Copyright © 2019 by the author(s). Published here under license by the Resilience Alliance. Rueda, X., M. A. Velez, L. Moros, and L. A. Rodriguez. 2019. Beyond proximate and distal causes of land-use change: linking Individual motivations to deforestation in rural contexts. Ecology and Society 24(1):4. https://doi.org/10.5751/ES-10617-240104 Research, part of a Special Feature on Seeking sustainable pathways for land use in Latin America Beyond proximate and distal causes of land-use change: linking Individual motivations to deforestation in rural contexts Ximena Rueda 1, Maria Alejandra Velez 1, Lina Moros 1,2 and Luz Angela Rodriguez 3 ABSTRACT. Most of the literature on the causes of tropical deforestation has focused on the proximate and distal causes. However, research exploring the psychological drivers of deforestation, i.e., motivations, is still scant despite being crucial to understand the processes of land-use change and individual decision making within social-ecological systems. We studied the combined effect of structural and individual causes of deforestation, with particular emphasis on motivations, for a sample of rural households in Colombia’s foremost tropical deforestation frontier. We implemented a new instrument based on self-determination theory to measure five different types of motivations to protect the forests: intrinsic, guilt/regret, social, extrinsic motivations, and amotivation (lack of motivation). Our findings show that, controlling for the structural and household drivers widely identified in the deforestation literature, intrinsic motivations positively correlate with less self-reported deforestation. Also, amotivated people and those with extrinsic motives, such as expected payments for conservation, are more likely to deforest. Our results show that motivations can explain variation in land-use decisions and thus should be considered when designing, implementing, and evaluating conservation policies aiming to halt deforestation. Key Words: agricultural expansion; Colombia; deforestation frontier; land-use change; motivations INTRODUCTION Ruiz-Mallén et al. 2015). However, to our knowledge, no study Tropical deforestation continues to be a major concern in the has attempted to determine the interaction between developing world. About 129 million ha of forest was lost between proenvironmental motivations of rural dwellers and stated land- 1990 and 2015 mainly in the tropical regions of South America use change. Understanding baseline motivations of rural and Africa (Food and Agriculture Organization of the United inhabitants and forest users is important given that the design of Nations 2015). Identifying distal and proximate drivers of tropical policy instruments aiming at reducing deforestation may have deforestation has been one of the main concerns of the literature unintended effects on motivations, which, in turn, can affect on land-use change (Kaimowitz and Angelsen 1998, Lambin et conservation outcomes and behaviors (see, e.g., Rico García- al. 2001, Geist and Lambin 2002, DeFries et al. 2010). Most of Amado et al. 2013, Moros et al. 2017). these studies focus on both the behavior of landholders and the We studied the effect of motivations on the stated decision to structural processes that affect such behavior, based on objective deforest for a group of farmers, controlling for individual and or observable factors. These studies have explored the structural variables. We used the questionnaire designed by Moros demographic, economic, institutional, and cultural factors that et al. (2017), based on the self-determination theory (SDT) lead agents to clear forests (Geist and Lambin 2002). Such proposed by Ryan and Deci (2000), to explore the different types research has been developed at different scales: from the of motivations and their effect on the reported decision of farmers household or firm level to regional, national, and global scales, to deforest. Using a set of different types of motivations allows and using analytical, empirical, or simulation models (Kaimowitz us to capture more precisely the specific reasons behind preserving and Angelsen 1998). forests and, as such, design and improve programs and policies Although civic values and behavioral factors have been described suited to particular types of individuals. We applied a survey to as important drivers of deforestation (Geist and Lambin 2001), a group of 64 farmers in the rural area of Florencia, the capital little systematic effort has been made to link motivations, i.e., the city of Caquetá, one of the regions with the highest rates of set of reasons to protect the forests, to the observed patterns of deforestation in Colombia. Our findings show that, controlling land-use change in tropical settings. Other fields of study, such as for socio-economic factors, intrinsic motivations negatively environmental psychology, have focused on understanding the correlate with self-reported deforestation. That is, more relationship between proenvironmental motivations and intrinsically motivated individuals report less deforestation. Also, behaviors, mostly in urban settings, e.g., car use, energy saving, amotivated individuals are more likely to deforest, as well as and recycling. In general, this body of literature has established people concerned with external motivations such as expected that both contextual and individual factors such as attitudes, payments for conservation. Our findings contribute to the motivations, goals, values, beliefs, concerns, and self-identity discussion of land-use change beyond the proximate and distal influence behaviors that benefit the environment (Steg and Vlek or underlying causes of deforestation usually considered in the 2009). Also, the literature on environmental studies and policy literature. Although our empirical strategy does not allow us to has attempted to establish the effect of conservation programs on claim causality, our results open an urgent and unexplored motivations (Rico García-Amado et al. 2013, Moros et al. 2017) research agenda and raise considerations for the implementation and drivers of engagement in particular conservation practices of conservation policies, such as payment for ecosystems services, or programs (Greiner et al. 2009, Souto et al. 