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In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests. [ABSTRACT FROM AUTHOR]

In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey. [ABSTRACT FROM AUTHOR]

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth's key periods is feasible and effective. [ABSTRACT FROM AUTHOR]

Administrative strategies to cope with drought are steadily changing, from emergency procedures to day-to-day monitoring. More consideration must be paid to long-term and preventive drought control measures in the future. This paper discusses the risk of hydrological drought in the Yellow River Basin. The standardized runoff index (SRI) was used to characterize hydrological drought, and the run theory was used to identify drought states and quantify drought characteristic variables. Based on the drought severity and duration, a drought development plan was proposed and a three-dimensional copula function was constructed to obtain the joint distribution function of three-dimensional drought characteristic variables. A drought risk assessment system based on the loss × probability risk theory was constructed to explore the spatial and temporal characteristics of hydrological drought risk in the Yellow River Basin. Finally, according to the risk assessment results, the risk level was divided into low, medium and high risk, and a Bayesian network was used to explore the probability of hydrological drought. The main results are as follows: (1) From 1960 to 2018, the severity of hydrological drought in the Yellow River Basin increased, the duration lengthened, and the development speed accelerated. (2) The hydrological drought risk in the Yellow River Basin showed an overall upward trend, with the fastest increase in the HJ region of 0.041/10a. The highest annual average drought risk in the TDG region is 0.598. (3) The spatial transmission of hydrological drought risk is divided into three types: constant, enhanced and mitigation types, of which the constant type is the most common. The transmission probabilities of low, medium and high risk of hydrological drought from the HYK region to the low, medium and high risk of hydrological drought in the LJ region are 0.68, 0.66 and 0.78, respectively. [ABSTRACT FROM AUTHOR]

This document discusses the challenges faced by plants in arid and semi-arid regions due to harsh environmental conditions such as limited rainfall, high temperatures, and poor soil nutrition. The document highlights the importance of understanding plant adaptation strategies to extreme environments in order to protect and manage local vegetation and ecosystems. The document presents research papers that cover various aspects of plant adaptation in arid regions, including water uptake mechanisms, physiological and biochemical adaptations, plant-soil microbial interactions, and ecosystem services. The research findings provide insights into how plants in arid regions adapt to environmental changes and maintain ecological balance. However, further research is needed to fully understand the processes and mechanisms of plant adaptation in extreme environments. [Extracted from the article]

Drought stress (DS) is one of the abiotic stresses that plants encounter commonly in nature, which affects their life, reduces agricultural output, and prevents crops from growing in certain areas. To enhance plant tolerance against DS, abundant exogenous substances (ESs) have been attempted and proven to be effective in helping plants relieve DS. Understanding the effect of each ES on alleviation of plant DS and mechanisms involved in the DS relieving process has become a research focus and hotspot that has drawn much attention in the field of botany, agronomy, and ecology. With an extensive and comprehensive review and summary of hundred publications, this paper groups various ESs based on their individual effects on alleviating plant/crop DS with details of the underlying mechanisms involved in the DS-relieving process of: (1) synthesizing more osmotic adjustment substances; (2) improving antioxidant pathways; (3) promoting photosynthesis; (4) improving plant nutritional status; and (5) regulating phytohormones. Moreover, a detailed discussion and perspective are given in terms of how to meet the challenges imposed by erratic and severe droughts in the agrosystem through using promising and effective ESs in the right way and at the right time.

The area surrounding the Po River, known as the Po Valley, provides a central contribution in the economy of Italy and is highly devoted to agriculture. Recently it has been hit by multiple droughts, among which the exceptional event of summer 2022 is considered the worst dry period of the past 200 years. In the near future, the frequency of such exceptional events is predicted to rise; thus, a deep knowledge of the past droughts that hit the area, the variables used to characterize the events, the impacts they caused and the mitigation strategies adopted to deal with dry periods is of the utmost importance for policy definitions and planning. This study maps the scientific literature published from 2000 to February 2024 on the topic of drought in the Po Valley using the Scopus and Web of Science databases. Overall, 44 articles have been identified and grouped in three main classes: event identification and characterization, impact analysis and management strategies. The main gaps found in the collected papers are the lack of evaluations of the impacts of drought events on human health, hydroelectric energy production and tourism. Furthermore, comprehensive drought management and planning in the area is never addressed in the considered articles. The mentioned aspects deserve more attention, especially the development of drought management plans and policies and the evaluation of their effectiveness. [ABSTRACT FROM AUTHOR]

This paper discusses the increasing severity of droughts due to climate change. It emphasises the complexity of defining drought and the diverse perspectives among stakeholders. Lots of stakeholders with unclear responsibilities are involved, which can lead to uncertainty and indecisiveness in addressing the issue. To tackle this, the present paper proposes a methodology to dissect drought systems and reveal the intricate relationships between their components. This approach combines a comprehensive definition of drought with the "Water Resilience Assessment Framework" and an "Actor-Relational Approach", visualised through network analysis. The methodology was applied to a case study situated in the Leie Basin of Flanders, Belgium. By employing this strategy, policymakers and mediators can gain a deeper understanding of drought, identify its root causes, and prioritise necessary changes for more effective drought and water management. [ABSTRACT FROM AUTHOR]

In times of climate change, periods of drought will occur more frequently. This causes challenges for water use, ranging from limitations on the navigability of water courses, limited availability of water for irrigation and drinking water supply, reduced hydropower production, increasing concentrations of pollutants, deteriorating water quality, and ecosystem degradation. Dealing with droughts, however, is a complex puzzle due to the multi-level governance characteristics of international river basins and the need to meet the freshwater demands of all sectors involved. This increases the need to address drought issues in a coordinated way, along all levels of decision making. Thus far, the way this must be executed has been under-researched. This paper addresses this knowledge gap as it aims to provide design principles for good multi-level drought risk governance in international river basins. In order to meet our aim, we first reviewed literature on multi-level and good governance and established an assessment framework. This framework was applied in a case study on drought risk governance in the international Rhine basin. Policy documents were analyzed and key informants interviewed. We found that although the governance practice in the basin meets most of our framework criteria, differences between the international level, the Netherlands, Germany, and Switzerland also occurred. We have synthesized our findings into a list of 10 design principles for good multi-level drought risk governance, which could function as a starting point for the analysis and improvement of other multi-level drought risk governance practices. [ABSTRACT FROM AUTHOR]

