Understanding coffee farmers’ poverty, food insecurity and adaptive responses to climate stress. Evidence from the dry corridor of western Honduras

Understanding coffee farmers’ poverty, food insecurity and adaptive responses to climate stress. 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Evidence from the dry corridor of western Honduras Fernando Rodriguez-Camayo, Christian Borgemeister, Julian Ramirez-Villegas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4145448/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Central America faces significant vulnerability to climatic variations. In recent years, national and international organizations have been working on climate-smart agricultural (CSA) to support coffee farmers in adapting to climate change. However, limited scientific evidence exists regarding the efficacy of these strategies in mitigating vulnerability. This study aims to assess the suitability of CSA practices promoted by Honduras' coffee sector in addressing the needs and vulnerability of coffee-farming households. Here, we integrated quantitative and qualitative methods, to assess how coffee farmers' livelihoods, poverty levels, and food insecurity status relate to their dependence on coffee income, prevailing stressors, and responses from farmers and value chain stakeholders. Data from a survey of 348 coffee farmers in western Honduras, along with key stakeholder interviews and focus group discussions, inform our analyses. Results indicate that poverty levels rise with increased reliance on coffee income, while diversified income sources correlate with greater food security among households. Nevertheless, despite efforts to enhance coffee tree productivity and soil resilience, most CSA practices neglect the food insecurity concerns of coffee farmers. Interviews and discussions reveal uncertainty among farmers regarding maintaining food security under extreme hazards. Consequently, coffee households remain vulnerable to climate and non-climate hazards, leading to crop losses, income instability, and food insecurity. Our findings underscore the need for a fundamental shift in the scope of coffee CSA practices towards a more holistic approach that addresses food security and income. Figures Figure 1 Figure 2 Figure 3 1. Introduction Coffee supports the livelihoods of about 25 million people in tropical regions, including vulnerable rural families (Baca et al., 2014 ; Bacon, 2005 ; Morel et al., 2019 ) Despite being the most traded commodity in the world, 80% of the coffee farmers live with less than USD 1.25 per day (FAO, 2015 ). In Central America, including Honduras, where coffee production is vital for rural economies, extreme weather events such as tropical storms, hurricanes, and irregular rains have had negative effects on production, income, and food security (Harvey et al., 2018 ). Morel et al. ( 2019 ) reported up to 30% reductions in coffee productivity and incomes from interannual climatic variations. Food insecurity and malnutrition, especially among the most vulnerable population, have worsened because of the droughts in the southern and western regions of Honduras, known as the dry corridor . Climate change is projected to reduce about 50% of the area suitable for coffee in Central America by 2050 (Ovalle-Rivera et al., 2015 ), therefore likely worsening food insecurity and poverty throughout the region. Central American farmers often lack the capacity to adapt to climate-related stressors, rendering the region highly vulnerable (Bouroncle et al., 2017 ; Hannah et al., 2013 ). In Honduras and Guatemala, a recent study found that 56% of farmers faced recurrent food insecurity and 36% experienced episodic food insecurity due to extreme climate events (Alpízar et al., 2020 ). Furthermore, access to and quality of basic services, including extension, input and seed markets, water for irrigation, household use and health, are well below global standards across rural areas in Central America (Bouroncle et al., 2019 ; Palma et al., 2020 ; Ward et al., 2017 ). Over the past decade, Honduras and Guatemala have promoted climate-resilient agricultural practices and technologies (Bunn et al., 2019 ; Djufry & Wulandari, 2021 ; Reay, 2019 ). Climate smart agriculture (CSA) addresses three pillars, namely, increasing food security and incomes, adapting to climate change, and removing or reducing greenhouse gas (GHG) emissions (FAO, 2021 ; Lipper et al., 2014 ). A growing body of evidence suggests that CSA has substantial potential to improve farmers resilience, especially at the interface of food security, productivity, and climate adaptation (e.g., Aggarwal et al., 2018 ; Prestele & Verburg, 2020 ; Sain et al., 2017 ). Yet, evidence shows so far limited adoption of CSA especially in smallholder contexts in middle- and low-income countries (Amadu, McNamara, & Miller, 2020 ; García de Jalón, Silvestri, & Barnes, 2017 ; McCarthy et al., 2011 ; Vernooy & Bouroncle, 2019 ). This may be due to strong differences between farmers’ priorities and CSA offered by supply services. (Akhter Ali & Olaf Erenstein, 2017). Furthermore, CSA programs have primarily focused on enhancing crop productivity and the biophysical environment, often ignoring adoption constraints, local context, social values, or food insecurity (Groot et al., 2019 ; Khatri-chhetri, Aggarwal, Joshi, & Vyas, 2017 ; Long, Blok, & Poldner, 2016 ; Westermann, Thornton, & Förch, 2015 ). Little evidence exists to date on whether and how CSA adoption improves food security of farmers’ households, particularly in Central America, where knowledge gaps persist regarding suitable adaptation strategies for farmers' specific vulnerability conditions and food security (Donatti et al., 2019 ). Here we investigate whether CSA practices promoted by the coffee sector in Honduras are responding to the needs and vulnerability conditions of coffee farmers' households. More specifically, we aim to answer the following questions: How do coffee farmers’ livelihoods, poverty and food security conditions vary with respect to climate and non-climate hazards and income dependency on coffee? What are farmers’ strategies to reduce food insecurity under climate stress? What are coffee value chain actors’ strategies to reduce farmers’ food insecurity under climate stress? To address these questions, we conducted a randomized household survey of 348 Honduran coffee farmers to analyze socioeconomic conditions, food insecurity, and poverty levels. In the survey, we also identified the main climate and non-climate stressors affecting coffee producers, and the producer responses to these stressors. We also conducted 55 semi-structured interviews with value chain stakeholders to better understand their responses to climate impacts at the farm level. Finally, we discuss the results considering existing knowledge on climate adaptation and food security for coffee farmers in the region and elsewhere and draw recommendations for research and practice in climate adaptation for coffee cultivation. 2. Materials and methods 2.1 Study area This study focuses on coffee systems in western Honduras, covering farms located in the departments of Ocotepeque and Copan (Fig. 1 ) which are part of the dry corridor. Farm livelihoods in this region depend on the cultivation of coffee and staple crops like beans, maize, and rice. The region is an important coffee producer in Honduras, especially for specialty coffee and export to international markets. It has a wide range of elevations, spanning from 850 meters above sea level (m.a.s.l.) to 1,800 m.a.s.l. Coffee is grown across the entire elevational gradient. The coffee farmers’ households are situated within the red square (Fig. 1 B). Most of the coffee exporters and the national coffee institutions are based in the main cities of Tegucigalpa and San Pedro Sula (marked with stars in Fig. 1 B). 2.2 Coffee household and value chain stakeholder data We used three methods to collect primary data on household characteristics and adaptation strategies at both farmer and value chain levels: (i) a structured household survey, and (ii) semi-structured qualitative stakeholder interviews and observations. To select farmers and other stakeholders for data collection, we first identified relevant institutions working on CSA within the target region via a stakeholder mapping. Thereafter we performed a pre-screening process to assess their interest in participating in our research. The pre-screening consisted of an in-person meeting with representatives of each farmers’ organization and emails and calls with exporters, national institutions, and non-governmental organizations (NGOs) where we explained the objectives of the research, the nature of the work (i.e., academic research), and the potential relevance of the results for their own purposes. The pre-screening meeting also included explanations about the ethical aspects of the research and addressed any concerns related to conflicts of interest. The pre-screening resulted in 22 stakeholders interviewed, including representatives of cooperatives (n = 5 interviews), coffee exporters (n = 3), national institutions (e.g., Honduran Coffee Institute –IHCAFE, n = 6), agronomists (n = 5), and NGOs (n = 3). Lastly, we conducted three focus group discussions, one with several agronomists (n = 9) and the other two with 12 coffee farmers each (n = 24 total). It is noteworthy that the coffee exporters did not explicitly work with CSA due to their exclusive focus on the export process. However, they purchased large volumes of coffee, and thus constituted an important player in the coffee value chain. The semi-structured interviews captured perceptions of food insecurity among the coffee farming households, resilience of farmers to climate change and the roles of national coffee sector actors (exporters, national institutions, research centers) to ensure coffee farming households’ food security and resilience to climate variation. The farmer sample for the household survey was drawn from the population of farmers associated with the said five coffee cooperatives. These coffee cooperatives are working on climate resilience strategies with support from national institutions (e.g. IHCAFE) and other members of the coffee sector such as coffee industry representatives and coffee research centers through a project focused on coffee and climate resilience in the region. To draw the sample, we prepared a first list of potential household respondents based on individual membership lists provided by each cooperative. This list was further revised with the support of cooperative leaders and technical assistants, to ensure that only active members of the cooperatives were surveyed. We applied a simple random sampling method with 90% confidence level and 5% precision level, stratified by elevation. The elevation strata used were as proposed by CIAT (2017) with two categories related to the impacts of climate change: “remain suitable” for households located between 1,200 and 1,800 m.a.s.l., and “substantial stress or worse” for households below 1,200 m.a.s.l. We surveyed 348 individuals from a total population of 716. We conducted the survey during September and October 2019 using SurveyCTO. Data cleaning was performed in STATA to identify duplicates and remove records with missing data or with clearly erroneous answers. The household survey captured (a) the Poverty Probability Index® (PPI), which is a poverty measurement to characterize households’ asset ownership through 10 questions and calculates the probability of living below the poverty line. After accomplishing the survey, the poverty of a coffee household can be calculated by summing the score of answers (from 0 to 100) and using the look-up table to convert the score and poverty likelihood (%) related to a poverty line for the country. The poverty probability of a household increases when its PPI score is low and vice versa. We further recorded (b) the Months of Adequate Household Food Provisioning (MAHFP) (Swindale & Bilinsky, 2010 ), a proxy to measure changes of household food access during a year; and (c) the Household Dietary Diversity Score (HDDS) (FAO, 2010 ; Swindale & Bilinsky, 2006 ), which assesses a household’s economic access to food and which is calculated by adding up the number of food groups (0–12) eaten over the past 24 hours by all households members, where (1) cereals, (2) white tubers and roots, (3) vegetables, (4) fruits, (5) meat, (6) eggs, (7) fish and sea food, (8) legumes, nuts and seeds, (9) milk and dairy products, (10) oils and fats, (11) sweets, and (12) spices, condiments and beverages. The HDDS focuses on dietary diversity, but its scope is limited to only the last 24 hours. We also gathered and then incorporated data on (d) the Food Insecurity Experience Scale (FIES) (Ballard, et al., 2013 ) into our data collection methods to assess food insecurity levels among coffee households. FIES is based on the household’s direct responses to questions about their experiences facing constrained access to food for the previous 12 months. FIES consist of eight questions that were integrated into the structured households survey, covering various aspects of food access such as concern about food security, changes in dietary diversity, to skipping meals or staying without eating for a whole day (Ballard, et at., 2013). These questions are designed to capture the severity of food insecurity experienced by households, with their responses reflecting the extent of their challenges. Therefore, the severity level of food insecurity is considered an unobservable trait, and the experiences reported by households’ respondents are closely linked to the FIES question set. Compared to the MAHFP and HDDS, the FIES is a more comprehensive food insecurity indicator. The MAHFP and HDDS measures household food provisioning during the year, therefore measuring availability and access, but not severe levels of food insecurity. Consequently, the more severe a households’ food insecurity, the greater the likelihood of reporting associated experiences. Finally, the survey also recorded (e) climate-smart practices applied by farmers; (f) farmers’ strategies for food security; (g) extreme events, including climate hazards and non-climate hazards perceived during the last five years; and (h) households’ responses to extreme climate events. Lastly, we conducted three focus group discussions. The first one was with nine agronomists to understand the various climate adaptation practices promoted in the field by the technicians and by any project to which they had participated. The nine interviewed constituted all the agronomists working for the five cooperatives, as well as those that worked directly with NGOs. The other two focal groups were conducted with coffee farmers to understand whether and how climate-smart practices contributed to climate resilience, food security as well as the incentives for adoption of these practices. For the farmer focal group discussions, we drew two random samples of 12 farmers from the household survey sample. 