2014, Greiner 2015, aimed at changing behavior. In particular, they highlight the 1 School of Management, Universidad de los Andes, Bogotá, Colombia, 2Institute of Environmental Science and Technology (ICTA), Universitat Autònoma de Barcelona, 3School of Environmental and Rural Studies, Pontificia Universidad Javeriana, Bogotá, Colombia Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ importance of considering the impacts on motivations when urbanization. Thus, instead of focusing exclusively on the local designing, implementing, and evaluating the effects of such types causes of land change, newer streams of research have identified of policies to avoid possible crowding-out effects (Agrawal et al. the importance of its distant drivers. Currently, the main agents 2015). of deforestation are private agricultural enterprises, i.e., well- capitalized ranchers, farmers, and loggers, that have led the LITERATURE REVIEW activity, in particular in Southeast Asia and the Amazon basin, Evolution of the drivers of land-use change to satisfy global markets (Rudel et al. 2009, DeFries et al. 2010). There is a large body of literature that attempts to unravel the It is the demand for agricultural products in distant urban and causes of land-use change (Turner et al. 2007). Original studies international locations that is fueling current deforestation on tropical deforestation found population to be the main (Leblois et al. 2017). Globalization affects land-use change explanatory variable, either because of the so-called frontier directly by increasing the opportunity cost of forested land. Also, theory, in which entrepreneurs and smallholders colonized the the “geographies of trade” that interconnect distant social- frontier in search of profitable economic activities, or because ecological systems cause leakage or indirect effects on land use they were pushed out of the agricultural frontier and into within and across countries as a consequence of local or national marginal lands, i.e., the “immiseration” theory (Rudel and Roper interventions (Meyfroidt et al. 2013). For example, policies to 1997). When more variables were included, population started to promote sustainable land use in a specific site may have lose its prominence as the main driver of deforestation, and other unintended effects by displacing land change to less regulated elements such as road building, state-sponsored development places. These studies focus on global-scale social processes that programs, and credit availability began to combine with are shaping decisions on the land. Urbanization, for instance, population to explain deforestation in areas such as the Brazilian increases the demand for agricultural commodities, such as Amazon (Pfaff 1999). vegetable oils, widely used in processed food. This demand is manifested in price transmission to local land users, increasing The causes of deforestation have since been classified as direct or the profitability of soy or palm cultivation in tropical regions. proximate and distal or underlying. According to Geist and Because the main agents of land change are companies and Lambin (2002), proximate causes include local-level human entrepreneurs, decision making is mainly related to extrinsic, activities such as agricultural expansion or infrastructure profit-seeking motives. In some instances, reputational risks for development that affect land-use decisions and, therefore, forest branded companies might deter them from engaging in cover. Underlying driving forces are social processes, such as deforestation, either direct or indirectly through their providers, human population dynamics or agricultural policies that support but the underlying motives are profit driven (Mayer and Gereffi the proximate causes. Underlying causes can operate at a local, 2010). national, or global level. As the role of international markets becomes more prominent in Empirical studies of drivers of deforestation have been explaining land transformation, agricultural intensification has summarized in two seminal meta-analyses (Kaimowitz and been suggested as a strategy to reduce the use of forested land for Angelsen 1998, Geist and Lambin 2002). Kaimowitz and agriculture while satisfying agricultural demand. However, Angelsen (1998) identify farm-level explanatory factors of empirical analysis has shown a weak or nonexistent relationship deforestation, such as wages, availability of off-farm employment, between intensification and land sparing for conservation (Phelps and the price of agricultural inputs. As wages increase, off-farm et al. 2013). Intensification can increase future agricultural land employment is more abundant, and the price of agricultural rents, which creates incentives to clear more land for agricultural inputs rises, as the pressure on forests decreases. Geist and Lambin expansion, particularly when driven by market pressures (Phalan (2002) separate the causes of deforestation into biophysical, et al. 2011, Byerlee et al. 2014, Carrasco et al. 2014). Thus, whether technological or economic, institutional, demographic, and farmers have market outlets for their crops, especially in the cultural. Their analysis shows that different combinations of international market, could be a significant driver of proximate and underlying driving forces determine tropical forest deforestation. All of these studies are based on the assumption decline worldwide (Lambin et al. 2001). The basic assumption of that only extrinsic, instrumental reasons explain farmers’ these studies is that land users behave like rational agents who behavior toward deforestation. We take into account some of respond to price incentives, given by the opportunity cost of these variables to predict self-reported deforestation but add a deforestation. Within such models, only instrumental reasons more complex and wider array of motivations for engaging in guide land-use decisions. In some cases, other considerations, such deforestation in the forest frontier. as values and beliefs, are mentioned, but how they shape people’s behaviors is not theorized (Geist and Lambin 2002). Motivations and environmental behavior Although behavioral factors have been described as important in More recent studies have reported that clearly established driving