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts. [ABSTRACT FROM AUTHOR]

Agricultural drought events have become more frequent in the Inner Mongolia inland river basin in recent years, and the spatio-temporal evolution characteristics and development rules can be accurately and comprehensively understood using the three-dimensional identification method. In this paper, standardized soil moisture index (SSMI) was used to characterize agricultural drought, and modified Mann–Kendall trend test (MMK) and 3D recognition of drought events were used to analyze the spatio-temporal evolution characteristics of agricultural drought events in this basin and reveal the drought development law. The relationships between drought and temperature (T), precipitation (P), evapotranspiration (E), and humidity (H) were analyzed using a cross-wavelet method. The results are as follows: (1) When the time scale of agricultural drought was short (monthly scale), the alternations of dry and wet were frequent, but the SSMI index of all scales showed a downward trend; (2) The spatial distribution characteristics of drought change trend in four seasons were similar, but the area with a significant downward trend of drought in spring was the largest, and the area of high frequency region was also the largest, and the drought trend was the most obvious; (3) The most serious agricultural drought event occurred from October 2000 to May 2002, and reached its maximum value in September 2001 (drought area and drought severity of 2.26 × 105 km2 and 3.61 × 105 months·km2, respectively), which mainly experienced five processes—drought onset–intensification–decay–re-intensification–termination—and the migration path of the drought center showed the characteristics of southwest–northeast transmission; (4) All the four meteorological factors were correlated with SSMI, and P had a greater impact on SSMI. This article aims to reveal the spatio-temporal evolution of agricultural drought events in the Inner Mongolia inland river basin, and provide a new way to accurately evaluate the spatio-temporal evolution of drought. [ABSTRACT FROM AUTHOR]

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method's high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. [ABSTRACT FROM AUTHOR]

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions. [ABSTRACT FROM AUTHOR]

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans. [ABSTRACT FROM AUTHOR]

Cities around the world are facing increased sensitivity to drought effects. Climate-change-induced drought affects not only the natural hydrology of the broad macroclimate but also those in the urban microclimates. The increasing frequency and duration of droughts are creating challenges for urban water utilities to convey water through distribution systems to customers reliably and consistently. This has led many urban areas like San Francisco, California, to search for unique alternative water supply projects to help bolster the drought resilience of the coupled human and natural water system. This paper focuses on applying the features of resilience (i.e., plan, adapt, absorb, and recover) through a drought resilience matrix to water supply alternatives to analyze how the addition of these projects would increase the overall water system's drought resilience. San Francisco, California, was used as the case study to test the use of this matrix. Three portfolios (modifying existing supply, recycling, and desalination, as well as local approaches) were created and tested in the matrix. Each portfolio is composed of various alternative water supply projects that the San Francisco Public Utilities Commission (SFPUC) is considering for implementation. Results concluded that the local approaches portfolio provided the most drought resilience, with the recycling and desalination portfolio providing the least resilience. The study approach and the presented findings will provide guidance to water utility professionals in supply planning to enhance drought resilience. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network's edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system's real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture. [ABSTRACT FROM AUTHOR]

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future. [ABSTRACT FROM AUTHOR]

Sand prevention and control are the main tasks of desertification control. The MU Us Sandy Land (MUSL), one of China's four main deserts, frequently experiences droughts and has a very fragile biological environment. Climate change is the main factor leading to drought, and it may result in more serious drought situations in the future. The Temperature Vegetation Dryness Index (TVDI) was established using land surface temperature and normalized difference vegetation index data. In this paper, we investigate spatial and temporal change characteristics, future change trends, and the time-lag effect of TVDI on climate factors at different scales in MUSL from 2001 to 2020 using Sen + Mann–Kendall trend analysis, Hurstexponent, partial correlation analysis, and lag analysis methods. The results show that (1) the overall drought shows a spatial characteristic of gradually alleviating from west to east (TVDI = 0.6). A significant drying trend dominated 38.5% of the pixels in the fall (Z = 1.99), and a highly significant drying trend dominated the rest of the three seasons (Z average = 2.95) and the whole year (Z = 3.47). (2) In the future, dry autumn, winter, and the whole year will be dominated by continuous drying, and spring and summer will mainly change from dry to wet. The main relationships between winter TVDI and temperature (−0.06) and precipitation (−0.07) were negative, while evapotranspiration (0.18) showed a positive correlation. The six land use types in spring, summer, fall, and the whole year were primarily non-significantly positively correlated with temperature and evapotranspiration. (3) At the seasonal scale, the sensitive factors in spring and autumn were opposite, with spring TVDI responding quickly to precipitation (0.3 months) and being less sensitive to temperature (1.8 months) and evapotranspiration (2 months). At the interannual scale, desert land TVDI was most sensitive to precipitation (2.6 months) and least responsive to temperature (3 months). [ABSTRACT FROM AUTHOR]

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes. [ABSTRACT FROM AUTHOR]

Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson's test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management. [ABSTRACT FROM AUTHOR]

