Operation Of Infrastructure Research Articles

Study Report on Optimum Utilisation Plan of Ngoma 22 Irrigation Scheme

The study was conducted in Ngoma 22 irrigation scheme located in Remera and Rurenge sectors of Ngoma District in Eastern Province of Rwanda between November 2018 and January 2019. It was recommended by RAB Board of Directors. The methodology used encompasses the study area, sampling procedures, data collection and analysis tools. The main data collection tool used was FGD which targeted site agronomists and household head farmers producing rice, fruits and vegetables as high value crops in the scheme area. Through pair wise ranking technique most profitable cash crops in the scheme were ranked as follows: (1) tree tomato, (2) maracuja, (3) egg plants, (4) water melon, (5) tomato, (6) irish potatoes, (7) french beans, (8) onions, (9) carrots, (10) cabbages, (11) beetroots. From this point of views, findings from the study show only two most profitable crops: maracuja with 696 Frw/kg followed by tree tomato with Frw 249 Frw/kg. The third ranked cash crop being Irish potato (with a loss of -40 Frw/kg), the fourth is egg plant with a loss of- 177 Frw. The existing paddy rice production has shown a loss of – 270 Frw inspite the water availability all the year-round. Maize and bush bean show respectively losses of -543 Frw/kg and -1743 Frw. Hence it is understandable why farmers in the scheme area have already abandoned maize and beans cultivation. Findings show also water fees (20,000 Frw/season) determined based on maintenance costs and farmers income. For capacity building model, more than one thousand beneficiary farmers in Ngoma 22 Irrigation Scheme need strong farmer organisation for ownership of the scheme and increased profitability. To that effect, they need continued capacity building as stated in the tripartite IMTA: administrative and financial management, record keeping, and conflicts resolution management, integrated water management, operation and maintenance of irrigation infrastructures, best agricultural practices for sustainable farmers’ organisations and commercial farming. Key recommendations include fixing minimum prices for agricultural products in relation to costs of production- or setting subsidy prices, increase yield for each crop by meeting all requirements: lime, fertilisers and improvement of irrigation technologies in order to reach automatically increased yield. Farmers should continue to leave out traditional crops and adopt profitable cash crops identified in this study (e.g maracuja and tree tomato). They should also increase the ownership spirit through IWUA and cooperatives performance. They should adopt collective marketing of their products and pay water fees per season.

YOLOv8-CDD: an improved concrete defect detection method combined CNN with transformer

Abstract Concrete defect detection is a critical task to ensure the safe and stable operation of concrete infrastructure. In order to effectively solve the problems of low efficiency, high cost and poor performance of existing methods, a high-precision concrete defect detection method YOLOv8-CDD (Concrete Defect Detection) combining convolutional neural network and transformer is proposed in this paper. Firstly, based on the features with a large span of concrete defect features, a bot-transformer module that can effectively extract the global information of defect features is proposed to improve the network’s ability to extract global features. Secondly, in order to further strengthen the interaction between defect feature channels and spatial information, a convolutional triplet attention module is introduced into the feature enhancement network to effectively integrate the information of different dimensions of defect features and improve the model detection accuracy. Additionally, in order to enhance the learning of samples with different degrees of difficulty, the introduction of Focaler-CIoU instead of the original boundary regression loss function can optimize the model training process. Finally, the dataset was collected and organized in concrete scenarios from bridge towers, dams, and tunnel corridors, and our method achieved 0.898 average precision, 0.893 average recall, 0.031 average FPR, 0.895 average F1 score, 0.929 mAP50, and 0.731 mAP50:95 on the dataset. The experimental results show that the proposed method achieves the best performance in concrete defect detection.

Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises

Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system.

