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1. Forest dieback caused by drought-induced tree mortality has been observed world-wide. Forecasting which trees in which locations are vulnerable to drought-induced mortality is important to predict the consequences of drought on forest structure, biodiversity and ecosystem function. 2. In this paper, our central aim was to compile a synthesis of tree traits and associated abiotic variables that can be used to predict drought-induced mortality. 3. We reviewed the literature that specifically links drought mortality to functional traits and site conditions (i.e. edaphic variables and biotic conditions), targeting studies that show clear use of tree traits in drought analysis. We separated the review into five climatic zones to determine global vs. regionally restricted relationships between traits and mortality. 4. Our synthesis identifies a number of traits that have clear relationships with droughtinduced mortality (e.g. wood density at the species level and tree size and growth at the individual level). However, the lack of direct relationships between most traits and droughtinduced mortality highlights areas where future research should focus to broaden our understanding. 5. Synthesis and applications. Our synthesis highlights established relationships between traits and drought-induced mortality, presents knowledge gaps for future research focus and suggests monitoring and research avenues for improving our understanding of drought-induced mortality. It is intended to assist ecologists and natural resource managers choose appropriate and measurable parameters for predicting local and regional scale tree mortality risk in different climatic zones within constraints of time and funding availability.

Agroforestry systems (AFS) and practices followed in India are highly diverse due to varied climatic conditions ranging from temperate to humid tropics. The estimated area under AFS in India is 13.75 million ha with the highest concentration being in the states of Uttar Pradesh (1.86 million ha), followed by Maharashtra (1.61 million ha), Rajasthan (1.55 million ha) and Andhra Pradesh (1.17 million ha). There are many forms of agroforestry practice in India ranging from intensified simple systems of monoculture, such as block plantations and boundary planting, to far more diverse and complex systems, such as home gardens. As a result, the biomass production and carbon sequestration potential of AFS are highly variable across different agro-climatic zones of India. Studies pertaining to the assessment of biomass and carbon storage in different agroforestry systems in the Indian sub-continent are scanty and most of these studies have reported region and system specific carbon stocks. However, while biomass and carbon stock data from different AFS at national scale has been scanty hitherto, such information is essential for national accounting, reporting of C sinks and sources, as well as for realizing the benefits of carbon credit to farmers engaged in tree-based production activities. Therefore, the objective of this study was to collate and synthesize the existing information on biomass carbon and SOC stocks associated with agroforestry practices across agro-climatic zones of India. The results revealed considerable variation in biomass and carbon stocks among AFS, as well as between different agro-climatic zones. Higher total biomass (>200 Mg ha−1) was observed in the humid tropics of India which are prevalent in southern and northeastern regions, while lower total biomass (<50 Mg ha−1) was reported from Indo-Gangetic, western and central India. Total biomass carbon varied in the range of 1.84 to 131 Mg ha−1 in the agrihorticulture systems of western and central India and the coffee agroforests of southern peninsular India. Similarly, soil organic carbon (SOC) ranged between 12.26–170.43 Mg ha−1, with the highest SOC in the coffee agroforests of southern India and the lowest in the agrisilviculture systems of western India. The AFS which recorded relatively higher SOC included plantation crop-based practices of southern, eastern and northeastern India, followed by the agrihorticulture and agrisilviculture systems of the northern Himalayas. The meta-analysis indicated that the growth and nature of different agroforestry tree species is the key factor affecting the carbon storage capacity of an agroforestry system. The baseline data obtained across various regions could be useful for devising policies on carbon trading or financing for agroforestry.

Globally, agriculture is recognized as a highly vulnerable sector to climate change and risks from climatic aberrations pose an imminent danger to the food security and sustainability of livelihoods. To bring robustness in climate adaptation planning, evaluation of resilience across homogenous regions is essential for developing and scaling suitable location-need-context specific interventions and policies that build the resilience of the agricultural system. In this paper, we present an analysis and discussion of multi-scalar and multi-indicator assessment, by profiling resilience across agro-climatic zones of India, based on the development of a Climate-Resilient Agriculture Index embracing environmental, technological, socio-economic, and institutional and infrastructural dimension. A total of 26 indicators, spread across these four dimensions, were employed to purport inter- and intra-agro-climatic zone differentials in the level of resilience. Among the zones, it was found that West Coast Plains & Ghats and Tans-Gangetic Plains had the highest degree of resilience to manage climate risks. Most of the districts lying within Eastern Himalayan Region, Middle Gangetic Plains, Eastern Plateau & Hills, and Western Dry Region had a lower degree of resilience. The study places greater emphasis on deciphering region-specific drivers and barriers to resilience at a further disaggregated scale for improving rural well-beings. It is construed that devising action plans emphasizing awareness, preservation of natural resources, diversification, building physical infrastructure, strengthening of grass-root institutions, and mainstreaming climate adaptation in the developmental policy is crucial for climate-resilient pathways.

