Perceptions and adaptation strategies to climate change in coffee farming: a systematic review

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Climate change directly affects coffee production and quality, with consequences for producing regions and communities that depend on this activity. This study aimed to identify, through a systematic literature review, methodological approaches used in research on coffee agroecosystems and climate change, to analyze adaptation strategies adopted, and to understand factors influencing coffee farmers’ perceptions across different territorial contexts regarding climate change. The methodology followed the PRISMA-RR protocol, which organizes the systematic review process into four stages: (i) literature identification, (ii) screening, (iii) eligibility assessment, and (iv) retrieval of conclusive evidence. Searches were conducted on the Web of Science and Scopus databases. Most studies adopted a mixed-methods approach (70%), combining qualitative and quantitative tools. Among production systems analyzed, hybrid and agroecological systems showed the greatest diversity of adaptation strategies to climate change. Territorial factors, production systems, and socioeconomic conditions directly influenced how farmers perceived and responded to climatic events, as well as adoption of adaptation strategies. Results indicate that adaptation in coffee farming is conditioned by structural and territorial factors, reinforcing the need for interdisciplinary approaches that integrate ecological, productive, and socioeconomic dimensions when addressing climate change. Family farming Farmer perceptions Coffee production systems Agricultural resilience. Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Coffee farming is an agricultural activity of major global importance because of its economic and social contributions. An estimated 17.5 million family farmers worldwide depend on coffee production for their livelihoods (Pham et al., 2019 ). Brazil, the world’s largest producer, accounts for 35.5% of Coffea arabica production and 22.25% of Coffea canephora production. Vietnam leads global C. canephora production with 37.7%, whereas Colombia contributes 13.20% of global C. arabica production (BRAZIL, 2025). Coffee farming also supports development in rural communities and producing regions by generating employment and income in countries such as Ethiopia, Mexico, and Rwanda (Jawo et al., 2023 ; Tesfaye, 2021; Bro, 2020 ). Currently, coffee farming faces increasing risks associated with climate change across producing countries (Gomes et al., 2020; Dias, Martins, and Martins, 2024). The most widely cultivated coffee species require distinct climatic conditions for optimum development. C. arabica , commonly used for specialty coffee production, performs best under mean annual temperatures of 18–22°C and annual rainfall of 1,200–2,000 mm (DaMatta et al., 2007). C. canephora is generally more resilient and productive, although often considered lower in beverage quality, and performs best under mean temperatures of 22–28°C (Magrach and Ghazoul, 2015) and annual rainfall of 1,200–1,800 mm (de Souza et al., 2025). Under future climate scenarios, projections for Brazil indicate yield losses in C. arabica caused by thermal stress and greater susceptibility to infestation by coffee leaf miner ( Leucoptera coffeella Guérin-Mèneville) and coffee rust ( Hemileia vastatrix Berkeley and Broome) (Dias; Martins; Martins, 2024). According to these projections, approximately 35% to 75% of Arabica plantations in the country may be negatively affected and could become economically unviable during the twenty-first century. In addition, Venancio et al. (2020) reported that prolonged droughts and elevated temperatures may reduce C. canephora productivity by up to 41%. How farmers perceive and respond to climate change is central to understanding adaptation dynamics within agricultural communities and coffee agroecosystems (Nguyen et al., 2025). Such perceptions do not always align with meteorological records, as they are shaped by farmers’ empirical experiences and direct observations of climatic impacts (Jawo et al., 2023 ; Tesfaye, 2021). Studies conducted in producing regions such as Mana, southwestern Ethiopia, and Puebla, Mexico, show that perceptions of increasing temperatures and declining rainfall are critical drivers of adaptation strategies (Tesfaye, 2021; Jawo et al., 2023 ). In Vietnam, recognition of drought periods by 91% of farmers and excessive rainfall by 23% was decisive for adopting adaptation measures (Lan et al., 2023 ). Reported strategies include agroforestry systems, shade-tree management, crop diversification, and adjustments in agricultural calendars (Shinbrot et al., 2019; Sánchez et al., 2023 ). In Rwanda, Gather and Wollni ( 2024 ) found that farmers’ risk perceptions varied mainly according to prior experiences with climatic events. Farmers who perceived stronger climatic changes were more likely to adopt shading practices and diversify income through cultivation of additional crops (Gather and Wollni, 2024 ). Conversely, limited perception of climate change may reflect insufficient knowledge of its effects on coffee production, thereby constraining adoption of adaptive strategies (Hasibuan et al., 2023 ). Analyzing coffee growers’ perceptions of climate change requires consideration of territorial, socio-environmental, and production contexts. Geographic location, agroecosystem type, and socioeconomic structure influence both how climate risks are understood and which production and adaptation strategies are adopted. Therefore, understanding climate adaptation in coffee farming requires recognition that farmers’ responsiveness is directly shaped by these variables, which may differ substantially across territories (Jawo et al., 2023 ; Tesfaye, 2021). Although climate change adaptation strategies have been widely documented, a gap remains in scientific literature regarding farmers’ perceptions, particularly how such perceptions influence adoption of adaptive practices. This gap is especially relevant because available evidence indicates that perception of climate risk is a key determinant of decision-making and implementation of adaptation measures. Accordingly, this study aimed to identify methodological approaches used in research on coffee agroecosystems and climate change, analyze adaptation strategies adopted, and understand factors influencing coffee growers’ perceptions of climate change across different territorial contexts. 2. Materials and Methods For the systematic literature review, the core collection of the Web of Science platform, originally developed by Thomson Reuters – Institute for Scientific Information (ISI) and currently maintained by Clarivate Analytics, together with the Scopus database by Elsevier, were used. The review was conducted according to guidelines of the PRISMA-RR protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Rapid Reviews) (Stevens et al., 2018 ). This protocol is particularly relevant for interdisciplinary and rapidly evolving topics, such as climate change and agricultural adaptation, because it helps ensure that evidence syntheses are robust, comparable, and useful for guiding technical and policy decisions (Stevens et al., 2018 ; Mouratiodou et al., 2024 ). PRISMA-RR organizes the review process into four phases: (I) literature identification, (II) screening, (III) eligibility assessment, and (IV) retrieval of conclusive evidence. In Phase I, the identification stage, the following search strategy was applied: Documents-Topic [("climate change" AND "coffee" AND "percept")]**. Keywords were defined through exploratory searches conducted within the databases. The terms climate change, coffee, and percept were selected because they represent core concepts of this investigation, spanning climatic impacts on coffee farming and subjective perceptions associated with farmers’ experiences and management practices. The Boolean operator AND restricted retrieval to articles containing all specified terms simultaneously (Shah et al., 2021), thereby ensuring direct relevance to each theme. The wildcard asterisk (*) was used to capture word variants, whereas quotation marks ensured exact phrase matching for specified terms (Mejia et al., 2021 ). Publications available in all languages up to March 2025 were considered. In Phase II, the screening stage, inclusion and exclusion criteria were applied (Table 1 ). Review articles, book chapters, editorial materials, conference papers, and duplicate records were excluded because they did not provide original data and/or presented only partial results related to study objectives. In parallel, titles and abstracts of selected records were examined to confirm whether they effectively addressed the proposed topic and should therefore be included in the review. Table 1 Inclusion and exclusion criteria applied during the screening phase. Inclusion criteria Exclusion criteria Original research articles only Review articles, book chapters, editorial materials, conference papers, and duplicate records. Peer-reviewed publications Non-peer-reviewed publications Studies presenting original information on farmers’ perceptions of climate change Studies not presenting original information on farmers’ perceptions Full-text articles available for access Full-text articles not available for access Source The author (2025). In Phase III, corresponding to the eligibility stage, previously selected articles were organized and subjected to full-text review to confirm their alignment with study objectives. At this stage, the treatment of farmers’ perceptions of climate change in coffee agroecosystems was assessed, considering both direct and indirect expressions of such perceptions. In addition, information reported in each study was examined, with particular attention to how perceptions were described and analyzed. Articles that did not address these aspects were excluded from the final dataset. In Phase IV, the evidence retrieval stage, selected articles were organized in a spreadsheet using Microsoft Excel containing the following information: (a) reference data, including title, authors, publication year, country of publication, objectives, abstract, and conclusions; (b) territories analyzed, including country and region where the study was conducted; (c) categorization of agroecosystems; (d) methodological approaches, including classification as qualitative, quantitative, or mixed methods, and tools applied (interviews, questionnaires, focus groups, statistical tests, or combinations of these tools); (e) farmers’ perceptions, including assessment of coffee growers’ perceptions of climate change; and (f) adaptation strategies, including actions and practices adopted in response to climate-related impacts. All protocol stages were conducted collaboratively by two researchers, thereby increasing methodological rigor and reliability. During the screening phase, disagreements were resolved through final evaluation by a third reviewer. Data were systematically organized and tabulated to enable detailed analysis of approaches employed in reviewed studies. Initially, 151 records were identified, including 59 from Web of Science and 92 from Elsevier Scopus. These records were assessed according to steps described above. After application of PRISMA-RR criteria (Stevens et al., 2018 ), the final dataset comprised 53 articles from both databases. A summary of the methodological procedure based on the PRISMA-RR flowchart is presented in Fig. 1 . Source The author (2025). Based on evidence retrieval (Phase IV), analysis focused on central elements of research question and study problem. Accordingly, results are discussed according to three main axes: (I) methodological approaches and tools applied in the studies; (II) territories and agroecosystems; and (III) farmers’ perceptions of climate change and adaptation strategies. 3. Results The temporal distribution of publications indicates more consistent growth in this topic from 2017 onward, with clear intensification in recent years and notable peaks after 2020. In 2025, a reduction in the number of studies was observed, possibly associated with the temporal cutoff adopted for data collection rather than reflecting decreased scientific interest in the subject (Fig. 2 ). Most of the 53 articles included in the systematic review were published in English (n = 47), followed by publications in Spanish (n = 6) (Appendix 1). Regarding methodological approaches, studies using mixed methods predominated, representing 38 articles (70%). Exclusively qualitative approaches were identified in 13 studies (23%), whereas exclusively quantitative approaches were found in only 2 articles (3% of the total) (Table 2 ). Table 2 Methodological approaches identified in the reviewed articles. Methodological approach Perception analysis References Number of articles Mixed Integrates qualitative and quantitative methodological approaches and research tools Hernández-Castán and Tapia-Hervert Calderón, ( 2023 ); Quiroga et al. (2020); Teodoro et al. (2024); Gather and Wollni, ( 2024 ); Ayalew et al. ( 2024 ); Há et al. (2024); Rodríguez-Barillas, Poortvliet, Klerkx (2024); Bartl, ( 2024 ); Gomm et al. ( 2024 ); Rodríguez-Barillas, Poortvliet, Klerkx (2023); Hasibuan et al. ( 2023 ); Ayal et al. ( 2023 ); Jawo et al. ( 2023 ); Sánchez et al. ( 2023 ); Tesfaye (2021); Jaramillo-Villanueva et al. ( 2022 ); Pons et al. (2021); Tran and Chen ( 2021 ); Eise, Lambert and Wiemer (2021); Mbwambo, Maurice and Tarimo (2021); Nguyen and Drakou ( 2021 ); Adhikari et al. (2021); Jezeer et al. ( 2019 ); Capitani et al. (2018); Asayehegn et al. (2017); Barrucand, Vieira and Canziani (2019); Quiroga, Cristina, Suárez and Solís ( 2014 ); Nghiepa et al. (2024); Beristain-Moreno et al. (2024); Lan et al. ( 2023 ); Msuya and Mahonge ( 2022 ); Byrareddy et al. (2021); Wagner et al. ( 2022 ); Le et al., ( 2020 ); Shinbrot et al. (2019); Harvey et al. (2018); Chengappa, Devika and Rudragouda ( 2017 ); Eakin et al. (2013); Torres-Lezama et al. (2011). 