2.3 Data analysis We performed three types of analysis: (1) descriptive analysis of household socioeconomic characteristics, poverty, and food insecurity levels; (2) model-based analysis linking poverty and food insecurity with stressors and household characteristics; and (3) qualitative analysis of food insecurity perceptions and stakeholder strategies for household food security and climate resilience. For (1) and (2) we used the household survey data, whereas for (3) we used the stakeholder interviews and focus group discussions. The descriptive analysis described household demographic and socioeconomic characteristics for the entire farmer sample and three coffee income dependency groups. These coffee income dependency groups were determined based on the share of coffee income to total household income. A group termed “diversified” was defined as containing households with coffee incomes below 50% of total household income. A second group, termed “coffee specialized”, contained households with coffee incomes between 50% and 75%. The third group, labelled “coffee dependent”, was defined to contain households with coffee incomes above 75%. For the whole sample, and for each income group, we calculated the mean value of all relevant demographic and socioeconomic variables. For the analysis of food insecurity, we used the data from the eight questions FIES applied: 1) Were you worried you would not have enough food to eat? 2) Were you unable to eat healthy and nutritious food? 3) Did you eat only a few kinds of foods? 4) Did you have to skip a meal? 5) Did you eat less than you thought you should? 6) Did your household run out of food? 7) Were you hungry but did not eat? 8) Did you go without eating for a whole day? The methodology used for analyzing FIES data employs Item Response Theory (IRT), which examines responses to survey or test questions. Specifically, the Rasch model, a component of IRT utilized in analyzing FIES data, aims to improve measurement accuracy and reliability by systematically evaluating response data. This model not only provides a theoretical framework but also includes a set of statistical tools that facilitate interpretation of the responses (Nord, 2014 ). We applied a probabilistic model, linking unobservable traits with respondents’ experiences, following a procedure developed by the United Nations Food and Agriculture Organization (FAO) (Cafiero, Viviani, & Nord, 2018 ) to assess the prevalence of food insecurity within each households’ coffee income dependency groups. Quantitative categorical types of data were analyzed using percentages, frequency distributions, and cross-tabulation, while quantitative continuous data were analyzed using means, and standard deviations. The Kruskal-Wallis and Fishers’ Exact test were used to investigate potential differences in numeric and categorical variables, respectively, among households’ coffee income dependency groups. For non-normally distributed variables, the Dunn Bonferroni test was used by pairwise comparison among households’ coffee income dependency groups. The model-based analyses used the household survey data to explain the variability in poverty and food insecurity using climate and non-climate stressors, and household characteristics as explanatory variables. As we were interested in understanding what variables contributed to explaining the variability in poverty (measured by the PPI score) or food security (measured through the weights’ percentages resulting from the reported food insecurity experience or not, derived from the FIES questions) across the sample of households, these two were used separately as response variables. The PPI (continuous) and the FIES (ordinal) scores are variables of different nature, and this necessitated a different type of model. For PPI we therefore used multiple linear regression (MLR) with the following formula: Where ( Y ) is the dependent variable (PPI-Poverty), ( β o ) is the intercept, and ( β i ) is a slope coefficient of the independent variables. The independent variables were CCLASS (climate impact class; remain suitable, substantial stress or worse), FSIZE (farm size), EDUC (education), HHSIZE (number of household members), HHSEX (sex of the household head), PWATER (availability of piped water in household; Yes/No), ICLASS (income class; diversified, specialized, dependent), DROU (experienced drought; Yes/No), LPCOFFEE (experienced low price shocks; Yes/No), PDCOFFEE (experienced pest and disease shocks; Yes/No), OHAZARD (experienced other hazards; Yes/No). For food security, we structured the modeling process into a sequential framework, encompassing both the tuning and assessment of models built various classification algorithms. Initially, we transformed the raw score into a binary outcome: food secure, when the raw FIES score was equal to zero, and food insecure when the raw score was greater than zero. Next, we validated the distribution of the food security variable, observing no imbalance among classes. Subsequently, we scrutinized the correlation between dependent and independent variables, revealing an absence of correlation. For the independent categorical variables, we employed the One-Hot Encoding technique to convert them into binary variables suitable for automatic learning models. Furthermore, we partitioned the data, allocating 75% for training and 25% for testing purposes. The evaluation phase involved four classification algorithms, namely, Logistic Regression, Gradient Boosting Model (GBM), Random Forest, and XGBoost. We fine-tuned and assessed each model using metrics such as Precision, Recall, F1-score, AUC-ROC, and accuracy. To optimize hyperparameters, we conducted a comprehensive search through GridSearchCV, exploring multiple hyperparameter combinations to identify optimal settings that maximized model performance based on the assessed metrics. Additionally, we implemented K-Fold cross-validation to ensure a robust estimation of model performance, mitigating the risk of bias in model evaluation. This strategy provides a thorough exploration of the model's generalizability, enhancing the reliability of our findings. In these classification models, explanatory variables included all household characteristics collected in the survey, namely, the level of income dependency on coffee, farm size, education level of the household head, number of household members, sex of the household head, and whether the household had piped water. We did not include outmigration of household members because it is likely to be a result, rather than a driver of poverty. Farm size, educational level and household size were all considered as continuous variables, whereas PWATER (Yes/No) and ICLASS (diversified, coffee specialized, dependent) were both categorical. We also included climate and non-climate hazards and long-term climate change impacts. For the hazards, we explicitly included the three most common ones as reported by the surveyed households, namely, drought (DROU; Yes/No), low coffee prices (LPCOFFEE; Yes/No), and coffee pests and diseases (PDCOFFEE; Yes/No). To account for the rest of the hazards we also included a variable on the occurrence of any other hazard (OHAZARD; Yes/No). For long-term climate change impacts, we used the climate change impact by elevation gradient proposed by CIAT ( 2018 ) as a categorical variable (CCLASS) with two classes, i.e., “remain suitable”, and “substantial stress or worse”. For food security, we also added the PPI score as an explanatory variable, under the rationale that food security is an outcome that arises in part from the household assets and poverty levels (Hyman et al., 2005 ; Pretty et al., 2003 ; Saravanakumar et al., 2020 ). For synthesizing perceptions and strategies, we transcribed and analyzed the interviews and focus group discussions using the Atlas.ti software. In Atlas.ti, once the transcription process was completed, we created codes for the various categories and concepts that were relevant to this work. These included concepts such as food insecurity, poverty, but also categories such as perceptions, impacts, and strategies. Once the codes were created, we then mapped segments of the transcribed data to the relevant codes. After this we synthesized the interviews and focus group discussion data into a synthesis narrative on (1) farmers’ and other value chain stakeholders’ perceptions of household food insecurity, and (2) the stakeholder strategies to address food insecurity and climate-related stress. 3. Results 3.1 Coffee households’ livelihoods, poverty, and food insecurity according to climate and non-climate hazards and households’ coffee income dependency groups. 3.1.1 Households description (demographic variables) After data cleaning, 348 households were included in the final dataset. Predominantly, households were male-headed (86%), averaging 3.86 members, operating relatively small farms (3.06 ha on average) at a range of elevations (800–1,800 m.a.s.l.). The age of household heads ranged from 21 to 80 years (average 49 years). Education levels varied, with only 9% having no formal education, 26% attending high school or technical education, and 7% completing a university degree. However, 65% had attended at least secondary school (Table 1 ). In terms of income sources, households relied on coffee (65%), off-farm labor (18%), external sources (7%), other crops (6%), and animals (4%). Off-farm labor included work at other farms, jobs in coffee cooperatives, and small businesses. External sources encompassed remittances, government cash transfers, or donations. Other crops included staples like beans and maize, while animals referred to cows, pigs, and poultry. Notably, income from forest-related activities was absent due to legal restrictions in Honduras. Income distribution varied among households, with 37% classified as diversified, 33% specialized, and 29% coffee dependent. The diversified group balanced incomes from coffee (39%), off-farm labor (34%), external sources (13%), other crops (6%), and animals (6%). The specialized group relied primarily on coffee (68%), followed by off-farm labor (13%), other crops (8%), external sources (6%), and animals (5%). Coffee-dependent households primarily relied on coffee (94%), with minimal contributions from other sources. Poverty and food insecurity rates were high, with 31% of households living below the national poverty line (Table 1 ). Greater dependency on coffee correlated with a higher likelihood of poverty, with specialized and dependent households being 10% and 12% more likely to be below the poverty line, respectively (Table 1 ). There is a high prevalence of food insecurity, especially regarding quality and diversity of food consumption. The FIES data show only 49% of the surveyed households were food secure, while 51% of households had a level of food insecurity in the last 12 months (Table 1 ). The percentage of food secure households went down from 55% “diversified” to 47% “coffee-specialized” and 43% “coffee dependent” as the dependency on coffee for income increased. The Rash model, used to analyze the FIES questions, revealed that among households, the prevalence of food insecurity (moderate and severe) was highest in the “coffee dependent” group at 15%, following the “diversified” group at 5%, and the “coffee-specialized” group at 3%. Consistent with the FIES results, the MAHFP data show that households in the study area have access to adequate food provisioning for the whole year (mean of 11.8 months), with no significant differences between the three coffee income distribution groups. In the HDDS, we found that the mean for the total sample was 9.33 on the scale from 0 to 12. We also found that the HDDS is lower when households depend more on coffee incomes. The “coffee dependent” households have a score of 8.8, which means less access to diversified groups of food than the “coffee-specialized” (score of 9.4) and the “coffee dependent” (score 9.7) ones (see Table 1 ). Table 1 Demographic variables of total households and coffee income distribution groups Coffee incomes distribution Variables Total (n = 348) Diversified (n = 130) Specialized (n = 116) Dependent (n = 102) SD SD SD PPI - Probability to be below of the national poverty line – (%) † 31% 25%* 10.9 34%* 10.9 36%* 12.8 Number of households members (Mean)† 3.9 3.8 1.7 4.1 1.4 3.8 1.2 Age of household head – (Mean) † 47 46* 13.7 50* 13.2 51 13.5 Size of farm (Ha) – (Mean) † 3.1 3.4 2.5 2.7 4.6 3.1 4.6 Household head male – (%) 86% 85% 86% 85% Access to clean water (public system) 97% 95% 99% 97% The education of the head of household – (%) No studies 9% 9% 54% 26% 9% 66% 17% 7% 7% 76% 14% 3% Elementary 65% Middle 20% Higher 7% 11% Income distribution – (%) HHs incomes with > 75% of coffee 29% - HHs incomes with 50% 33% - HHs incomes with < 50% 37% Mean Access to Adequate Households Food Provisioning Months (MAHFP) – (Mean) † 11.8 11.8 0.9 11.7 0.6 11.8 0.9 Low (1–9 months) 3% 3% 4% 2% Moderate (10–11 months) 7% 2% 8% 10% High (12 months) 90% 95% 88% 88% Mean Households Dietary Diversity Score – HDDS – (Mean) † 9.3 9.7* 1.5 9.4 1.5 8.8* 1.6 Food access – FIES – (%) Food security 49% 55% 47% 43% Farm elevation (m.a.s.l.) – (Mean) 1271 1289 191.1 1245 181.6 1276 160.3 Elevation farm groups – (%) † Substantial stress ( 1,200–1,800 m.a.s.l.) 56% 62% 47% 58% Households with a family member migrated in the last five years – (%) 24% 17% 28% 27% Households with a family member is thinking of migrating – (%) 7% 9% 8% 9% Note: Symbol † indicates non-normally distributed variables. Kruskal-Wallis and Fishers’ Exact tests were used to test for statistical differences, * indicate a significant difference between groups, followed by pairwise comparisons based on the Dunn-Bonferroni post hoc test was used for non-normally distributes variables. SD is the standard deviation. 