# Small steps in the right direction: Preferences of small-scale farmers for sustainable cattle systems in Guaviare, Colombian Amazon

Catalina Posada-Borrero, Driss Ezzine-De-Blas, Emmanuelle Lavaine,
Sébastien Roussel

► To cite this version:

Catalina Posada-Borrero, Driss Ezzine-De-Blas, Emmanuelle Lavaine, Sébastien Roussel. Small steps in the right direction: Preferences of small-scale farmers for sustainable cattle systems in Guaviare, Colombian Amazon. Ecological Economics, 2025, 239, pp.108744. 10.1016/j.ecolecon.2025.108744. hal-05234900

HAL Id: hal-05234900

https://hal.inrae.fr/hal-05234900v1

Submitted on 2 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

## ANALYSIS

# Small steps in the right direction: Preferences of small-scale farmers for sustainable cattle systems in Guaviare, Colombian Amazon

<figure><img src="image_3.png" /></figure>

Catalina Posada-Borrero<sup>a, b, c</sup>, Driss Ezzine-de-Blas<sup>b</sup>, Emmanuelle Lavaine<sup>a</sup>, Sébastien Roussel<sup>c, d</sup>

## ARTICLE INFO

JEL classification:

Q01

Q15

Q16

Keywords:

Discrete choice experiment

Incentive programs

Payments for Ecosystem Services (PES)

Silvopastoral systems (SPS)

Agroforestry

Cash bonus

Colombia

Guaviare

## ABSTRACT

This paper investigates the preferences of smallholder farmers residing in the agricultural frontier of the Colombia Amazon's Forest, specifically in the department of Guaviare, regarding interventions aimed at promoting the adoption of sustainable land-use systems. We focus on the transition from extensive livestock systems to sustainable livestock systems and agroforestry systems. The adoption of silvopastoral systems presents a significant opportunity for sustainable development, offering land-saving advantages that can be leveraged by programs to encourage other sustainable value chains, such as timber in agroforestry systems. Through a discrete choice experiment, we examine farmer preferences concerning land allocation, cash bonuses for permanence, and technical assistance. Additionally, we assess how socioeconomic factors influence farmers' decision-making in participating in such programs. Our findings indicate that while farmers exhibit a preference for allocating land to silvopastoral systems over agroforestry, they also show considerable interest in interventions involving a small proportion of timber in agroforestry systems. Furthermore, our analysis reveals that permanence bonuses tied to individual effort can enhance participation, while collective goals may hinder it.

## 1. Introduction

The Amazonian region is a biodiversity hotspot, with a significant influence on global climate and carbon atmospheric levels. However, it is currently threatened by climate change and deforestation (Malhi et al., 2008; Siegel et al., 2022). One of the most diverse, yet more jeopardized, locations in the Amazon is the Colombian Andean Amazon Piedmont. In 2017, the main driver of deforestation in the Colombian Amazon was illegal land-grabbing for cattle ranching (60%), followed by illegal coca crops (20%) and other causes (20%) (Rodríguez-de Francisco et al., 2021). Clerici et al. (2019) highlight the need for international organizations and governments to promote projects for the preservation and sustainable management of the protected and unprotected areas in the Andes-Amazon bridge, given its key role in biodiversity exchange for the Neotropic. This region is not only economically crucial but also socio-politically complex, requiring targeted strategies that balance conservation with rural livelihoods (Arvor et al., 2018).

To address these challenges, various initiatives have promoted sustainable production systems in Colombia. Silvopastoral systems (SPS)

combine trees and shrubs with pastures, increasing carbon sequestration, soil nutrition and animal welfare (Montagnini et al., 2013). Adopting SPS presents a major opportunity for the sustainability of livestock systems development (Calle et al., 2012; Álvarez et al., 2021). This practice is a promising climate change adaptation and mitigation strategy, which has the potential of enhancing farmers' financial returns (Chará et al., 2017). In particular, rotating livestock systems allow for higher stocking rates than traditional systems and, therefore, require less land than extensive systems for an equal level of production. The stocking rate will depend on the climatic conditions and the type of SPS.<fnref n="1" />

Alongside SPS, forest conservation agreements and agroforestry systems offer further potential for sustainable development. SPS very often support the transition from traditional livestock systems to sustainable intensive livestock systems, combined with financial and technical assistance to farmers conditional on a commitment to conserve and restore the forest (Castro-Nunez et al., 2021). Indeed, incentives can be decisive in the transition of livestock practices because the main constraints that small-holder farmers face for SPS adoption are knowledge

<footnote><sup>a</sup> CEE-M, Univ. Montpellier, CNRS, INRAE, Institut Agro, Montpellier, France</footnote>

<footnote><sup>b</sup> Centre de coopération Internationale en Recherche Agronomique - CIRAD, France</footnote>

<footnote><sup>c</sup> CEE-M, Univ. Montpellier, CNRS, INRAE, Institut Agro, Univ. Montpellier, Paul-Valéry, Montpellier, France</footnote>

<footnote>* Correspondence to: Bureau 503 UFR Economie Avenue Raymond Dugrand CS79606, Cedex 2, 34960, Montpellier, France.</footnote>

<footnote>E-mail address: catalina.posada-borrero@umontpellier.fr (C. Posada-Borrero).</footnote>

<footnote n="1">¹ Murgueitio et al. (2015) provide instances of increasing up to 6 times the stocking rate. This means that farmers could potentially allocate land to other activities, without having to decrease their overall cattle population.</footnote>
and financial barriers to acquiring the material needed (e.g., wooden poles, barbed wire, grass seeds) (Calle et al., 2013). Rasch et al. (2021) highlight that once farmers overcome the liquidity constraints for the initial investment, the system can increase the net present value of their production. Agroforestry, in turn, improves biodiversity conservation, soil enrichment, and air and water quality, and is a tool for poverty alleviation (Jose, 2009).

These initiatives fall under Payments for Ecosystem Services (PES) schemes. Tacconi (2012) defines PES schemes as a transparent system for the additional provision of environmental services through conditional payments to voluntary providers. PES ideally are based on conditionality and can potentially be effective during and after interventions (Andersson et al., 2018; Wunder et al., 2020), where farmers receive conditional incentives to adopt sustainable practices.<fnref n="2" />

However, there is a lack of empirical evidence of their effectiveness (Bauch et al., 2014) and multiple critiques about the accomplishment of their goals (Gockel and Gray, 2009) largely related to the lack of conditionality on the reception of benefits. PES interventions in which cash payments are substituted with in-kind benefits and technical assistance, conditional on signing and respecting conservation agreements, are designed to correct this weakness. One of the tools to fine-tune program design prior to its full-scale implementation is through the elicitation of preferences of the targeted population using the discrete choice experiment methodology (DCE).

In this article, using a DCE,<fnref n="3" /> we explore the preferences of small-holder cattle farmers settled on the agricultural frontier of the Amazon forest of Colombia, specifically in the Guaviare department, with regard to interventions that encourage the adoption of sustainable productive systems that would have both environmental and financial benefits for the population. Carriazo et al. (2020) conducted a valuation study in the Orinoco region of Colombia that evidences a willingness to adopt sustainable livestock practices that increase with payments and technical assistance. We also examine agroforestry systems as a complementary activity, focusing on farmers preferences for systems that prioritize timber production (e.g., abarco, amarillo) while generating short- and medium-term returns from food crops (e.g., plantain, cassava) and fruit trees (e.g., copoazú, cacao) which are particularly relevant to the area (Barrera García et al., 2017).

One of the main challenges for the transitions towards sustainable practices in developing countries has been ensuring the changes brought by the intervention are long-lasting or permanent. Even when farmers adopt SPS with external support, they might stop implementation due to several reasons, such as climate conditions and the complexity of the systems *per se* (Lee et al., 2020). Hence, the design of incentives needs to be optimized for the long-term permanence of the intervention results. Besides permanence, another key issue is collective action, as a lever to reach larger-scale outcomes in the landscape such as biodiversity corridors. However, building collective action through PES can prove elusive (Villamayor-Tomas et al., 2021). To assess this issue in our research, we explore farmers' preferences concerning cash bonuses tied to permanence, encompassing both individual and collective objectives. Although we can only base our results on declarative

data and can only give insights on the effect this would have on participation, it sheds light on the general response farmers could have to this type of incentive.

The remainder of this paper is organized as follows. In Section 2, we present the background of our case study. In Section 3 we explain the data collection process, the methodology used, the design of the DCE and the estimation models. In Section 4, we report the main findings of the study and in Section 5 we discuss these findings. In Section 6, we provide a conclusion and policy recommendations.

## 2. Context and study area

### 2.1. Guaviare (Colombia)

The study was conducted across the department of Guaviare (Fig. 1). Guaviare is one of the four departments of Colombia located in the Andes-Amazon Transition Belt (AATB). It has a privileged location with various hydric sources and native forests that give it unique environmental biodiversity (Enciso et al., 2018). However, forest loss and fragmentation have been very high in Colombian Amazon (Armenteras et al., 2006) as the result of various waves of colonization that started in 1955, some of which were encouraged by the government (Murillo-Sandoval et al., 2021; Fernando, 1995). Subsequently, the state started declaring protected areas in 1959 (Hein et al., 2020) some of them already partially occupied by settlers. This paradoxical situation resulted in deforested areas becoming protected areas with settlers, which in turn created tenancy insecurity. These circumstances have persisted until this day. Although deforestation declined substantially in both Colombia and Guaviare in 2023, the department continued to rank among the most affected nationwide, contributing 14.5% of total forest loss (IDEAM, 2024). Different armed groups, such as the FARC and paramilitaries established control of the area over different periods. In 2016 the peace agreement was signed, and one of the unintended consequences was a rise of 44% in deforestation of the country, partly due to the lack of control of some areas after the retreat of the FARC (Rodríguez-de Francisco et al., 2021). The violence has not been completely left behind, with organized crime groups including FARC dissidents and paramilitaries still present in uncontrolled areas (Clerici et al., 2019). Nevertheless, the agreement has increased security and has opened the door to many organizations for the implementation of projects for rural development and environmental protection.

This complicated social context and geographical location have continuously restrained the economic development of the Guaviare department. Furthermore, given the poor road infrastructure and the low access to capital, few value chains have been able to persist, apart from conflict-resilient agricultural systems such as ranching and coca cultivation (Murillo-Sandoval et al., 2021). This explains why the most frequent activity for small-holder farmers in the Colombian Amazon is dual-purpose livestock, for milk and fattening animals for beef (Buurman et al., 2007). According to the latest National Agricultural Survey (Encuesta Nacional Agropecuaria, ENA, 2017–2019), about 55% of productive land in Guaviare is used for livestock, around 38% for forest, and just 4% for agriculture (DANE, 2020b).

Cattle represent liquidity for producers, who receive stable payments for milk and can sell animals in times of need. Unlike other activities, it has a secured demand, it does not require a large initial investment nor exclusive knowledge, and the infrastructure conditions for its transportation are not very high. Extensive livestock systems are the most popular productive systems in the area, consisting of grazing areas with few or no fences that are prepared using fires to eradicate weeds (Bautista, 2020). Moreover, long-lasting deforested soils coupled with high levels of stocking have resulted in a heightened likelihood of having degraded pastures and infertile soils in the Piedmont region of the Colombian Amazon (Álvarez et al., 2021).

<footnote n="2">² However, some programs also share characteristics with Integrated Conservation and Development Projects (ICDPs). ICDPs aim to reconcile biodiversity conservation with socio-economic development interests at the local and international levels (Franks and Blomley, 2004; Wells and McShane, 2004). This type of program is useful to align conservation with the economic interests of local populations (McShane and Wells, 2004), which pursue broader socio-environmental goals but often lack conditionality. Distinguishing between PES and ICDPs is essential to design effective, targeted interventions.</footnote>

<footnote n="3">³ Choice experiments have been widely implemented to study policy incentives focused on the adoption of sustainable practices and technologies for agricultural intensification (e.g., Cranford and Mourato (2014), Vorlauser et al. (2017), Haile et al. (2019), Nong et al. (2021)), and in conservation (e.g., Balderas et al. (2013), Kaczan et al. (2013), Costedoa et al. (2016)).</footnote>
<figure><img src="image_4.png" /><figcaption>Fig. 1. Map of the Guaviare Department (Colombia).</figcaption></figure>

### 2.1.1. Sustainable productive activities in the context of Guaviare

The post-conflict period provides an opportunity to encourage sustainable development and strengthen environmental protection with the objective of stabilizing the agricultural frontier (Morales, 2017). Within the variety of strategies that the government and international and national cooperation agencies are implementing, two productive systems stand out in areas already deforested.

