Four-stage Methodology Research Articles

Cross-Border Cooperation and Land-Use: Studying Two European Cities in the Period 2000–2018

ABSTRACT Cross-Border Cooperation (CBC) denotes the joint effort and harmonization of endeavors between neighboring countries or geographically connected regions. It encompasses collaborative initiatives spanning economic, political, social, and environmental domains. Land Use (LU) dynamics are one of the main topics in the interests of sustainable development studies, particularly in the territories facing high socioeconomic and environmental concerns and challenges. Therefore, we used a four-stage methodology: data collection; case study selection criteria; analysis of case studies; and, finally, the relation between CBC and LU for sustainable development. The study provides us with some exciting guidelines about actions to promote sustainable development through CBC (in the case studies) – i.e. promote knowledge sharing and best practices among successful cities; Offer capacity-building programs to enhance skills in sustainable land use planning and CBC; or foster collaboration on common projects and create communication networks for regular interaction.

Flexural properties of fiber-reinforced concrete using hybrid recycled steel fibers and manufactured steel fibers

This study investigates the flexural properties of fiber-reinforced concrete (FRC) using a hybrid mix of recycled steel fibers (RSF) from waste tires and manufactured steel fibers (MSF). The aim of this study is to demonstrate hybrid steel fiber reinforcement as a more environmentally sustainable alternative to traditional single-fiber systems, by assessing whether hybrid reinforcement can achieve or surpass the performance of conventional single-manufactured steel fiber reinforcement. Using a novel four-stage methodology, this paper presents a comprehensive and detailed evaluation of mechanical and ductility performance through three-point bending tests, digital image correlation (DIC), CT-scans, and scanning electron microscopy (SEM). The results from flexural stress-deflection curves were used to evaluate the flexural strength and toughness. This was followed by an in-depth eight-stage DIC analysis and Surface Damage Index (SDI) curves throughout the test. CT-scan images were used to quantify the proportion of fibers classified as pulled-out, ruptured, or bridging the crack, while SEM analyzed the efficiency of fiber reinforcement through microstructural evaluations, further validating the findings of this study. Results show that hybrid reinforcement significantly enhances both toughness and ductility while reducing surface damage. MSFs in the hybrid system improve post-peak ductility and energy absorption by bridging large cracks, while RSFs prevent micro-cracks, increasing pre-peak rigidity. CT-scan analysis reveals that the inclusion of RSFs reduces pulled-out MSFs by 26%, further boosting flexural performance. SEM confirms the superior bond of MSFs with the concrete matrix in hybrid samples. Additionally, higher fiber content in FRC enhances toughness and ductility while reducing surface damage. This research advances our understanding of hybrid fiber-reinforced concrete and promotes more sustainable construction practices through the use of recycled steel fibers. By leveraging the combination of new methodologies, the study effectively targets gaps in the current research landscape, offering a comprehensive approach to improving performance and sustainability in construction materials.

Bacterial and Parasitic Characterization of the Rivers in Cuenca, Ecuador

Cryptosporidium and Giardia are infectious parasitic forms widely distributed in aquatic ecosystems and resistant to disinfection of drinking water. Their presence was investigated in the lower areas of the city’s four rivers through a four-stage methodology. Between December 2017 and April 2018, three monitoring campaigns were conducted, with results ranging between not detected to 500 oocysts/L for Cryptosporidium, and between not detected and 300 for Giardia. Cryptosporidium was more abundant, especially in the Machángara River. In the same period, the bacteriological quality of the rivers was also reviewed using Total Streptococci and Fecal Enterococci expressed in colony-forming units (CFU)/100 mL as indicators. The results showed a progressive increase in pollution as the course of the rivers progressed. The sensitivity of bacterial indicators to changes in quality is also observed, which is why their use in specific studies is recommended. It is concluded that untreated domestic wastewater discharges may be the main source of contamination by bacteria and parasites and that there is a relationship between their concentration and the seasonal period. In dry weather, the concentration is higher for both microorganisms. This study fills a gap in knowledge in the region, due to the absence of data on parasitic indicators with great impacts on public health.

Evaluating Crowdsourcing Applications with Map-Based Storytelling Capabilities in Cultural Heritage

Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates.

