Logistic Approach Research Articles

Intra-specific variation in mortality of even-aged Cryptomeria japonica (L. f.) D. Don. forests can be explained using relationships among long-term stand characteristics

Abstract Context Understanding tree mortality is critical for sustainable forest management. Long-term tree mortality may differ depending on the stand development process and can be influenced by forest management regimes. Logistic regression is widely used to explain tree mortality based on site productivity, age, size, and competition. However, the explanatory variables are interrelated. Thus, we attempted to explain long-term Japanese cedar tree mortality by considering interrelated variables. Aims The aim of this study was to elucidate the direct and indirect effects of site productivity, age, individual size, and competition on the long-term mortality of Japanese cedars. Methods Data were collected from 5130 even-aged Japanese cedar trees over approximately 50 years. We compared each variable between dead and living trees. We then constructed a mortality model using a conventional logistic approach and selected the best model for the stepwise methods. Finally, we applied a piecewise structural equation model (SEM) to identify these variables’ direct and indirect effects. We compared the conventional logistic model and piecewise SEM models and discussed the advantage of applying the SEM models. Results Annual mortality was approximately 4% in the most fertile stands, increasing gradually with decreasing site fertility. Dead tree size and competition status differed according to age and site productivity. Competition, individual size, and stand density were selected for the best logistic model (area under the curve (AUC) = 0.74, Brier score = 0.042), whereas age and site productivity were not (p > 0.05). The piecewise SEM results showed that age and site productivity indirectly affected tree mortality through individual size and stand density (Fisher’s C = 4.569, p = 0.102). Conclusion Long-term Japanese cedar tree mortality can be explained by individual size and competition as direct influencing factors and age and site productivity as indirect influencing factors. This indicated that hidden factors cannot be explained using the conventional logistic approach. Further studies are required to explore the potential factors contributing to tree mortality thoroughly.

Understanding the role of information and digital technologies of industry 4.0 on last-mile logistics: current state and prospective research avenues

This work aims to provide an overview of the current state of the literature linking Information and Digital Technologies of Industry 4.0 (IDT of I4.0) and last-mile logistics. The systematic literature review (SLR) was used to identify, select, and evaluate papers covering the period from 2001 to 2022, inclusively. Due to the large number of identified articles, inclusion criteria prioritized those recognized among the highest-rated journals according to the AJG (ABS) classification resulting 97 papers. The paper provides a novel classification of the literature into three lines of research: (1) Mature IDT in last-mile logistics, (2) Emerging IDT in last-mile logistics, and (3) General approach in last-mile logistics. The study identifies and proposes future lines of research to address gaps.

Integrated optimization of humanitarian relief chains: a multi-product nonlinear programming approach for disaster logistics under uncertainty

Integrated optimization of humanitarian relief chains: a multi-product nonlinear programming approach for disaster logistics under uncertainty

Advanced integration of bidirectional long short-term memory neural networks and innovative extended Kalman filter for state of charge estimation of lithium-ion battery

The state of charge (SoC) of a battery is a crucial monitoring indicator for battery management systems and it helps to assess how much further an electric vehicle can travel. This work proposes a novel approach for predicting battery SoC by developing a closed-loop system that integrates a bidirectional long short-term memory neural network with an innovative algorithm-extended Kalman filter. A second-order equivalent circuit model is selected, and its parameters are computed using the variational and logistic map cuckoo search approach. Further, an Extended Kalman filter is combined with an innovation algorithm to update process noise in real-time, and a bidirectional long short-term memory neural network takes the input from the Extended Kalman filter and gives the compensated error value for the final SoC estimation. 75% of dynamic stress test data from the Extended Kalman filter is used for training purposes, remaining data sets are used for testing purposes. The addressed algorithm is validated by evaluating its performance in comparison to individual algorithms and various combined approaches. Empirical analysis demonstrates that the proposed model achieves a root mean square error of 0.11 % and mean absolute error of 0.1 % positioning it as a valuable tool for battery management systems.