38 Qualitative Exclusively qualitative approaches and tools Moreno et al. (2024); Rodríguez-Barillas et al. (2024); Bro, ( 2020 ); Altea, (2020); Pham et al. (2020); Borsky and Spata, (2017). Eise, Lambert, Wiemer (2021); Borsky and Spata (2017); Sánchez and Bello (2020); Gerlicz et al. ( 2018 ); Vigueira et al. (2018); Vigueira et al., (2019); de los Ríos and Almeida ( 2010 ); Palacios et al. (2023). 13 Quantitative Exclusively quantitative approaches and tools Lan et al., ( 2023 ); Venancio et al., (2024). 2 Source: Own elaboration based on studies from the systematic review (2025). Among tools employed in mixed-methods studies, the most frequent were interviews (n = 22; 57.9%), structured and semi-structured questionnaires (n = 21; 55.3%), regression models and analysis of variance (n = 21; 55.3%), focus groups (n = 11; 28.9%), and descriptive statistics (n = 12; 31.6%) (Table 3 ). In qualitative studies, interviews combined with direct observation of agroecosystems predominated (n = 9; 69.2%), followed by workshops (n = 3; 23.1%) and interactive games with participatory workshops (n = 2; 15.4%) (Table 3 ). Among the few studies with an exclusively quantitative focus, use of questionnaires (n = 1; 50.0%), descriptive statistics, and mean comparison tests (n = 2; 100.0%) was identified (Table 3 ). Table 3 Predominant methodological tools used in mixed, qualitative, and quantitative approaches among the reviewed articles. Methodological approach Tools Number of articles Mixed Interviews 22 Structured and semi-structured questionnaires 21 Regression models and analysis of variance 21 Focus groups 11 Descriptive statistics 12 Qualitative Interviews combined with direct observation 9 Interactive games and participatory workshops 2 Quantitative Questionnaires 1 Descriptive statistics and mean comparison tests 2 Source Own elaboration based on studies from the systematic review (2025). Most studies were conducted in Latin America (40%), Africa (26%), and Asia (21%). Among countries with the highest number of studies, Ethiopia (n = 9), Vietnam (n = 8), Mexico (n = 7), and Costa Rica (n = 4) were most prominent. In addition, three studies each were conducted in Colombia, Guatemala, Tanzania, Honduras, and Nicaragua (Fig. 3 ). Based on reading and analysis of the studies, four categories of farming systems were identified: agroecological, conventional, hybrid/transitional, and traditional/subsistence (Table 4 ). Agroecological systems are characterized by adoption of sustainable practices such as ecological soil and water management, crop shading, agroforestry systems, crop diversification, and use of biological inputs. In contrast, conventional systems are based on intensive use of chemical inputs and monoculture, with little or no incorporation of sustainable practices such as ecological soil and water management. Hybrid or transitional systems combine agroecological elements with use of local resources, while still maintaining some dependence on conventional practices, such as occasional pesticide application. Finally, traditional or subsistence systems rely on local knowledge and maintain strong integration with surrounding ecosystems, prioritizing agroecological practices mainly directed toward household food security. Categorization of production systems showed predominance of hybrid or transitional systems (44%), followed by agroecological (22%), conventional (14%), and traditional or subsistence systems (5%). It should also be noted that in 12% of articles analyzed, production systems could not be categorized because of insufficient information (Table 4 ). Table 4 Categories of production systems and their frequency of occurrence in the reviewed studies. Production Systems Frequency (%) References Agroecological 22% Gather and Wollni ( 2024 ); Gom et al. (2024); Ayal et al. ( 2023 ); Jawo et al. ( 2023 ); Tesfaye (2021); Mbwambo et al. ( 2021 ); Altea (2019); Capitani et al. (2018); Barrucand, Vieira and Canziani (2016); Ayalew et al. ( 2024 ); Beristain-Moreno et al. (2024); Sánchez and Bello (2020); Shinbrot et al. (2018); Conventional 14% Lan et al., ( 2023 ); Jaramillo-Villanueva et al., ( 2022 ); Nguyen and Drakou ( 2021 ); Pham et al., (2020); Le et al. ( 2020 ); Viguera et al., ( 2019 ); Chengappa, Devika and Rudragouda ( 2017 ); Torres-Lezama et al. (2012); Hybrid/Transitional 44% Ha et al. (2024); Rodríguez-Barillas, Poortvliet and Klerkx (2024); Bartl ( 2024 ); Teodoro et al. (2024); Sánchez et al. ( 2023 ); Hernández-Castán and Tapia-Hervert Calderón ( 2023 ); Pons et al. (2021); Tran and Chen ( 2021 ); Bro ( 2020 ); Adhikari et al. (2021); Quiroga et al. (2019); Jezzer et al. (2019); Asayehegn et al. (2017); Tucker, Eakin and Castellanos ( 2009 ); Nghiepa et al. (2024); Palacios et al. (2023); Msuya and Mahonge ( 2022 ); Byrareddy et al. (2021); Wagner et al. (2021); Gerlicz et al. ( 2018 ); Viguera et al. (2018); Harvey et al. (2018); Eakin et al. (2013). Traditional 5% Ayalew et al. ( 2024 ); Hasibuan et al. ( 2023 ); de los Ríos and Almeida ( 2010 ). Non-categorized 12% Rodríguez-Barillas, Klerkx and Poortvliet (2023); Eise, Lambert and Wiemer (2021); Borsky and Spata (2017); Quiroga, Suárez and Solís (2009); Rodríguez-Barillas, Klerkx and Poortvliet (2024); Lan et al. ( 2023 ); Tran and Chen ( 2021 ). Source: Own elaboration based on studies from the systematic review (2025). Analysis of the regional distribution of production systems showed that hybrid systems predominated in Latin America, traditional/subsistence systems were more common in East Africa, Asia was characterized by intensive conventional systems, and some producing regions of Africa, particularly Ethiopia, showed a substantial presence of agroecological systems (Table 5 ). Table 5 Distribution of production systems and their characteristics by region and country. Region Predominant production system Countries References Latin America Hybrid/Transitional Mexico, Guatemala, Costa Rica, and Honduras Hernández-Castán and Calderón, ( 2023 ); Sánchez et al. ( 2023 ); Gerlicz et al. ( 2018 ); Viguera et al., (2018); Barillas, Poortvliet, Klerkx, (2024); Palacios et al. (2023); Tucker, Eakin and Castellanos ( 2009 ); Jezeer et al. ( 2019 ); Bro ( 2020 ); Tran and Chen ( 2021 ); Pons et al. (2021). East Africa Traditional/Subsistence Ethiopia, Gomm et al. 2024 ; Ayalew et al. ( 2024 ). Asia Conventional/Intensive Vietnam and Indonesia Le, Le, Cowal, (2020); Pham et al., (2020); Nguyen and Drakou ( 2021 ); Lan et al. ( 2023 ). East Africa Agroecological Ethiopia and Tanzania Gom et al. (2024); Ayal et al. ( 2023 ); Tesfaye, (2021); Altea, (2019); Capitani et al., (2018); Jawo et al. ( 2023 ); Mbwambo et al., ( 2021 ); Capitani et al. (2018). Source: Own elaboration based on studies from the systematic review (2025). Analysis of the reviewed articles allowed identification of four territory-related factors that influence farmers’ perceptions of and adaptation to climate change in coffee farming. In Vietnam, local climatic conditions and access to credit were reported as key determinants of farmers’ perceptions and decision-making. In Mexico, access to markets and technologies increased capacity for adaptive responses. In Colombia, social organization and participation in cooperatives were identified as important facilitators of collective climate adaptation strategies (Table 6 ). Table 6 Relationship between territorial factors and their implications for farmers’ perceptions of climate change. Territory-Related Factors Implications for perception and adaptation strategies Country References Climate conditions, water deficit, and access to credit Influence farmers’ perceptions and decision-making Vietnam Nguyen and Drakou ( 2021 ); Byrareddy et al. (2021). Pham et al. (2020) Access to markets and technology through cooperatives and associations Increases adaptative response capacity Mexico Jaramillo- Villanueva et al. (2022); Sánchez and Bello (2020). Shinbrot et al. (2019). Social organization and cooperatives Facilitate collective adaptation strategies Colombia Eise, Lambert, Wiemer (2021); Barrucand, Vieira, Canziani (2016); Borsky and Spata, (2017). Land tenure structure Influences how farmers perceive climate risks Rwanda and Ethiopia Ayalew et al. (2025); Gather and Wollni, ( 2024 ) Source: Own elaboration based on studies from the systematic review (2025). Among strategies adopted to adapt coffee agroecosystems to climate change, the most prominent were agroforestry systems and shade management (33.9%), diversification of agricultural crops (32.1%), ecological soil and water management (32.0%), use of resistant varieties (21.4%), access to credit, participation in cooperatives, and fair-trade arrangements (8.9%), and reduced pesticide use combined with greater adoption of biological inputs (8.9%) (Table 7 ). Table 7 Main adaptation strategies identified in coffee agroecosystems in response to climate change, expected actions, and frequency of reporting in the reviewed studies. Adaptation Strategy Expected Action Frequency (%) Agroforestry systems and shade management Shading, soil conservation, improved water regulation, and a more favorable microclimate. 33.9% Diversification of agricultural production Reduced vulnerability through multiple income sources. 32.1% Ecological soil and water management Increased biodiversity, greater soil water retention, and water reuse. 32% Use of resistant varieties Cultivars tolerant to water deficit, pests, and diseases such as rust and cercosporiosis. 21.4% Access to credit, cooperatives, and markets Greater investment capacity in adaptive technologies and market access. 8.9% Reduction in pesticide use and increased use of biological inputs Enhanced soil biodiversity and lower input costs. 8.9% Source Own elaboration based on studies from the systematic review (2025). Territorial context and production systems seemed to influence farmers’ perceptions of climate change, which, when mediated by socioeconomic and institutional factors, are associated with adoption of different adaptation strategies. Figure 4 schematically illustrates this dynamic. 4. Discussion The predominance of mixed-methods approaches (Table 2 ) reflects researchers’ efforts to address both subjective dimensions of climate perception, such as farmers’ narratives, values, and beliefs, and measurable variables related to climate change impacts and adaptive responses (Beristain-Moreno et al., 2024; Mbwambo et al., 2021 ; Jezeer et al., 2019 ). Qualitative methods enable identification of empirical perceptions constructed through direct observation of nature and knowledge acquired through lived experience (Beristain-Moreno et al., 2024; Bro, 2020 ). Studies adopting this approach generally emphasize intuitive, historical, and affective dimensions of human perception, thereby providing deeper understanding of relationships between farmers and their environments (Altea, 2019; Viguera et al., 2018). This perspective reinforces the view that individual experiences with nature play a significant role in shaping climate perceptions and in how people interpret environmental change. In contrast, quantitative approaches are valuable for risk assessment, particularly regarding crop productivity and decision-making. They allow identification of generalizable patterns and establishment of statistical relationships between perception and adoption, or non-adoption, of adaptive practices (Lan et al., 2023 ). However, when perception is treated as an isolated variable without examining motivations and meanings underlying responses, quantitative approaches may overlook the social and cultural contexts in which such perceptions are formed (Moreno et al., 2024). This limitation may help explain the relatively small number of exclusively quantitative studies identified in this review (Table 2 ). The mixed-methods approach effectively integrates subjective and measurable evidence, establishing itself as the predominant methodology among analyzed articles. Jezeer et al. ( 2019 ), for example, linked climate perceptions with livelihood assets and selection of sustainable practices in Peru. This finding illustrates how human perceptions can be connected with empirical data to explain climate-related decisions. Likewise, Quiroga et al. (2019) showed that distinct levels of perception directly influenced adoption of diversification strategies in Nicaragua coffee farming. In this case, the mixed-methods approach enabled correlation between farmers’ perceptions and their economic and agricultural decisions. By integrating qualitative evidence with quantitative data, mixed methods provide a broader analytical framework for understanding complex issues such as climate adaptation (Shinbrot et al., 2019; Le et al., 2020 ). Among methodological tools employed (Table 3 ), interviews, structured and semi-structured questionnaires, regression models, and analysis of variance were most prominent, appearing in more than half of studies that adopted mixed methods. Understanding coffee growers’ perceptions of climate change requires consideration of territorial, socio-environmental, and production contexts in which they are embedded. Although research methods strongly influence how perception is interpreted, factors such as geographic location, agroecosystem type, and socioeconomic structure shape not only production strategies but also how climate risks are perceived and addressed (Jawo et al., 2023 ; Tesfaye, 2021). Most studies were conducted in countries of the Global South (Fig. 3 ), widely recognized as major coffee producers. Vietnam, the world’s second-largest coffee producer and responsible for 37.7% of global C. canephora production (Brazil, 2025), and Ethiopia, the largest producer in Africa and responsible for approximately 5% of global C. arabica production (USDA, 2023), were particularly prominent in number of publications. Although Brazil is the leading global coffee producer, accounting for 35.5% of global C. arabica production and 22.25% of C. canephora production (Brazil, 2025), it