Shortfall Risk Research Articles

Climate Smart Agriculture: Innovating Sustainable Practices for a Changing Climate

Climate Smart Agriculture (CSA) is a strategy aimed at lowering greenhouse gas emissions, strengthen climate change resilience, raising crop production and incomes significantly. In a variety of agro-ecological zones, the implementation of climate smart agriculture methods has significantly improved the availability of food, adaptability to fluctuations in the climate, and emissions reduction. One of the key benefits of CSA is its ability to improve agricultural productivity and stabilize food security, particularly for small-scale farmers in developing nations who are most vulnerable to climate change. By integrating sustainable techniques such as conservation agriculture, crop diversification, and agroforestry, CSA helps ensure stable yields, even amid erratic weather patterns. Enhanced soil fertility, enhanced irrigation practices and the use of varieties of crops that are climate resilient all contribute to sustained productivity, mitigating the risks of crop failure and food shortages. CSA also strengthens agricultural systems' resilience to climate impacts by promoting practices like zero tillage, residue management, and efficient irrigation, which improve soil health and water retention, making crops more resistant to droughts and floods. In addition to boosting resilience, CSA contributes to climate change mitigation by reducing agriculture's carbon footprint. Precision farming, organic agriculture, and other low-emission practices help curb greenhouse gas emissions from farming activities. Mobile technology is also emerging as a key driver of CSA by providing real-time information, decision-making support, and access to essential resources, further enhancing the adaptability and sustainability of farming systems. The significance of CSA as a strategic solution to the threats that climate change poses to food security and agricultural sustainability is being more widely acknowledged by governments. As a result, numerous initiatives have been launched at national, regional, and international levels to encourage the widespread adoption of CSA practices. In order to achieve sustainable agricultural development and ensure food security in the face of climate change, CSA must be scaled up around the world.

Differences in Drug Shortages in the US and Canada

Drug shortages are a persistent public health issue that increased during the COVID-19 pandemic. Both the US and Canada follow similar regulatory standards and require reporting of drug-related supply chain issues that may result in shortages. However, it is unknown what proportion are associated with meaningful shortages (defined by a significant decrease in drug supply) and whether differences exist between Canada and the US. To compare how frequently reports of drug-related supply chain issues in the US vs Canada were associated with drug shortages. Longitudinal cross-sectional study conducted from January 2023 to March 2024 using drug-related reports of supply chain issues from 2017 to 2021 that were less than 180 days apart in Canada and the US. Shortages were assessed using data from the IQVIA Multinational Integrated Data Analysis database, comprising 89% of US and 100% of Canadian drug purchases. Country (Canada vs US), timing of report issuance (before vs after the COVID-19 pandemic), and characteristics of the supply chain prior to the reports of drug-related supply chain issues (including World Health Organization essential medicine status, Health Canada tier 3 medicine [moderate risk classification], whether there was sole-source manufacturing of the drug, the formulation, the price per unit, ≥20 years since drug approval, and the number of therapeutic alternatives). A drug shortage (a decrease of ≥33% in monthly purchased standardized drug units) within 12 months, relative to the average units purchased during the 6 months prior to the report of supply chain issues to a US or Canadian reporting system. Among the 104 drug-related reports of supply chain issues in both countries, 49.0% (95% CI, 39.3%-59.7%) were associated with drug shortages in the US vs 34.0% (95% CI, 25.0%-45.0%) in Canada (adjusted hazard ratio [HR], 0.53 [95% CI, 0.36-0.79]). The lower risk of drug shortages in Canada vs the US was consistent before the COVID-19 pandemic (adjusted HR, 0.47 [95% CI, 0.30-0.75]) and after the pandemic (adjusted HR, 0.31 [95% CI, 0.15-0.66]). After combining reports of supply chain issues in both countries, the shortage risk was double for sole-sourced drugs (adjusted HR, 2.58 [95% CI, 1.57-4.24]) and nearly half for Canadian tier 3 medicines (moderate risk) (adjusted HR, 0.56 [95% CI, 0.32-0.98]). Drug-related reports of supply chain issues were 40% less likely to result in meaningful drug shortages in Canada compared with the US. These findings highlight the need for international cooperation between countries to curb the effects of drug shortages and improve resiliency of the supply chain for drugs.

INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM

E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.