3.1.2. Relationships between poverty and food security and climate and other hazards and households’ demographic variables. The MLR model explained 47% of the variance in the PPI score, with level of education and income dependency on coffee, farm size, number of members in the household, and farm elevation as the most important variables (Table 2 ). Table 2 The multiple linear regression results for relation demographic variables, hazards, and poverty (y = PPI) Variables Acronym Coefficients Standard Error Intercept 0.6126 0.08125 HH with coffee incomes > 50% 75% ICLASS_Depen 0.0390** 0.02144 Farm size FSIZE -0.0058*** 0.00210 The education of the head of HH EDUC -0.0250*** 0.00663 Sex of the HH head HHSEX 0.0308 0.02480 HH had piped water PWATER 0.0004 0.04908 Drought as a hazard DROU 0.0096 0.02060 Low coffee prices as a hazard LPCOFFEE -0.0262 0.02212 Pest and diseases as a hazard PDCOFFEE 0.0268 0.02076 Number of HH members HHSIZE 0.0199*** 0.00587 Elevation gradient CCLASS -0.0002*** 0.00004 Any other hazard OHAZARD -0.0266 0.02476 Note: *** p > 0.01; **p < 0.05; *p < 0.1 indicates a significant difference Model results show that education, farm elevation and farm size have a positive relationship with the PPI score; that is, the larger the farm, or the greater the education level or higher the elevation, the lower the probability to be below the national poverty line. The converse relationship is found for the number of household members, with poverty increasing (i.e., PPI score decreasing) with a greater number of households members. Importantly, consistent with the descriptive results shown in Table 1 , the MLR model shows that poverty increases when the level of income dependency on coffee increases. Overall, therefore, the PPI model results suggest that households with many members, with small farms, limited education, and high dependency on coffee for incomes are more likely to be below the poverty line. For the food insecurity indicator model, the metrics assessed for each classification algorithm are summarized in Table 3 . GBM demonstrated the best performance in precision and F1-score compared to other models. Therefore, GBM exhibits superior generalization capabilities, emphasizing its effectiveness in our modeling process. Consequently, for food security, from here onwards we present and discuss results only for the GBM model (Freund & Schapire, 1997 ; Friedman, 2002 ). Table 3 Metrics assessed for each classification algorithm Metric Logistic Regression Gradient Boosting Classifier Random Forest Classifier XGBoost Classifier Accuracy 0.53 0.57 0.52 0.54 Precision 0.52 0.56 0.52 0.54 Recall 0.73 0.73 0.66 0.63 F1-Score 0.61 0.63 0.58 0.58 AUC-ROC 0.52 0.57 0.52 0.54 For the food security indicator, the GBM model shows the greatest contribution to explained variance came from the PPI score (Fig. 2 ). According to the model, the variables with a high contribution to food security are the following: income dependency of coffee, size of farm, and education level of the household head. The hazards and long-term climate change impacts that the model found important are the low coffee prices, the pest and diseases and droughts (Fig. 2 ). It is noteworthy, however, that although the coffee pests/diseases were herein categorized by non-climate events, the technicians and farmers focal groups associated this hazard with the coffee rust disease, caused by the fungus Hemileia vastatrix , whose occurrence is highly dependent on weather conditions such as high humidity and high temperatures. The regression and classification analysis confirmed the descriptive results that the variability of both the PPI score and the food security indicator (transformed FIES score) can be partly explained through the coffee income dependency. Other variables also contributed significantly to explaining the variability of the two dependent variables (Table 1 ). 3.1.3. Climate and non-climate hazards and their effects on households With an understanding that climate and non-climate hazards affect poverty and food insecurity, we analyzed the individual hazards in terms of reporting frequency, their impacts at farm- and household-level, and the resulting household coping strategies. A total of 233 out of the 348 households reported to have experienced non-climate hazards, from which 60 households reported at least two non-climate hazards and 11 households reported three non-climate hazards during the last five years. A total of 152 households out of the 348 households reported experiencing climate hazards. From these 152 households, seven reported to have experienced two climate hazards, and one household reported three events over the last five years, for a total of 160 individual climate hazard events reported. The most frequently mentioned non-climate events were coffee pest and diseases (38%), and low coffee prices (23%), whereas the most frequent climate hazard for the households surveyed was drought, with 36% of the households perceiving it in the last five years. A closer look at the intersection between the most reported climate and non-climate events, i.e., pests and diseases, low coffee prices and droughts, shows the complexity of the interplay between the various hazards to which farmers are exposed, with a considerable proportion having experienced two or more concurrently. In particular, coffee farmers are challenged by drought and pests and diseases (Fig. 3 ). The most reported hazards are coffee pest/diseases (38%), drought (36%) and low coffee incomes (23%). At least 32% of households reported two or more of these three events the last five years. A total 76 out of the 348 households (22%) perceived coffee pest/diseases and droughts, and 180 households (52%) perceived droughts or coffee pest/diseases (Fig. 3 ). Note that a total of 211 households (60.6% of total) indicated that at least one of the three events affected them. The reported impacts of the climate events show that these affected mainly coffee yields and food security (Table 4 ). Fifty-three percent (53%) of these households said they noted a reduced coffee yields due to an extreme event in the last five years. Technicians and coffee farmers in focus groups mentioned an extended dry period during the coffee flowering or beyond, reduces the nutrient uptake by coffee plants, causing flower abortion or wilting of coffee trees, resulting in partial or total loss of the next coffee harvest, especially for coffee-dependent households. At least 38% of households reported food insecurity as a main impact of climate events. The food security impacts “Household food reduction” due to climate hazards show a greater perception of such impacts for the coffee-specialized and coffee-dependent income groups (Table 4 ). Table 4 The impact of climate and non-climate hazards and responses from coffee households Impacts of climate events Coffee income groups (%) 152 households reporting 160 events Total (%) Diversified Specialized Dependent n = 48 n = 64 n = 40 Reduction of yields 10 7 3 3 Partial loss of harvest 26 8 14 16 Total loss of harvest 26 8 21 7 Household food reduction 38 15 20 17 Lower incomes 0 - - - Households’ responses Diversified Specialized Dependent Sold assets 0 - - - Accessed to loans or savings 26 1 2 5 Looked for another type of income 5 12 10 14 Somebody of the household migrated for looking a job 0 - - - Adjust the household budget 1 - - - Applied CSA practices 7 2 3 4 Bought basic grains 1 - 1 1 Planting or renovation of the coffee plantation 8 2 1 7 Others 5 1 3 4 Without strategies 47 12 23 31 Impacts of non-climate events 233 households reporting 300 events Total (%) Diversified Specialized Dependent n = 86 n = 77 n = 70 Reduction of yields 18 7 3 8 Partial loss of harvest 16 6 7 3 Total loss of harvest 23 8 9 7 Destabilization of household income and food reduction 17 5 6 6 Lower incomes 48 19 14 14 Other 6 2 2 2 Households’ responses Diversified Specialized Dependent Sold assets 4 2 1 1 Access to loans or saving 65 24 19 21 Somebody of the household migrated for looking a job 2 0 1 1 Others 1 0.4 0.4 0.4 Renovation coffee trees or pest/diseases management 32 15 9 9 Without strategies 24 7 10 7 The reported non-climate events primarily impact income, total loss of harvest and reduction of yields (Table 4 ). Sixty-five percent (65%) of the 233 households said that they experienced a reduction of incomes or redistribution of household spending (including a reduction of food consumed) due to a non-climate hazard in the last five years. Also, farmers and technicians in focal groups mentioned heavily reduced income from coffee when prices and/or the production dropped (due to pests/diseases). Moreover, farmers must continue to pay back the credits they took to invest in coffee, often carrying over these debts into the following coffee season. 3.2 Coffee households’ response against the impacts of climate and non-climate events Despite many households having experienced climate hazards (Table 4 ) nearly half of those surveyed (47%) had no response against the impacts of the extreme climate events. Most coffee households know the climate event when it hits them but have no mechanism to be prepared for it. Although the coffee households did not report storage of staple food (beans and maize) as a strategy to cope with impacts of the climate events in the survey, the farmers focal groups reported storage of staple food as a traditional households’ strategy to keep food during periods of food shortage. The coffee farmers procure staple food (beans and maize) to store at home according to their financial capacity. According to the interviewed households and the focus groups discussions, this strategy is often hindered by low coffee prices, as households do not earn enough to procure beans and maize for the next months. Only seven percent of households reported having implemented CSA practices such as intercropping before the climate event occurred. According to interviews with coffee farmers and farmers focus groups discussions, coffee farmers used to do intercropping (planting beans and maize) every year as part of their livelihood strategies before the concept of CSA came to their attention. Many of the farmers and local technicians agree that intercropping coffee with beans and maize is an important strategy in response to climate impacts. Interestingly, however, they did not consider this a CSA practice. According to the survey, in the context of income reductions due to non-climate hazards such as low coffee prices and pest and diseases, 151 households (50%) used their savings or accessed credits as a coping strategy to be able to pay debts, health care, access food, or pay wages and inputs. It is noteworthy that the use of savings or access to credits was also the most important household strategy to cope with climate hazards. In both cases, coffee households used a financial strategy as a coping mechanism against extreme hazards. 3.3. Actions and strategies designed and applied by the coffee stakeholders on coffee farms to deal with climate change. The coffee stakeholders interviewed have been promoting CSA practices through projects and activities in the field (farms of the coffee households, experimental coffee research stations, experimental farms), supported by capacity building through workshops with farmers and local agronomists. These activities typically seek to boost the climate resilience capacities of coffee farmers against climate change conditions such as high temperatures, heat waves, and long dry seasons, additional data given in Online Resource 1. Farmers reported as the most common practices applied soil management (minimum or no tillage), cover crops (no bare soil fallows), vegetative barriers with, e.g., Dracaena sansevieria , shade management using for instance Cajanus cajan , and soil cover like Brachiaria ruziziensis grass between the rows of the coffee trees. Soil management interventions included ways to improve water retention and reducing high temperatures in the soil for instance by using Brachiaria ruziziensis . The adoption of those practices is high (Online Resource 1). Yet, according to experts and local agronomists, the use of several of these practices is associated with certification requirements and it is unclear whether they respond to farmers’ actual climate adaptation needs or whether they are motivated by avoiding price penalties due to not being certified. During the focus groups farmers explained that some practices are not new to them. Practices such as vegetative barriers with Yucca gigantea , soil cover with lemongrass ( Cymbopogon spp.), as well as intercropping with beans and maize, were already used before the initiation of recent climate projects. However, with the introduction of new coffee projects, farmers reported adopting additional practices tailored to their specific soil types and local weather conditions. As one farmer expressed, “ We adopted the practices according to our type of soil and weather around our farms, so, the technician brings practices such zacate (grass), avocado tree or sunflowers and we choose the most suitable option ” (A coffee farmer, focus group interview, 2020). Another farmer mentioned, “ All of us adopted these practices, as failure to do so could lead to loss of certification and subsequently, the price premium ” (A coffee farmer, focus group interview, 2020). However, some farmers noted that while certain crops introduced through these practices, such as grasses, improve water retention and soil quality, they also required additional labour and competed with coffee plants for nutrients. Coffee farmers anticipate that these practices will ultimately reduce the cost of coffee production and increase coffee incomes. As one participant remarked, “ The grass retains water and enhances soil quality, but we need to mow it every 30 or 40 days to prevent it from competing with coffee trees for nutrients ”. Another coffee farmer stated, “ As these practices are part of the certification, we are hopeful that the certification price premium will increase and we could access a better food ” (A coffee farmer, focus group interview, 2020). These voices exemplify the perspectives of coffee farmers regarding climate resilience practices promoted by projects in the study region. However, these resilience practices are done with a fixed menu of technologies and options. That is, without an adequate process of co-design with the end users, i.e., the farming households. Ultimately, this results in a top-down technology adoption process that lacks consideration of farmers’ climate and socioeconomic vulnerability conditions and adaptation pathways that are suitable for them. Finally, the national institutions led by the Ministry of Agriculture and Livestock (SAG) provide climate-information services as a climate resilience strategy for agronomic management decisions such as planting times for annual crops and fertilizer management. Although the national coffee sector also uses and shares these information services with coffee farmers to decide the date to fertilize the coffee, it was not clear how exactly coffee farmers employed these climate services to manage their farms, or what benefits they derived from them. 4. Discussion In our study coffee households in the dry corridor of western Honduras with more dependence on coffee incomes are poorer, more vulnerable and food insecure than the households with diversified incomes such as off-farm labor and additional on-farm activities like animal husbandry and other staple crops. Anderzén et al. (2020) reported that Mexican coffee farmers with more diversified strategies tend to have higher incomes and are more food secure than households only involved in coffee cultivation. In that case, however, the households were more focused on on-farm and market-oriented strategies such as honey and coffee (cash flow) and maize and beans (staple food). Similarly, van Asselt and Useche (2022) reported that coffee income dependence for small-scale farmers households in Guatemala has a negative impact on their nutrition due to decreases in food production diversity, and no impact on-farm income. Furthermore, small-scale coffee farmers from Colombia with a strong focus on coffee (farm certification and/or high-quality coffee) may preclude increases in total household incomes as coffee activities demand substantial input in terms of time and labor from family members (Vellema et al., 2015). Despite these challenges, coffee is a very important livelihood strategy for many rural communities in Latin America. In Guatemala for instance, coffee households enjoy better food security and higher incomes than farmers households growing only maize (Lopez-Ridaura et al., 2019). Yet it is clear that diversifying incomes is key to improve levels of food security and prosperity. This underscores the importance of understanding the local economy, the income distribution in the target population sufficiently early in the design of agricultural development interventions. Projects and intervention programs should seek to contribute to balancing coffee households’ income sources between on-farm strategies such as coffee, other crops and animals, with market-oriented