Confronting intrinsic variability: How farmers understand, manage, and cope with synchronous alternate bearing in a perennial crop system

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Garcia, Laura Kuhl, Colin M. Orians This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4193379/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Human Ecology → Version 1 posted 7 You are reading this latest preprint version Abstract Global efforts aim to support the socio-ecological resilience of farms and farmers to environmental disturbance. Farmers of many perennial crops also contend with intrinsic yield fluctuations, or alternate bearing (AB), which can synchronize across regional and national scales. Synchronous AB across a farm has direct implications for farmer livelihoods but is absent from discussions of resilience. We conducted a socio-ecological study on farm-scale AB in Coffea arabica to assess (a) how farmers understand, manage, and cope with AB, and (b) opportunities for, and constraints upon, their capacity to limit it. We integrate semi-structured interviews (n=29) with quantitative analyses of AB across participant farms. Farmers identify AB as an inherent challenge with differential impacts on management based on whether they perceive AB as extrinsically- or intrinsically driven. The former employ strategies to ameliorate the effects of weather and pests, while the latter prioritize fertilization and plant renovation strategies. Quantitative analyses found that pruning and fertilizer management are unrelated to signals of AB, but AB decreases significantly with farm elevation, perhaps due to lower pest pressure which can exacerbate AB. Synchrony within and across farms increased after a regional pest outbreak, supporting the synchronizing potential of environmental disturbances. These findings indicate that AB persists despite management efforts and may be outside farmer influence, raising questions about coping strategies. Farmer-reported coping strategies for low years include loans, external income, and limits on household spending, with implications for broader resilience capacity. Intrinsic AB merits greater attention as a determinant of resilience in perennial crops. alternate bearing resilience crop yield agroecosystems synchrony biennial bearing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The mounting pressures of environmental change on food systems have motivated global research and policy efforts towards securing socio-ecological resilience of coupled farms and farmers (Antwi-Agyei et al. 2021 né et al. 2016; Food and Agriculture Organization of the United Nations 2018 ; Roosevelt et al. 2023 ; Tendall et al. 2015 ; Zurek et al. 2022 ). Much of the existing research on the socio-ecological resilience of farm systems (i.e., their capacity to adapt to and cope with change) has focused on the social conditions that influence the response trajectory of farm households following extrinsic disturbances to the agroecosystem (e.g., climate extremes, pest outbreaks) (Dar et al. 2013 ; Jezeer et al. 2019 ; Marrero et al. 2022 ; Philpott et al. 2008 ). Yet farmers of many perennial crops also contend with the challenge of intrinsic variability in crop yield, known as alternate (or biennial) bearing (Goldschmidt and Sadka 2021 ; Monselise and Goldschmidt 1982 ), thus far absent in discussions of socio-ecological resilience. Alternate bearing (AB), a pattern of plant reproduction in which high-yielding years are followed by low-yielding years, is exhibited by numerous crops of global socio-economic importance (e.g., apple, olive, avocado, coffee, among others). AB is generally understood to be driven by intrinsic plant-level resource allocation tradeoffs (i.e., overbearing) which can become synchronous across space (Esmaeili et al. 2021 ; Garcia and Orians 2022 ; Ye and Sakai 2016 ). The conditions that induce synchrony in crops are unresolved, though extrinsic environmental and biotic shocks are hypothesized to synchronize intrinsic variability in seed output across natural populations of mast seeding trees (i.e., the environmental veto hypothesis) (Bogdziewicz et al. 2019, 2017 ; Crone et al. 2009 ; Vacchiano et al. 2021 ). A recent global analysis found that signals of synchronous AB persist even in national-scale crop yields, despite extensive agronomic research and practice to limit AB via breeding and management (e.g., fruit thinning, pruning) (Garcia et al. 2021 ). As signals of synchrony decay with increasing spatial scale (Noble et al. 2018 ), this evidence suggests that AB cycles are also often synchronous at the farm scale, with direct implications for farmer livelihoods in perennial cropping systems. Synchronous farm-scale AB (i.e., yield fluctuations across a farm) may pose a particular threat to smallholder farmers, (< 10ha) for whom stable yield and income are recognized as key building blocks for resilience capacity (Baca et al. 2014 ; Cohn et al. 2017 ; Collins et al. 2010 ; Jezeer et al. 2019 ; Nawaz et al. 2018 ). An account of farmer perspectives and experiences of AB is thus far lacking from the literature, limiting an applied understanding of the phenomenon and its impact on farmer livelihoods, management decision-making, and their broader resilience capacity. Quantitative analyses of synchronous farm-scale AB are similarly scarce. The patterns and mechanisms of farm-scale synchrony remain remarkably unresolved across systems, and little is known about farmer ability to effectively manage synchrony. To address these gaps, we conducted a socio-ecological assessment of farm-scale alternate bearing using smallholder coffee (Coffea arabica ) farms as a system. We integrated (a) in-depth interviews to capture how farmers understand, manage, and cope with AB as a source of yield variability, with (b) quantitative analyses of AB within and across participant farms to assess opportunities for, and constraints upon, farmer capacity to limit synchrony. Drawing on existing scientific understanding, we developed a novel conceptual model of hypothesized socio-ecological components and relationships underlying farm-scale AB (Fig. 1 ). Specifically, we expect patterns of synchronous farm-scale AB arise from linkages between intrinsic plant-level resource tradeoffs (Garcia and Orians 2022 ), management practices and other farm features that impact resource dynamics (e.g., plant pruning, nutrient inputs, elevation) (Baitelle et al. 2019 ; Haberman et al. 2019 ; Sarmiento-Soler et al. 2022 ), and extrinsic environmental and biotic shocks (e.g., pest outbreak, drought, failed pollination). Here we provide an analytical structure for integrating empirical qualitative and quantitative findings to explore the impact of AB in these socio-ecological systems. Despite apparent socio-ecological linkages underlying crop AB, most existing studies on AB have a narrow agronomic focus and predominantly address plant-level mechanisms (Bolivar-Medina et al. 2019 ; Fernández et al. 2015 ; Garcia and Orians 2022 ; Haberman et al. 2019 ; Kallsen et al. 2007 ; Zuo et al. 2018 ). The degree to which farmer understanding of underlying drivers aligns with scientific understanding of intrinsic resource allocation tradeoffs (Ye and Sakai 2016 ; Esmaeili et al. 2021 ; G. M. Garcia and Orians 2022 ), and whether this in turn shapes management approaches, is thus far unresolved. To understand farmer decision-making around AB management and coping strategies, it is important to characterize how they perceive the phenomenon and what they understand to be the key drivers (Antwi-Agyei et al. 2021 ; Campbell and Beckford 2009 ; Frank et al. 2011 ; Jezeer et al. 2019 ; Quiroga et al. 2015 ). If farmers perceive that AB is driven by resource tradeoffs, they may prioritize management practices such as plant pruning and fruit thinning expected to limit resource allocation tradeoffs to achieve more consistent yields over time (Baitelle et al. 2019 ; Grigorian and Bidarigh Sharemi 2003 ; Meland 2009 ). Conversely, and perhaps less intuitively, applying fertilizer to increase available resources and maximize yield may exacerbate AB, as high yields in one year can further deplete resources for seed production the following year (Garcia and Orians 2020 ). If farmers instead understand AB to result primarily from extrinsic factors, management decisions may seek to limit plant susceptibility to extrinsic variability, through for example pest or microclimate management. In addition to plant-level practices, farmers may also attempt to manage AB at the farm scale by maintaining asynchronous AB cycles among plants or plots, such as through serial pruning or planting. Critically, due to the paucity of quantitative assessments of farm-level synchrony, evidence is also lacking regarding whether AB management practices, intentionally or not, create feedback for farm-level AB (i.e., if and how farm-scale AB impacts management decision-making, and if and how management impacts farm-scale AB). When management fails to eliminate farm-scale synchrony, which evidence suggests is often the case (Esmaeili et al. 2021 ; Garcia et al. 2021 ; Noble et al. 2018 ; Rosenstock et al. 2011 ), we expect it impacts farmer livelihoods and broader resilience capacity to stressors. Existing literature on smallholder resilience has focused on the links between livelihood assets, capacities, and extrinsic (e.g., climatic, financial) risks (Darnhofer 2010 ; Hung Anh et al. 2019 ; Jezeer et al. 2019 ; Rahn et al. 2014 ). While important progress has been made in describing features of resilient socio-ecological systems (Eakin and Luers 2006 ; Folke et al. 2016 ; Grafton et al. 2019 ; Turner et al. 2003 ), intrinsic yield variability, perhaps due to a lack of awareness regarding the socio-economic importance of the phenomenon and/or its underlying mechanisms, has not been incorporated. We hypothesize that AB plays a much more central role to the socio-ecological resilience of perennial cropping systems than previously acknowledged, and farmer responses to AB could result in negative or positive resilience pathways. To illustrate, a farmer may take out a short-term loan to cope with a low-yielding year in the AB cycle, making them especially vulnerable to unpredictable disturbances that disrupt the projected high-yielding year (e.g., drought, hurricane, etc.) as consecutive low years would likely leave them in debt. Alternatively, AB could motivate crop diversification as a buffer against income fluctuations and increase their broader resilience capacity to variability (Blesh et al. 2023 ; Darnhofer 2010 ; Shapiro-Garza et al. 2020 ). Coping strategies for farm-level yield fluctuations may extend beyond the farm, as well, such as through off-farm livelihood diversification (Jezeer et al. 2019 ; Scoones 1998 ). Characterizing farmer responses to AB is key to identifying opportunities to support farmer resilience capacity to both intrinsic and extrinsic challenges. The aim of the present study was to document farmer knowledge and experience of farm-scale AB and assess opportunities for, and constraints upon, their capacity to limit synchrony. Specifically, using coupled coffee farms and farmers as a system, we asked: (Q1) How do farmers understand, manage, and cope with AB? (Q2) Is AB related to variability among farms in pruning practices, fertilizer management, or elevation? In the analysis phase, we discovered that a fungal rust outbreak across the farms, which occurred at the midpoint of crop production timeseries gathered for Q2, afforded us a unique opportunity to conduct a follow-up assessment of environmental disturbance as a potential driver of synchrony, leading to: (Q3) Did signals of synchrony within and among farms increase following a fungal rust break? In synthesizing qualitative findings from in-depth farmer interviews with quantitative assessments of farm-scale AB, we advance an empirical, socio-ecological understanding of farm-scale AB in coffee and pilot a novel integrated approach that could be adapted to other AB systems. Alternate Bearing in Coffee Predominantly cultivated by smallholders, coffee is known to exhibit AB at the plant level, and noticeable national-scale yield fluctuations in some countries are indicative of widespread synchrony (Bote and Jan 2016 ; DaMatta 2004 ; Garcia et al. 2021 ; Garcia and Orians 2022 ; Jaramillo-Botero et al. 2010 ). While there has been ample research on the socio-ecological dynamics of coffee systems in the past (Borges-Méndez and Caron 2019 ; Cerdán et al. 2012 ; Imbach et al. 2017 ; Jezeer et al. 2019 ; Perfecto et al. 2019 ; Ricketts et al. 2004 ; Shapiro-Garza et al. 2020 ), it has tended to focus on external drivers of yield variability (e.g., weather, pests, and pollinators) or the social conditions (e.g., local ecological knowledge) and ecological consequences of farm management decisions (e.g., wildlife habitat) and not on AB. The fact that coffee exhibits AB across spatial scales implies that internal dynamics of plants are an important determinant of yield and thus should be integrated with an understanding of socio-ecological resilience in the system. Coffee-farming landscapes feature substantial heterogeneity, making them well-suited for investigating differences across farms. Farms range from high input to low input systems and span a broad elevation gradient (Moguel and Toledo 1999 ; Sarmiento-Soler et al. 2022 ). The multitude of factors influencing smallholder decision-making both on and off the farm are complex and dynamic (Adane and Bewket 2021 ; Blesh et al. 2023 ; Darnhofer 2010 ; Hung Anh et al. 2019 ; Jezeer et al. 2019 ; Ndiritu et al. 2022 ), but the role of AB in these decisions is thus far unresolved. With the exception of shade-grown cultivation, which can limit AB relative to sun-grown monocultures but is most common in lower elevation regions with suboptimal climate (Schnabel et al. 2018 ; Vaast et al. 2006 ), little is known about how management practices feed back, in turn, to influence AB. We assess whether signals of AB are related to three additional variables expected to influence resource dynamics: pruning practices, fertilizer inputs, and farm elevation. We hypothesized that farms where plants are pruned to have fewer vertical (i.e., orthotropic) stems per plant to limit resource depletion would exhibit weaker signals of AB, consistent with evidence from experimental conditions (Baitelle et al. 2019 ). We similarly expected that signals of farm-scale AB would be negatively related to farm elevation, since slower growth rates and lower yields at higher elevation may limit overbearing (Sarmiento-Soler et al. 2020 ), and positively related to fertilization rate, as increased resource availability is expected to exacerbate resource-driven overbearing (Garcia and Orians 2020 ). Methods Study site and participant selection The study was conducted in the coffee-farming community of Santa María de Dota, Costa Rica. Located in the region of Los Santos, Santa María de Dota is renowned for producing high-quality, high-elevation coffee (1500-2000m). Soils are ultisols of alluvial origin (Castro-Tanzi et al. 2014 ), annual precipitation averages 2400 mm/year and the regional production constitutes roughly 40% of Costa Rican coffee output (Icafe 2020 ). Between June and August of 2017, participants (n = 30) were selected using a random sampling scheme from the 508 coffee farmer associates of the local cooperative, CoopeDota, stratified to capture the local 500m elevation gradient of coffee production (1500-2000m). The selection criteria included willingness to participate and ownership of an active coffee farm. In this community, farm owners typically make management decisions and play an active role in farm labor, though both farm managers and owners were interviewed as needed to obtain accurate information