Conditional shortfall risk of lifetime consumption

Conditional shortfall risk of lifetime consumption

Dynamic process simulation for life cycle inventory data acquisition – Environmental assessment of biological and chemical phosphorus removal

In Sweden, phosphorus is commonly removed from municipal wastewater treatment by chemical precipitation (CP). Recently, such alternatives as enhanced biological phosphorus removal (EBPR) have garnered interest due to the increased risk of chemical shortage. In this study, a life cycle assessment (LCA) was performed to compare EBPR and CP in three scenarios: 1) baseline – precipitation chemicals available, 2) stricter effluent requirements – precipitation chemicals available, and 3) chemical shortage – no precipitation chemicals available. Data acquisition that was based on dynamic process simulation was useful, yielding more site-specific results, in contrast to standard literature values. The results indicated substantial differences in greenhouse gas emissions between configurations (around three times higher methane emissions for EBPR compared to CP configurations – although this finding requires further validation). These differences suggest that different emission factors for EBPR and CP should be considered. Furthermore, it is suggested to include waterline methane emissions, at least when the configuration incorporates anaerobic reactors in the water line. Further validation of emissions is necessary, especially for EBPR plants with side-stream hydrolysis and digester reject water treatment. The LCA results showed a similar overall environmental impact for both configurations, but the results of individual impact categories differed. EBPR caused greater climate impact due to the larger direct emissions of methane. Toxicity was more important for CP, based on the inherent heavy metal content in precipitation chemicals. Freshwater eutrophication was similar for both configurations, assuming that precipitation chemicals were available. However, if the recipient is sensitive, implementing EBPR reduces the freshwater eutrophication potential by 75% during a chemical shortage, and should be considered.

Stochastic Risk-driven Bidding of a Solar and Storage Aggregator in Primary Frequency and Energy Markets: A Performance-based Capacity Allocation Approach

The ever-increasing impact of deploying renewable energy resources reducing power system inertia, requires distributed energy resource (DER) aggregators to secure high-performance, fast-responding primary frequency reserve (PFR) in ancillary service markets. However, enabling immediate and local regulation of the operating point upon detecting frequency deviations imposes the necessity of allocating specific headroom for DERs. The individual headroom susceptibility to uncertainty and response speed of a certain DER not only compromises aggregated headroom performance in the PFR market but also leads to incurring unnecessary opportunity costs in the energy market and inadequate frequency control in the power system. To address this challenge, this paper introduces an integrated probabilistic performance index for DERs that considers their individual interactive heterogeneous response speed and uncertainty. Using this index, aggregators overseeing photovoltaic and smart buildings equipped with energy storage can evaluate, allocate, and remunerate optimal headroom capacity for individual DERs based on their performance. This approach ensures maximum aggregated profits and minimum opportunity costs in the PFR and energy markets while improving the performance of the provided PFR capacity, respectively. These tasks are aimed by a novel optimal capacity allocation strategy for an aggregator in both markets. This strategy employs a mixed-integer linear programming model to find the optimal solution and the conditional value-at-risk measure to tackle the uncertainties faced with the problem. The efficiency of the proposed model is assessed within a system reflecting the ERCOT market structure, demonstrating improved aggregated headroom performance, reduced energy opportunity costs, diminished risk of capacity shortage, and improved load frequency control (LFC) from the power system perspective.

Hobli wise hydro geomorphological evaluation of the groundwater prospects mapping

ABSTRACT Numerous studies highlight the urgent issue of groundwater depletion due to overexploitation. The United Nations’ World Water Development Report (2023) warns that if current extraction rates persist, many regions could face severe groundwater shortages in the coming decades. A 2021 study in Nature reports that about one-third of the world’s largest groundwater basins are depleting faster than they are replenished, escalating the risk of critical shortages. In Chickballapur District, excessive agricultural activity has led to an imbalance between groundwater loss and recharge. To address this challenge, a weighted overlay analysis of spatial layers in QGIS was employed to identify potential groundwater zones. This methodical approach involved mapping topographic features and integrating six spatial layers: slope, geomorphology, lithology, drainage, lineament and land use/cover. Each subcategory was ranked and scored based on expert opinion and existing literature. The Central Ground Water Board data was used to validate the resulting groundwater prospect map. The study further included an assessment of groundwater prospects across administrative units by superimposing the administrative boundaries on the final map. Calculating the percentage of land within each groundwater prospect category is crucial for effective groundwater management at the administrative level.

Mycorrhizal types determined the response of yield of woody bioenergy crops to environmental factors.