Drought is a significant environmental limiting factor that restricts the growth of Quercus wutaishanica Mayr. The MYB transcription factor plays a wide role in controlling the growth of plants. In this study, the QwMYB108 gene was cloned and the bioinformatics was analyzed, and we examined how QwMYB108 responded to various gradient drought stresses. The results demonstrated that QwMYB108 encoded 275 amino acids using an 828 bp open reading frame. Subcellular localization indicated that the gene was located in the nucleus. Phylogenetic analysis showed that QwMYB108 was close to Q. robur, and that the highest level of expression was found in leaves, which was significantly different from other tissues. The expression of QwMYB108 increased as the stress degree rose when drought stress was present, and there was a significant difference between severe drought stress and other gradient stress. In this study, the function of QwMYB108 in drought stress response was investigated, and the drought response function gene of Q. wutaishanica was further explored to provide a theoretical basis. [ABSTRACT FROM AUTHOR]

We conducted a multi-scaled Ecoregional Conservation Assessment for the Southern Rockies (~14.5 M ha) and its trailing edge, the Santa Fe Subregion (~2.2 M ha), from Wyoming to New Mexico, USA. We included a representation analysis of Existing Vegetation Types (EVTs), mature and old-growth forests (MOG), and four focal species—Canada lynx (Lynx canadensis), North American wolverine (Gulo gulo luscus), Mexican spotted owl (Strix occidentalis lucida), and northern goshawk (Accipiter gentilis)—in relation to 30 × 30 and 50 × 50 conservation targets. To integrate conservation targets with wildfire risk reduction to the built environment and climate change planning, we overlaid the location of wildfires and forest treatments in relation to the Wildland–Urban Interface (WUI) and included downscaled climate projections for a lower (RCP4.5) and higher (RCP8.5) emission scenario. Protected areas were highly skewed toward upper-elevation EVTs (most were >50% protected), underrepresented forest types (<30% protected), especially MOG (<22% protected) and riparian areas (~14% protected), and poorly represented habitats (<30%) for at least three of the focal species, especially in the subregion where nearly all the targets underperformed compared to the ecoregion. Most (>73%) forest-thinning treatments over the past decade were >1 km from delineated WUI areas, well beyond the distance at which vegetation management can effectively reduce structure ignition risk (<50 m from structures). Extreme heat, drought, snowpack reductions, altered timing of peak stream flows, increasing wildfires, and potential shifts in the climate, favoring woodlands over conifer forests, may impact forest-dependent species, while declining snowpack may impact wolverines that den at upper elevations. Strategically targeting the built environment for fuel treatments would improve wildfire risk reduction and may allow for expansion of protected areas held up in controversy. Stepped-up protection for roadless areas, adoption of wilderness proposals, and greater protection for MOG and riparian forests are critical for meeting representation targets. [ABSTRACT FROM AUTHOR]

Drought is, among natural hazards, one of the most harmful to humanity. The forecasting of droughts is essential to reduce their impact on the economy, agriculture, tourism and water resource systems. In this study, drought forecast in the central region of the state of Zacatecas, a semi-arid region of Mexico, is explored by means of artificial neural networks (ANNs), forecasting numerical values of three drought indices—the standardized precipitation index (SPI), the standardized precipitation and evapotranspiration index (SPEI) and the reconnaissance drought index (RDI)—in an effort to establish the most suitable index for drought forecasting with ANNs in semi-arid regions. Records of 52 years of monthly precipitation and temperature were used. The indices were calculated in three different time scales: 3, 6 and 12 months. The analyzed models showed great capacity to forecast the values of the three drought indices, and it was found that for the trial set, the RDI was the drought index that was best fitted by the models, with the evaluation metrics R2 (determination coefficient), RMSE (root mean square error), MAE (mean absolute error) and MBE (Mean Bias Error) showing ranges of 0.834–0.988, 0.099–0.402, 0.072–0.343 and 0.017–0.095, respectively. For the validation set, the evaluation metrics were slightly better. [ABSTRACT FROM AUTHOR]

Portulaca oleracea L. is a wild edible plant with high potential for exploitation in commercial cropping systems due to its nutritional value and great adaptability to abiotic stress conditions. The present study aimed to investigate the response of purslane plants grown under drought stress conditions (100%, 80%, and 60% of field capacity (FC)) and the implementation of biostimulant amendments (control without amendment, plant growth-promoting rhizobacteria (PGPR), mycorrhiza, and effective microorganisms (EMs)) for two consecutive years. In the two-year experiment, the greatest height was recorded in plants grown under no-stress conditions and inoculated with PGPR. The highest branch number, and fresh and dry weight of aboveground and underground parts were observed under no-stress conditions at the mycorrhiza treatment. Moreover, mycorrhiza application in plants growing under 100% FC resulted in the highest N, P, total carbohydrates, and vitamin C and the lowest nitrate and proline contents in leaves. Purslane plants grown under 100% FC and inoculated with PGPR treatment resulted in the highest K and total chlorophyll leaf contents. Additionally, growing plants under mild drought stress (80% FC) combined with biostimulant application (e.g., inoculation with mycorrhiza, PGPR, and EM) may improve plant growth characteristics and mitigate negative stress effects. In general, the applied biostimulant amendments alleviated the adverse effects of drought on plant growth and leaf chemical composition indicating the importance of sustainable strategies to achieve high yield and sufficient quality within the climate change scenario. [ABSTRACT FROM AUTHOR]