deforestation (Geist and Lambin 2001), little systematic property rights are associated with less deforestation (Robinson effort has been made to link intrinsic motivations, i.e., the set of et al. 2014). Additionally, because deforestation decisions depend noninstrumental reasons to protect the forests, to the observed on expected profits, neighborhood deforestation influences the patterns of land-use change. One stream of literature, particularly probability of deforestation in a particular parcel. There are, in environmental psychology, has studied the role of values, therefore, patterns of spatial interactions in deforestation beliefs, and attitudes to understand the determinants of urban (Robalino and Pfaff 2012). proenvironmental behavior, e.g., energy saving, recycling, and Drivers and patterns of deforestation also evolve over time composting. In addition, studies applied to rural contexts focus because of changes in international markets, globalization, and on the effect of attitudes on environmental practices and Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ conservation behavior, but few efforts have been made to link such extrinsic motivation. For example, social interventions perceived psychological aspects to land-use change, which is our objective. as controlling have an effect on individuals’ feelings of autonomy, thus negatively affecting intrinsic motivations to perform a task. Different theories are used to model the determinants of proenvironmental behavior (e.g., theory of reasoned action: As described by Moros et al. (2017), empirical analyses in Fishbein and Ajzen 1975; values-norms beliefs: Stern 2000; theory environmental psychology and environmental studies commonly of planned behavior: Ajzen 1991, among others). We use SDT rely on self-reported responses to questionnaire items to identify (Ryan and Deci 2000) as our framework because of its predictive both the frequency of environmental practices and the patterns power in explaining proenvironmental behaviors (Festré and of attitudes, beliefs, and motivations that drive such reported Garrouste 2015). SDT, instead of conceiving motivations as a behavior (Steg and Vlek 2009). Ruiz-Mallén et al. (2015) and dichotomous phenomenon of motivated and amotivated people, Souto et al. (2014), for example, explored engagement in proposes a model with six types of motives guiding human community-based conservation activities, while Greiner and behavior: intrinsic, integrated, introjected, identified, external, colleagues (Greiner et al. 2009, Greiner 2015) analyzed farmers’ and amotivated. People are intrinsically motivated if they protect willingness to participate in biodiversity contracts. These studies forests because it is inherently interesting or enjoyable, whereas make use of semistructured interviews, surveys based on Likert people who are amotivated lack an interest or see no reason to scales, and focus groups to capture individuals’ reasons to do so. In between, there are four different types of extrinsic participate in conservation programs. However, this body of motivations that vary in their degree of autonomy and literature refers to attitudes, motivations, social norms, emotions, internalization. Autonomy is understood as the psychological and perceptions as if they were equivalent, which limits its need for “the experience of choice” (Moller et al. 2006:104) and comparability, because no unified language is used. internalization, as “a proactive process through which people Nonetheless, despite being measured and labeled in different ways, transform regulation by external contingencies into regulation by most of the studies in this field report two broad categories: internal processes” (Deci et al. 1991:328). It is important to instrumental versus noninstrumental reasons. Instrumental understand extrinsic motivation because most of the tasks reasons include economic benefits derived from ecosystem individuals undertake are not inherently valued or enjoyable. services or social rewards such as recognition or reputation, The least autonomous form of extrinsic motivation is external whereas noninstrumental reasons include a sense of moral duty, regulation in which an individual’s behavior is motivated by an respect for nature and animals, or stewardship ethics (Rode et al. expected external reward or punishment and there is no 2015). In general, strong noninstrumental reasons favoring internalization process involved. Introjected regulation is conservation positively correlate to proenvironmental behavior somewhat more autonomous given that feelings of guilt, anxiety, (Lynne et al. 1988, Beedell and Rehman 1999, Ryan et al. 2003, or pride, among others, drive behavior. In other words, Price and Leviston 2014), whereas strong instrumental reasons introjection occurs when behavior is motivated by self-esteem toward profit maximization affect proenvironmental behavior contingencies and controlled by feelings of pressure to maintain either negatively or not at all (Ryan et al. 2003, Greiner et al. or enhance self-esteem and the feeling of worth. A more 2009). autonomous form of extrinsic motivation is identified In both environmental psychology and environmental studies, motivation, which occurs when an individual identifies with the different types of motivations are reported to guide personal importance of the behavior “and has thus accepted its proenvironmental behavior. However, most of the studies applied regulation as his or her own” (Ryan and Deci 2000:62), but the to rural contexts focus on the effect of attitudes/motivations in drivers of action continue to be somewhat external to the self. The environmental practices and conservation behavior (Gelcich et al. most autonomous form of extrinsic motivation is integrated 2008, Greiner et al. 2009, Greiner 2015). To our knowledge, we motivation, in which identified motivations have been fully are the first to link such psychological aspects to self-reported assimilated to the self. Integrated and intrinsic motivations are land-use change. both autonomous forms of motivations; however, they differ in the sense that under integrated motivation the action is motivated METHODS by its instrumental value, whereas under intrinsic motivation the action is motivated by its inherent value. In