Intensive SPS system is the first one since it stands as a credible alternative to traditional livestock systems (Améndola et al., 2019). Pastures in this system have trees spread or planted in clusters and can have multiple divisions through fences (Grebner et al., 2022). These pastures require brief grazing periods and long recovery periods and can sustain high stocking rates (Calle et al., 2013). SPS regenerates the soil and facilitates the growth of grass, allowing for significantly higher production and reducing animal stress by providing shadow. In general, SPS contributes to climate change mitigation through the increase of soil and tree carbon capture (Montagnini et al., 2013). Moreover, it is a mechanism of adaptation to climate change since trees moderate temperature avoiding grass droughts while improving animals' welfare and productivity (Montagnini et al., 2013). This is crucial in Guaviare, where the lowering levels of water sources induced by climate change have been identified as a bottleneck in the industry (Enciso et al., 2018). A recent example of an intervention project that promoted SPS to small producers in the area is *Caminemos Territorios Sostenibles*. The project provided resources and assistance for the transformation of 3 ha to SPS and aimed at increasing the stocking rate from 1 to at least 1.5 animals per hectare.<fnref n="4" />

Agroforestry systems is the second one and can be an efficient strategy for rural development by improving environmental services while providing timber for smallholder farmers, overcoming some of the frequent limitations of reforestation programs in developing regions (de Sousa et al., 2016). Agroforestry has the potential to improve climate change resilience through environmental stability and food security (Duffy et al., 2021). Other agroforestry systems that consist of subsistence crops, cash crops and timber can have even higher environmental benefits. A recent study by Taou (2022) found through quasi-experimental econometric methods that these agroforestry programs can stall deforestation and even increase permanent forest cover in Guaviare. Overall, agroforestry serves as a multi-functional strategy that can tackle the intertwined issues related to food security, climate change, biodiversity conservation, and social well-being (Waldron et al., 2017).

## 3. Materials and methods

### 3.1. Data collection

Prior to the survey, we conducted a field visit across the four main municipalities of Guaviare, in this case San José del Guaviare, El Retorno, Calamar and Miraflores, to better understand the local context. This preparatory phase included five focus groups and several semi-structured interviews with local experts. These consultations informed the selection of attributes and levels in the Discrete Choice Experiment (DCE) and shaped the overall survey design. A pilot survey with 32 farmers was carried out to test respondents understanding of the experiment, assess the potential cognitive burden of the number of choice tasks, and derive preliminary parameter estimates for refining the DCE design.

The survey aimed to include both farmers who had previously participated in the *Caminemos* program and those who had not, in order to capture potential variation in preferences related to prior exposure to silvopastoral practices. Approximately equal representation from each group was sought. Within these two groups, we aimed for geographic coverage across the four municipalities and sought to approximate stratified random sampling by location.

Due to logistical and security constraints, fully random sampling was not feasible. Participants were recruited in coordination with local farmer associations during community meetings and through trusted intermediaries. To reduce selection bias, local leaders were asked to avoid selective recruitment and to suggest families randomly within their networks whenever possible. Relying on trusted intermediaries helped reach a diverse group of farmers, including those who might not have participated otherwise.

The final survey was administered face-to-face individually with tablets, using the Kobocollect app between March and May 2023. A total of 205 farmers completed the survey, which corresponds to 2,460 choice observations, after excluding 2 incomplete surveys. Only one respondent chose invariably the status-quo option, but the observation was not excluded from the analysis since we did not consider it a protest response. Following Barrio and Loureiro (2013), we did a careful classification to identify protest responses, and given that this participant was interested in the programs offered but did not have enough land to implement them it is considered a legitimate response.

Regarding geographic representativeness, the 2022 National Bovine Census reports (ICA, 2023) 3638 cattle farmers with fewer than 100 animals in Guaviare, with 52% located in San José del Guaviare, 31% in El Retorno, 12% in Calamar, and 4% in Miraflores. In our sample, 32% of respondents are from San José del Guaviare, 56%

<footnote n="4">⁴ More information about the project is available at https://onfandina.com/caminemos-territorios-sostenibles/.</footnote>
from El Retorno, 11% from Calamar, and 1% from Miraflores. The sample captures all four municipalities, with good representation of the two major neighboring areas, San José and El Retorno, although the latter is somewhat overrepresented relative to the former. The two more isolated municipalities, Calamar and Miraflores, are included in proportions broadly consistent with their population shares.

## 3.2. Design of the choice experiment

To understand farmer preferences regarding the projects we use a DCE, which is a quantitative method for eliciting stated preferences by asking respondents to choose between different hypothetical intervention alternatives (Lancaster, 1966). The DCE methodology and econometric models rationale are presented in Appendix A. Estimates of all models were computed using R software. We first analyze the data using a standard Conditional Logit (CL) model. To explore the heterogeneity of preferences across individuals, we employ a Random Parameter Model (RPL) and a Latent Class Model (LCM). The RPL allows us to model the choice as a function of the chooser's specific characteristics by permitting parameters to vary from one individual to another. We include interactions of the ASC (Alternative-Specific Constant) with farmers' characteristics to further understand what drives the decision to opt-out. The LCM allows us to identify groups of respondents that share similar preferences. The farmer's characteristics serve as class probability variables that explain belonging to a specific group. Moreover, Willingness To Accept (WTA) with regards to each attribute were then computed from the RPL model. To test the robustness of our results, we ran the CL model for the sub-sample excluding individuals that were more likely to have ignored certain attributes or to be non-compliant.

### 3.2.1. Attributes

The selection of attributes and attribute levels, summarized in Table 1, was based on the literature as well as on discussions with experts and focus group discussions with farmers in the field. The main objective was to select attributes that were important for the decision-making of the participants, describing realistic future programs and, therefore, allowing us to provide valid policy advice. It was equally important to have a set of programs with attributes that balanced the commitments farmers would have to make with the incentives provided by the program if it was implemented (Mamine et al., 2020). In addition, the number of attributes was limited to consider the size of the sample and the cognitive burden on the participants. Following the results of Rose and Bliemer (2013), we aimed to have a few levels over a wide range of values to ensure a higher reliability in the parameter estimates. The attributes were coded with linear and effect coding.<fnref n="5" />

The first attribute captures the land use commitment required from participants, defined as the number of hectares currently under extensive livestock that must be transformed into either silvopastoral systems (SPS) or agroforestry systems (AS) for timber. The levels vary in both the total area to be transformed and the combination of SPS and AS. This attribute reflects the type of transformation effort required, as well as the potential costs and benefits associated with it. The explanations provided to participants included estimated monetary and time costs, along with potential benefits. This attribute allows us to evaluate how participants value different land use pathways, from systems more compatible with livestock production to those involving diversification. We chose to express these commitments in absolute hectares rather than as percentages of landholdings to reduce cognitive complexity and to reflect how land-based programs are typically structured. Combining SPS and AS into a single attribute also helped us avoid high correlation

between attributes. Although the land use requirements vary across levels, the expected material costs are relatively similar, so differences in utility are more likely to reflect preferences for the type and extent of transformation rather than differences in perceived financial burden.

The second attribute corresponds to a permanence bonus in cash. The meta-analysis conducted by Mamine et al. (2020) on contract designs for the adoption of agri-environmental practices includes several studies that have used bonuses as incentives for quality commitments or collective goals. In this paper, we study a bonus conditional on permanence. Long-term transformations are one of the biggest concerns for the adoption of sustainable practices promoted by external entities in the area (IUCN and WRI, 2014; Calle, 2020). With the DCE we cannot measure the impact of a bonus on long-term permanence, but we can evaluate if participants take this long-term incentive into account in their participation decisions as a proxy for permanence probabilities. The bonus is a cash payment given 8 years after the start of the program if at least 80% of the total hectares subscribed to the program have remained transformed.

Additionally, we compare two types of bonus schemes that both provide individual payments, but differ in the type of effort required for eligibility. In the *individual-effort bonus*, each participant receives a cash payment 8 years after enrolling in the program, as long as they have maintained the new productive systems on at least 80% of the hectares they initially committed to transform. In the *collective-effort bonus*, individuals receive the payment only if the group as a whole meets the target: at least 80% of the total hectares committed by all participants must remain transformed over the 8-year period. While the reward remains individual, the path to earning it can vary significantly. Coordination toward a collective target can be challenging, especially in contexts where trust and communication are limited. However, it can also generate stronger group motivation and foster collaboration. Previous studies, in different contexts, have shown that collective conditions in bonuses can increase farmer enrollment (Kuhfuss et al., 2026; Vaissière et al., 2018). However, in Guaviare, where agricultural systems have long operated under conditions shaped by informality and corruption, we expect most farmers to prefer the *individual-effort bonus*, reflecting broader concerns about relying on collective outcomes in a low-trust environment.

The third attribute corresponds to the number of months of technical assistance offered by the programs. Variations of this attribute are common in the literature of DCE for schemes that promote the adoption of new practices and technologies, (e.g., Carriazo et al. (2020), Nong et al. (2021), Kuhfuss et al. (2026)). It is a crucial component for the SPS transition, as noted by past programs. We hypothesize that most farmers will value positively technical assistance. However, those who find technical assistance useless might value it negatively.

The fourth attribute is the compensation level, a linear attribute expressed in Colombian Pesos (COP). While it represents a monetary value, we explicitly stated in the experiment that compensation would be provided in inputs rather than cash. The main rationale is that the program aims to promote land use transformations, and cash transfers could easily be diverted to other uses. In contrast, input-based compensation creates a stronger commitment to the program's goals. Although agricultural inputs can technically be resold, doing so incurs effort and transaction costs, which reduces the likelihood of such deviations. Even within a hypothetical setting, we aimed to simulate a situation where participants would perceive a real need to engage with the intervention, thereby enhancing the credibility of their choices. This choice also reflects the implementation model of past interventions in the area and aligns with likely future approaches. Notably, Vorlaufer et al. (2017) found that input payments were preferred over cash or voucher payments, possibly due to the transaction costs of sourcing agricultural inputs independently. While we cannot assume these findings fully translate to our context, they support the idea that input-based payments are a viable and context-appropriate alternative.

<footnote n="5">⁵ In Ngene, this changes automatically from 0 to -1 in each column for the base attribute level; hence, the base level is equivalent to the negative sum of the other parameter estimates, rather than being equal to zero.</footnote>
<table><caption>**Table 1**
Attributes and levels.</caption><thead><tr><th>Attributes</th><th>Description</th><th>Levels</th><th>Coding</th></tr></thead><tbody><tr><td>Land transformation</td><td>The hectares of their property that they must transform into silvopastoral or agroforestry systems.</td><td>Effects coding<br>6 ha of SPS (ref.)<br>4.5 ha of SPS and 0.5 ha of AS<br>3 ha of SPS and 1 ha of AS</td><td>0<br>1<br>2</td></tr><tr><td>Permanence bonus</td><td>A bonus of 2.5 million COP could be given 8 years after the start of the program. The collective effort bonus is awarded to all individuals if 80% of the total amount of hectares registered in the program have remained transformed and the individual effort bonus is awarded to every individual that has maintained at least 80% of the hectares registered on their land.</td><td>Effects coding<br>No bonus (ref.)<br>Collective effort bonus<br>Individual effort bonus</td><td>0<br>1<br>2</td></tr><tr><td>Technical assistance</td><td>The technical assistance time (training and support) offered by the program</td><td>Linear coding<br>6 months<br>18 months<br>30 months</td><td>6<br>18<br>30</td></tr><tr><td>Compensation</td><td>The amount equivalent to the compensation, which would be given in materials such as posts, hoses, cables, trees, fertilizer and other materials that are necessary to implement the SLUS.</td><td>Linear coding<br>9.5 million COP (2021 USD)<br>11.5 million COP (2446 USD)<br>13.5 million COP (2872 USD)</td><td>9.5<br>11.5<br>13.5</td></tr></tbody></table>

We opted for a one-time up-front payment, rather than annual disbursements, primarily to address the entry barriers to adoption, while also making the choice tasks easier to navigate. The specific payment levels were determined through consultations with multiple experts and were informed by detailed cost assessments and reports from past interventions in the region. The levels were designed to reflect realistic scenarios, with the lowest level approximating the full cost of materials needed for the transformation, and higher levels allowing for increased flexibility in material quality, quantity, or potential use on other plots. These values are not conservative (the approximate cost per hectare is 1,375,000 COP for silvopastoral systems and 4,125,000 COP for agroforestry, therefore, about 8,250,000 COP for the total land converted in the proposed programs), as the actual material needs may vary depending on what farmers already own. In many cases, participants might need to incur additional expenses, particularly for water systems. This could also compensate for some short-term opportunity costs associated with labor or land use trade-offs.