regarding management practices. One participant withdrew from the study; 29 participants remained. Farmer understanding, management, and coping strategies Semi-structured interviews (n = 29) were conducted in Spanish in June 2019 by the lead author (GMG), who is fluent in Spanish and had built rapport with the participants over a two-year period through conversations and farm visits. Verbal consent was audio recorded from all participants prior to conducting interviews. All interview materials were approved by the Tufts University Institutional Review Board under IRB study #1705017. Interviews were recorded for subsequent transcription and coding. Interview questions first established farmer demographics (years of experience in coffee farming; education level; percent of household income derived from coffee production) and farm characteristics and management (farm size, fertilization rate, pest and weed management; interview questions available in S1). Before delving into the subject of AB, participants were asked to identify the biggest challenges facing coffee farmers to position AB in this broader context. Further questions addressed personal experience with AB, understanding of the underlying causes, awareness and use of management strategies, and barriers to their implementation. Interviews also explored perceived severity of AB in recent years and the spatial extent of synchrony (farm-level, regional). A qualitative content analysis was conducted based on the conceptual framework in Fig. 1 to (a) characterize farmer understanding of AB, including its drivers and relationship to farm management and elevation, (b) document the range of farm management practices reported for AB, and (c) assess the motivations and barriers behind AB management choices with particular attention to the role of AB in driving farmer decision-making on- and off-farm. As farm-scale AB is likely to be especially challenging for those whose livelihood relies entirely on coffee revenue, the degree of reliance was determined and used to assess patterns in practices and perceptions across farmer subsets. Knowledge and perceptions of AB were coded by the following themes: underlying causes, drivers and extent of synchrony, personal experiences and perceived severity, and relation to management practices and elevation. Management and coping strategies were coded by employed and preferred practices (pest and weed management; fertilizer applications) and their relation to AB, barriers to preferred practices, their additional benefits and disadvantages, and strategies to cope with and prepare for low-yielding years. Pattern strength was assessed by consistency in farmer responses. Farm management, physical farm features, and indicators of alternate bearing We assessed patterns of AB across participant farms, and their relation to pruning, fertilizer, and elevation, at two relevant focal scales: 10-year time series of farm-scale production to capture signals of farm-wide synchrony and 2 years of plant-scale reproduction on each farm to capture underlying plant-level tradeoffs. We requested 10 years of production data (2008–2017) from the local coffee cooperative with participant consent. Production data were available and granted for n = 25 interviewees. As production records maintained by the cooperative were not associated with farm area (see S2 for a detailed data description), analyses were conducted on total farm-level production rather than yield. We note that this necessary limitation would be likely to mask a signal of AB rather than exaggerate it, resulting in conservative farm-level estimates. Four of the 25 farms had less than 10 years of data available; since their inclusion did not alter the results, results are reported below for the full set of n = 25 farms. Farm-level AB indices (ABI farm ) were calculated as \({\text{A}\text{B}\text{I}}_{\text{f}\text{a}\text{r}\text{m}}=\frac{\sum \frac{\left|{a}_{i+1 }–{ a}_{i}\right|}{{a}_{i+1}+{ a}_{i}}}{n-1}\) where a i is the production value in the i th year and n is the total number of years (Monselise and Goldschmidt 1982 ). Alternate bearing index (ABI) values can range from 0 (no AB) to 1 (severe AB). Production data were paired with data on pruning practices, farm elevation, and fertilizer load obtained through the interviews and farm visits to assess their relationships with ABI farm, Information on fertilizer load was obtained during farmer interviews. Farm visits conducted in June 2017 to obtain farm elevation and pruning practices. At 10 randomly selected points across each farm, elevation was measured with a Garmin™GPS64s and the number of orthotropic stems were counted on adjacent plants and averaged across the farm. All statistical analyses were performed in R Studio software. We fit a linear model that included ABI farm as the response variable and scaled averages of farm elevation, fertilizer load per hectare, and orthotropic stems per plant as predictor variables. Results of the best fit model, determined by the Akaike Information Criterion, are presented below. To capture signals of AB at a plant level, study plots measuring 10m x 6m plots were established on farms owned by study participants in June 2018 and followed for 2 years. Plot location within the farm was determined by randomly selecting 1 of the 10 sampling points from the 2017 farm visits, excluding those near farm limits or barriers. Garmin™ GPS64s was used to record the plot’s location and elevation. Ten plants were randomly selected within each plot for sampling. Measurements were taken on sample plants at fruit initiation (June) and maturity (December) for two consecutive reproductive cycles: 2018 and 2019. At each sample plant, the number of orthotropic stems was recorded and 4 horizontal (plagiotropic) branches (one in each cardinal direction per (Garcia and Orians 2022 ; Jaramillo-Botero et al. 2010 ) were tagged for measurements at fruit initiation and maturity in 2018. Four new branches were selected in 2019. To minimize variability, focal branches were selected with the following criteria: fruit-bearing, located within the upper third portion of the plant, no lateral shoots, and a healthy branch tip. At each sampling point, branch length was measured and the number of fruited nodes, total leaves, and diseased or infected leaves, and fruits were recorded. At fruit maturity, any dead branches were also recorded. As three plots were not able to be assessed during the second year (due to sale of the land or other unforeseeable reasons), the analyses reported below capture a final sample of n = 26 plots. Tradeoffs in reproductive traits between years were assessed using generalized linear models with 2019 fruited nodes or 2019 fruits per node as the response variable and 2018 fruited nodes or fruits per node as the predictor, respectively. Separate models were fit to look for between-year tradeoffs at fruit initiation and fruit maturity. A Poisson distribution with a log link was used, and a random effect of plot ID was included when its inclusion significantly improved the model’s Akaike Information Criterion, assessed with the function ICtab() in the package bbmle (Bolker and R Development Core Team 2020). As tradeoffs were only significant in the number of initiated fruits per node between years (but not fruited nodes; see Results), we calculated the ABI in initiated fruits per node and tested for a relationship with pruning, fertilizer, and elevation. The ABI for fruits per node (ABI fruits ) was calculated as $${\text{A}\text{B}\text{I}}_{\text{f}\text{r}\text{u}\text{i}\text{t}\text{s}}=\frac{|{fruits}_{2019}–{fruits}_{2018}|}{{fruits}_{2019}+{fruits}_{2018}}$$ where fruits 2019 is the average plant-level fruits per node in 2019 and fruits 2018 is the average plant-level fruits per node in 2018. We fit a linear model with the same model structure as the farm-scale data, substituting ABI fruits as the response variable. The effect of initial fruit load on branch death rate, another tradeoff that can exacerbate AB, was also analyzed using a generalized linear model with a binomial distribution and random effects of plant ID and plot ID. The same predictors (fertilizer, plant orthotropic stems, and elevation) were included in addition to the initial fruit load per branch. Finally, since both total reproductive effort and variability are important for farmer decision-making, we analyzed cumulative reproduction using the same model structure but substituting the total fruits per node and fruited nodes across years as the response variables. Pairwise Synchrony after Fungal Outbreak A fungal rust outbreak struck coffee-growing regions in Central America in 2012, at the midpoint of the farm-level production timeseries (Avelino et al. 2015 ). To assess whether signals of synchrony increased after the outbreak, which would support the hypothesis that a biotic shock can induce synchrony, we analyzed a subset of the data including only farms with a complete 10 years of data (n = 21 farms). Timeseries were split before (2007–2011) and after (2012–2016) the rust outbreak. To capture whether farm-scale ABI (as a metric of farm-scale synchrony) increased, we calculated and compared average ABI farm before and after the rust outbreak. To assess whether synchrony among farms increased, we calculated and compared pairwise correlations (Pearson’s r) in farm-scale production data across all 210 pairs of farms. Results Farmer understanding, management, and coping strategies Interview participants were predominantly smallholders (median farm size: 4 ha; range 0.25-21 ha). Farmer demographics and management practices are presented in Table 1 . Note that while 6 of the interviewed farm owners were female, 100% of farm managers (those making on-farm management decisions) were male. In instances where owners were not the farm managers, the farm manager was also interviewed to obtain accurate data on management practices. To contextualize AB within the broader spectrum of challenges facing farmers, participants were asked what they considered to be their greatest challenges as coffee farmers. Farmers reported five key contemporary challenges: economic viability (prices received for coffee relative to the cost of inputs; 77% respondents), climate variability (31%), pests and diseases (23%), and achieving yields of sufficient quantity (19%) and quality (11%) (n = 26). Most farmers characterized AB as an intermediate problem (65%) though some described it as a severe challenge (20%) and a few as a minor challenge (15%). All respondents reported personal experience with AB at some point in their coffee-growing careers. Opinions varied regarding whether patterns of AB have shifted in recent years, with 15% reporting it has become more severe, 33% reporting that it has always been the same, and another 33% reporting that it has improved (the remainder were unclear or unsure). Most farmers (85%) reported a degree of asynchrony among plants or parcels, leading to greater stability at the farm level than plant level. At a regional level, one third of respondents (38%) reported that yields fluctuate similarly due to weather, though several pointed out that there are exceptions to the regional trend and that patterns also depend on farm management. Table 1 Farmer demographics and management practices. Demographics Gender identity Male (78%); Female (22%) Formal education Median: 6 years; Range: 6–18 years Coffee-farming experience Median: 25 years; Range: 6–51 years Reliance on coffee revenue 100% of income derived from coffee (37%); 50–99% of income derived from coffee (30%); <50% of income derived from coffee (33%) Management Practices Inorganic fertilizer Applications/year Median: 3; Range: 0–3 Kg/ha/year Median: 570 kg; Range: 125–820 kg Weed control Applications/year (Chemical) Median: 1; Range: 0–2 Applications/year (Manual) Median: 2.75; Range: 1–5 Fungicide Applications/year Median: 3; Range: 0–5 Shade cover Farm-level estimate (2017) Median: 20%; Range: 8–38% Perceived relationships between alternate bearing, management, and farm features Farmers identified a range of causes of AB. The most frequently reported cause was “weather”, an external driver (48%). For these farmers (n = 13), effective pest and disease management were viewed as key management strategies, suggesting an implicit link between poor weather, an increased incidence of pest and disease, and AB. This group was also more likely to identify shade as a management strategy, presumably given the buffering effects against radiation and precipitation extremes (38%). The second most widely reported cause of AB was “poor farm management” (41%), which in this context aligns with an understanding of intrinsically-driven AB. Among this subset of respondents (n = 11), fertilizer (55%) and plant renovation (pruning or entirely replacing old trunks with juvenile plants; 36%) were identified as key management strategies; both of which target a plant’s nutritional status and vigor. Additionally, several farmers explicitly reported intrinsic drivers of AB in terms of plant stress and resource exhaustion (22%) while two more farmers (7%) stated, “It’s just the way coffee is.” Another farmer reported, “[AB] is not a grave issue because one has always worked this way. One knows that if there is a good harvest, the next one will be lower.” Pruning was the only factor that was reported as both a cause of AB (19%) and a management strategy (30%). This apparent discrepancy may reflect two approaches to pruning employed by coffee farmers. In one approach, all the orthotropic stems on a plant are cut back (i.e., coppicing), leaving only the trunk from which new stems will sprout to bear fruit in three or four years. Farmers who identified pruning as a cause of AB likely referred to this approach, as they reported that plants can take several years to recover after intensive pruning. If done systematically across a farm, however, this approach can also be a management strategy to achieve farm-level asynchrony by attempting to maintain some plants in the productive phase while others are in the recovery phase. The other pruning approach involves selectively pruning orthotropic stems on a plant such that the remaining stems can achieve a better resource balance (Baitelle et al. 2019 ). This approach is intended to prevent individual plants from overbearing and thus targets plant-level yield consistency. Overall, re-juvenilizing the farm, either through plant replacement or pruning, was the top reported management strategy for alternate bearing (48%), followed by fertilizer (41%) and pest/disease management (33%). Only about a quarter of interviewees (26%) identified shade as a management strategy for AB, though several other benefits of shade cultivation were mentioned, including microclimate regulation (33%) and improved soil fertility and weed control when pruned debris is left as ground cover (22%). Perceived disadvantages, however, included decreased coffee yields (19%) and increased fungal disease incidence (11%). There was little consensus regarding the relationship between farm elevation and AB. Some asserted no effect of farm elevation on AB (37%), others a stabilizing effect (lower AB; 30%) with increasing farm elevation, and still others reported the opposite (19%). The remainder were unclear or unsure of the relationship. Adaptive and coping strategies for low-yielding years When asked if there were any strategies that they employed to prepare for low years, the most common response was “nothing” (37%), yet nearly half of these respondents were entirely reliant on coffee for their income. Diversifying income, either on or off the farm, was the second most frequently reported strategy (30%), followed by efforts to budget or save during higher yielding years (26%). Most (70%) reported that they did not change their