this framework, there Site description is no ideal sequence or “evolution” of motivations, or, in the The study was carried out in the region of El Caraño within the authors’ own words, “we do not suggest that the continuum rural area of Florencia, an Amazonian city in southwestern underlying types of extrinsic motivation is a developmental Colombia. The study area is part of the Amazon foothills, a region continuum” (Ryan and Deci 2000:62). that links the Andes with the Amazon basin, a key biodiversity ecotone and a major area of deforestation. Our study involves Three types of feelings determine how people move through participants from 13 rural districts from El Caraño, a colonization different types of motivations: autonomy, competence, and social frontier where displaced families from other regions in Colombia relatedness. Any external reward that affects one of these three have moved over the last 40 years. These villages were included in “moderators” is expected to affect intrinsic motivations. the study because they are located in the headwaters of the Hacha Interventions that support autonomy and feelings of self-efficacy, River, the main water source for the 170,000 urban inhabitants of i.e., feeling capable or as having the skills to perform an activity, the city of Florencia, and because of the significant area of and that enhance or activate one’s relationship to others as well remaining forest they harbor (Moros et al. 2017). as one’s self-image (social relatedness) are expected to maintain intrinsic motivation for a task and more self-determined types of Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Table 1. Description of items and the correspondent statements of motivations survey. Source: Moros et al. (2017). Motivation Type Survey Statements Intrinsic 1. “I enjoy when I do not clear the forest.” 2. “I see myself as someone who does not clear the forests.” Guilt or regret 1. “I would feel guilty if I clear the forests.” 2. “I would regret if I clear the forests.” Social 1. “I would be criticized by my neighbors if I clear the forests.” 2. “Significant others would be upset if I clear the forests.” Extrinsic, payments “I would take care of forests only if I am paid to do so.” Extrinsic, fines “I do not cut down the forests because of fear [of] fines that might be imposed by environmental authorities.” Amotivation “I do not see what I can get from protecting the forests. There is no point in doing so.” Agriculture and cattle ranching have driven forest clearing in El We designed a logit model in which we explain the decision of Caraño (Vásquez 2015), and the area is populated by more than farmers to clear forest as a function of different social, economic, 100 families, mainly smallholders who cultivate staple foods biophysical, and motivational factors, following those usually combined with coffee, sugarcane, and pastures. There are few measured in the literature. The biophysical variables linked to large farms; average farm size in the area is 35 ha, and the median deforestation are slope, altitude, and distance to markets. Farmers is 15 ha (Vélez et al. 2016). are expected to clear forest for agriculture if the land is suitable for cultivation in terms of slope and altitude, and if they are close In our fieldwork, farmers mentioned deforestation as their main to markets, which reduces transaction costs. The economic environmental concern caused mainly by conversion to characteristics include self-reported measures of household subsistence agriculture. There are no official records of income; farm size, as a proxy for wealth; and whether households deforestation rates for our study area, but, based on available engage in commercial cultivation, i.e., coffee, sugar, and cattle forest cover data for the 13 rural districts that were part of the ranching. We expect wealthier households and those engaged in study zone, 7879 ha of the 10,588 ha (74%) is still forested. Almost commercial activities to have engaged in forest clearing. As for 19% of the forest in the zone is highly vulnerable to deforestation the social characteristics of the household, we include the time of because of its proximity to roads and to the agricultural frontier. residence on the farm; household size; a binary variable relating On average, forests cover 50% of the farms, whereas the rest is to land tenure, i.e., property title; age of the household head; and allocated to pastures; cash crops, such as coffee and sugarcane; a binary variable for participation in local, voluntary and subsistence crops. The villages in the study area are located organizations. We ran a comparison of these variables between at higher altitudes and are adjacent to two national protected the samples to identify variables that could influence the decision areas (Vélez et al. 2016). to cut forest. For this, we used a two-sample Fligner-Policello Smallholders live in poverty: Incomes are low, access to education robust rank-order test for ordinal or interval data and chi-square is limited, and they possess few or no assets. Average household test for categorical data. The two-sample Fligner-Policello robust size is 4 people, and migration patterns have been very dynamic: rank-order test is an alternative of the Wilcoxon-Mann-Whitney People arrived in the region 11 years ago, on average, but there is test that assumes neither normality, nor equal variances, nor equal a large dispersion in the data, with some households having distribution (Feltovich 2003). resided in the area for more than 40 years, and others having Finally, we rely on the questionnaire developed by Moros et al. resided there for just a few months. Fewer than 40% of households (2017) to measure motivations among rural dwellers to protect declare that they have a legal land title (Vélez et al. 2016). the forest. This questionnaire follows SDT and Pelletier et al.’s Data and variables (1998) motivation toward the environment scale. The survey Our source of data is a household survey conducted by Vélez et contains 9 statements intended to measure 5 different types of al. (2016) between June and September 2016 in 13 villages. reasons to protect the forests: intrinsic, guilt/regret, social, Although we tried to contact all households in the study area, we extrinsic motivations, and