was the focus of only one study assessing coffee growers’ perceptions of climate change (Venancio et al., 2020). This finding reveals a substantial research gap on the topic when compared with other producing countries. Among cropping systems, the hybrid/transitional system, which combines conventional and agroecological practices, was predominant among retrieved studies (Table 4 ) and was mainly concentrated in Latin America, particularly Mexico, Guatemala, and Colombia (Table 5 ). Adoption of hybrid systems suggests a transition toward more sustainable agricultural models driven by climate change impacts, in which farmers implement adaptation strategies often stimulated by lived experience, participation in farmer associations (Shinbrot et al., 2019), and exchange of information (Eise, Lambert, and Wiemere, 2021). Farmers operating under hybrid systems frequently adopt strategies for both adaptation and income generation. Notable practices include shade management, in which companion tree species are introduced to provide canopy cover for coffee plants (Jezeer et al., 2019 ), introduction of cultivars more resistant to prolonged drought (Harvey et al., 2018), and diversification of crop species to generate income and enhance food supply (Sánchez et al., 2023 ; Hernández-Castán and Calderón, 2023 ). These practices indicate progress toward production systems that are more climate-adapted and better integrated into territorial contexts. The agroecological system was the second most frequently studied category (Table 4 ) and was concentrated in East Africa, especially Ethiopia and Tanzania (Table 5 ). Agroecological practices are widely adopted by rural communities that structure their agroecosystems through traditional knowledge and well-established socioecological relationships (Altea, 2019; Mbwambo et al., 2021 ; Capitani et al., 2018). In some regions of Ethiopia, coffee cultivation is integrated into natural forests or along fragments and edges of native vegetation (Gomm et al., 2024 ), forming multifunctional landscapes that may simultaneously support environmental conservation and agricultural production. In this context, agroecological production extends beyond technical and productive functions, assuming a vital role in management of common resources and sociocultural continuity of local communities (Ayal et al., 2023 ). The conventional or intensive production system was among the least cited in reviewed articles, ranking above only traditional systems (Table 4 ), and occurred mainly in Southeast Asia, particularly Vietnam and Indonesia (Table 5 ). Vietnam and Indonesia together account for more than 24% of global coffee production (FAO, 2023). This output is largely based on conventional systems characterized by monoculture, high input dependence, and strong orientation toward export markets (Le et al., 2020 ; Nguyen and Drakou, 2021 ; Pham et al., 2020). These findings indicate that studies addressing farmers’ perceptions of climate change are more strongly associated with hybrid/transitional and agroecological systems, which are generally linked to family farming and small- to medium-scale producers. For this production profile, conventional systems are often regarded as costly and unsustainable from both environmental and economic perspectives (Lan et al., 2023 ; Torres-Lezama, 2012 ). The limited number of studies on conventional systems, such as Nguyen and Drakou ( 2021 ) and Le et al. ( 2020 ), examined broader grower profiles and regions, while treating climate perception and adaptation strategies as secondary issues. Although less frequently cited, traditional systems identified in reviewed studies were concentrated mainly in Ethiopia (Table 4 ). Under climate change pressures, these systems remain important for food sovereignty, biodiversity conservation (Ayalew et al., 2024 ), and maintenance of local ways of life (Hasibuan et al., 2023 ). Climate perception is not homogeneous, but rather emerges from interaction among local experience, structural conditions, and existing support networks (Jaramillo-Villanueva et al., 2022 ). Perception and adaptation are contextual processes shaped by factors such as access to technology, social organization, land tenure structure, and local climatic conditions. Therefore, methodological approaches that ignore territorial diversity tend to oversimplify a complex phenomenon and may compromise understanding of resilience dynamics in coffee farming under climate change (Sánchez et al., 2023 ; Jezeer et al., 2019 ). Studies conducted in Vietnam (Table 6 ) indicate that water deficit, deforestation, and rising temperatures influence producers’ perceptions and encourage adaptive practices (Pham et al., 2020). However, adoption of these strategies is constrained by barriers such as limited access to credit and resistance among smallholders to adopt shaded or diversified systems because of concerns over reduced cultivated areas and lower economic returns (Pham et al., 2020; Nguyen and Drakou, 2021 ; Byrareddy et al., 2021). Access to markets and technologies (Table 6 ) also proved to be a determining factor in expanding adaptive capacity in coffee farming, as reported by Jaramillo-Villanueva et al. ( 2022 ), Sánchez and Bello (2020), and Shinbrot et al. (2019). Concern over low coffee prices and high input costs often meant that, although climate change was recognized as a central issue, it was not always perceived as the most immediate challenge. Social and productive organization (Table 6 ), particularly through cooperatives, favors development of collective adaptation strategies such as information networks, workshops, technical training, and participation in fair-trade schemes (Eise, Lambert, and Wiemer, 2021; Borsky and Spata, 2017; Barrucand, Vieira, and Canziani, 2016). In Ethiopia, Jawo et al. ( 2023 ) reported that producers linked to cooperatives had greater access to climate information, sustainability certification programs, and community initiatives focused on environmental conservation, thereby strengthening collective response capacity to climate change. Land tenure structure, often characterized by smallholdings, also appears to influence how farmers perceive climate risks, as observed in studies conducted in countries such as Rwanda and Ethiopia (Ayalew et al., 2025; Gather and Wollni, 2024 ). On smallholdings, perception tends to be more empirical, grounded in direct environmental observation and traditional knowledge developed collectively. Farmers’ perceptions of climate change are a key element for understanding adaptation processes in agricultural systems. Rather than being merely technical phenomenon, perception is socially constructed and mediated by practical experience, territorial conditions, and ability to access information, collective networks, and public policies (Eise et al., 2021; Mbwambo et al., 2021 ). The reviewed studies indicate that coffee growers’ climate perceptions generally arise from direct experience with extreme events and visible environmental changes, such as altered rainfall and temperature patterns, increased incidence of pests and diseases, reduced productivity, and impacts on household income (Jezeer et al., 2019 ; Quiroga et al., 2020; Ayal et al., 2023 ). This empirical form of perception is especially pronounced in regions with limited access to climate monitoring technologies, which may directly affect agricultural management conditions of these communities, as reported in studies conducted in Ethiopia and Uganda (Jawo et al., 2023 ; Bartl, 2023). However, farmers do not perceive or interpret climate change uniformly. The intensity and nature of perception are conditioned by factors such as years in farming, education level, access to technical information, and participation in collective organizations (Adhikari et al., 2019). Farmers with higher educational attainment and stronger integration into cooperatives tend to recognize signs of climate change more readily and adopt better-structured adaptation strategies, such as crop diversification, agroforestry systems, and soil conservation practices (Jawo et al., 2023 ; Jezeer et al., 2019 ). Despite clear recognition of climate risks, studies indicate that perception does not automatically translate into adaptive action (Quiroga et al., 2020; Sánchez et al., 2023 ). Economic, cultural, and institutional barriers often limit farmers’ ability to implement effective responses, highlighting need for public policies that strengthen connections between perception and action (Asayehegn et al., 2017). The analysis also suggests that farmers who interpret climate change systemically, understanding it as part of a broader process of environmental transformation, tend to adopt more diversified and long-term strategies (Adhikari et al., 2019; Mbwambo et al., 2021 ). In contrast, more fragmented perceptions focused only on isolated extreme events tend to generate short-term responses that are insufficient to build resilience (Wagner et al., 2021). The adaptation strategies (Table 7 ) adopted by farmers across different territorial contexts reflect both local perceptions of risk and resources available in each region. Agroforestry systems (AFS) and shade management were the most recurrent adaptation strategies identified in reviewed studies. Incorporation of tree species into production systems is described as a practice adopted to regulate microclimate, reduce crop exposure to high temperatures and intense solar radiation, and promote conservation of air and soil moisture (Beristain-Moreno et al., 2024; Palacios et al., 2023; Venancio et al., 2020). In this context, shading is presented as an important strategy for coping with climatic variability (Jezeer et al., 2019 ; Bro, 2020 ). In the reviewed studies, this practice was treated not only as an agronomic technique but also as an integrated ecological response to climatic pressures. Research conducted in Ethiopia by Ayal et al. ( 2023 ) and Jawo et al. ( 2023 ) indicates that farmers widely adopt shading to mitigate rising temperatures and irregular rainfall, highlighting strong dependence of coffee systems on natural resources and local ecological processes. Diversification of production, including cultivation of crops other than coffee, aims to promote more stable and economically sustainable farming systems. In Rwanda, Gather and Wollni ( 2024 ) reported adoption of income diversification strategies through planting of additional crops and shade trees, motivated by previous experiences with extreme weather events. Incorporation of distinct species into production systems helps reduce vulnerability to climatic shocks, expand income sources, and promote ecological balance and sustainable soil management (Ha et al., 2024). Ecological soil and water management also emerged as an important adaptive strategy. Practices such as maintaining permanent ground cover, applying organic fertilizers, intercropping, terracing, and rainwater harvesting were described as measures intended to increase soil water retention, reduce losses caused by surface runoff, and improve water availability under irregular rainfall conditions (Rodríguez-Barillas et al., 2024; Hernández-Castán and Calderón, 2023 ; Adhikari et al., 2021). Water storage and reuse, in turn, appeared as initiatives aimed at increasing water autonomy within production systems (Mbwambo et al., 2021 ; Adhikari et al., 2021). Collectively, these actions indicate a strategy directed toward strengthening ecological foundations of agroecosystems, based on recognition that soil and water conservation are central elements in adaptation to climate change. Adoption of resistant varieties has proven to be a relevant measure in countries such as Mexico and Tanzania. As reported by Jaramillo-Villanueva et al. ( 2022 ) and Mbwambo et al. ( 2021 ), producers have invested in cultivars that are more tolerant to water deficit and fungal diseases such as coffee rust ( Hemileia vastatrix Berkeley and Broome). Adoption of these varieties represents an effort to maintain productivity under climate instability, particularly in response to rising temperatures and irregular rainfall (Beristain-Moreno et al., 2024). Access to credit and collective organizations, such as cooperatives, also emerged as strategic factors in climate adaptation. As noted by Borsky and Spata (2017), cooperatives and associations increase capacity to invest in adaptive technologies while facilitating access to differentiated markets and certification programs. Institutional coordination, combined with mechanisms such as fair trade, strengthens social capital and broadens opportunities to respond to climate-related challenges (Rodríguez-Barillas et al., 2024). Reduced pesticide use associated with adoption of biological inputs was also highlighted in reviewed studies as an adjustment strategy in coffee agroecosystems. This transition is described as an initiative aimed at reducing dependence on external inputs and mitigating environmental impacts (Jezeer et al., 2019 ; Mbwambo et al., 2021 ), while promoting soil biodiversity and strengthening ecological processes linked to biological balance. In addition, partial replacement of chemical inputs with biological alternatives is frequently cited as a measure intended to reduce production costs, especially under rising prices of fertilizers and pesticides (Tucker et al., 2009 ; Jawo et al., 2023 ). Understanding responses to climate change depends largely on relationships among territory, farmers’ perceptions, and adaptation strategies. This connection shows that local responses to climate change are not determined solely by environmental conditions, but are also strongly shaped by sociocultural, economic, and institutional factors that influence farmers’ capacity to act (Fig. 4 ). Coffee growers’ perceptions are largely built from empirical experiences, such as observing irregular rainfall patterns and temperature fluctuations (Tesfaye, 2022 ; Jawo et al., 2023 ). However, recognition of climate change does not necessarily lead to adoption of adaptive measures. Implementation of adaptive responses is conditioned by structural factors such as availability of financial resources, access to credit, technical assistance, and market integration (Jezeer et al., 2019 ; Mbwambo et al., 2021 ; Nguyen et al., 2021). These elements function as mediators between perception of environmental change and farmers’ practical capacity to respond. Therefore, even when environmental changes are clearly recognized, implementation of adaptation strategies may remain limited by institutional, economic, and organizational constraints. 