investment in farm management to cope with low years, and several stressed the importance of maintaining a consistent management regimen despite lower farm income to avoid a downward spiral of farm health. In the words of one farmer, “When there are bad harvests it is difficult to maintain a living. I make the sacrifice of doing the same [management] on the farm.” Those that did report restricting their fertilizer application following a low year (22%) did not rely as heavily on the coffee harvest for their livelihood (median relative income derived from coffee was 15% for these respondents). Only two farmers (7%) reported using cheaper fertilizer products as a coping mechanism. To cover the costs of maintaining farm management following a reduced harvest, farmers reported supplementing with other income (33%) or taking out loans from the cooperative (22%). The farmers using other sources of income differed from those taking out loans; the former were less reliant on coffee (50% median coffee income values compared to 100%) and had smaller farms (2.1 ha compared to 8.75 ha). Cutting back on household spending (30%) was another prominent coping strategy and included putting off repairs on the home or car and limiting any leisure spending. Three farmers (11%) reported not taking any measures to cope with a low year; two of whom were not reliant on coffee for their livelihoods. The third owned the largest coffee farm included in the study (21 ha) and reported not being affected much by AB. Alternate bearing index (ABI) in relation to management and elevation Farm-level ABI (ABI farm ; Fig. 2 ) between 2008–2017 ranged from 0.08 to 0.72 with a mean of 0.26 (SD = 0.13) across participants (n = 25; Fig. 3 ). There was a significant negative relationship between ABI farm and farm elevation (χ 2 = 5.04; df = 1; p = 0.025; Fig. 3 ) which explained 18% of the variability. ABI farm was not significantly related to fertilizer rate or pruning (mean number of orthotropic stems). At a plant level, field assessments confirmed intrinsic resource tradeoffs as an underlying driver of coffee AB, but tradeoffs were not significantly related to elevation, fertilizer, or orthotropic stems. Specifically, plants that initiated a greater number of fruits per fruited node in 2018 initiated significantly less in 2019, consistent with a between-year tradeoff in the number of fruits per fruited node (n = 221 plants across 26 plots; χ 2 = 11.8, df = 1, p < 0.001; Fig. 4 A). The ABI for the number of initiated fruits per fruited node (ABI fruits ) ranged from 0.008 to 0.893 with a mean of 0.326 (SD = 0.224) across sampled plants but was not significantly related to any of the focal predictor variables. A high initial branch-level fruit load was associated with higher rates of branch death, which can contribute to an AB cycle (n = 8505 branches, 253 plants, 26 plots: χ 2 = 29.317, df = 1, p < 0.0001; Fig. 4 B). There was again no significant relationship between fertilizer, elevation, or orthotropic stems and branch death, or with cumulative fruited nodes and cumulative fruits per node across years. Signals of synchrony pre- and post-disturbance Signals of synchronous AB within and among farms were higher after the fungal rust outbreak than they were before. Mean ABI farm , an indicator of farm-scale synchrony, was nearly 70% greater over the period following the outbreak (0.272 ± 0.023 SE) compared to before (0.189 ± 0.025 SE). Among all pairs of 21 farms with complete, farm-scale production data (n = 210 pairwise combinations), average synchrony increased 2.5 times immediately following the fungal rust outbreak (Pearson’s r = 0.573 ± 0.025 SE vs. 0.221 ± 0.033 SE; Fig. 5 ). Discussion In a Costa Rican coffee farming community, we conducted a socio-ecological study to characterize farmer knowledge and experience of AB as a source of intrinsic yield variability (including impacts on farmer livelihoods, its role in management decision-making, and predominant coping strategies) and assess opportunities and constraints upon farmer capacity to limit farm-scale synchrony. Farmer interviewees identified AB as an inherent challenge in the system, further supported by signals of AB in their farm-scale production data, and that low-yielding years often have negative impacts on farmer livelihoods, particularly for those reliant on coffee. Notably, while farmer knowledge of AB plays an important role in management decision-making, variability among farms in pruning and fertilizer practices is not significantly related to signals of AB at either the farm- or plant scale. Higher elevation farms, however, exhibit significantly less AB in farm-scale production data (lower ABI farm values), a pattern not perceived by most farmers. In a follow-up analysis, we found that synchrony within and among farms increased following a fungal rust outbreak across the region, offering preliminary support for the hypothesis that environmental and biotic shocks induce synchrony among AB plants (referred to in the mast-seeding literature as “environmental vetoes”; (Bogdziewicz et al. 2019, 2018 ; Pearse et al. 2016 ). The collective findings confirm that farm-scale AB poses an important challenge to farming livelihoods and suggest that farmer capacity to achieve more consistent yields in this system is limited, consistent with existing evidence of synchronous AB at greater spatial scales (i.e., in regional and national scale coffee yield data). Supporting farmer capacity to cope with low-yielding years is therefore critical to securing the resilience of smallholder farmers of perennial crops. Below we discuss the results further in relation to each of the socio-ecological components and a priori and posteriori linkages captured in the conceptual model (Fig. 1 ). Perceived impacts and drivers of AB Farmer participants generally possessed a detailed understanding of AB, including intrinsic plant-level resource tradeoffs as a driving mechanism, which they described in terms of the plant being “tired” or “exhausted” following a heavy crop year. The two-year field assessment further demonstrated these tradeoffs at a plant level, as higher branch death rates were related to heavy fruit load and there was a negative correlation in the number of fruits initiated per fruiting node between years, consistent with prior research on coffee AB (Garcia and Orians 2022 ). Farmers also frequently reported a failure to properly manage the resource costs of reproduction as a cause of AB, consistent with perceptions of AB as an intrinsic component of the system. This view may help explain why AB was not explicitly named among the key challenges they face as coffee producers, which were predominantly extrinsic sources of variability (pests, changing climate, rising costs), despite subsequent reports that AB has negative impacts on farming livelihoods and plays a role in management decision-making. In the resilience literature, predictable or gradual changes (also termed persistent stressors) are understood to be less of a threat to farmer livelihoods than sudden, unexpected disturbances, in part because human agents can learn from past experiences and apply accumulated knowledge to enhance their resilience capacity (Darnhofer 2010 ; Eakin and Luers 2006 ; Luers et al. 2003 ). AB arguably constitutes a persistent stressor in perennial cropping systems. Therefore, if farmers perceive that they can accumulate and apply knowledge of the phenomenon to limit its impact, such as by determining appropriate management practices and coping strategies, it follows that AB would be perceived as a lesser threat than factors that are largely outside their control. Farm management choices We found that AB, a unique and understudied source of intrinsic variability, influences management decisions as potential adaptive strategies. Farmers perceptions of underlying drivers were related to the management practices they reported employing to limit AB. Nearly half of the respondents identified the weather, an extrinsic variable, as a driver of AB; these farmers more frequently reported shade and pest control efforts as key practices to limit AB, which buffer vulnerability to weather extremes and variable pest populations, respectively. Those that understood AB to be intrinsically driven more frequently reported fertilizer and pruning as management strategies, intended to balance plant resource pools more directly. Pruning coffee plants is among the principal management strategies for AB recommended in the literature (Baitelle et al. 2019 ) but was not significantly related to AB at the farm or plant scale. Fertilizer was similarly unrelated to ABI in the present study. Previous research on the effect of fertilizer on coffee AB has been inconclusive, with some studies citing no effect (Garcia and Orians 2022 ; Jaramillo-Botero et al. 2010 ) and others observing a more pronounced AB pattern under increased fertilization, consistent with ecological theory (Beaumont and Fukunaga 1958 ; Garcia and Orians 2020 ; Schnabel et al. 2018 ). Notably, both of these scenarios (no effect and an exacerbating effect) contradict farmer reports that increased fertilization limits AB. Because farmers recognize plant exhaustion as a key driver of AB, they may expect that additional nutrition would allow plants to produce more consistently without becoming exhausted, instead of simply investing more in the “high” years. A mitigating effect of fertilizer has been supported in some other AB systems, including olive and apple (Haberman et al. 2019 ; Raese et al. 2007 ), underscoring a need to better elucidate system-specific relationships between management and AB. Farmers also reported pest and disease management as strategies (e.g., fungicide application) to combat AB. Experimental research on the role of pests and diseases in crop AB is limited, though some existing evidence suggests that plant resource investment in plant defenses could exacerbate resource-driven reproductive tradeoffs underlying AB (Cerda et al. 2017 ). Our observation that high initial fruit loads was positively associated with subsequent branch death, which typically results from Colletotrichum fungal infection (i.e., anthracnose disease) in this system, offers further support for this hypothesis. While shade cultivation was reported by some as a strategy to limit AB, it was also often reported as increasing pest incidence, and therefore expected to exacerbate AB. Farmers reported weighing these tradeoffs between perceived advantages and disadvantages in their choice of shade implementation. The lack of consensus regarding the relationship between shade and AB may be due to the high elevation of the focal coffee growing community, whereas the benefits of shade, particularly for achieving consistent and high-quality yields, are best supported at lower elevations. Farm elevation Farm-scale elevation was the only significant predictor of farm-scale ABI, which decreased significantly with increasing elevation. To the best of our knowledge, this is the first study to assess the relationship between farm elevation and AB in any perennial crop. There are several possible explanations for the observed yield-stabilizing effect of increasing elevation. Some farmers suggested more stability at high elevation due to overall smaller yields, since colder temperatures slow growth and development. To the contrary, elevation was not a significant predictor of cumulative fruit load in the two-year plant-level assessments, indicating that more consistent yield at high elevation may not come with a cost of limited overall yield. Elevation may instead have an indirect effect on AB in this system, as relatively lower temperatures on high elevation farms can also limit pest and disease pressure (Jonsson et al. 2015 ) which may in turn synchronize AB across scales. We found preliminary support for this explanation; a fungal rust outbreak across the region was associated with increased synchrony in the AB cycle within and across farms. The novel finding that AB becomes less severe with increasing elevation, even across a relatively small elevational gradient (500m), highlights a need for further research into the links between AB and farm elevation across AB crops, especially given that most farmers reported no such relationship. Environmental disturbance In addition to intrinsic resource dynamics, a minority of farmers pointed to the role of climate in regional AB. Our finding that synchrony among pairs of farms increased more than two-fold following the 2012 fungal rust outbreak offers novel support for the hypothesis that environmental and biotic disturbances (i.e., environmental vetoes) can synchronize seed output across a population (Bogdziewicz et al. 2019, 2018 ). The observed increase in synchrony also highlights the difficulty that farmers face in their efforts to secure more consistent farm-scale yields. Indeed, even successful interventions may be short-lived, particularly as farmers contend with a rapidly changing climate and disturbance regime (Baca et al. 2014 ; Jezeer et al. 2019 ; Marrero et al. 2022 ; Petersen-Rockney et al. 2021 ). Coping strategies and resilience capacity Farmer-reported strategies to adapt to and cope with low-yielding years varied with the degree of coffee “specialization”, or the relative reliance of the farm household on coffee revenue. Unsurprisingly, farmers that had diversified their income viewed this as a key coping strategy for low-yielding years in the AB cycle, whereas coffee specialists were more likely to take out loans. Diversifying incomes on or off the farm is a well-supported strategy to increase the resilience of smallholder farmers to a variety of stressors (Blesh et al. 2023 ; Darnhofer 2010 ; Scoones 1998 ; Stratton et al. 2021 ; Wezel et al. 2020 ). In contrast, loans are likely maladaptive in the long run as coffee price volatility and climate variability make returns on on-farm investment difficult to predict (Bunn et al. 2015 ; Craparo et al. 2015 ; Ovalle-Rivera et al. 2015 ). The other frequently reported strategy, cutting back on household spending, is also likely to compromise resilience to concurrent stressors. The findings suggest that expanding income diversification and minimizing management costs incurred by reliance on external agrochemical inputs, such as through regenerative soil practices and integrated pest management approaches (Andrade et al. 2020 ; Caudill et al. 2017 ; Elevitch et al. 2018 ; Kremen and Miles 2012 ; Rahn et al. 2014 ), could be promising avenues to enhance smallholder capacity to adapt to and cope with low-yielding years in this system and their resilience more broadly. Synthesis and Conclusion Synchronous farm-scale AB can result in variable income for the farm household, recognized as a barrier to smallholder adaptive capacity (Baca et al. 2014 ; Collins et al. 2010 ; Nawaz et al. 2018 ). Patterns of synchronous crop AB have received little attention in the literature, and largely failed to incorporate the human dimension. To help fill this gap, we conducted a socio-ecological study in a Costa Rican coffee farming community in which we paired in-depth farmer interview analysis with quantitative analyses of farm-scale AB. Farmer reports and farm-scale production timeseries confirmed that AB is often synchronous at the farm-scale with negative impacts on farmer livelihoods. AB is generally perceived as a challenge that is inherent to the system, consistent with discussions of “persistent stressors” in the broader socio-ecological resilience literature, and therefore poses a lesser threat than reported challenges of rising costs, climatic variability, and increasing pest pressure. Farmer understanding of the drivers of AB was reflected in their reported management strategies, which we categorize as practices to limit susceptibility