2D-convolutional neural network based fault detection and classification of transmission lines using scalogram images

The reliable operation of power transmission systems is essential for maintaining the stability and efficiency of the electrical grid. Rapid and accurate detection of faults in transmission lines is crucial for minimizing downtime and preventing cascading failures. This research presents a novel approach to fault detection and classification in transmission lines employing 2D Convolutional Neural Networks (2D-CNN).The proposed methodology leverages the inherent spatial characteristics of fault signals, converting them as 2D scalogram images for input to the CNN model. By converting fault signals into scalogram representations, the network can capture both temporal and frequency domain features, enabling a more comprehensive analysis of fault patterns. The 2D-CNN architecture is designed to automatically learn hierarchical features, allowing for effective discrimination between different fault types. To evaluate the performance of the proposed approach, extensive simulations and experiments were conducted using MATLAB/SIMULINK modeled transmission line data. The results demonstrate the superior fault detection accuracy and classification capabilities of the 2D-CNN model. The performance of the proposed model is evaluated using 10-fold cross-validation, and its effectiveness is assessed by comparing it with current state-of-the-art techniques. Proposed 2D-CNN model has evidenced an accuracy of 99.9074 with ideal dataset for 12- class fault classification and performing consistently in presence of noise, having an accuracy of 99.629 %,99.72 % and 99.814 % in 20.30 and 40 dB noises respectively. The proposed model also verified in high resistance fault condition. The model exhibits robustness to noise and is capable of generalizing well to various fault scenarios. The proposed methodology offers a scalable and efficient solution for transmission line fault analysis, paving the way for the integration of advanced machine learning techniques into the operation and maintenance of power transmission infrastructure.

Supporting the infrastructure operators with customer specific AE-monitoring solutions

Cost-effective and efficient monitoring of infrastructure is crucial for planning maintenance and repairs while ensuring the safety of the structure. Acoustic emission is a method particularly suitable for long-term monitoring. However, the necessary extended monitoring period incurs high costs for the operator, primarily due to the exceptionally high costs of measuring equipment. The need for a tailored monitoring system for infrastructure applications is evident. TÜV AUSTRIA is developing a monitoring system called RISE, which represents a customized solution for steel bridge monitoring. The innovative online monitoring solution RISE assists experts with structural monitoring. Due to its compact design, low power consumption, and easy installation, the measuring system is specifically designed for continuous monitoring. Specifically, RISE is introduced for the hotspot monitoring of railway steel bridges. The passage of trains triggers a material response that can be recorded by the measuring system. This is used to perform crack monitoring on bridges and to have a tool for assessing crack activity and the degree of damage. This enables targeted planning of repairs and maintenance activities. Reducing the operating costs of the structure and ensures it save operation. The system is being developed to function robustly and provide a high level of automation. A demonstration of AE-monitoring of active cracks on a main bridge girder was done during a fatigue test in cooperation with the TU Graz. The girder was removed from a deconstructed bridge.

Maintenance factory platform for in-space manufacturing: Conceptualizing design architecture

Commerce, long-term habitation, and far-location explorations are the objectives of the next space era, also called Space 2.0. These objectives are the focus of in-space servicing, assembly, and manufacturing (ISAM) research. Current research in this area is focused on demonstrating various manufacturing processes under microgravity using the International Space Station (ISS) as a platform and relies on an Earth-based supply chain. Building operational infrastructure and supply chains enabling in-space manufacturing is critical for the sustained growth of ISAM to meet the goals of Space 2.0. This paper is specifically focused on discussing the need and potential architecture for a maintenance factory in space to enable servicing, maintenance, and repairs in space environments. Space presents a hostile environment and a new set of challenges to establish a robust infrastructure necessary for supporting these objectives. The realization of such an infrastructure, including physical and digital footprints, is demanding new manufacturing science and engineering tools and platforms for maintenance. The authors present requirements of a robust maintenance platform, called a “factory in space” to deliver multi-functional maintenance service station(s) across various operational points in space such as low-earth orbit, lunar surface, and Martian surface. Such factories are projected to be an essential part of the in-space infrastructure for short as well as long-term commerce, habitation, and exploration. A critical analysis of this concept is proposed through an analysis of the state of the art, boundary conditions in space and requirements of maintenance, the role of manufacturing, and the design as well as sustainability considerations for a maintenance factory in space.