and off-farm strategies like small local business and jobs with better salaries. Our analysis suggests that households with many members, with small farms, limited education, at lower elevations and high dependency of coffee incomes are more likely to find themselves below the national Honduran poverty line. These households tend to also experience more hazards events (climate and non-climate), further exacerbating food insecurity and poverty. The combined effects of climate and non-climate hazards such as low coffee prices, pests and diseases (for instance coffee rust), and recurrent droughts, coupled with the unavailability of efficient coping strategies, enhances the vulnerability of such coffee households (Avelino et al., 2015). In general, high climate vulnerability leads to food insecurity and outmigration for coffee households in Latin America (Bacon et al., 2021; Dupre et al., 2022; Harvey et al., 2018; Ruiz et al., 2015). The majority of the respondents in our study either possessed no coping strategies in the face of climate hazards or had to resort to financial interventions such as using savings, reducing expenses, and/or accessing credits. Lopez-Ridaura et al. (2021) for Guatemala, Honduras, El Salvador, and Mexico, and Harris et al. (2020) for India, reported similar financial strategies for farmers during shocks. Although it is encouraging that there are at least some response strategies, it is likely that there are clear limits to such coping mechanisms. More specifically, the current strategies are unsustainable considering the projected long-term climate changes for Central America which suggest warmer temperatures and increasingly frequent extreme events (IPCC, 2012, 2021; Ruiz et al., 2015). In our study, although most of the climate-resilience practices provided by the coffee sector are focused on improving coffee tree productivity in the face of climate variability, they do not appear to respond to actual household climate adaptation needs. This reinforces the notion of a disconnect between the needs of end-users of technologies and resilient practices offered by service providers (Akhter & Erenstein, 2017). There are two implications of this finding. First, although it could be in principle an attractive strategy to protect one of the main sources of gross income of households (coffee), the current strategies such as soil management with fodder crops and vegetative barriers do not have a direct effect on improving household food security, prosperity, adaptive capacity, or other farmers’ basic needs (also see Groot et al., 2019; Khatri-chhetri et al., 2017; Long et al., 2016; Westermann et al., 2015). Thus, a focus on resilience to improve coffee production alone may not help producers enough to become more resilient to food insecurity and climatic variation. Second, the incentives needed for the adoption of such practices (e.g., certification schemes) create more, rather than less, vulnerability, which ultimately hinders agricultural development (Vellema et, al., 2015). It will remain a topic of future study whether the lack of broader farming and food system focus when promoting climate-smart practices and technologies 'locks’ farmers into a maladaptation pathway. Therefore, understanding the vulnerability of households with a multidisciplinary view (social, biophysical, and economic) can shed light on the debate around building inclusive climate adaptation strategies for coffee farmers (Donatti et al., 2019), improve the adoption of climate-resilient practices (Amadu et al., 2020; García de Jalón et al., 2017) and climate adaptation policies and programs (Harvey et al., 2018). Building resilience and adaptive capacity should include households needs such as income diversification as well as food availability and food production but designing such necessitates the participation of affected households. Conclusions This study analyzed coffee farmer food insecurity, poverty, and their exposure and responses to climate and non-climate hazards, seeking to understand whether and how CSA practices promoted by coffee stakeholders are responding to households’ socioeconomic vulnerability and their adaptation needs. Our findings suggest that diversified households (whose income depends less on coffee) which engage more in off-farm labor have a greater probability of being food secure, while the converse is true for coffee specialized and coffee-dependent households. Importantly, although coffee sector stakeholders invest in climate-resilient coffee production techniques, the coffee households’ exposure to and impacts from climate and non-climate hazards often result in crop and income loss, and food insecurity. Moreover, in the absence of effective coping strategies, coffee farmers only can rely on financial mechanisms to cope with these impacts. Yet, such coping mechanisms are unlikely to work in the long run, especially if climate conditions continue to worsen in the region. Thus, to truly address climate impacts, food insecurity and poverty, the coffee sector should seek to better understand the role of households’ food security as part of climate resilience and design strategies that generate benefits for the coffee value chain but also for the farming households’ livelihoods. 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CCAFS Working Paper , (256), 48 pp.-48 pp. Ward, R., Gonthier, D., & Nicholls, C. (2017). Ecological resilience to coffee rust: Varietal adaptations of coffee farmers in Copán, Honduras. Agroecology and Sustainable Food Systems , 41 (9–10), 1081–1098. https://doi.org/10.1080/21683565.2017.1345033 Westermann, O., Thornton, P., & Förch, W. (2015). Working Paper Reaching more farmers . Retrieved from https://cgspace.cgiar.org/bitstream/handle/10568/68403/Scaling-Up FINAL.pdf?sequence=1 Footnotes Ecologically speaking, the Central American Dry Corridor (CADC) is a tropical dry forest region that extends throughout Mesoamerica, from the Pacific Coast of Chiapas (Mexico) to the western part of Costa Rica and western provinces of Panama. Guatemala, El Salvador, Honduras, and Nicaragua are the most exposed to precipitation extremes and drought (van der Zee et al., 2012). 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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-4145448","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288113703,"identity":"e81b39c3-8a6f-43cb-aed5-5797005ba659","order_by":0,"name":"Fernando Rodriguez-Camayo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBAC9gYwxczABqYrGKAMPIDnAIqWM6RoAQPGNiIcxsPAfIC5osI6n49/jeHDn/Ps7PrYzz5g+LinFo8WtgTGM2fSLdsk3hgbSG5LTm7jSTdgnPHsOE4t9gw8BoyNbYcN2CTOmEkYbjuQzMaQxsDMc+AYHlv4P8C0mP9InAPUwv+MkBYeBogW/h4zhoMNB+zYJMC21ODWwsxmcLDhTDrQFrZiyYZjyQlsEs8YDs44cAC3Fvbmhw8bKqwN5PsPb/z4o8bOXr4/jfHBhwN1OLWAYgRioEQCmEpsYACLHMatBQ74ITrtoVw8toyCUTAKRsFIAwAnlUx6T8QgaAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5058-138X","institution":"University of Bonn: Rheinische Friedrich-Wilhelms-Universitat Bonn","correspondingAuthor":true,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Rodriguez-Camayo","suffix":""},{"id":288113704,"identity":"49d1f698-8588-4ec5-92c6-aac9e8fa223a","order_by":1,"name":"Christian Borgemeister","email":"","orcid":"","institution":"University of Bonn: Rheinische Friedrich-Wilhelms-Universitat Bonn","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Borgemeister","suffix":""},{"id":288113705,"identity":"95c02f36-d641-425b-ac8d-1b340961e7a9","order_by":2,"name":"Julian Ramirez-Villegas","email":"","orcid":"","institution":"Alliance of Bioversity International and CIAT: Alliance of Bioversity International and International Center for Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"","lastName":"Ramirez-Villegas","suffix":""},{"id":288113706,"identity":"f93167d6-4abb-4cc1-b0b4-6e4d4990ad10","order_by":3,"name":"Mark Lundy","email":"","orcid":"","institution":"Centro Internacional de agricultura Tropical CIAT: Alliance of Bioversity International and International Center for Tropical Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Lundy","suffix":""},{"id":288113707,"identity":"070c3acb-93fa-4beb-bba1-f9f818ebc0d1","order_by":4,"name":"Tina Beuchelt","email":"","orcid":"","institution":"University of Bonn: Rheinische Friedrich-Wilhelms-Universitat Bonn","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"","lastName":"Beuchelt","suffix":""}],"badges":[],"createdAt":"2024-03-21 18:58:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4145448/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4145448/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54430167,"identity":"4b16ae57-2afb-43e6-b2a7-5f3832dec9c1","added_by":"auto","created_at":"2024-04-10 10:49:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152810,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area in Honduras. (1a) Location of Honduras in Central America; The square in western Honduras encloses Copan and Ocotepeque departments (1b). Surveyed coffee households are distributed within the red square, and red dots represent the farmers cooperatives close to the villages and local towns. Source: own elaboration based on the Atlas of Honduras (2022)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4145448/v1/1f7658ae9bf5a9e2cc6e2b28.png"},{"id":54430516,"identity":"657537c1-019e-499d-94d3-4795c8e839a8","added_by":"auto","created_at":"2024-04-10 10:57:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27468,"visible":true,"origin":"","legend":"\u003cp\u003eVariable importance plot for food security variable using the gradient boosting model (GBM)\u003c/p\u003e\n\u003cp\u003eNote: The following acronyms stand for the explanatory variables: probability of households to be below of the national poverty line (PPI_score), the level of income dependency on coffee (ICLASS), farm size (FSIZE), education level of the household head (EDUC), number of household members (HHSIZE), sex of the household head (HHSEX), and whether the household had piped water (PWATER). Also, included the three most common ones, drought (DROU), low coffee prices (LPCOFFEE), and coffee pests and diseases (PDCOFFEE), any other hazard (OHAZARD) and elevation gradient (CCLASS).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4145448/v1/20bacc72333b7c666393372a.png"},{"id":54430165,"identity":"53f3cffa-2b74-4d1e-a386-2003b2f777bb","added_by":"auto","created_at":"2024-04-10 10:49:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70628,"visible":true,"origin":"","legend":"\u003cp\u003eRelative distribution of the co-occurrence for the three most frequently reported climate (droughts) and non-climate events (coffee pest and diseases and low coffee prices) as reported by coffee households\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4145448/v1/57d94470df1361defd6b67d0.png"},{"id":76504270,"identity":"ab27f735-dfca-4e3f-bcc3-d2b28d32ba7d","added_by":"auto","created_at":"2025-02-17 22:48:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4145448/v1/a0ac50f8-6ae8-4bf1-9f83-53182fea0055.pdf"},{"id":54430168,"identity":"b3b075a8-085f-44d5-85e4-23a340b0a9bc","added_by":"auto","created_at":"2024-04-10 10:49:40","extension":"tiff","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":13772168,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-4145448/v1/83baa7befbbc471028800b08.tiff"}],"financialInterests":"","formattedTitle":"Understanding coffee farmers’ poverty, food insecurity and adaptive responses to climate stress. Evidence from the dry corridor of western Honduras","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCoffee supports the livelihoods of about 25\u0026nbsp;million people in tropical regions, including vulnerable rural families (Baca et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Bacon, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Morel et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) Despite being the most traded commodity in the world, 80% of the coffee farmers live with less than USD 1.25 per day (FAO, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Central America, including Honduras, where coffee production is vital for rural economies, extreme weather events such as tropical storms, hurricanes, and irregular rains have had negative effects on production, income, and food security (Harvey et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Morel et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported up to 30% reductions in coffee productivity and incomes from interannual climatic variations. Food insecurity and malnutrition, especially among the most vulnerable population, have worsened because of the droughts in the southern and western regions of Honduras, known as the dry corridor\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e. Climate change is projected to reduce about 50% of the area suitable for coffee in Central America by 2050 (Ovalle-Rivera et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), therefore likely worsening food insecurity and poverty throughout the region.\u003c/p\u003e \u003cp\u003eCentral American farmers often lack the capacity to adapt to climate-related stressors, rendering the region highly vulnerable (Bouroncle et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hannah et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In Honduras and Guatemala, a recent study found that 56% of farmers faced recurrent food insecurity and 36% experienced episodic food insecurity due to extreme climate events (Alp\u0026iacute;zar et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Furthermore, access to and quality of basic services, including extension, input and seed markets, water for irrigation, household use and health, are well below global standards across rural areas in Central America (Bouroncle et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Palma et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ward et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past decade, Honduras and Guatemala have promoted climate-resilient agricultural practices and technologies (Bunn et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Djufry \u0026amp; Wulandari, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reay, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Climate smart agriculture (CSA) addresses three pillars, namely, increasing food security and incomes, adapting to climate change, and removing or reducing greenhouse gas (GHG) emissions (FAO, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lipper et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A growing body of evidence suggests that CSA has substantial potential to improve farmers resilience, especially at the interface of food security, productivity, and climate adaptation (e.g., Aggarwal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Prestele \u0026amp; Verburg, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sain et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Yet, evidence shows so far limited adoption of CSA especially in smallholder contexts in middle- and low-income countries (Amadu, McNamara, \u0026amp; Miller, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Garc\u0026iacute;a de Jal\u0026oacute;n, Silvestri, \u0026amp; Barnes, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McCarthy et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vernooy \u0026amp; Bouroncle, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This may be due to strong differences between farmers\u0026rsquo; priorities and CSA offered by supply services. (Akhter Ali \u0026amp; Olaf Erenstein, 2017). Furthermore, CSA programs have primarily focused on enhancing crop productivity and the biophysical environment, often ignoring adoption constraints, local context, social values, or food insecurity (Groot et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khatri-chhetri, Aggarwal, Joshi, \u0026amp; Vyas, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Long, Blok, \u0026amp; Poldner, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Westermann, Thornton, \u0026amp; F\u0026ouml;rch, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Little evidence exists to date on whether and how CSA adoption improves food security of farmers\u0026rsquo; households, particularly in Central America, where knowledge gaps persist regarding suitable adaptation strategies for farmers' specific vulnerability conditions and food security (Donatti et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere we investigate whether CSA practices promoted by the coffee sector in Honduras are responding to the needs and vulnerability conditions of coffee farmers' households. More specifically, we aim to answer the following questions:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHow do coffee farmers\u0026rsquo; livelihoods, poverty and food security conditions vary with respect to climate and non-climate hazards and income dependency on coffee?