The DSSAT CANEGRO model was calibrated and verified using field experimental data from five Tamil Nadu Agroclimatic Zones (1981–2022). The genetic coefficients of the sugarcane cultivar (CO-86032) were calculated. R 2 obtained between measured and simulated stalk fresh mass was 0.9 with the nRMSE (0.01) and RMSE (1.6) and R 2 between measured and simulated sucrose mass was 0.9 with the nRMSE (0.16) and RMSE (1.2). For yield R 2 obtained between measured and simulated was 0.9 with the nRMSE (0.01) and RMSE (1.6). As a result, the CANEGRO model may be used to mimic the phenology and yield features of the sugarcane cultivar in Tamil Nadu's Agro Climatic Zones. Temperature increases in Agro Climatic Zones resulted in varying yield reductions, with 2 °C increases causing a 3% loss, 3 °C increases 5%, and 4 °C increases 9%. The Water Requirement rose throughout all of the ACZ due to the high temperature, but to differing degrees. A 2 °C increase often results in an average 4% increase in the WR. 3 °C rise in temperature increased WR to 9% and WR rose by 13% when the temperature was raised by 4 °C.

Understanding the long-term rainfall trends and characteristics of meteorological drought resulting from scanty rainfall, under the influence of changing climate, is vital in addressing the challenges associated with the management of water resources. An attempt has been made to analyze the changes in long-term rainfall trends and meteorological drought characteristics in agro-climatic zones of two different regions, i.e., Tamil Nadu and Rajasthan, India. A 52-year daily gridded precipitation dataset (0.25° × 0.25°) for the period of 1969 to 2020 has been obtained from the India Meteorological Department (IMD) to analyze region-wise agro-climatic rainfall variability and drought characteristics. In addition, IMD grids of both regions are divided into three zones based on the classification of agro-climatic zones (ACZs) of India. The ACZs-wise precipitation irregularity has been evaluated using the Rainfall Anomaly Index (RAI). The Standardized Precipitation Index (SPI) was also performed at different time scales (three, six, twelve, and twenty-four months) to identify the meteorological droughts. Further, the run theory was applied to characterize drought assessment for both regions. SPI results showed that in Tamil Nadu, the East coast plains and hills region (zone 3 ECH) experienced more frequent but less severe and short-lived droughts, while the West coast plains and hills region (zone 1 WCG) experienced less frequent but more severe and long-lasting droughts, whereas in Rajasthan, the Western Dry Region (zone 3 WDR) experienced more frequent but less severe droughts, while the Trans Gangetic Plains (zone 1 TGP) experienced less but more severe and long-lasting droughts. Findings revealed that the regional topography and moisture availability perform a major role in regional precipitation variability. A comparative evaluation of drought characteristics in different climatic regions using agro-climatic zones provides valuable information to planners for adopting management strategies to easily tackle drought conditions.

This study uses data from 1901 to 2023 to investigate the long-term spatiotemporal variations and trends in monsoon rainfall. It also looks at how these changes may affect Kharif Paddy production in the state’s agro-climatic zones from 2000 to 2022. The Pettitt test was used to identify sudden alterations in rainfall patterns, and the Mann–Kendall (MK) test was used to assess rainfall trends. The findings show that most districts in Odisha have no significant change in monsoon rainfall, indicating a generally consistent pattern over the past 123 years. Only Sundergarh in the North-Western Plateau Zone showed a significant negative trend (-2.51), highlighting potential localized vulnerabilities. Change detection analysis shows the probable change years vary by districts (under agro-climatic zones) ranging from 1919 to 2009, whereas inter-seasonal rainfall variability was recorded to increase after 1980. The relationship between GPP and rainfall revealed non-linear characterises. Meanwhile, the seasonal trend from 2000 to 2022 showed a favourable increase in Gross Primary Productivity (GPP), averaging 10.88 gC/m 2 per year. Sensitivity analysis revealed that the GPP of forested areas in a region or district is more responsive to rainfall fluctuations than cropped areas within Odisha’s agro-climatic zones. Additionally, threshold analysis was conducted to identify the optimal range of monsoon rainfall that maximizes GPP for the studied districts across different agro-climatic zones. Understanding long-term rainfall variability is crucial for ensuring sustainable agricultural productivity, particularly in monsoon-dependent regions like Odisha, where shifting precipitation patterns can significantly affect Kharif paddy production.