### 3.2.2. Properties

Given that the design of a DCE must focus on identification (the independent estimation of effects) and efficiency (precision of the parameter estimates) (Hoyos, 2010), the recent trend of the literature and the advantages in the model estimation of efficient designs resulting in smaller standard errors for small samples explained by Rose and Bliemer (2013), we decided to implement a D-efficient design. We used the Ngene software to produce a set of 12 choice tasks, all of which were presented to each participant.<fnref n="6" /> The minimum number of tasks required for our design was six, based on the number of alternatives and parameters to estimate, following Rose and Bliemer (2013)'s formula. We opted for a larger design to reduce both D-error and S-error, thereby improving the efficiency of the experiment, while keeping the cognitive burden manageable. This approach reduced the required sample size and provided more information per respondent.

### 3.3. Questionnaire

The questionnaire was implemented with face-to-face interviews. At the start of the interview, the purpose of the DCE and its attributes and levels were carefully explained, including estimated costs and benefits of the hypothetical programs to enhance realism and respondent engagement (supplementary material S.1). To try to reduce hypothetical bias a *cheap talk* was included, asking respondents to choose as if

this was real life (Murphy et al., 2005). Following Ladenburg and Olsen's (2014) suggestion, participants were reminded to choose the opt-out option when the program's compensation was not enough to compensate for their effort. Fig. 2 illustrates an example of a choice task.

Follow-up questions were included to assess openness to additional environmental commitments. Additionally, to identify potential attribute non-attendance, we included a question on which attributes participants considered most important for their decisions, as suggested by both Mariel et al. (2021) and Hensher et al. (2007). We also included additional questions to better understand farmers' preferences and compliance behaviors. Finally, we asked about socioeconomic characteristics and individual traits that could influence decision-making. These included the Tenure Security Index developed by Jones et al. (2022)<fnref n="7" />; an adapted Spanish version of the Revised Environmental Identity Scale (EID), previously used with farmers in Peru (Clayton et al., 2021)<fnref n="8" />; and a proxy for intertemporal judgment, particularly patience, based on items adapted from Frederick (2005).

## 4. Results

### 4.1. Descriptive statistics

In the second semester of 2019, there were 4969 cattle ranchers in Guaviare with a mean age of 54.6 years, from which 66% had achieved elementary school level, and 23% were women (DANE, 2020a). In our sample, the average age is slightly below the population's age, while the education level is very similar. Women are over-represented in our sample, this could be explained because, in a lot of marriages, the man is the official cattle rancher even when the woman is also involved in the activity. Despite some deviations, the sample aligns with key characteristics of the population. Table 2 summarizes the key characteristics of the sample.

In our sample, the average farm size is approximately 47 hectares, including an average of 12 hectares of forest, 27 hectares of grasslands, and an average livestock count of 33. Over 64% of respondents have participated in projects promoting silvopastoral or agroforestry activities. On average, participants have 5.7 hectares of silvopastoral systems and 1.1 hectares of agroforestry systems. A significant 68% of participants stated that they own their property and the average participant expressed a high perception of tenure security.

<footnote n="6">⁶ The full factorial design, including all possible combinations of attributes and levels, would have a large number of alternatives (3 × 3 × 3 × 3 = 81) that would result in an unfeasible number of choice sets (81 × 80/2 = 3240).</footnote>

<footnote n="7">⁷ Item 4, related to insecurity from “external groups”, was excluded given the delicate security situation in the region.</footnote>

<footnote n="8">⁸ The original EID was developed to assess an individual's interdependence and connectedness to nature by Clayton (2003).</footnote>
<table><caption>Fig. 2. Choice task.</caption><thead><tr><th>1</th><th>PROJECT A</th><th>PROJECT B</th><th>NEITHER PROJECT</th></tr></thead><tbody><tr><td>Hectares to be transformed into Silvopastoral System (SPS) or Agroforestry System (AS)</td><td>6 ha SPS</td><td>3 ha SPS and 1 ha AS</td><td></td></tr><tr><td>Permanence Bonus</td><td></td><td></td><td></td></tr><tr><td>Technical Assistance</td><td>30 Months</td><td>6 Months</td><td></td></tr><tr><td>Amount of Compensation in Materials</td><td>$ 9.500.000</td><td>$ 13.500.000</td><td></td></tr></tbody></table>

The mean of the Environmental Incentive Scale (EIS) is notably high, closely aligning with the highest-ranking score achieved in the Peruvian population in a cross-cultural validation study of the EIS (Clayton et al. 2021). This is consistent with perceptions gathered through focus groups, reflecting participants' close interaction with nature. Another key point in understanding the population's behavior is the notably low patience level of the average participant, relative to a cross-country comparison of waiting tendencies (Wang et al., 2016). Finally, community interactions appear to be strong, with high levels of advice and support exchange among participants. However, only a quarter of the respondents state that they consider their neighbors' opinions in the management of their farms.

## 4.2. Conditional logit (CL) and random parameter logit (RPL) models

Table 3 summarizes the results of the basic CL and RPL models.<fnref n="9" /> The RPL model relaxes the assumptions of the CL and allows for heterogeneity between individuals. The compensation parameter was left equal among individuals, for all the other attributes we assumed normally distributed random parameters (Boxall and Adamowicz, 2002).

In the CL model, the ASC parameter associated with the status quo is positive and significant, implying that on average respondents prefer the status quo to the contractual limitations and bureaucracy of a

<footnote n="9">⁹ The RPL model is estimated with 1000 random Halton draws.</footnote>
<table><caption>**Table 2**
Variable description.</caption><thead><tr><th>Variable</th><th>Description</th><th>Mean</th><th>StdDev</th></tr></thead><tbody><tr><td><strong>Socioeconomic characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>Age</td><td>Age in years</td><td>50.478</td><td>11.016</td></tr><tr><td>Gender</td><td>1 = female</td><td>0.439</td><td></td></tr><tr><td rowspan="4">Education</td><td>1 = no education</td><td>0.093</td><td></td></tr><tr><td>1 = primary education</td><td>0.659</td><td></td></tr><tr><td>1 = high school education</td><td>0.180</td><td></td></tr><tr><td>1 = higher education</td><td>0.068</td><td></td></tr><tr><td rowspan="5">Income</td><td>1 = income less than 1 million COP</td><td>0.244</td><td></td></tr><tr><td>1 = income from 1 to 1.25 million COP</td><td>0.346</td><td></td></tr><tr><td>1 = income from 1.25 to 1.5 million COP</td><td>0.254</td><td></td></tr><tr><td>1 = income from 1.5 to 2 million COP</td><td>0.127</td><td></td></tr><tr><td>1 = income from 2 to 3 million COP</td><td>0.029</td><td></td></tr><tr><td><strong>Farm characteristics</strong></td><td></td><td></td><td></td></tr><tr><td rowspan="3">Property rights</td><td>1 = property owner</td><td>0.683</td><td></td></tr><tr><td>1 = tenancy</td><td>0.010</td><td></td></tr><tr><td>1 = possession</td><td>0.244</td><td></td></tr><tr><td>Land size</td><td>Total number of hectares</td><td>47.327</td><td>39.983</td></tr><tr><td>Grassland area</td><td>Number of hectares of grassland</td><td>26.670</td><td>27.209</td></tr><tr><td>Silvopastoral area</td><td>Number of hectares of silvopastoral</td><td>5.676</td><td>8.197</td></tr><tr><td>Agroforestry area</td><td>Number of hectares of agroforestry systems</td><td>1.171</td><td>2.261</td></tr><tr><td>Forest area</td><td>Number of hectares of forest</td><td>12.174</td><td>15.157</td></tr><tr><td>Household workforce</td><td>Number of household members working on the farm</td><td>1.868</td><td>0.763</td></tr><tr><td>Animals</td><td>Number of animals in the farm</td><td>32.829</td><td>23.351</td></tr><tr><td>Milk income</td><td>1 = milk is a main income source</td><td>0.688</td><td></td></tr><tr><td>Agriculture income</td><td>1 = agriculture is a main income source</td><td>0.210</td><td></td></tr><tr><td>Other livestock income</td><td>1 = fattening, selling animals, or renting grass is a main income source</td><td>0.780</td><td></td></tr><tr><td>Other income</td><td>1 = other is a main income source</td><td>0.088</td><td></td></tr><tr><td>Caminemos</td><td>1 = participated in Caminemos project</td><td>0.420</td><td></td></tr><tr><td>Similar project</td><td>1 = projects related</td><td>0.644</td><td></td></tr><tr><td><strong>Farmers' perceptions</strong></td><td></td><td></td><td></td></tr><tr><td>Tenure Security</td><td>Tenure Security score out of 5</td><td>4.349</td><td>0.428</td></tr><tr><td>Environmental Identity</td><td>Environmental Identity score out of 5</td><td>4.336</td><td>0.405</td></tr><tr><td>Patience</td><td>1 = lower discount rate</td><td>0.270</td><td></td></tr><tr><td>Advice</td><td>1 = receives advice from neighbors</td><td>0.722</td><td></td></tr><tr><td>Opinion</td><td>1 = considers neighbor's opinion</td><td>0.254</td><td></td></tr><tr><td>Support</td><td>1 = feels supported by community</td><td>0.888</td><td></td></tr><tr><td>Collective activities</td><td>1 = participates in community activities</td><td>0.985</td><td></td></tr></tbody></table>

Notes: N = 205. Education and income are coded as ordinal variables for regression analysis: education from 1 = no education to 4 = higher education; income from 1 = less than 1 million COP to 5 = 2 to 3 million COP.

<table><caption>**Table 3**
Conditional Logit (CL) model and Random Parameter Logit (RPL) estimates.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th>RPL</th><th rowspan="2">SD (Std.err.)</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.146 *** (0.201)</td><td>0.528 (0.743)</td><td>5.050 *** (0.585)</td></tr><tr><td>Compensation</td><td>0.225 *** (0.015)</td><td>0.485 *** (0.032)</td><td></td></tr><tr><td>Technical Assistance</td><td>-0.007 ** (0.003)</td><td>-0.014 *** (0.004)</td><td>-0.011 (0.008)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.811 *** (0.043)</td><td>-2.233 *** (0.223)</td><td>2.490 *** (0.203)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.315 *** (0.038)</td><td>0.769 *** (0.098)</td><td>0.911 *** (0.108)</td></tr><tr><td>6 ha SPS</td><td>0.496</td><td>1.268</td><td></td></tr><tr><td>Individual Bonus</td><td>0.307 *** (0.040)</td><td>0.757 *** (0.072)</td><td>-0.155 (0.184)</td></tr><tr><td>Collective Bonus</td><td>-0.288 *** (0.040)</td><td>-0.686 *** (0.089)</td><td>0.77 *** (0.098)</td></tr><tr><td>No Bonus</td><td>-0.019</td><td>-0.071</td><td></td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>2460</td><td>2460</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.119</td><td>0.401</td><td></td></tr><tr><td>final log-likelihood</td><td>-2076.02</td><td>-1403.87</td><td></td></tr><tr><td>AIC</td><td>4166.03</td><td>2833.74</td><td></td></tr><tr><td>BIC</td><td>4206.69</td><td>2909.2</td><td></td></tr></tbody></table>

Notes: N = 205. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

contract. In the RPL model, the estimate is not significant. However, the standard deviation is significant, suggesting high heterogeneity in the preferences regarding the status quo. In both models, the compensation estimate parameter is positive and significant, as expected, higher levels of compensation are preferred.

The coefficient associated with the technical assistance parameter is negative and significant in both models, but its magnitude is very low. This means that people prefer contracts with less technical assistance within the interval of 6 months to 30 months. Moreover, there is no

evidence of heterogeneity, implying that this is a general tendency of the sample.