5. Conclusions Most research on climate perception in coffee farming adopts mixed-methods approaches that integrate qualitative and quantitative techniques to capture subjective variables, such as farmers’ narratives and experiences, together with measurable variables, such as productivity and incidence of pests and diseases. This methodological choice reflects an effort to understand not only what farmers observe and perceive, but also how they construct interpretations and strategies in response to challenges posed by climate change. Territorial conditions associated with production systems are determining factors in the capacity of coffee farmers to adopt adaptive strategies under climate-related pressures. Farmers’ perceptions, built through direct observation of environmental change, are mediated by socioeconomic, structural, and territorial factors such as access to credit, technical assistance, and participation in collective organizations, including associations and cooperatives. These elements are essential for transforming perceptions into concrete adaptation strategies and for strengthening both productivity and community resilience. The reviewed studies demonstrate progress in integrating subjective dimensions, including lived experiences, observations, and local knowledge, with technical analyses based on climatic and agronomic data. However, further investigations are still needed to better connect farmers’ perceptions, management practices, adaptation strategies, and effects of these actions on resilience of their agroecosystems. Declarations Disclosure Statement The authors declare no potential conflicts of interest. Ethical Approval This article does not contain any studies involving human participants or animals. Author Contribution Author Contributions[Author CA], [Author AF], [Author AG], and [Author LM] contributed equally to the conception and planning of the study, as well as to the development of the systematic review protocol. Authors [Author CA] and [Author AG] independently performed the literature search, screening, and data extraction to ensure methodological rigor and reliability.[Author LM] acted as a third reviewer during the screening phase, resolving disagreements when consensus could not be reached.All authors contributed to data interpretation, manuscript writing, and critical content review. All authors read and approved the final version of the manuscript. Acknowledgement AcknowledgmentsThe authors thank the Coordination for the Improvement of Higher Education Personnel (CAPES), Brazil, under Funding Code 001. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9545672","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":632558600,"identity":"f9883dfc-b689-45e2-9f7f-36c2d74ade13","order_by":0,"name":"Caroline Alves Ferreira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYLCCBwZAgh3MtKnvB1EJBQS0JIC0MIOZaYwzG2AieLUwwLUcZtxwAETj0cLffvbhg4SCO3L8zTyGDz78OcxsfH514gegU+X5xQ5g1SJxJt3YIMHgmbHEYR5jw5lt6WxmN95ulgA6zHDm7ASsWgwY0tiACg4nNhxmS5PmbbDmMbtxdgNIS4LBbRxa+J+BtdTPP8yW/vvPH2YJ4xlnN//Aq0UCYgsQMR9jZmBzNjDg792G1xaJG8+YgX45bLjxMPNhyd62tASJG7zbLBIMJHD6hb8/jREUUPJyxxsbP/z4Y5PA3392880fFTby/NLYtWCzGKxSgljlYIsPkKJ6FIyCUTAKRgAAAJUkX1yPxUq7AAAAAElFTkSuQmCC","orcid":"","institution":"Federal University of São Carlos - UFSCar","correspondingAuthor":true,"prefix":"","firstName":"Caroline","middleName":"Alves","lastName":"Ferreira","suffix":""},{"id":632558602,"identity":"3401cf78-d223-47db-a75a-5402d3a522d2","order_by":1,"name":"Anastácia Fontanetti","email":"","orcid":"","institution":"Federal University of São Carlos - UFSCar","correspondingAuthor":false,"prefix":"","firstName":"Anastácia","middleName":"","lastName":"Fontanetti","suffix":""},{"id":632558606,"identity":"e47e875b-8964-4a60-b432-ee0f0d0ed507","order_by":2,"name":"Anderson Souza Gallo","email":"","orcid":"","institution":"Federal University of São Carlos - UFSCar","correspondingAuthor":false,"prefix":"","firstName":"Anderson","middleName":"Souza","lastName":"Gallo","suffix":""},{"id":632558607,"identity":"d66d9006-fbc1-4604-bc9b-5135347b67e2","order_by":3,"name":"Leonardo Pinto Magalhães","email":"","orcid":"","institution":"Federal University of São Carlos - UFSCar","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"Pinto","lastName":"Magalhães","suffix":""}],"badges":[],"createdAt":"2026-04-27 19:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9545672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9545672/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109067643,"identity":"203a1a9f-3ab4-4907-aa80-c4f213f38730","added_by":"auto","created_at":"2026-05-12 09:58:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":702069,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological summary of the PRISMA-RR flowchart, comprising four main phases:(I) Identification, (II) Screening, (III) Eligibility, and(IV) Evidence Retrieval.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/6c2771e81c56e477fb09bcaa.png"},{"id":109067796,"identity":"8eeb1bd8-9883-4988-8d11-0c116fa41359","added_by":"auto","created_at":"2026-05-12 10:01:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20623,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of publications included in the review.\u003c/p\u003e\n\u003cp\u003eSource: Authors’ elaboration based on studies included in the systematic review (2025).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/21857539c43f080ae2fadc26.png"},{"id":108828453,"identity":"966cfb7c-7bb2-4d14-9dd6-1285311e63bc","added_by":"auto","created_at":"2026-05-08 18:32:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160903,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of analyzed studies by region and country.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/cf54296848c841862df5bbbb.png"},{"id":108977522,"identity":"3c2c414f-59fb-486f-8cb8-e931763e0780","added_by":"auto","created_at":"2026-05-11 11:31:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":646437,"visible":true,"origin":"","legend":"\u003cp\u003eDynamics of farmers’ perceptions of climate change and adaptation strategies in coffee farming.\u003c/p\u003e\n\u003cp\u003eSource: Author’s elaboration.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/214eaeadefa0ca2577ab0a8e.png"},{"id":109069248,"identity":"5ae3b978-2888-4183-9e5a-4d46cfc205dd","added_by":"auto","created_at":"2026-05-12 10:21:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1860152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/38544790-cb7c-4f64-b19c-7698c41a5bb3.pdf"},{"id":108828451,"identity":"965662c1-322d-406e-9829-e38d6bab2d95","added_by":"auto","created_at":"2026-05-08 18:32:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":177454,"visible":true,"origin":"","legend":"","description":"","filename":"reviewmatrix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9545672/v1/dbaa27d494385685bfb1f8c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Perceptions and adaptation strategies to climate change in coffee farming: a systematic review","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eCoffee farming is an agricultural activity of major global importance because of its economic and social contributions. An estimated 17.5\u0026nbsp;million family farmers worldwide depend on coffee production for their livelihoods (Pham et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBrazil, the world\u0026rsquo;s largest producer, accounts for 35.5% of \u003cem\u003eCoffea arabica\u003c/em\u003e production and 22.25% of \u003cem\u003eCoffea canephora\u003c/em\u003e production. Vietnam leads global \u003cem\u003eC. canephora\u003c/em\u003e production with 37.7%, whereas Colombia contributes 13.20% of global \u003cem\u003eC. arabica\u003c/em\u003e production (BRAZIL, 2025). Coffee farming also supports development in rural communities and producing regions by generating employment and income in countries such as Ethiopia, Mexico, and Rwanda (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tesfaye, 2021; Bro, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, coffee farming faces increasing risks associated with climate change across producing countries (Gomes et al., 2020; Dias, Martins, and Martins, 2024). The most widely cultivated coffee species require distinct climatic conditions for optimum development. \u003cem\u003eC. arabica\u003c/em\u003e, commonly used for specialty coffee production, performs best under mean annual temperatures of 18\u0026ndash;22\u0026deg;C and annual rainfall of 1,200\u0026ndash;2,000 mm (DaMatta et al., 2007). \u003cem\u003eC. canephora\u003c/em\u003e is generally more resilient and productive, although often considered lower in beverage quality, and performs best under mean temperatures of 22\u0026ndash;28\u0026deg;C (Magrach and Ghazoul, 2015) and annual rainfall of 1,200\u0026ndash;1,800 mm (de Souza et al., 2025).\u003c/p\u003e \u003cp\u003eUnder future climate scenarios, projections for Brazil indicate yield losses in \u003cem\u003eC. arabica\u003c/em\u003e caused by thermal stress and greater susceptibility to infestation by coffee leaf miner (\u003cem\u003eLeucoptera coffeella\u003c/em\u003e Gu\u0026eacute;rin-M\u0026egrave;neville) and coffee rust (\u003cem\u003eHemileia vastatrix\u003c/em\u003e Berkeley and Broome) (Dias; Martins; Martins, 2024). According to these projections, approximately 35% to 75% of Arabica plantations in the country may be negatively affected and could become economically unviable during the twenty-first century. In addition, Venancio et al. (2020) reported that prolonged droughts and elevated temperatures may reduce \u003cem\u003eC. canephora\u003c/em\u003e productivity by up to 41%.\u003c/p\u003e \u003cp\u003eHow farmers perceive and respond to climate change is central to understanding adaptation dynamics within agricultural communities and coffee agroecosystems (Nguyen et al., 2025). Such perceptions do not always align with meteorological records, as they are shaped by farmers\u0026rsquo; empirical experiences and direct observations of climatic impacts (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tesfaye, 2021).\u003c/p\u003e \u003cp\u003eStudies conducted in producing regions such as Mana, southwestern Ethiopia, and Puebla, Mexico, show that perceptions of increasing temperatures and declining rainfall are critical drivers of adaptation strategies (Tesfaye, 2021; Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Vietnam, recognition of drought periods by 91% of farmers and excessive rainfall by 23% was decisive for adopting adaptation measures (Lan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Reported strategies include agroforestry systems, shade-tree management, crop diversification, and adjustments in agricultural calendars (Shinbrot et al., 2019; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Rwanda, Gather and Wollni (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that farmers\u0026rsquo; risk perceptions varied mainly according to prior experiences with climatic events. Farmers who perceived stronger climatic changes were more likely to adopt shading practices and diversify income through cultivation of additional crops (Gather and Wollni, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, limited perception of climate change may reflect insufficient knowledge of its effects on coffee production, thereby constraining adoption of adaptive strategies (Hasibuan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalyzing coffee growers\u0026rsquo; perceptions of climate change requires consideration of territorial, socio-environmental, and production contexts. Geographic location, agroecosystem type, and socioeconomic structure influence both how climate risks are understood and which production and adaptation strategies are adopted. Therefore, understanding climate adaptation in coffee farming requires recognition that farmers\u0026rsquo; responsiveness is directly shaped by these variables, which may differ substantially across territories (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tesfaye, 2021).\u003c/p\u003e \u003cp\u003eAlthough climate change adaptation strategies have been widely documented, a gap remains in scientific literature regarding farmers\u0026rsquo; perceptions, particularly how such perceptions influence adoption of adaptive practices. This gap is especially relevant because available evidence indicates that perception of climate risk is a key determinant of decision-making and implementation of adaptation measures.\u003c/p\u003e \u003cp\u003eAccordingly, this study aimed to identify methodological approaches used in research on coffee agroecosystems and climate change, analyze adaptation strategies adopted, and understand factors influencing coffee growers\u0026rsquo; perceptions of climate change across different territorial contexts.