Abstract
Although agroforestry is a promising approach to reconcile biodiversity conservation and local development, uptake has proven challenging, justifying the introduction of incentives to promote adoption. We firstly conducted a discrete choice experiment (DCE) over a sample comprised by 223 cacao farmers from the province Padre Abad in the Peruvian Amazon to gain insights into their preferences for four attributes of a proposed biodiversity-oriented agroforestry- Payment for Ecosystem Services (PES) scheme: (1) requirement for tree canopy cover in enrolled plots, (2) modalities for a biodiversity connectivity bonus, (3) farmers' participation in monitoring, and (4) the compensatory cash amount. Although we found that a PES scheme would have a wide acceptance among eligible farmers, their preferences are highly heterogeneous regarding assessed attributes. For disentangling such differences, we perform a latent class (LC) model and identify three farmer classes: first, the larger one (56% of the sample) comprised by farmers that are more likely to enroll in the proposed PES as the cash compensation for accepting both high required canopy level and collective bonus rises, and they are allowed to participate in monitoring tasks. Meanwhile, preferences of the second group (34% of the sample) seemed to be less sensitive to changes in levels of the assessed PES attributes. Finally, the third farmers group (10% of the sample) features, in average, a willingness to accept (WTA) lower than the first farmers group for required high canopy level and a larger preference for individual rather than collective connectivity bonus. These findings highlight the necessity of tailoring incentives to address differentiated farmer preferences for increasing scheme effectiveness and equity.
1 Introduction
Recent evaluations highlight declining biodiversity worldwide at alarming rates (; ) mainly driven by the rapid expansion of monocultures and intensive livestock production (). Biodiversity conservation is critical for sustaining the provision of key ecosystem services benefiting agriculture including pollination, soil fertility and natural pest control (). Agroforestry systems with perennial crops have been seen as a promising approach to reconcile socioeconomic targets with biodiversity conservation (; ; Tscharntke et al., 2011). The evidence base for positive impacts of agroforestry practices in cocoa and coffee plots on biodiversity is well established (; ; Udawatta et al., 2021). Based on metanalysis of 23 studies, () conclude that cocoa agroforestry systems, with higher canopy cover retain a comparable number of bird species as nearby forest, yet potentially compromising community composition (). These results are coherent with findings from case studies in coffee landscapes (; ), highlighting the importance of maintaining forests patches in agroforestry landscapes for boosting biodiversity outcomes ().
Conversely, the existing evidence is more contested in relation to the financial performance of agroforestry systems vis-a-vis monocultures. While some authors argue that the profitability of agroforestry systems is higher than monocultures due to lower input requirements and higher prices obtained in differentiated markets [e.g. (), ()], several empirical analyses question such arguments by noting that the adoption of agroforestry might imply additional demand for labor and arising trade-offs at the short-term between the yields of main crop and other ecosystem services (i.e. carbon storage and biodiversity) (; ; ; Thiesmeier and Zander, 2023). In addition to such considerations, other relevant factors may limit the uptake of agroforestry practices in different contexts including scarcity of productive inputs such as suitable land and labor (), insecure land tenure (), and insufficient technical and institutional support (; Tranchina et al., 2024).
Frequently, diverse incentives have been introduced to promote agroforestry adoption in the presence of the mentioned limiting factors. This has on occasions produced negligible or even adverse impacts as reported by recent reviews (; Tedesco et al., 2023). For instance, () report that a randomized intervention which provided technical assistance and diverse crop seeds in Zambia increased smallholders' perceived land security but had no impact on agroforestry uptake. () found that an agroforestry program promoting crop-fruit trees associations in rural China decreased incomes for participants as the additional time required to manage the promoted agroforestry systems reduced possibilities to obtain off-farm incomes.
The increased interest in developing biodiversity markets that could help companies meet their nature-positive commitments has led to the emergence and functioning of several biodiversity offset mechanisms with some of these relying on active restoration approaches such as agroforestry (). Importantly, some flaws into the issuance and trading of these instruments with the consequential risk of nil biodiversity contributions have been flagged (Zu Ermgassen et al., 2020). These are analogous to problems that have arisen in the voluntary carbon markets (West et al., 2023). Despite these potential problems, biodiversity market enthusiasts argue that these initiatives might facilitate the injection of the funding required to implement sustainable pathways in agri-food systems (), including for example, through the provision of market incentives to farmers including the adoption of biodiversity-friendly land management practices. Some nascent initiatives rely on certification mechanisms for incentivizing farmers to implement a diverse range of sustainable practices in their cocoa and coffee plots oriented to conserve bird habitat (Williams et al., 2021).
Payment for Ecosystem Services (PES) constitutes another key mechanism through which biodiversity markets might facilitate the injection of funds required to implement sustainable pathways in agri-food systems. PES schemes have been widely introduced over the last decades, particularly in the Global South to incentivize actions oriented to reverse critical environmental challenges including climate change, hydrological deficits, and biodiversity loss (). When introduced in agroforestry interventions, PES aim to compensate initial losses in main crop yields, especially during the transition phase when new measures are being implemented (). Incorporating PES might also lead to more equitable outcomes when implementing agroforestry programs (). However, the evidence of PES effects on biodiversity is still limited as such outcomes have been rarely considered in the design phase and seldom monitored (); despite strong connections between the state of biodiversity on PES intervened areas and their capacity to provide ecosystem services (). This resonates with the debates around the role that biodiversity conservation has within the ecosystem services framework (). Nevertheless, given that many PES initiatives and other conservation policies consciously target areas featuring high biodiversity levels, many implementers of such interventions have started to assess their biodiversity contributions in a more explicit way (; ; zu Ermgassen et al., 2023).
Despite these advances, there is still a scarcity of literature that focuses on assessing the feasibility of introducing PES incentives for upscaling biodiversity outcomes via adoption of more sustainable agricultural practices (). With this study, we aim to provide insight into potential opportunities and challenges for designing and implementing a scheme of this type oriented to upscale cocoa biodiversity-friendly agroforestry in an emblematic agricultural landscape of the Peruvian Amazon. We combined a discrete choice experiment (DCE) with a Latent Class (LC) model for assessing and disentangling preferences of farmers across the study area in relation to a proposed PES scheme framed within a hypothetical voluntary biodiversity offsetting mechanism implemented by local organizations. Through this analysis, we address the following research questions:
Do environmental perceptions and attitudes present in cocoa farmers who are settled across our study area have limited agroforestry uptaking?
What are the preferences of cacao farmers across the study area regarding design features of a proposed agroforestry-based PES scheme?
How should potential heterogeneity among potential PES participants in relation to their preferences regarding its attributes inform design of incentives?
By addressing these questions, we expect to contribute to the existing literature related to sustainable agri-food production by: (1) analyzing farmer preferences for interventions that explicitly promote sustainable agricultural practices in the rural context of a developing country by using in-person rather than online questionnaires; (2) documenting heterogeneity in motivations for participation in a proposed agroforestry-based PES and determine whether these might coexist even in a quiet localized intervention area, an aspect that has been scarcely addressed in previous studies despite its important implications for the design of this type of interventions; (3) advancing in the understanding about how farmers' perceptions and attitudes toward agroforestry and biodiversity might exert influence on farmers' enrollment in this type of intervention. This manuscript is structured as follows: in Section 2, we describe the study area, methods and statistical analyses. In Section 3 we present the main results. In Section 4, our findings are discussed and concluding remarks are provided.
2 Materials and methods
2.1 Study case: the province of Padre Abad, Peruvian Amazon
The study area is in the province of Padre Abad, one of the four provinces of Ucayali, the second largest region of Peru, situated in the Amazon (Figure 1). We focused on three of the seven districts within the province: Alexander Von Humbolt, Neshuya and Irazola. These three districts were selected due to the role of cocoa as a main economic activity and the rapid increase of its production over the last three decades across such territories as consequence of numerous ‘alternative development' programs that have had focused on the area to counter rising coca (Erythroxylum coca) plantations since the eighties (Zegarra, 2004). The climatic conditions in the province are typical of the humid tropics. Such ecological conditions jointly with the construction and successive improvements of the Federico Basadre highway connecting the regional capital Pucallpa, to the country capital Lima, have promoted internal immigration since the 1940s, especially during the 1981–1993 period, when the population in the province increased by 125% (). This rapid demographic transition precipitated considerable loss and degradation of forest in the province as a direct consequence of activities carried out by colonist farmers including livestock production, logging, planting of cash crops such as oil palm, cocoa, corn, pineapple and others ().