There is a positive and significant estimate parameter for the programs that require the transformation of 4.5 hectares to silvopastoral systems and 0.5 hectares to agroforestry systems. The estimated parameter associated with the transformation of 3 hectares into silvopastoral systems and 1 hectare into agroforestry systems is negative and significant. We can observe high heterogeneity between participants given the significant standard deviation of the RPL model for both estimates. The calculated coefficient for the reference level of 6ha of SPS is the
<table><caption>**Table 4**
Estimates of the CL and RPL models with covariates.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th>RPL</th><th rowspan="2">SD (Std.err.)</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.406** (0.606)</td><td>0.575 (1.815)</td><td>4.839*** (0.532)</td></tr><tr><td>Compensation</td><td>0.225*** (0.018)</td><td>0.487*** (0.044)</td><td></td></tr><tr><td>Technical Assistance</td><td>-0.007*** (0.002)</td><td>-0.013*** (0.004)</td><td>0.004 (0.011)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.813*** (0.086)</td><td>-2.088*** (0.320)</td><td>2.560*** (0.299)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.315*** (0.046)</td><td>0.811*** (0.124)</td><td>0.908*** (0.132)</td></tr><tr><td>6 ha SPS</td><td>0.498</td><td>1.277</td><td></td></tr><tr><td>Individual Bonus</td><td>0.309*** (0.037)</td><td>0.753*** (0.086)</td><td>0.226 (0.202)</td></tr><tr><td>Collective Bonus</td><td>-0.290*** (0.045)</td><td>-0.675*** (0.116)</td><td>0.806*** (0.158)</td></tr><tr><td>No Bonus</td><td>-0.019</td><td>-0.078</td><td></td></tr><tr><td>Workforce</td><td>0.477** (0.212)</td><td>1.337** (0.566)</td><td></td></tr><tr><td>Milk Income</td><td>0.501 (0.356)</td><td>0.293 (1.659)</td><td></td></tr><tr><td>Property owner</td><td>-0.948** (0.338)</td><td>-2.035** (0.734)</td><td></td></tr><tr><td>Income level</td><td>-0.272† (0.162)</td><td>-0.414 (0.588)</td><td></td></tr><tr><td>Projects related</td><td>-0.333 (0.358)</td><td>-0.047 (1.283)</td><td></td></tr><tr><td>Patience</td><td>-0.615 (0.400)</td><td>-1.580* (0.771)</td><td></td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>2460</td><td>2460</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.247</td><td>0.476</td><td></td></tr><tr><td>Final log-likelihood</td><td>-2022.70</td><td>-1397.50</td><td></td></tr><tr><td>AIC</td><td>4071.41</td><td>2833.00</td><td></td></tr><tr><td>BIC</td><td>4146.91</td><td>2943.35</td><td></td></tr></tbody></table>

Notes: N = 205. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses.†, *, **, *** indicate significance at 10%, 5%, 1%, and 0.1%, respectively.

largest positive coefficient. This indicates that the preferred option is the adoption of 6 ha of SPS, followed by the transformation of 4.5 hectares to SPS and 0.5 hectares to agroforestry systems. The least preferred option is largely the option of 3 ha of SPS and 1 ha to AS.

In both models, the individual bonus has a strong positive significant estimate parameter that evidences the preference for an individual bonus, with no evidence for heterogeneity. Conversely, the collective bonus has a negative significant estimate parameter, and the RPL model indicates high heterogeneity for this estimate between individuals. The reference level of No bonus is associated with a negative yet very small coefficient. Hence, the individual bonus is the preferred option and the least preferred option is the collective bonus, followed by the no bonus option.

### 4.2.1. Predictions

The following charts (Fig. 3) illustrate the prediction of the percentage of farmers that would choose to participate in a project, instead of choosing the status-quo alternative. Predictions are made with the Apollo package using the model on the initial data set, along with 30 iterations of randomly selected samples from the parameter estimates vector (Hess and Palma, 2019). Market share analysis, as done by Coste-daat et al. (2016), can be complementary to the interpretation of preferences. Predictions can be valuable for specific recommendations on projects and policies, but their calculation has various limits (Mariel et al., 2021). Therefore, these forecasts should be approached with caution, but they offer reasonable participation probabilities for simulated projects. Aligned with the interpretation of the estimates, we can observe how participation increases with the individual bonus and decreases with the collective bonus, as well as how it increases with compensation. We also find that the difference in participation levels between the land-use alternative of 6 ha of SPS and 4.5 ha of SPS and 0.5 ha of AS is not large, while the gap in the participation levels from 4.5 ha of SPS and 0.5 ha of AS to 3 ha of SPS and 1 ha of AS is very important.

### 4.2.2. CL and RPL with covariates

To understand the role of participants' characteristics in the decision to participate or not in a contract, we build upon the basic model adding interactions with the ASC. Table 4 summarizes the results of the CL and RPL models with covariates.

Overall, the estimates of the main parameters are similar to the basic model, confirming that the estimates are robust (Nong et al., 2021;

<table><caption>**Table 5**
WTA estimates and confidence intervals.</caption><thead><tr><th>Attribute</th><th>WTA estimates</th><th>Confidence interval</th></tr></thead><tbody><tr><td>Technical Assistance</td><td>27,900</td><td>[12,808; 42,991]</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>7,622,800</td><td>[5,804,933; 9,440,666]</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>1,430,800</td><td>[500,405; 2,361,194]</td></tr><tr><td>Individual Bonus</td><td>-1,709,100</td><td>[-2219866; -1198333]</td></tr><tr><td>Collective Bonus</td><td>1,266,900</td><td>[619327; 1914472]</td></tr></tbody></table>

Note: N = 205.

Chèze et al., 2020). Being a property owner increases the probability of participating in a program. This is very intuitive considering the fear of losing your land when participating in a program expressed by many farmers. Surprisingly, farmers with more available workforce are less likely to participate in the programs. Having participated previously in other similar programs is not significant. Given the structure of our sample, to further analyze the possible impact of previous participation in projects, we compare two types of groups: On one hand, farmers who had participated in Caminemos to farmers who had not (the table is presented in Appendix B). On the other hand, farmers who had participated in projects for silvopastoral systems or agroforestry systems to farmers who had not (the table is presented in Appendix C). We observe that all groups exhibit similar tendencies with no particular differences statistically significant. Finally, the RPL model suggests that farmers who have a higher waiting tendency are more likely to participate in programs. The reason could be that even if compensation in materials is given at the start of the program, you need time and effort to start receiving direct economic benefits.

### 4.3. Willingness to accept (WTA)

Table 5 presents the willingness-to-accept (WTA) estimates and their confidence intervals for the average respondent, based on the marginal rate of substitution between attributes (see methodology, Appendix A). These estimates are derived from the basic Random Parameters Logit (RPL) model and confidence intervals were calculated using the Delta Method.

Farmers are willing to accept 27,900 COP if the project has one more month of technical assistance. Farmers would require an additional compensation of 7,622,800 COP to transform 3 hectares into silvopastoral systems and one hectare into agroforestry system, to
<figure><img src="image_5.png" /><figcaption>Fig. 3. Participation predictions (%).</figcaption></figure>

maintain the same level of utility of projects that require a transformation to 6 ha to silvopastoral systems. To transform 4.5 hectares into silvopastoral systems and 0.5 hectares into agroforestry systems, they would need 1,430,800 COP more to maintain that same utility level. Regarding the permanence bonuses, farmers would be willing to receive 1,709,100 COP less if the program offered the individual permanence bonus and would require 1,266,900 COP more if the program had a collective bonus to maintain the same utility of a program with no bonus. We also calculated the WTA for the subgroups presented above (results are presented in Appendices D and E).

4.4. Latent class model (LCM) results

The Latent Class Model (LCM) reveals three distinct groups of farmers, each different preferences for the project attributes. The number of classes was selected based on its good performance compared to alternative class specifications (see Appendix F). Table 6 presents the estimated parameters for each latent class, along with predicted class memberships derived from covariates.

Across all classes, preferences for compensation and bonuses align with the main results. The coefficient for technical assistance is also
<table><caption>**Table 6**
Latent class model with three classes.</caption><thead><tr><th>Variable</th><th>Class 1</th><th>Class 2</th><th>Class 3</th></tr></thead><tbody><tr><td>Average class probability</td><td>42.4</td><td>45.7</td><td>11.9</td></tr><tr><td>ASC</td><td>1.219** (0.408)</td><td>-0.211 (1.062)</td><td>4.127*** (0.968)</td></tr><tr><td>Compensation</td><td>0.218*** (0.032)</td><td>0.345*** (0.035)</td><td>0.260*** (0.046)</td></tr><tr><td>Technical Assistance</td><td>-0.024*** (0.005)</td><td>0.002 (0.003)</td><td>-0.014 (0.016)</td></tr><tr><td>3 ha land and 1 ha alt.</td><td>-3.190*** (0.309)</td><td>-0.341** (0.113)</td><td>0.506 (0.415)</td></tr><tr><td>0.5 ha land and 4.5 ha alt.</td><td>1.150*** (0.186)</td><td>0.413*** (0.083)</td><td>-0.630<sup>†</sup> (0.352)</td></tr><tr><td>6 ha SPS</td><td>2.040</td><td>0.256</td><td>-0.124</td></tr><tr><td>Individual bonus</td><td>0.439*** (0.105)</td><td>0.510*** (0.063)</td><td>0.517** (0.169)</td></tr><tr><td>Collective bonus</td><td>-0.635*** (0.136)</td><td>-0.374*** (0.073)</td><td>-0.178 (0.139)</td></tr><tr><td>No bonus</td><td>0.196</td><td>-0.136</td><td>0.178</td></tr><tr><td>Class probability variables</td><td></td><td></td><td></td></tr><tr><td>delta</td><td>(fixed)</td><td>1.267<sup>†</sup> (0.767)</td><td>0.584 (1.746)</td></tr><tr><td>Workforce</td><td>(fixed)</td><td>0.386 (0.266)</td><td>1.032** (0.336)</td></tr><tr><td>Milk income</td><td>(fixed)</td><td>-0.986* (0.400)</td><td>-0.272 (0.847)</td></tr><tr><td>Property owner</td><td>(fixed)</td><td>0.445 (0.399)</td><td>-1.140<sup>†</sup> (0.651)</td></tr><tr><td>Blocks income</td><td>(fixed)</td><td>-0.673*** (0.192)</td><td>-1.071<sup>†</sup> (0.625)</td></tr><tr><td>Project participation</td><td>(fixed)</td><td>0.272 (0.392)</td><td>-0.573 (0.795)</td></tr><tr><td>Patience</td><td>(fixed)</td><td>-0.285 (0.396)</td><td>-0.950 (1.204)</td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>2460</td><td></td><td></td></tr><tr><td>Adj. Rho-squared</td><td>0.41</td><td></td><td></td></tr><tr><td>Final log-likelihood</td><td>-1559.44</td><td></td><td></td></tr><tr><td>AIC</td><td>3188.89</td><td></td><td></td></tr><tr><td>BIC</td><td>3392.16</td><td></td><td></td></tr></tbody></table>

Notes: N = 205. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

consistent, but is not statistically significant in Classes 2 and 3. The main point of differentiation between classes lies in their preferences for land-use transformation options. In Class 1, which includes 42.4% of participants, the order of preferences mirrors the main results, the most preferred option is the conversion of all 5 hectares to silvopastoral systems and the least preferred is the conversion of 3 hectares to silvopastoral systems and 1 hectare to agroforestry systems.

In contrast, Class 2, the largest group (45.7%), shows a different ranking of preferences, the most preferred option is the conversion of 4.5 hectares to silvopastoral systems and 0.5 hectares to agro-forestry systems, followed closely by the conversion of 6 hectares to silvopastoral systems. The option with 1 hectare of agroforestry and 3 hectares of silvopastoral systems remains the least preferred, although the strength of rejection is weaker than in Class 1. A key insight from the results is that nearly half of participants prefer the intermediate option that incorporates agroforestry, indicating interest in diversification and small-scale adoption. Finally, Class 3, a minority group representing 11.9% of the sample, shows mostly non-significant coefficients for the land-use transformation attribute, indicating weaker or more heterogeneous preferences.

The class probability model reveals systematic differences in class membership relative to the reference group (Class 1). Participants whose main source of income includes milk production, as well as those with higher total income, are less likely to belong to Class 2 than to Class 1. This result is coherent with the fact that Class 1 exhibits the strongest preference for converting all land to silvopastoral systems, a land use particularly compatible with dairy production, as noted by Castro-Nunez et al. (2021). During focus group discussions it was explained that good pasture is essential for dairy and that intensive systems are ideal because cattle must be managed daily, which is essential for the rotation required of silvopastoralism. This may make silvopastoral systems especially appealing to these producers, in contrast to those whose primary income comes from activities such as agriculture or livestock for beef production. Moreover, the tendency for higher-income participants to avoid options involving agroforestry may suggest that they place less value on the subsistence food production associated with agroforestry systems.

# 5. Discussion

## 5.1. Transition to sustainable productive systems

Our predictions showcase a strong willingness of farmers to participate in the proposed interventions, with a general preference for the option that relies exclusively on silvopastoral systems, while the option containing a larger share of agroforestry is the least preferred. This positive reaction to silvopastoral coincides with the results of Carriazo et al. (2020). We complement the DCE results with insights from a short follow-up survey (Appendix G) to better understand the motivations behind farmers' choices. Nearly all respondents (97%) said they would adopt silvopastoral systems, and 77% named productivity or economic gains as their principal motive (Fig. 4a). In contrast, only about 12% chose environmental benefits as their top reason and adding second choices raises this to about 30%, suggesting these benefits are acknowledged but not the main driver of adoption. Focus groups reinforce that farmers recognize the role of trees in providing shade for livestock, conserving water sources, and regulating farm temperatures, although these benefits are valued mainly through their impact on productivity rather than as independent environmental goals. If they are aware of these benefits, yet the current adoption of silvopastoral is low, entry barriers might be the main obstacle to their adoption with almost 70% of respondents believing it is too expensive.

