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Climate change exerts a substantial influence on global livelihood security. This research aims to elucidate the risk faced by agroforestry managers of urban and rural areas. Adhering to the IPCC risk framework, we structured the experimental design and adopted an indicator-based methodology to delineate the risk dimensions. Altogether, 105 households from 7 villages in Aizawl district, Mizoram, India, were considered for the study. For indicator identification, we conducted a comprehensive literature review and subsequently employed principal component analysis to select relevant indicators. Finally, risk was determined using the index value of hazard, exposure, and vulnerability. Additionally, we also developed a regression model and integrated it into ArcGIS to generate a spatial risk map. Out of 69 indicators identified, 52 were selected for final assessment after PCA analysis. Our findings underscore the higher susceptibility of urban agroforestry managers to climate change which was in agreement to our hypothesis that the risk index of agroforestry households increases with altitude while it decreases with the distance from Aizawl headquarter. Furthermore, we observed that households residing at higher altitudes exhibit greater vulnerability. Key determinants contributing to elevated risk in the region encompass land ownership constraints, diminished yields, traditional farming practices with no institutional help, and a dearth of available labour resources. The study advocates the implementation of climate smart agroforestry practices integrated with agricultural credit schemes and an educational policy designed to enrol dropout youths. [ABSTRACT FROM AUTHOR]

Uncertainties continue to prevail in the potential of natural forests and plantations in carbon stock assessment. The present study was carried out to assess the carbon stock in natural and plantation forests of Manipur using geo-informatics in Imphal East and West districts. The integrated approach of geospatial technology, along with field inventory based data, was used in spatial modeling of biomass carbon of selected natural and plantation forests. The stand density was similar for both LNG and TRS forests (680 individuals ha−1) and lowest for KHP forest (640 individuals ha−1). Paulownia fortunei (770 individuals ha−1) showed highest density among tree species while Tectona grandis (54.07 m2 ha−1) followed by Gmelina arborea (42.18 m2 ha−1) had higher basal area compared to other tree species. The soil moisture content (%) in the natural forest ranged from 19.13 ± 0.47 to 26.9 ± 0.26%. The soil moisture content in the plantation forest ranged from 19.16 ± 0.98 to 25.83 ± 0.06%. The bulk density of natural forests ranged from 1.27 g cm−3 to 1.37 g cm−3 while for plantation forests it ranged from 1.18 g cm−3 to 1.34 g cm−3. Among the studied sites of natural forest, TRS forest had both the highest AGBC value of 132.74 t ha−1 as well as the BGBC value of 38.49 t ha−1. Similarly, among the plantations, T. grandis plantation showed the highest AGBC (193 t ha−1) and BGBC (55.97 t ha−1). On the other hand, Tharosibi forest and T. grandis plantation had the highest total carbon stock for natural and plantation forest with values of 274.824 t ha−1 and 390.88 t ha−1, respectively. The total above-ground carbon stock estimated for the natural forest of KHP, LNG and TRS were 109.60 t ha−1, 79.49 t ha−1 and 132.74 t ha−1, respectively. On the other hand, the estimated total above-ground carbon stock in plantation of GA, PD, PF and TG were 62.93 t ha−1 62.81 t ha−1, 45.85 t ha−1 and 193.82 t ha−1. In the present study, the relationship with the biomass was observed to be better in SAVI compared to NDVI and TVI. The linear regression analysis performed to determine the relationship between the estimated and predicted biomass resulted in a correlation coefficient of R2 = 0.85 for the present study area, which is an indication of a good relationship between the estimated and predicted biomass. [ABSTRACT FROM AUTHOR]

Soil carbon sequestration is vital to reduce the atmospheric carbon concentration, which is expected to increase within future climatic scenarios. The present study aims to investigate the effects of land use changes to different plantation systems on SOC stocks, and simulate these changes using the RothC model in Mizoram, India. With this aim, four land uses, viz., secondary forest, oil palm, orange, and arecanut plantations, established on degraded shifting cultivation lands, and a control natural forest were selected for this study. The soils were sampled 0–30 cm in the secondary forest, plantations, and the natural forest, at an interval of five years. Measured SOC stocks were the highest in the secondary forest (67.0 Mg C ha−1) and the lowest under the oil palm plantation (37.4 Mg C ha−1), 10 years after land use conversion. The climate change projections for 2021–2035 and 2036–2050 indicated that temperature and rainfall changes, projected to increase by 0.8 and 2.0 °C, and 5.9 and 5.4%, respectively, will affect SOC stocks in the future differently, depending on the land use and carbon input from vegetation. Baseline climate simulations under land use change showed the highest increase in the SOC stock under the secondary forest (116%), and the lowest in the oil palm plantation (27%). Overall, the model predicted that SOC stocks would increase, but the rate of change (0.23–1.86 Mg C ha−1 yr−1) varied with different land uses, plant species, and land management practices. The model results indicated that restoring secondary forest following the abandonment of a shifting cultivation and orange plantations are the best options to improve SOC stocks within future climate change scenarios. Conversely, arecanut and oil palm need to be reduced because the SOC storage is lower. [ABSTRACT FROM AUTHOR]