Figure 1
To date, the zone is still an active forest frontier in the Peruvian Amazon where local claims for greater State support to agricultural activities and greater market access contrast with ecological concerns from some stakeholders due to the massive loss of forest across the province (). Forest loss has often been enabled by government-led interventions rolled out to achieve development goals. Despite these threats, natural ecosystems in the province still present high levels of biodiversity as the region overlaps the Andes-Amazon transition zone, identified as one of the most important biodiversity hotspots worldwide (). This has led to establishment of legal protection over some areas to conserve biodiversity values present in these, as well as to promote sustainable land practices such as cocoa and oil palm agroforestry (). Despite such efforts, the study area features high levels of fragmentation across its landscape due to the loss of 90,437 ha – equivalent to ~44% of primary forests during the 2001–2023 period ()—which has severely affected the connectivity across existing habitats in the area, and harmed ultimately the region's biodiversity values. Mosaics comprised of scattered forest patches, cocoa, oil palm and pastures dominate the landscape across the study area (). It is estimated that around 2,000 cocoa farmers manage 9,205 ha of cocoa—corresponding to the 36% of Ucayali's cocoa harvesting area—are settled across the three districts comprising the study area. The cacao productivity reaches around 900 kg ha−1 on average ().1 In addition, the most recent regional assessment of the cocoa sector indicates that around 5% of the cocoa production in Ucayali is produced under agroforestry systems, and that 95% of cocoa farmers in the region had obtained returns lower than PEN 12,000 (equivalent to US$ ~3 428) in 2019 ().
2.2 Econometric framework
We conducted a discrete choice experiment (DCE) to model the preferences of cocoa farmers across the study area regarding attributes for a proposed biodiversity-oriented PES scheme. We apply two complementary approaches. The first approach is based on the assumption that the alternative chosen by each farmer represents the one generating the highest utility among those available and therefore has the highest probability of being selected (). The probability of choosing the alternative associated to the highest utility might be derived from an indirect utility function estimated through a Random Utility Model: Uki = Vki+εki (), where U represents the kth farmer utility function for option i, which has a deterministic term, V; and an independent and identically distributed (iid) random term ε. This framework allows us to estimate their marginal willingness to accept (WTA) enrollment into the proposed PES scheme in relation to the status quo situation, this is:
Where WTA represents farmers' maximum willingness to accept the changes required to get enrolled into the PES scheme and move from the status quo situation. The term βk reflects a proposed change in attributes of the PES scheme and αk the corresponding amount that compensates for the proposed changes. In case the random term is proven to not be iid, we implement econometric specifications that do not require this condition (e.g. mixed logit) to calculate these parameters.
A second complementary approach derived from the first one helped us to explore potential variations in farmers' preferences due to heterogeneous characteristics. This was done by performing a latent class model, which provides insights regarding differences in preferences by identifying and characterizing classes within the sample (). Different farmer classes are detected based on patterns of farmers' responses to choice scenarios after which farmers are allocated to classes using a probabilistic approach. This process implies that farmers with similar preferences are allocated to the same class. Preferences are assumed to be homogeneous within classes and may vary across classes (). We extended our analyses by assessing if farmers' environmental attitudes and demographic characteristics might be a source of heterogeneity among classes. Considering N as the available option number in the set, and t as the choice situation, the probability of individual i selecting alternative j conditioned on the latent class c and scale class membership k might be represented as:
where βcvXitkt is the deterministic portion of the utility functions, with X capturing the attributes, including the compensation, and βcv capturing class-specific marginal utilities for each class variable.
2.3 Survey design and implementation
The survey instrument was designed using information collected through (1) semi-structured interviews with key regional actors involved in the forestry and cacao sectors, (2) two focus groups with eligible participants of the proposed initiative, and (3) secondary sources including publications and internal reports from organizations working in the study area. In the discrete choice experiment (DCE), we consulted about cocoa growers' preferences regarding three alternatives (i.e., Option A, B and C) per round. The following four attributes were consulted to participants of the DCE: (1) canopy cover (%) and number of tree strata required for areas enrolled in the PES, (2) monitoring modalities, (3) options for a connectivity bonus for boosting enrollment of neighboring farms in the initiative, and (4) the cash compensation amount for participating in the PES scheme to be paid annually after the fifth year of enrollment and conditional on successful verification of ecological commitments (Table 1). The selection of these attributes and corresponding levels is based on insights that we collected from the focus groups and semi structured interviews as well as from previous relevant studies [e.g. (), ()]. In all choice cards presented to cocoa farmers, Option A represented the status quo reflecting the current situation regarding each of assessed attributes built using information from interviews conducted to local experts. Options B and C represented attribute combinations of a possible initiative aiming to enhance the capacity of existing cocoa plots to provide biodiversity related ecosystem services which would be compensated accordingly.
Table 1
| Attributes | Levels* |
|---|---|
| Minimum canopy cover required in PES enrolled cocoa plots | •Low (< 30%, 25 shade trees/ha, ≤1 tree stratum in the cocoa plot) |
| •Medium (30%−50%, 25–50 shade trees/ha, ≥ 2 tree strata in the cocoa plot) | |
| •High (>50%, more than 100 shade trees/ha, ≥3 tree strata in the cocoa plot) | |
| Monitoring modalities | •None |
| •External monitoring: performed only by third-party actors with passive involvement of enrolled participants | |
| •Participatory monitoring: external monitoring + active involvement of enrolled participants | |
| Connectivity bonus | •None |
| •Individual sponsorship bonus (PEN 300 or US$ ~86) | |
| •Collective bonus to village-level organization/Agglomeration bonus (PEN 300 or US$ ~86) | |
| Cash compensation per enrolled ha—annually delivered after the 5th year of enrolment and verification of ecological compliance | •0 |
| •PEN 500 (US$ ~143) | |
| •PEN 1800 (US$ ~514) | |
| •PEN 3000 (US$ ~857) |
Summary of attributes and corresponding levels for the discrete choice experiment (DCE).
*Status quo in bold.
Based on consultations with local experts, we define one hectare (ha) to be the minimum size of a cocoa plot that could be enrolled in the proposed PES scheme. For the attribute related to canopy cover values required in PES enrolled cocoa plots, three levels (low, medium and high) were defined. The first level here refers to the status quo situation characterized by low canopy cover ( ≤ 30%), less than 25 shade trees ha−1 and equal or less one stratum of trees in the cocoa plots. Meanwhile, the high level we proposed in the experiment is characterized by canopy cover above 50% or more than 100 individual trees (including both native and introduced species with the predominance of natives) within the cocoa plot, and at least three strata. The intermediate level for this attribute was a cocoa plot with 30–50% canopy cover, at least two strata of shade trees and between 25–50 shade trees ha−1. These canopy cover values are based on existing scientific evidence that confirm the positive correlation between canopy levels and diverse biodiversity parameters in perennial crops (; ; ; Wynter et al., 2025), and are also coherent with existing biodiversity standards currently being applied to cocoa agroforestry systems [e.g. Smithsonian ()]. To facilitate the understanding of this attribute during interviews, we use graphical material presented in ().
The three levels of the second attribute assessed in the DCE were related to farmers' participation in monitoring biodiversity for the proposed PES scheme. The first level reflects the status quo for this attribute in which no biodiversity monitoring occurs within cocoa plots. In the proposed second level, monitoring is exclusively performed by external actors commissioned by the implementer of the initiative (e.g. private investor or certification body) using remote sensing technologies as well as some field measurements (e.g. camera traps, acoustic recorders, etc.), meanwhile, in the third level, farmers are allowed to participate in biodiversity monitoring, which would imply some training as well as periodic reporting to complement external tracking.
The third attribute assessed through the DCE relates to mechanisms for boosting the enrollment of contiguous plots into the scheme following the assumption that numerous farms under agroforestry management nearby should provide greater habitat connectivity and therefore more robust biodiversity outcomes. The lack of connectivity can heavily compromise ecological processes within the landscape, such as seed and animal dispersals as well as local species abundance which, on turn, may lead to biodiversity loss (; ). For this attribute, the first level (status quo) consisted of no mechanism for this purpose existing on the ground. Based on the existing literature (; ), we proposed the following two levels for this attribute in addition to the status quo: a one-time cash bonus (PEN 300 or US$ ~86 per new participant, considering an US$/PEN exchange rate of 3.5) on top of the PES compensation that would be paid to individual farmers sponsoring the enrolment of nearby participants—those managing eligible cocoa plots up to 3 km away from the sponsor's enrolled plot; after the latter has complied with the rules of the PES scheme for five consecutive years to guarantee additional outcomes from implementing the proposed initiative. The third level for this attribute consists in that newer enrolment bonuses are added at the village level and then periodically delivered to corresponding organizations comprised of participants in the same village once newly enrolled farmers have stayed in the scheme for five consecutive years and complied with their environmental commitments. This strategy is analogous to an “agglomeration bonus” () with the variant that, for this study, we did not establish any distribution rules in advance as such decisions should depend on each village-level organization. To facilitate the analysis of farmers' preferences between both approaches, we set the amount of this agglomeration bonus to be equal to the individual sponsorship bonus.