On the other hand, our results highlight that participants tend to require high compensation for the adoption of agroforestry systems. Only 54.6% of farmers expressed interest in adopting this activity, and over 20% suggested they had no compelling reason to do so. When participants were asked to rank the reasons for adopting agroforestry systems, 31.2% cited environmental benefits as their first motivation and nearly 70% of respondents mentioned them among their top two motivations (Fig. 4b). Focus group participants similarly expressed pride in planting trees and acknowledged their contribution to sustainable farming, but emphasized that decisions to adopt sustainable practices ultimately depended on the perceived economic viability and labor requirements of maintaining these systems. While the environmental benefits can directly affect their utility, this might partly be seen as an altruistic reason. Only 10.7% of the people chose long-term economic benefits as the first reason to have agroforestry systems. This might be because the average participant is 50 years old and might not expect to receive
<figure><img src="image_6.png" /><figcaption>Fig. 4. Farmers' motivations for adopting silvopastoral and agroforestry systems, by rank.</figcaption></figure>

these benefits. It may also reflect widespread concerns: nearly half of respondents doubted that agroforestry systems would work well or believed there would be insufficient demand to sell their products. Still, it shows that most of them do not believe that a large economic benefit can be derived from this activity. This opens the room for future interventions to carefully design agroforestry pilot systems that address farmers' concerns and create a favorable environment for future wider adoption.

Overall, even though the environmental identity scores among farmers are high, our results suggest that when it comes to adopting new productive systems, decision-making remains primarily driven by short- and medium-term economic considerations. This finding highlights the importance of aligning environmental objectives with clear, tangible livelihood benefits in program design. Nonetheless, the middle option, transforming 4.5 hectares to SPS and 0.5 hectares to agroforestry, is not far behind the most preferred choice, and even slightly preferred by a significant proportion of participants, suggesting that combining small-scale agroforestry interventions with more familiar practices like silvopastoralism could be an effective pathway to gradually introduce and promote agroforestry systems.

Finally, follow-up responses suggest strong potential for scaling up, with 60% of farmers willing to double and 48% willing to triple the land committed across all programs they initially chose, if compensation increased proportionally. This is especially relevant given concerns that small-scale transformations may limit both the impact and long-term sustainability of these interventions.

## 5.2. Permanence and collective goals

Including a potential bonus after 8 years reduces the amount of compensation required for individuals to adopt the intervention offered, pointing towards its possible positive effect to foster participation. The fact that producers are not indifferent to this attribute signals that many of them find it plausible to achieve the goal of continuing with the transformed hectares after the end of the program. In an impact evaluation of the long-term effects of short-term PES that promoted SPS in Colombia, Calle (2020) concluded that the profitability of SPS was enough for framers to not reverse the transformations made during the program. However, those results are from forest coverage and here it would be expected for farmers to also continue the rotating systems associated with SPS.

However, the collective bonus provokes a negative reaction compared to the no-bonus option. This is objectively irrational since they have nothing to lose, even if the possibility of obtaining the bonus is remote. This negative valuation might signal a rejection of collective action, a lack of trust in the community, or a fear of unfairness. Indeed, when asked about the percentage of people that would likely continue to keep the transformed hectares years after the end of the program, only 18% of the respondents said that more than 70% would continue. Moreover, 73.43% of the respondents stated that the opinion of their neighbors or colleagues from productive associations did not have any effect on the way they manage their farms. Such generalized opinion underscores again that it might be difficult to get to an arrangement with neighbors to ensure the collective bonus. In line with this finding, Corbera et al. (2020) state that PES interventions in communities characterized by disparities in land tenure and fragmented collective action are likely to have inefficient outcomes in creating shared agreements.

Nonetheless, this does not implicate a rejection of all collective activities. Most participants stated that they were willing to participate in collective reforestation activities. Hence, entities should be careful not to base their projects on collective goals or agreements, but should still promote simple community activities unlikely to affect the cost-benefit rationale of participants.

## 5.3. Rejection of technical assistance

A counterintuitive result of our study is the estimated negative valuation of technical assistance. The questionnaire further showed that 53.2% of participants said that they would only hire technical assistance if it was strictly necessary. While 40% said that they would probably not or would never hire technical assistance, showcasing an important rejection of paid technical assistance. Carriazo et al. (2020) found that farmers in the Orinoco region valued positively technical assistance for sustainable cattle practices and preferred more complete assistance over basic assistance. Seemingly, Ezzine de Blas et al. (2009) find that long-term and more complete external support is often preferred by local forest communities. However, during our focus groups, it came to light that some farmers did not like to be instructed or did not think assistance was useful. A few farmers expressed that often they had more knowledge of the land than the technical assistants. To further understand their overall preferences towards technical assistance we asked if they would hire technical assistance on their own if they
<figure><img src="image_7.png" /><figcaption>Fig. 5. Willingness of farmers to accept different environmental commitments.</figcaption></figure>

thought it was necessary after the end of the program, and only 6.9% said they would definitely or most probably hire technical assistance. This supports the idea that most farmers do not value technical assistance although we cannot conclude on the reasons behind such rejection.

## 5.4. Additional environmental commitments

While the adoption of these practices brings clear environmental benefits, their financial profitability justifies requesting additional environmental commitments from participants, both to enhance impact and to serve as safeguards against potential externalities. Fig. 5 shows the distribution of farmers' responses to the follow-up questions on their willingness to accept different environmental commitments.

Most farmers were willing to reaffirm their participation even when it required signing a conservation contract to protect their existing forests, suggesting that basic conservation commitments were perceived as low-cost and acceptable. Similarly, the majority expressed willingness to join the programs under the condition of participating in community reforestation activities for 5 days per year. Although willingness declined as demands increased, most remained willing to accept a condition of 12 days of reforestation per year. In contrast, there was significant resistance to the option requiring an organization to reforest one hectare of their currently cleared land.

This aligns with our focus group discussions, where most farmers expressed little resistance to conservation requirements. In the context of widespread forest scarcity, they recognized the importance of maintaining the forest that remains. Similarly, many appeared willing to dedicate time to reforestation efforts, reflecting both their concern for forest recovery and a strong environmental identity. Nonetheless, active reforestation was rejected by many farmers. This distinction highlights that environmental motivations are sensitive to opportunity costs: farmers may support conservation measures when they align with existing land uses, but are reluctant to forgo productive land without adequate compensation or short-term benefits. Focus group participants repeatedly emphasized fears about losing land under conservation agreements, suggesting that reforestation may be perceived as restricting land use. This indicates that while farmers are willing to conserve forest, their support remains conditional on maintaining land-use flexibility and a sense of control over their land.

## 5.5. Limits

Although the randomization of the sample posed some challenges, the study offers valuable insights into patterns among smallholder farmers in the area, with some caution warranted when generalizing the findings. Beyond sampling considerations, the DCE design itself presents some limitations. One of them is the possible consequences of non-attendance, summary statistics on attribute attendance are presented in Appendix H. The CL analysis of the sub-sample of participants who considered all attributes very important or important for their choices gives similar estimates to our main results (Appendix I). However, we only asked for attendance to attributes at the end of the choice experiment for simplicity, while Hensher et al. (2007) point out that is best to ask respondents after each choice task about attribute attendance. Similarly, we reminded respondents before the choice experiment to consider the opt-out alternative, but due to time constraints, we did not do it before every choice set as suggested by Ladenburg and Olsen (2014).

Finally, a possible bias specific to our scenario is that some respondents chose to participate with the intention of selling the materials rather than complying. To account for this “cheating behavior”, we ran a CL model excluding respondents who answered that more than 30% of participants would sell the materials in our follow-up questions, as this may signal non-compliant individuals who overestimate how common non-compliance is among others (Arias and Sutton, 2013). We again obtained very similar estimates (Appendix I).

# 6. Conclusion

In recent years, Guaviare has been a target for interventions aimed at rural development and environmental sustainability. Our research brings valuable insights into the preferences of farmers, in the hope that future programs can use this information to better adjust contracts to these preferences. We provide strong evidence of farmers' willingness to adopt silvopastoral systems. Even when the benefits of this practice are clear, this remains noteworthy in Guaviare, where moving from traditional practices appears to be challenging. This is an opportunity to mitigate environmental impact and improve farmer's welfare. Moreover, the direct welfare benefits that farmers can pull from silvopastoral systems could be leveraged to require additional environmental commitments on their part.

Most of Guaviare's territory is part of the buffer zone to the Seranáa del Chiribiquete National Park, the world's largest tropical rainforest and a UNESCO World Heritage site with substantial biodiversity and archeological value (WWF, 2018). With this in mind, going from traditional livestock practices to silvopastoral systems is necessary but not sufficient. As noted by White et al. (2001), while improved pastures can have a positive relationship with forest cover, this dynamic may not hold in the long term with increased herd expansion. In our context, this risk appears limited due to current forest scarcity within farms and the localized scale of interventions, which offer few possibilities for expansion outside already cleared areas. However, as a precaution, incentives should be promoted carefully and in parallel with environmental safeguards to prevent deforestation leakage from SPS adoption (Castro-Nunez et al., 2021). In this paper, we proposed combining silvopastoral and agroforestry systems as a more integrated approach to sustainable land use, and explored participants willingness to engage in additional environmental commitments. The balanced option of 4.5 ha of silvopastoral systems and 0.5 ha of agroforestry systems is well accepted by participants, which is encouraging considering the environmental benefits over the reference option. Furthermore, conservation agreements and reforestation efforts were also widely accepted.

Our results have evidenced most farmers require large incentives to partake in the adoption of agroforestry on a larger scale. For these systems to become an instrument of rural development, the bottlenecks
of the value chain have to be addressed at a large level. If livestock is to be partially replaced with more sustainable economic activities in the future in Guaviare, extensive research and policy efforts are needed to determine how to make it economically sustainable for the population.

Another factor explored in this research is using individual cash bonuses to compensate for effort and permanence. Programs could reallocate a small part of resources from fixed economic support and offer individual effort bonuses. Our results indicate that at the very least, if participants trust the entity that provides the contracts, this would increase participation. Further research could explore the impact of these bonuses on permanence.

Finally, our research highlights two challenges that should be considered for the design of interventions. On the one hand, farmers might have a rejection towards technical assistance. Although this does not drastically harm participation rates, we can assume it plays a role in the motivation of farmers once they are part of a project. Assistance and training need to be tailored to farmers' needs, and further research is needed to understand their needs in this aspect. On the other hand, the collective dimension of interventions needs careful design. In this complex context, our results indicate that collective goals may hinder participation rates. Nevertheless, other collective dimensions, such as community activities, remain crucial instruments for achieving rural development. Our results are particularly important in the current political context of the Guaviare-Caqueta region, where the Colombian National Government is partnering with local authorities and international organizations to implement different interventions to stop the advance of the agricultural frontier. Supporting the transition from extensive cattle ranching towards SPS with reduced surfaces under agroforestry systems and coupled with collective reforestation activities is a promising policy to shift the regional culture and mental model of these regions. Current policies such as Vision Amazonia and taxes coming from carbon mining could fully support such interventions, to aim at a gradual although substantial and long-term change in regional productive behaviors.

### CRediT authorship contribution statement

**Catalina Posada-Borrero:** Writing – review & editing, Writing – original draft, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

**Driss Ezzine-de-Blas:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Data curation, Conceptualization. **Emmanuelle Lavaine:** Writing – review & editing, Validation, Supervision, Investigation. **Sébastien Roussel:** Writing – review & editing, Validation, Supervision, Investigation.

### Ethics

The whole procedure was reviewed and approved by the University of Montpellier Institutional Review Board (n° UM 2023-016Bis). The Review Board considers that this project perfectly follows the rules established in the Ethical Charter.

### Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT to rephrase some sentences and for formatting support. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Driss Ezzine-de-Blas reports financial support was provided by European Union. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

We are grateful to the ONFA Andina team at the San José del Guaviare office and the field data collectors for their invaluable logistical support, and to the Caminemos Territorios Sostenibles project for the information provided. We also acknowledge the DeSIRA-IDEAS project, funded by the European Union.

### Appendix A. Choice experiment method

This methodology is based on the theory of Lancaster (1966), which states that the utility of a good is the sum of the value derived from its characteristics. The DCE survey contains various choice sets with alternative scenarios, described by attributes with specific levels. The attributes correspond to certain characteristics of the intervention alternatives and the levels describe the ranges over which attributes vary (Hoyos, 2010). The analysis of DCE is based on the random utility theory of McFadden et al. (1973), which states it is only possible to find the probability of an individual choosing one alternative over another. It is assumed that individual *i* makes utility-maximizing decisions with the utility ($U_{ij}$) for a program *j* consisting of a deterministic component ($V_{ij}$) and a stochastic component ($e_{ij}$):

$$U_{ij} = V_{ij}(X_j) + e_{ij}$$

The deterministic element $V$ depends on the characteristics of the programs ($X_j$) defined by the attributes and levels, including the compensation given by the program to the individual. The vector also contains an alternative specific constant (ASC), associated with the status-quo. This parameter represents unobserved sources of utility of this alternative (Hoyos, 2010). The stochastic element $e$ is an independently and identically distributed (IID) extreme value Type 1 random error, unobserved by the researcher.