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat are farmers\u0026rsquo; strategies to reduce food insecurity under climate stress?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWhat are coffee value chain actors\u0026rsquo; strategies to reduce farmers\u0026rsquo; food insecurity under climate stress?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo address these questions, we conducted a randomized household survey of 348 Honduran coffee farmers to analyze socioeconomic conditions, food insecurity, and poverty levels. In the survey, we also identified the main climate and non-climate stressors affecting coffee producers, and the producer responses to these stressors. We also conducted 55 semi-structured interviews with value chain stakeholders to better understand their responses to climate impacts at the farm level. Finally, we discuss the results considering existing knowledge on climate adaptation and food security for coffee farmers in the region and elsewhere and draw recommendations for research and practice in climate adaptation for coffee cultivation.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eThis study focuses on coffee systems in western Honduras, covering farms located in the departments of Ocotepeque and Copan (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) which are part of the dry corridor. Farm livelihoods in this region depend on the cultivation of coffee and staple crops like beans, maize, and rice. The region is an important coffee producer in Honduras, especially for specialty coffee and export to international markets. It has a wide range of elevations, spanning from 850 meters above sea level (m.a.s.l.) to 1,800 m.a.s.l. Coffee is grown across the entire elevational gradient. The coffee farmers\u0026rsquo; households are situated within the red square (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Most of the coffee exporters and the national coffee institutions are based in the main cities of Tegucigalpa and San Pedro Sula (marked with stars in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Coffee household and value chain stakeholder data\u003c/h2\u003e\n \u003cp\u003eWe used three methods to collect primary data on household characteristics and adaptation strategies at both farmer and value chain levels: (i) a structured household survey, and (ii) semi-structured qualitative stakeholder interviews and observations.\u003c/p\u003e\n \u003cp\u003eTo select farmers and other stakeholders for data collection, we first identified relevant institutions working on CSA within the target region via a stakeholder mapping. Thereafter we performed a pre-screening process to assess their interest in participating in our research. The pre-screening consisted of an in-person meeting with representatives of each farmers\u0026rsquo; organization and emails and calls with exporters, national institutions, and non-governmental organizations (NGOs) where we explained the objectives of the research, the nature of the work (i.e., academic research), and the potential relevance of the results for their own purposes. The pre-screening meeting also included explanations about the ethical aspects of the research and addressed any concerns related to conflicts of interest.\u003c/p\u003e\n \u003cp\u003eThe pre-screening resulted in 22 stakeholders interviewed, including representatives of cooperatives (n\u0026thinsp;=\u0026thinsp;5 interviews), coffee exporters (n\u0026thinsp;=\u0026thinsp;3), national institutions (e.g., Honduran Coffee Institute \u0026ndash;IHCAFE, n\u0026thinsp;=\u0026thinsp;6), agronomists (n\u0026thinsp;=\u0026thinsp;5), and NGOs (n\u0026thinsp;=\u0026thinsp;3). Lastly, we conducted three focus group discussions, one with several agronomists (n\u0026thinsp;=\u0026thinsp;9) and the other two with 12 coffee farmers each (n\u0026thinsp;=\u0026thinsp;24 total). It is noteworthy that the coffee exporters did not explicitly work with CSA due to their exclusive focus on the export process. However, they purchased large volumes of coffee, and thus constituted an important player in the coffee value chain. The semi-structured interviews captured perceptions of food insecurity among the coffee farming households, resilience of farmers to climate change and the roles of national coffee sector actors (exporters, national institutions, research centers) to ensure coffee farming households\u0026rsquo; food security and resilience to climate variation.\u003c/p\u003e\n \u003cp\u003eThe farmer sample for the household survey was drawn from the population of farmers associated with the said five coffee cooperatives. These coffee cooperatives are working on climate resilience strategies with support from national institutions (e.g. IHCAFE) and other members of the coffee sector such as coffee industry representatives and coffee research centers through a project focused on coffee and climate resilience in the region. To draw the sample, we prepared a first list of potential household respondents based on individual membership lists provided by each cooperative. This list was further revised with the support of cooperative leaders and technical assistants, to ensure that only active members of the cooperatives were surveyed. We applied a simple random sampling method with 90% confidence level and 5% precision level, stratified by elevation. The elevation strata used were as proposed by CIAT (2017) with two categories related to the impacts of climate change: \u0026ldquo;remain suitable\u0026rdquo; for households located between 1,200 and 1,800 m.a.s.l., and \u0026ldquo;substantial stress or worse\u0026rdquo; for households below 1,200 m.a.s.l. We surveyed 348 individuals from a total population of 716. We conducted the survey during September and October 2019 using SurveyCTO. Data cleaning was performed in STATA to identify duplicates and remove records with missing data or with clearly erroneous answers.\u003c/p\u003e\n \u003cp\u003eThe household survey captured (a) the Poverty Probability Index\u0026reg; (PPI), which is a poverty measurement to characterize households\u0026rsquo; asset ownership through 10 questions and calculates the probability of living below the poverty line. After accomplishing the survey, the poverty of a coffee household can be calculated by summing the score of answers (from 0 to 100) and using the look-up table to convert the score and poverty likelihood (%) related to a poverty line for the country. The poverty probability of a household increases when its PPI score is low and vice versa. We further recorded (b) the Months of Adequate Household Food Provisioning (MAHFP) (Swindale \u0026amp; Bilinsky, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), a proxy to measure changes of household food access during a year; and (c) the Household Dietary Diversity Score (HDDS) (FAO, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Swindale \u0026amp; Bilinsky, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), which assesses a household\u0026rsquo;s economic access to food and which is calculated by adding up the number of food groups (0\u0026ndash;12) eaten over the past 24 hours by all households members, where (1) cereals, (2) white tubers and roots, (3) vegetables, (4) fruits, (5) meat, (6) eggs, (7) fish and sea food, (8) legumes, nuts and seeds, (9) milk and dairy products, (10) oils and fats, (11) sweets, and (12) spices, condiments and beverages. The HDDS focuses on dietary diversity, but its scope is limited to only the last 24 hours. We also gathered and then incorporated data on (d) the Food Insecurity Experience Scale (FIES) (Ballard, et al., \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) into our data collection methods to assess food insecurity levels among coffee households. FIES is based on the household\u0026rsquo;s direct responses to questions about their experiences facing constrained access to food for the previous 12 months. FIES consist of eight questions that were integrated into the structured households survey, covering various aspects of food access such as concern about food security, changes in dietary diversity, to skipping meals or staying without eating for a whole day (Ballard, et at., 2013). These questions are designed to capture the severity of food insecurity experienced by households, with their responses reflecting the extent of their challenges. Therefore, the severity level of food insecurity is considered an unobservable trait, and the experiences reported by households\u0026rsquo; respondents are closely linked to the FIES question set. Compared to the MAHFP and HDDS, the FIES is a more comprehensive food insecurity indicator. The MAHFP and HDDS measures household food provisioning during the year, therefore measuring availability and access, but not severe levels of food insecurity. Consequently, the more severe a households\u0026rsquo; food insecurity, the greater the likelihood of reporting associated experiences. Finally, the survey also recorded (e) climate-smart practices applied by farmers; (f) farmers\u0026rsquo; strategies for food security; (g) extreme events, including climate hazards and non-climate hazards perceived during the last five years; and (h) households\u0026rsquo; responses to extreme climate events.\u003c/p\u003e\n \u003cp\u003eLastly, we conducted three focus group discussions. The first one was with nine agronomists to understand the various climate adaptation practices promoted in the field by the technicians and by any project to which they had participated. The nine interviewed constituted all the agronomists working for the five cooperatives, as well as those that worked directly with NGOs. The other two focal groups were conducted with coffee farmers to understand whether and how climate-smart practices contributed to climate resilience, food security as well as the incentives for adoption of these practices. For the farmer focal group discussions, we drew two random samples of 12 farmers from the household survey sample.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data analysis\u003c/h2\u003e\n \u003cp\u003eWe performed three types of analysis: (1) descriptive analysis of household socioeconomic characteristics, poverty, and food insecurity levels; (2) model-based analysis linking poverty and food insecurity with stressors and household characteristics; and (3) qualitative analysis of food insecurity perceptions and stakeholder strategies for household food security and climate resilience. For (1) and (2) we used the household survey data, whereas for (3) we used the stakeholder interviews and focus group discussions.\u003c/p\u003e\n \u003cp\u003eThe descriptive analysis described household demographic and socioeconomic characteristics for the entire farmer sample and three coffee income dependency groups. These coffee income dependency groups were determined based on the share of coffee income to total household income. A group termed \u0026ldquo;diversified\u0026rdquo; was defined as containing households with coffee incomes below 50% of total household income. A second group, termed \u0026ldquo;coffee specialized\u0026rdquo;, contained households with coffee incomes between 50% and 75%. The third group, labelled \u0026ldquo;coffee dependent\u0026rdquo;, was defined to contain households with coffee incomes above 75%. For the whole sample, and for each income group, we calculated the mean value of all relevant demographic and socioeconomic variables.\u003c/p\u003e\n \u003cp\u003eFor the analysis of food insecurity, we used the data from the eight questions FIES applied: 1) Were you worried you would not have enough food to eat? 2) Were you unable to eat healthy and nutritious food? 3) Did you eat only a few kinds of foods? 4) Did you have to skip a meal? 5) Did you eat less than you thought you should? 6) Did your household run out of food? 7) Were you hungry but did not eat? 8) Did you go without eating for a whole day? The methodology used for analyzing FIES data employs Item Response Theory (IRT), which examines responses to survey or test questions. Specifically, the Rasch model, a component of IRT utilized in analyzing FIES data, aims to improve measurement accuracy and reliability by systematically evaluating response data. This model not only provides a theoretical framework but also includes a set of statistical tools that facilitate interpretation of the responses (Nord, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). We applied a probabilistic model, linking unobservable traits with respondents\u0026rsquo; experiences, following a procedure developed by the United Nations Food and Agriculture Organization (FAO) (Cafiero, Viviani, \u0026amp; Nord, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) to assess the prevalence of food insecurity within each households\u0026rsquo; coffee income dependency groups.\u003c/p\u003e\n \u003cp\u003eQuantitative categorical types of data were analyzed using percentages, frequency distributions, and cross-tabulation, while quantitative continuous data were analyzed using means, and standard deviations. The Kruskal-Wallis and Fishers\u0026rsquo; Exact test were used to investigate potential differences in numeric and categorical variables, respectively, among households\u0026rsquo; coffee income dependency groups. For non-normally distributed variables, the Dunn Bonferroni test was used by pairwise comparison among households\u0026rsquo; coffee income dependency groups.\u003c/p\u003e\n \u003cp\u003eThe model-based analyses used the household survey data to explain the variability in poverty and food insecurity using climate and non-climate stressors, and household characteristics as explanatory variables. As we were interested in understanding what variables contributed to explaining the variability in poverty (measured by the PPI score) or food security (measured through the weights\u0026rsquo; percentages resulting from the reported food insecurity experience or not, derived from the FIES questions) across the sample of households, these two were used separately as response variables.