Flash droughts, characterized by their abrupt onset and rapid intensification, are predicted to increase in frequency and severity under global warming. Understanding the incidence and progression of a flash drought and its impact on maize growth is crucial for maize production to withstand flash drought events. This study used the evaporative demand drought index (EDDI) method to evaluate the incidence of summer drought in Liaoning during the period 1961–2020. It examined the incidence and characteristics of summer flash droughts in Liaoning Province in the period of 1961–2020 and evaluated the factors responsible and the impact on maize during the critical development period. The ratio of the number of stations recording a disaster to total number of stations (IOC) curve, i.e., the ratio of the number of stations recording disasters and total stations, for summer flash droughts in Liaoning showed an upward trend during the period of 1961–2020, with large-scale, regional, and local flash droughts occurring in 8, 10, and 31 years, respectively. Summer flash droughts in Liaoning were mainly in the extreme drought category and ranged in frequency from 10% to 20% in most areas. Before the flash drought occurrence in three typical years (1989, 1997, and 2018), a precipitation deficit without large-scale high-temperature events was observed, and the cumulative water deficit caused the flash drought. Regional or large-scale high-temperature events were often accompanied by flash droughts, and the drought intensified rapidly, owing to the influence of heat waves and water deficits. Summer flash droughts caused a reduction in total primary productivity (GPP) of maize by more than 20% in most areas in the three typical years. The yield reduction rate in 1989, 1997, and 2018, was 27.6%, 26.4%, and 5%, respectively. The degree of decline in maize productivity and yield was associated with the onset and duration of the flash drought. The atmospheric conditions of summer flash droughts were characterized by high-pressure anomalies and atmospheric subsidence, which were unconducive for precipitation but conducive to flash drought occurrence. The continuous high-pressure anomaly promoted the maintenance of the flash drought. [ABSTRACT FROM AUTHOR]

Nutrient deficiencies are a major cause of yield loss under abiotic stress conditions, so proper nutrient management can reduce the negative effects of stress to some extent. Nutrients can alleviate stress by activating resistance genes, enhancing antioxidant enzyme activity, creating osmoprotectants in cells, reducing reactive oxygen species (ROS) activity, increasing cell membrane stability, synthesizing proteins associated with stress tolerance, and increasing chlorophyll content in leaves. The current review highlights changes in soybean metabolic activity caused by drought stress and changes in vital functions caused by the deficiency of primary (N, K, P) and secondary macronutrients (Ca, Mg, S). The role of macronutrients in reducing the adverse effects of water deficit stress is highlighted. Under stressed conditions, appropriate nutrient management options can be implemented to minimize the effects of drought and ensure good yields. Balanced nutrient fertilization helps activate various plant mechanisms to mitigate the effects of abiotic stresses and improve soybean drought resistance/tolerance. Nutrient management is therefore a viable technique for reducing environmental stress and increasing crop productivity. [ABSTRACT FROM AUTHOR]

The increasing variability of precipitation, higher temperatures, and recurring droughts in the semi-arid regions due to climate change are leading to increased aridity, resulting in scarcer water resources for crops. The present study aimed to analyse the spatial distribution of climate variables related to water resources in the olive-growing areas throughout Extremadura, southwestern Spain. To perform this task, three climate variables were used: the potential evapotranspiration of the crop, the FAO aridity index, and the annual water requirement. Considering data from 58 weather stations located throughout Extremadura and 17 along boundaries with at least a 30-year length (within the 1991–2021 period), each variable was computed at each station. After calculating some descriptive statistics, a multivariate geostatistical (regression-kriging) algorithm, incorporating secondary information on elevation and latitude, was used to accurately map each climate variable. Later, temporal trends and their magnitude were analysed using the Mann–Kendall test and the Sen's estimator, respectively. The highest evapotranspiration and water requirements are located in the southern part of the region, which has large areas dedicated to olive cultivation. In the northern part of the region, there is greater spatial variability in evapotranspiration and, consequently, in water requirements for olive groves due to the more rugged topography. Similarly, the olive-growing areas with the highest aridity are also in the south of Extremadura. In most areas of Extremadura, olive cultivation requires appropriate irrigation for optimal productivity. According to evapotranspiration trends, the water requirements will become greater in the future. However, it is not guaranteed that the water supply will be sufficient in olive-growing areas where aridity is higher and water resources are scarce. The results of this study are very important for evaluating water deficit and water resources in vulnerable olive-growing areas throughout Extremadura. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Characteristics of Hydrological and Meteorological Drought Based on Intensity-Duration-Frequency (IDF) Curves" published in the journal Water. The authors of the article realized that they had not included an acknowledgement in the original publication, which is important for Mr. Ahmad Abu Arra and his Ph.D. thesis. The authors have now added the acknowledgement, stating that the scientific conclusions of the article remain unaffected. This correction has been approved by the Academic Editor and the original publication has been updated. [Extracted from the article]

Drought is a critical factor constraining plant growth in arid regions. However, the performance and adaptive mechanism of Atriplex canescens (A. canescens) under drought stress remain unclear. Hence, a three-year experiment with three drought gradients was performed in a common garden, and the leaf functional traits, biomass and biomass partitioning patterns of A. canescens were investigated. The results showed that drought stress had significant effects on A. canescens leaf functional traits. A. canescens maintained the content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), but the peroxidase (POD) and catalase (CAT) activity decreased, and the content of proline (Pro) and soluble sugar (SS) increased only under heavy drought stress. Under drought stress, the leaves became smaller but denser, the specific leaf area (SLA) decreased, but the dry matter content (LDMC) maintained stability. Total biomass decreased 60% to 1758 g under heavy drought stress and the seed and leaf biomass was only 10% and 20% of non-stress group, but there had no significant difference on root biomass. More biomass was allocated to root under drought stress. The root biomass allocation ratio was doubled from 9.62% to 19.81% under heavy drought, and the root/shoot ratio (R/S) increased from 0.11 to 0.25. The MDA was significantly and negatively correlated with biomass, while the SPAD was significantly and positively correlated with total and aboveground organs biomass. The POD, CAT, Pro and SS had significant correlations with root and seed allocation ratio. The leaf morphological traits related to leaf shape and weight had significant correlations with total and aboveground biomass and biomass allocation. Our study demonstrated that under drought stress, A. canescens made tradeoffs between growth potential and drought tolerance and evolved with a conservative strategy. These findings provide more information for an in-depth understanding of the adaption strategies of A. canescens to drought stress and provide potential guidance for planting and sustainable management of A. canescens in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