The fourth attribute assessed corresponds to the cash compensation amount that would be received by the participant per ha after five consecutive years of enrolment and verification of compliance with environmental commitments. The status quo for this attribute consists of receiving zero compensation [PEN 0]. In addition, we offered three compensation levels that reflect an increasing bid vector that would be introduced through the initiative [PEN 500, 1,800 and 3,000] (equivalent to US$ ~143, ~514 and ~857, respectively) as defined using information collected from interviews with local experts and focus groups who were consulted about their opinion regarding widely acceptable compensation in cash for conservation efforts and potential financial losses from changing agricultural practices. Finally, to facilitate and increase farmers' understanding of attributes and levels under consultation when conducting the DCE, local surveyors were trained by the first and third authors to use colloquial words for explaining technical terms as well as use diverse visual material previously designed for this purpose (figures, photographs, etc.). Table 1 summarizes the attributes and levels used in the DCE.
We followed guidelines from () and designed the survey instrument to cover information related to individual environmental attitudes, preferences for the proposed intervention, and socioeconomic aspects. We also consider recommendations to reduce hypothetical bias. For instance, we include six modules in the survey: the first three sections include questions to inquire about survey participants' environmental attitudes including those related to biodiversity and agroforestry. The fourth survey module collected information about interviewees' land management practices as well as their economic activities and income sources. In the fifth module, the choice experiment was introduced by first providing information about assessed attributes and levels and then presenting the hypothetical scenario to be considered during selection of the most preferable options. Given the large number of possible combinations for the DCE (3 × 3 × 3 × 4 = 108) we apply an efficient D-design to optimize the number of choice cards to 20 representing combinations that are proven to be relevant for the proposed initiative (Walker et al., 2018). After asking and confirming whether the instructions about each attribute and corresponding levels have been well understood by survey participants, they received the instructions that they might select among three options each out of four times during the interview. Considering 2 alternatives to the status quo for each choice round and 4 rounds, each surveyed farmer was consulted for their preferences in relation to 8 alterative scenarios in total. Following each round, they were asked about their certainty about decisions made, and after all the rounds were conducted, some consequential questions (i.e. about the policy and survey) were also asked to mitigate hypothetical bias (). We show an example of choice cards presented to participants in Supplementary material (Figure S1).
Between September and November of 2024, 300 surveys were completed by three local surveyors using the digital version of the survey installed into the ODK application for tablets. However, we report our results based on 223 farmers' responses who were certain of their answers and considered the proposed options or survey as realistic or consequential, respectively during all the voting questions. This subsample represents 75% of the collected sample and about 9% of the total number of cocoa producers settled within the three districts comprising our study area. To identify survey participants, we contacted natural resource managers working with local projects promoting cocoa agroforestry in the study area and consulted them about existing lists of cocoa farmers across the area from which we could randomly select 100 farmers per district. Selected farmers were contacted and visited by our surveyors. Although this was the most efficient way to contact farmers in the area, we acknowledge that this would also generate some bias as a disproportionate number of sampled farmers could have already been exposed to programs and information about biodiversity and cocoa agroforestry.
This study forms part of the Project Biomonitor4CAP (Project 101081964-2022) that obtained ethical clearance by the Independent Ethical Committee of the European Commission as requirement for its approval. The activities involving this research were conducted in accordance with the local legislation and institutional requirements. Research participants were enrolled on voluntary basis, and all of them provided their prior informed consent in written form. To ensure data confidentiality, all participants' responses were anonymized and presented in an aggregated way.
3 Results
3.1 Sample description
Out of the 300 original sample, 223 farmers were certain of their responses and form the final sample. Eighty one percent were men and were on average 47 years old (Table 2, Column 3). Due to our sampling procedure, most participants identified themselves as farmers (91%). Most farmers in the sample attained educational levels higher than primary school (56%) and manage less than 10 ha of land (58%). Small farmers in our sample reported an average annual on-farm income of 18 thousand PEN (US$ ~5.3 thousand), while annual on-farm incomes of larger farmers amount in average 68 thousand PEN (US$ ~19.5 thousand), more than three times the average income earned by the former. Also, we report incomes calculated by aggregating self-reported annual revenues from the most relevant on-farm economic activities across the study area besides cocoa production, mainly livestock and fish farming. Additional descriptive statistics about land uses present on managed lands of surveyed farmers are shown as Supplementary material (Table S1).
Table 2
| Characteristics | ANS 2024 (n = 252)* | Sample for this study (n = 223) |
|---|---|---|
| Age | 50.96 | 47 (35, 60) |
| Sex | ||
| Male | 79% | 180 (80.7%) |
| Female | 21% | 43 (19.3%) |
| Educational level | ||
| No formal education or no answer | 3% | 11 (4.9%) |
| Incomplete primary school | 27% | 33 (14.8%) |
| Complete primary school | 22% | 53 (23.8%) |
| Incomplete high school | 22% | 44 (19.7%) |
| Complete high school | 22% | 58 (26.0%) |
| Superior studies (university or technical) | 9% | 24 (10.7%) |
| Main occupation | ||
| Farmer | n.a. | 203 (91.0%) |
| Self-employed off-farma | n.a. | 1 (4.0%) |
| Employment off-farmb | n.a. | 6 (2.7%) |
| Otherc | n.a. | 5 (2.2%) |
| Farmer types according to their managed land size | ||
| Small (< 10 ha) | 14% | 128 (58.2%) |
| Medium (between 10 and 20 ha) | 22% | 48 (21.8%) |
| Big (>20 ha) | 63% | 44 (20.0%) |
| Gross annual income in average per farmers' type (in PEN) | ||
| Small (< 10 ha) | n.a. | 18 469.41 |
| Medium (between 10 and 20 ha) | n.a. | 31 500.83 |
| Big (>20 ha) | n.a. | 68 330.00 |
| Environmental attitudes (yes = 1) | ||
| Knows what the term “biodiversity” refers | n.a. | 124 (55.6%) |
| Thinks biodiversity is important | n.a. | 195 (87.4%) |
| A good farmer is productive | n.a. | 132 (59.2%) |
| A good farmer protects the forest | n.a. | 140 (62.8%) |
| A good farmer does not pollute | n.a. | 132 (59.2%) |
| Agreement with agroforestry benefits (yes = 1) | ||
| Agroforestry improves the ecosystem's health | n.a. | 164 (73.5%) |
| Agroforestry improves soil fertility | n.a. | 92 (41.3%) |
| Agroforestry helps control pests and diseases | n.a. | 83 (37.2%) |
| Agroforestry increases cacao yields | n.a. | 106 (47.5%) |
| Agroforestry increases my total income | n.a. | 208 (93.3%) |
| Agroforestry mitigates climate change | n.a. | 208 (93.3%) |
Summary of socioeconomic features of sampled farmers.
*Agriculture National Survey (). n.a., not available.
aFor instance, kiosk or restaurant owners, transport driver.
bFor instance, schoolteacher, either government or private sector employee.
cFor instance, either a university or technical school student, retired employee, or housewife.
Table 2 allows us to compare the characteristics of our final sample with features of farmers settled across the study area who were included in the 2024 Peruvian National Agricultural Survey (2024 ANS) to explore the presence of representativeness biases. We observe that our sample feature a similar average age than farmers included in the 2024 ANS, but male farmers are slightly overrepresented in our sample. Although farmers in both samples are similar in terms of primary and secondary education attainment, farmers with superior studies and those without formal education are slightly overrepresented in our study sample. With respect to their land size distribution, our sample mostly consists of smallholders (< 10 ha) while the 2024 ANS included a larger share of farmers possessing larger holdings (>20 ha). This might be indicative that our results are better suited for design intervention targeting vulnerable smallholders. Disaggregated farmers' information per district is presented in Supplementary Materials (Table S2).
Our survey also captured important insights about sampled farmers' attitudes and beliefs toward biodiversity, farming and agroforestry. Slightly more than a half of farmers in the sample considers that they know what the term biodiversity refers to 56%, and 87% though that it is important. Sixty three percent of the sampled farmers agreed that a good farmer protects the forest, which is a slightly higher share than the percentage of farmers who expressed that at a good farmer is productive (59%) or does not pollute—for instance, by using chemical fertilizers (59%). In terms of their attitude toward agroforestry, 93% of sampled farmers agree that it both increases their total income, and it contributes to climate change mitigation. Seventy four percent of farmers believe that agroforestry improves ecosystem health, but most farmers in the sample think that this benefit could be in detrimental to their production as only the 48% of sampled farmers agree with the statement that agroforestry contributes to increased cacao yields and only 37% believe that agroforestry might help to control pests and diseases. Forty one percent of farmers in the final sample think that agroforestry improves soil fertility.
