Search Results (46)

Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period. Full article

To identify seedling traits closely associated with drought resistance and to screen for drought-tolerant germplasm, 202 cotton varieties (lines) were evaluated under controlled indoor conditions using a nutrient soil cultivation method. Seedling-stage traits measured included plant height, cotyledon node diameter, true leaf number, chlorophyll content, and fresh and dry biomass of both shoots and roots. Drought resistance was assessed using drought resistance coefficients for each trait, followed by descriptive statistics, principal component analysis (PCA), partial correlation analysis, and comprehensive evaluation via the entropy weight method. PCA and partial correlation analysis revealed that plant height, cotyledon node diameter, aboveground fresh weight, and underground fresh weight were strongly associated with drought resistance at the seedling stage. The comprehensive drought resistance index (D-value) classified the 202 cotton lines into four categories: highly drought-resistant, moderately drought-resistant, drought-sensitive, and highly drought-sensitive. Physiological assays indicated that malondialdehyde (MDA) content in drought-resistant lines first increased and then declined with prolonged drought stress, while it continued to increase in sensitive lines. In contrast, proline (Pro) content and superoxide dismutase (SOD) activity increased steadily in drought-resistant lines but showed negligible changes in sensitive lines. These four morphological traits and three physiological indicators represent reliable criteria for evaluating drought resistance in cotton seedlings. Four highly drought-resistant and thirteen moderately drought-resistant lines were identified, providing valuable germplasm for genetic improvement of drought tolerance in cotton. Full article

Precipitation plays a vital role in the hydrological cycle, directly affecting water resource management and influencing flood and drought risk prediction. This study proposes a Bayesian Model Averaging (BMA) framework to integrate multiple precipitation datasets. The framework enhances estimation accuracy for hydrological simulations. The BMA framework synthesizes four precipitation products—Climate Hazards Group Infrared Precipitation with Station (CHIRPS), the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), Global Satellite Mapping of Precipitation (GSMaP), and Integrated Multi-satellitE Retrievals (IMERG)—over China’s Ganjiang River Basin from 2008 to 2020. We evaluated the merged dataset’s performance against its constituent datasets and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) at daily, monthly, and seasonal scales. Evaluation metrics included the correlation coefficient (CC), root mean square error (RMSE), and Kling–Gupta efficiency (KGE). The Variable Infiltration Capacity (VIC) hydrological model was further applied to assess how these datasets affect runoff simulations. The results indicate that the BMA-merged dataset substantially improves precipitation estimation accuracy when compared with individual inputs. The merged product achieved optimal daily performance (CC = 0.72, KGE = 0.70) and showed superior seasonal skill, notably reducing biases in autumn and winter. In hydrological applications, the BMA-driven VIC model effectively replicated observed runoff patterns, demonstrating its efficacy for regional long-term predictions. This study highlights BMA’s potential for optimizing hydrological model inputs, providing critical insights for sustainable water management and risk reduction in complex basins. Full article

Bitter vetch (Vicia ervilia L. Willd.) is an ancient Mediterranean legume, well adapted to dry climates, that has recently gained attention for its potential in organic farming and as a suitable source of bioactive compounds. This study analyzed the agrobiological variability of 12 bitter vetch accessions from the IPGR-Sadovo genebank in two-year field trials. Yield-related traits were recorded, and grains were assessed for protein, sugar, starch, free amino acids, phenols, and antitrypsin content. Statistical analyses included variance, correlation, cluster, principal component, and path-coefficient methods. Significant variation was observed in plant branching, pod and grain numbers, and grain weight per plant. Grain yield correlated strongly with pod number (r = 0.910**), grains per pod (r = 0.867**) and per plant (r = 0.965**), and pod size. Positive direct effects on grain yield had the traits germination−50% flowering, number of seeds per plant, height to first pod, and harvest index. An indirect impact was found for the number of pods per plant, number of seeds per pod, and seed starch content. Accessions formed four main clusters. BGR6207, B9E0168, and C3000003 showed high yield potential. C3000001, C3000003, C3000007, and C3000006 exhibited early maturity. C3E0118, C3000007, and C3000003 seeds had lower amounts of phenols. BGR13526 presented lower protein and antitrypsin but higher carbohydrate and phenol levels. Tolerance to moderate osmotic stress (150 mM NaCl or 10% Polyethylene glycol 6000) varied. BGR3052, BGR13526, and A3BM0178 were found to be resistant to both stressors, while accessions C3000001 and C3000007 were identified as sensitive to both adversities. C3000006 was determined as sensitive to salinity but resistant to drought, and BGR3051and C3000003 were relatively sensitive to drought but resistant to salinity. Root elongation and thinning were observed in half of the accessions as adaptive responses to stress. These findings highlight some of the advantages of the evaluated bitter vetch accessions for breeding and reintroduction into sustainable agricultural practices. Full article

Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia. Full article

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. Full article

The root is an important organ by which plants directly sense variation in soil moisture. The discovery of drought stress-responsive genes in roots is very important for the improvement of drought tolerance in wheat varieties via molecular approaches. In this study, transcriptome sequencing was conducted on the roots of drought-tolerant wheat cultivar YH1818 seedlings at 0, 2, and 7 days after treatment (DAT). Based on a weighted gene correlation network analysis of differentially expressed genes (DEGs), 14 coexpression modules were identified, of which five modules comprising 3107 DEGs were related to 2 or 7 DAT under drought stress conditions. A total of 223,357 single-nucleotide polymorphisms (SNPs) of these DEGs were retrieved from public databases. Using the R language package and GAPIT program, association analysis was performed between the 223,357 SNPs and the drought tolerance coefficient (DTC) values of six drought resistance-related traits in 114 wheat germplasms. The results revealed that 18 high-confidence SNPs of 10 DEGs, including TaPK, TaRFP, TaMCO, TaPOD, TaC3H-ZF, TaGRP, TaDHODH, TaPPDK, TaLectin, and TaARF7-A, were associated with drought tolerance. The RT–qPCR results confirmed that these genes were significantly upregulated by drought stress at 7 DAT. Among them, TaARF7-A contained three DTC-related SNPs, which presented two haplotypes in the tested wheat germplasms. YH1818 belongs to the Hap1 allele, which is involved in increased drought tolerance. This study revealed key modules and candidate genes for understanding the drought-stress response mechanism in wheat roots. Full article

Soybean is a major legume and oilseed crop with enormous economic importance, but its production is highly dependent on optimal rainfall or ample irrigation. In Kazakhstan, soybean production is highly vulnerable to drought and irrigation shortages. The aim of this study was to assess the level of drought escape and tolerance of soybean genotypes in different maturity groups, grown in well-watered conditions or without irrigation. Field trials were conducted in the very dry conditions of Kazakhstan with the hydrothermal coefficient 0.46–0.67. Nineteen soybean cultivars from five maturity groups were tested over four seasons under two conditions, with and without irrigation. The main indicators of drought tolerance were seed yield, seed weight per plant, percentage of seeds with smooth coats compared to wrinkled and shriveled ones, and 1000 seed weight. Under drought, seed yield of the studied genotypes decreased by 45.5–69.5% compared to well-watered controls. The most optimal genotypes for cultivation without irrigation were soybean cultivars from medium maturity group MG I (Vilana, Cheremosh, Xin-D11-252, and Desna) with a vegetation period of 115–128 days when avoiding drought during flowering, and the average yield for the group (1.7 t/ha) was slightly below that of those in drought-tolerant genotypes from medium–late/late maturity groups MG II–III (1.9–2.0 t/ha). Based on yield under drought, the best cultivars were identified as follows: Ivushka (1.2 t/ha) for MG 00 group; Ustya (1.3 t/ha) for MG 0; Vilana (1.8 t/ha) for MG I; Zen (2.3 t/ha) for MG II; and Sponsor (2.5 t/ha) for MG III. The identified genotypes can be used in breeding programs to reduce drought effects on soybean crops. Full article