lack of motivation (see Table 1 for a reached only 64 respondents, equivalent to approximately 60% of summary of survey statements). We would like to point out that household heads in the villages, according to our own field the original motivation questionnaire was made up of 24 items. assessment because there are no recent census data for the region. The 9 items we used to capture each type of motivation are those Dwellers are mainly mestizo peasants, from several regions in that after conducting a factor analysis, had a factor loading of Colombia that have been forced to leave more productive rural above 0.55 as suggested by Hair et al. (2009). The survey, designed areas and move to this agricultural frontier. and implemented in Spanish by native speakers, used a 4-point bipolar Likert scale to capture variations in the motivations to In the survey, we asked about motivations to protect forests, socio- protect forests. The scale is symmetric, ranging from 1 (totally demographics, productive practices, perceptions of environmental disagree) to 4 (totally agree). Items were presented in the same problems, existing forms of social capital, and land-use history order each time. Although we did not control for order effects, (see Appendix 1 for full questionnaire in Spanish). The data items were spread through the entire survey, thus reducing the helped us establish whether farmers have cleared forests since their risk of consistency across responses in relation to the motivations. arrival in the region and which socio-demographic and economic For intrinsic, guilt/regret, and social motivations, we created conditions might have influenced that behavior. Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ indexes of responses giving equal weights to each item considering Table 4 reports a correlation matrix showing that, in fact, four that, to our knowledge, there are no theoretical reasons to impute correlations are statistically significant. Intrinsic and guilt-related different weights to particular items. motivations are, as expected by SDT theory, positively correlated. Payments for conservation, a specific type of extrinsic motivation, Following SDT, we expect individuals with higher levels of is negatively correlated with intrinsic and guilt motivations, i.e., intrinsic, guilt, and social motivations to be less likely to clear the the two types of most internalized and autonomous motivations. forests because they have internalized the importance of taking We found an unexpected significant correlation between fines and care of the forests either because of pleasure, i.e., intrinsic social motivations, which are positively correlated, meaning that motivations; self-approval, i.e., guilt- and regret-related people who reported fines to be an important motive for forest motivations; or social approval, i.e., social motivations. Further, conservation also reported peer pressure from neighbors and we expect intrinsic motivations to be stronger predictors of forest relatives as an important driver for the task. We also calculated protection compared with less self-determined types of the variance inflation factor for these variables to analyze the motivations (guilt, regret, social, or external) because, as severity of multicollinearity. We found that all motivation mentioned previously, intrinsic motivations have been shown to coefficients are inflated by an order of 4 or more, indicating the increase proenvironmental behaviors (De Groot and Steg 2010). impossibility of combining all the variables in the same model. For extrinsic motivators, such as potential payments and fines, Thus, to have stable and reliable beta coefficients, we first test the we expect a positive relationship with deforestation because, for different motivations independently in models (2) to (7) because these individuals, taking care of forest is contingent on external it is in our interest to disentangle the differences between types of rewards or punishments, i.e., proenvironmental behavior is not motivations and deforestation behavior. However, as a robustness internalized. In this line of reasoning, we expect amotivated test, in Table 5, we repeat the models in Table 3 with different individuals to be the most likely to deforest when compared with combinations of motivations. In each version, models (8) to (13), all other motivational types because this type of motivation is the we include those motivations that are not significantly correlated least autonomous and self-determined. with each other, and results are consistent. We do not include a RESULTS model with a motivation index, because our interest, beyond asserting the value of motivations in decision making, is to find Descriptive results out which of them are significantly associated with specific We used two nonparametric tests, the two-sample Fligner- behaviors. By presenting an aggregate index, we would lose that Policello robust rank-order test for ordinal or interval data and information. SDT theory precisely argues the relevance of the chi-square test for categorical data, to explore whether understanding motivation as a continuum and not as an households that had cleared forests since their arrival at the farm aggregated dichotomous phenomenon. In fact, policy (33% of respondents) were significantly different from their instruments may affect each motivation type differently (see counterparts (Table 2). We observed that the two samples are Moros et al. 2017). similar in most biophysical and productive characteristics, except that farms that report having cut down forests are larger, have Variables commonly reported in the literature as drivers of more land in forests, and are farther away from main roads than deforestation, such as farm size, altitude, distance to nearest road, farms that have not been deforested. Also, the percentages of and plot slope, do not affect the likelihood of cutting down forests sugarcane growers and cattle ranchers are significantly higher for in our study, nor do socio-demographic variables, such as age, those households that have cut down forests. The households that education, or household size, predict deforestation. Nonetheless, have deforested are significantly more dependent on crop-related households that report having