Chir pine (Pinus roxburghii, Sarg.) forests are dominant in the Indian Himalayan region and act as a huge carbon (C) sink. However, measuring the C sink in soil is complex and time‐intensive, and therefore the present study attempts to estimate the soil organic carbon (SOC) through a remote sensing (RS) approach. We estimated SOC stock of chir pine forests along an altitudinal gradient at three soil depths (0–30, 30–60 and 60–100 cm) in the Garhwal Himalaya, Uttarakhand. Fourteen forest stands at four altitudes, viz., <1000 m above sea level (m asl), 1001–1400 m asl, 1401–1800 m asl and >1801 m asl were surveyed and served for data collection. A model for predicting SOC was developed through stepwise regression analysis based on vegetation information and altitude as independent variables with the field data on SOC. For vegetation information, we used the normalized difference vegetation index (NDVI) measured through remote sensing (RS). The mean SOC stock up to 100 cm depth was increased with increasing altitude and were in the order of 69.66 ± 19.86, 85.27 ± 17.53, 95.68 ± 7.90 and 148.41 ± 71.39 million g ha−1 (million gram per hectare) for <1000, 1001–1400, 1401–1800 and >1801 m_asl, respectively. The result showed that NDVI was a good predictor for SOC estimation. The model predicted SOC stock between 57 and 152 million g ha−1 with a mean of 93 million g ha−1, which was close to the SOCs from field inventory. Therefore, RS could be used to precisely map the SOC stock in the chir pine forests of the Himalayas through NDVI and provide information to policymakers for forest management. [ABSTRACT FROM AUTHOR]

Climate change is intricately linked with amplifying existing risk across multiple sectors, and assessment of the indigenous farmers practising jhum (jhumias) is highly necessary for developing adaptation strategies in the biogeographically-diverse northeast Himalayan region. The current study estimates the Shared Socio-Economic Pathway 2 (SSP2) – 4.5 hazard scenario for the year 2040–2059 using Coupled Model Inter-comparison Project 6 (CIMP6) (mean projection) and Multi-Model Ensemble. The remaining risk dimensions (exposure and vulnerability) were derived from household survey data involving 450 households from 18 villages representing six districts, three states, and four watershed basins. This framework used an indicator approach for defining risk dimensions and a spatial approach for the final risk presentation and classification. Principal Component Analysis (PCA) was used to select and group the indicators. All chosen indicators underwent varimax rotation, and factors with eigenvalues greater than one were considered for grouping into components. Different weights for each indicator were defined using the Shanon entropy method. Climate change risk was found to be highest in Khudengthabi village in Chandel district, followed by Sangshak village in Ukhrul district. Lack of land ownership, low yield, no alternative livelihood option, an insufficient market, and unavailability of labour force are some of the major factors contributing to higher risk in the area. The study recommends skill development and alternative livelihood programmes with a new education policy that encourages the dropout youth to continue in schools and colleges. Subsequent policy initiatives should prioritize villages, indicators and components with high index scores to optimize resource allocation and facilitate quicker adaptation. [Display omitted] • Jhumias sole dependence on climate variability has called for the risk analysis. • Shared Socio-Economic Pathway 2–4.5 (2040–2059) was used for hazard model. • Indicator approach, augmented by Entropy weight method was used for index estimation. • Result found Khudengtabi village (Manipur) and watershed-180 to have highest risk. • Recommend policy change towards land ownership, livelihood options, and education. • The exclusive reliance of Jhumias on climate variability necessitates a risk analysis. • Hazard model was based on the Shared Socio-Economic Pathway 2–4.5 (2040–2059). • Index estimation used an indicator approach, supplemented by Entropy weight method. • Results showed highest risk in Khudengtabi village under watershed-180. • Suggest policy change towards land ownership, livelihood opportunities and education. [ABSTRACT FROM AUTHOR]

We studied flowering phenological events of Rhododendron arboreum Sm. at two altitudes (1945 m and 2157 m asl) in Phawngpui National Park for 4-years period. Our results revealed that all the flowering phenophases (onset, peak and end) were significantly influenced by altitude mediated atmospheric temperature and soil surface temperature. The low altitude which experienced higher surface and atmospheric temperature induced early onset of flowering of this species by 9-16.days, peak flowering by 7-9 days and showed better flowering synchrony and higher flowering amplitude compared to those plants at high altitude. However, flowering duration was not affected by the variations in temperature induced by altitude. These findings provide clues for climate change influence on the altering phenological pattern of this species. [ABSTRACT FROM AUTHOR]

This study utilizes household data to examine the Jhumias susceptibility to the changing climate by using the lens of socio-economic vulnerability framework. 150 households spread across 6 villages were surveyed to understand association between Jhumias and their physical and social surroundings. The data was comprehensively consolidated and scrutinized to assess the vulnerability. Principle Component Analysis (PCA) was used to select and group the indicators. Varimax rotation was executed on all selected indicators and factors with eigenvalue >1 was incorporated for weight assignment. Results revealed that Muallungthu was socio-economically the most vulnerable village while the least vulnerable village was Chhungte. Households which fall short to adopt any adaptation strategies to the impacts of climate change were most vulnerable. Exposure to natural hazards was the basic indicator of climate change in the region and the sole dependence on agriculture made the households highly sensitive to climate variability. With the aim to improve the economic status of Jhumias, Climate-smart agriculture (CSA) interventions were advocated for climate resilient approach in Jhum. The study calls for policy makers and development planners to invest in education and rural income diversification. Effort should be made to assess the effect of previous disasters and hazard events so as to come up with obligatory preventive measures and consequently emphasize in adopting new measures for households to make them less vulnerable henceforth. • Sole dependence on agriculture makes Jhumia households highly sensitive. • Poor education and Jhum management degrades adaptive capacity. • CSA interventions are crucial for improving climate resilience in Jhum. • Calls for lawmakers and development planners to diversify rural income. [ABSTRACT FROM AUTHOR]