A Comparative Study on Customer Churn Analysis Using Machine Learning and Data Enrichment Techniques

With the increasing amount of online shopping, companies can collect more customer data. Companies use this data to get to know their customers better and provide customized services. Churn analysis is one of the most essential analyses derived from the vast amount of data collected, which provides information about when a customer will stop shopping with the company. In this study, we perform a churn analysis using machine learning (ML) algorithms to analyse the customer behavior data of a fashion retail company. To perform churn analysis, we performed a four-stage methodology. First, we carried out data preparation and visualization studies, and then we created models using various ML algorithms. After examining the baseline data, we added the RFM (Recency, Frequency, Monetary) score to the data with the data enrichment technique and performed the analysis again. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate the data irregularity and performed parameter optimization on the algorithms in SMOTE data. We compared the accuracy and F1 score values obtained after this four-stage process and examined the effect of the algorithms. In the last stage, we divided whole data into clusters using the k-means technique and applied ML algorithms to clustered data. Then, we compared all these results and examined the effect of segmentation on the results. The analysis shows that the extreme gradient boosting algorithm provides better accuracy and F1 score values. Using these results, the company can identify customers likely to churn and begin funding Customer Relationship Management (CRM) efforts. Additionally, experts can determine the company's development directions by organizing campaigns for these customers and analysing their reasons for churn in more detail.

Second-best electricity pricing in France: Effectiveness of existing rates in evolving power markets

The adoption of time-varying pricing in the French electricity market has been historically low despite their availability to consumers well before the deployment of smart metering. However, as the share of variable renewable electricity increases and carbon prices grow, the demand-side response will become increasingly important to achieve efficiency gains. Relying on the historical hourly consumption of French electricity consumers and multiple prospective weather years, we study the gain allowed by the broader adoption of time-varying electricity retail rates in low-carbon power systems. We develop a four-stage methodology to assess the efficiency and stability of the electricity supply cost component of the electricity bill in France for historical and prospective years. Our analysis demonstrates that peak pricing schemes have increasing interest in the context of further deployment of renewable capacity, capturing 25% to 50% of the welfare gain reached by real-time pricing schemes. The corresponding deadweight loss of peak pricing schemes represents 0.4 to 1 bn EUR per year in 2030 compared to real-time prices (RTP) schemes. Conversely, deadweight loss of current time-of-use (ToU) rates represents 1 to 1.2 bn EUR per year in 2025 and increases over time. The current design of TOU rates does not provide adequate incentives in future power systems. Besides the tariff efficiency, the results underlined an increasing price difference between on-peak and off-peak rates. This questions the social acceptance of time-varying retail rates for consumers unable to hedge against peak prices. By analysing the effectiveness and limitations of time-varying rates, our findings underline the importance and value of price-based DR in future power systems in general. Our results highlight the need to revise the end-user tariff design in the French power system, notably with regard to the conveyed price signals.

Urban Resilience Index for Critical Infrastructure: A Scenario-Based Approach to Disaster Risk Reduction in Road Networks

Floods pose a significant threat to road networks globally, disrupting transportation, isolating communities, and causing economic losses. This study proposes a four-stage methodology (avoidance, endurance, recovery, and adaptability) to enhance the resilience of road networks. We combine analysis of constructed assets and asset system performance with multiple disaster scenarios (Reactive Flood Response, Proactive Resilience Planning, and Early Warning Systems). Advanced flood Geospatial-AI models and open data sources pinpoint high-risk zones affecting crucial routes. The study investigates how resilient assets and infrastructure scenarios improve outcomes within Urban Resilience Index (CRI) planning, integrating performance metrics with cost–benefit analysis to identify effective and economically viable solutions. A case study on the Lisbon Road network subjected to flood risk analyzes the effectiveness and efficiency of these scenarios, through loss and gain cost analysis. Scenario 2, Proactive Resilience Planning, demonstrates a 7.6% increase compared to Scenario 1, Reactive Flood Response, and a 3.5% increase compared to Scenario 3, Early Warning Systems Implementation. By considering asset performance, risk optimization, and cost, the study supports resilient infrastructure strategies that minimize economic impacts, while enabling communities to withstand and recover from flood events. Integrating performance and cost–benefit analysis ensures the sustainability and feasibility of risk reduction measures.

