Introduction
Climate change has emerged as one of the greatest challenges facing global agricultural production in the 21st century. Current climate change research primarily focuses on developing predictive models and assessing the impacts of climate change, extreme weather events, adaptation and vulnerability, risk assessment, as well as mitigation solutions (IPCC, 2021; IPCC, 2023; Hoque et al., 2019; Tran et al., 2022). The evaluation of barriers that limit adaptive capacity in agriculture has become a critical research domain, requiring interdisciplinary approaches.
The assessment of barriers limiting adaptive capacity to climate change in agricultural production has been explored through various methodological approaches, with the Garrett ranking technique commonly employed to identify and prioritize barriers faced by farmers. Claessens et al. (2012) introduced a comprehensive impact assessment framework, the TOA-MD model, which simulates technology adoption and evaluates economic, environmental, and social outcomes across heterogeneous farm populations, providing deeper insights into adaptation strategies (Claessens et al., 2012). Conversely, analysis of barriers in agricultural production for climate change adaptation reveals a multidimensional picture influenced by socio-economic, environmental, and informational factors (Thein, et al., 2024). Several studies highlight the importance of resource accessibility and support systems as primary barriers (Batungwanayo et al., 2023; Reddy et al., 2022). Bryan et al. (2009) emphasized that in Ethiopia and South Africa, farmers' adaptation decisions were strongly influenced by assets, access to extension services, credit, and fertile land, demonstrating that economic and informational barriers constrain adaptive capacity. Similarly, studies have identified lack of access to extension services as a significant barrier for smallholder farmers in South Africa, emphasizing the role of institutional support in facilitating adaptation (Popoola et al., 2020; Elum et al., 2017).
Contemporary research examining agricultural production constraints within the Mekong Delta has elucidated a complex array of challenges fundamentally associated with climate change adaptation processes (ICEM, 2013). The multidimensional nature of adaptive systems, characterized by intricate interdependencies among biophysical, socio-economic, and institutional determinants, poses substantial methodological complexities in establishing constraint hierarchies and identifying critical adaptation barriers (Tran et al., 2020; FAO, 2015). To address these analytical challenges, Garrett's ranking methodology has been established as a sophisticated quantitative framework for systematic constraint assessment and prioritization.
This methodological approach has demonstrated considerable efficacy in household-level analyses, particularly in evaluating technical efficiency constraints among rice farming systems, thereby providing empirically-grounded insights into adaptation impediments (Claessens et al., 2012; Connor et al., 2012). Subsequent investigations have revealed that agricultural system transformation processes are substantially mediated by multifarious constraints that influence crop management strategies and resource allocation patterns (Tran et al., 2018; Dang et al., 2020). Additionally, comprehensive analyses of land use and land cover dynamics have demonstrated that constraints pertaining to land tenure arrangements and resource governance mechanisms critically determine adaptive capacity within agricultural landscapes (Di Bene et al., 2022; World Bank, 2024). The systematic implementation of Garrett's ranking technique facilitates rigorous constraint quantification and comparative analysis, thereby enabling the formulation of targeted intervention strategies. Empirical applications focusing on constraints related to land use rights, resource accessibility, and infrastructural deficiencies have yielded critical evidence for informed policy development in agricultural development frameworks (Troost et al., 2015; Becker et al., 2024).
In Thanh Phu district specifically and the Mekong Delta region in general, studies analyzing constraints and barriers in agricultural production for climate change adaptation have rarely been directly addressed in published research works (Tan et al., 2020; Feder et al., 1985), which primarily focus on adaptation themes and vulnerability of agricultural livelihoods to climate impacts (Nguyen et al., 2019). The results of these studies also demonstrate the contribution of the agricultural sector to greenhouse gas emissions and the potential of integrated production systems for climate change mitigation and adaptation (Stewart and Coclanis, 2011). Although the findings of these studies are not directly related to Garrett's ranking methodology, they have provided relevant information for understanding challenges and potential solutions in the context of climate change adaptation in the region. Therefore, to further clarify the broader impacts of climate change on global agricultural production and food security (Nguyen, 2019; Le & Nguyen, 2025). This study aims to identify and analyze the main barriers affecting adaptive capacity in agricultural production under the impacts of climate change. By applying Garrett's ranking method, barriers are ranked according to their importance level, providing a foundation for developing appropriate solutions. Data were collected from a survey of 200 farm households, used to assess nine main barriers in the agricultural production process in Thanh Phu district to determine the importance of climate change adaptation in the district's agricultural sector.