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eFor the systematic literature review, the core collection of the Web of Science platform, originally developed by Thomson Reuters \u0026ndash; Institute for Scientific Information (ISI) and currently maintained by Clarivate Analytics, together with the Scopus database by Elsevier, were used. The review was conducted according to guidelines of the PRISMA-RR protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Rapid Reviews) (Stevens et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This protocol is particularly relevant for interdisciplinary and rapidly evolving topics, such as climate change and agricultural adaptation, because it helps ensure that evidence syntheses are robust, comparable, and useful for guiding technical and policy decisions (Stevens et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mouratiodou et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePRISMA-RR organizes the review process into four phases: (I) literature identification, (II) screening, (III) eligibility assessment, and (IV) retrieval of conclusive evidence. In Phase I, the identification stage, the following search strategy was applied: Documents-Topic [(\"climate change\" AND \"coffee\" AND \"percept\")]**. Keywords were defined through exploratory searches conducted within the databases. The terms climate change, coffee, and percept were selected because they represent core concepts of this investigation, spanning climatic impacts on coffee farming and subjective perceptions associated with farmers\u0026rsquo; experiences and management practices. The Boolean operator AND restricted retrieval to articles containing all specified terms simultaneously (Shah et al., 2021), thereby ensuring direct relevance to each theme. The wildcard asterisk (*) was used to capture word variants, whereas quotation marks ensured exact phrase matching for specified terms (Mejia et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Publications available in all languages up to March 2025 were considered.\u003c/p\u003e \u003cp\u003eIn Phase II, the screening stage, inclusion and exclusion criteria were applied (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Review articles, book chapters, editorial materials, conference papers, and duplicate records were excluded because they did not provide original data and/or presented only partial results related to study objectives. In parallel, titles and abstracts of selected records were examined to confirm whether they effectively addressed the proposed topic and should therefore be included in the review.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInclusion and exclusion criteria applied during the screening phase.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOriginal research articles only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReview articles, book chapters, editorial materials, conference papers, and duplicate records.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeer-reviewed publications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-peer-reviewed publications\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudies presenting original information on farmers\u0026rsquo; perceptions of climate change\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies not presenting original information on farmers\u0026rsquo; perceptions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-text articles available for access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-text articles not available for access\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eThe author (2025).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn Phase III, corresponding to the eligibility stage, previously selected articles were organized and subjected to full-text review to confirm their alignment with study objectives. At this stage, the treatment of farmers\u0026rsquo; perceptions of climate change in coffee agroecosystems was assessed, considering both direct and indirect expressions of such perceptions.\u003c/p\u003e \u003cp\u003eIn addition, information reported in each study was examined, with particular attention to how perceptions were described and analyzed. Articles that did not address these aspects were excluded from the final dataset.\u003c/p\u003e \u003cp\u003eIn Phase IV, the evidence retrieval stage, selected articles were organized in a spreadsheet using Microsoft Excel containing the following information: (a) reference data, including title, authors, publication year, country of publication, objectives, abstract, and conclusions; (b) territories analyzed, including country and region where the study was conducted; (c) categorization of agroecosystems; (d) methodological approaches, including classification as qualitative, quantitative, or mixed methods, and tools applied (interviews, questionnaires, focus groups, statistical tests, or combinations of these tools); (e) farmers\u0026rsquo; perceptions, including assessment of coffee growers\u0026rsquo; perceptions of climate change; and (f) adaptation strategies, including actions and practices adopted in response to climate-related impacts.\u003c/p\u003e \u003cp\u003eAll protocol stages were conducted collaboratively by two researchers, thereby increasing methodological rigor and reliability. During the screening phase, disagreements were resolved through final evaluation by a third reviewer.\u003c/p\u003e \u003cp\u003eData were systematically organized and tabulated to enable detailed analysis of approaches employed in reviewed studies. Initially, 151 records were identified, including 59 from Web of Science and 92 from Elsevier Scopus. These records were assessed according to steps described above. After application of PRISMA-RR criteria (Stevens et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the final dataset comprised 53 articles from both databases. A summary of the methodological procedure based on the PRISMA-RR flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSource\u003c/strong\u003e \u003cp\u003eThe author (2025).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBased on evidence retrieval (Phase IV), analysis focused on central elements of research question and study problem. Accordingly, results are discussed according to three main axes: (I) methodological approaches and tools applied in the studies; (II) territories and agroecosystems; and (III) farmers\u0026rsquo; perceptions of climate change and adaptation strategies.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe temporal distribution of publications indicates more consistent growth in this topic from 2017 onward, with clear intensification in recent years and notable peaks after 2020. In 2025, a reduction in the number of studies was observed, possibly associated with the temporal cutoff adopted for data collection rather than reflecting decreased scientific interest in the subject (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost of the 53 articles included in the systematic review were published in English (n\u0026thinsp;=\u0026thinsp;47), followed by publications in Spanish (n\u0026thinsp;=\u0026thinsp;6) (Appendix 1).\u003c/p\u003e\n\u003cp\u003eRegarding methodological approaches, studies using mixed methods predominated, representing 38 articles (70%). Exclusively qualitative approaches were identified in 13 studies (23%), whereas exclusively quantitative approaches were found in only 2 articles (3% of the total) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMethodological approaches identified in the reviewed articles.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethodological approach\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePerception analysis\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of articles\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMixed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntegrates qualitative and quantitative methodological approaches and research tools\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHern\u0026aacute;ndez-Cast\u0026aacute;n and Tapia-Hervert Calder\u0026oacute;n, (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Quiroga et al. (2020); Teodoro et al. (2024); Gather and Wollni, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Ayalew et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); H\u0026aacute; et al. (2024); Rodr\u0026iacute;guez-Barillas, Poortvliet, Klerkx (2024); Bartl, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Gomm et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Rodr\u0026iacute;guez-Barillas, Poortvliet, Klerkx (2023); Hasibuan et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Ayal et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Jawo et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); S\u0026aacute;nchez et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Tesfaye (2021); Jaramillo-Villanueva et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Pons et al. (2021); Tran and Chen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Eise, Lambert and Wiemer (2021); Mbwambo, Maurice and Tarimo (2021); Nguyen and Drakou (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Adhikari et al. (2021); Jezeer et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Capitani et al. (2018); Asayehegn et al. (2017); Barrucand, Vieira and Canziani (2019); Quiroga, Cristina, Su\u0026aacute;rez and Sol\u0026iacute;s (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e); Nghiepa et al. (2024); Beristain-Moreno et al. (2024); Lan et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Msuya and Mahonge (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Byrareddy et al. (2021); Wagner et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Le et al., (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Shinbrot et al. (2019); Harvey et al. (2018); Chengappa, Devika and Rudragouda (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); Eakin et al. (2013); Torres-Lezama et al. (2011).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQualitative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExclusively qualitative approaches and tools\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMoreno et al. (2024); Rodr\u0026iacute;guez-Barillas et al. (2024); Bro, (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Altea, (2020); Pham et al. (2020); Borsky and Spata, (2017). Eise, Lambert, Wiemer (2021); Borsky and Spata (2017); S\u0026aacute;nchez and Bello (2020); Gerlicz et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); Vigueira et al. (2018); Vigueira et al., (2019); de los R\u0026iacute;os and Almeida (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e); Palacios et al. (2023).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuantitative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExclusively quantitative approaches and tools\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLan et al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Venancio et al., (2024).\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Own elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eAmong tools employed in mixed-methods studies, the most frequent were interviews (n\u0026thinsp;=\u0026thinsp;22; 57.9%), structured and semi-structured questionnaires (n\u0026thinsp;=\u0026thinsp;21; 55.3%), regression models and analysis of variance (n\u0026thinsp;=\u0026thinsp;21; 55.3%), focus groups (n\u0026thinsp;=\u0026thinsp;11; 28.9%), and descriptive statistics (n\u0026thinsp;=\u0026thinsp;12; 31.6%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn qualitative studies, interviews combined with direct observation of agroecosystems predominated (n\u0026thinsp;=\u0026thinsp;9; 69.2%), followed by workshops (n\u0026thinsp;=\u0026thinsp;3; 23.1%) and interactive games with participatory workshops (n\u0026thinsp;=\u0026thinsp;2; 15.4%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAmong the few studies with an exclusively quantitative focus, use of questionnaires (n\u0026thinsp;=\u0026thinsp;1; 50.0%), descriptive statistics, and mean comparison tests (n\u0026thinsp;=\u0026thinsp;2; 100.0%) was identified (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePredominant methodological tools used in mixed, qualitative, and quantitative approaches among the reviewed articles.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMethodological approach\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTools\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNumber of articles\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eMixed\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterviews\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStructured and semi-structured questionnaires\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRegression models and analysis of variance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFocus groups\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDescriptive statistics\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQualitative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInterviews combined with direct observation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInteractive games and participatory workshops\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQuantitative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuestionnaires\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDescriptive statistics and mean comparison tests\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u0026nbsp;\u003c/strong\u003eOwn elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eMost studies were conducted in Latin America (40%), Africa (26%), and Asia (21%). Among countries with the highest number of studies, Ethiopia (n\u0026thinsp;=\u0026thinsp;9), Vietnam (n\u0026thinsp;=\u0026thinsp;8), Mexico (n\u0026thinsp;=\u0026thinsp;7), and Costa Rica (n\u0026thinsp;=\u0026thinsp;4) were most prominent. In addition, three studies each were conducted in Colombia, Guatemala, Tanzania, Honduras, and Nicaragua (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eBased on reading and analysis of the studies, four categories of farming systems were identified: agroecological, conventional, hybrid/transitional, and traditional/subsistence (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAgroecological systems are characterized by adoption of sustainable practices such as ecological soil and water management, crop shading, agroforestry systems, crop diversification, and use of biological inputs. In contrast, conventional systems are based on intensive use of chemical inputs and monoculture, with little or no incorporation of sustainable practices such as ecological soil and water management. Hybrid or transitional systems combine agroecological elements with use of local resources, while still maintaining some dependence on conventional practices, such as occasional pesticide application. Finally, traditional or subsistence systems rely on local knowledge and maintain strong integration with surrounding ecosystems, prioritizing agroecological practices mainly directed toward household food security.