\u003c/p\u003e\n \u003cp\u003eThe PPI (continuous) and the FIES (ordinal) scores are variables of different nature, and this necessitated a different type of model. For PPI we therefore used multiple linear regression (MLR) with the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere (\u003cem\u003eY\u003c/em\u003e) is the dependent variable (PPI-Poverty), (\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sub\u003e) is the intercept, and (\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) is a slope coefficient of the independent variables. The independent variables were CCLASS (climate impact class; remain suitable, substantial stress or worse), FSIZE (farm size), EDUC (education), HHSIZE (number of household members), HHSEX (sex of the household head), PWATER (availability of piped water in household; Yes/No), ICLASS (income class; diversified, specialized, dependent), DROU (experienced drought; Yes/No), LPCOFFEE (experienced low price shocks; Yes/No), PDCOFFEE (experienced pest and disease shocks; Yes/No), OHAZARD (experienced other hazards; Yes/No).\u003c/p\u003e\n \u003cp\u003eFor food security, we structured the modeling process into a sequential framework, encompassing both the tuning and assessment of models built various classification algorithms. Initially, we transformed the raw score into a binary outcome: food secure, when the raw FIES score was equal to zero, and food insecure when the raw score was greater than zero. Next, we validated the distribution of the food security variable, observing no imbalance among classes. Subsequently, we scrutinized the correlation between dependent and independent variables, revealing an absence of correlation. For the independent categorical variables, we employed the One-Hot Encoding technique to convert them into binary variables suitable for automatic learning models. Furthermore, we partitioned the data, allocating 75% for training and 25% for testing purposes.\u003c/p\u003e\n \u003cp\u003eThe evaluation phase involved four classification algorithms, namely, Logistic Regression, Gradient Boosting Model (GBM), Random Forest, and XGBoost. We fine-tuned and assessed each model using metrics such as Precision, Recall, F1-score, AUC-ROC, and accuracy. To optimize hyperparameters, we conducted a comprehensive search through GridSearchCV, exploring multiple hyperparameter combinations to identify optimal settings that maximized model performance based on the assessed metrics. Additionally, we implemented K-Fold cross-validation to ensure a robust estimation of model performance, mitigating the risk of bias in model evaluation. This strategy provides a thorough exploration of the model\u0026apos;s generalizability, enhancing the reliability of our findings.\u003c/p\u003e\n \u003cp\u003eIn these classification models, explanatory variables included all household characteristics collected in the survey, namely, the level of income dependency on coffee, farm size, education level of the household head, number of household members, sex of the household head, and whether the household had piped water. We did not include outmigration of household members because it is likely to be a result, rather than a driver of poverty. Farm size, educational level and household size were all considered as continuous variables, whereas PWATER (Yes/No) and ICLASS (diversified, coffee specialized, dependent) were both categorical. We also included climate and non-climate hazards and long-term climate change impacts. For the hazards, we explicitly included the three most common ones as reported by the surveyed households, namely, drought (DROU; Yes/No), low coffee prices (LPCOFFEE; Yes/No), and coffee pests and diseases (PDCOFFEE; Yes/No). To account for the rest of the hazards we also included a variable on the occurrence of any other hazard (OHAZARD; Yes/No). For long-term climate change impacts, we used the climate change impact by elevation gradient proposed by CIAT (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) as a categorical variable (CCLASS) with two classes, i.e., \u0026ldquo;remain suitable\u0026rdquo;, and \u0026ldquo;substantial stress or worse\u0026rdquo;. For food security, we also added the PPI score as an explanatory variable, under the rationale that food security is an outcome that arises in part from the household assets and poverty levels (Hyman et al., \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pretty et al., \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e; Saravanakumar et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor synthesizing perceptions and strategies, we transcribed and analyzed the interviews and focus group discussions using the Atlas.ti software. In Atlas.ti, once the transcription process was completed, we created codes for the various categories and concepts that were relevant to this work. These included concepts such as food insecurity, poverty, but also categories such as perceptions, impacts, and strategies. Once the codes were created, we then mapped segments of the transcribed data to the relevant codes. After this we synthesized the interviews and focus group discussion data into a synthesis narrative on (1) farmers\u0026rsquo; and other value chain stakeholders\u0026rsquo; perceptions of household food insecurity, and (2) the stakeholder strategies to address food insecurity and climate-related stress.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Coffee households\u0026rsquo; livelihoods, poverty, and food insecurity according to climate and non-climate hazards and households\u0026rsquo; coffee income dependency groups.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.1 Households description (demographic variables)\u003c/h2\u003e\n \u003cp\u003eAfter data cleaning, 348 households were included in the final dataset. Predominantly, households were male-headed (86%), averaging 3.86 members, operating relatively small farms (3.06 ha on average) at a range of elevations (800\u0026ndash;1,800 m.a.s.l.). The age of household heads ranged from 21 to 80 years (average 49 years). Education levels varied, with only 9% having no formal education, 26% attending high school or technical education, and 7% completing a university degree. However, 65% had attended at least secondary school (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn terms of income sources, households relied on coffee (65%), off-farm labor (18%), external sources (7%), other crops (6%), and animals (4%). Off-farm labor included work at other farms, jobs in coffee cooperatives, and small businesses. External sources encompassed remittances, government cash transfers, or donations. Other crops included staples like beans and maize, while animals referred to cows, pigs, and poultry. Notably, income from forest-related activities was absent due to legal restrictions in Honduras.\u003c/p\u003e\n \u003cp\u003eIncome distribution varied among households, with 37% classified as diversified, 33% specialized, and 29% coffee dependent. The diversified group balanced incomes from coffee (39%), off-farm labor (34%), external sources (13%), other crops (6%), and animals (6%). The specialized group relied primarily on coffee (68%), followed by off-farm labor (13%), other crops (8%), external sources (6%), and animals (5%). Coffee-dependent households primarily relied on coffee (94%), with minimal contributions from other sources.\u003c/p\u003e\n \u003cp\u003ePoverty and food insecurity rates were high, with 31% of households living below the national poverty line (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Greater dependency on coffee correlated with a higher likelihood of poverty, with specialized and dependent households being 10% and 12% more likely to be below the poverty line, respectively (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). There is a high prevalence of food insecurity, especially regarding quality and diversity of food consumption. The FIES data show only 49% of the surveyed households were food secure, while 51% of households had a level of food insecurity in the last 12 months (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The percentage of food secure households went down from 55% \u0026ldquo;diversified\u0026rdquo; to 47% \u0026ldquo;coffee-specialized\u0026rdquo; and 43% \u0026ldquo;coffee dependent\u0026rdquo; as the dependency on coffee for income increased. The Rash model, used to analyze the FIES questions, revealed that among households, the prevalence of food insecurity (moderate and severe) was highest in the \u0026ldquo;coffee dependent\u0026rdquo; group at 15%, following the \u0026ldquo;diversified\u0026rdquo; group at 5%, and the \u0026ldquo;coffee-specialized\u0026rdquo; group at 3%.\u003c/p\u003e\n \u003cp\u003eConsistent with the FIES results, the MAHFP data show that households in the study area have access to adequate food provisioning for the whole year (mean of 11.8 months), with no significant differences between the three coffee income distribution groups. In the HDDS, we found that the mean for the total sample was 9.33 on the scale from 0 to 12. We also found that the HDDS is lower when households depend more on coffee incomes. The \u0026ldquo;coffee dependent\u0026rdquo; households have a score of 8.8, which means less access to diversified groups of food than the \u0026ldquo;coffee-specialized\u0026rdquo; (score of 9.4) and the \u0026ldquo;coffee dependent\u0026rdquo; (score 9.7) ones (see Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic variables of total households and coffee income distribution groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCoffee incomes distribution\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;348)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDiversified (n\u0026thinsp;=\u0026thinsp;130)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSpecialized (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDependent (n\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\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\u003ePPI - Probability to be below of the national poverty line \u0026ndash; (%) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e25%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36%*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of households members (Mean)\u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge of household head \u0026ndash; (Mean) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e46*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSize of farm (Ha) \u0026ndash; (Mean) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold head male \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to clean water (public system)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe education of the head of household \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\" rowspan=\"3\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003cp\u003e54%\u003c/p\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003cp\u003e14%\u003c/p\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome distribution \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHHs incomes with \u0026gt;\u0026thinsp;75% of coffee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHHs incomes with \u0026lt;\u0026thinsp;75% \u0026gt; 50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHHs incomes with \u0026lt;\u0026thinsp;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Access to Adequate Households Food Provisioning Months (MAHFP) \u0026ndash; (Mean) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow (1\u0026ndash;9 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate (10\u0026ndash;11 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh (12 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Households Dietary Diversity\u003c/p\u003e\n \u003cp\u003eScore \u0026ndash; HDDS \u0026ndash; (Mean) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e9.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFood access \u0026ndash; FIES \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFood security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm elevation (m.a.s.l.) \u0026ndash; (Mean)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e191.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e181.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation farm groups \u0026ndash; (%) \u0026dagger;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstantial stress (\u0026lt;\u0026thinsp;1,200 m.a.s.l.)\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\" colspan=\"3\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemain suitable and suitable (\u0026gt;\u0026thinsp;1,200\u0026ndash;1,800 m.a.s.l.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with a family member migrated in the last five years \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHouseholds with a family member is thinking of migrating \u0026ndash; (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"10\"\u003e\n \u003cp\u003eNote: Symbol \u0026dagger; indicates non-normally distributed variables. Kruskal-Wallis and Fishers\u0026rsquo; Exact tests were used to test for statistical differences, * indicate a significant difference between groups, followed by pairwise comparisons based on the Dunn-Bonferroni post hoc test was used for non-normally distributes variables. SD is the standard deviation.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2. Relationships between poverty and food security and climate and other hazards and households\u0026rsquo; demographic variables.