extrinsic sources of variability (i.e., weather extremes, pest pressure) versus those to limit intrinsic resource tradeoffs (i.e., pruning, fertilizer). Our quantitative analyses supported interacting roles of intrinsic resource tradeoffs and susceptibility to extrinsic pest and disease pressure. Specifically, high fruit loads were negatively associated with subsequent fruit load and positively associated with branch death (typically caused by a fungal infection, an extrinsic factor, in coffee). We therefore recommend that farmer-facing institutions raise awareness of the interacting intrinsic and extrinsic variables associated with AB and support farmer implementation of practices that together limit susceptibility to overbearing, pests and disease, and weather extremes. At the same time, the fact that we observe synchronous AB within and across farms suggests that management practices are ineffective at eliminating AB altogether. Higher farm elevation, largely outside the scope of farmer influence, is the only variable we found to be significantly associated with a lower farm-scale ABI. High elevation may have buffered these farms against the fungal rust outbreak, which we found to be related to increased synchrony at the farm- and between-farm scale. The observed increase in synchrony offers novel empirical support for the hypothesis that environmental and biotic shocks may induce synchrony across scales (Bogdziewicz et al. 2019). Importantly, our findings also suggest that even successful efforts to limit farm-scale AB may be short-lived if shocks outweigh the buffering effects of management. Failure to acknowledge that synchronous AB is not easily eliminated places undue burden on farmers (Garcia and Orians 2022 ; Jaramillo-Botero et al. 2010 ) and greater attention must be given to broader opportunities to support socio-ecological resilience to the challenge of AB. Here, reported coping mechanisms for low-yielding years were primarily aimed at maintaining consistent farm management, including agrochemical inputs, despite lower farm revenue; specific approaches varied according to the degree of household reliance on coffee farming, with more reliant farmers taking out loans to cover on-farm expenses and less reliant farmers supplementing with their external income streams. We recommend that institutions seeking to build resilience among smallholder farmers of AB crops prioritize efforts to expand income diversification and regenerative practices that minimize reliance on external agrochemical inputs. We hope this study provides a framework for further socio-ecological inquiry into crop AB across systems and elevates its consideration as a key determinant in the resilience of perennial crop systems. Declarations Acknowledgements We thank the coffee farmer participants for sharing their experiences and granting us permission to access their farms and production data. We also thank Coopedota for facilitating initial contact with farmers and for providing production data. We are grateful to Drs. Elizabeth Crone and Eric Scott, for comments of the manuscript and statistical consultation, and to Javiera Garcia, Madeline Bondy, Ida Weiss, Andres Vega, and Angie Navarro for assistance in data collection. Gabriela Garcia acknowledges the National Science Foundation Socio-environmental Synthesis Center for their support and workshops on synthesizing data to address socio-ecological challenges. Competing Interests The authors declare no competing interests. Ethical Approval All interview materials were reviewed by the Tufts University Institutional Review Board and granted exempt status under IRB study # 1705017. Verbal consent was audio recorded from all participants and their anonymity was guaranteed in reporting of the results. Funding The project was funded by fellowships awarded to G.G. by the NSF Graduate Research Fellowship Program (DGE-1842474), the National GEM Consortium, and Tufts Institute of the Environment. Availability of data and materials The data that support the findings of this study are available from Northeastern University Digital Repository Service, but restrictions apply to the availability of this human subject data as anonymized participants could be re-identified by production values and farm elevation data. The data are, however, available from the corresponding author upon request with assurance that the confidentiality of participants will be maintained. 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Plant Nutr. 30, 1585–1604. doi:https://doi.org/10.1080/01904160701615483 Rahn, E., Läderach, P., Baca, M., Cressy, C., Schroth, G., Malin, D., van Rikxoort, H., Shriver, J., 2014. Climate change adaptation, mitigation and livelihood benefits in coffee production: where are the synergies? Mitigation and Adaptation Strategies for Global Change 19. doi:https://doi.org/10.1007/s11027-013-9467-x Ricketts, T.H., Daily, G.C., Ehrlich, P.R., Michener, C.D., 2004. Economic value of tropical forest to coffee production. Proc. Natl. Acad. Sci. U. S. A. 101, 12579–12582. doi:https://doi.org/10.1073/pnas.0405147101 Roosevelt, M., Raile, E.D., Anderson, J.R., 2023. Resilience in Food Systems: Concepts and Measurement Options in an Expanding Research Agenda. Agronomy 13, 444. doi:https://doi.org/10.3390/agronomy13020444 Rosenstock, T.S., Hastings, A., Koenig, W.D., Lyles, D.J., Brown, P.H., 2011. Testing Moran’s theorem in an agroecosystem. 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Syst. 92, 1535–1549. doi:https://doi.org/10.1007/s10457-017-0100-y Scoones, I., 1998. Sustainable rural livelihoods: A framework for analysis, IDS Working paper. Brighton, England. Shapiro-Garza, E., King, D., Rivera-Aguirre, A., Wang, S., Finley-Lezcano, J., 2020. A participatory framework for feasibility assessments of climate change resilience strategies for smallholders: lessons from coffee cooperatives in Latin America. Int. J. Agric. Sustainability 18, 21–34. doi:https://doi.org/10.1080/14735903.2019.1658841 Stratton, A.E., Wittman, H., Blesh, J., 2021. Diversification supports farm income and improved working conditions during agroecological transitions in southern Brazil. Agron. Sustain. Dev. 41. doi:https://doi.org/10.1007/s13593-021-00688-x Tendall, D.M., Joerin, J., Kopainsky, B., Edwards, P., Shreck, A., Le, Q.B., Kruetli, P., Grant, M., Six, J., 2015. Food system resilience: Defining the concept. Global Food Security 6, 17–23. doi:https://doi.org/10.1016/j.gfs.2015.08.001 Turner, B.L., 2nd, Kasperson, R.E., Matson, P.A., McCarthy, J.J., Corell, R.W., Christensen, L., Eckley, N., Kasperson, J.X., Luers, A., Martello, M.L., Polsky, C., Pulsipher, A., Schiller, A., 2003. A framework for vulnerability analysis in sustainability science. Proc. Natl. Acad. Sci. U. S. A. 100, 8074–8079. doi:https://doi.org/10.1073/pnas.1231335100 Vaast, P., Bertrand, B., Perriot, J.-J., Guyot, B., Génard, M., 2006. Fruit thinning and shade improve bean characteristics and beverage quality of coffee (Coffea arabica L.) under optimal conditions. J. Sci. Food Agric. 86, 197–204. doi:https://doi.org/10.1002/jsfa.2338 Vacchiano, G., Pesendorfer, M.B., Conedera, M., Gratzer, G., Rossi, L., Ascoli, D., 2021. Natural disturbances and masting: from mechanisms to fitness consequences. Philos. Trans. R. Soc. Lond. B Biol. Sci. 376, 20200384. doi:https://doi.org/10.1098/rstb.2020.0384 Wezel, A., Herren, B.G., Kerr, R.B., Barrios, E., Gonçalves, A.L.R., Sinclair, F., 2020. Agroecological principles and elements and their implications for transitioning to sustainable food systems. A review. Agron. Sustain. Dev. 40, 40. doi:https://doi.org/10.1007/s13593-020-00646-z Ye, X., Sakai, K., 2016. A new modified resource budget model for nonlinear dynamics in citrus production. Chaos Solitons Fractals 87, 51–60. doi:https://doi.org/10.1016/j.chaos.2016.03.016 Zuo, X., Zhang, D., Wang, S., Xing, L., Li, Y., Fan, S., Zhang, L., Ma, J., Zhao, C., Shah, K., An, N., Han, M., 2018. Expression of genes in the potential regulatory pathways controlling alternate bearing in “Fuji” (Malus domestica Borkh.) apple trees during flower induction. Plant Physiol. Biochem. 132, 579–589. doi:https://doi.org/10.1016/j.plaphy.2018.10.003 Zurek, M., Ingram, J., Sanderson Bellamy, A., Goold, C., Lyon, C., Alexander, P., Barnes, A., Bebber, D.P., Breeze, T.D., Bruce, A., Collins, L.M., Davies, J., Doherty, B., Ensor, J., Franco, S.C., Gatto, A., Hess, T., Lamprinopoulou, C., Liu, L., Merkle, M., Norton, L., Oliver, T., Ollerton, J., Potts, S., Reed, M.S., Sutcliffe, C., Withers, P.J.A., 2022. Food System Resilience: Concepts, Issues, and Challenges. Annu. Rev. Environ. Resour. 47, 511–534. doi:https://doi.org/10.1146/annurev-environ-112320-050744 Additional Declarations No competing interests reported. Supplementary Files GarciaetalSupportingInformation20240330.docx Cite Share Download PDF Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Human Ecology → Version 1 posted Editorial decision: Revision requested 10 Apr, 2025 Reviews received at journal 12 Oct, 2024 Reviewers agreed at journal 15 Sep, 2024 Reviewers invited by journal 07 Apr, 2024 Submission checks completed at journal 02 Apr, 2024 Editor assigned by journal 02 Apr, 2024 First submitted to journal 30 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Garcia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie2OIWsDMRTHUwKpCa3NQbl8hQsHg0Hp5L5GSmCqzE81NacGp6v2FTY3MZESUZNxNqMTPQplYoPVrVCxpDMVCVRO5CeSl/fej38ASCT+MRh0pb/VSU+FFk8V7De4P+B5CgCEn6nQawU/9i+jQX/+tdjc/bzn/bpxxTPIe5YHFWY4urzfCkxWt8I9tiWxArJXA8ospkiOCqwUBqvJRSa5HksLUTarwPgxptRrxA5OoW/mqEwfGt3dO2UaUyjhcONTCouPCi+UQB2n8CKiFKSt4EAJzMykZPJGsycrSvcxwuZmHU6phd59qtFVvjSslUNN82bR7mbVkPaWkRTVqUhoEGz+pUgAv6PTRCKRSHh+AW7XZxm6JirWAAAAAElFTkSuQmCC","orcid":"","institution":"Northeastern University","correspondingAuthor":true,"prefix":"","firstName":"Gabriela","middleName":"M.","lastName":"Garcia","suffix":""},{"id":286398004,"identity":"c8269479-885d-48af-b0b9-2f8484fdc546","order_by":1,"name":"Laura Kuhl","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Kuhl","suffix":""},{"id":286398005,"identity":"0d22d863-369b-47e9-9866-4eefe9983ed4","order_by":2,"name":"Colin M. Orians","email":"","orcid":"","institution":"Tufts University","correspondingAuthor":false,"prefix":"","firstName":"Colin","middleName":"M.","lastName":"Orians","suffix":""}],"badges":[],"createdAt":"2024-03-30 19:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4193379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4193379/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10745-025-00638-1","type":"published","date":"2025-10-06T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54180260,"identity":"d3a1e12c-e62c-4e77-9b81-71943fb107a3","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84729,"visible":true,"origin":"","legend":"\u003cp\u003eSocio-ecological model of farm-scale alternate bearing; components and hypothesized relationships are depicted by colored boxes and black arrows, respectively. Blue corresponds to social variables, green to environmental variables largely outside of farmer influence, and orange to the focal yield patterns. Relationships between elevation and susceptibility to disturbance and between disturbance and AB were supported by a follow-up analysis (see “Q3” in the set of questions below) but were not included in the initial conceptualization of the study or, therefore, the a priori model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/327439f830cfbc99fb2a0da8.png"},{"id":54180259,"identity":"8a03f982-d256-45a0-97ee-3b41bd25c0d2","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66506,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrative examples of log-detrended production farm-scale patterns over the study period and corresponding ABI values, plotted on the y-axis on Fig. 3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/5ba21444a9b968656b2df50d.png"},{"id":54180261,"identity":"a90ddf55-b554-423f-8c62-7a6ccd4a53ab","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28284,"visible":true,"origin":"","legend":"\u003cp\u003eObserved negative relationship (p=0.04) between farm-scale ABI and farm elevation. Points depict individual farms.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/cd74fe750c229b5a8291ddf3.png"},{"id":54180262,"identity":"db26d705-369c-496f-85d5-dec9db15bebe","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98557,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant plant-level tradeoffs of high fruit load related to AB:\u003cstrong\u003e \u003c/strong\u003eRelationship in initiated fruits per fruited node between years (A); relationship between initial fruit load and probability of branch death (B). Points in A represent individual plants; points in B represent branches.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/7787910b8ca39e7aa93cc1cd.png"},{"id":54180263,"identity":"a30cec2b-d9e2-492d-b188-62acbe92b516","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":198238,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise synchrony in farm production before and after an outbreak of coffee rust (H. vastatrix) in 2012. Average synchrony was 2.5 times higher after the outbreak than before the outbreak. Farms are aligned in the same order on both axes, based on hierarchical clustering of post-shock synchrony.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/efe6762d5118df27b955a2cb.png"},{"id":93420222,"identity":"71db8f5c-9d95-40f6-97fd-0480518171d6","added_by":"auto","created_at":"2025-10-13 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1418393,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/4c78a420-9c7a-4eb7-bc49-f1083e7fe825.pdf"},{"id":54180258,"identity":"5cfe1d0d-f80b-48ef-a856-1f5d049f3080","added_by":"auto","created_at":"2024-04-05 16:29:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23579,"visible":true,"origin":"","legend":"","description":"","filename":"GarciaetalSupportingInformation20240330.docx","url":"https://assets-eu.researchsquare.com/files/rs-4193379/v1/192bf25033a53fed30a4c89c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Confronting intrinsic variability: How farmers understand, manage, and cope with synchronous alternate bearing in a perennial crop system","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe mounting pressures of environmental change on food systems have motivated global research and policy efforts towards securing socio-ecological resilience of coupled farms and farmers (Antwi-Agyei et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003en\u0026eacute; \u003cem\u003eet al.