Research Methods
Research framework
This research framework (Fig. 1) aims to identify the primary barriers affecting farmers' adaptation processes in Thanh Phu District, Ben Tre Province, to support effective policy intervention planning. The study employs the Garrett ranking method to analyze agricultural production barriers faced by local residents, thereby assessing the relative impact of each factor.
The research procedure comprises several key steps: identifying the list of barriers through preliminary surveys and literature review; collecting data from farming households using questionnaires that require ranking the impact level of each barrier; converting rankings into Garrett scores through standardized conversion tables; calculating total and average scores for each barrier to determine relative impact levels and prioritize them accordingly. This method enables the transformation of subjective qualitative assessments from residents into quantitative results that are easily analyzed and compared (Garrett & Woodworth, 1969).
The application of the Garrett method not only helps quantify farmers' perceptions of production constraints but also supports the identification of priority areas in climate change adaptation strategies (Wens et al., 2020). The research findings are expected to provide scientific evidence to inform agricultural policy development and sustainable development in coastal delta regions
Study area and data collection
Thạnh Phu District is positioned at the southernmost boundary of Ben Tre Province, geographically situated between the Ham Luong and Co Chien distributaries of the Tien River within the Mekong Delta region (Fig. 2). The district's downstream coastal location confers exceptional vulnerability to saltwater intrusion and brackish water fluctuations. The district encompasses a total area of 425.7 km2, representing approximately 18% of the provincial territory, and maintains a population density of 300 inhabitants per km2. Climate projections under the RCP/SSP (AR5/AR6) scenario (MONRE, 2022) indicate substantial vulnerability for Thạnh Phu District, with modeling estimates suggesting that approximately 45% of the population may face flood exposure by 2100 due to projected sea-level rise (MARD, 2020). The predominantly agricultural economic base of the resident population experiences increasing pressures from multiple environmental stressors, including saltwater intrusion, periodic inundation, coastal erosion, and progressive natural resource depletion.
In 2023, Thanh Phu District recorded a population of over 146,000 inhabitants distributed across approximately 37,000 households, of which more than 25,000 households were engaged in agricultural activities, accounting for nearly 70% of the total households (Ben Tre DOST, 2023). A survey was conducted from December 2023 to January 2024, employing direct face-to-face interviews using a structured questionnaire comprising 33 items targeting household heads whose livelihoods depend on agriculture (Table 1). The questionnaire focused on nine main components to identify the following barriers: (i) Climate change impacts on natural resources, (ii) Insufficient information, (iii) Prolonged timeframes for observing adaptation measure outcomes, (iv) Insufficient social and institutional support, (v) Deficient agricultural techniques and low educational attainment, (vi) High investment costs and financial capital deficiency, (vii) Elderly age and poor health conditions, (viii) Inadequate infrastructure and material support, and (ix) Restricted credit accessibility. To ensure optimal sample size, approximately 200 households were selected to participate in the survey. To guarantee representativeness and statistical reliability of the collected data, the sample size was determined using Yamane 's formula (1967), which is widely employed in large-scale quantitative sociological research, as presented below:
The optimal sample size was estimated using Yamane’s (1967) formula, yielding n = 25,000/(1 + 25,000 × 0.0049) ≈ 202 households with ± 7% margin of error.. For feasibility, a final sample of 200 households was adopted, which satisfies the requirements of statistical rigor and population representativeness (Israel, 1992). To capture ecological diversity, five communes: Giao Thanh, My Hung, Dai Dien, An Nhon, and An Quy were purposively selected to represent three distinct ecological zones of Thanh Phu District. Within each commune, approximately 40 households were surveyed, resulting in a stratified sampling design with equal allocation across strata. This approach combined purposive commune selection with proportional household distribution, thereby ensuring ecological representativeness and demographic balance, and aligning with established methodological standards in large scale quantitative research.
Applying the Garrett ranking method to analyze adaptation barriers in agricultural production
In this study, to convert the rankings of factors into statistically meaningful scores, thereby determining the priority order of different factors to identify the main barriers affecting the adaptation process of farm households (Garrett and Woodworth, 1969). By converting the rankings of factors into percentage positions, then transforming them into standardized Garrett scores, this approach enables the synthesis of subjective assessments into comparable quantitative values (Anser et al., 2020).