Methodology for improving the assessment of the economic efficiency of transport infrastructure development

Transport infrastructure, especially railway infrastructure, plays a key role in economic development, ensuring efficient movement of goods and people. It is characterised by high capital intensity, long operational lifespans, and generation of socio-economic effects. Therefore, there is a need to improve the methodology for assessing the economic efficiency of creating and developing transport infrastructure objects. The traditional approach to evaluation is based on discounting cash flows. When applying this method, significant capital investments are compared with devalued effects in subsequent years due to discounting, during which the infrastructure object reaches the specified estimated load. Moreover, it does not consider the fact that the transport infrastructure benefits and holds value not only for the current generation but also for the future one. Additionally, it, as an economic good, cannot be seen as a one-timely consumed product: this transportation service cannot be accumulated. For a more accurate and comprehensive assessment of the efficiency, particularly in the case of railway transport, it is proposed to consider the results of long-term operation of the transport infrastructure at full (estimated) load not in the future but in the present (present сontinious). The economic assessment indicators include initially invested and adjusted capital, annual effect, efficiency ratio, and the value of the transport infrastructure object. The research results aim to provide an adequate assessment of project implementation efficiency in the field in question. In the future, the methodology can be enhanced by defining risks of not ensuring normal infrastructure load throughout the entire calculation period as well as by assessing the reduction in profitability or increase in transportation costs.

DIAGNOSTIC EVALUATION OF URBAN METRO TRANSIT SYSTEM POST-COVID-19

Public transportation usage in Delhi has declined, with the Delhi Metro having a significant share. However, due to fare hikes and COVID-19 restrictions, the DM's share has been decreasing further. To improve ridership, a study is being conducted to evaluate the DM's performance and identify areas for improvement in passenger convenience and comfort. The Magenta line is investigated through an on-board survey to collect primary data. The survey covers commuter perceptions of safety & security, financial & economic factors, infrastructure & comfort and functional & operational features. The Relative Importance Index approach is used to analyse the data and evaluate DM performance. An ANN model is also presented to determine the factors influencing the choice to travel on the DM, with the “metro fare per trip” factor being a key consideration. Based on the analysis results, recommendations are made to improve the DM's performance. The study found that safety and security had the highest RII, followed by efficiency and viability, functional and operational features, infrastructure and comfort, and financial and economic factors. The subway fare had the lowest RII. The ANN model is adapted to understand the reasons behind low metro ridership.

MANAGEMENT OF SCALABILITY IN CLOUD-BASED APPLICATIONS

The following is an abstract of the article. The article presents an analysis of the challenges associated with monitoring and managing the scalability of a cloud application. To this end, a module for monitoring and managing the scalability of a cloud application has been developed as part of this study. The development process included the introduction of automatic scaling, and monitoring using Prometheus and Grafana, which allows for a high level of availability and resource efficiency. The study comprised a series of phases, including requirements analysis, system design, development, testing, and evaluation. Consequently, the system's performance, stability, and capacity to scale in response to fluctuating workloads were enhanced. The module exhibits a high degree of adaptability to changes in system requirements and load, which is a crucial attribute for the dynamic development of business applications. This solution assists in optimizing the allocation of resources and reducing infrastructure costs. The project has been found to fully meet the set goals and objectives, as well as the requirements for effective resource management of the Amazon Web Services cloud platform using Terraform, Prometheus, and Grafana. The practical value of the developed module is evidenced by a significant improvement in resource efficiency, service stability and cost optimisation. The module design has been subjected to rigorous testing and has been successfully implemented in a test environment, thereby demonstrating the sustainability and efficiency of the developed solution. The experience gained in the implementation and operation of this solution may prove useful for further expansion and optimization of cloud solutions in other projects and companies specializing in the provision of cloud solutions. The findings of this study were validated in a test environment at an IT company with a specialization in cloud technologies. The objective was to ascertain the functionality and efficiency of the developed module in a real-world context of cloud infrastructure operation. The testing process entailed the configuration of the module on pre-existing cloud infrastructure systems, its integration with Prometheus and Grafana for monitoring purposes, and the execution of a series of stress tests designed to assess the module's scalability. As a result of this testing, a number of critical points were identified that required further optimization. The results of the study and the issues identified during the project testing have enabled the identification of several areas for further improvement and development of the system. First and foremost, the optimization of automatic scaling algorithms represents a crucial avenue for improvement. The development of these algorithms should be oriented towards utilizing historical monitoring data to anticipate potential shifts in system load. Another pivotal area for enhancement is the precision of monitoring systems. The integration of supplementary tools and the expansion of existing monitoring systems' functionality will facilitate the acquisition of more comprehensive insights into the system's condition. This, in turn, will facilitate the expedient identification and eradication of potential issues.