\u003c/em\u003e 2016; Food and Agriculture Organization of the United Nations \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Roosevelt et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tendall et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zurek et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Much of the existing research on the socio-ecological resilience of farm systems (i.e., their capacity to adapt to and cope with change) has focused on the social conditions that influence the response trajectory of farm households following extrinsic disturbances to the agroecosystem (e.g., climate extremes, pest outbreaks) (Dar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marrero et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Philpott et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Yet farmers of many perennial crops also contend with the challenge of intrinsic variability in crop yield, known as alternate (or biennial) bearing (Goldschmidt and Sadka \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Monselise and Goldschmidt \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1982\u003c/span\u003e), thus far absent in discussions of socio-ecological resilience. Alternate bearing (AB), a pattern of plant reproduction in which high-yielding years are followed by low-yielding years, is exhibited by numerous crops of global socio-economic importance (e.g., apple, olive, avocado, coffee, among others). AB is generally understood to be driven by intrinsic plant-level resource allocation tradeoffs (i.e., overbearing) which can become synchronous across space (Esmaeili et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ye and Sakai \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The conditions that induce synchrony in crops are unresolved, though extrinsic environmental and biotic shocks are hypothesized to synchronize intrinsic variability in seed output across natural populations of mast seeding trees (i.e., the environmental veto hypothesis) (Bogdziewicz et al. 2019, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Crone et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vacchiano et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA recent global analysis found that signals of synchronous AB persist even in national-scale crop yields, despite extensive agronomic research and practice to limit AB via breeding and management (e.g., fruit thinning, pruning) (Garcia et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As signals of synchrony decay with increasing spatial scale (Noble et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this evidence suggests that AB cycles are also often synchronous at the farm scale, with direct implications for farmer livelihoods in perennial cropping systems. Synchronous farm-scale AB (i.e., yield fluctuations across a farm) may pose a particular threat to smallholder farmers, (\u0026lt;\u0026thinsp;10ha) for whom stable yield and income are recognized as key building blocks for resilience capacity (Baca et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cohn et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Collins et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nawaz et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn account of farmer perspectives and experiences of AB is thus far lacking from the literature, limiting an applied understanding of the phenomenon and its impact on farmer livelihoods, management decision-making, and their broader resilience capacity. Quantitative analyses of synchronous farm-scale AB are similarly scarce. The patterns and mechanisms of farm-scale synchrony remain remarkably unresolved across systems, and little is known about farmer ability to effectively manage synchrony. To address these gaps, we conducted a socio-ecological assessment of farm-scale alternate bearing using smallholder coffee \u003cem\u003e(Coffea arabica\u003c/em\u003e) farms as a system. We integrated (a) in-depth interviews to capture how farmers understand, manage, and cope with AB as a source of yield variability, with (b) quantitative analyses of AB within and across participant farms to assess opportunities for, and constraints upon, farmer capacity to limit synchrony. Drawing on existing scientific understanding, we developed a novel conceptual model of hypothesized socio-ecological components and relationships underlying farm-scale AB (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, we expect patterns of synchronous farm-scale AB arise from linkages between intrinsic plant-level resource tradeoffs (Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), management practices and other farm features that impact resource dynamics (e.g., plant pruning, nutrient inputs, elevation) (Baitelle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haberman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sarmiento-Soler et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and extrinsic environmental and biotic shocks (e.g., pest outbreak, drought, failed pollination). Here we provide an analytical structure for integrating empirical qualitative and quantitative findings to explore the impact of AB in these socio-ecological systems.\u003c/p\u003e \u003cp\u003eDespite apparent socio-ecological linkages underlying crop AB, most existing studies on AB have a narrow agronomic focus and predominantly address plant-level mechanisms (Bolivar-Medina et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fern\u0026aacute;ndez et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Haberman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kallsen et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zuo et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The degree to which farmer understanding of underlying drivers aligns with scientific understanding of intrinsic resource allocation tradeoffs (Ye and Sakai \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Esmaeili et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G. M. Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and whether this in turn shapes management approaches, is thus far unresolved. To understand farmer decision-making around AB management and coping strategies, it is important to characterize how they perceive the phenomenon and what they understand to be the key drivers (Antwi-Agyei et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Campbell and Beckford \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Frank et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Quiroga et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). If farmers perceive that AB is driven by resource tradeoffs, they may prioritize management practices such as plant pruning and fruit thinning expected to limit resource allocation tradeoffs to achieve more consistent yields over time (Baitelle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Grigorian and Bidarigh Sharemi \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Meland \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Conversely, and perhaps less intuitively, applying fertilizer to increase available resources and maximize yield may exacerbate AB, as high yields in one year can further deplete resources for seed production the following year (Garcia and Orians \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). If farmers instead understand AB to result primarily from extrinsic factors, management decisions may seek to limit plant susceptibility to extrinsic variability, through for example pest or microclimate management. In addition to plant-level practices, farmers may also attempt to manage AB at the farm scale by maintaining asynchronous AB cycles among plants or plots, such as through serial pruning or planting. Critically, due to the paucity of quantitative assessments of farm-level synchrony, evidence is also lacking regarding whether AB management practices, intentionally or not, create feedback for farm-level AB (i.e., if and how farm-scale AB impacts management decision-making, and if and how management impacts farm-scale AB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen management fails to eliminate farm-scale synchrony, which evidence suggests is often the case (Esmaeili et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garcia et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Noble et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rosenstock et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), we expect it impacts farmer livelihoods and broader resilience capacity to stressors. Existing literature on smallholder resilience has focused on the links between livelihood assets, capacities, and extrinsic (e.g., climatic, financial) risks (Darnhofer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hung Anh et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rahn et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). While important progress has been made in describing features of resilient socio-ecological systems (Eakin and Luers \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Folke et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Grafton et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Turner et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), intrinsic yield variability, perhaps due to a lack of awareness regarding the socio-economic importance of the phenomenon and/or its underlying mechanisms, has not been incorporated. We hypothesize that AB plays a much more central role to the socio-ecological resilience of perennial cropping systems than previously acknowledged, and farmer responses to AB could result in negative or positive resilience pathways. To illustrate, a farmer may take out a short-term loan to cope with a low-yielding year in the AB cycle, making them especially vulnerable to unpredictable disturbances that disrupt the projected high-yielding year (e.g., drought, hurricane, etc.) as consecutive low years would likely leave them in debt. Alternatively, AB could motivate crop diversification as a buffer against income fluctuations and increase their broader resilience capacity to variability (Blesh et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Darnhofer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Shapiro-Garza et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coping strategies for farm-level yield fluctuations may extend beyond the farm, as well, such as through off-farm livelihood diversification (Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Scoones \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Characterizing farmer responses to AB is key to identifying opportunities to support farmer resilience capacity to both intrinsic and extrinsic challenges.\u003c/p\u003e \u003cp\u003eThe aim of the present study was to document farmer knowledge and experience of farm-scale AB and assess opportunities for, and constraints upon, their capacity to limit synchrony. Specifically, using coupled coffee farms and farmers as a system, we asked: (Q1) How do farmers understand, manage, and cope with AB? (Q2) Is AB related to variability among farms in pruning practices, fertilizer management, or elevation? In the analysis phase, we discovered that a fungal rust outbreak across the farms, which occurred at the midpoint of crop production timeseries gathered for Q2, afforded us a unique opportunity to conduct a follow-up assessment of environmental disturbance as a potential driver of synchrony, leading to: (Q3) Did signals of synchrony within and among farms increase following a fungal rust break? In synthesizing qualitative findings from in-depth farmer interviews with quantitative assessments of farm-scale AB, we advance an empirical, socio-ecological understanding of farm-scale AB in coffee and pilot a novel integrated approach that could be adapted to other AB systems.\u003c/p\u003e\n\u003ch3\u003eAlternate Bearing in Coffee\u003c/h3\u003e\n\u003cp\u003ePredominantly cultivated by smallholders, coffee is known to exhibit AB at the plant level, and noticeable national-scale yield fluctuations in some countries are indicative of widespread synchrony (Bote and Jan \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; DaMatta \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Garcia et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaramillo-Botero et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). While there has been ample research on the socio-ecological dynamics of coffee systems in the past (Borges-M\u0026eacute;ndez and Caron \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cerd\u0026aacute;n et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Imbach et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Perfecto et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ricketts et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Shapiro-Garza et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), it has tended to focus on external drivers of yield variability (e.g., weather, pests, and pollinators) or the social conditions (e.g., local ecological knowledge) and ecological consequences of farm management decisions (e.g., wildlife habitat) and not on AB. The fact that coffee exhibits AB across spatial scales implies that internal dynamics of plants are an important determinant of yield and thus should be integrated with an understanding of socio-ecological resilience in the system.\u003c/p\u003e \u003cp\u003eCoffee-farming landscapes feature substantial heterogeneity, making them well-suited for investigating differences across farms. Farms range from high input to low input systems and span a broad elevation gradient (Moguel and Toledo \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Sarmiento-Soler et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The multitude of factors influencing smallholder decision-making both on and off the farm are complex and dynamic (Adane and Bewket \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Blesh et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Darnhofer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hung Anh et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ndiritu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but the role of AB in these decisions is thus far unresolved.\u003c/p\u003e \u003cp\u003eWith the exception of shade-grown cultivation, which can limit AB relative to sun-grown monocultures but is most common in lower elevation regions with suboptimal climate (Schnabel et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vaast et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), little is known about how management practices feed back, in turn, to influence AB. We assess whether signals of AB are related to three additional variables expected to influence resource dynamics: pruning practices, fertilizer inputs, and farm elevation. We hypothesized that farms where plants are pruned to have fewer vertical (i.e., orthotropic) stems per plant to limit resource depletion would exhibit weaker signals of AB, consistent with evidence from experimental conditions (Baitelle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). We similarly expected that signals of farm-scale AB would be negatively related to farm elevation, since slower growth rates and lower yields at higher elevation may limit overbearing (Sarmiento-Soler et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and positively related to fertilization rate, as increased resource availability is expected to exacerbate resource-driven overbearing (Garcia and Orians \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy site and participant selection\u003c/h2\u003e \u003cp\u003eThe study was conducted in the coffee-farming community of Santa Mar\u0026iacute;a de Dota, Costa Rica. Located in the region of Los Santos, Santa Mar\u0026iacute;a de Dota is renowned for producing high-quality, high-elevation coffee (1500-2000m). Soils are ultisols of alluvial origin (Castro-Tanzi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), annual precipitation averages 2400 mm/year and the regional production constitutes roughly 40% of Costa Rican coffee output (Icafe \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Between June and August of 2017, participants (n\u0026thinsp;=\u0026thinsp;30) were selected using a random sampling scheme from the 508 coffee farmer associates of the local cooperative, CoopeDota, stratified to capture the local 500m elevation gradient of coffee production (1500-2000m). The selection criteria included willingness to participate and ownership of an active coffee farm. In this community, farm owners typically make management decisions and play an active role in farm labor, though both farm managers and owners were interviewed as needed to obtain accurate information regarding management practices. One participant withdrew from the study; 29 participants remained.