species found across the traditional agroforestry systems, Artocarpus heterophyllus, had the highest occurrence (50%), followed by Parkia timoriana (37.5) and Amoora rohituka, Delonix regia, Mangifera indica, and Toona ciliata (25% for each species). Farmers’ preference to cash return of a species, trees density, and basal area were the determinant factors in the carbon stock potential of these systems. The present study suggests that the farmers’ preferred and dominant species in their agroecosystems have a limited scope of enhanced biomass carbon storage. Therefore, improvement of traditional agroforestry systems through selective incorporation of biomass carbon important tree species is recommended to enhance the carbon sink capacity of these systems.
Показать больше [+] Меньше [-]Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India
2021
Watson, Ancy S | Bai R, Sudha
Air pollutant concentration of Trivandrum, the capital of Kerala, exceeded the limits of National Ambient Air Quality (NAAQ) standards, according to a study conducted in 2015 by NATPAC. These polluted corridors harbour vegetation on roadsides and traffic islands, planted solely for aesthetic appeal. Analysis of air pollution tolerance levels of existing plants can act as a scientific basis for efficient planning of the urban landscape. Sixty-seven species, including flowering, fruit-bearing, ornamental, shade-providing and timber-yielding species, were screened for their relative resistance to air pollution. Based on leaf pH, relative water content, chlorophyll and ascorbic acid levels, the Air Pollution Tolerance Indices (APTI) of each species were formulated and they were grouped into the following: tolerant, moderately tolerant, intermediate and sensitive groups. Agave americana (18.40), Cassia roxburghii (17.63), Anacardium occidentale (11.97), Cassia fistula (11.60), Mangifera indica (11.59) and Saraca asoca (10.88) may be considered for planting near green spaces like roundabouts and near pollution prone industrial areas, as they belong to tolerant category. Comparison of APTI during summer and monsoon also revealed the stability of Agave americana, Saraca asoca, Ficus benghalensis, Peltophorum pterocarpum, Ficus elastica, Ixora finlaysoniana, Mangifera indica, Canna indica and Delonix regia in maintaining pollution tolerance even during water disparity. Agave americana, Anacardium occidentale, Ficus elastica, Mangifera indica, Syzygium cumini, Ficus benghalensis, Nerium oleander and Ficus benjamina were found to be suited for mass planting, as was evident from their Anticipated Performance Indices (API).
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