Smart City Capacities: Extant Knowledge and Future Research for Sustainable Practical Applications

Throughout the smart city literature, there are mentions of capacities, the application of which is claimed to result in the sustainable achievement of objectives. Because of the often desperate need for smart city objectives to be met, we sought to understand which were the capacities and whether the components of these capacities are explained sufficiently for them to be effective in practice. We applied a four-stage methodology commencing with a search of multiple databases for smart city capacity knowledge. We next assembled the evidence from the items identified in that search using a thematic analysis that identified the capacity to exploit technology, innovate, collaborate, and orchestrate. Next, we followed the threads of knowledge, iteratively allocating the knowledge to each of the four capacities to a typology of what, why, and who. The fourth stage was a cross-capacity analysis that generated further refinement and identified important factors. We identified that capacities are not sufficiently explained. In addition to the need for more levels of detail as to practical implementation, we identified significant underdevelopment of the literature as to the impact of institutional complexity and the influence of stakeholders. We propose research directed at increasing the effectiveness of capacities, define the concept of smart city capacities, propose a framework of the components of capacities, and draw on established stakeholder theory to create a stakeholder influence research framework.

Employees’ reskilling and upskilling for industry 5.0: Selecting the best professional development programmes

The current research aims at developing a multi-criteria decision-making methodology, based on analytic network process (ANP), for selecting the appropriate professional development programmes for enhancing employees' reskilling and upskilling, in the Industry 5.0 context. Thus, a four-stage methodology, which combines the qualitative approach of the Delphi study with the quantitative approach of the multi-criteria decision-making technique of ANP, is proposed and tested in the three largest companies from the Romanian manufacture of coke and refined petroleum products industry. The results show that teamwork, online communities of practice and enterprise social networks are among the best options. Furthermore, the most influential factors are employees' psychological profile (namely, interpersonal trust and self-efficacy) and human capital (especially, inclusiveness and job satisfaction). These findings have both theoretical and practical implications. At the theoretical level, they provide a nexus between human resource management and operations management by showing how a methodology based on ANP can be applied for solving a human resource issue. At the practical level, they help managers to decide which professional development programmes to implement for employees' reskilling and upskilling by providing them an easily customizable tool that takes into account employees' characteristics and company's human and structural capital.

Occupational Therapy to Improve Productivity of the Elderly by Creating Photo Frames at Panti Werdha Batam

Aging is accompanied by physiological changes and an increased prevalence of acute and chronic diseases, leading to a decline in organ function and a reduced ability to adapt to environmental factors. Occupational therapy, encompassing activities related to personal care, recreation, and productivity, holds significance for individuals. This study aimed to identify the needs and analyze the situation of elderly residents at the Budi Sosial Foundation's Elderly Home. The four-stage methodology involved situational analysis, health assessments, and physical activity education sessions. The implementation phase included simulation and assistance, where participants were engaged in arranging popsicle sticks into frame shapes. Monitoring and evaluation were conducted to assess the effectiveness of the health assessments and physical activity education, with all 17 (100%) active elderly participants observed. The results demonstrate the potential benefits of occupational therapy in promoting physical activity and overall health among elderly individuals. These findings imply the importance of integrating occupational therapy interventions to enhance the well-being and functional abilities of the elderly population.
 Highlights:
 
 Occupational therapy: A beneficial intervention for elderly individuals' well-being and functional abilities.
 Physical activity promotion: Enhancing the overall health and adaptability of the aging population.
 Health assessment: Evaluating the effectiveness of occupational therapy interventions in elderly care.
 