The survey has $S$ choice sets, and the individual must choose one of $J$ program alternatives for each one. The individual will choose an alternative $m$, out of a finite set of $J$ alternatives in choice task $s$, if the indirect utility of $m$ is larger than the indirect utility of any other alternatives $j$.

$$U_{im} \geq U_{ij} \rightarrow V_{im} + e_{im} \geq V_{ij} + e_{ij} \quad \forall j \neq m; \quad j, m \in J$$

The probability of that choice is given by:

$$P_{ims} = \frac{\exp(\beta_i X_{ims})}{\sum_j \exp(\beta_j X_{jjs})}$$

where $\beta_i$ is the vector of estimated parameters.

To calculate the willingness to accept (WTA) a marginal change of linear attribute, we use the ratio of the attribute's coefficient and the coefficient of the payment attribute:

$$WTA = \frac{\beta_{attribute}}{\beta_{payment}}$$

Effect-coded attributes have a different estimation for the marginal values (Mariel et al., 2021), given the following formula:

$$WTA = \frac{-(2\beta_{attributelevel1} + \beta_{attributelevel2})}{\beta_{payment}}$$

### Appendix B. Comparative analysis of individuals who participated and did not participate in the caminemos project, respectively

see Tables 7 and 8.

### Appendix C. Comparative analysis of individuals who participated and did not participate in similar projects, respectively

see Tables 9 and 10.
<table><caption>**Table 7**
Subsample caminemos.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th>RPL</th><th rowspan="2">SD (Std.err.)</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>0.614 (0.322)</td><td>0.547 (1.066)</td><td>3.729 *** (0.391)</td></tr><tr><td>Compensation</td><td>0.233 *** (0.024)</td><td>0.486 *** (0.049)</td><td>–</td></tr><tr><td>Technical Assistance</td><td>-0.010 ** (0.004)</td><td>-0.018 **(0.006)</td><td>0.007 (0.009)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.898 *** (0.068)</td><td>-2.246 *** (0.327)</td><td>2.578 *** (0.328)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.224 *** (0.059)</td><td>0.604 *** (0.128)</td><td>0.787 *** (0.168)</td></tr><tr><td>6 ha SPS</td><td>0.674</td><td>1.642</td><td>–</td></tr><tr><td>Individual Bonus</td><td>0.265 *** (0.064)</td><td>0.623 *** (0.117)</td><td>-0.463 ** (0.162)</td></tr><tr><td>Collective Bonus</td><td>-0.277 *** (0.063)</td><td>-0.633 *** (0.136)</td><td>0.787 *** (0.168)</td></tr><tr><td>No Bonus</td><td>-0.012</td><td>0.010</td><td>–</td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>1032</td><td>1032</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.149</td><td>0.356</td><td></td></tr><tr><td>Final log-likelihood</td><td>-771.37</td><td>-576.42</td><td></td></tr><tr><td>AIC</td><td>1556.73</td><td>1178.85</td><td></td></tr><tr><td>BIC</td><td>1591.31</td><td>1243.06</td><td></td></tr></tbody></table>

Notes: N = 86. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

<table><caption>**Table 8**
Subsample NO Caminemos.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th>RPL</th><th rowspan="2">SD (Std.err.)</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.489 *** (0.264)</td><td>1.647 (0.892)</td><td>5.548 *** (0.677)</td></tr><tr><td>Compensation</td><td>0.225 *** (0.020)</td><td>0.529 *** (0.047)</td><td>–</td></tr><tr><td>Technical Assistance</td><td>-0.004 (0.003)</td><td>-0.010 (0.006)</td><td>-0.018 (0.012)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.756 *** (0.056)</td><td>-2.379 *** (0.324)</td><td>2.558 *** (0.271)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.382 *** (0.051)</td><td>0.928 *** (0.150)</td><td>1.221 *** (0.193)</td></tr><tr><td>6 ha SPS</td><td>0.374</td><td>1.451</td><td>–</td></tr><tr><td>Individual Bonus</td><td>0.345 *** (0.052)</td><td>0.921 *** (0.108)</td><td>-0.205 (0.161)</td></tr><tr><td>Collective Bonus</td><td>-0.301 *** (0.054)</td><td>-0.789 *** (0.142)</td><td>0.882 *** (0.134)</td></tr><tr><td>No Bonus</td><td>-0.044</td><td>-0.132</td><td>–</td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>1428</td><td>1428</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.103</td><td>0.424</td><td></td></tr><tr><td>Final log-likelihood</td><td>-1276.6</td><td>-812.46</td><td></td></tr><tr><td>AIC</td><td>2567.19</td><td>1650.92</td><td></td></tr><tr><td>BIC</td><td>2604.04</td><td>1719.35</td><td></td></tr></tbody></table>

Notes: N = 119. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

<table><caption>**Table 9**
Subsample project related.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th>RPL</th><th rowspan="2">SD (Std.err.)</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.497 *** (0.261)</td><td>0.432 (0.988)</td><td>5.892 *** (0.836)</td></tr><tr><td>Compensation</td><td>0.263 *** (0.020)</td><td>0.567 *** (0.046)</td><td>–</td></tr><tr><td>Technical Assistance</td><td>-0.006 (0.003)</td><td>-0.011 * (0.005)</td><td>0.010 (0.007)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.861 *** (0.055)</td><td>-2.298 *** (0.298)</td><td>2.716 *** (0.287)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.217 *** (0.048)</td><td>0.610 *** (0.100)</td><td>0.562 *** (0.135)</td></tr><tr><td>6 ha SPS</td><td>0.644</td><td>1.688</td><td>–</td></tr><tr><td>Individual Bonus</td><td>0.296 *** (0.052)</td><td>0.741 *** (0.100)</td><td>-0.414 ** (0.147)</td></tr><tr><td>Collective Bonus</td><td>-0.304 *** (0.051)</td><td>-0.714 *** (0.119)</td><td>0.853 *** (0.150)</td></tr><tr><td>No Bonus</td><td>-0.008</td><td>-0.027</td><td>–</td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>1584</td><td>1584</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.143</td><td>0.422</td><td></td></tr><tr><td>Final log-likelihood</td><td>-1275.98</td><td>-852.85</td><td></td></tr><tr><td>AIC</td><td>2565.95</td><td>1731.7</td><td></td></tr><tr><td>BIC</td><td>2603.53</td><td>1801.48</td><td></td></tr></tbody></table>

Notes: N = 132. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.
<table><caption>**Table 10**
Subsample NO project related.</caption><thead><tr><th rowspan="2"></th><th>CL</th><th colspan="2">RPL</th></tr><tr><th>Estimates (Std.err.)</th><th>Estimates (Std.err.)</th><th>SD (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>0.635 (0.327)</td><td>0.632 (1.348)</td><td>4.371 ** (1.423)</td></tr><tr><td>Compensation</td><td>0.167 *** (0.025)</td><td>0.404 *** (0.052)</td><td></td></tr><tr><td>Technical Assistance</td><td>-0.009 * (0.004)</td><td>-0.022 ** (0.008)</td><td>-0.026 * (0.011)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>-0.745 *** (0.071)</td><td>-2.057 *** (0.491)</td><td>2.748 *** (0.463)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>0.482 *** (0.064)</td><td>1.341 *** (0.255)</td><td>1.41 ** (0.273)</td></tr><tr><td>6 ha SPS</td><td>0.263</td><td>1.179</td><td></td></tr><tr><td>Individual Bonus</td><td>0.325 *** (0.064)</td><td>0.878 *** (0.131)</td><td>0.048 (0.232)</td></tr><tr><td>Collective Bonus</td><td>-0.258 *** (0.067)</td><td>-0.644 *** (0.155)</td><td>0.839 *** (0.177)</td></tr><tr><td>No Bonus</td><td>-0.067</td><td>-0.234</td><td></td></tr><tr><td>Model statistics</td><td></td><td></td><td></td></tr><tr><td>Number of choices</td><td>876</td><td>876</td><td></td></tr><tr><td>Adj.Rho-squared</td><td>0.090</td><td>0.382</td><td></td></tr><tr><td>Final log-likelihood</td><td>-781.72</td><td>-523.32</td><td></td></tr><tr><td>AIC</td><td>1577.43</td><td>1072.64</td><td></td></tr><tr><td>BIC</td><td>1610.86</td><td>1134.72</td><td></td></tr></tbody></table>

Notes: N = 73. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

<table><caption>**Table 11**
WTA estimates and confidence intervals (Caminemos and NO Caminemos).</caption><thead><tr><th rowspan="2">Attribute</th><th colspan="2">WTA estimates (Caminemos)</th><th colspan="2">WTA estimates (NO Caminemos)</th><th rowspan="2">Difference</th></tr><tr><th>WTA estimates</th><th>Confidence interval</th><th>WTA estimates</th><th>Confidence interval</th></tr></thead><tbody><tr><td>Technical Assistance</td><td>36,200</td><td>[3,820; 35,180]</td><td>18,900</td><td>[-2,855; 40,655]</td><td>-17,300</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>7,467,300</td><td>[5,068,190; 9,678,810]</td><td>6,330,900</td><td>[4,362,508; 8,299,291]</td><td>1,136,400</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>1,878,200</td><td>[928,753; 3,225,047]</td><td>361,100</td><td>[-721,780; 1,443,980]</td><td>1,517,100</td></tr><tr><td>Individual Bonus</td><td>-1,276,700</td><td>[-1,935,229; -753,371]</td><td>-2,018,300</td><td>[-2,641,372; -1,395,227]</td><td>-741,600</td></tr><tr><td>Collective Bonus</td><td>1,332,400</td><td>[519,718; 2,009,682]</td><td>1,212,700</td><td>[382,263; 2,043,136]</td><td>119,700</td></tr></tbody></table>

Note: N = 205.

<table><caption>**Table 12**
WTA estimates and confidence intervals (Project Related and NO Project Related).</caption><thead><tr><th rowspan="2">Attribute</th><th colspan="2">WTA estimates (Project Related)</th><th colspan="2">WTA estimates (NO Project Related)</th><th rowspan="2">Difference</th></tr><tr><th>WTA estimates</th><th>Confidence interval</th><th>WTA estimates</th><th>Confidence interval</th></tr></thead><tbody><tr><td>Technical Assistance</td><td>19,500</td><td>[15,032; 57,367]</td><td>52,400</td><td>[15,552; 89,247]</td><td>-32,900</td></tr><tr><td>3 ha SPS and 1 ha AS ha AS</td><td>7,373,500</td><td>[5,068,190; 9,678,810]</td><td>5,004,000</td><td>[1,514,480; 8,493,520]</td><td>2,369,500</td></tr><tr><td>4.5 ha SPS and 0.5</td><td>2,076,900</td><td>[928,753; 3,225,046]</td><td>-2,354,400</td><td>[-5,029,947; 321,147]</td><td>4,431,300</td></tr><tr><td>Individual Bonus</td><td>-1,344,300</td><td>[-1,935,229; -753,370]</td><td>-2,719,300</td><td>[-3,759,649; -1,678,951]</td><td>1,375,000</td></tr><tr><td>Collective Bonus</td><td>1,264,700</td><td>[519,718; 2,009,682]</td><td>1,039,000</td><td>[-324,155; 2,402,155]</td><td>225,700</td></tr></tbody></table>

Note: N = 205.

<table><caption>**Table 13**
Selection criteria for the number of latent classes.</caption><thead><tr><th>Number of classes</th><th>Estimated parameters</th><th>Final log-likelihood</th><th>AIC</th><th>BIC</th><th>Adjusted R<sup>2</sup></th></tr></thead><tbody><tr><td>2</td><td>21</td><td>-1803.90</td><td>3649.80</td><td>3771.77</td><td>0.2288</td></tr><tr><td>3</td><td>35</td><td>-1559.44</td><td>3188.89</td><td>3392.16</td><td>0.3272</td></tr><tr><td>4</td><td>49</td><td>-1559.44</td><td>3216.89</td><td>3501.47</td><td>0.3221</td></tr><tr><td>5</td><td>63</td><td>-1499.75</td><td>3125.49</td><td>3491.39</td><td>0.3423</td></tr></tbody></table>

## Appendix D. WTA of individuals who participated and did not participate in the caminemos project, respectively

see Table 11.

## Appendix E. WTA of individuals who participated and did not participate in similar projects, respectively

see Table 12.

## Appendix F. Model selection criteria for the number of latent classes

see Table 13.