\u003c/p\u003e\n\u003cp\u003eCategorization of production systems showed predominance of hybrid or transitional systems (44%), followed by agroecological (22%), conventional (14%), and traditional or subsistence systems (5%). It should also be noted that in 12% of articles analyzed, production systems could not be categorized because of insufficient information (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCategories of production systems and their frequency of occurrence in the reviewed studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eProduction Systems\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgroecological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGather and Wollni (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Gom et al. (2024); Ayal et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Jawo et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Tesfaye (2021); Mbwambo et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Altea (2019); Capitani et al. (2018); Barrucand, Vieira and Canziani (2016); Ayalew et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Beristain-Moreno et al. (2024); S\u0026aacute;nchez and Bello (2020); Shinbrot et al. (2018);\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLan et al., (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Jaramillo-Villanueva et al., (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Nguyen and Drakou (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Pham et al., (2020); Le et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Viguera et al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Chengappa, Devika and Rudragouda (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e); Torres-Lezama et al. (2012);\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHybrid/Transitional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHa et al. (2024); Rodr\u0026iacute;guez-Barillas, Poortvliet and Klerkx (2024); Bartl (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Teodoro et al. (2024); S\u0026aacute;nchez et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Hern\u0026aacute;ndez-Cast\u0026aacute;n and Tapia-Hervert Calder\u0026oacute;n (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Pons et al. (2021); Tran and Chen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Bro (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Adhikari et al. (2021); Quiroga et al. (2019); Jezzer et al. (2019); Asayehegn et al. (2017); Tucker, Eakin and Castellanos (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e); Nghiepa et al. (2024); Palacios et al. (2023); Msuya and Mahonge (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e); Byrareddy et al. (2021); Wagner et al. (2021); Gerlicz et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); Viguera et al. (2018); Harvey et al. (2018); Eakin et al. (2013).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTraditional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAyalew et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e); Hasibuan et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); de los R\u0026iacute;os and Almeida (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-categorized\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRodr\u0026iacute;guez-Barillas, Klerkx and Poortvliet (2023); Eise, Lambert and Wiemer (2021); Borsky and Spata (2017); Quiroga, Su\u0026aacute;rez and Sol\u0026iacute;s (2009); Rodr\u0026iacute;guez-Barillas, Klerkx and Poortvliet (2024); Lan et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Tran and Chen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Own elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eAnalysis of the regional distribution of production systems showed that hybrid systems predominated in Latin America, traditional/subsistence systems were more common in East Africa, Asia was characterized by intensive conventional systems, and some producing regions of Africa, particularly Ethiopia, showed a substantial presence of agroecological systems (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDistribution of production systems and their characteristics by region and country.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRegion\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePredominant production system\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountries\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLatin America\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHybrid/Transitional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMexico, Guatemala, Costa Rica, and Honduras\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHern\u0026aacute;ndez-Cast\u0026aacute;n and Calder\u0026oacute;n, (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); S\u0026aacute;nchez et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Gerlicz et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e); Viguera et al., (2018); Barillas, Poortvliet, Klerkx, (2024); Palacios et al. (2023); Tucker, Eakin and Castellanos (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e); Jezeer et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e); Bro (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e); Tran and Chen (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Pons et al. (2021).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEast Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTraditional/Subsistence\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEthiopia,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGomm et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ayalew et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAsia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConventional/Intensive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVietnam and Indonesia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLe, Le, Cowal, (2020); Pham et al., (2020); Nguyen and Drakou (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Lan et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEast Africa\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgroecological\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEthiopia and\u003c/p\u003e\n\u003cp\u003eTanzania\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGom et al. (2024); Ayal et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Tesfaye, (2021); Altea, (2019); Capitani et al., (2018); Jawo et al. (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e); Mbwambo et al., (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Capitani et al. (2018).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Own elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eAnalysis of the reviewed articles allowed identification of four territory-related factors that influence farmers\u0026rsquo; perceptions of and adaptation to climate change in coffee farming. In Vietnam, local climatic conditions and access to credit were reported as key determinants of farmers\u0026rsquo; perceptions and decision-making. In Mexico, access to markets and technologies increased capacity for adaptive responses. In Colombia, social organization and participation in cooperatives were identified as important facilitators of collective climate adaptation strategies (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eRelationship between territorial factors and their implications for farmers\u0026rsquo; perceptions of climate change.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTerritory-Related Factors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eImplications for perception and adaptation strategies\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eReferences\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClimate conditions, water deficit, and access to credit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInfluence farmers\u0026rsquo; perceptions and decision-making\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVietnam\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNguyen and Drakou (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); Byrareddy et al. (2021). Pham et al. (2020)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccess to markets and technology through cooperatives and associations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreases adaptative response capacity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMexico\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJaramillo-\u003c/p\u003e\n\u003cp\u003eVillanueva et al. (2022); S\u0026aacute;nchez and Bello (2020). Shinbrot et al. (2019).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial organization and cooperatives\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFacilitate collective adaptation strategies\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eColombia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEise, Lambert, Wiemer (2021); Barrucand, Vieira, Canziani (2016); Borsky and Spata, (2017).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLand tenure structure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInfluences how farmers perceive climate risks\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRwanda and Ethiopia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAyalew et al. (2025); Gather and Wollni, (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource: Own elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eAmong strategies adopted to adapt coffee agroecosystems to climate change, the most prominent were agroforestry systems and shade management (33.9%), diversification of agricultural crops (32.1%), ecological soil and water management (32.0%), use of resistant varieties (21.4%), access to credit, participation in cooperatives, and fair-trade arrangements (8.9%), and reduced pesticide use combined with greater adoption of biological inputs (8.9%) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab7\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMain adaptation strategies identified in coffee agroecosystems in response to climate change, expected actions, and frequency of reporting in the reviewed studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAdaptation Strategy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExpected Action\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eFrequency (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAgroforestry systems and shade management\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShading, soil conservation, improved water regulation, and a more favorable microclimate.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiversification of agricultural production\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduced vulnerability through multiple income sources.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEcological soil and water management\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIncreased biodiversity, greater soil water retention, and water reuse.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUse of resistant varieties\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCultivars tolerant to water deficit, pests, and diseases such as rust and cercosporiosis.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.4%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAccess to credit, cooperatives, and markets\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreater investment capacity in adaptive technologies and market access.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReduction in pesticide use and increased use of biological inputs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEnhanced soil biodiversity and lower input costs.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource\u0026nbsp;\u003c/strong\u003eOwn elaboration based on studies from the systematic review (2025).\u003c/p\u003e\n\u003cp\u003eTerritorial context and production systems seemed to influence farmers\u0026rsquo; perceptions of climate change, which, when mediated by socioeconomic and institutional factors, are associated with adoption of different adaptation strategies. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e schematically illustrates this dynamic.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe predominance of mixed-methods approaches (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reflects researchers\u0026rsquo; efforts to address both subjective dimensions of climate perception, such as farmers\u0026rsquo; narratives, values, and beliefs, and measurable variables related to climate change impacts and adaptive responses (Beristain-Moreno et al., 2024; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQualitative methods enable identification of empirical perceptions constructed through direct observation of nature and knowledge acquired through lived experience (Beristain-Moreno et al., 2024; Bro, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies adopting this\u003c/p\u003e \u003cp\u003eapproach generally emphasize intuitive, historical, and affective dimensions of human perception, thereby providing deeper understanding of relationships between farmers and their environments (Altea, 2019; Viguera et al., 2018). This perspective reinforces the view that individual experiences with nature play a significant role in shaping climate perceptions and in how people interpret environmental change.