 Keywords: aging, occupational therapy, physical activity, health assessment, elderly population

Exploring Bipartite Network Approach in Hand, Foot and Mouth Disease Hotspot Identification

Mathematical modeling of hand, foot, and mouth disease (HFMD) mainly focuses on compartmental modeling approaches. It classifies human population into compartments and assumes homogeneity that regards every human has equal chance of contacting other individuals in the population. However, the transmission of HFMD is complicated and dynamic with the interactions of the intertwined biomedical and social factors. Describing the disease transmission dynamic that involves high-dimensional space is mathematically challenging. The graph theoretic bipartite network modeling (BNM) approach has the potential to handle this challenge by abstracting the real-world disease transmission system and incorporating the individual features of the bipartite nodes. This study aims to seize the advantages portrayed by the BNM approach in capturing the heterogeneous features of the entities within a disease transmission system. It intends to explore adopting the BNM approach in modeling the transmission of HFMD at Kuching, Malaysia and identify the hotspot by employing the BNM approach comprising a four-stage methodology adapted from the BNM methodology framework. The bipartite HFMD contact (BHC) network is formulated with the basic building block consisting of the location and human nodes. The individual parameters of the location and human node are incorporated. The resulting BHC network formulated comprises 10 human nodes, 20 location nodes, and 23 edges. Then, six top-ranked location nodes were identified and agreed with the chosen benchmark system. The potential HFMD hotspots are thus identified by determining the location nodes ranking. The result from this study has enabled timely and effective measures and policies to be customized accordingly by the public health authorities and related policymakers.

Pandemic resiliency and flexibility assessment of dormitory buildings in the post-Covid-19 era

The Covid-19 pandemic affected the education system, causing distance learning in most parts of the world. Thanks to the rapid vaccination, face-to-face instruction started again, university students returned to colleges, and dormitories were again used. To respond to the changing living conditions and ensure a healthy indoor environment, strategies for pandemic-resistant and flexible design of dormitories have been discussed, which is the focus of this article. To achieve this goal, state dormitories in Turkey, their current condition, and functional/ technical solutions were studied in detail using a four-stage methodology developed with this study, and their adaptation potential for new flexible and pandemic-resistant designs in the post-pandemic world was discussed. The research results showed that the case dormitory is unlikely to adapt to pandemic conditions in terms of pandemic resilience, flexibility, and Turkey Covid-19 Guidelines and that the methodology has the potential to be applicable to the evaluation of similar buildings.

Bus-bar splitting for enhancing voltage stability under contingencies

As one of the network topology optimization ways, bus-bar splitting has been applied for various purposes. In this paper, the performance of bus-bar splitting on a look-ahead (i.e., short-term, say 30 min) power system (i.e., pre-splitting base case) with contingencies is evaluated. The proposed problem aims to find an ‘optimal’ bus-bar splitting scheme that can maximize the load margin of a look-ahead base case and ensure the load margin of each contingency case with a desirable level. Due to the complexity and nonlinear nature of the proposed problem, it is very difficult to be solved with the available solvers, and a large number of contingencies (including N−1 and N−k contingencies) and bus-bar splitting schemes need to be examined. A four-stage methodology is presented for online computing efficiency. Moreover, the group properties of contingencies are explored and illustrated in this paper, which is utilized to reduce the number of nonlinear constraints from a large number of contingencies and relieve the computational burden. The other advantage of the proposed methodology is that it can derive multiple effective solutions from which the operators can choose one. The effectiveness of the proposed methodology is demonstrated on the IEEE 118-bus and 1648-bus power systems with promising results.

Reconsidering Urban Densification for Microclimatic Improvement: Planning and Design Strategies for Istanbul

One of the key issues of the urban planning agenda is how urban density be decided in the spatial configurations of future neighbourhoods to overcome complex challenges such as urban warming. This paper aims to reconsider urban density as a spatial planning instrument to develop effective densification policies, planning and design strategies in terms of surface urban heat island (SUHI) mitigation in Istanbul. The quantitative research embraced a four-stage methodology including grid-based sampling design, decoding the taxonomy of urban density-matrix (UDM), land surface temperature mapping, and ANOVA tests. Tests were conducted on the UDM consisting of nine building typologies representing the horizontal and vertical urban density. The research indicated that the impact of urbanisation on SUHI can be mitigated by controlling densities and urban forms based on quantitative findings. The highest temperatures were recorded in areas with high-coverage-mid-rise and mid-coverage-mid-rise development. The different levels of SUHI in different building typologies having the same density indicated the mitigation potentials of the built-form in Istanbul's local urban warming. Low coverage and high-rise building forms were an optimal solution for mitigating SUHI in densely populated urban areas. The research gives insight into an ongoing debate among urban professionals in Istanbul concerning the impacts of density and the urban form for climate adaptation. It enables professionals to understand the impact of urban planning decisions on microclimate and integrate them into the operational processes. Considering quantitative research findings as a strong foundation for developing policy recommendations and using them as a guideline may create new opportunities for researchers, practitioners, and policymakers. The study has an original value for exploring design strategies to improve microclimate and promoting sustainable urban development.