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eFarmer understanding, management, and coping strategies\u003c/h2\u003e \u003cp\u003e Semi-structured interviews (n\u0026thinsp;=\u0026thinsp;29) were conducted in Spanish in June 2019 by the lead author (GMG), who is fluent in Spanish and had built rapport with the participants over a two-year period through conversations and farm visits. Verbal consent was audio recorded from all participants prior to conducting interviews. All interview materials were approved by the Tufts University Institutional Review Board under IRB study #1705017. Interviews were recorded for subsequent transcription and coding.\u003c/p\u003e \u003cp\u003eInterview questions first established farmer demographics (years of experience in coffee farming; education level; percent of household income derived from coffee production) and farm characteristics and management (farm size, fertilization rate, pest and weed management; interview questions available in S1). Before delving into the subject of AB, participants were asked to identify the biggest challenges facing coffee farmers to position AB in this broader context. Further questions addressed personal experience with AB, understanding of the underlying causes, awareness and use of management strategies, and barriers to their implementation. Interviews also explored perceived severity of AB in recent years and the spatial extent of synchrony (farm-level, regional).\u003c/p\u003e \u003cp\u003eA qualitative content analysis was conducted based on the conceptual framework in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e to (a) characterize farmer understanding of AB, including its drivers and relationship to farm management and elevation, (b) document the range of farm management practices reported for AB, and (c) assess the motivations and barriers behind AB management choices with particular attention to the role of AB in driving farmer decision-making on- and off-farm. As farm-scale AB is likely to be especially challenging for those whose livelihood relies entirely on coffee revenue, the degree of reliance was determined and used to assess patterns in practices and perceptions across farmer subsets. Knowledge and perceptions of AB were coded by the following themes: underlying causes, drivers and extent of synchrony, personal experiences and perceived severity, and relation to management practices and elevation. Management and coping strategies were coded by employed and preferred practices (pest and weed management; fertilizer applications) and their relation to AB, barriers to preferred practices, their additional benefits and disadvantages, and strategies to cope with and prepare for low-yielding years. Pattern strength was assessed by consistency in farmer responses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFarm management, physical farm features, and indicators of alternate bearing\u003c/h2\u003e \u003cp\u003eWe assessed patterns of AB across participant farms, and their relation to pruning, fertilizer, and elevation, at two relevant focal scales: 10-year time series of farm-scale production to capture signals of farm-wide synchrony and 2 years of plant-scale reproduction on each farm to capture underlying plant-level tradeoffs.\u003c/p\u003e \u003cp\u003eWe requested 10 years of production data (2008\u0026ndash;2017) from the local coffee cooperative with participant consent. Production data were available and granted for n\u0026thinsp;=\u0026thinsp;25 interviewees. As production records maintained by the cooperative were not associated with farm area (see S2 for a detailed data description), analyses were conducted on total farm-level production rather than yield. We note that this necessary limitation would be likely to mask a signal of AB rather than exaggerate it, resulting in conservative farm-level estimates. Four of the 25 farms had less than 10 years of data available; since their inclusion did not alter the results, results are reported below for the full set of n\u0026thinsp;=\u0026thinsp;25 farms. Farm-level AB indices (ABI\u003csub\u003efarm\u003c/sub\u003e) were calculated as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{A}\\text{B}\\text{I}}_{\\text{f}\\text{a}\\text{r}\\text{m}}=\\frac{\\sum \\frac{\\left|{a}_{i+1 }\u0026ndash;{ a}_{i}\\right|}{{a}_{i+1}+{ a}_{i}}}{n-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ea\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the production value in the \u003cem\u003ei\u003c/em\u003eth year and n is the total number of years (Monselise and Goldschmidt \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Alternate bearing index (ABI) values can range from 0 (no AB) to 1 (severe AB).\u003c/p\u003e \u003cp\u003eProduction data were paired with data on pruning practices, farm elevation, and fertilizer load obtained through the interviews and farm visits to assess their relationships with ABI\u003csub\u003efarm,\u003c/sub\u003e Information on fertilizer load was obtained during farmer interviews. Farm visits conducted in June 2017 to obtain farm elevation and pruning practices. At 10 randomly selected points across each farm, elevation was measured with a Garmin\u0026trade;GPS64s and the number of orthotropic stems were counted on adjacent plants and averaged across the farm.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed in R Studio software. We fit a linear model that included ABI\u003csub\u003efarm\u003c/sub\u003e as the response variable and scaled averages of farm elevation, fertilizer load per hectare, and orthotropic stems per plant as predictor variables. Results of the best fit model, determined by the Akaike Information Criterion, are presented below.\u003c/p\u003e \u003cp\u003eTo capture signals of AB at a plant level, study plots measuring 10m x 6m plots were established on farms owned by study participants in June 2018 and followed for 2 years. Plot location within the farm was determined by randomly selecting 1 of the 10 sampling points from the 2017 farm visits, excluding those near farm limits or barriers. Garmin\u0026trade; GPS64s was used to record the plot\u0026rsquo;s location and elevation. Ten plants were randomly selected within each plot for sampling. Measurements were taken on sample plants at fruit initiation (June) and maturity (December) for two consecutive reproductive cycles: 2018 and 2019. At each sample plant, the number of orthotropic stems was recorded and 4 horizontal (plagiotropic) branches (one in each cardinal direction per (Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaramillo-Botero et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) were tagged for measurements at fruit initiation and maturity in 2018. Four new branches were selected in 2019. To minimize variability, focal branches were selected with the following criteria: fruit-bearing, located within the upper third portion of the plant, no lateral shoots, and a healthy branch tip. At each sampling point, branch length was measured and the number of fruited nodes, total leaves, and diseased or infected leaves, and fruits were recorded. At fruit maturity, any dead branches were also recorded. As three plots were not able to be assessed during the second year (due to sale of the land or other unforeseeable reasons), the analyses reported below capture a final sample of n\u0026thinsp;=\u0026thinsp;26 plots.\u003c/p\u003e \u003cp\u003eTradeoffs in reproductive traits between years were assessed using generalized linear models with 2019 fruited nodes or 2019 fruits per node as the response variable and 2018 fruited nodes or fruits per node as the predictor, respectively. Separate models were fit to look for between-year tradeoffs at fruit initiation and fruit maturity. A Poisson distribution with a log link was used, and a random effect of plot ID was included when its inclusion significantly improved the model\u0026rsquo;s Akaike Information Criterion, assessed with the function ICtab() in the package \u003cem\u003ebbmle\u003c/em\u003e (Bolker and R Development Core Team 2020). As tradeoffs were only significant in the number of initiated fruits per node between years (but not fruited nodes; see Results), we calculated the ABI in initiated fruits per node and tested for a relationship with pruning, fertilizer, and elevation. The ABI for fruits per node (ABI\u003csub\u003efruits\u003c/sub\u003e) was calculated as\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\text{A}\\text{B}\\text{I}}_{\\text{f}\\text{r}\\text{u}\\text{i}\\text{t}\\text{s}}=\\frac{|{fruits}_{2019}\u0026ndash;{fruits}_{2018}|}{{fruits}_{2019}+{fruits}_{2018}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003efruits\u003c/em\u003e\u003csub\u003e2019\u003c/sub\u003e is the average plant-level fruits per node in 2019 and \u003cem\u003efruits\u003c/em\u003e\u003csub\u003e2018\u003c/sub\u003e is the average plant-level fruits per node in 2018. We fit a linear model with the same model structure as the farm-scale data, substituting ABI\u003csub\u003efruits\u003c/sub\u003e as the response variable.\u003c/p\u003e \u003cp\u003eThe effect of initial fruit load on branch death rate, another tradeoff that can exacerbate AB, was also analyzed using a generalized linear model with a binomial distribution and random effects of plant ID and plot ID. The same predictors (fertilizer, plant orthotropic stems, and elevation) were included in addition to the initial fruit load per branch. Finally, since both total reproductive effort and variability are important for farmer decision-making, we analyzed cumulative reproduction using the same model structure but substituting the total fruits per node and fruited nodes across years as the response variables.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003ePairwise Synchrony after Fungal Outbreak\u003c/h2\u003e \u003cp\u003eA fungal rust outbreak struck coffee-growing regions in Central America in 2012, at the midpoint of the farm-level production timeseries (Avelino et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). To assess whether signals of synchrony increased after the outbreak, which would support the hypothesis that a biotic shock can induce synchrony, we analyzed a subset of the data including only farms with a complete 10 years of data (n\u0026thinsp;=\u0026thinsp;21 farms). Timeseries were split before (2007\u0026ndash;2011) and after (2012\u0026ndash;2016) the rust outbreak. To capture whether farm-scale ABI (as a metric of farm-scale synchrony) increased, we calculated and compared average ABI\u003csub\u003efarm\u003c/sub\u003e before and after the rust outbreak. To assess whether synchrony among farms increased, we calculated and compared pairwise correlations (Pearson\u0026rsquo;s r) in farm-scale production data across all 210 pairs of farms.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eFarmer understanding, management, and coping strategies\u003c/h2\u003e \u003cp\u003eInterview participants were predominantly smallholders (median farm size: 4 ha; range 0.25-21 ha). Farmer demographics and management practices are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Note that while 6 of the interviewed farm owners were female, 100% of farm managers (those making on-farm management decisions) were male. In instances where owners were not the farm managers, the farm manager was also interviewed to obtain accurate data on management practices.\u003c/p\u003e \u003cp\u003eTo contextualize AB within the broader spectrum of challenges facing farmers, participants were asked what they considered to be their greatest challenges as coffee farmers. Farmers reported five key contemporary challenges: economic viability (prices received for coffee relative to the cost of inputs; 77% respondents), climate variability (31%), pests and diseases (23%), and achieving yields of sufficient quantity (19%) and quality (11%) (n\u0026thinsp;=\u0026thinsp;26). Most farmers characterized AB as an intermediate problem (65%) though some described it as a severe challenge (20%) and a few as a minor challenge (15%). All respondents reported personal experience with AB at some point in their coffee-growing careers. Opinions varied regarding whether patterns of AB have shifted in recent years, with 15% reporting it has become more severe, 33% reporting that it has always been the same, and another 33% reporting that it has improved (the remainder were unclear or unsure). Most farmers (85%) reported a degree of asynchrony among plants or parcels, leading to greater stability at the farm level than plant level. At a regional level, one third of respondents (38%) reported that yields fluctuate similarly due to weather, though several pointed out that there are exceptions to the regional trend and that patterns also depend on farm management.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFarmer demographics and management practices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGender identity\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale (78%); Female (22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFormal education\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMedian: 6 years; Range: 6\u0026ndash;18 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCoffee-farming experience\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMedian: 25 years; Range: 6\u0026ndash;51 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eReliance on coffee revenue\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e100% of income derived from coffee (37%); 50\u0026ndash;99% of income derived from coffee (30%); \u0026lt;50% of income derived from coffee (33%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eManagement Practices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eInorganic fertilizer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplications/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 3; Range: 0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKg/ha/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 570 kg; Range: 125\u0026ndash;820 kg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eWeed control\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplications/year (Chemical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 1; Range: 0\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplications/year (Manual)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 2.75; Range: 1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFungicide\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApplications/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 3; Range: 0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eShade cover\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarm-level estimate (2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian: 20%; Range: 8\u0026ndash;38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003ePerceived relationships between alternate bearing, management, and farm features\u003c/h2\u003e \u003cp\u003eFarmers identified a range of causes of AB. The most frequently reported cause was \u0026ldquo;weather\u0026rdquo;, an external driver (48%). For these farmers (n\u0026thinsp;=\u0026thinsp;13), effective pest and disease management were viewed as key management strategies, suggesting an implicit link between poor weather, an increased incidence of pest and disease, and AB. This group was also more likely to identify shade as a management strategy, presumably given the buffering effects against radiation and precipitation extremes (38%).\u003c/p\u003e \u003cp\u003eThe second most widely reported cause of AB was \u0026ldquo;poor farm management\u0026rdquo; (41%), which in this context aligns with an understanding of intrinsically-driven AB. Among this subset of respondents (n\u0026thinsp;=\u0026thinsp;11), fertilizer (55%) and plant renovation (pruning or entirely replacing old trunks with juvenile plants; 36%) were identified as key management strategies; both of which target a plant\u0026rsquo;s nutritional status and vigor. Additionally, several farmers explicitly reported intrinsic drivers of AB in terms of plant stress and resource exhaustion (22%) while two more farmers (7%) stated, \u0026ldquo;It\u0026rsquo;s just the way coffee is.