Reverse osmosis (RO) and nanofiltration (NF) are ubiquitous technologies in modern water treatment, finding applications across various sectors. However, the availability of high-quality water suitable for RO/NF feed is diminishing due to droughts caused by global warming, increasing demand, and water pollution. As concerns grow over the depletion of precious freshwater resources, a global movement is gaining momentum to utilize previously overlooked or challenging water sources, collectively known as “marginal water”. Fouling is a serious concern when treating marginal water. In RO/NF, biofouling, organic and colloidal fouling, and scaling are particularly problematic. Of these, organic fouling, along with biofouling, has been considered difficult to manage. The major organic foulants studied are natural organic matter (NOM) for surface water and groundwater and effluent organic matter (EfOM) for municipal wastewater reuse. Polymeric substances such as sodium alginate, humic acid, and proteins have been used as model substances of EfOM. Fouling by low molecular weight organic compounds (LMWOCs) such as surfactants, phenolics, and plasticizers is known, but there have been few comprehensive reports. This review aims to shed light on fouling behavior by LMWOCs and its mechanism. LMWOC foulants reported so far are summarized, and the role of LMWOCs is also outlined for other polymeric membranes, e.g., UF, gas separation membranes, etc. Regarding the mechanism of fouling, it is explained that the fouling is caused by the strong interaction between LMWOC and the membrane, which causes the water permeation to be hindered by LMWOCs adsorbed on the membrane surface (surface fouling) and sorbed inside the membrane pores (internal fouling). Adsorption amounts and flow loss caused by the LMWOC fouling were well correlated with the octanol-water partition coefficient (log P). In part 2, countermeasures to solve this problem and applications using the LMWOCs will be outlined. Full article

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems. Full article

The Altay Mountains’ forests are vital to Xinjiang’s terrestrial ecosystem, especially water regulation and conservation. This study evaluates vegetation evapotranspiration (ET) from 2000 to 2017 using temperature, precipitation, and ET data from the China Meteorological Data Sharing Service. The dataset underwent quality control and was interpolated using the inverse distance weighted (IDW) method. Correlation analysis and climate trend methodologies were applied to assess the impacts of temperature, precipitation, drought, and extreme weather events on ET. The results indicate that air temperature had a minimal effect on ET, with 68.34% of the region showing weak correlations (coefficients between −0.2 and 0.2). Conversely, precipitation exhibited a strong positive correlation with ET across 98.91% of the area. Drought analysis, using the standardized precipitation evapotranspiration index (SPEI) and the Temperature Vegetation Dryness Index (TVDI), showed that ET was significantly correlated with the SPEI in 96.47% of the region, while the TVDI displayed both positive and negative correlations. Extreme weather events also significantly influenced ET, with reductions in the Simple Daily Intensity Index (SDII), heavy precipitation days (R95p, R10), and increases in indicators like growing season length (GSL) and warm spell duration index (WSDI) leading to variations in ET. Based on the correlation coefficients and their significance, it was confirmed that the SII (precipitation intensity) and R95p (heavy precipitation) are the main factors causing vegetation ET increases. These findings offer crucial insights into the interactions between meteorological variables and ET, essential information for sustainable forest management, by highlighting the importance of optimizing water regulation strategies, such as adjusting species composition and forest density to enhance resilience against drought and extreme weather, thereby ensuring long-term forest health and productivity in response to climate change. Full article