Modern contraceptive utilization among street beggar women in the Sidama region of Ethiopia using bayesian model approach

IntroductionContraceptive methods are techniques used to prevent pregnancy during sexual intercourse. The primary goal of contraception is to inhibit the fertilization of an egg by sperm or to prevent the implantation of a fertilized egg in the uterus. Unwanted pregnancy is a major challenge for street beggar women. Due to limited use of modern contraception, unwanted pregnancy and STDs are key issues faced by this population. This study aims to assess modern contraceptive utilization and explore factors influencing contraceptive choices among street beggar women in the Sidama region, Ethiopia by using Bayesian logistic approaches.MethodsA cross-sectional quantitative study was conducted on reproductive-aged (15–49) street beggar women in the Sidama Region. Data was collected using a pre-tested, structured interview questionnaire in the local language from each town in the Sidama region. Descriptive and inferential statistics were used to analyze the data using STATA version 17. A Bayesian logistic approach, with Markov Chain Monte Carlo methods, was used to model the association between modern contraceptive utilization and independent variables, allowing for estimation of model parameters and credible intervals.ResultThe study found that 38.9% of street women in the Sidama region used modern contraceptives, primarily implants and injections. Bayesian logistic regression identified key factors influencing contraceptive use including, women aged 25–34 (AOR: 2.69 (95% CI: 1.57, 4.26)), married women (AOR: 4.91 (95% CI: 2.67, 8.51)), Widowed women (AOR: 2.81 (95% CI: 1.49)) and educated women (AOR: 2.16 (95% CI: 1.48, 3.01)) were more likely to use contraceptives. In contrast, those living on the street (AOR: 0.12 (95% CI: 0.05, 0.27)), with no history of pregnancy (AOR: 0.07 (95% CI: 0.02, 0.16)), and no sexual activity in the last six months (AOR: 0.11 (95% CI: 0.01, 0.03)), had lower usage rates. Additionally, women whose only job was begging (AOR: 0.43 (95% CI: 0.13, 0.92)) and those lacking health advice (AOR: 0.25 (95% CI: 0.07, 0.59)) also showed reduced utilization. Lack of discussion about contraceptives with partners (AOR: 0.18 (95% CI: 0.08, 0.33)) and no women's approval (AOR: 0.18 (95% CI: 0.08, 0.33)) further contributed to lower usage. Conversely, good knowledge of modern contraceptives was positively associated with utilization (AOR: 2.33 (95% CI: 1.44, 3.64)).ConclusionThe findings of this study identified several factors significantly associated with modern contraceptive use among street beggar women in the Sidama region, including age, marital status, Education level, disability, Job in addition to begging, Current living situation, history of sexual assault/rape, History of pregnancy after street life, discussion of modern contraception with a sexual partner, Advice from a health professional, Sexual activity in the last six months, time taken to arrive at a health facility on foot, approval of modern contraception, and knowledge. The main barriers reported were fear of side effects and lack of knowledge about contraceptive methods. The study suggests that concerned stakeholders should intervene to address these factors and improve modern contraceptive uptake in this vulnerable population.

Threshold modeling for antibiotic stewardship in Oman

Antimicrobial stewardship supports rational antibiotic use. However, balancing access to antibiotic treatment while controlling resistance is challenging. This research used a threshold logistic modeling approach to identify targets for antibiotic usage associated with carbapenem-resistant Acinetobacter baumannii, carbapenem-resistant Klebsiella pneumonia, and extended-spectrum β-lactamases-producing Escherichia coli incidence in hospitals. This study utilizes an ecological population-level design. Monthly pathogen cases and antibiotic use were retrospectively determined for inpatients between January 2015 and December 2023. The hospital pharmacy and microbiology information systems were used to obtain this data. Thresholds were identified by applying nonlinear modeling and logistic regression. Incidence rates of 0.199, 0.175, and 0.146 cases/100 occupied bed-days (OBD) for carbapenem-resistant A baumannii, carbapenem-resistant K pneumonia, and extended-spectrum β-lactamases-producing E coli, respectively, were determined as the cutoff values for high (critical) incidence rates. Thresholds for aminoglycosides (0.59 defined daily dose [DDD]/100 OBD), carbapenems (6.31 DDD/100 OBD), piperacillin-tazobactam (3.71 DDD/100 OBD), third-generation cephalosporins (3.71 DDD/100 OBD), and fluoroquinolones (1.91 DDD/100 OBD), were identified. Exceeding these thresholds would accelerate the gram-negative pathogens' incidence rate above the critical incidence levels. Threshold logistic models can help inform and implement effective antimicrobial stewardship interventions to manage resistance within hospital settings.