\u0026rdquo; Another farmer reported, \u0026ldquo;[AB] is not a grave issue because one has always worked this way. One knows that if there is a good harvest, the next one will be lower.\u0026rdquo;\u003c/p\u003e \u003cp\u003ePruning was the only factor that was reported as both a cause of AB (19%) and a management strategy (30%). This apparent discrepancy may reflect two approaches to pruning employed by coffee farmers. In one approach, all the orthotropic stems on a plant are cut back (i.e., coppicing), leaving only the trunk from which new stems will sprout to bear fruit in three or four years. Farmers who identified pruning as a cause of AB likely referred to this approach, as they reported that plants can take several years to recover after intensive pruning. If done systematically across a farm, however, this approach can also be a management strategy to achieve farm-level asynchrony by attempting to maintain some plants in the productive phase while others are in the recovery phase. The other pruning approach involves selectively pruning orthotropic stems on a plant such that the remaining stems can achieve a better resource balance (Baitelle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This approach is intended to prevent individual plants from overbearing and thus targets plant-level yield consistency. Overall, re-juvenilizing the farm, either through plant replacement or pruning, was the top reported management strategy for alternate bearing (48%), followed by fertilizer (41%) and pest/disease management (33%). Only about a quarter of interviewees (26%) identified shade as a management strategy for AB, though several other benefits of shade cultivation were mentioned, including microclimate regulation (33%) and improved soil fertility and weed control when pruned debris is left as ground cover (22%). Perceived disadvantages, however, included decreased coffee yields (19%) and increased fungal disease incidence (11%).\u003c/p\u003e \u003cp\u003eThere was little consensus regarding the relationship between farm elevation and AB. Some asserted no effect of farm elevation on AB (37%), others a stabilizing effect (lower AB; 30%) with increasing farm elevation, and still others reported the opposite (19%). The remainder were unclear or unsure of the relationship.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAdaptive and coping strategies for low-yielding years\u003c/h2\u003e \u003cp\u003eWhen asked if there were any strategies that they employed to prepare for low years, the most common response was \u0026ldquo;nothing\u0026rdquo; (37%), yet nearly half of these respondents were entirely reliant on coffee for their income. Diversifying income, either on or off the farm, was the second most frequently reported strategy (30%), followed by efforts to budget or save during higher yielding years (26%).\u003c/p\u003e \u003cp\u003eMost (70%) reported that they did not change their investment in farm management to cope with low years, and several stressed the importance of maintaining a consistent management regimen despite lower farm income to avoid a downward spiral of farm health. In the words of one farmer, \u0026ldquo;When there are bad harvests it is difficult to maintain a living. I make the sacrifice of doing the same [management] on the farm.\u0026rdquo; Those that did report restricting their fertilizer application following a low year (22%) did not rely as heavily on the coffee harvest for their livelihood (median relative income derived from coffee was 15% for these respondents). Only two farmers (7%) reported using cheaper fertilizer products as a coping mechanism. To cover the costs of maintaining farm management following a reduced harvest, farmers reported supplementing with other income (33%) or taking out loans from the cooperative (22%). The farmers using other sources of income differed from those taking out loans; the former were less reliant on coffee (50% median coffee income values compared to 100%) and had smaller farms (2.1 ha compared to 8.75 ha). Cutting back on household spending (30%) was another prominent coping strategy and included putting off repairs on the home or car and limiting any leisure spending. Three farmers (11%) reported not taking any measures to cope with a low year; two of whom were not reliant on coffee for their livelihoods. The third owned the largest coffee farm included in the study (21 ha) and reported not being affected much by AB.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAlternate bearing index (ABI) in relation to management and elevation\u003c/h2\u003e \u003cp\u003eFarm-level ABI (ABI\u003csub\u003efarm\u003c/sub\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) between 2008\u0026ndash;2017 ranged from 0.08 to 0.72 with a mean of 0.26 (SD\u0026thinsp;=\u0026thinsp;0.13) across participants (n\u0026thinsp;=\u0026thinsp;25; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was a significant negative relationship between ABI\u003csub\u003efarm\u003c/sub\u003e and farm elevation (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.04; df\u0026thinsp;=\u0026thinsp;1; p\u0026thinsp;=\u0026thinsp;0.025; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) which explained 18% of the variability. ABI\u003csub\u003efarm\u003c/sub\u003e was not significantly related to fertilizer rate or pruning (mean number of orthotropic stems).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt a plant level, field assessments confirmed intrinsic resource tradeoffs as an underlying driver of coffee AB, but tradeoffs were not significantly related to elevation, fertilizer, or orthotropic stems. Specifically, plants that initiated a greater number of fruits per fruited node in 2018 initiated significantly less in 2019, consistent with a between-year tradeoff in the number of fruits per fruited node (n\u0026thinsp;=\u0026thinsp;221 plants across 26 plots; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.8, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The ABI for the number of initiated fruits per fruited node (ABI\u003csub\u003efruits\u003c/sub\u003e) ranged from 0.008 to 0.893 with a mean of 0.326 (SD\u0026thinsp;=\u0026thinsp;0.224) across sampled plants but was not significantly related to any of the focal predictor variables. A high initial branch-level fruit load was associated with higher rates of branch death, which can contribute to an AB cycle (n\u0026thinsp;=\u0026thinsp;8505 branches, 253 plants, 26 plots: χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;29.317, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). There was again no significant relationship between fertilizer, elevation, or orthotropic stems and branch death, or with cumulative fruited nodes and cumulative fruits per node across years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSignals of synchrony pre- and post-disturbance\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignals of synchronous AB within and among farms were higher after the fungal rust outbreak than they were before. Mean ABI\u003csub\u003efarm\u003c/sub\u003e, an indicator of farm-scale synchrony, was nearly 70% greater over the period following the outbreak (0.272\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023 SE) compared to before (0.189\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025 SE). Among all pairs of 21 farms with complete, farm-scale production data (n\u0026thinsp;=\u0026thinsp;210 pairwise combinations), average synchrony increased 2.5 times immediately following the fungal rust outbreak (Pearson\u0026rsquo;s r\u0026thinsp;=\u0026thinsp;0.573\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025 SE vs. 0.221\u0026thinsp;\u0026plusmn;\u0026thinsp;0.033 SE; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn a Costa Rican coffee farming community, we conducted a socio-ecological study to characterize farmer knowledge and experience of AB as a source of intrinsic yield variability (including impacts on farmer livelihoods, its role in management decision-making, and predominant coping strategies) and assess opportunities and constraints upon farmer capacity to limit farm-scale synchrony. Farmer interviewees identified AB as an inherent challenge in the system, further supported by signals of AB in their farm-scale production data, and that low-yielding years often have negative impacts on farmer livelihoods, particularly for those reliant on coffee. Notably, while farmer knowledge of AB plays an important role in management decision-making, variability among farms in pruning and fertilizer practices is not significantly related to signals of AB at either the farm- or plant scale. Higher elevation farms, however, exhibit significantly less AB in farm-scale production data (lower ABI\u003csub\u003efarm\u003c/sub\u003e values), a pattern not perceived by most farmers. In a follow-up analysis, we found that synchrony within and among farms increased following a fungal rust outbreak across the region, offering preliminary support for the hypothesis that environmental and biotic shocks induce synchrony among AB plants (referred to in the mast-seeding literature as \u0026ldquo;environmental vetoes\u0026rdquo;; (Bogdziewicz et al. 2019, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pearse et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The collective findings confirm that farm-scale AB poses an important challenge to farming livelihoods and suggest that farmer capacity to achieve more consistent yields in this system is limited, consistent with existing evidence of synchronous AB at greater spatial scales (i.e., in regional and national scale coffee yield data). Supporting farmer capacity to cope with low-yielding years is therefore critical to securing the resilience of smallholder farmers of perennial crops. Below we discuss the results further in relation to each of the socio-ecological components and \u003cem\u003ea priori\u003c/em\u003e and \u003cem\u003eposteriori\u003c/em\u003e linkages captured in the conceptual model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePerceived impacts and drivers of AB\u003c/h2\u003e \u003cp\u003eFarmer participants generally possessed a detailed understanding of AB, including intrinsic plant-level resource tradeoffs as a driving mechanism, which they described in terms of the plant being \u0026ldquo;tired\u0026rdquo; or \u0026ldquo;exhausted\u0026rdquo; following a heavy crop year. The two-year field assessment further demonstrated these tradeoffs at a plant level, as higher branch death rates were related to heavy fruit load and there was a negative correlation in the number of fruits initiated per fruiting node between years, consistent with prior research on coffee AB (Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Farmers also frequently reported a failure to properly manage the resource costs of reproduction as a cause of AB, consistent with perceptions of AB as an intrinsic component of the system.\u003c/p\u003e \u003cp\u003eThis view may help explain why AB was not explicitly named among the key challenges they face as coffee producers, which were predominantly extrinsic sources of variability (pests, changing climate, rising costs), despite subsequent reports that AB has negative impacts on farming livelihoods and plays a role in management decision-making. In the resilience literature, predictable or gradual changes (also termed persistent stressors) are understood to be less of a threat to farmer livelihoods than sudden, unexpected disturbances, in part because human agents can learn from past experiences and apply accumulated knowledge to enhance their resilience capacity (Darnhofer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Eakin and Luers \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Luers et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). AB arguably constitutes a persistent stressor in perennial cropping systems. Therefore, if farmers perceive that they can accumulate and apply knowledge of the phenomenon to limit its impact, such as by determining appropriate management practices and coping strategies, it follows that AB would be perceived as a lesser threat than factors that are largely outside their control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFarm management choices\u003c/h2\u003e \u003cp\u003eWe found that AB, a unique and understudied source of intrinsic variability, influences management decisions as potential adaptive strategies. Farmers perceptions of underlying drivers were related to the management practices they reported employing to limit AB. Nearly half of the respondents identified the weather, an extrinsic variable, as a driver of AB; these farmers more frequently reported shade and pest control efforts as key practices to limit AB, which buffer vulnerability to weather extremes and variable pest populations, respectively. Those that understood AB to be intrinsically driven more frequently reported fertilizer and pruning as management strategies, intended to balance plant resource pools more directly. Pruning coffee plants is among the principal management strategies for AB recommended in the literature (Baitelle et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) but was not significantly related to AB at the farm or plant scale. Fertilizer was similarly unrelated to ABI in the present study. Previous research on the effect of fertilizer on coffee AB has been inconclusive, with some studies citing no effect (Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaramillo-Botero et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and others observing a more pronounced AB pattern under increased fertilization, consistent with ecological theory (Beaumont and Fukunaga \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1958\u003c/span\u003e; Garcia and Orians \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schnabel et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, both of these scenarios (no effect and an exacerbating effect) contradict farmer reports that increased fertilization limits AB. Because farmers recognize plant exhaustion as a key driver of AB, they may expect that additional nutrition would allow plants to produce more consistently without becoming exhausted, instead of simply investing more in the \u0026ldquo;high\u0026rdquo; years. A mitigating effect of fertilizer has been supported in some other AB systems, including olive and apple (Haberman et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Raese et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), underscoring a need to better elucidate system-specific relationships between management and AB.