Review on techno-socio-economic studies of electric vehicles in electrical energy systems

Transportation electrification is a practical solution for the world's environmental, economic, and technical challenges. During the last decades, numerous studies have focused on electric vehicles (EVs) as one of the essential energy fields due to the growth rate of EV penetration into energy systems. Researchers have investigated different operation and long-term planning models of EV charging infrastructure and prediction and uncertainty-handling approaches for EV drivers’ behavior (i.e., the arrival/departure time to/from charging stations and the associated state-of-charge) as well as fixed and mobile charging stations (MCSs) and social equity access to EV charging infrastructure. This study provides a detailed analysis and discussion of the daily operation and long-term planning of EV charging stations with economic and technical aspects. Also, the prediction of EV drivers’ behavior and approaches to dealing with uncertain EV parameters are studied. Furthermore, the role of MCSs and social equity access in planning EVs’ charging station placement is investigated. The authors also assess the applications of permanent and MCSs and the social equity access to EV infrastructure. Finally, the current study discusses EVs future trends and their effect on electrical systems as future subjects of research studies.

Nonlinear Analysis of the Mechanical Response of an Existing Tunnel Induced by Shield Tunneling during the Entire Under-Crossing Process

The safety of existing tunnels during the entire under-crossing process of a new shield tunnel is critically important for ensuring the sustainable operation of urban transportation infrastructure. The nonlinear behavior of surrounding soils plays a significant role in the mechanical response of tunnel structures. In order to assess the mechanical response of the existing tunnel more reasonably, this study attempts to propose a novel theoretical solution and calculation method by simultaneously considering the nonlinear characteristics of surrounding soils and the tunneling effects of a new tunnel during its entire under-crossing process. Firstly, the additional stresses acting on the existing tunnel stemming from the tunneling effects of a new shield tunnel during different under-crossing stages are calculated using the typical Mindlin solution, as well as the Loganathan and Poulos solutions. The influences of the additional thrust, friction force, and grouting pressure and the loss of surrounding soils are taken into account. Then, the nonlinear Pasternak foundation model is introduced to characterize the behavior of surrounding soils, and the governing differential equation for the mechanical response of the existing tunnel is derived using the typical Euler–Bernoulli beam model. Subsequently, a novel theoretical solution and calculation approach are established using the finite difference formula and the Newton iteration method for assessing the mechanical response of the existing tunnel. Finally, one case study is performed to illustrate the mechanical behavior of the existing tunnel during the whole under-crossing process of a new shield tunnel, and the validity of the developed solution is verified against both the computed result of finite element simulation and the field measurements. In addition, the influences from the ultimate resistance and reaction coefficient of surrounding soils and those from the vertical distance and intersection angle between existing and newly constructed tunnels are analyzed and discussed in detail.