MANAGEMENT REPORTING IN THE COST MANAGEMENT SYSTEM OF LOGISTICS ENTERPRISES

Purpose. Management of logistics costs includes making optimal decisions based on internal and external information, which is used to analyze and calculate a large number of indicators characterizing the logistics system from the point of view of costs. The existing problems and debatable issues regarding the relevance of the information of the accounting system on the reflection of costs related to the logistics activity at the enterprise are given. Design/methodology/approach. The theoretical basis of the research was the fundamental provisions of economic theory, the work of domestic and foreign scientists. In the process of research, the principles of system and process approaches were applied as components of the concept of managing logistics costs of the enterprise. The method of scientific abstraction is used to formulate theoretical generalizations and conclusions regarding the function of management reporting in the system of cost management mechanisms of the enterprise. The methodological basis of the study is based on a dialectical approach and a systematic method of studying the problems of information support for cost management at logistics enterprises. Findings. On the basis of the conducted research, the purpose, principles, functions, tasks and requirements for the introduction of management reporting into the logistics cost management system were determined. The classification features of logistics costs of enterprises for separate reports of internal management reporting regarding its volume, forms and terms of submission are substantiated. It has been proven that the correct structuring of logistics costs and methods of their distribution make it possible to identify, evaluate and optimize costs, justify management decisions aimed at their minimization Practical implications.. The implementation of logistics approaches by domestic enterprises in flow process management systems allows to quickly manage product and material flows, assess the demand for goods, plan inventory indicators, synthesize the tasks of supply, production and sale of finished products. The formation of a subsystem of management accounting and internal business reporting targeted at logistics costs and, based on them, analytical support of logistics operations strengthens the effectiveness of the management mechanism. logistics chains of the enterprise. Originality/value. The expediency of using the ABC costing (Activity Based Costing) method in domestic theory and practice is argued for managing logistics costs and on the basic basis of this method to form the content parameters of goal-oriented management reporting. The expediency of building management reporting is justified by the fact that it is possible to implement a differentiated cost accounting method and organize management accounting of costs by function.

Green Logistics 5.0: a review of sustainability-oriented innovation with foundation models in logistics

PurposeThe paper uses foundation models to integrate the green approach in Logistics 5.0. Such integration is innovative in logistics and leads to a more sustainable and prosperous future. By harnessing the power of foundation models and incorporating sustainable principles, this paper can systematize the logistics industry’s environmental framework, increase its social responsibility and ensure its long-term economic viability.Design/methodology/approachGeneralizing environmental sustainability goals requires a multi-layered innovation approach incorporating corporate philosophy, products, processes and business models. In this paper, this comprehensive approach is not just a strategy but a necessity in the current global context. This paper uses the sustainability-oriented innovation (SOI) method, crucial for achieving explicit environmental, social and economic impacts.FindingsArtificial intelligence, especially foundation models, can contribute to green logistics by optimizing routes, reducing packaging waste, improving warehouse layouts and other functions presented in the paper. At the same time, they can also consider social, economic and governance goals.Research limitations/implicationsArtificial intelligence algorithms present challenges such as high initial investment, regulatory compliance and technological integration.Practical implicationsThe paper contains implications for developing environmentally sustainable logistics, which is currently one of the most significant challenges. The framework presented can apply to logistics companies.Originality/valueThis paper fulfills an identified need to study sustainability in logistics. The framework is entirely original and not present in the literature. It is essential to help design and implement innovative logistics approaches.

Future of Earthquake-Based Disaster Logistics and Futuristic Approaches

Disasters that occur in the world can be caused by natural as well as human-induced factors. These disasters cause damage to cities, villages, and inhabited areas, and also result in loss of life and property. Accurately and effectively planned disaster logistics management activities are crucial during these moments when life comes to a halt. Today, it is evident that cities suffer damage or even face extinction due to deficiencies in planning, coordination, and opportunities within these practices. To mitigate the consequences of disasters and minimize damage, it is essential to always be prepared for such events and manage them well before, during, and after they occur. Disaster logistics practices should be dynamic and tailored to the specific event, suitable for addressing the situation at hand. Each disaster requires a unique set of solutions that depend on the area and geography affected. Therefore, disaster logistics must adopt and develop new approaches with a sustainable perspective. Given that earthquakes are among the disasters that occur frequently in our country, there is a critical need to create earthquake-based disaster management and action plans. Looking at disaster logistics from a futuristic perspective, it is clear that digital transformation technologies can have a significant and beneficial impact, making disaster logistics services more efficient, innovative, and solution oriented. The study conducted a literature review within the framework of qualitative research methodology, providing a detailed analysis of the role of digital transformation technologies in earthquake logistics processes. The literature review was conducted by reviewing relevant academic articles, reports, books, and scientific research. Special emphasis was placed on studies published in the last 10 years, focusing on the intersection of disaster logistics and digital transformation technologies.

