Search

This study used a descriptive-analytical method to assess how vulnerable farmers in Fars Province's rural regions near the Tashk and Bakhtegan lakes are to the effects of climate change. For this purpose, 17 villages around Tashk and Bakhtegan lakes with 2511 households, who are engaged in agricultural activities, were investigated. To achieve the research goals, a broad range of indexes were determined with the dimensions of exposure, sensitivity, and adaptive capacity to climate change according to the Climate Vulnerability Index (CVI) and were evaluated in the field studies. According to the results, 52.93% of communities were highly vulnerable to climate change. On the other hand, only 23.52% of farmers had very low vulnerability, and 23.52% had moderate vulnerability. The local farmers were aware of the climate change in the studied region with an average of 4.56. Given that more than half of the villagers are highly vulnerable to climate change, the government and relevant institutions need to increase their resilience to vulnerability. Increasing vulnerability requires careful measurement and monitoring of all related variables, increasing adaptation to the generalization of insurance, access to facilities, and reducing climatic sensitivities by increasing livelihood diversification, reducing unemployment, and using drought-resistant plants. [ABSTRACT FROM AUTHOR]

In recent years, Rwanda, especially its Eastern Province, has been contending with water shortages, primarily due to prolonged dry spells and restricted water sources. This situation poses a substantial threat to the country's agriculture-based economy and food security. The impact may escalate with climate change, exacerbating the frequency and severity of droughts. However, there is a lack of comprehensive spatiotemporal analysis of meteorological and agricultural droughts, which is an urgent need for a nationwide assessment of the drought's impact on vegetation and agriculture. Therefore, the study aimed to identify meteorological and agricultural droughts by employing the Standardized Precipitation Evapotranspiration Index (SPEI) and the Vegetation Health Index (VHI). VHI comprises the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI), both derived from the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). This study analyzed data from 31 meteorological stations spanning from 1983 to 2020, as well as remote sensing indices from 2001 to 2020, to assess the spatiotemporal patterns, characteristics, and adverse impact of droughts on vegetation and agriculture. The results showed that the years 2003, 2004, 2005, 2006, 2013, 2014, 2015, 2016, and 2017 were the most prolonged and severe for both meteorological and agricultural droughts, especially in the Southern Province and Eastern Province. These extremely dry conditions led to a decline in both vegetation and crop production in the country. It is recommended that policymakers engage in proactive drought mitigation activities, address climate change, and enforce water resource management policies in Rwanda. These actions are crucial to decreasing the risk of drought and its negative impact on both vegetation and crop production in Rwanda. [ABSTRACT FROM AUTHOR]

The effects of the guaranteed and purchasing price policy on agronomic crop cultivation patterns were investigated under three scenarios in the Qazvin plain of Iran. In all scenarios, the surface under cultivation of water-saving products, especially wheat, decreased and on the contrary, the surface under cultivation of water-taking products, such as alfalfa and tomato, increased. The average income of farmers in the region increases under all scenarios; the lowest increase is related to the second scenario in which farmers' income rises by 7.5–7.7%, and the highest increase is related to the third scenario in which farmers' income rises by 8.5%. [ABSTRACT FROM AUTHOR]

In the context of global warming, an increase in atmospheric aridity and global dryland expansion under the future climate has been expected in previous studies. However, this conflicts with observed greening over drylands and the insignificant increase in hydrological and ecological aridity from the ecohydrology perspective. Combining climatic, hydrological, and vegetation data, this study evaluated global dryland aridity changes at meteorological stations from 2003 to 2019. A decoupling between atmospheric, hydrological, and vegetation aridity was found. Atmospheric aridity represented by the vapor pressure deficit (VPD) increased, hydrological aridity indicated by machine-learning-based precipitation minus evapotranspiration (P - ET) data did not change significantly, and ecological aridity represented by the leaf area index (LAI) decreased. P - ET showed nonsignificant changes in most of the dominant combinations of the VPD, LAI, and P - ET. This study highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models. [ABSTRACT FROM AUTHOR]