The calculation of Garrett scores begins with determining the percentage position for each rank according to the formula:
Where:
PPi: percentage position corresponding to rank i
Ri: ordinal rank (1, 2, 3, ...)
N: total number of factors under consideration
These percentage positions are subsequently transformed into Garrett scores using the Garrett conversion table, which is based on the psychological distribution of response patterns. To obtain accurate Garrett scores for each percentage position, linear interpolation is applied using the following formula:
Where:
G(PP): Garrett score at percentage position PP
(PP1, G1): first point in the Garrett table
(PP2, G2): second point in the Garrett table
Condition: PP1 ≤ PP ≤ PP2
Given that 10 ranks are utilized in the study, the corresponding Garrett conversion Table 2 is presented as follows.
The research evaluated barriers using a multivariate synthesis methodology, an approach that has been effectively employed in various prior studies. Based on content properties and logical associations, relevant variables were categorized into 9 primary barriers encompassing: high investment costs and financial capital deficiency, restricted credit accessibility, insufficient information, climate change impacts on natural resources, deficient agricultural techniques and low educational attainment, elderly age and poor health conditions, inadequate infrastructure and material support, insufficient social and institutional support, and prolonged timeframes for observing adaptation measure outcomes. The composite Garrett score for each barrier was systematically calculated according to the formula:
Where:
TGSc: aggregate Garrett score for barrier c
Vc: set of variables associated with barrier c
fvr: frequency of respondents rating variable v at rank r
Gr: Garrett score associated with rank r
N: total number of ranking positions
Finally, the mean Garrett score is calculated using the formula:
With n: number of study participants
This integrated approach enables a comprehensive assessment of the relative influence of each barrier, consistent with the multidimensional methodology recommended by Smith et al. (2003) for climate change adaptation research. By synthesizing evaluations across multiple related variables, the study constructs a holistic perspective on the constraints faced by agricultural households, thereby providing a scientific basis for formulating appropriate and effective adaptation strategies.
Results and Discussion
Ranking results of the identified barriers
Following formulas (2) through (5), the aggregate scores for each barrier were computed by multiplying the frequency of respondents assigning a given rank with the corresponding Garrett score for that rank. The mean Garrett scores were then derived, and barriers were ordered in descending sequence, such that those with higher mean scores were assigned top rankings, while those with lower scores received correspondingly lower positions (Pokiya et al., 2024). Data for this analysis were collected through a structured questionnaire. Variables with multiple values were assessed using a ranking test, aggregated, and subsequently classified into scales ranging from 1 to 10. The interpretation of these scales varied depending on the characteristics of each variable, as defined in the questionnaire and in the supplementary reference tables. Afterward, the frequency of household responses for each factor was calculated, and barriers were organized from the most frequently selected to the least selected, thereby providing a clear prioritization of adaptation constraints.
Base on formula 4, Table 3 presents the results of Garrett ranking analysis examining climate change adaptation barriers encountered by farmers in Thanh Phu district. The Garrett ranking model required respondents to rank barriers according to their perceived importance from R1 (most important) to R10 (least important), with overall scores calculated by aggregating all ranking selections across positions. The analysis revealed that "Climate change impacts on natural resources" emerged as the most critical barrier, receiving 1,007 first-rank (R1) selections and 796 second-rank (R2) selections, demonstrating strong consensus among farmers regarding the priority of this issue. "High investment costs and financial capital deficiency" constituted the second most pressing barrier with 298 R1 selections. The substantial variation in total responses across different barriers (ranging from 200 to 2,800) reflects the multi-faceted nature of certain constraints and their broader recognition among the farming community. The ranking distribution patterns revealed that some barriers demonstrated high concentration in top positions (R1–R3), indicating strong consensus regarding their importance, while others exhibited more even distribution across ranking positions, suggesting divergent opinions within the farming community about the relative significance of specific climate adaptation barriers.