Meeting the demand for energy solely through fossil fuels has posed challenges. To mitigate the risk of energy shortage, woody bioenergy crops as a renewable energy feedstock have been the subject of many researchers. Also, mycorrhizas play an important role in crop productivity and inevitably affect the biomass yield of woody bioenergy crops. Based on a global synthesis of biomass yield of woody bioenergy crops, a framework for identifying and comparing bioenergy crop biomass in response to mycorrhizal type was developed. Our results found that the biomass yield of woody bioenergy crops in descending order was ectomycorrhizas (ECM) crops (10.2 ton DM ha-1year-1) > arbuscular mycorrhizas (AM)+ECM crops (8.8 ton DM ha-1year-1) > AM crops (8.0 ton DM ha-1year-1). In addition, our analysis revealed that the climate had the strongest effect on biomass yield in AM and ECM crops, whereas geography exerted the most significant influence on biomass yield in AM+ECM crops. Furthermore, there were differences in the biomass yield response of different mycorrhizal and plant types to geographic (latitude and elevation) and climatic factors (mean annual temperature (MAT) and mean annual precipitation (MAP)). When cultivating AM crops, we can focus more on temperature conditions-warmer locations, whereas for ECM crops, selecting regions with higher precipitation levels is advantageous. This study revealed the relationship between mycorrhizae and bioenergy crops. It provides data and theoretical support to rationalize differences in different woody bioenergy crops and their different responses to global change and increased production of bioenergy crops.

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Assessment and pre-assessment of compound hot and drought events over Yangtze River Basin

Abstract The severe compound hot and drought event (CHDE) in 2022 had serious impacts on water resources, agriculture, power supply, and ecosystems in the Yangtze River Basin (YRB). Have similar spatiotemporally overlapping compound events, like the one observed in 2022, occurred historically in the basin? How can we pre-assess the impacts of such potential events on subseasonal timescales? To address these, regional CHDEs in the YRB since 1961 were systematically identified and comprehensively assessed using a modified Intensity-Area-Duration (IAD) technique and hazard assessment method. Additionally, the subseasonal pre-assessment capability for CHDEs in the YRB using the China Meteorological Administration's third-generation climate model prediction system (CPSv3-S2S) was demonstrated. A total of 140 CHDEs occurred during 1961 to 2022, with the 21st century showing a remarkably high risk of such events. Among these, 2022 was the most intense year, with the event from July 26th to August 30th being unprecedented in severity. CPSv3-S2S effectively forecast this event’s occurrence within a 1-25 lead days, particularly in pre-assessing its extensively impacted area and high-hazard centers in the upper and middle reaches of the main river course. This pre-assessment method serves as a valuable reference in decision-making to anticipate and mitigate risks of hydropower supply shortages and heightened electricity demand during extremely high temperatures in the upstream area.

Online Estimation and Optimization of Utility-Based Shortfall Risk

Utility-based shortfall risk (UBSR) is a risk metric that is increasingly popular in financial applications, owing to certain desirable properties that it enjoys. We consider the problem of estimating UBSR in a recursive setting, in which samples from the underlying loss distribution are available one at a time. We cast the UBSR estimation problem as a root-finding problem and propose stochastic approximation-based estimation schemes. We derive nonasymptotic bounds on the estimation error in the number of samples. We also consider the problem of UBSR optimization within a parameterized class of random variables. We propose a stochastic gradient descent–based algorithm for UBSR optimization and derive nonasymptotic bounds on its convergence.

Establishing the robustness of chip trade networks by dynamically considering topology and risk cascading propagation

Risk cascading propagation research mostly focuses on complex theoretical networks. Recently, the vulnerability of international chip supply has increased notably, and it is strategically important to explore how shortage risk affects the emergence dynamics of the real chip trade systems. This study abstracts the global chip trade relationship data for 2009–2021 into multiple asymmetrically weighted networks. Using macro-network and micro-node indicators, we explore the topological traits of international chip trade networks and their evolutionary laws. Accordingly, we propose risk cascading propagation models driven by node failure and edge restraint and further innovate to open up the research paradigm of focused-edge networks. Furthermore, a community infection-driven risk cascading propagation mechanism that incorporates community risk absorption capacity is introduced, considering both the propagation attenuation effects and the trade dependency degree. A multi-dimensional dynamic perspective reveals the hidden systemic risks in international chip trade. The main results are as follows: first, international chip trade networks are highly connected and cohesive, consistent with small-world characteristics. Second, the proportion of economies that collapse because of supply shortage risk shocks increases with the impact coefficient α/β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha /\\beta$$\\end{document}. The dominant power in chip crisis propagation has shifted from Europe and America to Asia, and mainland China’s risk penetration capacity has enhanced significantly. Third, focused-edge networks conform to a multi-hub radiation pattern. Before the COVID-19 pandemic, the degree of control and spillover effects of chip supply shortages in hub economies on the international trade was increasing progressively. Fourth, an increase in absorption capacity λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document} or attenuation factor γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\gamma$$\\end{document} consistently leads to a decline in avalanche scale, with λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda$$\\end{document} exhibiting critical thresholds. These findings will help policymakers pursue efficient management strategies for chip trade, thereby improving supply stability and security.