3.2 Willingness to accept
Table 3 firstly presents the results of the MCL and MXL models where the probability of uptake for an agroforestry-based PES scheme is described according to each of the consulted attributes. Both models were estimated using the 223 farmers subsample as they provide the better fit for our data (Table S3 in Supplementary Materials) as well as the most conservative results. Results shown in Table 3 indicate that the MCL model projects, as expected, that increases in the cash amount that participants would receive for engaging and complying with the PES scheme overall rise the probability that farmers will engage. Also, establishment of high canopy cover requirement is associated with reductions in farmers' utility and hence in the possibility to enroll in the proposed initiative. The agglomeration bonus paid collectively is less preferred than the individual sponsorship bonus among the farmers surveyed presumably due to individualistic productive behavior featured by most of farmers settled in the area, and incorporating a participatory monitoring mechanism in the PES scheme rather than one reliant on third-party verification increase the probability that farmers would enroll in the PES.
Table 3
| Variable | MCL | MXL | GMNL | ||
|---|---|---|---|---|---|
| Mean | SD | Mean | SD | ||
| Status quo | −2.21509*** | −3.26392*** | −1.65107*** | −7.74659*** | 2.42649*** |
| (0.30437) | (0.47615) | (0.33887) | (2.88551) | (1.14580) | |
| PES cash amount | 0.00009* | 0.00012 | 0.00079*** | 0.00030 | 0.00173*** |
| (0.00005) | (0.00008) | (0.00011) | (0.00021) | (0.00074) | |
| High canopy cover | −0.16954 | −0.26780** | 0.02936 | −0.56194* | −0.60080* |
| (0.10347) | (0.11893) | (0.29677) | (0.30251) | (0.36040) | |
| Participatory monitoring | 0.14505 | 0.29003*** | 0.52842** | 0.64537** | −0.68735* |
| (0.09202) | (0.11137) | (0.21192) | (0.28756) | (0.40906) | |
| Connectivity bonus | −0.24789** | −0.24333* | 0.17476 | −0.39460* | 0.46125 |
| (0.10513) | (0.12659) | (0.22423) | (0.23552) | (0.39059) | |
| tau | 1.45948 | ||||
| (0.28980)*** | |||||
| gamma | −0.30750 | ||||
| (0.20307) | |||||
| LL(final) | −786.599 | −772.306 | −766.076 | ||
| AIC | 1,585.197 | 1,564.612 | 1,556.152 | ||
| BIC | 1,620.550 | 1,623.532 | 1,626.857 | ||
| Observations | 2,676 | 2,676 | 2,676 | ||
| Farmers | 223 | 223 | 223 | ||
Regression results.
Results from performing the following econometric specifications: MCL, conditional logit model; MXL, mix logit; GMNL, generalized multinomial logit. Significance at the one, five, and ten percent levels is indicated by ***, **, and *, respectively. Standard errors are shown in parentheses.
When testing for the Independence of Irrelevant Alternative (IIA) assumption to the MCL model, we found that it does not hold (χ2 = 0.0363) for our sample. For this reason, we estimate the mixed logit model (MXL) as it relaxes IIA. Standard deviation coefficients estimated through the MXL model (Table 3, Column 3) allow us to confirm heterogeneous farmers' preferences as most of them present statistical significance. All the MXL parameters representing consulted attribute means (Column 2 in Table 3) are statistically significant and follow the same relationships with WTA found through the MCL model. The status quo is mostly rejected, and farmers are willing to accept compensation for participating in the initiative when high canopy cover is required in PES enrolled areas. We also confirmed that farmers prefer receiving an individual rather than collective bonus as mechanism to promote the connectivity of PES enrolled areas. However, the statistically significant standard deviation coefficients estimated through the MXL model suggest that not all farmers would obtain the same utility levels from being compensated or participating in monitoring tasks within the frame of the proposed PES scheme. We consider all the attributes consulted in surveys as random as such assumption provides the best explanations for our sample (Tables S4, S5 in Supplementary Material).
Although MXL captures heterogeneity in preferences through random parameters, it assumes homogeneous error variance across all decision-makers (Train, 2009). We thus extend this framework using the Generalized Multinomial Logit (GMNL) model whose results are reported in Column 4 of Table 3, which simultaneously accounts for both preference heterogeneity and scale heterogeneity (). This econometric specification yields more robust and consistent estimates as evidenced by the superior Log Likelihood (LL) and the Akaike Information Criterium (AIC) values. It also shows that our participants exhibit different levels of choice consistency (τ = 1.246, p < 0.01). However, the non-significance of the mixing parameter (γ = −0.308, p > 0.10) indicates ambiguity into signaling the source of preferences variation ().
Regarding the amount required by participants to enroll in the proposed PES scheme, the models reported in this Section do not provide statistically significant PES compensation amounts. These were estimated using the delta method (Wooldridge, 2010) and are reported in Supplementary material (Figure 2). Given the ambiguity of the variation source in the GMNL model, we complement our analysis with a Latent Class (LC) Model to identify discrete behavioral segments in the population, as well as potential marginal willingness to accept (MWTA) for different groups. We report results from performing such a model in the next Section.
Figure 2
3.3 Heterogeneity and latent class (LC) model
Since we found strong heterogeneity in farmers' preferences for levels in the amount required to compensate their enrollment in the PES scheme, we implement a LC model to investigate which factors might shape these differences. Based on theoretical suggestions () and prior empirical analyses [e.g. ()], we employ the presence of certain attitudinal, demographic and socioeconomic features defined posterior the data analyses as membership variables to select the optimal number of identified classes. We again use LL and AIC as main criteria (Table S6 in Supplementary Material) to estimate the optimal class number as three as shown in Figure 2.2 The first identified class (Class I) represents the largest group (55.4% of the sample). They are more likely to enroll in the proposed scheme if the PES payment increases, and they are allowed to participate in monitoring tasks. Also, they are less likely to participate if high canopy level in enrolled plots is required or if the connectivity bonus is distributed collectively (at the village level) within the proposed PES. This class rejects the status quo scenario in larger magnitude than farmers in Class II as shown by corresponding coefficients and can be consider the “economically rational” class as they increase their enrollment when the payment per ha also increases. We estimated the average amount required to compensate utility losses of Class I farmers for adopting high canopy cover and collective connectivity bonus in PEN 737 and 807 (equivalent to US$ ~ 210 and ~ 230), respectively (Figure 2).
Meanwhile, farmers in Class II (34.3% of the sample) also reject the status quo but their marginal WTA for all the assessed attributes of the proposed PES scheme are statistically not different from zero. We presume that such results could signal the elasticity of this farmer group regarding levels in assessed attributes for the proposed PES scheme as consequence of altruistic values present to some extent in this class. On the other hand, Class III (10.3% of the sub sample) presents a positive and significant marginal WTA for high canopy values in their enrolled plots (352 PEN or US$ ~100 per ha). This group can be categorized as an individualistic class. Although they also present altruistic values to some extent, their responses indicates an average yet not significant negative marginal utility for the collective bonus, which could reflect some distrust on other village members. Based on the analysis of membership variables, the presence of positive attitudes toward forest conservation and being younger than 60 years old increases the likelihood of belonging to Class III compared to Class I. These results suggest that farmers younger than 60 years old with positive attitudes toward forest conservation are important features associated with this individualistic class. Other membership variables like possessing a small size farm, being a woman or educational attainment, do not correlate with probability of belonging to class II or III. These results remain robust even after using alternative specifications of the PES compensation amount, like estimations using net present values that incorporate an overall discount rate of 3.7% as suggested to evaluate profitability of national public projects with environmental contributions ()—Table S8 in Supplementary Material.
4 Discussion
Based on the results from our DCE performed in an emblematic area of the Peruvian Amazon that contains high biodiversity values, in this section we discuss some emerging insights that could inform the design and implementation of a PES scheme oriented to upscale biodiversity-friendly cocoa agroforestry across the study area. Such insights might be grouped around three topics that are elaborated in detail in the following paragraphs: (1) the role of farmers' attitudes and perception in the adoption and upscaling of cocoa agroforestry across the region; (2) the existence of heterogeneous preferences in relation to the proposed PES scheme in the study area; and finally, (3) the incorporation of these differentiated preferences in designing and implementing the proposed PES intervention. Such topics are also coherent with the research questions guiding this analysis.