Following formula 4, the computational results presented in Table 4 illustrate the Garrett scores assessing the relative importance of each barrier ranking derived from 200 participating agricultural households. As previously established, each barrier category was disaggregated into multiple constituent elements through structured questionnaire components, with respondents assigning priority rankings to all elements within each barrier from 1 to 10. During the ranking process for elements within identical barrier categories, respondents were permitted to assign equivalent importance rankings to multiple elements. Consequently, within a single barrier category, several factors could simultaneously occupy the R1 position when respondents perceived them as possessing equal significance. This methodological approach explains the occurrence of overlapping selections across ranking positions. The Garrett scoring system employs predetermined coefficients for each ranking position: rank 1 corresponds to a coefficient of 81, rank 2 to 70, with coefficients decreasing progressively for subsequent positions. The ranking score for each barrier is calculated by multiplying selection frequency by the corresponding positional coefficient. For instance, "Climate change impacts on natural resources" accumulated 1,007 selections at the R1 position (reflecting multiple households' designation of this factor as paramount), generating 81,567 points when multiplied by the coefficient of 81. The composite score for each element represents the summation of points across all ranking positions, thereby enabling systematic evaluation of barrier priority levels according to agricultural household perceptions.
Table 5 presents the hierarchical assessment of adaptive barriers in agricultural production, revealing distinct patterns in farmer perceptions. Climate change impacts on natural resources emerged as the predominant constraint, achieving the highest mean Garrett score of 990.95 and securing first rank. This barrier demonstrates overwhelming significance, recording a score nearly three times higher than the second-ranked barrier. The analysis identifies a descending order of barrier importance, with insufficient information ranking second (345.01), followed by prolonged timeframes for observing adaptation measure outcomes in third position (318.78), and insufficient social and institutional support ranking fourth (239.60). These four primary barriers exhibit substantially elevated mean scores relative to remaining constraints, establishing a clear demarcation between primary and secondary concerns among agricultural practitioners.
The middle-tier barriers encompass deficient agricultural techniques and low educational attainment (206.84, fifth rank), high investment costs and financial capital deficiency (155.26, sixth rank), and elderly age with poor health conditions (88.63, seventh rank). Economic constraints, specifically inadequate infrastructure and material support (78.00, eighth rank) and restricted credit accessibility (74.54, ninth rank), demonstrate relatively lower rankings than anticipated. This finding suggests that farmers prioritize environmental and informational challenges over economic barriers in their adaptation decision-making processes. The ranking distribution patterns exhibit considerable variation across barriers, with certain constraints demonstrating concentrated responses within specific rank ranges, while others span the complete ranking spectrum. These patterns reflect varying degrees of consensus among respondents regarding the perceived severity of individual barriers.
Barrier analysis by level of influence
High-impact barriers (Mean Garrett score > 300)
"Impact of climate change on natural resources" was identified as the most significant barrier with the highest mean Garrett score (990.95), accounting for 39.68% of the total score. This finding aligns with the research by Phuong et al. (2024), which demonstrated that climate change represents the most severe threat to agriculture in developing countries, particularly in the Southeast Asian region.
"Insufficient information" ranked second with a mean score of 345.01 (13.81%). Bryan et al. (2013) indicated that the lack of information regarding appropriate adaptation measures and their potential benefits constitutes a significant barrier to farmers' adoption of adaptation strategies.
"Prolonged timeframes for observing adaptation measure outcomes " ranked third with a mean score of 318.78 (12.76%). According to the research by Bryan et a l. (2009) on risk perception and adaptive capacity, farmers typically evaluate costs and benefits based on short-term timeframes, while many climate change adaptation measures require extended periods before their effectiveness becomes apparent.
Medium-impact barriers (Mean Garrett score from 100 to 300)
"Insufficient social and institutional support" ranked fourth with a mean score of 239.60 (9.59%). Thein et a l. (2024) emphasized the importance of social capital in the adaptation process, arguing that social organizations play a crucial role in supporting farmers' access to information, resources, and new technologies.
"Deficient agricultural techniques and low educational attainment" ranked fifth with a mean score of 206.84 (8.28%). Batungwanayo et al. (2023) demonstrated that educational level and technical knowledge significantly influence farmers' capacity for climate change perception and adaptation.
"High investment costs and financial capital deficiency" ranked sixth with a mean score of 155.26 (6.22%). According to Connor et al., (2012) initial investment costs remain a significant barrier to the adoption of adaptation measures at larger scales, particularly for smallholder farmers.
Low-impact barriers (Mean Garrett score < 100)
"Advanced age and poor health" ranked seventh with a mean score of 88.63 (3.55%). Rabby et al. (2019) indicated that age and health status may affect the ability to adopt adaptation measures that require intensive labor, but the significance of this factor may vary according to local contexts.