Reflecting on the past—a key to facilitating learning in strategy practice?

PurposeThe literature on the past triggering learning in strategy practice is scant. To fill this gap, this study aims to examine the meaning of the past to learning in strategy practice and expands on the strategy-as-practice (SAP) literature. Understanding the relationship between the past and learning in strategy practice is important because learning is what keeps strategy practice in motion and remains in place, even if organizations and strategy practitioners change.Design/methodology/approachThe authors used a longitudinal case study design combined with historical methods to examine how the past is embedded in present strategy practice. To capture learning in strategy practice over time, the authors applied a four-stage methodology in our analysis of document and interview data.FindingsThe authors identified four dimensions of the past embedded in the present strategy practice. These dimensions emerged from the analysis of the interviews and document data. The study’s results showed that the past appears in structures and routines, materiality, positioning and reflecting over repeated rounds of strategic planning. According to the study’s results, reflecting on strategy practice draws on past structures and routines, positioning and materiality. The past facilitates reflecting and reflecting on the past enables learning in strategy practice.Originality/valueThe authors constructed a conceptual model and showed that in strategy practice, reflection triggers learning. The authors contributed to theory development by demonstrating how the past is embedded in present strategy practice and is available for use by strategy practitioners. The authors showed that strategy practice is a continuous learning process.

Sustainable construction through energy management practices: an integrated hierarchal framework of drivers in the construction sector.

Reducing energy usage and promoting energy management practices remain hot issues in the construction sector. Construction firms are not interested to adopt energy conservation and management practices in their projects. Despite the successful integration of energy management practices in developed nations, their adaptability in developing countries, especially in Pakistan, is at a slow pace. Therefore, drivers to energy management practices need to be realized for its adoption. Based on this, the current study intends to evaluate the drivers of energy management practices adopted in the construction sector of Pakistan by using a four-stage methodology. Fuzzy Delphi method (FDM), interpretive structural modeling (ISM), and Matrice d’Impacts Croises Multiplication Appliques a un Classement (MICMAC) analysis were integrated with prioritizing essential drivers. Increased tax imposition on construction companies for energy usage and pollution contribution, promotion of investment subsidies for energy efficiency technologies, and increased enforcement of government rules and regulations regarding on-site energy management practices arose as significant drivers to adoption of energy management practices in the construction sector of Pakistan. These results will be helpful for policymakers to develop effective policies for integrating energy management practices in the construction sector. This study contributes significantly by developing a novel model of drivers affecting EMP adoption in the Pakistani construction sector. Further research might be expanded to other developing countries to validate current results.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-21928-x.

Exploring the use of water resource recovery facility instrument data to visualise dynamic resilience to environmental stressors

Water resource recovery facilities (WRRF) face increasingly dynamic stressors, such as higher rainfall intensity and extended dry periods, which can exert stress on ageing water infrastructure and processes. These events can generate process stresses, which lead to wastewater process failures which result in pollution events that could be identified from instrument data used for operational/compliance monitoring. This extraction can be performed on two levels (1) for discrete processes that generate data to monitor process control variables and (2) at the WRRF process boundary (global), which is mainly used for compliance. Both levels of data hold valuable information on the dynamic influence of environmental stressors (cause) and the resulting process stress or resilience (effect) as ‘dynamic resilience’. This paper proposes a novel methodology that uses actual water company instrument data to evaluate the ‘discrete’ (unit processes) and ‘global’ (WRRF boundary) dynamic resilience of a WRRF in the south of the UK. Dynamic resilience is presented as a four-stage methodology, which; (1) cleans WRRF data and extracts a standard operating condition; (2) identifies dynamic high and low flow environmental stressor events (one in five years); (3) models the process stresses and resilience generated by the imposed dynamic stressor before; (4) generating a contoured heat map of process-related stresses or resilience as a self ordering window. These methods demonstrate the possibility of visualising the dynamics of WRRF resilience (dynamic stressors and process stresses/resilience) resulting from high and low flow dynamic environmental stressors. Despite some challenges experienced with self ordering window scaling, the results demonstrate the possibility of identifying zones of process stress and resilience. It may also be possible to expand the methods developed to incorporate storm flows and combined sewer discharges.