Firstly, our study provides suggestive evidence indicating that the current low levels of agroforestry adoption across the study area might be related to farmers' low agreement with its purported benefits, in particular in helping to control pests and diseases and to maintain soil fertility as only 41% and 37% of our sample agree with these statements, respectively. Furthermore, we also found that less than half of our final sample (48%) agrees with the statement that claim positive impacts of agroforestry on cocoa yields. We identify that limitations of the technical assistance available across the area might shed some light to understand this finding. As was mentioned earlier, the sharp increase of cocoa production across the region over last decades is largely a product of successive “alternative development” programs whose main purpose has been to replace illegal coca with commercial crops including cocoa. In this sense, these interventions have focused largely on providing farmers with inputs and some financial resources to produce cocoa and other commercial crops (e.g. oil palm) delegating the provision of technical assistance to cocoa growers newly incorporated into these schemes to poorly resourced cooperatives and local public agencies with limited capacity to deliver these services in a tailored way. Furthermore, in most cases such extension providers aggregate and channel cocoa toward low-quality conventional markets not requiring any filtering for environmental or social standards (). Furthermore, although a few cooperatives operating across the area have started to promote agroforestry in cocoa, such an offer is not tailored to the necessities and limitations of most smallholders settled across the area who face considerable constraints in terms of labor, land, and financial resources (). The confluence of these factors have contributed to configure the landscape of the study area as one where the development of the cocoa sector has affected forest cover, biodiversity values and traditional livelihoods [e.g. (); ()].
The second topic we discuss in this section addresses the question of whether introducing a PES scheme might contribute to increasing biodiversity-friendly cocoa agroforestry uptake across the region. In this regard, our analysis also provides some insights. Our main results indicate that introducing PES incentives to increase agroforestry across the area would have a wide acceptance among eligible cocoa farmers. However, we also identify that their preferences regarding the attributes of the proposed initiative are quite heterogeneous. This last finding deserves careful consideration by interested implementers of this type of initiative in the region. Overlooking it might compromise the effectiveness and equity of any proposed policy intervention of this type across the area, as hinted at by previous studies (; ). Results derived from our latent class model allow us to identify three differentiated farmers groups whose preferences in relation to specific attributes of the proposed intervention differ, namely in terms of the canopy level required in PES enrolled areas and the connectivity modality. We find that decisions to enroll in the proposed PES scheme for the first and largest class of farmers (56% of the final sample) is strongly driven by economic rationality (; Singh et al., ). These farmers are more likely to enroll in a proposed PES scheme for monetary compensation. This group clearly recognizes that increased canopy cover might reduce yields and would require economic compensation to compensate the yield losses. This group also favored an individual bonus for enrolment of nearby farms to increase ecological connectivity and would also prefer that they were involved in monitoring tasks. This latter corroborates recent findings from prospective assessments of farmers' preferences in relation to similar proposed initiatives in Mexico () and Germany ().
The preferences of the second group seem to be more elastic in relation to all measured attributes. Moreover, the WTA estimates for the high canopy level required by the proposed PES is negative on average suggesting the emergence of a possible utility surplus for adopting such a requirement in this group of farmers (). We presume that this is due to altruistic values present in this group due to the positive correlation between membership in this group and agreement to the “good farmers are good forest stewards” statement (Table S7 in Supplementary material). Such a finding resonates with recent evidence highlighting the presence of diverse values driving pro-environmental attitudes found in participants of similar interventions implemented in Colombia (). Finally, the third farmers group features a WTA for required high canopy level that is lower on average than the first farmers group's WTA and, on average, a larger preference for an individual rather than collective connectivity bonus. We also find that being younger than 60-years-old and agreement with the statement claiming that good farmers are forest stewards is significantly correlated with membership to this group. Such findings highlights the relevance of offering customized rather than uniform incentives to match differentiated necessities and expectations present across eligible farmers in the study area and the potential of introducing tailored designed incentives to increase the uptake of biodiversity-friendly agroforestry practices (; ; ).
As a third discussion point, we address the question of how differentiated farmers' preferences might be incorporated in a PES program oriented toward upscaling biodiversity-friendly cocoa agroforestry across the region. In this regard, although the proposed PES and most of its attributes has a wide acceptance among eligible farmers, based on our findings we consider that still there is some room for customization in at least two aspects. The first aspect relates to leaving open the possibility of both individual and collective connectivity bonuses into the frame of the PES scheme. Providing PES participants scope to codesign and modify the rules of the game during periodic reviews and negotiation between farmers and implementers of the scheme, ideally after each five-year period of continuous annual payments, as was formulated in our conducted DCE, could foster inclusion and ongoing improvements. On the other hand, while the potential of an agglomeration bonus to achieve cost-effective biodiversity outcomes has been increasingly discussed from a theoretical perspective, its implementation has been rare and insights about potential effectiveness comes mostly from developed countries (). The limited number of empirical analyses show some discouraging results due to high transaction cost which can result in low participation and declines in coordination over time, ultimately compromising contributions of this type of policy on biodiversity connectivity (; ). In the same way, (), using a similar experimental approach, find that farmers in France are indifferent in selecting between PES contracts with no bonus option, and a contract offering the possibility to gain sponsorship bonuses (sometimes combined with the possibility of receiving a collective result bonus) on top of the PES compensation. The scarce evidence supporting the effectiveness of such instruments suggests that a cautionary position in relation to these instruments should be adopted. The second entry point for customization could consist in letting enrolled farmers decide if they want payments to be delivered after each year of compliance with their contractual obligations or accumulate these payments in the form of individual contingency funds. This would imply that, independently of their estimated WTA, the PES amount could be fixed across all participant farmers and set to the level of the first identified class (PEN 737 or US$ ~ 210 annually per ha enrolled after the 5th year of enrolment and verification of ecological compliance) for all participants in order to simplify and operationalize such a recommendation. This claim is also based on our knowledge that egalitarian principles rule farmers' notions of fairness across the study area mainly when it is about sharing financial benefits among them.
Some relevant caveats should be kept in mind when interpreting our results: firstly, our findings come from a field-framed experimental setting conducted in the rural context of a developing nation. In this sense, our results are based on survey responses which, despite our efforts already explained in detail in Section 2, eventually could have been influenced by hypothetical and cognitive biases. Secondly, in order to simplify the setting for our DCE we have simplified some relevant factors that might compromise validity of our findings in a real-world context, in particular we assumed that farmers in our sample have similar time discounting preferences and do not face time restrictions for participating in monitoring activities. Thirdly, given the particularities of the study area and the timeline of farmer colonization of study area, our findings are highly dependent on the context in which this analysis was conducted. Finally, we rely on using traditional econometric approaches to estimate the distribution of the utility coefficients instead of the distribution of WTA/WTA-space, directly (), which could have led to overestimate MWTA values even after checking the robustness of our estimates by performing several specifications (i.e. MCL, MXL, GMNL, LC). Future research might investigate the external validity of our findings by conducting similar experimental studies on other sites in the Amazon region and beyond. Also, another potential research might combine our findings with spatial analyses to identify specific villages and plots with larger potential for providing biodiversity benefits and therefore provide a more complete assessment about the economic feasibility of the proposed PES scheme to upscale biodiversity-friendly agroforestry across the study area and disseminate their promising benefits.
To sum up, here we provide experimental evidence that could guide the design of a PES intervention aiming to upscale biodiversity-friendly cocoa agroforestry across an important forest frontier of the Peruvian Amazon. Despite some particularities present in the study area, we believe that these findings might also be applicable to inform and guide the design of other agroforestry-based initiatives that are being developed across other tropical regions with the purpose of providing multiple key ecosystem services such as carbon storage and biodiversity conservation (Williams et al., 2021). Furthermore, given the increased interest by private stakeholders in biodiversity markets, our results contribute to unveiling the preferences of potential suppliers (i.e. cocoa growers) to these markets regarding the type and amount of ecosystem services that they might be willing to offer in these without compromising their livelihoods and wellbeing.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethics procedures applicable to this study were reviewed and approved by an Independent Ethical Committee of the European Commission (Project ID: 101081964). This research was conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.
Author contributions
JM-Z: Writing – review & editing, Supervision, Data curation, Writing – original draft, Formal analysis, Methodology, Investigation, Conceptualization. JF-F: Writing – review & editing, Writing – original draft, Formal analysis, Data curation, Methodology, Visualization, Investigation. LG: Resources, Project administration, Methodology, Supervision, Writing – review & editing, Investigation. ER: Writing – review & editing, Resources, Validation, Visualization, Data curation. LO-A: Writing – review & editing. BL: Conceptualization, Project administration, Supervision, Writing – review & editing, Funding acquisition, Resources.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This study is part of the project BioMonitor4CAP funded by the European Union's Horizon Europe programme under grant agreement N° 101081964. Additional funds to JM-Z were provided by the Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (CONCYTEC) and the Programa Nacional de Investigación Científica y Estudios Avanzados (PROCIENCIA) within the frame of the call “E067-2023-01 Proyectos Especiales: Proyectos de Incorporación de Investigadores Postdoctorales en Instituciones Peruanas” (Contract No. PE501084943-2023).