The amino acid proline accumulates in plants during abiotic stresses such as drought and salinity and is considered a reliable marker of environmental stress. While its accumulation is well established, its precise role in stress tolerance and its underlying molecular mechanism remain less clear. To address these issues, we performed a meta-analysis—a robust statistical technique that synthesizes results from multiple independent studies while accounting for experimental differences. We focused on 16 physiological and morphological parameters affected by drought and salt stress in transgenic plants expressing proline metabolic genes. For each parameter, we calculated the effect size as the response ratio ( R R ), which represents the logarithm of the mean value in the transgenic group over the mean value of the control group ( ln R R ). Under stress, most parameters exhibited significantly higher response ratios in the transgenic group, confirming the beneficial effects of proline during drought and salt stress. Surprisingly, under non-stressed conditions, most stress markers showed no significant differences between transgenic and non-transgenic plants, despite elevated proline levels in the former. These results suggest that the benefits of proline may be related to proline catabolism or may only become apparent during stress, possibly due to interactions with reactive oxygen species (ROS), which accumulate predominantly under stress conditions. [ABSTRACT FROM AUTHOR]

Maize is most sensitive to drought stress at the floral stage by reducing tassel and silk quality, and thus improving drought tolerance at this stage may help preserve yield. It has been reported that BRs (brassinosteroids) promote floral development under drought stress. However, the function of the brassinosteroid biosynthesis gene ZmDWARF11 (ZmD11) on floral growth under drought stress has not been elucidated. This study found that under normal growth conditions, the heterologous over-expression of ZmD11 significantly enhanced both the vegetative growth and floral development of tobacco. Under drought stress, overexpressing ZmD11 reduced stress-induced tobacco flower size reduction, while it did not affect vegetative growth. After drought treatment, the activities of protective enzymes, including CAT (Catalase), SOD (Superoxide Dismutase), and POD (Peroxidase), were higher, while the content of MDA (Malondialdehyde) was lower in ZmD11 over-expression tobacco lines than that in the wild type control. The relative expression of dehydrin-related genes NtLeat5 and NtERD10 was increased in ZmD11 over-expression tobacco lines compared to that in the control. In summary, we reported that ZmD11 plays a role in tobacco floral development under drought stress. Our data are valuable in understanding the functions of BRs in regulating plant floral development under drought stress. [ABSTRACT FROM AUTHOR]

Justification: Water-use efficiency (WUE) is the amount of carbon assimilated as biomass or grain produced per unit of water the crop uses, and it is considered a critical factor in maintaining the balance between carbon gain and water loss during photosynthesis, particularly in the face of global warming and drought challenges. Improving agricultural WUE is essential for sustainable crop production in water-scarce regions. Objective: This article explores the significance of WUE enhancement in agriculture, especially under drought conditions, and discusses various strategies to optimize WUE for improved crop productivity. Methods: We searched the scientific literature for articles on water-use efficiency published between 2010 and 2023 and selected the 42 most relevant studies for a comprehensive overview of strategies, technologies, and approaches to investigate sustainable agricultural practices to improve water-use efficiency in agriculture, particularly focusing on agronomic methods such as mulching, cover crops, canopy management, deficit irrigation, and irrigation modernization. Results: This review highlights several practical techniques for enhancing WUE, including sustainable irrigation practices, crop-specific agronomic strategies, and innovative technological solutions. By adopting these approaches, farmers can improve water management efficiency, reduce crop vulnerability to water stress, and ultimately enhance agricultural sustainability. In conclusion, improving water-use efficiency is an essential factor for ensuring food security in the face of climate change and water scarcity. By implementing innovative strategies and exploiting the power of technology, we can enhance WUE in agriculture, optimize crop production, conserve natural resources, and contribute to a more sustainable future. [ABSTRACT FROM AUTHOR]

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world's largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan's heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country. [ABSTRACT FROM AUTHOR]

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts. [ABSTRACT FROM AUTHOR]

Proso millet (Panicum miliaceum L.) is resilient to abiotic stress, especially to drought. However, the mechanisms by which its roots adapt and tolerate salt stress are obscure. In this study, to clarify the molecular mechanism of proso millet in response to drought stress, the physiological indexes and transcriptome in the root of seedlings of the proso millet cultivar 'Yumi 2' were analyzed at 0, 0.5, 1.0, 1.5, and 3.0 h of stimulated drought stress by using 20% PEG-6000 and after 24 h of rehydration. The results showed that the SOD activity, POD activity, soluble protein content, MDA, and O2−· content of 'Yumi 2' increased with the time of drought stress, but rapidly decreased after rehydration. Here, 130.46 Gb of clean data from 18 samples were obtained, and the Q30 value of each sample exceeded 92%. Compared with 0 h, the number of differentially expressed genes (DEGs) reached the maximum of 16,105 after 3 h of drought, including 9153 upregulated DEGs and 6952 downregulated DEGs. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that upregulated DEGs were mainly involved in ATP binding, nucleus, protein serine/threonine phosphatase activity, MAPK signaling pathway–plant, plant–pathogen interactions, and plant hormone signal transduction under drought stress, while downregulated DEGs were mainly involved in metal ion binding, transmembrane transporter activity, and phenylpropanoid biosynthesis. Additionally, 1441 TFs screened from DEGs were clustered into 64 TF families, such as AP2/ERF-ERF, bHLH, WRKY, NAC, MYB, and bZIP TF families. Genes related to physiological traits were closely related to starch and sucrose metabolism, phenylpropanoid biosynthesis, glutathione metabolism, and plant hormone signal transduction. In conclusion, the active oxygen metabolism system and the soluble protein of proso millet root could be regulated by the activity of protein serine/threonine phosphatase. AP2/ERF-ERF, bHLH, WRKY, NAC, MYB, and bZIP TF families were found to be closely associated with drought tolerance in proso millet root. This study will provide data to support a subsequent study on the function of the drought tolerance gene in proso millet. [ABSTRACT FROM AUTHOR]