sugarcane on their farm are more income than their counterparts. Demographic characteristics likely to deforest across models. Likewise, households farther such as education, household size, and age of the household head away from main roads are more likely to have cut down forests are not significantly different between the two samples. Only the since their arrival to the plot. For our particular interest, those time of residence is different, with households that have engaged heads of households who report more intrinsic motivations to in deforestation having resided in the region for longer. Regarding protect forests are less likely to deforest, whereas those that report motivations, intrinsic and social motivations are, as expected, payments as the only reason for forest protection are more likely significantly lower among heads of households that report having to cut down forests. This result is robust to different specifications, cut down forests. even those that include different motivations simultaneously. Heads of households lacking any motivation to protect forests Logistic model are also more likely to deforest. However, the amotivated variable We built a logistic model to assess the role of different variables loses significance when we include other types of motivations in in predicting the binary decision of having cut down forest in the the same model. None of the other motivations, i.e., guilt, social, past (has cut down forest = 1; has not cut down forest = 0). We or fines, significantly explain having cut down forest areas in any report odds ratios for 8 models in Table 3. Model (1) includes of the models reported. biophysical, socio-economic, and income source variables. Models (2) to (7) include a different type of motivation in each DISCUSSION regression. We do not report a model including all types of We move beyond the proximate and distal causes of land-use motivations at the same time because, although they capture change and consider the effect of individual motivations on different types of motives, some of them are correlated causing deforestation in the region with the highest deforestation rates in multicollinearity. This correlation implies that each motivation Colombia. Controlling for socio-economic factors, we find that coefficient in models (2) to (7) could be capturing other motives. intrinsic motivations negatively correlate with self-reported deforestation. In addition, amotivated and extrinsically Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Table 2. Descriptive characteristics of farmers. Note: Asterisks represent differences between groups, using two-sample Fligner-Policello robust rank-order test for ordinal or interval data and chi-square test for categorical data. ***P < 0.01, **P < 0.05, *P < 0.1. Standard errors in parentheses. Households that Have Cut Households that Have P value Forests Not Cut Forests N=21 N=43 Biophysical and productive characteristics of the farm Size of the farm (hectares) 30.14** (25.82) 25.55 (42.21) 0.0334 † Size of the productive area (hectares) 8.91 (6.70) 9.73 (18.65) 0.1562 Proportion of productive area relative to total area (%) 29.90 (18.09) 32.47 (23.44) 0.4093 Size of area in forest (hectares) 13.26** (17.69) 10.66 (19.08) 0.0375 Proportion of area in forests relative to total area (%) 39.66* (22.12) 31.66 (23.36) 0.0756 Proportion of area in forests/stubble relative to total area (%) 54.41 (17.48) 59.32 (23.40) 0.1570 Altitude (meters above sea level) 1065.05 (149.71) 999.62 (210.83) 0.1263 Slope (degrees) 44.469 (8.399) 41.562 (12.361) 0.1346 Distance to nearest road (meters) 2167.82*** (1910.30) 806.40 (1258.94) 0.0096 Productive uses of the land Coffee (% farmers) 71.42 (46.29) 65.11 (48.22) 0.614 Coffee (hectares) 1.40 (0.973) 1.12 (0.7441) 0.219 Sugarcane (% farmers) 80.95** (40.24) 51.16 (50.58) 0.022 Sugarcane (hectares) 95.25 (1.078) 80.74 (0.969) 0.484 Pasture (% farmers) 85.71 (35.86) 67.44 (47.41) 0.120 Pasture (hectares) 10.19 (13.65) 7.39 (11.73) 0.128 Cattle (% farmers) 61.90** (49.76) 34.88 (48.22) 0.041 Cattle (units) 8.38 (8.99) 9.86 (12.93) 0.372 Socioeconomic characteristics of the household Time living on the farm (years) 15.76** (14.78) 10.93 (13.01) 0.0371 Household size 3.67 (1.65) 3.72 (1.67) 0.4778 Formal property of land (%) 38.09 (49.76) 44.18 (50.25) 0.643 Education level of most educated member of household (years) 6.67 (3.71) 6.66 (3.84) 0.4622 Education level of the household head (years) 4.09 (3.88) 3.61 (3.25) 0.4454 Age of the household head (years) 50.05 (15.03) 50.70 (13.58) 0.4506 Sex of the household head (% male) 80.95 (40.23) 81.4 (39.37) 0.966 ‡ Monthly income (US$) 142.95 (120.53) 178.15 (143.66) 0.1842 Preference for their offspring to become farmers (%) 65.00 (48.94) 68.29 (47.11) 0.8357 Social capital and social networks Household head member of productive or community organization (%) 19.04 (40.24) 16.27 (37.35) 0.783 People in this rural district (1-3 scale): 2.14 (0.654) 2.23 (0.751) 0.2993 Help each other a lot (%) 28.57 41.86 Do not help each other that much (%) 57.14 39.53 Do not help each other at all (%) 14.29 18.6 Income sources (% farmers) Crops 80.95 (40.24) 65.11 (48.22) 0.193 Cattle ranching 33.33 (48.30) 18.60 (39.37) 0.192 Wage laborer 57.14 (50.71) 53.48 (50.47) 0.783 Self-employed 0 (0) 6.98 (25.78) 0.215 Employee 9.5 (30.08) 13.95 (35.06) 0.615 Diversified income source 95.24 (21.82) 72.09 (45.38) 0.1351 Main income source (% farmers) Crops 57.14* (50.71) 32.55 (47.41) 0.060 Cattle ranching 14.28 (35.86) 4.65 (21.31) 0.177 Wage laborer 9.52 (30.08) 25.58 (44.15) 0.134 Self-employed 0 (0) 4.65 (21.31) 0.315 Employee 4.76 (21.83) 9.30 (29.39) 0.525 Motivations (scale 1-4) Intrinsic 2.85** (0.76) 3.22 (0.69) 0.0306 "I see myself as someone who does not clear the forests." 2.66* (1.02) 3.09 (0.95) 0.0639 "I enjoy when I do not clear the forest." 3.04* (0.74) 3.34 (0.78) 0.0644 Guilt or regret 3.09 (0.77) 3.24 (0.69) 0.2527 "I would feel guilty if I clear the forests." 3.09 (0.89) 3.37 (0.69) 0.1653 "I would regret if I clear the forests." 3.09 (0.94) 3.11 (0.90) 0.4760 Social 2.26* (0.83) 2.62 (0.92) 0.0673 "I would be criticized by my neighbors if I clear the forests." 2.19 (1.17) 2.25 (1.16) 0.4178 "Significant others would be upset if I clear the forests." 