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Publisher’s note
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2025.1645422/full#supplementary-material
Footnotes
1.^More information about cacao production per district considered in this study is provided in Supplementary material (Table S2).
2.^Table S7 and Figure S3 in Supplementary material show results in detail from performing the selected specification (k = 3) and the posterior class membership probabilities by PES compensation bids, respectively.
References
1
Arias-ArévaloP.Pacheco-ValdésN.García-LozanoA. (2025). “With or without payment I conserve”: the fabric of intrinsic, instrumental and relational values in payments for ecosystem services. J. Environ. Manag.387:125744. doi: 10.1016/j.jenvman.2025.125744
2
AslamU.TermansenM.FleskensL. (2017). Investigating farmers' preferences for alternative PES schemes for carbon sequestration in UK agroecosystems. Ecosyst. Serv. 27, 103–112. doi: 10.1016/j.ecoser.2017.08.004
3
BassD. A.McFaddenB. R.MesserK. D. (2021). A case for measuring negative willingness to pay for consumer goods. Food Policy104:102126. doi: 10.1016/j.foodpol.2021.102126
4
BenjaminE. O.OlaO.BuchenriederG. (2018). Does an agroforestry scheme with payment for ecosystem services (PES) economically empower women in sub-Saharan Africa?Ecosyst. Serv.31, 1–11. doi: 10.1016/j.ecoser.2018.03.004
5
BenjaminE. O.OlaO.SauerJ.BuchenriederG. (2021). Interaction between agroforestry and women's land tenure security in sub-Saharan Africa: a matrilocal perspective. For. Policy Econ.133:102617. doi: 10.1016/j.forpol.2021.102617
6
BennettA.RavikumarA.CronkletonP. (2018). The effects of rural development policy on land rights distribution and land use scenarios: the case of oil palm in the Peruvian Amazon. Land Use Policy70, 84–93. doi: 10.1016/j.landusepol.2017.10.011
7
BennettR. E.SillettT. S.RiceR. A.MarraP. P. (2022). Impact of cocoa agricultural intensification on bird diversity and community composition. Conserv. Biol.36:e13779. doi: 10.1111/cobi.13779
8
BhagwatS. A.WillisK. J.BirksH. J. B.WhittakerR. J. (2008). Agroforestry: a refuge for tropical biodiversity?Trends Ecol. Evol.23, 261–267. doi: 10.1016/j.tree.2008.01.005
9
Blundo-CantoG.Cruz-GarciaG. S.TalsmaE. F.FrancesconiW.LabartaR.Sanchez-ChoyJ.et al. (2020). Changes in food access by mestizo communities associated with deforestation and agrobiodiversity loss in Ucayali, Peruvian Amazon. Food Secur.12, 637–658. doi: 10.1007/s12571-020-01022-1
10
BoufousS.HudsonD.CarpioC. (2023). Farmers' willingness to adopt sustainable agricultural practices: a meta-analysis. PLOS Sustain. Transf.2:e0000037. doi: 10.1371/journal.pstr.0000037
11
BoxallP. C.AdamowiczW. L. (2002). Understanding heterogeneous preferences in random utility models: a latent class approach. Environ. Resour. Econ.23, 421–446. doi: 10.1023/A:1021351721619
12
BremerL. L.AuerbachD. A.GoldsteinJ. H.VoglA. L.ShemieD.KroegerT.et al. (2016). One size does not fit all: natural infrastructure investments within the Latin American water funds partnership. Ecosyst. Serv.17, 217–236. doi: 10.1016/j.ecoser.2015.12.006
13
BremerL. L.FarleyK. A.DeMaagdN.SuárezE.Cárate TandallaD.Vasco TapiaS.et al. (2019). Biodiversity outcomes of payment for ecosystem services: lessons from páramo grasslands. Biodivers. Conserv.28, 885–908. doi: 10.1007/s10531-019-01700-3
14
BrochS. W.VedelS. E. (2012). Using choice experiments to investigate the policy relevance of heterogeneity in farmer agri-environmental contract preferences. Environ. Resource Econ.51, 561–581. doi: 10.1007/s10640-011-9512-8
15
BullJ. W.StrangeN. (2018). The global extent of biodiversity offset implementation under no net loss policies. Nat. Sustain. 1, 790–798. doi: 10.1038/s41893-018-0176-z
16
CanessaC.VenusT. E.WiesmeierM.MennigP.SauerJ. (2023). Incentives, rewards or both in payments for ecosystem services: drawing a link between farmers' preferences and biodiversity levels. Ecol. Econ.213:107954. doi: 10.1016/j.ecolecon.2023.107954
17
CastleS. E.MillerD. C.OrdonezP. J.BaylisK.HughesK. (2021). The impacts of agroforestry interventions on agricultural productivity, ecosystem services, and human well-being in low- and middle-income countries: a systematic review. Campbell Syst. Rev.17:e1167. doi: 10.1002/cl2.1167
18
CBD Secretariat (2020). Global Biodiversity Outlook 5. Convention on Biological Diversity (CBD). Available online at: https://www.cbd.int/gbo/gbo5/publication/gbo-5-en.pdf (Accessed February 12, 2025).
19
CharryA.PereaC.RamírezK.ZambranoG.YoveraF.SantosA.et al. (2025). The bittersweet economics of different cacao production systems in Colombia, Ecuador and Peru. Agric. Syst.224:104235. doi: 10.1016/j.agsy.2024.104235
20
CharryA.TorresN.FonsecaC.NarjesM. (2023). The Cocoa Value Chain in Ucayali. Analysis to Identify Business Models with Agroecological Potential. CGIAR Transformational Agroecology. Available online at: https://cgspace.cgiar.org/server/api/core/bitstreams/055e4f2f-8e36-42eb-80c1-5efc0149ccc5/content (Accessed August 24, 2025).
21
Clavo PeraltaZ. M.Vela AlvaradoJ. W.Alvez-VallesC. M. (2022). “Biodiversity Islands and dominant species in agricultural landscapes of the South Western Amazon, Perú,” in Biodiversity Islands: Strategies for Conservation in Human-Dominated Environments, ed F. Montagnini (London: Springer International Publishing), 207–235. doi: 10.1007/978-3-030-92234-4_9
22
DaiX.PuL.RaoF. (2017). assessing the effect of a crop-tree intercropping program on smallholders' incomes in rural Xinjiang, China. Sustainability9:1542. doi: 10.3390/su9091542
23
DollC. A.BurtonM. P.PannellD. J.RollinsC. L. (2023). Are greenspaces too green? Landscape preferences and water use in urban parks. Ecol. Econ.211:107896. doi: 10.1016/j.ecolecon.2023.107896
24
DucrosA.SteeleP. (2022). Biocredits to Finance Nature and People. Emerging Lessons. London: International Institute for Environment and Development (IIED).
25
Ezzine-de-BlasD.WunderS.Ruiz-PérezM.Moreno-SanchezR. D. P. (2016). Global patterns in the implementation of payments for environmental services. PLoS ONE11:e0149847. doi: 10.1371/journal.pone.0149847
26
FiebigD. G.KeaneM. P.LouviereJ.WasiN. (2010). The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Mark. Sci.29, 393–421. doi: 10.1287/mksc.1090.0508
27
GarbachK.MilderJ. C.MontenegroM.KarpD. S.DeClerckF. A. J. (2014). “Biodiversity and ecosystem services in agroecosystems,” in Encyclopedia of Agriculture and Food Systems (Amsterdam: Elsevier), 21–40. doi: 10.1016/B978-0-444-52512-3.00013-9
28
Gobierno Regional de Ucayali (2019). Plan de Competitividad del Cacao Ucayali 2019–2029. Dirección Regional de Desarrollo Económico GOREU.
29
Gobierno Regional de Ucayali (2022). Estrategia Regional para el Desarrollo Rural Bajo en Emisiones en el Departamento de Ucayali. Sintesis para tomadores de decisiones. Versión Preliminar. Earth Innovation Institute (EII). Available online at: https://projectflow.earthinnovation.org/documents/ (Accessed March 12, 2025).