Development path for China's mega-large urban agglomerations based on the water-energy-food nexus

The Beijing-Tianjin-Hebei (BTH) mega-large urban agglomeration of China (CMUA) has significantly contributed to Chinese socioeconomic development. However, it faces severe shortages in the supply to meet the increasing demand of water, energy, and food resources (WEF). In order to safeguard the resource security of CMUA-BTH and the synergistic development of cities, and to compensate for the lack of dynamic analysis between urban agglomerations and WEF in previous studies. In this study, a coupled CMUA-WEF simulation model is developed based on the system dynamics approach. The model not only studies the three elements of WEF, supply and demand, but also conducts a comparative analysis of the three regions of BTH, and designs a total of four scenarios based on the possibility of future development, so as to facilitate the depiction of the trend of the WEF supply and demand system under different scenarios, and to provide theoretical basis for the government to formulate effective policy. The results show that: (1) Regarding demand, the aggregate demand for energy, food and water in the BTH will increase by about 3–5 times, 3 times, and 2 times, respectively, from 2000 to 2050, among which the total demand for energy will increase most significantly. In contrast, the variation of local resource supply side is small, which leads to the deterioration of resource security. (2) The risk of water shortage is the greatest in resource security, with less than 30% self-sufficiency in water security in all three regions. (3) Scenario analysis for CMUA-BTH indicates that “choking flow” (S3) yields more pronounced effects compared to “open source” (S2), and therefore adhering to the reduction of food waste, promoting energy-saving technologies, and intensifying water-saving measures can effectively reduce the risk of water shortages in the three regions. Optimal resource security in S4 increases Hebei's water pressure by 1.5%, alleviates Beijing's pressure by 7.3%, and the implementation of the Xiongan New Area and the new energy development policy can help realize the synergistic development of BTH and the inter-regional linkage.

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

An integrated adaptive allocation model for unified optimization of conventional and unconventional water resources based on fairness and efficiency

Unconventional water (UW) use has pros and cons, presenting the challenge of matching water utilization with regional water use characteristics. This study proposed the concept of amount-centered adaptive water allocation and built an integrated adaptive allocation model that includes conventional water (CW) and UW adaptability. The model consists of two modules. The first module introduces the regional composite index and inequality index to match CW and UW allocation amount with the sub-region’s characteristics. The second module defines the efficiency of CW and UW allocation, employs the Zero-Sum Gains Data Envelopment Analysis to find the solution set with the highest efficiency on the Pareto front, and further modifies it to reach the optimal allocation efficiency. Then the proposed model was applied to Jiangsu Province, China. Empirical and comparative analyses of the 54 weighting plans proved the validity and practicability of the model. The results showed: (1) The model tallies with the principles of fairness and efficiency and reduces the spatial heterogeneity of water shortage risk; (2) When available water supply decreases by over 20% due to future water-related emergency incidents, CW allocation would first reach its upper limit, while UW fails to effectively bridge the supply–demand gap; (3) Only by improving the maximum UW consumption by over 40% can the future risk of extreme water shortages be effectively addressed. The model proposed in this study has excellent practicality and application potential, providing a novel perspective for incorporating UW into the unified management of water resources.

Drug Shortages: A Scoring System to Evaluate and Prioritize the Clinical Importance of Drugs in Canada

Drug shortages are an ongoing global health issue and pose significant challenges to policy-makers and health care providers. They may result from various factors (e.g., supply chain disruptions and recalls) and have a negative impact on patient outcomes and health care expenditures. The ability to accurately predict drugs at risk of a shortage would be a vital aspect in the effective management of drug shortages and help ensure proactive measures for consistent drug supply and optimized patient care; however, these drugs must also be prioritized in a meaningful way to identify high priority targets among the large number of drugs in the market and help ensure resources are allocated to the most important at-risk drugs. The purpose of this Environmental Scan aimed to identify a systematic method of objectively evaluating and ranking the value of drugs based on important clinical criteria and adapt the selected tool to prioritize the clinical importance of drugs in Canada. A total of 5 potential scoring systems were considered, and 1 scoring system was selected and adapted (based on expert panel member feedback). Finally, an assessment of 9 clinical criteria was made across 5 broad categories. The criteria include an evaluation of disease severity, size of the population, clinical guidelines, comparator interventions limitations (unmet needs), improvement of efficacy or effectiveness, improvement of safety and tolerability, improvement of patient-reported outcomes (PRO), type of medical service (e.g., symptom relief, prolonging life, cure), and public health interest. The proposed tool can be used in situations where decision-making requires an objective and systematic evaluation of drugs' clinical importance. Additionally, it will help inform a drug shortage risk framework in Canada, which will consider both the clinical importance and supply chain risks of drugs as part of the Health Canada Drug Shortages Task Force.