\u003c/p\u003e \u003cp\u003eIn contrast, quantitative approaches are valuable for risk assessment, particularly regarding crop productivity and decision-making. They allow identification of generalizable patterns and establishment of statistical relationships between perception and adoption, or non-adoption, of adaptive practices (Lan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, when perception is treated as an isolated variable without examining motivations and meanings underlying responses, quantitative approaches may overlook the social and cultural contexts in which such perceptions are formed (Moreno et al., 2024). This limitation may help explain the relatively small number of exclusively quantitative studies identified in this review (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe mixed-methods approach effectively integrates subjective and measurable evidence, establishing itself as the predominant methodology among analyzed articles. Jezeer et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), for example, linked climate perceptions with livelihood assets and selection of sustainable practices in Peru. This finding illustrates how human perceptions can be connected with empirical data to explain climate-related decisions. Likewise, Quiroga et al. (2019) showed that distinct levels of perception directly influenced adoption of diversification strategies in Nicaragua coffee farming. In this case, the mixed-methods approach enabled correlation between farmers\u0026rsquo; perceptions and their economic and agricultural decisions. By integrating qualitative evidence with quantitative data, mixed methods provide a broader analytical framework for understanding complex issues such as climate adaptation (Shinbrot et al., 2019; Le et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong methodological tools employed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), interviews, structured and semi-structured questionnaires, regression models, and analysis of variance were most prominent, appearing in more than half of studies that adopted mixed methods.\u003c/p\u003e \u003cp\u003eUnderstanding coffee growers\u0026rsquo; perceptions of climate change requires consideration of territorial, socio-environmental, and production contexts in which they are embedded. Although research methods strongly influence how perception is interpreted, factors such as geographic location, agroecosystem type, and socioeconomic structure shape not only production strategies but also how climate risks are perceived and addressed (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tesfaye, 2021).\u003c/p\u003e \u003cp\u003eMost studies were conducted in countries of the Global South (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), widely recognized as major coffee producers. Vietnam, the world\u0026rsquo;s second-largest coffee producer and responsible for 37.7% of global \u003cem\u003eC. canephora\u003c/em\u003e production (Brazil, 2025), and Ethiopia, the largest producer in Africa and responsible for approximately 5% of global \u003cem\u003eC. arabica\u003c/em\u003e production (USDA, 2023), were particularly prominent in number of publications.\u003c/p\u003e \u003cp\u003eAlthough Brazil is the leading global coffee producer, accounting for 35.5% of global \u003cem\u003eC. arabica\u003c/em\u003e production and 22.25% of \u003cem\u003eC. canephora\u003c/em\u003e production (Brazil, 2025), it was the focus of only one study assessing coffee growers\u0026rsquo; perceptions of climate change (Venancio et al., 2020). This finding reveals a substantial research gap on the topic when compared with other producing countries.\u003c/p\u003e \u003cp\u003eAmong cropping systems, the hybrid/transitional system, which combines conventional and agroecological practices, was predominant among retrieved studies (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and was mainly concentrated in Latin America, particularly Mexico, Guatemala, and Colombia (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdoption of hybrid systems suggests a transition toward more sustainable agricultural models driven by climate change impacts, in which farmers implement adaptation strategies often stimulated by lived experience, participation in farmer associations (Shinbrot et al., 2019), and exchange of information (Eise, Lambert, and Wiemere, 2021). Farmers operating under hybrid systems frequently adopt strategies for both adaptation and income generation. Notable practices include shade management, in which companion tree species are introduced to provide canopy cover for coffee plants (Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), introduction of cultivars more resistant to prolonged drought (Harvey et al., 2018), and diversification of crop species to generate income and enhance food supply (S\u0026aacute;nchez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hern\u0026aacute;ndez-Cast\u0026aacute;n and Calder\u0026oacute;n, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These practices indicate progress toward production systems that are more climate-adapted and better integrated into territorial contexts.\u003c/p\u003e \u003cp\u003eThe agroecological system was the second most frequently studied category (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and was concentrated in East Africa, especially Ethiopia and Tanzania (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Agroecological practices are widely adopted by rural communities that structure their agroecosystems through traditional knowledge and well-established socioecological relationships (Altea, 2019; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Capitani et al., 2018). In some regions of Ethiopia, coffee cultivation is integrated into natural forests or along fragments and edges of native vegetation (Gomm et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), forming multifunctional landscapes that may simultaneously support environmental conservation and agricultural production. In this context, agroecological production extends beyond technical and productive functions, assuming a vital role in management of common resources and sociocultural continuity of local communities (Ayal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe conventional or intensive production system was among the least cited in reviewed articles, ranking above only traditional systems (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and occurred mainly in Southeast Asia, particularly Vietnam and Indonesia (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVietnam and Indonesia together account for more than 24% of global coffee production (FAO, 2023). This output is largely based on conventional systems characterized by monoculture, high input dependence, and strong orientation toward export markets (Le et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen and Drakou, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pham et al., 2020).\u003c/p\u003e \u003cp\u003eThese findings indicate that studies addressing farmers\u0026rsquo; perceptions of climate change are more strongly associated with hybrid/transitional and agroecological systems, which are generally linked to family farming and small- to medium-scale producers. For this production profile, conventional systems are often regarded as costly and unsustainable from both environmental and economic perspectives (Lan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Torres-Lezama, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The limited number of studies on conventional systems, such as Nguyen and Drakou (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Le et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), examined broader grower profiles and regions, while treating climate perception and adaptation strategies as secondary issues.\u003c/p\u003e \u003cp\u003eAlthough less frequently cited, traditional systems identified in reviewed studies were concentrated mainly in Ethiopia (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Under climate change pressures, these systems remain important for food sovereignty, biodiversity conservation (Ayalew et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and maintenance of local ways of life (Hasibuan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate perception is not homogeneous, but rather emerges from interaction among local experience, structural conditions, and existing support networks (Jaramillo-Villanueva et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Perception and adaptation are contextual processes shaped by factors such as access to technology, social organization, land tenure structure, and local climatic conditions. Therefore, methodological approaches that ignore territorial diversity tend to oversimplify a complex phenomenon and may compromise understanding of resilience dynamics in coffee farming under climate change (S\u0026aacute;nchez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies conducted in Vietnam (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) indicate that water deficit, deforestation, and rising temperatures influence producers\u0026rsquo; perceptions and encourage adaptive practices (Pham et al., 2020). However, adoption of these strategies is constrained by barriers such as limited access to credit and resistance among smallholders to adopt shaded or diversified systems because of concerns over reduced cultivated areas and lower economic returns (Pham et al., 2020; Nguyen and Drakou, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Byrareddy et al., 2021).\u003c/p\u003e \u003cp\u003eAccess to markets and technologies (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) also proved to be a determining factor in expanding adaptive capacity in coffee farming, as reported by Jaramillo-Villanueva et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), S\u0026aacute;nchez and Bello (2020), and Shinbrot et al. (2019). Concern over low coffee prices and high input costs often meant that, although climate change was recognized as a central issue, it was not always perceived as the most immediate challenge.\u003c/p\u003e \u003cp\u003eSocial and productive organization (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), particularly through cooperatives, favors development of collective adaptation strategies such as information networks, workshops, technical training, and participation in fair-trade schemes (Eise, Lambert, and Wiemer, 2021; Borsky and Spata, 2017; Barrucand, Vieira, and Canziani, 2016). In Ethiopia, Jawo et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) reported that producers linked to cooperatives had greater access to climate information, sustainability certification programs, and community initiatives focused on environmental conservation, thereby strengthening collective response capacity to climate change.\u003c/p\u003e \u003cp\u003eLand tenure structure, often characterized by smallholdings, also appears to influence how farmers perceive climate risks, as observed in studies conducted in countries such as Rwanda and Ethiopia (Ayalew et al., 2025; Gather and Wollni, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On smallholdings, perception tends to be more empirical, grounded in direct environmental observation and traditional knowledge developed collectively.\u003c/p\u003e \u003cp\u003eFarmers\u0026rsquo; perceptions of climate change are a key element for understanding adaptation processes in agricultural systems. Rather than being merely technical phenomenon, perception is socially constructed and mediated by practical experience, territorial conditions, and ability to access information, collective networks, and public policies (Eise et al., 2021; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reviewed studies indicate that coffee growers\u0026rsquo; climate perceptions generally arise from direct experience with extreme events and visible environmental changes, such as altered rainfall and temperature patterns, increased incidence of pests and diseases, reduced productivity, and impacts on household income (Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Quiroga et al., 2020; Ayal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis empirical form of perception is especially pronounced in regions with limited access to climate monitoring technologies, which may directly affect agricultural management conditions of these communities, as reported in studies conducted in Ethiopia and Uganda (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bartl, 2023).\u003c/p\u003e \u003cp\u003eHowever, farmers do not perceive or interpret climate change uniformly. The intensity and nature of perception are conditioned by factors such as years in farming, education level, access to technical information, and participation in collective organizations (Adhikari et al., 2019). Farmers with higher educational attainment and stronger integration into cooperatives tend to recognize signs of climate change more readily and adopt better-structured adaptation strategies, such as crop diversification, agroforestry systems, and soil conservation practices (Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite clear recognition of climate risks, studies indicate that perception does not automatically translate into adaptive action (Quiroga et al., 2020; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Economic, cultural, and institutional barriers often limit farmers\u0026rsquo; ability to implement effective responses, highlighting need for public policies that strengthen connections between perception and action (Asayehegn et al., 2017).\u003c/p\u003e \u003cp\u003eThe analysis also suggests that farmers who interpret climate change systemically, understanding it as part of a broader process of environmental transformation, tend to adopt more diversified and long-term strategies (Adhikari et al., 2019; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, more fragmented perceptions focused only on isolated extreme events tend to generate short-term responses that are insufficient to build resilience (Wagner et al., 2021).