30
GrandeT. O.AguiarL. M. S.MachadoR. B. (2020). Heating a biodiversity hotspot: connectivity is more important than remaining habitat. Landsc. Ecol.35:639–657. doi: 10.1007/s10980-020-00968-z
31
HaileK. K.TirivayiN.TesfayeW. (2019). Farmers' willingness to accept payments for ecosystem services on agricultural land: the case of climate-smart agroforestry in Ethiopia. Ecosyst. Serv.39:100964. doi: 10.1016/j.ecoser.2019.100964
32
HarveyC. A.González VillalobosJ. A. (2007). Agroforestry systems conserve species-rich but modified assemblages of tropical birds and bats. Biodivers. Conserv.16, 2257–2292. doi: 10.1007/s10531-007-9194-2
33
HäuslerM. -M.ZabelA. (2024). Sites side by side: can an agglomeration bonus with an adjacency rule connect agri-environmental sites?Ecol. Econ.224:108287. doi: 10.1016/j.ecolecon.2024.108287
34
HuberR.ZabelA.SchleifferM.VroegeW.BrändleJ. M.FingerR. (2021). Conservation costs drive enrolment in agglomeration bonus scheme. Ecol. Econ.186:107064. doi: 10.1016/j.ecolecon.2021.107064
35
HuntingtonH.ShenoyA. (2021). Does insecure land tenure deter investment? Evidence from a randomized controlled trial. J. Development Econ.150:102632. doi: 10.1016/j.jdeveco.2021.102632
36
HyseniC.HeinoJ.BiniL. M.BjelkeU.JohanssonF. (2021). The importance of blue and green landscape connectivity for biodiversity in urban ponds. Basic Appl. Ecol.57, 129–145. doi: 10.1016/j.baae.2021.10.004
37
INEI (2009a). Perfil Sociodemográfico del Departamento de Ucayali. Instituto Nacional de Estadística e Informática (INEI). Available online at: https://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0838/libro11/libro.pdf (Accessed March 14, 2025).
38
INEI (2009b). Sistema de Consulta de Centros Poblados. [Dataset]. Available online at: http://sige.inei.gob.pe/test/atlas/ (Accessed May 2, 2025).
39
INEI (2025). Agrarian National Survey [Dataset]. Available online at: https://www.datosabiertos.gob.pe/ (Accessed August 25, 2025).
40
IPBES (2019). Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services (Version summary for policy makers). Zenodo.
41
IvanovaY.TristánM.RomeroM.CharryA.LemaS.ChoyJ.et al. (2020). Moving Towards a Deforestation-Free Cacao and Chocolate Value Chain With Low Greenhouse Gas Emissions. CIAT Publication No. 502. Cali: International Center for Tropical Agriculture (CIAT), 136.
42
Izquierdo-TortS.AlatorreA.Arroyo-GeralaP.Shapiro-GarzaE.NaimeJ.DuprasJ. (2024). Exploring local perceptions and drivers of engagement in biodiversity monitoring among participants in payments for ecosystem services schemes in southeastern Mexico. Conserv. Biol.38:e14282. doi: 10.1111/cobi.14282
43
JaureguiberryP.TiteuxN.WiemersM.BowlerD. E.CosciemeL.GoldenA. S.et al. (2022). The direct drivers of recent global anthropogenic biodiversity loss. Sci. Adv.8:eabm9982. doi: 10.1126/sciadv.abm9982
44
JezeerR. E.VerweijP. A.SantosM. J.BootR. G. A. (2017). Shaded coffee and cocoa—double dividend for biodiversity and small-scale farmers. Ecol. Econ.140, 136–145. doi: 10.1016/j.ecolecon.2017.04.019
45
JhaS.KaecheleH.SieberS. (2021). Factors influencing the adoption of agroforestry by smallholder farmer households in Tanzania: case studies from Morogoro and Dodoma. Land Use Policy103:105308. doi: 10.1016/j.landusepol.2021.105308
46
JohnstonR. J.BoyleK. J.AdamowiczW. (Vic), Bennett, J.BrouwerR.CameronT. A.et al. (2017). Contemporary guidance for stated preference studies. J. Assoc. Environ. Resour. Econ.4, 319–405. doi: 10.1086/691697
47
KeaneM.WasiN. (2013). Comparing alternative models of heterogeneity in consumer choice behavior. J. Appl. Econometrics28, 1018–1045. doi: 10.1002/jae.2304
48
Le GlouxF.Ropars-ColletC.IssanchouA.DuprazP. (2025). Payments for environmental services with ecological thresholds: farmers' preferences for a sponsorship bonus. J. Environ. Plan. Manage.68, 2042–2069. doi: 10.1080/09640568.2024.2303738
49
LeleS.Springate-BaginskiO.LakerveldR.DebD.DashP. (2013). Ecosystem services: origins, contributions, pitfalls, and alternatives. Conserv. Soc.11:343. doi: 10.4103/0972-4923.125752
50
LouviereJ.StreetD.CarsonR.AinslieA.DeshazoJ. R.CameronT.et al. (2002). Dissecting the random component of utility. Marketing Lett.13, 177–193. doi: 10.1023/A:1020258402210
51
MaceG. M.NorrisK.FitterA. H. (2012). Biodiversity and ecosystem services: a multilayered relationship. Trends Ecol. Evol.27, 19–26. doi: 10.1016/j.tree.2011.08.006
52
MansonS.NekarisK. A. I.NijmanV.CamperaM. (2024). Effect of shade on biodiversity within coffee farms: a meta-analysis. Sci. Total Environ.914:169882. doi: 10.1016/j.scitotenv.2024.169882
53
Martinez-NuñezC.Velado-AlonsoE.AvelinoJ.ReyP. J.Ten HoopenG. M.Pe'erG.et al. (2024). Tailored policies for perennial woody crops are crucial to advance sustainable development. Nat. Sustain.8, 133–141. doi: 10.1038/s41893-024-01483-8
54
McFaddenD. (1980). Econometric models for probabilistic choice among products. J. Bus. 53:S13. doi: 10.1086/296093
55
MEF (2011). Cálculo de la Tasa Social de Descuento para Proyectos de Inversión Pública Ambientales. Ministerio de Economía y Finanzas. Available online at: https://mef.gob.pe/contenidos/inv_publica/docs/estudios_documentos/estudios/Calculo_TSD_PIP_Ambientales_Noviembre_2011.pdf (Accessed August 25, 2025).
56
MercerD. E.PattanayakS. K. (2003). “Agroforestry adoption by smallholders,” in Forests in a Market Economy, Vol. 72, eds E. O. Sills, and K. L. Abt (Netherlands: Springer Netherlands), 283–299. doi: 10.1007/978-94-017-0219-5_16
57
MINAM (2017). Geobosque. Plataforma de Monitoreo de Cambios sobre la Cobertura de los Bosques. Available online at: https://geobosques.minam.gob.pe/geobosque/view/index.php (Accessed May 10, 2025).