2.33** (1.06) 3 (1.09) 0.0108 External, payments "I would take care of the forests only if I am paid to do so." 3.43 (1.07) 3.11 (1.16) 0.1622 External, fines "I do not cut down the forest because of fear [of] fines." 1.95 (1.07) 2.23 (1.21) 0.2396 A-motivation "I do not see what I can get by protecting the forests." 2 (1.18) 1.744 (1.05) 0.2341 † Note that only 25.6% individuals that reported to have not cut forest and 47.6% that reported to have cut forest knew and reported the size of the productive area. ‡ Kolmogorov-Smirnov test of equality of distributions has a P value of 0.415, so there are not significant differences in the distributions of income between the two groups. Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Table 3. Logistic model for binary variable of having cut down forests in the past controlling for biophysical, socio-demographic, and motivation variables. ***P < 0.01, **P < 0.05, *P < 0.1. Standard errors in parentheses. Variables (1) (2) (3) (4) (5) (6) (7) Size of the farm 0.00936 0.0648 0.00768 0.00824 0.0119 0.00740 0.00225 (0.0205) (0.0496) (0.0208) (0.0207) (0.0223) (0.0208) (0.0214) Altitude -0.00174 0.00216 -0.00188 -0.00201 -0.000534 -0.00214 -0.00132 (0.00306) (0.00455) (0.00320) (0.00309) (0.00383) (0.00315) (0.00395) Distance to nearest 0.000575 0.00226* 0.000535 0.000618 0.000404 0.000644 0.000893* road (0.000402) (0.00122) (0.000408) (0.000421) (0.000432) (0.000432) (0.000492) Slope 0.0433 0.267* 0.0507 0.0491 0.0603 0.0537 0.0250 (0.0511) (0.145) (0.0533) (0.0542) (0.0556) (0.0567) (0.0607) Coffee -0.548 -4.188* -0.392 -0.562 -1.175 -0.720 -1.096 (1.069) (2.312) (1.101) (1.058) (1.180) (1.112) (1.263) Sugarcane 2.062* 7.476** 2.119* 2.051* 2.048* 1.959* 2.396** (1.114) (3.371) (1.111) (1.123) (1.146) (1.131) (1.190) Cattle ranching 1.570 7.282* 1.645 1.560 2.443 1.684 1.934 (1.286) (3.912) (1.301) (1.280) (1.556) (1.316) (1.418) Time living on the 0.0165 -0.123 0.0183 0.0153 0.0185 0.0184 0.0419 farm (0.0384) (0.0859) (0.0383) (0.0386) (0.0409) (0.0387) (0.0442) Household size -0.177 0.0158 -0.133 -0.159 -0.141 -0.146 -0.143 (0.276) (0.668) (0.289) (0.276) (0.322) (0.276) (0.308) Formal property -1.205 -1.471 -1.248 -1.094 -1.609 -1.219 -1.563 of land (1.196) (2.000) (1.186) (1.215) (1.317) (1.203) (1.375) Crops main 0.660 -4.144 0.488 0.662 0.883 0.615 0.574 income source (1.026) (2.927) (1.099) (1.028) (1.154) (1.034) (1.218) Income -0.00190 -0.0238 -0.00166 -0.00120 -0.00574 -0.00131 -0.00375 US$ (0.00772) (0.0176) (0.00753) (0.00778) (0.00804) (0.00792) (0.00782) Member of -0.555 6.466* -0.384 -0.815 0.163 -0.728 -0.597 productive or (1.413) (3.684) (1.468) (1.540) (1.531) (1.463) (1.546) community organization Intrinsic -6.354** motivations (2.528) Guilt -0.352 (0.684) Social motivations -0.235 (0.487) External 1.172* motivation, (0.604) payments External -0.248 motivation, fines (0.407) Amotivated 1.262** (0.610) Constant -3.141 -1.427 -2.403 -2.674 -8.890* -2.785 -5.331 (3.130) (7.370) (3.476) (3.253) (5.147) (3.188) (3.937) Observations 50 50 50 50 50 50 50 Table 4. Correlation matrix of motivation variables. P values in parentheses. Intrinsic Guilt Social Payments Fines Guilt 0.4472 (0.0002) Social -0.0555 0.0590 (0.6634) (0.6431) Payments -0.4396 -0.2881 0.1536 (0.0003) (0.0210) (0.2257) Fines 0.0202 0.0521 0.5194 0.1084 (0.8739) (0.6826) (0.0000) (0.3938) Amotivated -0.0972 -0.0883 0.0256 0.1591 0.0567 (0.4448) (0.4876) (0.8411) (0.2092) (0.6565) Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Table 5. Logistic model for binary variable of having cut down forests in the past controlling for biophysical, socio-demographic, and motivation variables. ***P < 0.01, **P < 0.05, *P < 0.1. Standard errors in parentheses. Variables (8) (9) (10) (11) (12) (13) Size of the farm 0.0549 0.0512 0.00460 0.00382 0.00740 0.000555 (0.0579) (0.0643) (0.0221) (0.0219) (0.0264) (0.0257) Altitude 0.00221 0.00181 -0.00141 -0.00161 5.33e-05 -0.000418 (0.00538) (0.00477) (0.00384) (0.00393) (0.00446) (0.00460) Distance to nearest road 0.00277* 0.00296* 0.00110* 0.00121* 0.000683 0.000887 (0.00158) (0.00160) (0.000613) (0.000640) (0.000538) (0.000582) Slope 0.252 0.252* 0.0215 0.0299 0.0700 0.101 (0.158) (0.151) (0.0709) (0.0791) (0.0737) (0.0890) Coffee -4.882* -5.372* -1.633 -2.104 -1.734 -2.469 (2.910) (3.169) (1.494) (1.668) (1.338) (1.577) Sugarcane 8.116** 8.151** 2.265* 2.066* 2.634* 2.460* (3.823) (3.847) (1.232) (1.246) (1.368) (1.303) Cattle ranching 7.460* 7.616* 1.722 2.193 2.682 3.844* (4.172) (4.013) (1.425) (1.585) (1.654) (2.208) Time living on the farm -0.112 -0.0906 0.0382 0.0520 0.0248 0.0491 (0.0922) (0.0965) (0.0473) (0.0503) (0.0477) (0.0495) Household size -0.138 -0.256 -0.160 -0.169 -0.0690 -0.161 (0.758) (0.730) (0.323) (0.319) (0.341) (0.357) Formal property of land -1.584 -2.022 -1.221 -1.615 -1.503 -2.533 (2.237) (2.268) (1.545) (1.564) (1.441) (1.663) Crops main income source -4.113 -3.924 0.865 0.845 0.833 0.640 (3.098) (3.033) (1.317) (1.350) (1.312) (1.359) Income -0.0252 -0.0248 -0.00466 -0.00506 -0.00628 -0.00578 US$ (0.0197) (0.0197) (0.00823) (0.00859) (0.00788) (0.00848) Member of productive or 6.338 5.886 -1.254 -1.311 -0.558 -0.500 community organization (4.142) (4.059) (1.742) (1.682) (1.797) (1.780) Intrinsic motivations -6.430** -6.479** (2.825) (2.859) Guilt 0.645 0.704 (0.932) (0.897) Social motivations -0.0861 -0.432 -0.860 (0.847) (0.572) (0.702) External motivation, payments 1.282* 1.394* (0.760) (0.792) External motivation, -0.438 -0.542 -0.943 fines (0.843) (0.505) (0.646) Amotivated 1.096 1.442 1.474** 1.647** 0.730 1.031 (1.096) (1.413) (0.712) (0.788) (0.645) (0.733) Constant -1.937 -0.785 -6.201 -6.739 -10.04* -11.90* (8.822) (8.467) (4.555) (4.702) (6.101) (6.780) Observations 50 50 50 50 50 50 motivated individuals report more deforestation. These results consistent with the lack of enforcement and legitimacy of local are novel and contribute to understanding the drivers of environmental authorities in the study region. deforestation. One should be cautious about claiming causality with our Our results are important because motivational factors can empirical strategy and results. Our data report farmers’ explain differences in self-reported deforestation even when motivations at a certain point in time. Three months later, they