The performance of a soybean genotype under water deficit stress at an early vegetative stage might be related to its general tolerance to drought. To investigate the plasticity of root and shoot seedling traits in response to drought at an early vegetative stage, a set of 32 soybean genotypes adapted to southeast European growing conditions was grown under polyethylene glycol (PEG) 8000-induced drought stress and well-watered control conditions. Under drought, mean tap root length (RL), shoot length (SL), root fresh weight (RFW) and shoot fresh weight (SFW) decreased significantly by 11, 17, 38 and 34%, respectively, while root dry matter (RDM) and shoot dry matter (SDM) increased significantly by 13 and 11%, respectively. Of the four derived traits, the ratios of RL/SL, RL/RFW and SL/SFW increased significantly by 8, 45 and 28%, respectively, under drought, while the ratio of RFW/SFW decreased significantly by 4%. However, a wide variation between genotypes was observed for all 10 studied seedling traits under both control and drought conditions. Broad sense heritability ranged from 0.53 (RL) to 0.97 (SL) under control conditions and from 0.56 (RL/RFW) to 0.96 (SL) under drought conditions. The correlation coefficients between the traits were either weak or moderate, indicating that the studied traits can be modified independently by selection. Full article

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman’s correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato’s ‘government of the wise’ principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. Full article

The development of an accurate root water-uptake model is pivotal for evaluating crop evapotranspiration; understanding the combined effect of drought and waterlogging stresses; and optimizing water use efficiency, namely, crop yield [kg/ha] per unit of ET [mm]. Existing models often lack quantitative approaches to depicting crop root water uptake in scenarios of concurrent drought and waterlogging moisture stresses. Addressing this as our objective; we modified the Feddes root water-uptake model by revising the soil water potential response threshold and by introducing a novel method to calculate root water-uptake rates under simultaneous drought and waterlogging stresses. Then, we incorporated a water stress lag effect coefficient, $\phi \left(W_s\right)$, that investigated the combined effect of historical drought and waterlogging stress events based on the assumption that the normalized influence weight of each past stress event decreases with an increase in the time interval before simulation as an exponential function of the decay rate. Further, we tested the model parameters and validated the results obtained with the modified model using data from three years (2016–2018) of rice (Oryza sativa, L) trails with pots in Bengbu, China. The modified Feddes model significantly improved precision by 9.6% on average when calculating relative transpiration rates, particularly post-stress recovery, and by 5.8% on average when simulating soil moisture fluctuations during drought periods. The root mean square error of relative transpiration was reduced by 60.8%, and soil water was reduced by 55.1%. By accounting for both the accumulated impact of past moisture stress and current moisture conditions in rice fields, the modified model will be useful in quantifying rice transpiration and rice water use efficiency in drought–waterlogging-prone areas in southern China. Full article

Drought stress, which often occurs repeatedly across the world, can cause multiple and long-term effects on plant growth. However, the repeated drought–rewatering effects on plant growth remain uncertain. This study was conducted to determine the effects of drought–rewatering cycles on aboveground growth and explore the underlying mechanisms. Perennial ryegrass plants were subjected to three watering regimes: well-watered control (W), two cycles of drought–rewatering (D2R), and one cycle of drought–rewatering (D1R). The results indicated that the D2R treatment increased the tiller number by 40.9% and accumulated 28.3% more aboveground biomass compared with W; whereas the D1R treatment reduced the tiller number by 23.9% and biomass by 42.2% compared to the W treatment. A time-course transcriptome analysis was performed using crown tissues obtained from plants under D2R and W treatments at 14, 17, 30, and 33 days (d). A total number of 2272 differentially expressed genes (DEGs) were identified. In addition, an in-depth weighted gene co-expression network analysis (WGCNA) was carried out to investigate the relationship between RNA-seq data and tiller number. The results indicated that DEGs were enriched in photosynthesis-related pathways and were further supported by chlorophyll content measurements. Moreover, tiller-development-related hub genes were identified in the D2R treatment, including F-box/LRR-repeat MAX2 homolog (D3), homeobox-leucine zipper protein HOX12-like (HOX12), and putative laccase-17 (LAC17). The consistency of RNA-seq and qRT-PCR data were validated by high Pearson’s correlation coefficients ranging from 0.899 to 0.998. This study can provide a new irrigation management strategy that might increase plant biomass with less water consumption. In addition, candidate photosynthesis and hub genes in regulating tiller growth may provide new insights for drought-resistant breeding. Full article