Predictors of Blood Lipid and Renal Function Screening among Adults in a Peri-Urban Community in Ghana: A Combined Logistic and Dominance Analysis Approach

Objective: Despite the critical role of screening in reducing the burden of non-communicable diseases (NCDs), its uptake remains low, particularly in peri-urban settings. This study aimed to identify and rank predictors of screening behaviors for blood lipid/cholesterol and renal function in a peri-urban community in Ghana. Methods: Secondary cross-sectional data from 136 adults aged 18–60, collected in January 2023, were analyzed. Associations and relative importance were examined using bootstrapped logistic regression and dominance analysis models. Results: Multivariate logistic regression analysis identified age (35-60 years) (aOR:7.6, 95% CI: 1.2–50.6) and employment status (aOR:4.4, 95% CI: 1.1–17.6) as significant predictors of renal screening. For blood lipid screening, significant predictors included body mass index (BMI) screening (aOR:3.6, 95% CI: 1.4–9.1) and diploma-level education (aOR:5.4, 95% CI: 1.3–21.8). Dominance analysis, which assesses the relative importance of predictors, revealed that age, blood glucose screening, and employment were the most important predictors for renal screening. In contrast, BMI and a history of raised blood pressure were the leading predictors for blood lipid screening. Conclusions: These findings highlight the need for targeted health promotion strategies that integrate comprehensive screening packages within broader health services, addressing the specific needs of various educational and occupational groups. Enhanced public health interventions could improve screening rates and contribute to better management of NCDs in peri-urban settings.

Fixing imbalanced binary classification: An asymmetric Bayesian learning approach.

Most statistical and machine learning models used for binary data modeling and classification assume that the data are balanced. However, this assumption can lead to poor predictive performance and bias in parameter estimation when there is an imbalance in the data due to the threshold election for the binary classification. To address this challenge, several authors suggest using asymmetric link functions in binary regression, instead of the traditional symmetric functions such as logit or probit, aiming to highlight characteristics that would help the classification task. Therefore, this study aims to introduce new classification functions based on the Lomax distribution (and its variations; including power and reverse versions). The proposed Bayesian functions have proven asymmetry and were implemented in a Stan program into the R workflow. Additionally, these functions showed promising results in real-world data applications, outperforming classical link functions in terms of metrics. For instance, in the first example, comparing the reverse power double Lomax (RPDLomax) with the logit link showed that, regardless of the data imbalance, the RPDLomax model assigns effectively lower mean posterior predictive probabilities to failure and higher probabilities to success (21.4% and 63.7%, respectively), unlike Logistic regression, which does not clearly distinguish between the mean posterior predictive probabilities for these two classes (36.0% and 39.5% for failure and success, respectively). That is, the proposed asymmetric Lomax approach is a competitive model for differentiating binary data classification in imbalanced tasks against the Logistic approach.

The Relationship Between Chief Financial Officer Gender and Investment Efficiency

Objective: This study aims to examine the relationship between the gender of the chief financial officer (CFO) and the efficiency of corporate investment, especially over-investment. Methods: The independent variable is the gender of the CFO and the dependent variable is over-investment. The research method is correlation type and multivariate regression using panel data with a logistic regression model approach. The data of manufacturing companies in the Tehran Stock Exchange has been collected from 2017 to 2021. Results: The findings of the research show that the presence of a female financial manager is significantly related to the reduction of the company's over-investment level, so gender plays a role in the economic decision-making and investment of companies. Conclusion: Female chief financial managers are more risk-averse compared to male chief financial managers and consider the interests of shareholders in the company’s economic decisions. Therefore, companies should pay special attention to gender diversity at the level of company managers in making decisions and economic investments.

A logistic model for precise tomato fruit-growth prediction based on diameter-time evolution

Monitoring tomato fruit growth in large greenhouse environments is crucial for estimating harvest volume and maximising labour efficiency. Mathematical modelling is commonly used to characterise crop and fruit growth and to understand crop responses to environmental influences. This study presents a logistic approach for estimating tomato fruit maturity based on fruit diameter. A set of logistic models were evaluated, and their precision and ability to forecast tomato fruit growth were analysed. Non-linear least square regression was used to fit each model to individual tomato fruits from a sample of over 600 fruits of two species (small and large fruit-bearing tomatoes) grown within one year. The focus was on fruit diameter and the necessary measurement precision for fruit maturity estimation. Low-dispersion variables were identified within our logistic model functions, and fruit species-specific model parameters were determined. Furthermore, we reduced the number of regression variables by identifying parameters of low variance. This allowed for high prediction precision (>80%) at the early fruit maturity stages (<30%). The fruit diameter data were collected manually using a caliper. Our results establish an upper limit for the measurement precision of automated fruit size estimation approaches utilising photogrammetry, with an optical range of 98% for 3 mm and 90% for 5 mm precision.