58
MongeO.DullingerS.FusaniL.SchulzeC. H. (2022). Taxonomic, functional and phylogenetic bird diversity response to coffee farming intensity along an elevational gradient in Costa Rica. Agric. Ecosyst. Environ.326:107801. doi: 10.1016/j.agee.2021.107801
59
Montoya-ZumaetaJ. G.WunderS.TacconiL. (2021). Incentive-based conservation in Peru: assessing the state of six ongoing PES and REDD+ initiatives. Land Use Policy108:105514. doi: 10.1016/j.landusepol.2021.105514
60
MTC (2015). Road map, including principal and secondary roads [Dataset]. https://portal.mtc.gob.pe/estadisticas/descarga.html
61
MyersN.MittermeierR. A.MittermeierC. G.Da FonsecaG. A. B.KentJ. (2000). Biodiversity hotspots for conservation priorities. Nature403, 853–858. doi: 10.1038/35002501
62
NguyenC.Latacz-LohmannU.HanleyN.SchilizziS.IftekharS. (2022). Spatial coordination incentives for landscape-scale environmental management: a systematic review. Land Use Policy114:105936. doi: 10.1016/j.landusepol.2021.105936
63
NietherW.JacobiJ.BlaserW. J.AndresC.ArmengotL. (2020). Cocoa agroforestry systems versus monocultures: a multi-dimensional meta-analysis. Environ. Res. Lett.15:104085. doi: 10.1088/1748-9326/abb053
64
NunooI.OwusuV. (2017). Comparative analysis on financial viability of cocoa agroforestry systems in Ghana. Environ. Dev. Sustain.19, 83–98. doi: 10.1007/s10668-015-9733-z
65
Orozco-AguilarL.López-SampsonA.Leandro-MuñozM. E.RobiglioV.ReyesM.BordeauxM.et al. (2021). Elucidating pathways and discourses linking cocoa cultivation to deforestation, reforestation, and tree cover change in Nicaragua and Peru. Front. Sustain. Food Syst.5:635779. doi: 10.3389/fsufs.2021.635779
66
OwusuV.Akoto-AdjepongV.AcheampongE.BarnesV. R. (2022). Farmer perceptions and economic performance of cocoa agroforestry shade levels in Ghana. J. Sustain. For.41, 922–940. doi: 10.1080/10549811.2021.1883444
67
ParkhurstG. M.ShogrenJ. F.BastianC.KiviP.DonnerJ.SmithR. B. W. (2002). Agglomeration bonus: an incentive mechanism to reunite fragmented habitat for biodiversity conservation. Ecol. Econ.41, 305–328. doi: 10.1016/S0921-8009(02)00036-8
68
PattanayakS. K.Evan MercerD.SillsE.YangJ. -C. (2003). Taking stock of agroforestry adoption studies. Agrofor. Syst.57, 173–186. doi: 10.1023/A:1024809108210
69
PorroR.Lopez-FeldmanA.Vela-AlvaradoJ. W. (2015). Forest use and agriculture in Ucayali, Peru: livelihood strategies, poverty and wealth in an Amazon frontier. For. Policy Econ.51, 47–56. doi: 10.1016/j.forpol.2014.12.001
70
ReadyR. C.ChampP. A.LawtonJ. L. (2010). Using respondent uncertainty to mitigate hypothetical bias in a stated choice experiment. Land Econ.86, 363–381. doi: 10.3368/le.86.2.363
71
RiceR. A.GreenbergR. (2000). Cacao cultivation and the conservation of biological diversity. AMBIO: A J. Hum. Environ.29, 167–173. doi: 10.1579/0044-7447-29.3.167
72
RodriguesP.ShumiG.DorresteijnI.SchultnerJ.HanspachJ.HylanderK.et al. (2018). Coffee management and the conservation of forest bird diversity in southwestern Ethiopia. Biol. Conserv.217, 131–139. doi: 10.1016/j.biocon.2017.10.036
73
SalzmanJ.BennettG.CarrollN.GoldsteinA.JenkinsM. (2018). The global status and trends of payments for ecosystem services. Nat. Sustain.1, 136–144. doi: 10.1038/s41893-018-0033-0
74
ScarpaR.ThieneM.TrainK. (2008). Utility in willingness to pay space: a tool to address confounding random scale effects in destination choice to the Alps. Am. J. Agric. Econ.90, 994–1010. doi: 10.1111/j.1467-8276.2008.01155.x
75
SchrothG.HarveyC. A. (2007). Biodiversity conservation in cocoa production landscapes: an overview. Biodivers. Conserv.16, 2237–2244. doi: 10.1007/s10531-007-9195-1
76
Singh I. Squire L. Strauss J. World Bank (eds.). (1986). Agricultural Household Models: Extensions, Applications, and Policy. Baltimore, MD: Johns Hopkins University Press.
77
Smithsonian (2023). Smithsonian Bird Friendly. Normas para cacao. Washington, D.C.: Smithsonian's Migratory Birds Center.
78
SommervilleM. M.Milner-GullandE. J.JonesJ. P. G. (2011). The challenge of monitoring biodiversity in payment for environmental service interventions. Biol. Conserv.144, 2832–2841. doi: 10.1016/j.biocon.2011.07.036
79
Steffan-DewenterI.KesslerM.BarkmannJ.BosM. M.BuchoriD.ErasmiS.et al. (2007). Tradeoffs between income, biodiversity, and ecosystem functioning during tropical rainforest conversion and agroforestry intensification. Proc. Natl. Acad. Sci.104, 4973–4978. doi: 10.1073/pnas.0608409104
80
TadesseT.BerhaneT.MulatuD. W.RannestadM. M. (2021). Willingness to accept compensation for afromontane forest ecosystems conservation. Land Use Policy105:105382. doi: 10.1016/j.landusepol.2021.105382
81
TedescoA. M.BrancalionP. H. S.HepburnM. L. H.WaljiK.WilsonK. A.PossinghamH. P. (2023). The role of incentive mechanisms in promoting forest restoration. Philos. Trans. R. Soc. B. Biol. Sci.378:20210088. doi: 10.1098/rstb.2021.0088
82
ThiesmeierA.ZanderP. (2023). Can agroforestry compete? A scoping review of the economic performance of agroforestry practices in Europe and North America. For. Policy Econ.150:102939. doi: 10.1016/j.forpol.2023.102939
83
TrainK. (2009). “Mixed logit,” in Discrete Choice Methods with Simulation, 2nd Edn (Cambridge: Cambridge University Press), 134–147.
84
TranchinaM.ReubensB.FreyM.MeleM.MantinoA. (2024). What challenges impede the adoption of agroforestry practices? A global perspective through a systematic literature review. Agrofor. Syst.98, 1817–1837. doi: 10.1007/s10457-024-00993-w
85
TscharntkeT.CloughY.BhagwatS. A.BuchoriD.FaustH.HertelD.et al. (2011). Multifunctional shade-tree management in tropical agroforestry landscapes—a review. J. Appl. Ecol.48, 619–629. doi: 10.1111/j.1365-2664.2010.01939.x
86
UdawattaR. P.RankothL. M.JoseS. (2021). “Agroforestry for biodiversity conservation,” in Agroforestry and Ecosystem Services, eds R. P. Udawatta, and S. Jose (Springer International Publishing), 245–274. doi: 10.1007/978-3-030-80060-4_10
87
WalkerJ. L.WangY.ThorhaugeM.Ben-AkivaM. (2018). D-efficient or deficient? A robustness analysis of stated choice experimental designs. Theor. Decis.84, 215–238. doi: 10.1007/s11238-017-9647-3
88
WestT. A. P.WunderS.SillsE. O.BörnerJ.RifaiS. W.NeidermeierA. N.et al. (2023). Action needed to make carbon offsets from forest conservation work for climate change mitigation. Science381, 873–877. doi: 10.1126/science.ade3535
89
WilliamsA.DayerA. A.Hernandez-AguileraJ. N.PhillipsT. B.Faulkner-GrantH.GómezM. I.et al. (2021). Tapping birdwatchers to promote bird-friendly coffee consumption and conserve birds. People Nat.3:312–324. doi: 10.1002/pan3.10191
90
WooldridgeJ. M. (2010). Econometric Analysis of Cross Section and Panel Data. Cambridge: The MIT Press. JSTOR. Available online at: http://www.jstor.org/stable/j.ctt5hhcfr (Accessed August 14, 2025).
91
WynterV.Milner-GullandE. J.PooreJ. (2025). A global comparison of the biodiversity impacts of coffee agricultural systems—-from monoculture to diverse agroforestry. Agric. Syst.229:104449. doi: 10.1016/j.agsy.2025.104449
92
ZegarraE. (2004). Estudio en Profundidad sobre los Impactos de los Proyectos de Desarrollo Alternativo en la Cuenca del Aguaytía. GRADE, 100. Available online at: http://www.grade.org.pe/upload/publicaciones/archivo/download/pubs/Informeproy234.pdf (Accessed February 2, 2025).
93
zu ErmgassenS. O. S. E.DevenishK.SimmonsB. A.GordonA.JonesJ. P. G.MaronM.et al. (2023). Evaluating the impact of biodiversity offsetting on native vegetation. Glob. Chang. Biol.29, 4397–4411. doi: 10.1111/gcb.16801
94
Zu ErmgassenS. O. S. E.MaronM.Corlet WalkerC. M.GordonA.SimmondsJ. S.StrangeN.et al. (2020). The hidden biodiversity risks of increasing flexibility in biodiversity offset trades. Biol. Conserv.252:108861. doi: 10.1016/j.biocon.2020.108861
Summary
Keywords
choice experiment, latent class model, cacao, agriculture, stated preferences methods, biodiversity offsets, trade-off, canopy level
Citation
Montoya-Zumaeta JG, Fiestas-Flores J, Gutiérrez LJ, Rojas E, Orozco-Aguilar L and Ladd B (2026) Disentangling farmers' preferences for conditional incentives to upscale biodiversity-friendly agroforestry in the Amazon. Front. Sustain. Food Syst. 9:1645422. doi: 10.3389/fsufs.2025.1645422
Received
11 June 2025
Revised
07 November 2025
Accepted
15 December 2025
Published
21 January 2026
Volume
9 - 2025
Edited by
Ranjana Bhattacharjee, International Institute of Tropical Agriculture (IITA), Nigeria
Reviewed by
Marlon Enrique López Torres, Fundacion Hondurena de Investigacion Agricola, Honduras
Adriana Molina Garzon, Indiana University, Purdue University Indianapolis, United States
Updates
Copyright
© 2026 Montoya-Zumaeta, Fiestas-Flores, Gutiérrez, Rojas, Orozco-Aguilar and Ladd.
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*Correspondence: Javier G. Montoya-Zumaeta, jmontoya.iq@gmail.com
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