Review of peer-to-peer energy trading: Advances and challenges

The power system is confronting progress due to the high integration of distributed energy resources (DERs). These DERs are expected to cause challenges for power system operations. Therefore, innovative management approaches were proposed for integrating DERs in future power systems and maximizing DER owners’ benefits, such as peer-to-peer (P2P) energy trading. Direct energy trading between users is made possible by P2P energy trading, which supports bulk power infrastructure operations while supporting local power and energy balance. P2P energy trading is a promising approach to expanding the installation of renewable energy sources and achieving the system flexibility required for the shift to low-carbon energy. The grid is anticipated to gain from P2P energy trading by having lower reserve requirements, peak demand, and network losses. Many studies and pilot projects have shown how P2P energy trading benefits prosumers as well as the grid. However, the widespread use of such trading models remains limited in today's electrical markets. This paper reviews recent advances in the P2P energy system and a perceptive discussion of the challenges that are keeping P2P from becoming a viable energy management solution in the present electrical market. First, the energy network is covered in this paper's description of these new P2P markets; next, the types of P2P energy trading, moving on to the market structure. Then, the technologies and technical approaches behind P2P energy trading are covered. After that, P2P energy trading advances in different systems are discussed. Finally, we identify challenges before making some concluding remarks.

Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics

Abstract Uncrewed surface vehicles (USVs) equipped with echosounders have the potential to replace or enhance acoustic observations from conventional research vessels (RVs), increase spatial and temporal coverage, and reduce cost and carbon emission. We discuss the objectives, system requirements, infrastructure, and regulations for using USVs with echosounders to conduct ecological experiments, acoustic-trawl surveys, and long-term monitoring. We present four example applications of USVs with lengths <8 m, and highlight some advantages and disadvantages relative to RV-based data acquisitions. Sail-driven USVs operate continuously for months and are more mature than motorized USVs, but they are slower. To maintain the pace of an RV, multiple sail-powered USVs sample in coordination. In comparison, motorized USVs can travel as fast as RVs and therefore may facilitate a combined survey, interleaving USV and RV transects, with RV-based biological sampling. Important considerations for all USVs include platform design, noise and transducer motion mitigation, communications and operations infrastructure, onboard data processing, biological sampling approach, and legal requirements. This technology is evolving and applied in multiple disciplines, but further development and institutional commitment are needed to allow USVs equipped with echosounders to become ubiquitous and useful components of a worldwide network of autonomous ocean observation platforms.

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Rockfall, glacier recession, and permafrost degradation: long-term monitoring of climate change impacts at the Open-Air-Lab Kitzsteinhorn, Hohe Tauern

Abstract. Since 2010, comprehensive geoscientific monitoring has been established in the summit region of the Kitzsteinhorn (“Open-Air-Lab Kitzsteinhorn”), which focuses on the investigation of high-alpine climate change impacts in four monitoring domains: air temperature, glaciation, permafrost, and rock stability. Air and near-surface permafrost temperatures are currently rising with mean rates of almost +0.1 °C yr−1. The thickness of the local cirque glacier is decreasing by more than 1 m yr−1. Rockwall monitoring demonstrates that rockfall activity in freshly deglaciated rockwall sections has increased by 1 order of magnitude. The intensity of the observed processes is most likely unprecedented in recent human history. This emphasizes the importance of long-term monitoring efforts such as those carried out at the Kitzsteinhorn for improved geophysical understanding and for safe and sustainable infrastructure operation in high-alpine environments.

An Analysis of the Impact of Seaport Efficiency on Nigeria’s Economic Growth.

This research is driven by the objective to assess the impact of the seaport efficiency on Nigeria’s economic growth. The western ports of Lagos are selected as the Decision-Making Units (DMU) for this study covering the period of 2000 to 2020. Two stage estimation process is employed. Data Envelopment Analysis (DEA) was deployed in the first stage to generate the efficiency index required to test the relationships. In the second stage, the efficiency index so generated is introduced as one of the independent variables in a multiple regression model, using Autoregressive Distributed Lag Model (ARDL). The finding shows that seaport efficiency has a negative but significant relationship with economic growth in both the long run and short run. The study concludes that seaport efficiency has an inverse relationship with Nigeria’s economic growth. The result of this study suggests that reforms may not have deepened the level of efficiency in the Nigerian seaport to drive positive economic growth, hence the recommendation for further investment in the terminal operational equipment and technological infrastructure to drive further efficiency in the sector.