## Appendix G. Follow-up survey on participation motivations and expectations

see Table 14.
<table><caption>**Table 14**
Follow-up questions about your decisions (%).</caption><tbody><tr><td colspan="2"><strong>Would you like to implement (or continue implementing) silvopastoral systems?</strong></td></tr><tr><td>Yes</td><td>97.1</td></tr><tr><td>I do not know</td><td>0.98</td></tr><tr><td>No</td><td>1.9</td></tr><tr><td colspan="2"><strong>Main reasons for implementing silvopastoral systems:</strong></td></tr><tr><td colspan="2"><strong>First reason</strong></td></tr><tr><td>Land &amp; pasture productivity</td><td>77.1</td></tr><tr><td>Facilitates work with animals</td><td>5.9</td></tr><tr><td>Good for environment</td><td>11.7</td></tr><tr><td>Animal well-being</td><td>5.4</td></tr><tr><td>None</td><td>0</td></tr><tr><td colspan="2"><strong>Second reason</strong></td></tr><tr><td>Land &amp; pasture productivity</td><td>16.6</td></tr><tr><td>Facilitates work with animals</td><td>44.9</td></tr><tr><td>Good for environment</td><td>27.3</td></tr><tr><td>Animal well-being</td><td>11.2</td></tr><tr><td>None</td><td>0</td></tr><tr><td colspan="2"><strong>Consider the following as obstacles (Yes or No):</strong></td></tr><tr><td>It is expensive</td><td>66.8</td></tr><tr><td>The system may not be more productive or not work well</td><td>32.7</td></tr><tr><td>It requires a lot of time/work</td><td>30.7</td></tr><tr><td>Other</td><td>8.8</td></tr><tr><td colspan="2"><strong>Would you like to implement (or continue implementing) agroforestry systems?</strong></td></tr><tr><td>Yes</td><td>54.6</td></tr><tr><td>I do not know</td><td>7.3</td></tr><tr><td>No</td><td>38</td></tr><tr><td colspan="2"><strong>Main reasons for implementing agroforestry systems:</strong></td></tr><tr><td colspan="2"><strong>First reason</strong></td></tr><tr><td>Good for environment</td><td>31.2</td></tr><tr><td>Diversify activities</td><td>28.3</td></tr><tr><td>Use home garden food</td><td>17.1</td></tr><tr><td>Long-term savings</td><td>10.7</td></tr><tr><td>None</td><td>12.7</td></tr><tr><td colspan="2"><strong>Second reason</strong></td></tr><tr><td>Good for environment</td><td>38.0</td></tr><tr><td>Diversify activities</td><td>26.8</td></tr><tr><td>Use home garden food</td><td>14.1</td></tr><tr><td>Long-term savings</td><td>12.7</td></tr><tr><td>None</td><td>8.2</td></tr><tr><td colspan="2"><strong>Consider the following as obstacles:</strong></td></tr><tr><td>The system may not work well</td><td>56.1</td></tr><tr><td>It is expensive</td><td>49.3</td></tr><tr><td>No commercial partners or guarantee for product purchase</td><td>47.8</td></tr><tr><td>Benefits will only be perceived in a long time</td><td>36.6</td></tr><tr><td>It requires a lot of time/work</td><td>30.7</td></tr><tr><td>Other</td><td>2.4</td></tr><tr><td colspan="2"><strong>How many of these people do you think would sell the materials instead of using them for the agreed-upon productive system?</strong></td></tr><tr><td>Less than 5</td><td>24.4</td></tr><tr><td>Between 5 and 15</td><td>44.9</td></tr><tr><td>Between 15 and 30</td><td>24.9</td></tr><tr><td>More than 30</td><td>5.9</td></tr><tr><td colspan="2"><strong>If technical assistance for a project has ended and you still think it is necessary, would you hire it on your own?</strong></td></tr><tr><td>Yes, I definitely would hire technical assistance</td><td>1.5</td></tr><tr><td>Probably would hire technical assistance</td><td>5.4</td></tr><tr><td>Would only hire technical assistance if strictly necessary</td><td>53.2</td></tr><tr><td>Not sure if I would hire technical assistance</td><td>24.4</td></tr><tr><td>No, definitely would not hire technical assistance</td><td>15.6</td></tr><tr><td colspan="2"><strong>How many people do you think would continue the productive systems of the transformed hectares 12 years after the end of the program?</strong></td></tr><tr><td>Less than 15</td><td></td></tr><tr><td>Between 15 and 30</td><td>18.5</td></tr><tr><td>Between 30 and 50</td><td>30.7</td></tr><tr><td>Between 50 and 70</td><td>32.7</td></tr><tr><td>More than 70</td><td>18</td></tr></tbody></table>

Notes: N = 205. For the items labeled *Consider the following as obstacles*, respondents evaluated each obstacle independently, the proportion of respondents who answered Yes to each obstacle separately. For the other questions, respondents selected a single response option.
# Appendix H. Attribute's attendance

see Table 15.

<table><caption>**Table 15**
Proportions (%) of the importance given to attributes.</caption><thead><tr><th>Attribute</th><th>Very important</th><th>Important</th><th>Not so important</th></tr></thead><tbody><tr><td>Compensation</td><td>59.0</td><td>41.0</td><td>0</td></tr><tr><td>Technical Assistance</td><td>25.4</td><td>69.3</td><td>5.4</td></tr><tr><td>Land</td><td>44.9</td><td>54.6</td><td>0.5</td></tr><tr><td>Bonus</td><td>19.0</td><td>59.0</td><td>21.9</td></tr></tbody></table>

Note: N = 205.

# Appendix I. Robustness

see Table 16.

<table><caption>**Table 16**
Robustness analysis.</caption><thead><tr><th></th><th>Without non-attendance estimate (Std.err.)</th><th>Without noncompliance estimate (Std.err.)</th></tr></thead><tbody><tr><td>ASC</td><td>1.586 *** (0.236)</td><td>1.233 *** (0.209)</td></tr><tr><td>Compensation</td><td>0.252 *** (0.018)</td><td>0.233 *** (0.016)</td></tr><tr><td>Technical Assistance</td><td>-0.006 * (0.003)</td><td>-0.007 * (0.003)</td></tr><tr><td>3 ha SPS and 1 ha AS</td><td>0.388 *** (0.045)</td><td>0.314 *** (0.039)</td></tr><tr><td>4.5 ha SPS and 0.5 ha AS</td><td>-0.850 *** (0.051)</td><td>-0.813 *** (0.045)</td></tr><tr><td>6 ha SPS</td><td>0.328 *** (0.046)</td><td>0.292 *** (0.041)</td></tr><tr><td>Individual Bonus</td><td>-0.230 *** (0.047)</td><td>-0.269 *** (0.042)</td></tr><tr><td>Collective Bonus</td><td>0.328 *** (0.054)</td><td>0.294 *** (0.042)</td></tr><tr><td>No Bonus</td><td>0.328 *** (0.046)</td><td>0.292 *** (0.041)</td></tr><tr><td>Model statistics</td><td></td><td></td></tr><tr><td>Number of choices</td><td>1860</td><td>2316</td></tr><tr><td>Adj.Rho-squared</td><td>0.1261</td><td>0.1207</td></tr><tr><td>Final log-likelihood</td><td>-1576.89</td><td>-1944.26</td></tr><tr><td>AIC</td><td>3167.78</td><td>3902.53</td></tr><tr><td>BIC</td><td>3206.48</td><td>3942.76</td></tr></tbody></table>

Notes: N = 205. For the 6 ha SPS and No Bonus variables, the effect-coded base level's parameter is the negative sum of the other level estimates. Standard errors in parentheses. *, **, *** indicate significance at 5%, 1%, and 0.1%, respectively.

# Appendix J. Supplementary data

Supplementary material related to this article can be found online at https://doi.org/10.1016/j.ecolecon.2025.108744.

## Data availability

Data will be made available on request.

## References

Álvarez, F., Casanoves, F., Suárez, J.C., 2021. Influence of scattered trees in grazing areas on soil properties in the piedmont region of the Colombian Amazon. PLoS One 16 (12), e0261612.

Améndola, L., Solorio, F., Ku-Vera, J., Améndola-Massioti, R., Zarza, H., Mancera, K., Galindo, F., 2019. A pilot study on the foraging behaviour of heifers in intensive silvopastoral and monoculture systems in the tropics. Animal 13 (3), 606–616.

Andersson, K.P., Cook, N.J., Grillos, T., Lopez, M.C., Salk, C.F., Wright, G.D., Mwangi, E., 2018. Experimental evidence on payments for forest commons conservation. Nat. Sustain. 1 (3), 128–135.

Arias, A., Sutton, S.G., 2013. Understanding recreational fishers compliance with no-take zones in the great barrier reef marine park. Ecol. Soc. 18 (4).

Armenteras, D., Rudas, G., Rodriguez, N., Sua, S., Romero, M., 2006. Patterns and causes of deforestation in the Colombian Amazon. Ecol. Indic. 6 (2), 353–368.

Arvor, D., Daugeard, M., Tritsch, I., Mello-Thery, D., Aparecida, N., Thery, H., Dubreuil, V., 2018. Combining socioeconomic development with environmental governance in the Brazilian Amazon: The mato grosso agricultural frontier at a tipping point. Environ. Dev. Sustain. 20 (1), 1–22.

Balderas, Torres A., MacMillan, D.C., Skutsch, M., Lovett, J.C., 2013. Payments for ecosystem services and rural development: Landowners' preferences and potential participation in western Mexico. Ecosyst. Serv. 6, 72–81.

Barrera García, J.A., Giraldo Benavides, B., Castro, S., García, L., Daza, M., 2017. Sistemas agroforestales para la Amazonía. In: Instituto Amazónico de Investigaciones Científicas. SINCHI, Bogotá, Colombia, Printed by Instituto Amazónico de Investigaciones Científicas. Available at: www.sinchi.org.co.

Barrio, M., Loureiro, M., 2013. The impact of protest responses in choice experiments: an application to a biosphere reserve management program. For. Syst. 22 (1), 94–105.

Bauch, S.C., Sills, E.O., Pattanayak, S.K., 2014. Have we managed to integrate conservation and development? icdp impacts in the Brazilian Amazon. World Dev. 64, S135–S148.

Bautista, C., 2020. La ganadería en la frontera agrícola amazónica y las emisiones de Gases de Efecto Invernadero, caso del departamento del Guaviare. (Ph.D. thesis). Colombia Cattle Ranching in the Amazon Agricultural Frontier and Greenhouse Gas Emissions, a case study of the Guaviare department, Colombia.

Ezzine de Blas, D., Ruiz Pérez, M., Sayer, J.A., Lescuyer, G., Nasi, R., Karsenty, A., 2009. External influences on and conditions for community logging management in Cameroon. World Dev. 37 (2), 445–456.

Boxall, P.C., Adamowicz, W.L., 2002. Understanding heterogeneous preferences in random utility models: a latent class approach. Environ. Resour. Econ. 23, 421–446.

Buurman, P., Ibrahim, M., et al., 2007. Carbon Sequestration in Tropical Grassland Ecosystems. Wageningen Academic Publishers.

Calle, A., 2020. Can short-term payments for ecosystem services deliver long-term tree cover change? Ecosyst. Serv. 42, 101084.

Calle, Z., Murgueitio, E., Chará, J., Molina, C.H., Zuluaga, A.F., Calle, A., 2013. A strategy for scaling-up intensive silvopastoral systems in Colombia. J. Sustain. For. 32 (7), 677–693.

Calle, Z., Murgueitio, E., Chará, J., et al., 2012. Integrating forestry, sustainable cattle-ranching and landscape restoration. Unasylva 63 (1), 31–40.

Carriazo, F., Labarta, R., Escobedo, F.J., 2020. Incentivizing sustainable rangeland practices and policies in Colombias Orinoco region. Land Use Policy 95, 104203.

Castro-Nunez, A., Buriticá, A., Gonzalez, C., Villarino, E., Holmann, F., Perez, L., Del Río, M., Sandoval, D., Eufemia, L., Löhr, K., Durango, S., Romero, M., Lana, M., Sotelo, S., Rivera, O., Loboguerrero, A.M., Quintero, M., 2021. The risk of unintended deforestation from scaling sustainable livestock production systems. Conserv. Sci. Pr. 3 (9), e495, eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/csp.2.495.

Chará, J., Rivera, J., Barahona, R., Murgueitio, R.E., Deblitz, C., Reyes, E., Mauricio, R.M., Molina, J.J., Flores, M., Zuluaga, A., 2017. Intensive silvopastoral systems: economics and contribution to climate change mitigation and public policies. In: Integrating Landscapes: Agroforestry for Biodiversity Conservation and Food Sovereignty.

Chèze, B., David, M., Martinet, V., 2020. Understanding farmers' reluctance to reduce pesticide use: A choice experiment. Ecol. Econom. 167, 106349.