Forest aboveground biomass (AGB) is not simply affected by a single factor or a few factors, but also by the interaction between them in complex ways across multiple spatial scales. Understanding the joint effect of stand structural characteristics and climate factors on AGB on large scales is critical for accurate forest carbon storage prediction and sustainable management. Despite numerous attempts to clarify the relationships between stand structural characteristics (tree density/TD, diameter at breast height/DBH, basal area/BA), climate factors (mean annual temperature/MAT, mean annual precipitation/MAP), and AGB, they remain contentious on a large scale. Therefore, we explored the relationships between stand structural characteristics, climate factors, and AGB at a biome level by meta-analyzing datasets contained in 40 articles from 25 countries, and then answered the questions of how stand structural characteristics influence AGB at the biome level and whether the relationships are regulated by climate on a large scale. Through using regression analysis and the establishment of a structural equation model, the results showed that the influence of basal area on AGB at the biome level was more substantial than that of tree density and DBH, and the significant relationship between basal area and AGB was relatively stable regardless of biome variation, but the effects of tree density and DBH was non-negligible within the biome. Climatic factors (e.g., temperature and precipitation), should be considered. Our meta-analysis illustrated the complicated interactions between climate factors, stand structural characteristics, and the AGB of forests, highlighting the importance of climate effects on regulating stand structural characteristics and AGB relationships. We suggest that basal area be preferred and considered in forest sustainable management practice to optimize stand structure for increasing carbon storage potential, with close attention to local climate conditions. Overall, our meta-analysis will crucially aid forest management and conservation in the context of global environmental changes, and provide novel insights and a scientific reference to lead to future carbon storage research on large scales. [ABSTRACT FROM AUTHOR]

Using statistical methods that do not emphasize the systematic causality to attribute climate and plant traits to control ecosystem function may produce biased perceptions. We revisit this issue using a Bayesian network (BN) capable of quantifying causality. Based on expert knowledge and climate, vegetation, and ecosystem function data from the FLUXNET flux stations, we constructed a BN containing the causal relationship of 'climate-plant trait-ecosystem function'. Based on the sensitivity analysis function of the BN, we attributed the control of climate and plant traits to ecosystem function and compared the results with those based on Random forests and correlation analysis. The main conclusions of this study include: BN can be used for the quantification of causal relationships between complex ecosystems and climatic and environmental systems, and enables the analysis of indirect effects among variables. The control of ecosystem function by climate variables (especially mean temperature and mean vapor pressure deficit) may have been underestimated previously, and the mechanism of indirect effects of climate variables on ecosystem function through plant traits should be emphasized in future studies. Further inclusion of temporal information in BN holds promise for improving the analysis of lagged effects and interactions and feedback effects between variables. [ABSTRACT FROM AUTHOR]

With the rapid accumulation of water flux observations from global eddy-covariance flux sites, many studies have used data-driven approaches to model site-scale water fluxes with various predictors and machine learning algorithms used. However, systematic evaluation of such models is still limited. We therefore performed a meta21 analysis of 32 such studies, derived 139 model records, and evaluated the impact of various features on model accuracy throughout the modeling flow. SVM (average R-squared = 0.82) and RF (average R-squared = 0.81) outperformed over evaluated algorithms in both cross-study and intra-study (with the same training dataset) comparisons. The average accuracy of the model applied to arid regions is higher than other climate classes. The average accuracy of the model was slightly lower for forest sites (average R-squared = 0.76) than for cropland and grassland sites (average R-squared = 0.8 and 0.79), but higher than for shrub sites (average R-squared = 0.67). Among various predictor variables, the use of net/sun radiation, precipitation, air temperature, and the fraction of absorbed photosynthetically active radiation improved the model accuracy. Among the different validation methods, random cross-validation shows higher model accuracy than spatial cross-validation and temporal cross-validation, but spatial cross-validation is more important for the application for water flux predictive models when used for spatial extrapolation. The findings of this study are promising to guide future research on such machine learning-based modeling. [ABSTRACT FROM AUTHOR]