An intelligent dynamic cyber physical system threat detection system for ensuring secured communication in 6G autonomous vehicle networks

Smart cities have developed advanced technology that improves people’s lives. A collaboration of smart cities with autonomous vehicles shows the development towards a more advanced future. Cyber-physical system (CPS) are used blend the cyber and physical world, combined with electronic and mechanical systems, Autonomous vehicles (AVs) provide an ideal model of CPS. The integration of 6G technology with Autonomous Vehicles (AVs) marks a significant advancement in Intelligent Transportation Systems (ITS), offering enhanced self-sufficiency, intelligence, and effectiveness. Autonomous vehicles rely on a complex network of sensors, cameras, and software to operate. A cyber-attack could interfere with these systems, leading to accidents, injuries, or fatalities. Autonomous vehicles are often connected to broader transportation networks and infrastructure. A successful cyber-attack could disrupt not only individual vehicles but also public transportation systems, causing widespread chaos and economic damage. Autonomous vehicles communicate with other vehicles (V2V) and infrastructure (V2I) for safe and efficient operation. If these communication channels are compromised, it could lead to collisions, traffic jams, or other dangerous situations. So we present a novel approach to mitigating these security risks by leveraging pre-trained Convolutional Neural Network (CNN) models for dynamic cyber-attack detection within the cyber-physical systems (CPS) framework of AVs. The proposed Intelligent Intrusion Detection System (IIDS) employs a combination of advanced learning techniques, including Data Fusion, One-Class Support Vector Machine, Random Forest, and k-Nearest Neighbor, to improve detection accuracy. The study demonstrates that the EfficientNet model achieves superior performance with an accuracy of up to 99.97%, highlighting its potential to significantly enhance the security of AV networks. This research contributes to the development of intelligent cyber-security models that align with 6G standards, ultimately supporting the safe and efficient integration of AVs into smart cities.

Principles of Construction System for Monitoring the Psychoemotional State and Health of Critical Infrastructure Operators

Principles of Construction System for Monitoring the Psychoemotional State and Health of Critical Infrastructure Operators

Fusion of computer vision and piezoelectric tactility approach to measuring moving vehicle loads

Vehicle overloading seriously threatens the safety and long-term operation of global traffic infrastructure. The emergence of computer vision-based non-contact weigh-in-motion (NC-WIM) technique has made it possible to expand the coverage of vehicle load monitoring, but misjudgment of vehicle weight due to abnormal tire pressure is still an insurmountable drawback of this approach. In this study, we propose a novel approach to measure moving vehicle loads by fusing computer vision and piezoelectric tactility. Meanwhile, a visual-tactile sensing system based on the proposed method is built on a real road. Field tests show that the proposed method can effectively avoid the misjudgment caused by abnormal tire pressure, and the errors of vehicle weight estimation are within the range of ± 10 % in both underinflated and overinflated cases. The proposed solution not only ensures the accuracy of overload warnings, but also promises to provide support for the design, operation, and maintenance of transport infrastructure.

Contrasting corn acreage trends in the Midwest and Southeast: The role of yield, climate, economics, and irrigation

The US corn area footprint has changed significantly since the 20th century, declining in the southeastern states while exhibiting an increase or stable variations in the Midwest. As harvested acreage directly impacts the total corn production, understanding the influencing factors is crucial. This study assesses the role of potential drivers on the contrasting trajectories of harvested corn acreage between midwestern and southeastern US. Profit-acreage analysis reveals that antecedent profits/losses have a statistically significant influence on corn acreage changes, with southeastern US, which experienced more loss-making years, also experiencing more frequent reductions in corn acreage. The high number of loss-making years in the Southeast is primarily attributed to the region's low corn yield, influenced by climate and other agro-environmental factors. Using a panel regression model, we find that the loss-making years in the Southeast could have reduced to fewer than 26 out of the considered 45 years, or almost similar to the average in the Midwest, by just increasing the irrigated corn area to 50 %, a realistic irrigated corn area fraction already achieved in several Georgia counties. This underscores the potential for early policy interventions like irrigation facilitation to sustain and expand cropped acreage. However, we also find that this would only be economically feasible with incentives for both the installation and sustained operation of irrigation infrastructure.