This research aimed to identify the most appropriate probability distribution for modeling average monthly rainfall in the agro-climatic zones of West Bengal and to detect any trends in this data. The study utilized historical rainfall data spanning 51 years (1970-2020) obtained from the IMD in Pune. To determine the best-fitting distribution and assess trends, 23 different probability distributions were employed, with the Mann-Kendall test and Sen’s slope estimator used for trend analysis. Goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Chi-square tests, were employed to determine the most suitable distribution. The findings indicated that the Generalized Extreme Value, Gamma, and Lognormal (3-parameter) distributions were the best fits for two specific districts. The monthly rainfall distributions can be effectively used for predicting future monthly rainfall events in the region. The Mann-Kendall test revealed an increasing trend in rainfall for Kalimpong and Nadia Districts and a decreasing trend for Malda District.

Objective: The current study evaluated the greenhouse gas (GHG) emissions of dairy cattle through the Global Livestock Environmental Assessment Model (GLEAM) model and illustrated potential mitigation strategies by modifying nutrition interventions. Materials and Methods: A semi-structural questionnaire was developed to calculate dairy animal GHG emissions. This study comprised 40 farmers from four districts: river basin (Pabna), drought-prone (Chapainobabganj), floodplain (Nilphamari), and saline-prone (Sathkhira) areas. Ten lac¬tating cows (two cows from each farmer) were also selected to collect information on feeding practices, feed resources, feed intake (roughages and concentrate), water intake, and production and reproductive parameters for 7 days at each site during two seasons: dry (November– February) and wet (June–October). Results: The GHG emissions from the river basin area were significantly (p < 0.05) higher due to low-quality roughages (75%), whereas CH4/kg of milk production was the lowest (77.0 gm). In contrast, the area that frequently experiences drought showed a different pattern. For instance, the generation of CH4 from enteric fermentation was 1187.4 tons/year, while the production of CH4 and N2O from manure management was 323.1 tons/year and 4.86 tons/year, respectively. In comparison to other climatic areas, these values were the lowest because the supply of green grass was twice as abundant as in the other climatic areas (40%). The quantity of CH4/kg of milk produced in an area susceptible to drought did not vary. Conclusion: Implementing feeding systems in drought-prone areas is a successful approach to reducing GHG emissions in the dairy industry in Bangladesh. Consequently, implementing feed-balancing techniques can enhance productivity and foster environmentally sustainable ani¬mal production.

Accurate estimation of reference evapotranspiration (ET 0 ) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET 0 across diverse climate regimes over the past decades. In this study, the Python implementation for estimation of daily and monthly ET 0 values of representative stations of ten agro-climatic zones of Karnataka from 1979 to 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET 0 values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET 0 values was higher than temporal variation, indicating major difference in ET 0 values was with respect to the stations rather than years under study. The nonparametric Mann-Kendall test conducted at 1% significance level on the annual ET 0 values revealed a statistically significant increasing trend for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity, and solar radiation signifies their influence on the annual ET 0 values. The magnitude changes in the trends detected by the Theil Sen’s slope indicated that increasing values of mean temperature, solar radiation, and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET 0 values for the 10 stations. A trivial impact of wind speed on annual ET 0 values was observed for the stations. Kalburgi and Udupi stations exhibited a positive ET 0 trend with the highest and lowest annual values among ten stations.

Climate change is one of the most significant challenges of the 21st century, particularly its impact on surface water availability in arid and hyper-arid regions within the Euphrates River Basin. This study aims to analyze the impacts of climate change using five global climate models (GCMs) within the Coupled Model Intercomparison Project Phase 6 (CMIP6, IPCC 2021). Model outputs were statistically downscaled using Statistical Downscaling Model (SDSM 6.1) under three greenhouse gas emission scenarios, and bias correction was performed using Climate Model Data for Hydrologic Modeling (CMhyd 1.02). Performance evaluation was based on data from meteorological stations distributed across five climatic zones within the basin, covering the period from 1982 to 2023. The study results showed that the uncertainty-related biases in CMIP6 models limit the accuracy of climate projections in the basin. Using individual GCM models instead of relying solely on linear trends can reduce bias and uncertainty in estimating climate variables across different regions. Although the SDSM model provides a good fit with GCM outputs, the variability in spatial patterns and emission scenarios is reflected in the limited accuracy of some climate indices. Applying bias correction using the linear scaling method has been shown to improve the accuracy of statistical modeling. Projections indicate that rising temperatures due to increased greenhouse gas emissions may decrease rainfall in arid regions while increasing in humid regions. These results contribute to a better understanding of climate change’s impacts on water resources in river basins and provide a scientific basis for developing effective adaptation strategies and future planning.