Penggunaan CROPWAT 8.0 untuk Menentukan Kebutuhan Air Irigasi Tanaman Tomat pada Tanah Regosol di Kawasan Ternate Utara, Provinsi Maluku Utara

Tomato is an important crop in Ternate, North Maluku Province, Indonesia. This study aimed to determine the irrigation water requirements for tomato cultivation on Regosol soil in the North Ternate region using the CROPWAT 8.0 model. Climate data, crop data, and soil data were collected and processed using the CROPWAT 8.0 program. The results showed that the irrigation water requirements varied significantly between the rainy and dry seasons. During the rainy season, the total irrigation water requirement ranged from 60 to 107 mm, while in the dry season, it was much higher, ranging from 130 to 187 mm. This difference was primarily due to the low rainfall during the dry season. Simulations of planting at different periods showed relatively minor variations in crop water requirements, reflecting the stable weather conditions throughout the year in Ternate. The findings of this study can assist local tomato farmers in managing irrigation water more efficiently and effectively, thereby improving crop productivity and reducing the risk of water shortage.

A Review on Biotechnological Innovations in Developing Stress-Tolerant Crops for Adverse Environmental Conditions

The development of stress-tolerant crops through advanced biotechnological approaches is critical for enhancing global food security and sustainability in the face of climate change and increasing environmental stresses. Emerging tools such as CRISPR/Cas9 gene editing and synthetic biology are revolutionizing genetic engineering by enabling precise, targeted modifications of plant genomes to improve drought, salinity, heat, and cold tolerance. Integrative approaches that combine genomics, transcriptomics, proteomics, and metabolomics provide a comprehensive understanding of plant stress responses, facilitating the identification of key regulatory genes and metabolic pathways. High-throughput phenotyping and RNA interference (RNAi) technologies further enhance trait identification and manipulation, accelerating the development of robust stress-tolerant varieties. The deployment of these crops has demonstrated significant yield improvements and stability in adverse conditions, reducing the risk of crop failures and food shortages. Drought-tolerant maize and rice varieties have increased yields by 20-30% under water-scarce conditions, while salt-tolerant rice and wheat varieties have enabled cultivation in saline soils. Heat-tolerant crops maintain productivity in high-temperature regions, and cold-tolerant varieties extend growing seasons in temperate areas. These advancements not only enhance crop productivity but also promote sustainable agricultural practices by reducing the need for chemical inputs and supporting the resilience of food systems. Moreover, the socio-economic benefits include improved livelihoods for smallholder farmers through increased incomes and economic stability. Continued interdisciplinary research and collaboration are essential to fully realize the potential of these technologies in addressing global agricultural challenges and ensuring a stable food supply for future generations.

Synchronizing short-, mid-, and long-term operations of hydro-wind-photovoltaic complementary systems

Hydro-wind-photovoltaic (PV) complementary power systems (HWPCSs) offer a promising solution for integrating intermittent wind and PV power, leveraging the long-term energy storage capacity of reservoirs and the short-term flexible adjustability of hydro units. However, existing studies often neglect the reciprocal influences among short-, mid-, and long-term operations of HWPCSs, resulting in suboptimal operating strategies. To tackle this issue, the study proposes a generic framework for synchronizing operations of HWPCSs across multiple time scales. The framework simultaneously optimizes long-term operating rule curves, mid-term water consumption allocation, day-ahead power generation plan scheduling, and intraday economic operation of HWPCSs. A HWPCS located in the Yalong River Basin is selected as the case study. The results indicate that: (1) synchronized operation effectively reduces the output shortage risk (from 4.86 % to 0 %) and average absolute water consumption deviation (from 2.07 % to 0.09 %) compared to the conventional method; (2) synchronized operation provides more reasonable boundaries for short-term operations, reducing the average online time percentage (from 65.08 % to 63.57 %) and start-up/shut-down times (from 3185 to 2981) of hydro units, leading to an increase in the average hydropower efficiency (from 9.05 to 9.12). This study highlights the impracticality of the conventional method, underscoring the necessity of synchronized operation for HWPCSs. The proposed method provides a practical framework for the efficient operation of HWPCSs.

Fuzzy rule–based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule–Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7–day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.