Digital twin-driven safety management and decision support approach for port operations and logistics

Ports are of great significance in processing cargo containers and facilitating global marine logistics. Nevertheless, the susceptibility of the container shipping network for hazardous cargo is likely to intensify in the event of a significant disruption at a major port, such as adverse weather conditions, inadequate management practices, or unforeseen catastrophes. Such situations require the deployment of port protection emergency response and prevention in advance. This study proposes a digital twin (DT) model that employs extensive and trajectory data within containers to comprehensively analyze the occurrence of hazardous cargo failures within the port storage process. The virtual models of physical entities in the port are created through a data-driven approach, and the behavior of these entities in a port environment with big data is then simulated. A combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) algorithm is employed to provide predictions for the service layer of the DT system. The predicted correlation coefficients of temperature and humidity in the container reach 0.9855 and 0.9181, respectively. The developed system driven by DT models integrated with a CNN and the LSTM algorithm can more effectively assist the safety manager in achieving prevention in port operations. This study enables marine authorities and decision-makers to optimize emergency procedures, thereby reducing the probability of accidents in port operations and logistics.

Methodology of Shipboard Spare Parts Requirements Based on Whole Part Repair Strategy

This paper introduces an assessment method for shipboard spare parts requirements based on a whole-part repair strategy, aimed at enhancing the availability and combat effectiveness of naval equipment. Addressing the shortcomings of traditional repair strategies, this study innovatively adopts a whole-part rotation repair approach to reduce repair times and improve the rapid response capability of equipment. An evaluation model for support probability and fill rate is established, and Monte Carlo simulation techniques are applied to simulate the impact of different maintenance strategies on spare parts demand and equipment availability. This study also conducts a sensitivity analysis of key parameters, including Mean Time Between Failures (MTBF), repair demand probability, and faulty part repair cycle, to assess their influence on spare parts requirements and equipment availability. The results indicate that the whole-part repair strategy can effectively reduce spare parts demand and enhance equipment availability. In conclusion, the whole-part repair strategy demonstrates a distinct advantage in shipboard spare parts management, optimizing inventory management while ensuring combat readiness. This research provides a novel analytical approach for naval logistics and maintenance planning.

A discrete-event simulation model with a collaborative and lean logistic approach application to a dairy industry

A discrete-event simulation model with a collaborative and lean logistic approach application to a dairy industry

Towards human-centric warehousing: the impact of rack configuration and cognitive demands on order picking performance

Within warehouse logistics processes, design concepts and management decisions regarding operations systems determine the demands placed on human operators. While the literature proposes that human-centric work system design positively affects the operator, the impact on operator performance remains under-explored. We analyse the case of a German retail warehouse where the shelf numbering follows the pickers' walking direction from right to left. However, all human order pickers working in this system are familiar with a left-to-right writing system due to their schooling and training. Because cognitive psychology proposes that writing system direction impacts spatial orientation in goal-oriented visual search, we are motivated to explore the interplay of rack configuration and human cognitive demands. We ground our mixed-effects model on archival data, including 470,968 storage location visits. Our results indicate that order pickers work 18.7% faster when picking items starting from the left side of the shelf compared to starting their visual search from the right side. Our findings suggest that human search strategies in warehousing operations are influenced by attention templates like writing direction on a similar level as by system design elements of the warehouse itself. This insight underscores the potential for diversified future human-centric design approaches in warehouse logistics.