\u003c/h2\u003e\n \u003cp\u003eThe MLR model explained 47% of the variance in the PPI score, with level of education and income dependency on coffee, farm size, number of members in the household, and farm elevation as the most important variables (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe multiple linear regression results for relation demographic variables, hazards, and poverty (y\u0026thinsp;=\u0026thinsp;PPI)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcronym\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStandard Error\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHH with coffee incomes\u0026thinsp;\u0026gt;\u0026thinsp;50% \u0026lt;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICLASS_Speci\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0539***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHH with coffee incomes\u0026thinsp;\u0026gt;\u0026thinsp;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICLASS_Depen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0390**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFarm size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0058***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe education of the head of HH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEDUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0250***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex of the HH head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHHSEX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHH had piped water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePWATER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrought as a hazard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDROU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow coffee prices as a hazard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPCOFFEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePest and diseases as a hazard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDCOFFEE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of HH members\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHHSIZE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0199***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElevation gradient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCLASS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAny other hazard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOHAZARD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02476\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: *** p\u0026thinsp;\u0026gt;\u0026thinsp;0.01; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 indicates a significant difference\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eModel results show that education, farm elevation and farm size have a positive relationship with the PPI score; that is, the larger the farm, or the greater the education level or higher the elevation, the lower the probability to be below the national poverty line. The converse relationship is found for the number of household members, with poverty increasing (i.e., PPI score decreasing) with a greater number of households members. Importantly, consistent with the descriptive results shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the MLR model shows that poverty increases when the level of income dependency on coffee increases. Overall, therefore, the PPI model results suggest that households with many members, with small farms, limited education, and high dependency on coffee for incomes are more likely to be below the poverty line.\u003c/p\u003e\n \u003cp\u003eFor the food insecurity indicator model, the metrics assessed for each classification algorithm are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. GBM demonstrated the best performance in precision and F1-score compared to other models. Therefore, GBM exhibits superior generalization capabilities, emphasizing its effectiveness in our modeling process. Consequently, for food security, from here onwards we present and discuss results only for the GBM model (Freund \u0026amp; Schapire, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Friedman,\u0026nbsp;\u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMetrics assessed for each classification algorithm\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLogistic Regression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGradient Boosting Classifier\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRandom Forest Classifier\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eXGBoost Classifier\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\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrecision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF1-Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAUC-ROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\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\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFor the food security indicator, the GBM model shows the greatest contribution to explained variance came from the PPI score (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the model, the variables with a high contribution to food security are the following: income dependency of coffee, size of farm, and education level of the household head. The hazards and long-term climate change impacts that the model found important are the low coffee prices, the pest and diseases and droughts (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). It is noteworthy, however, that although the coffee pests/diseases were herein categorized by non-climate events, the technicians and farmers focal groups associated this hazard with the coffee rust disease, caused by the fungus \u003cem\u003eHemileia vastatrix\u003c/em\u003e, whose occurrence is highly dependent on weather conditions such as high humidity and high temperatures.\u003c/p\u003e\n \u003cp\u003eThe regression and classification analysis confirmed the descriptive results that the variability of both the PPI score and the food security indicator (transformed FIES score) can be partly explained through the coffee income dependency. Other variables also contributed significantly to explaining the variability of the two dependent variables (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3. Climate and non-climate hazards and their effects on households\u003c/h2\u003e\n \u003cp\u003eWith an understanding that climate and non-climate hazards affect poverty and food insecurity, we analyzed the individual hazards in terms of reporting frequency, their impacts at farm- and household-level, and the resulting household coping strategies.\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eA total of 233 out of the 348 households reported to have experienced non-climate hazards, from which 60 households reported at least two non-climate hazards and 11 households reported three non-climate hazards during the last five years. A total of 152 households out of the 348 households reported experiencing climate hazards. From these 152 households, seven reported to have experienced two climate hazards, and one household reported three events over the last five years, for a total of 160 individual climate hazard events reported. The most frequently mentioned non-climate events were coffee pest and diseases (38%), and low coffee prices (23%), whereas the most frequent climate hazard for the households surveyed was drought, with 36% of the households perceiving it in the last five years.\u003c/p\u003e\n \u003cp\u003eA closer look at the intersection between the most reported climate and non-climate events, i.e., pests and diseases, low coffee prices and droughts, shows the complexity of the interplay between the various hazards to which farmers are exposed, with a considerable proportion having experienced two or more concurrently. In particular, coffee farmers are challenged by drought and pests and diseases (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The most reported hazards are coffee pest/diseases (38%), drought (36%) and low coffee incomes (23%). At least 32% of households reported two or more of these three events the last five years. A total 76 out of the 348 households (22%) perceived coffee pest/diseases and droughts, and 180 households (52%) perceived droughts or coffee pest/diseases (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Note that a total of 211 households (60.6% of total) indicated that at least one of the three events affected them.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe reported impacts of the climate events show that these affected mainly coffee yields and food security (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Fifty-three percent (53%) of these households said they noted a reduced coffee yields due to an extreme event in the last five years. Technicians and coffee farmers in focus groups mentioned an extended dry period during the coffee flowering or beyond, reduces the nutrient uptake by coffee plants, causing flower abortion or wilting of coffee trees, resulting in partial or total loss of the next coffee harvest, especially for coffee-dependent households. At least 38% of households reported food insecurity as a main impact of climate events. The food security impacts \u0026ldquo;Household food reduction\u0026rdquo; due to climate hazards show a greater perception of such impacts for the coffee-specialized and coffee-dependent income groups (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe impact of climate and non-climate hazards and responses from coffee households\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eImpacts of climate events\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eCoffee income groups (%)\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\" colspan=\"2\"\u003e\n \u003cp\u003e152 households reporting 160 events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eReduction of yields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePartial loss of harvest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal loss of harvest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHousehold food reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLower incomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHouseholds\u0026rsquo; responses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSold assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAccessed to loans or savings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLooked for another type of income\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\" colspan=\"3\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSomebody of the household migrated for looking a job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdjust the household budget\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eApplied CSA practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBought basic grains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePlanting or renovation of the coffee plantation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\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\" colspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWithout strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eImpacts of non-climate events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233 households reporting 300 events\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u0026thinsp;=\u0026thinsp;70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReduction of yields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePartial loss of harvest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal loss of harvest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDestabilization of household income and food reduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e17\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\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLower incomes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHouseholds\u0026rsquo; responses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiversified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialized\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSold assets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to loans or saving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSomebody of the household migrated for looking a job\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenovation coffee trees or pest/diseases management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWithout strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe reported non-climate events primarily impact income, total loss of harvest and reduction of yields (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Sixty-five percent (65%) of the 233 households said that they experienced a reduction of incomes or redistribution of household spending (including a reduction of food consumed) due to a non-climate hazard in the last five years. Also, farmers and technicians in focal groups mentioned heavily reduced income from coffee when prices and/or the production dropped (due to pests/diseases). Moreover, farmers must continue to pay back the credits they took to invest in coffee, often carrying over these debts into the following coffee season.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Coffee households\u0026rsquo; response against the impacts of climate and non-climate events\u003c/h2\u003e\n \u003cp\u003eDespite many households having experienced climate hazards (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) nearly half of those surveyed (47%) had no response against the impacts of the extreme climate events. Most coffee households know the climate event when it hits them but have no mechanism to be prepared for it.\u003c/p\u003e\n \u003cp\u003eAlthough the coffee households did not report storage of staple food (beans and maize) as a strategy to cope with impacts of the climate events in the survey, the farmers focal groups reported storage of staple food as a traditional households\u0026rsquo; strategy to keep food during periods of food shortage. The coffee farmers procure staple food (beans and maize) to store at home according to their financial capacity. According to the interviewed households and the focus groups discussions, this strategy is often hindered by low coffee prices, as households do not earn enough to procure beans and maize for the next months.\u003c/p\u003e\n \u003cp\u003eOnly seven percent of households reported having implemented CSA practices such as intercropping before the climate event occurred. According to interviews with coffee farmers and farmers focus groups discussions, coffee farmers used to do intercropping (planting beans and maize) every year as part of their livelihood strategies before the concept of CSA came to their attention. Many of the farmers and local technicians agree that intercropping coffee with beans and maize is an important strategy in response to climate impacts. Interestingly, however, they did not consider this a CSA practice. According to the survey, in the context of income reductions due to non-climate hazards such as low coffee prices and pest and diseases, 151 households (50%) used their savings or accessed credits as a coping strategy to be able to pay debts, health care, access food, or pay wages and inputs. It is noteworthy that the use of savings or access to credits was also the most important household strategy to cope with climate hazards. In both cases, coffee households used a financial strategy as a coping mechanism against extreme hazards.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.3. Actions and strategies designed and applied by the coffee stakeholders on coffee farms to deal with climate change.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe coffee stakeholders interviewed have been promoting CSA practices through projects and activities in the field (farms of the coffee households, experimental coffee research stations, experimental farms), supported by capacity building through workshops with farmers and local agronomists. These activities typically seek to boost the climate resilience capacities of coffee farmers against climate change conditions such as high temperatures, heat waves, and long dry seasons, additional data given in Online Resource 1.\u003c/p\u003e\n \u003cp\u003eFarmers reported as the most common practices applied soil management (minimum or no tillage), cover crops (no bare soil fallows), vegetative barriers with, e.g., \u003cem\u003eDracaena sansevieria\u003c/em\u003e, shade management using for instance \u003cem\u003eCajanus cajan\u003c/em\u003e, and soil cover like \u003cem\u003eBrachiaria ruziziensis\u003c/em\u003e grass between the rows of the coffee trees. Soil management interventions included ways to improve water retention and reducing high temperatures in the soil for instance by using \u003cem\u003eBrachiaria ruziziensis\u003c/em\u003e. The adoption of those practices is high (Online Resource 1). Yet, according to experts and local agronomists, the use of several of these practices is associated with certification requirements and it is unclear whether they respond to farmers\u0026rsquo; actual climate adaptation needs or whether they are motivated by avoiding price penalties due to not being certified.