As one of the most influential environmental factors, drought stress greatly impacts the development and production of plants. Triploid-induced Passiflora edulis Sims 'Mantianxing' is an important new cultivar for multi-resistance variety selective breeding, which is one of the P. edulis breeding essential targets. However, the performance of triploid 'Mantianxing' under drought stress is unknown. In order to study the drought resistance of triploid 'Mantianxing', our study compared drought-related indicators in diploids and triploids under natural drought experiments, including morphological, physiological, and biochemical characteristics. Results showed that triploid P. edulis 'Mantianxing' showed variable responses to drought treatment. Compared with diploids, triploids showed higher photosynthesis and chlorophyll fluorescence, osmotic adjustment substances, and antioxidant enzyme activity under drought stress and faster chlorophyll biosynthesis and growth recovery after rewatering. Generally speaking, these results indicate that the drought resistance of triploid P. edulis is superior to diploid. This study provides scientific information for breeding stress tolerance variety of P. edulis 'Mantianxing' new cultivar. [ABSTRACT FROM AUTHOR]

Plant growth and productivity are predicted to be affected by rising CO2 concentrations, drought and temperature stress. The C3 crop model in a changing climate is Chenopodium quinoa Willd—a protein-rich pseudohalphyte (Amaranthaceae). Morphophysiological, biochemical and molecular genetic studies were performed on quinoa grown at ambient (400 ppm, aCO2) and elevated (800 ppm, eCO2) CO2 concentrations, drought (D) and/or high temperature (eT) treatments. Among the single factors, drought caused the greatest stress response, inducing disturbances in the light and dark photosynthesis reactions (PSII, apparent photosynthesis) and increasing oxidative stress (MDA). Futhermore, compensation mechanisms played an important protective role against eT or eCO2. The disruption of the PSII function was accompanied by the activation of the expression of PGR5, a gene of PSI cyclic electron transport (CET). Wherein under these conditions, the constant Rubisco content was maintained due to an increase in its biosynthesis, which was confirmed by the activation of rbcL gene expression. In addition, the combined stress treatments D+eT and eCO2+D+eT caused the greatest negative effect, as measured by increased oxidative stress, decreased water use efficiency, and the functioning of protective mechanisms, such as photorespiration and the activity of antioxidant enzymes. Furthermore, decreased PSII efficiency and increased non-photochemical quenching (NPQ) were not accompanied by the activation of protective mechanisms involving PSI CET. In summary, results show that the greatest stress experienced by C. quinoa plants was caused by drought and the combined stresses D+eT and eCO2+D+eT. Thus, drought consistently played a decisive role, leading to increased oxidative stress and a decrease in defense mechanism effectiveness. [ABSTRACT FROM AUTHOR]

As a type of cell-wall-relaxing protein that is widely present in plants, expansins have been shown to actively participate in the regulation of plant growth and responses to environmental stress. Wild soybeans have long existed in the wild environment and possess abundant resistance gene resources, which hold significant value for the improvement of cultivated soybean germplasm. In our previous study, we found that the wild soybean expansin gene GsEXLB14 is specifically transcribed in roots, and its transcription level significantly increases under salt and drought stress. To further identify the function of GsEXLB14, in this study, we cloned the CDS sequence of this gene. The transcription pattern of GsEXLB14 in the roots of wild soybean under salt and drought stress was analyzed by qRT-PCR. Using an Agrobacterium rhizogenes-mediated genetic transformation, we obtained soybean hairy roots overexpressing GsEXLB14. Under 150 mM NaCl- and 100 mM mannitol-simulated drought stress, the relative growth values of the number, length, and weight of transgenic soybean hairy roots were significantly higher than those of the control group. We obtained the transcriptomes of transgenic and wild-type soybean hairy roots under normal growth conditions and under salt and drought stress through RNA sequencing. A transcriptomic analysis showed that the transcription of genes encoding expansins (EXPB family), peroxidase, H+-transporting ATPase, and other genes was significantly upregulated in transgenic hairy roots under salt stress. Under drought stress, the transcription of expansin (EXPB/LB family) genes increased in transgenic hairy roots. In addition, the transcription of genes encoding peroxidases, calcium/calmodulin-dependent protein kinases, and dehydration-responsive proteins increased significantly. The results of qRT-PCR also confirmed that the transcription pattern of the above genes was consistent with the transcriptome. The differences in the transcript levels of the above genes may be the potential reason for the strong tolerance of soybean hairy roots overexpressing the GsEXLB14 gene under salt and drought stress. In conclusion, the expansin GsEXLB14 can be used as a valuable candidate gene for the molecular breeding of soybeans. [ABSTRACT FROM AUTHOR]

Drought, as a widespread natural calamity, leads to the most severe agricultural losses among all such disasters. Alterations in the yield of major global agricultural products are pivotal factors influencing food prices, food security, and land use decisions. China's rapidly expanding demand for sustenance will persist over the forthcoming decades, emphasizing the critical need for an accurate assessment of drought's impact on food production. Consequently, we conducted a comprehensive evaluation of the drought risk in China and its repercussions on agricultural output. Additionally, we delved into the underlying factors driving changes in yield for three primary grain crops (wheat, corn, and rice), which hold particular relevance for shaping effective strategies to mitigate future drought challenges. The findings divulge that both the number of drought months (DM) and the drought magnitude index (DMI) have displayed an upward trajectory over 60 years with a correlation coefficient of 0.96. The overall severity of meteorological drought has escalated across China, and it is particularly evident in regions such as the southwest and central parts of the Huang-Huai-Hai region, the northwestern middle region, and the Xinjiang region. Conversely, there has been some relief from drought conditions in southern China and the Yangtze River Delta. Shifts in the total grain output (TGO) during this period were compared: it underwent three stages, namely "fluctuating growth" (1961–1999), then a "sharp decline" (2000–2003), followed by "stable growth" (2004–2018). Similarly, changes in the grain planting area (GPA) experienced two stages, "continuous reduction" (1961–2003) succeeded by "stable growth" (2004–2018), while maintaining an upward trend for grain yield per unit area (GY) throughout. Furthermore, it was revealed that the drought grade serves as a significant constraint on continuous expansion within China's grain output—where the drought damage rate's influence on the TGO outweighs that from the GY. Our research outcomes play an instrumental role in deepening our comprehension regarding how drought impacts agricultural production within China while furnishing the scientific groundwork to devise efficacious policies addressing these challenges. [ABSTRACT FROM AUTHOR]