Sustainable fresh strawberry consumption: environmental, genetically modified food, and climate concerns in Europe and North Africa

Background and gap in the literatureSeasonal strawberries are a tasty fruit with many significant health and environmental benefits. Despite these attributes, the determinants influencing strawberry consumption remain underexplored. Few existing research studies are focused on conventional factors, such as taste, freshness, and price, leaving aside some critical, current dimensions, such as consumers’ environmental attitude, sustainability, genetically modified (GM) food knowledge, and climate change concerns. This article aims to explore the impact of the aforementioned factors on the purchasing behavior of strawberries among consumers.MethodologyA designed questionnaire for a final sample of 2,378 consumers from Italy, Germany, France, Spain, Turkey, and Morocco was used to collect data about sociodemographic attributes, strawberry purchasing habits, respondents’ level of knowledge on the topic of GM food, and climate change issue. The generalized ordinal logistic approach was performed deriving from the ordinal qualitative type of our two variables of interest (seasonal and non-seasonal strawberries purchase) and relaxing the assumption of parallel lines.Main resultsFrom the sociodemographic factors, the more educated individuals, living in urban areas and having higher income levels are more likely to purchase frequently seasonal strawberries, while older people buy less often non-seasonal strawberries. Individuals who are more aware of sustainability issues, exhibit sustainable behaviors, and have greater concerns about climate change are more likely to buy non-seasonal strawberries less frequently, which is the most important result of our analysis.ImplicationsThese results offer a comprehensive understanding of other drivers than the conventional ones related to seasonal and non-seasonal strawberry purchase patterns, giving significant insights for policymakers in formulating tailored interventions for other dimensions: promoting sustainable agricultural practices (following the seasonality of the fruit), increasing consumer awareness about the environmental implications of the non-seasonal fruit purchases, and shifting individual eating patterns toward more sustainable and healthy ones (fresh and seasonal fruit consumption).

Developing Grounded Theory Systematic Approach for Logistics and Supply Chain Management Research

ABSTRACTThe emergence of the supply chain management (SCM) discipline and its integration with other research fields, such as logistics, has sparked research that aims to combine scientific rigor with business utility. Calls for increased theory‐building and practically relevant research have driven the discipline's development. This article advocates the wider use of grounded theory methodology, providing practical guidance to ensure reliable theoretical contributions. The study employs a multi‐method approach (bibliometric analysis, mapping review, and state‐of‐the‐art analysis) to quantitatively review existing grounded theory research on logistics and SCM issues, investigate its methodological attributes, and outline its strengths and weaknesses. Based on these findings, guidelines are proposed for methodologically sound grounded theory building in the discipline. Derived from empirically grounded theory studies, these guidelines address issues that hinder the effective use of the method, such as misunderstanding its fundamentals, improper use of literature, coding challenges, inadequate research design, and difficulties in transitioning from analysis to theorizing. By adding an empirical perspective to supplement existing guidelines, the study aims to enhance the quality of SCM research. While grounded theory is not the only solution for advancing the discipline, its judicious use is advocated to bring rigorous, theory‐driven, and practice‐oriented contributions to logistics and SCM research.

Revolutionizing plant breeding: a historical perspective from cross-pollination to gene editing

The history of plant breeding can be broadly divided into four eras: the Pre-Mendelian era (before 1900), the Mendelian era (1900-1920), the post-Mendelian era (1921-1950), and the Modern era (after 1950). Significant milestones include the release of the first hybrid maize varieties in 1961, the first sorghum hybrid (CSH-1) in 1964, the first bajra hybrid (HB-1) in 1965, and the first pigeon pea hybrid (ICPH-8) in 1991. Achieving a world with zero hunger requires a sustainable increase in food production and distribution and elimination of poverty. This goal demands scientific, logistical, and humanitarian approaches to ensure food security from farmers and breeders to policymakers and governments. Conventional breeding techniques alone are insufficient to meet the challenges posed by climate change, biotic and abiotic stress, and the growing global population projected to reach 10 billion by 2050. Novel plant breeding techniques, such as genome editing, speed breeding, and omics technology integration, offer precise, cost-effective, and less time-consuming solutions. These techniques enable the editing of agriculturally significant genes, promoting hybrid seed production, induced apomixis, and resistance to biotic and abiotic stress. Genome editing technologies have evolved, with CRISPR/Cas9 and its variants, base editing (BE), and prime editing (PE) playing pivotal roles in generating transgene-free plants. These advancements have significant implications for crop improvement, meeting food and nutrition security, and catering to regional preferences. Syngenta's initiative to open rights to CRISPR-based technologies through the Shoots by Syngenta platform exemplifies the collaborative efforts needed to drive agricultural innovation and sustainability. The review discusses the current regulatory regimes governing genome-edited crops, prospects of new tools such as DNA-free editing systems and nanotechnology, and their applicability in crop improvement. A multidisciplinary approach involving political, social, economic, and scientific stakeholders is essential for the successful adoption of genome editing technologies, which will ultimately make agriculture a lucrative profession and attract youth to the field.