\u003c/p\u003e \u003cp\u003eThe adaptation strategies (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) adopted by farmers across different territorial contexts reflect both local perceptions of risk and resources available in each region.\u003c/p\u003e \u003cp\u003eAgroforestry systems (AFS) and shade management were the most recurrent adaptation strategies identified in reviewed studies. Incorporation of tree species into production systems is described as a practice adopted to regulate microclimate, reduce crop exposure to high temperatures and intense solar radiation, and promote conservation of air and soil moisture (Beristain-Moreno et al., 2024; Palacios et al., 2023; Venancio et al., 2020). In this context, shading is presented as an important strategy for coping with climatic variability (Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bro, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the reviewed studies, this practice was treated not only as an agronomic technique but also as an integrated ecological response to climatic pressures. Research conducted in Ethiopia by Ayal et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Jawo et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicates that farmers widely adopt shading to mitigate rising temperatures and irregular rainfall, highlighting strong dependence of coffee systems on natural resources and local ecological processes.\u003c/p\u003e \u003cp\u003eDiversification of production, including cultivation of crops other than coffee, aims to promote more stable and economically sustainable farming systems. In Rwanda, Gather and Wollni (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported adoption of income diversification strategies through planting of additional crops and shade trees, motivated by previous experiences with extreme weather events. Incorporation of distinct species into production systems helps reduce vulnerability to climatic shocks, expand income sources, and promote ecological balance and sustainable soil management (Ha et al., 2024).\u003c/p\u003e \u003cp\u003eEcological soil and water management also emerged as an important adaptive strategy. Practices such as maintaining permanent ground cover, applying organic fertilizers, intercropping, terracing, and rainwater harvesting were described as measures intended to increase soil water retention, reduce losses caused by surface runoff, and improve water availability under irregular rainfall conditions (Rodr\u0026iacute;guez-Barillas et al., 2024; Hern\u0026aacute;ndez-Cast\u0026aacute;n and Calder\u0026oacute;n, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Adhikari et al., 2021).\u003c/p\u003e \u003cp\u003eWater storage and reuse, in turn, appeared as initiatives aimed at increasing water autonomy within production systems (Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Adhikari et al., 2021). Collectively, these actions indicate a strategy directed toward strengthening ecological foundations of agroecosystems, based on recognition that soil and water conservation are central elements in adaptation to climate change.\u003c/p\u003e \u003cp\u003eAdoption of resistant varieties has proven to be a relevant measure in countries such as Mexico and Tanzania. As reported by Jaramillo-Villanueva et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Mbwambo et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), producers have invested in cultivars that are more tolerant to water deficit and fungal diseases such as coffee rust (\u003cem\u003eHemileia vastatrix\u003c/em\u003e Berkeley and Broome). Adoption of these varieties represents an effort to maintain productivity under climate instability, particularly in response to rising temperatures and irregular rainfall (Beristain-Moreno et al., 2024).\u003c/p\u003e \u003cp\u003eAccess to credit and collective organizations, such as cooperatives, also emerged as strategic factors in climate adaptation. As noted by Borsky and Spata (2017), cooperatives and associations increase capacity to invest in adaptive technologies while facilitating access to differentiated markets and certification programs. Institutional coordination, combined with mechanisms such as fair trade, strengthens social capital and broadens opportunities to respond to climate-related challenges (Rodr\u0026iacute;guez-Barillas et al., 2024).\u003c/p\u003e \u003cp\u003eReduced pesticide use associated with adoption of biological inputs was also highlighted in reviewed studies as an adjustment strategy in coffee agroecosystems. This transition is described as an initiative aimed at reducing dependence on external inputs and mitigating environmental impacts (Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while promoting soil biodiversity and strengthening ecological processes linked to biological balance. In addition, partial replacement of chemical inputs with biological alternatives is frequently cited as a measure intended to reduce production costs, especially under rising prices of fertilizers and pesticides (Tucker et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding responses to climate change depends largely on relationships among territory, farmers\u0026rsquo; perceptions, and adaptation strategies. This connection shows that local responses to climate change are not determined solely by environmental conditions, but are also strongly shaped by sociocultural, economic, and institutional factors that influence farmers\u0026rsquo; capacity to act (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoffee growers\u0026rsquo; perceptions are largely built from empirical experiences, such as observing irregular rainfall patterns and temperature fluctuations (Tesfaye, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jawo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, recognition of climate change does not necessarily lead to adoption of adaptive measures.\u003c/p\u003e \u003cp\u003eImplementation of adaptive responses is conditioned by structural factors such as availability of financial resources, access to credit, technical assistance, and market integration (Jezeer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mbwambo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nguyen et al., 2021). These elements function as mediators between perception of environmental change and farmers\u0026rsquo; practical capacity to respond. Therefore, even when environmental changes are clearly recognized, implementation of adaptation strategies may remain limited by institutional, economic, and organizational constraints.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eMost research on climate perception in coffee farming adopts mixed-methods approaches that integrate qualitative and quantitative techniques to capture subjective variables, such as farmers\u0026rsquo; narratives and experiences, together with measurable variables, such as productivity and incidence of pests and diseases. This methodological choice reflects an effort to understand not only what farmers observe and perceive, but also how they construct interpretations and strategies in response to challenges posed by climate change.\u003c/p\u003e \u003cp\u003eTerritorial conditions associated with production systems are determining factors in the capacity of coffee farmers to adopt adaptive strategies under climate-related pressures.\u003c/p\u003e \u003cp\u003eFarmers\u0026rsquo; perceptions, built through direct observation of environmental change, are mediated by socioeconomic, structural, and territorial factors such as access to credit, technical assistance, and participation in collective organizations, including associations and cooperatives. These elements are essential for transforming perceptions into concrete adaptation strategies and for strengthening both productivity and community resilience.\u003c/p\u003e \u003cp\u003eThe reviewed studies demonstrate progress in integrating subjective dimensions, including lived experiences, observations, and local knowledge, with technical analyses based on climatic and agronomic data. However, further investigations are still needed to better connect farmers\u0026rsquo; perceptions, management practices, adaptation strategies, and effects of these actions on resilience of their agroecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003eThis article does not contain any studies involving human participants or animals.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions[Author CA], [Author AF], [Author AG], and [Author LM] contributed equally to the conception and planning of the study, as well as to the development of the systematic review protocol. Authors [Author CA] and [Author AG] independently performed the literature search, screening, and data extraction to ensure methodological rigor and reliability.[Author LM] acted as a third reviewer during the screening phase, resolving disagreements when consensus could not be reached.All authors contributed to data interpretation, manuscript writing, and critical content review. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgmentsThe authors thank the Coordination for the Improvement of Higher Education Personnel (CAPES), Brazil, under Funding Code 001. They also acknowledge CAPES for the postdoctoral fellowships awarded to the second and third authors (under process n\u0026deg; 88887.691467/2022-00).\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eThe data supporting the conclusions of this study are available within the article and its supplementary materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eADHIKARI, D. (2021). Climate change impacts and adaptation strategies in Trans-Himalaya region of Nepal. \u003cem\u003eJournal of Forest and Livelihood\u003c/em\u003e, Kathmandu, v. 20, n. 1, pp. 16\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eALTEA L. 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Percepciones de cambio clim\u0026aacute;tico y respuestas adaptativas de caficultores Costarricenses de peque\u0026ntilde;a escala. \u003cem\u003eAgronom\u0026iacute;a Mesoamericana\u003c/em\u003e, \u003cem\u003ev, 30\u003c/em\u003e, 333\u0026ndash;351.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWAGNER, S., JASSOGNE, L., PRICE, E., JONES, M., \u0026amp; PREZIOSI, R. (2022). Impact of Climate Change on the Production of \u003cem\u003eCoffea arabica\u003c/em\u003e at Mt. Kilimanjaro, Tanzania. \u003cem\u003eAgriculture v\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e, 53.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Family farming, Farmer perceptions, Coffee production systems, Agricultural resilience.","lastPublishedDoi":"10.21203/rs.3.rs-9545672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9545672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoffee farming is an activity of major socioeconomic relevance, particularly in countries of the Global South, where it represents a primary source of income for millions of smallholder farmers. Climate change directly affects coffee production and quality, with consequences for producing regions and communities that depend on this activity. This study aimed to identify, through a systematic literature review, methodological approaches used in research on coffee agroecosystems and climate change, to analyze adaptation strategies adopted, and to understand factors influencing coffee farmers\u0026rsquo; perceptions across different territorial contexts regarding climate change. The methodology followed the PRISMA-RR protocol, which organizes the systematic review process into four stages: (i) literature identification, (ii) screening, (iii) eligibility assessment, and (iv) retrieval of conclusive evidence. Searches were conducted on the Web of Science and Scopus databases. Most studies adopted a mixed-methods approach (70%), combining qualitative and quantitative tools. Among production systems analyzed, hybrid and agroecological systems showed the greatest diversity of adaptation strategies to climate change. Territorial factors, production systems, and socioeconomic conditions directly influenced how farmers perceived and responded to climatic events, as well as adoption of adaptation strategies. Results indicate that adaptation in coffee farming is conditioned by structural and territorial factors, reinforcing the need for interdisciplinary approaches that integrate ecological, productive, and socioeconomic dimensions when addressing climate change.\u003c/p\u003e","manuscriptTitle":"Perceptions and adaptation strategies to climate change in coffee farming: a systematic review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 18:32:35","doi":"10.21203/rs.3.rs-9545672/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T09:00:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178277912714579816637695507967201970236","date":"2026-05-16T06:31:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252282334862572510176873221016982668670","date":"2026-04-30T15:07:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T13:23:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T02:10:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-29T02:09:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Ecology","date":"2026-04-27T19:38:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4e60e3e0-d892-47ee-9412-e72c053ada3b","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T09:00:18+00:00","index":14,"fulltext":""},{"type":"reviewerAgreed","content":"178277912714579816637695507967201970236","date":"2026-05-16T06:31:01+00:00","index":13,"fulltext":""},{"type":"reviewerAgreed","content":"252282334862572510176873221016982668670","date":"2026-04-30T15:07:27+00:00","index":10,"fulltext":""},{"type":"reviewersInvited","content":"9","date":"2026-04-30T13:23:04+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T18:32:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 18:32:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9545672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9545672","identity":"rs-9545672","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}