Water deficit affects the growth as well as physiological and biochemical processes in plants. The aim of this study was to determine differences in physiological and biochemical responses to drought stress in two wheat cultivars—Chinese Spring (CS) and SQ1 (which are parents of a mapping population of doubled haploid lines)—and to relate these responses to final yield and agronomic traits. Drought stress was induced by withholding water for 14 days, after which plants were re-watered and maintained until harvest. Instantaneous gas exchange parameters were evaluated on the 3rd, 5th, 10th, and 14th days of seedling growth under drought. After 14 days, water content and levels of chlorophyll a+b, carotenoids, malondialdehyde, soluble carbohydrates, phenolics, salicylic acid, abscisic acid (ABA), and polyamines were measured. At final maturity, yield components (grain number and weight), biomass, straw weight, and harvest index were evaluated. Physiological and biochemical parameters of CS responded more than those of SQ1 to the 14-day drought, reflected in a greater reduction in final biomass and yield in CS. Marked biochemical differences between responses of CS and SQ1 to the drought were found for soluble carbohydrates and polyamines. These would be good candidates for testing in the mapping population for the coincidence of the genetic control of these traits and final biomass and yield. [ABSTRACT FROM AUTHOR]

Amidst worsening climate change, drought stress imperils global agriculture, jeopardizing crop yields and food security, thereby necessitating the urgent exploration of sustainable methods like biopriming for the harnessing of beneficial microorganisms to bolster plant resilience. Recent research has revealed diverse biological compounds with versatile applications produced by Schizophyllum commune, rendering this fungus as a promising contender for biopriming applications. For the first time, this study aimed to investigate the potential of S. commune exo- (EPSH) and intra-polysaccharides (IPSH) isolated from two strains—Italian (ITA) and Serbian (SRB)—under submerged cultivation to enhance the resilience of Pisum sativum L. seeds through the biopriming technique. Testing of the seed quality for the bioprimed, hydroprimed, and unprimed seeds was conducted using a germination test, under optimal and drought conditions, while characterization of the PSHs included FTIR analysis, microanalysis, and determination of total protein content (TPC). The FTIR spectra of EPSH and IPSH were very similar but revealed the impurities, while microanalysis and TPC confirmed a different presence of proteins in the isolated PSHs. In optimal conditions, the IPSH SRB increased germination energy by 5.50% compared to the control; however, the highest percentage of germination (94.70%) was shown after biopriming with the PSH isolated from the ITA strain. Additionally, all assessed treatments resulted in a boost in seedling growth and biomass accumulation, where the ITA strain demonstrated greater effectiveness in optimal conditions, while the SRB strain showed superiority in drought conditions. The drought tolerance indices increased significantly in response to all examined treatments during the drought, with EPSH ITA (23.00%) and EPSH SRB (24.00%) demonstrating the greatest effects. Results of this preliminary study demonstrate the positive effect of isolated PSH, indicating their potential as biopriming agents and offering insights into novel strategies for agricultural resilience. [ABSTRACT FROM AUTHOR]

The TALE gene family plays a crucial role in regulating growth, development, and abiotic stress responses in plants. However, limited studies have been conducted on the functions of the ZmTALE gene family in maize under drought stress. This study identified 40 members of the ZmTALE family within the maize genome through Blast comparisons, distributed unevenly across the first nine chromosomes. Intraspecific collinearity analysis revealed 13 linked pairs. By constructing a phylogenetic tree with Arabidopsis AtTALE members as references, maize members were divided into two subfamilies, KNOX and BEL1-Like, with KNOX further divided into three branches (KNOX Class I, KNOX Class II, and KNOX Class III). The gene structure and motifs of ZmTALE genes within the same subfamily or branch showed similarities, as did their encoded proteins, which possess similar motifs and conserved domains. Analysis of the physicochemical properties of the ZmTALE proteins revealed that the proteins encoded by this family are stable. Expression analysis of ZmTALE genes in maize demonstrated their varied roles in development and drought stress regulation, confirmed through qRT-PCR. The identification, characterization, and expression analysis of ZmTALE genes provide a reference for future gene function research and aid in the genetic enhancement of maize to withstand drought stress. [ABSTRACT FROM AUTHOR]