\u003c/p\u003e\n \u003cp\u003eDuring the focus groups farmers explained that some practices are not new to them. Practices such as vegetative barriers with \u003cem\u003eYucca gigantea\u003c/em\u003e, soil cover with lemongrass (\u003cem\u003eCymbopogon\u003c/em\u003e spp.), as well as intercropping with beans and maize, were already used before the initiation of recent climate projects. However, with the introduction of new coffee projects, farmers reported adopting additional practices tailored to their specific soil types and local weather conditions. As one farmer expressed,\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eWe adopted the practices according to our type of soil and weather around our farms, so, the technician brings practices such zacate (grass), avocado tree or sunflowers and we choose the most suitable option\u003c/em\u003e\u0026rdquo; (A coffee farmer, focus group interview, 2020).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eAnother farmer mentioned,\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eAll of us adopted these practices, as failure to do so could lead to loss of certification and subsequently, the price premium\u003c/em\u003e\u0026rdquo; (A coffee farmer, focus group interview, 2020).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eHowever, some farmers noted that while certain crops introduced through these practices, such as grasses, improve water retention and soil quality, they also required additional labour and competed with coffee plants for nutrients. Coffee farmers anticipate that these practices will ultimately reduce the cost of coffee production and increase coffee incomes. As one participant remarked,\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eThe grass retains water and enhances soil quality, but we need to mow it every 30 or 40 days to prevent it from competing with coffee trees for nutrients\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eAnother coffee farmer stated,\u003c/p\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u0026ldquo;\u003cem\u003eAs these practices are part of the certification, we are hopeful that the certification price premium will increase and we could access a better food\u003c/em\u003e\u0026rdquo; (A coffee farmer, focus group interview, 2020).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003eThese voices exemplify the perspectives of coffee farmers regarding climate resilience practices promoted by projects in the study region. However, these resilience practices are done with a fixed menu of technologies and options. That is, without an adequate process of co-design with the end users, i.e., the farming households. Ultimately, this results in a top-down technology adoption process that lacks consideration of farmers\u0026rsquo; climate and socioeconomic vulnerability conditions and adaptation pathways that are suitable for them.\u003c/p\u003e\n \u003cp\u003eFinally, the national institutions led by the Ministry of Agriculture and Livestock (SAG) provide climate-information services as a climate resilience strategy for agronomic management decisions such as planting times for annual crops and fertilizer management. Although the national coffee sector also uses and shares these information services with coffee farmers to decide the date to fertilize the coffee, it was not clear how exactly coffee farmers employed these climate services to manage their farms, or what benefits they derived from them.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn our study coffee households in the dry corridor of western Honduras with more dependence on coffee incomes are poorer, more vulnerable and food insecure than the households with diversified incomes such as off-farm labor and additional on-farm activities like animal husbandry and other staple crops.\u0026nbsp;Anderzén et al. (2020)\u0026nbsp;reported that Mexican coffee farmers with more diversified strategies tend to have higher incomes and are more food secure than households only involved in coffee cultivation. In that case, however, the households were more focused on on-farm and market-oriented strategies such as honey and coffee (cash flow) and maize and beans (staple food). Similarly,\u0026nbsp;van Asselt and Useche (2022)\u0026nbsp;reported that coffee income dependence for small-scale farmers households in Guatemala has a negative impact on their nutrition due to decreases in food production diversity, and no impact on-farm income. Furthermore, small-scale coffee farmers from Colombia with a strong focus on coffee (farm certification and/or high-quality coffee) may preclude increases in total household incomes as coffee activities demand substantial input in terms of time and labor from family members\u0026nbsp;(Vellema et al., 2015). Despite these challenges, coffee is a very important livelihood strategy for many rural communities in Latin America. In Guatemala for instance, coffee households enjoy better food security and higher incomes than farmers households growing only maize\u0026nbsp;(Lopez-Ridaura et al., 2019). Yet it is clear that diversifying incomes is key to improve levels of food security and prosperity. This underscores the importance of understanding the local economy, the income distribution in the target population sufficiently early in the design of agricultural development interventions. Projects and intervention programs should seek to contribute to balancing coffee households’ income sources between on-farm strategies such as coffee, other crops and animals, with market-oriented and off-farm strategies like small local business and jobs with better salaries.\u003c/p\u003e\n\u003cp\u003eOur analysis suggests that households with many members, with small farms, limited education, at lower elevations and high dependency of coffee incomes are more likely to find themselves below the national Honduran poverty line. These households tend to also experience more hazards events (climate and non-climate), further exacerbating food insecurity and poverty. The combined effects of climate and non-climate hazards such as low coffee prices, pests and diseases (for instance coffee rust), and recurrent droughts, coupled with the unavailability of efficient coping strategies, enhances the vulnerability of such coffee households\u0026nbsp;(Avelino et al., 2015). In general, high climate vulnerability leads to food insecurity and outmigration for coffee households in Latin America\u0026nbsp;(Bacon et al., 2021; Dupre et al., 2022; Harvey et al., 2018; Ruiz et al., 2015).\u003c/p\u003e\n\u003cp\u003eThe majority of the respondents in our study either possessed no coping strategies in the face of climate hazards or had to resort to financial interventions such as using savings, reducing expenses, and/or accessing credits.\u0026nbsp;Lopez-Ridaura et al. (2021)\u0026nbsp;for Guatemala, Honduras, El Salvador, and Mexico, and\u0026nbsp;Harris et al. (2020)\u0026nbsp;for India, reported similar financial strategies for farmers during shocks. Although it is encouraging that there are at least some response strategies, it is likely that there are clear limits to such coping mechanisms. More specifically, the current strategies are unsustainable considering the projected long-term climate changes for Central America which suggest warmer temperatures and increasingly frequent extreme events\u0026nbsp;(IPCC, 2012, 2021; Ruiz et al., 2015).\u003c/p\u003e\n\u003cp\u003eIn our study, although most of the climate-resilience practices provided by the coffee sector are focused on improving coffee tree productivity in the face of climate variability, they do not appear to respond to actual household climate adaptation needs. This reinforces the notion of a disconnect between the needs of end-users of technologies and resilient practices offered by service providers\u0026nbsp;(Akhter \u0026amp; Erenstein, 2017). There are two implications of this finding. First, although it could be in principle an attractive strategy to protect one of the main sources of gross income of households (coffee), the current strategies such as soil management with fodder crops and vegetative barriers do not have a direct effect on improving household food security, prosperity, adaptive capacity, or other farmers’ basic needs\u0026nbsp;(also see Groot et al., 2019; Khatri-chhetri et al., 2017; Long et al., 2016; Westermann et al., 2015).\u0026nbsp;Thus, a focus on resilience to improve coffee production alone may not help producers enough to become more resilient to food insecurity and climatic variation. Second, the incentives needed for the adoption of such practices (e.g., certification schemes) create more, rather than less, vulnerability, which ultimately hinders agricultural development\u0026nbsp;(Vellema et, al., 2015). It will remain a topic of future study whether the lack of broader farming and food system focus when promoting climate-smart practices and technologies 'locks’ farmers into a maladaptation pathway.\u003c/p\u003e\n\u003cp\u003eTherefore, understanding the vulnerability of households with a multidisciplinary view (social, biophysical, and economic) can shed light on the debate around building inclusive climate adaptation strategies for coffee farmers\u0026nbsp;(Donatti et al., 2019), improve the adoption of climate-resilient practices\u0026nbsp;(Amadu et al., 2020; García de Jalón et al., 2017)\u0026nbsp;and climate adaptation policies and programs\u0026nbsp;(Harvey et al., 2018). Building resilience and adaptive capacity should include households needs such as income diversification as well as food availability and food production but designing such necessitates the participation of affected households.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study analyzed coffee farmer food insecurity, poverty, and their exposure and responses to climate and non-climate hazards, seeking to understand whether and how CSA practices promoted by coffee stakeholders are responding to households’ socioeconomic vulnerability and their adaptation needs. Our findings suggest that diversified households (whose income depends less on coffee) which engage more in off-farm labor have a greater probability of being food secure, while the converse is true for coffee specialized and coffee-dependent households. Importantly, although coffee sector stakeholders invest in climate-resilient coffee production techniques, the coffee households’ exposure to and impacts from climate and non-climate hazards often result in crop and income loss, and food insecurity. Moreover, in the absence of effective coping strategies, coffee farmers only can rely on financial mechanisms to cope with these impacts. Yet, such coping mechanisms are unlikely to work in the long run, especially if climate conditions continue to worsen in the region. Thus, to truly address climate impacts, food insecurity and poverty, the coffee sector should seek to better understand the role of households’ food security as part of climate resilience and design strategies that generate benefits for the coffee value chain but also for the farming households’ livelihoods. This necessitates more research on how climate variations affect local food systems and how market-oriented crop value chains can generate opportunities to improve local households' well-being and resilience against them.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAggarwal, P. K., Jarvis, A., Campbell, B. M., Zougmor\u0026eacute;, R. B., Khatri-chhetri, A., \u0026amp; Vermeulen, S. J. (2018). The climate-smart village approach : framework of an integrative strategy. \u003cem\u003eEcology and Society\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1).\u003c/li\u003e\n \u003cli\u003eAkhter Ali, \u0026amp; Olaf Erenstein. (2017). 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Retrieved from https://cgspace.cgiar.org/bitstream/handle/10568/68403/Scaling-Up FINAL.pdf?sequence=1\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Ecologically speaking, the Central American Dry Corridor (CADC) is a tropical dry forest region that extends throughout Mesoamerica, from the Pacific Coast of Chiapas (Mexico) to the western part of Costa Rica and western provinces of Panama. \u003cb\u003eGuatemala, El Salvador, Honduras, and Nicaragua\u003c/b\u003e are the most exposed to precipitation extremes and drought (van der Zee et al., 2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4145448/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4145448/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCentral America faces significant vulnerability to climatic variations. In recent years, national and international organizations have been working on climate-smart agricultural (CSA) to support coffee farmers in adapting to climate change. However, limited scientific evidence exists regarding the efficacy of these strategies in mitigating vulnerability. This study aims to assess the suitability of CSA practices promoted by Honduras' coffee sector in addressing the needs and vulnerability of coffee-farming households. Here, we integrated quantitative and qualitative methods, to assess how coffee farmers' livelihoods, poverty levels, and food insecurity status relate to their dependence on coffee income, prevailing stressors, and responses from farmers and value chain stakeholders. Data from a survey of 348 coffee farmers in western Honduras, along with key stakeholder interviews and focus group discussions, inform our analyses. Results indicate that poverty levels rise with increased reliance on coffee income, while diversified income sources correlate with greater food security among households. Nevertheless, despite efforts to enhance coffee tree productivity and soil resilience, most CSA practices neglect the food insecurity concerns of coffee farmers. Interviews and discussions reveal uncertainty among farmers regarding maintaining food security under extreme hazards. Consequently, coffee households remain vulnerable to climate and non-climate hazards, leading to crop losses, income instability, and food insecurity. Our findings underscore the need for a fundamental shift in the scope of coffee CSA practices towards a more holistic approach that addresses food security and income.\u003c/p\u003e","manuscriptTitle":"Understanding coffee farmers’ poverty, food insecurity and adaptive responses to climate stress. Evidence from the dry corridor of western Honduras","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 10:49:35","doi":"10.21203/rs.3.rs-4145448/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efdf6406-aa3d-49e9-9368-bde2d1114e9b","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T22:40:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 10:49:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4145448","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4145448","identity":"rs-4145448","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}