Non-Stationary Hydrological Regimes Due to Climate Change: The Impact of Future Precipitation in the Spillway Design of a Reservoir, Case Study: Sube y Baja Dam, in Ecuador

Changes in flood loads and reservoir levels, produced by climate change (CC), represent an increasing concern for dam safety managers and downstream populations, highlighting the need to define adaptation strategies based on the dam failure risk management framework. Currently, thousands of dams worldwide, varying in use, age, and maintenance, may represent a threat to downstream cities in the case of structural failure. Several studies relate the failure of dams to several issues in the spillway, which may be even more vulnerable in CC conditions. This study provides a review of dam safety threats due to CC and approaches for the design/redesign of the spillway to cope with CC. A general four-stage methodology is proposed: data gathering and hydro-climatic, hydrological, and hydraulic analyses. Afterward, this methodology is applied to the spillway design for the Sube y Baja dam in Ecuador. The Probable Maximum Precipitation (PMP) increases around 20% considering CC under the Representative Concentration Pathway 8.5. Such an increment derived a 25% increase in the spillway maximum flow. These results show that the non-stationary hydrological regimes related to CC require a revision of engineering design criteria for hydraulic structures in general, and call for a consensus on design variables under CC.

From evaluation to action: Testing nudging strategies to prevent food waste in school canteens

Food waste (FW) prevention is an essential measure to contribute to the achievement of the Sustainable Development Goal target 12.3, achieve more sustainable food systems and tackle the climate crisis. This paper aims at evaluating nudging strategies’ impact on FW prevention in school canteens. To do so, it applies a four-stage methodology in 4 school canteens of the Metropolitan Region of Barcelona as case studies. This study has three main contributions. First, it provided real FW data by estimating a daily FW of 46 g per dinner. Secondly, it observed a 41% FW prevention impact when specific nudging strategies were applied. Dessert was the course with bigger reductions, especially when fruits were served. In addition, strategies involving canteen staff seemed to be more effective. Third, it proposed a methodological framework to co-design and evaluate the impact of FW prevention strategies by quantifying plate waste at school canteens. Thus, this study suggests nudging strategies as innovative interventions to improve food systems’ sustainability by preventing FW in school canteens.

Temporary Crops in the Brazilian Agricultural Frontier

Food production remains one of the main challenges for humankind in this century, and Brazil is one of the largest food-producing countries that have yet some land for economically or technically profitable farming expansion. Moreover, knowing which areas constitute the Brazilian agricultural frontier is crucial for improving public policies and logistics infrastructure decisions. Data from the Brazilian Institute of Geography and Statistics from 1995 to 2019 were used in this study. We aimed to map and measure the expansion of agricultural areas in Brazil from 1995 to 2019 for temporary crops according to their mesoregions. We used a four-stage methodology, compared the results of two agglomerative clustering methods, and identified similar mesoregions based on their share trends in the Brazilian agricultural seeded area. Some mesoregions had higher positive trend values for their share of the Brazilian agricultural seeded area: Mato-grossense North (MT), Mato-grossense Northeast (MT), Mato Grosso do Sul Southwest (MS), Goiano South (GO), Extreme West Bahia (BA), Maranhense South (MA), Piauiense Southwest (PI), and Tocantins Eastern (TO). As a second leading group, the Paranaíba Upstream (MG), São José do Rio Preto (SP), Mato-grossense Southeast (MT), and Goiano East (GO), must be emphasized. Further research is recommended, including extending the study to permanent crops and applying top-down analysis targeting microregions or municipalities in the identified mesoregions.