Clayton, S., 2003. Environmental identity: A conceptual and an operational definition. In: Identity and the Natural Environment: The Psychological Significance of Nature. pp. 45–65.

Clayton, S., Czellar, S., Nartova-Bochaver, S., Skibins, J.C., Salazar, G., Tseng, Y.-C., Irkhin, B., Monge-Rodriguez, F.S., 2021. Cross-cultural validation of a revised environmental identity scale. Sustainability 13 (4), 2387.

Clerici, N., Salazar, C., Pardo-Díaz, C., Jiggins, C.D., Richardson, J.E., Linares, M., 2019. Peace in Colombia is a critical moment for neotropical connectivity and conservation: Save the northern Andes Amazon biodiversity bridge. Conserv. Lett. 12 (1), e12594.

Corbera, E., Costedoaat, S., Ezzine-de, Blas D., Van Hecken, G., 2020. Troubled encounters: payments for ecosystem services in Chiapas, Mexico. Dev. Chang. 51 (1), 167–195.

Costedoaat, S., Koetse, M., Corbera, E., Ezzine-de, Blas D., 2016. Cash only? unveiling preferences for a pes contract through a choice experiment in Chiapas, Mexico. Land Use Policy 58, 302–317.

Cranford, M., Mourato, S., 2014. Credit-based payments for ecosystem services: Evidence from a choice experiment in Ecuador. World Dev. 64, 503–520.

DANE, 2020a. Anexo Ena Demografía Productores Ganaderos 2019 Annex Ena Demography Livestock Producers 2019. National Administrative Department of Statistics (DANE), Bogotá, Colombia.

DANE, 2020b. Encuesta Nacional Agropecuaria 2019: Resultados Guaviare National Agricultural Survey 2019: Guaviare Results. National Administrative Department of Statistics (DANE), Bogotá, Colombia, Available at: https://www.dane.gov.co/files/investigaciones/agropecuario/enda/ena/2019/presentacion-ena-guaviare-2019.pdf.

de Sousa, K.F., Detlefsen, G., De Melo Virginio Filho, E., Tobar, D., Casanoves, F., 2016. Timber yield from smallholder agroforestry systems in nicaragua and honduras. Agrofor. Syst. 90, 207–218.

Duffý, C., Toth, G.G., Hagan, R.P., McKeown, P.C., Rahman, S.A., Widyaningsih, Y., Sunderland, T.C., Spillane, C., 2021. Agroforestry contributions to smallholder farmer food security in Indonesia. Agrofor. Syst. 95 (6), 1109–1124.

Enciso, K., Bravo Parra, A.M., Charry, A., Rosas Sandoval, G., Jager, M., Hurtado Bermudez, J.J., Romero Sánchez, M.A., Sierra, L., Quintero, M., Burkart, S., 2018. Estrategia sectorial de la cadena de ganadería doble propósito en caquetá, con enfoque agroambiental y cero deforestation sectorial strategy for dual-purpose livestock chain in caquetá, with agro-environmental focus and zero deforestation. Publicación CIAT.
Fernando, G.M., 1995. Coca, guerrilla y sociedad civil en el Guaviare: regulación de conflictos y otros controls [coca, guerrilla, and civil society in Guaviare: Conflict regulation and other controls]. Colomb. Int. (29), 18-29.

Rodríguez-de Francisco, J.C., del Cairo, C., Ortiz-Gallego, D., Velez-Triana, J.S., Vergara-Gutiérrez, T., Hein, J., 2021. Post-conflict transition and REDD+ in Colombia: Challenges to reducing deforestation in the Amazon. For. Policy Econ. 127, 102450.

Franks, P., Blomley, T., 2004. Fitting icd into a project framework: a care perspective. In: Getting Biodiversity Projects to Work: Towards more Effective Conservation and Development. Columbia University Press, pp. 77-97.

Frederick, S., 2005. Cognitive reflection and decision making. J. Econ. Perspect. 19 (4), 25-42.

Gockel, C.K., Gray, L.C., 2009. Integrating conservation and development in the Peruvian Amazon. Ecol. Soc. 14 (2).

Grebner, D.L., Bettinger, P., Siry, J.P., Boston, K., 2022. Chapter 11 - Common forestry practices. In: Grebner, D.L., Bettinger, P., Siry, J.P., Boston, K. (Eds.), Introduction to Forestry and Natural Resources (Second Edition), second ed. Academic Press, San Diego, pp. 265-294.

Haile, K.K., Tirivayi, N., Tesfaye, W., 2019. Farmers willingness to accept payments for ecosystem services on agricultural land: The case of climate-smart agroforestry in Ethiopia. Ecosyst. Serv. 39, 100964.

Hein, J., Del Cairo, C., Gallego, D.O., Gutiérrez, T.V., Velez, J.S., de Francisco, J.C.R., 2020. A political ecology of green territorialization: frontier expansion and conservation in the Colombian Amazon. DIE ERDE- J. Geogr. Soc. Berl. 151 (1), 37-57.

Hensher, D.A., Rose, J., Bertoia, T., 2007. The implications on willingness to pay of a stochastic treatment of attribute processing in stated choice studies. Transp. Res. Part E: Logist. Transp. Rev. 43 (2), 73-89.

Hess, S., Palma, D., 2019. Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application. J. Choice Model. 32, 100170.

Hoyos, D., 2010. The state of the art of environmental valuation with discrete choice experiments. Ecol. Econom. 69 (8), 1595-1603.

ICA, 2023. Censo pecuario nacional. Intituto Colombiano agropecuario (ICA). Retrieved from: https://www.ica.gov.co/areas/pecuaria/servicios/epidemiologia-veterinaria/censos-2016/censo-2018/censos-otras-especies-2022-1.aspx.

IDEAM, 2024. Monitoring of Forest Area and Deforestation in Colombia 2023 (Summary of Results). Instituto de Hidrología, Meteorología y Estudios Ambientales (IDEAM), Bogotá, Colombia.

IUCN, WRI, 2014. A Guide to the Restoration Opportunities Assessment Methodology (ROAM): Assessing Forest Landscape Restoration Opportunities at the National Or Sub-National Level. International Union for Conservation of Nature (IUCN) and World Resources Institute (WRI), Gland, Switzerland.

Jones, K., et al., 2022. Evaluation Design, Baseline Data Collection, and Analysis: Amazon Alive Activity. USAID, Washington, DC.

Jose, S., 2009. Agroforestry for ecosystem services and environmental benefits: an overview. Agrofor. Syst. 76, 1-10.

Kaczan, D., Swallow, B.M., et al., 2013. Designing a payments for ecosystem services (pes) program to reduce deforestation in Tanzania: An assessment of payment approaches. Ecol. Econom. 95, 20-30.

Kuhfuss, L., Préget, R., Thoyer, S., Hanley, N., Coent, P.L., Désolé, M., 2026. Nudges, social norms, and permanence in Agri-environmental schemes. Land Econ. 92 (4), 641-655.

Ladenburg, J., Olsen, S.B., 2014. Augmenting short cheap talk scripts with a repeated opt-out reminder in choice experiment surveys. Resour. Energy Econ. 37, 39-63.

Lancaster, K.J., 1966. A new approach to consumer theory. J. Political Econ. 74 (2), 132-157.

Lee, S., Bonatti, M., Löhr, K., Palacios, V., Lana, M.A., Sieber, S., 2020. Adoption potentials and barriers of silvopastoral system in Colombia: Case of Cundinamarca region. Cogent Environ. Sci. 6 (1), 1823632.

Malhi, Y., Roberts, J.T., Betts, R.A., Killeen, T.J., Li, W., Nobre, C.A., 2008. Climate change, deforestation, and the fate of the Amazon. Science 319 (5860), 169-172, Publisher: American Association for the Advancement of Science.

Mamine, F., Minviel, J.J., et al., 2020. Contract design for adoption of agri-environmental practices: a meta-analysis of discrete choice experiments. Ecol. Econom. 176, 106721.

Mariel, P., Hoyos, D., Meyerhoff, J., Czajkowski, M., Dekker, T., Glenk, K., Jacobsen, J.B., Liebe, U., Olsen, S.B., Sagebiel, J., et al., 2021. Environmental Valuation with Discrete Choice Experiments: Guidance on Design, Implementation and Data Analysis. Springer Nature.

McFadden, D., et al., 1973. Conditional logit analysis of qualitative choice behavior.

McShane, T.O., Wells, M.P., 2004. Getting Biodiversity Projects to Work: Towards more Effective Conservation and Development. Columbia University Press.

Montagnini, F., Ibrahim, M., Murgueitio, E., 2013. Silvopastoral systems and climate change mitigation in Latin America. Bois forêts Des Trop. 316 (2), 3-16.

Morales, L., 2017. Peace and environmental protection in Colombia. In: Proposals for Sustainable Rural Development. Inter-American Dialogue.

Murgueitio, E., Barahona, R., Martins, R., Flores, M.X., Chará, J., Solorio, F.J., Morelia, F.P.M., 2015. Intensive Silvopastoral Systems: Improving Sustainability and Efficiency in Cattle Ranching Landscapes. FAO, Rome, Italy.

Murillo-Sandoval, P.J., Gjerdseth, E., Correa-Ayram, C., Wrathall, D., Van Den Hoek, J., Dávalos, L.M., Kennedy, R., 2021. No peace for the forest: Rapid, widespread land changes in the Andes-Amazon region following the Colombian civil war. Glob. Environ. Chang. 69, 102283.

Murphy, J.J., Stevens, T., Weatherhead, D., 2005. Is cheap talk effective at eliminating hypothetical bias in a provision point mechanism? Environ. Resour. Econ. 30, 327-343.

Nong, Y., Yin, C., Yi, X., Ren, J., Chien, H., 2021. Smallholder farmer preferences for diversifying farming with cover crops of sustainable farm management: A discrete choice experiment in Northwest China. Ecol. Econom. 186, 107060.

Rasch, S., Wünscher, T., Casasola, F., Ibrahim, M., Storm, H., 2021. Permanence of PES and the role of social context in the regional integrated silvo-pastoral ecosystem management project in Costa Rica. 185, 107027.

Rose, J.M., Bliemer, M.C., 2013. Sample size requirements for stated choice experiments. Transportation 40 (5), 1021-1041.

Siegel, K., Perez, A.F., Kinnebrew, E., Mills-Novoa1, M., Ochoa, J., Shoffner, E., 2022. Integration of qualitative and quantitative methods for land-use-change modeling in a deforestation frontier. Conserv. Biol.

Tacconi, L., 2012. Redefining payments for environmental services. Ecol. Econom. 73, 29-36.

Taou, N., 2022. Agroforestry Programs in the Colombian Amazon: Selection, Treatment and Exposure. (Ph.D. thesis). Heriot-Watt University.

Vaissière, A.-C., Tardieu, L., Quétier, F., Roussel, S., 2018. Preferences for biodiversity offset contracts on arable land: a choice experiment study with farmers. Eur. Rev. Agric. Econ. 45 (4), 553-582.

Villamayor-Tomas, S., Sagebiel, J., Rommel, J., Olschewski, R., 2021. Types of collective action problems and farmers willingness to accept agri-environmental schemes in Switzerland. Ecosyst. Serv. 50, 101304.

Vorlauer, T., Falk, T., Dufhues, T., Kirk, M., 2017. Payments for ecosystem services and agricultural intensification: Evidence from a choice experiment on deforestation in Zambia. Ecol. Econom. 141, 95-105.

Waldron, A., Garrity, D., Malhi, Y., Girardin, C., Miller, D., Seddon, N., 2017. Agro-forestry can enhance food security while meeting other sustainable development goals. Trop. Conserv. Sci. 10, 1940082917720667.

Wang, M., Rieger, M.O., Hens, T., 2016. How time preferences differ: Evidence from 53 countries. J. Econ. Psychol. 52, 115-135.

Wells, M.P., McShane, T.O., 2004. Integrating protected area management with local needs and aspirations. AMBIO: A J. Hum. Environ. 33 (8), 513-519.

White, D., Holmann, F., Fujisaka, S., Reategui, K., Lascano, C., 2001. Will intensifying pasture management in Latin America protect forests-or is it the other way round? In: Agricultural Technologies and Tropical Deforestation. CAB International, Wallingford UK, pp. 91-111.

Wunder, S., Börner, J., Ezzine-de Blas, D., Feder, S., Pagiola, S., 2020. Payments for environmental services: Past performance and pending potentials. Annu. Rev. Resour. Econ. 12, 209-234.

WWF, 2018. Colombia's chiribiquete now world's largest tropical rainforest national park. Recuperado de: https://wwf.panda.org/wwf_news/?3330412/Colombias-Chiribiquete-now-worlds-largest-tropical-rainforest-national-park.