"Lack of infrastructure and material support" ranked eighth with a mean score of 78.00 (3.12%). Reddy et al. (2022) emphasized the role of infrastructure in supporting climate change adaptation, but also noted that the importance of this factor may be overshadowed by other barriers.
"Restricted credit accessibility" ranked last with a mean score of 74.54 (2.98%). This result differs from the research by Deressa et al. (2009), which identified financial barriers as primary limiting factors. This discrepancy may be attributed to the specific characteristics of the study region or the fact that existing financial support programs have reduced the impact of this factor.
Conclusion
This study successfully identified and ranked nine principal barriers influencing climate change adaptation capacity in agricultural production within Thanh Phu district, Ben Tre province, through the application of the Garrett ranking methodology to a sample of 200 farming households. The findings demonstrate distinct stratification among barriers according to their impact magnitude, with three primary categories identified.
The high-impact barrier category (Garrett mean score > 300) comprises: (1) Climate change impacts on natural resources (990.95 points, representing 39.68% of total scores), indicating farmers' predominant concern regarding direct climate change effects on the natural environment; (2) Information deficiency (345.01 points, 13.81%), reflecting informational gaps in accessing effective adaptation strategies; and (3) Extended timeframes required for observing adaptation measure outcomes (318.78 points, 12.76%), illustrating challenges concerning the long-term sustainability of adaptive strategies.
The medium-impact barrier category (100–300 points) encompasses: insufficient social and institutional support (239.60 points), inadequate agricultural techniques and low educational attainment (206.84 points), and elevated investment costs coupled with financial capital constraints (155.26 points). The low-impact barrier category (< 100 points) includes: advanced age and compromised health status (88.63 points), deficient infrastructure and material support (78.00 points), and restricted credit accessibility (74.54 points).
The research findings provide a comprehensive analysis of climate change adaptation barriers in agriculture within the coastal Mekong Delta region. The implementation of the Garrett ranking methodology facilitated objective quantification and prioritization of barriers, establishing a robust scientific foundation for evidence-based policy development.
The most significant finding of this investigation is the prioritization of barriers associated with climate change impacts on natural resources and information accessibility over conventional economic constraints. This necessitates a paradigmatic shift in policy approaches from exclusively emphasizing financial assistance toward adopting a more holistic framework encompassing resource management, information dissemination, and long-term adaptive capacity development.
Furthermore, this study contributes substantially to the scientific knowledge base regarding agricultural climate change adaptation through the systematic application of the Garrett ranking methodology for quantifying adaptation barriers. This represents among the inaugural comprehensive applications of this methodology within the Mekong Delta context, particularly in highly vulnerable coastal regions such as Thanh Phu district. Concurrently, it provides critical empirical evidence concerning barrier prioritization from farmers' perspectives, wherein factors related to climate change impacts on natural resources and information access are accorded greater significance than traditional economic barriers. This finding diverges from numerous previous studies that typically emphasized financial constraints as primary determinants, thereby highlighting the necessity for reconsidering policy priority assumptions in climate change adaptation frameworks.
Based on the ranking results, this study proposes a three-tiered framework of policy implications. At the commune level, emphasis should be placed on strengthening climate-adaptive farming practices through farmer training, demonstration models, and digital information platforms to ensure timely dissemination of adaptation knowledge. At the district level, integrated natural resource management systems need to be developed, particularly predictive and early warning mechanisms for salinity intrusion, drought, and flooding, in parallel with the promotion of climate-resilient crop and livestock varieties. At the provincial level, coordinated policy frameworks are required to align research and development, infrastructure investment, and multi-stakeholder collaboration in order to scale up successful pilot initiatives and enhance resilience across districts. Collectively, these findings contribute both theoretically and practically to advancing sustainable agricultural development in Vietnam and Southeast Asia, while providing critical scientific evidence to guide policy decisions aligned with the Sustainable Development Goals, particularly SDG 2 (Zero Hunger), SDG 13 (Climate Action), and SDG 15 (Life on Land).
In addition, barriers of different priority levels warrant differentiated policy responses. High-ranked barriers should be addressed through targeted measures such as financial assistance and improved access to credit. Medium-ranked barriers require strengthening of technical training programs and more effective dissemination of market information. Low-ranked barriers may be integrated into broader community awareness and communication campaigns. This tiered approach enhances the practical relevance of the findings and provides actionable guidance for local governments and agricultural extension agencies in policy formulation and implementation.
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