\u003c/p\u003e \u003cp\u003eFarmers also reported pest and disease management as strategies (e.g., fungicide application) to combat AB. Experimental research on the role of pests and diseases in crop AB is limited, though some existing evidence suggests that plant resource investment in plant defenses could exacerbate resource-driven reproductive tradeoffs underlying AB (Cerda et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our observation that high initial fruit loads was positively associated with subsequent branch death, which typically results from \u003cem\u003eColletotrichum\u003c/em\u003e fungal infection (i.e., anthracnose disease) in this system, offers further support for this hypothesis. While shade cultivation was reported by some as a strategy to limit AB, it was also often reported as increasing pest incidence, and therefore expected to exacerbate AB. Farmers reported weighing these tradeoffs between perceived advantages and disadvantages in their choice of shade implementation. The lack of consensus regarding the relationship between shade and AB may be due to the high elevation of the focal coffee growing community, whereas the benefits of shade, particularly for achieving consistent and high-quality yields, are best supported at lower elevations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFarm elevation\u003c/h2\u003e \u003cp\u003eFarm-scale elevation was the only significant predictor of farm-scale ABI, which decreased significantly with increasing elevation. To the best of our knowledge, this is the first study to assess the relationship between farm elevation and AB in any perennial crop. There are several possible explanations for the observed yield-stabilizing effect of increasing elevation. Some farmers suggested more stability at high elevation due to overall smaller yields, since colder temperatures slow growth and development. To the contrary, elevation was not a significant predictor of cumulative fruit load in the two-year plant-level assessments, indicating that more consistent yield at high elevation may not come with a cost of limited overall yield. Elevation may instead have an indirect effect on AB in this system, as relatively lower temperatures on high elevation farms can also limit pest and disease pressure (Jonsson et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) which may in turn synchronize AB across scales. We found preliminary support for this explanation; a fungal rust outbreak across the region was associated with increased synchrony in the AB cycle within and across farms. The novel finding that AB becomes less severe with increasing elevation, even across a relatively small elevational gradient (500m), highlights a need for further research into the links between AB and farm elevation across AB crops, especially given that most farmers reported no such relationship.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental disturbance\u003c/h2\u003e \u003cp\u003eIn addition to intrinsic resource dynamics, a minority of farmers pointed to the role of climate in regional AB. Our finding that synchrony among pairs of farms increased more than two-fold following the 2012 fungal rust outbreak offers novel support for the hypothesis that environmental and biotic disturbances (i.e., environmental vetoes) can synchronize seed output across a population (Bogdziewicz et al. 2019, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The observed increase in synchrony also highlights the difficulty that farmers face in their efforts to secure more consistent farm-scale yields. Indeed, even successful interventions may be short-lived, particularly as farmers contend with a rapidly changing climate and disturbance regime (Baca et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jezeer et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marrero et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Petersen-Rockney et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCoping strategies and resilience capacity\u003c/h2\u003e \u003cp\u003eFarmer-reported strategies to adapt to and cope with low-yielding years varied with the degree of coffee \u0026ldquo;specialization\u0026rdquo;, or the relative reliance of the farm household on coffee revenue. Unsurprisingly, farmers that had diversified their income viewed this as a key coping strategy for low-yielding years in the AB cycle, whereas coffee specialists were more likely to take out loans. Diversifying incomes on or off the farm is a well-supported strategy to increase the resilience of smallholder farmers to a variety of stressors (Blesh et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Darnhofer \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Scoones \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Stratton et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wezel et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, loans are likely maladaptive in the long run as coffee price volatility and climate variability make returns on on-farm investment difficult to predict (Bunn et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Craparo et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ovalle-Rivera et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The other frequently reported strategy, cutting back on household spending, is also likely to compromise resilience to concurrent stressors. The findings suggest that expanding income diversification and minimizing management costs incurred by reliance on external agrochemical inputs, such as through regenerative soil practices and integrated pest management approaches (Andrade et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Caudill et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Elevitch et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kremen and Miles \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rahn et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), could be promising avenues to enhance smallholder capacity to adapt to and cope with low-yielding years in this system and their resilience more broadly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis and Conclusion\u003c/h2\u003e \u003cp\u003eSynchronous farm-scale AB can result in variable income for the farm household, recognized as a barrier to smallholder adaptive capacity (Baca et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Collins et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Nawaz et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Patterns of synchronous crop AB have received little attention in the literature, and largely failed to incorporate the human dimension. To help fill this gap, we conducted a socio-ecological study in a Costa Rican coffee farming community in which we paired in-depth farmer interview analysis with quantitative analyses of farm-scale AB.\u003c/p\u003e \u003cp\u003eFarmer reports and farm-scale production timeseries confirmed that AB is often synchronous at the farm-scale with negative impacts on farmer livelihoods. AB is generally perceived as a challenge that is inherent to the system, consistent with discussions of \u0026ldquo;persistent stressors\u0026rdquo; in the broader socio-ecological resilience literature, and therefore poses a lesser threat than reported challenges of rising costs, climatic variability, and increasing pest pressure.\u003c/p\u003e \u003cp\u003eFarmer understanding of the drivers of AB was reflected in their reported management strategies, which we categorize as practices to limit susceptibility extrinsic sources of variability (i.e., weather extremes, pest pressure) versus those to limit intrinsic resource tradeoffs (i.e., pruning, fertilizer). Our quantitative analyses supported interacting roles of intrinsic resource tradeoffs and susceptibility to extrinsic pest and disease pressure. Specifically, high fruit loads were negatively associated with subsequent fruit load and positively associated with branch death (typically caused by a fungal infection, an extrinsic factor, in coffee). We therefore recommend that farmer-facing institutions raise awareness of the interacting intrinsic and extrinsic variables associated with AB and support farmer implementation of practices that together limit susceptibility to overbearing, pests and disease, and weather extremes.\u003c/p\u003e \u003cp\u003eAt the same time, the fact that we observe synchronous AB within and across farms suggests that management practices are ineffective at eliminating AB altogether. Higher farm elevation, largely outside the scope of farmer influence, is the only variable we found to be significantly associated with a lower farm-scale ABI. High elevation may have buffered these farms against the fungal rust outbreak, which we found to be related to increased synchrony at the farm- and between-farm scale. The observed increase in synchrony offers novel empirical support for the hypothesis that environmental and biotic shocks may induce synchrony across scales (Bogdziewicz \u003cem\u003eet al.\u003c/em\u003e 2019). Importantly, our findings also suggest that even successful efforts to limit farm-scale AB may be short-lived if shocks outweigh the buffering effects of management.\u003c/p\u003e \u003cp\u003eFailure to acknowledge that synchronous AB is not easily eliminated places undue burden on farmers (Garcia and Orians \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jaramillo-Botero et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and greater attention must be given to broader opportunities to support socio-ecological resilience to the challenge of AB. Here, reported coping mechanisms for low-yielding years were primarily aimed at maintaining consistent farm management, including agrochemical inputs, despite lower farm revenue; specific approaches varied according to the degree of household reliance on coffee farming, with more reliant farmers taking out loans to cover on-farm expenses and less reliant farmers supplementing with their external income streams. We recommend that institutions seeking to build resilience among smallholder farmers of AB crops prioritize efforts to expand income diversification and regenerative practices that minimize reliance on external agrochemical inputs. We hope this study provides a framework for further socio-ecological inquiry into crop AB across systems and elevates its consideration as a key determinant in the resilience of perennial crop systems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the coffee farmer participants for sharing their experiences and granting us permission to access their farms and production data. We also thank Coopedota for facilitating initial contact with farmers and for providing production data. We are grateful to Drs. Elizabeth Crone and Eric Scott, for comments of the manuscript and statistical consultation, and to Javiera Garcia, Madeline Bondy, Ida Weiss, Andres Vega, and Angie Navarro for assistance in data collection. Gabriela Garcia acknowledges the National Science Foundation Socio-environmental Synthesis Center for their support and workshops on synthesizing data to address socio-ecological challenges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll interview materials were reviewed by the Tufts University Institutional Review Board and granted exempt status under IRB study # 1705017.\u0026nbsp;Verbal consent was audio recorded from all participants and their anonymity was guaranteed in reporting of the results.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe project was funded by fellowships awarded to G.G. by the NSF Graduate Research Fellowship Program (DGE-1842474), the National GEM Consortium, and Tufts Institute of the Environment.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Northeastern University Digital Repository Service, but restrictions apply to the availability of this human subject data as anonymized participants could be re-identified by production values and farm elevation data. The data are, however, available from the corresponding author upon request with assurance that the confidentiality of participants will be maintained.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eG.G. conceived of the study, carried out the data collection, analysis, and interpretation, and drafted the manuscript; L.K. and C.O. participated in study design and data interpretation and critically revised the manuscript. All authors gave final approval for publication and agree to be held accountable for the work.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdane, A., Bewket, W., 2021. Effects of quality coffee production on smallholders\u0026rsquo; adaptation to climate change in Yirgacheffe, Southern Ethiopia. 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Resour. 47, 511\u0026ndash;534. doi:https://doi.org/10.1146/annurev-environ-112320-050744\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"alternate bearing, resilience, crop yield, agroecosystems, synchrony, biennial bearing","lastPublishedDoi":"10.21203/rs.3.rs-4193379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4193379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGlobal efforts aim to support the socio-ecological resilience of farms and farmers to environmental disturbance. Farmers of many perennial crops also contend with intrinsic yield fluctuations, or alternate bearing (AB), which can synchronize across regional and national scales. Synchronous AB across a farm has direct implications for farmer livelihoods but is absent from discussions of resilience. We conducted a socio-ecological study on farm-scale AB in \u003cem\u003eCoffea arabica \u003c/em\u003eto assess (a) how farmers understand, manage, and cope with AB, and (b) opportunities for, and constraints upon, their capacity to limit it. We integrate semi-structured interviews (n=29) with quantitative analyses of AB across participant farms. Farmers identify AB as an inherent challenge with differential impacts on management based on whether they perceive AB as extrinsically- or intrinsically driven. The former employ strategies to ameliorate the effects of weather and pests, while the latter prioritize fertilization and plant renovation strategies. Quantitative analyses found that pruning and fertilizer management are unrelated to signals of AB, but AB decreases significantly with farm elevation, perhaps due to lower pest pressure which can exacerbate AB. Synchrony within and across farms increased after a regional pest outbreak, supporting the synchronizing potential of environmental disturbances. These findings indicate that AB persists despite management efforts and may be outside farmer influence, raising questions about coping strategies. Farmer-reported coping strategies for low years include loans, external income, and limits on household spending, with implications for broader resilience capacity. Intrinsic AB merits greater attention as a determinant of resilience in perennial crops.\u003c/p\u003e","manuscriptTitle":"Confronting intrinsic variability: How farmers understand, manage, and cope with synchronous alternate bearing in a perennial crop system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-05 16:29:10","doi":"10.21203/rs.3.rs-4193379/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T17:40:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-12T20:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245726795246714036174717958630854249239","date":"2024-09-15T15:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-07T11:40:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-02T04:02:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-02T04:02:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Human Ecology","date":"2024-03-30T19:26:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"human-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"huec","sideBox":"Learn more about [Human Ecology](http://link.springer.com/journal/10745)","snPcode":"10745","submissionUrl":"https://submission.nature.com/new-submission/10745/3","title":"Human Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"add8450d-64c6-447b-b29a-e526e9f290ae","owner":[],"postedDate":"April 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:08:12+00:00","versionOfRecord":{"articleIdentity":"rs-4193379","link":"https://doi.org/10.1007/s10745-025-00638-1","journal":{"identity":"human-ecology","isVorOnly":false,"title":"Human Ecology"},"publishedOn":"2025-10-06 15:57:07","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2024-04-05 16:29:10","video":"","vorDoi":"10.1007/s10745-025-00638-1","vorDoiUrl":"https://doi.org/10.1007/s10745-025-00638-1","workflowStages":[]},"version":"v1","identity":"rs-4193379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4193379","identity":"rs-4193379","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}