Drought poses a significant threat to agricultural productivity, particularly affecting economic crops like tomatoes. To address this challenge, various alternatives have been explored, including the use of elicitors or biostimulants such as salicylic acid (SA). This study aims to assess the stimulatory action of SA in alleviating drought stress in tomato plants under greenhouse conditions. The experiment was designed with two main factors: water availability (controlled versus drought) and the foliar application of SA at four different concentrations ranging from 100 to 250 mg L−1. The application of SA, particularly at a concentration of 250 mg L−1, showed promising results in mitigating the adverse effects of drought stress followed by 200 mg L−1. This was evidenced by the increased activity of antioxidant enzymes such as superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT). Gene expression analysis revealed optimal APX expression with SA application at concentrations of 200 mg L−1 or 250 mg L−1. Additionally, the application of SA at 250 mg L−1 led to a high accumulation of bioactive compounds without compromising yield. Furthermore, SA application positively influenced both shoot and root weights, with the highest values observed at a concentration of 250 mg L−1. While SA is known to enhance plant tolerance to abiotic stress, further research is needed to fully elucidate its biochemical, physiological, and molecular mechanisms in supporting plant tolerance to drought stress. Utilizing salicylic acid can help growers mitigate environmental stresses, enhancing tomato crop yield and quality. Integrating SA treatments into agriculture offers a sustainable alternative elicitor for ensuring food security under challenging climate conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The water-retaining and yield-increasing capacity of super-absorbent polymer (SAP) are essential for soil remediation in arid and semi-arid areas. Water availability is an increasing challenge to plant development and crop yield. During the growing seasons in 2021 and 2022, the present study was conducted to evaluate the effect of the addition of different amounts of SAP on the development and yield of green beans (Phaseolus vulgaris L. cv Bronco) under varying water deficit stresses, compared with the control treatment without SAP and water deficit stress. The results demonstrated that a 50% reduction in water requirement (WR) resulted in significant decreases in leaf fresh weight, specific leaf area, leaf total chlorophyll content, pod number, leaf free water content, pod fresh weight per plant, and yield. Decreases were also found in pod total chlorophyll content, carotenoids, dry matter and total protein, leaf proline content, and crude fiber content. Additionally, leaf water saturation deficit was significantly increased under the stress compared with the full irrigation at 100% WR. However, irrigation at 75% WR increased pod contents of ascorbic acid, total sugars, and leaf bound water. The current study also indicated that addition of SAP significantly enhanced the above-mentioned growth characteristics under irrigation at 50% and 75% WR. Treatment with SAP at 3 g/plant was the most effective in mitigating the adverse effects of water deficiency, especially at the irrigation rate of 75% WR. Pearson's correlation analysis showed significantly positive correlations between the growth parameters, as well as pod yield, under water stress and SAP. This study provides a promising strategy for green bean cultivation by adding SAP to soil to alleviate water shortage stress. [ABSTRACT FROM AUTHOR]

This study investigates the economic impact of the 2018 agricultural drought in Korea on the agricultural field through input–output analysis. Using industry linkage tables provided by the Bank of Korea, various economic impacts, including socio-economic and industry linkage effects, such as production, value added inducement effects, and employment inducement effects in the agricultural field, were analyzed. Our findings show the following: (1) It was found that an increase of 1 billion KRW (South Korean won) in output of agricultural, forestry, and fishery products induces an average of 0.6544 KRW in production inducement effects in other industries, 0.23756 KRW in value-added inducement effects, and 3.11363 in employment inducement effects per 1 billion KRW. (2) The supply shortage effect of agricultural, forestry, and fishery products was analyzed to cause a decrease in production of 2.3932 KRW across all industries, and the price inducing effect of a 10% increase in price was found to be 0.19400%, on average. The highest production inducement effects in the food and beverage industry (0.16514 KRW) and the highest value-added inducement effects (0.04391 KRW) came from agricultural, forestry, and fishery products. (3) In the industry linkage effect analysis, agricultural, forestry, and fishery products were found to have a forward linkage coefficient of 0.95652 and a backward linkage coefficient of 0.98911. It is implied by this result that the economic impact of agriculture on other industrial sectors is not significant. This study emphasizes the economic importance of agriculture by providing analytical results that can be utilized in agricultural policy formulation and economic decision-making. It can be used as an important basis for policy development for sustainable development and economic stability of the agricultural field. It can also contribute to a better understanding of how agriculture interacts with other industrial sectors and to the development of effective response strategies to natural disasters such as agricultural drought. [ABSTRACT FROM AUTHOR]

Improving the drought resistance of rice is of great significance for expanding the planting area and improving the stable yield of rice. In our previous work, we found that ROLLED AND ERECT LEAF1 (REL1) protein promoted enhanced tolerance to drought stress by eliminating reactive oxygen species (ROS) levels and triggering the abscisic acid (ABA) response. However, the mechanism through which REL1 regulates drought tolerance by removing ROS is unclear. In this study, we identified REL1 interacting protein 5 (RIP5) and found that it directly combines with REL1 in the chloroplast. We found that RIP5 was strongly expressed in ZH11 under drought-stress conditions, and that the rip5-ko mutants significantly improved the tolerance of rice plants to drought, whereas overexpression of RIP5 resulted in greater susceptibility to drought. Further investigation suggested that RIP5 negatively regulated drought tolerance in rice by decreasing the content of ascorbic acid (AsA), thereby reducing ROS clearance. RNA sequencing showed that the knockout of RIP5 caused differential gene expression that is chiefly associated with ascorbate and aldarate metabolism. Furthermore, multiple experimental results suggest that REL1 is involved in regulating drought tolerance by inhibiting RIP5. Collectively, our findings reveal the importance of the inhibition of RIP5 by REL1 in affecting the rice's response to drought stress. This work not only explains the drought tolerance mechanism of rice, but will also help to improve the drought tolerance of rice. [ABSTRACT FROM AUTHOR]

The effects of the changing frequency and severity of drought events in Central Europe may become a growing concern for its forests. In this study, we looked into how Hungary's forests have been affected by the 2022 compound heatwave and drought, following an arid period from 2018 to 2021. We used our active intensive monitoring plots of the Forest Protection Measuring and Monitoring System (Level II in the ICP Forests) across the country between 2017 and 2022. We analyzed satellite images to support a survey of the large-scale drought utilizing moderate and high-resolution data. The health state of the forest calculated and mapped on the NDVI, ZNDVI, and NDWI indices showed damage and regeneration throughout the period studied. Overall, the forest stands observed tolerated the negative impacts of the drought (126–204 mm water deficit in 2022) based on our biomass data (the summer leaf loss was 14% in each monitoring plot). However, the classified Z-NDVI values of the Sentinel-2 satellite imagery for the period 2017–2022 showed a severe drought in 2022, which was followed by some improvement in 2023. [ABSTRACT FROM AUTHOR]