households do not have significant differences in their socio- are asked to report the history of forest clearing at a time when economic characteristics or the biophysical conditions of their deforestation is a fait accompli. It could be argued that farmers farms. Intrinsic motivations prevent deforestation, even in the are just creating a consistent narrative of forest use or that clearing context of an active deforestation frontier. Social motivations and practices determine motivations. In addition, we have no observed guilt did not explain self-reported deforestation. This could be behavior, i.e., actual changes in forest cover, and rely exclusively related to the lack of social cohesion among community members on self-reported practices. Thus, at this point, we can only claim in this ongoing deforestation frontier with low levels of association between our dependent and independent variables. In associativity, very loose or nonexistent kinship ties, and little addition, we acknowledge the small size of our sample and the social capital. Also, external motivations driven by expected fines possibility of some variables becoming significant once we for cutting the forest did not explain deforestation, a result increase it. Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Nevertheless, motivations are significant, and our results open an Ajzen, I. 1991. The theory of planned behavior. Organizational important and unexplored research agenda, and they raise Behavior and Human Decision Processes 50(2):179-211. http://dx. important considerations for the implementation of conservation doi.org/10.1016/0749-5978(91)90020-T policies aiming at behavioral changes such as payment for Beedell, J. D. C., and T. Rehman. 1999. Explaining farmers’ ecosystems services. In particular, our results highlight the conservation behaviour: why do farmers behave the way they do? importance of considering the impacts on motivations when Journal of Environmental Management 57:165-176. http://dx.doi. designing, implementing, and evaluating the effects of such types org/10.1006/jema.1999.0296 of policies to avoid possible crowding-out effects (Agrawal et al. 2015). Further research should move beyond self-reported Byerlee, D., J. Stevenson, and N. Villoria. 2014. Does behavior to actual behavior to establish a causal relationship intensification slow crop land expansion or encourage between motivation and deforestation. deforestation? Global Food Security 3(2):92-98. http://dx.doi. org/10.1016/j.gfs.2014.04.001 The literature on environmental psychology has explored the importance of motivations in determining urban proenvironmental Carrasco, L. R., C. Larrosa, E. J. Milner-Gulland, and D. P. behavior, whereas the experimental economics literature has Edwards. 2014. A double-edged sword for tropical forests. Science explored the effect of economic incentives on individual 346(6205):38-40. http://dx.doi.org/10.1126/science.1256685 motivation and forest conservation behavior with mixed results Deci, E. L., R. J. Vallerand, L. G. Pelletier, and R. M. Ryan. 1991. (Narloch et al. 2012, Moros et al. 2017, Salk et al. 2017). Thus, Motivation and education: the self-determination perspective. empirical studies exploring the effect of different types of Educational Psychologist 26(3-4):325–346. https://doi. motivations as drivers of deforestation are crucial for the design org/10.1080/00461520.1991.9653137 of tailored policies that consider fundamental drivers of individual behavior. Ignoring the role of motivations could affect DeFries, R. S., T. Rudel, M. Uriarte, and M. Hansen. 2010. the expected impact of conservation policies and the durability Deforestation driven by urban population growth and and stability of behavior over time. agricultural trade in the twenty-first century. Nature Geoscience 3(3):178-181. http://dx.doi.org/10.1038/ngeo756 Understanding the role of proenvironmental motivations as drivers of land-use change is the first step in a new research agenda De Groot, J. I. M., and L. Steg. 2010. Relationships between value aimed at designing policies that attempt to change mental models, orientations, self-determined motivational types and pro- via education and communication campaigns, along with environmental behavioural intentions. Journal of Environmental structural changes aimed at improving farmers’ poor social Psychology 30(4):368-378. http://dx.doi.org/10.1016/j.jenvp.2010.04.002 conditions. Motivations can play a crucial role in the success of Feltovich, N. 2003. Nonparametric tests of differences in interventions, especially in areas with low state presence and medians: comparison of the Wilcoxon–Mann–Whitney and limited capacity to enforce traditional command and control robust rank-order tests. Experimental Economics 6(3):273-297. interventions. New policies should aim to work on motivations, http://dx.doi.org/10.1023/A:1026273319211 because they are an important piece of the behavioral change puzzle. Festré, A., and P. Garrouste. 2015. Theory and evidence in psychology and economics about motivation crowding out: a possible convergence? Journal of Economic Surveys 29(2):339– Responses to this article can be read online at: 356. https://doi.org/10.1111/joes.12059 http://www.ecologyandsociety.org/issues/responses. Fishbein, M., and I. Ajzen. 1975. Belief, attitude, intention, and php/10617 behavior: an introduction to theory and research. Addison-Wesley, Reading, Massachusetts, USA. Food and Agriculture Organization of the United Nations (FAO). Acknowledgments: 2015. Global forest resources assessment 2015: desk reference. This research has been possible thanks to the support of the U.S. FAO, Rome, Italy. Agency for International Development (USAID), under the Geist, H. J., and E. F. Lambin. 2001. What drives tropical program “Conservación y Gobernanza en el Piedemonte deforestation. Land Use and Cover Change (LUCC) Report Series Amazónico,” implemented by Fondo Patrimonio Natural. Its No. 4. LUCC International Project Office, University of Louvain, content is the responsibility of the authors and does not necessarily Louvain-la-Neuve, Belgium. reflect the opinions of Patrimonio Natural, USAID, or the U.S. government. We thank Juan Sebastián Rodríguez for his excellent Geist, H. J., and E. F. Lambin. 2002. Proximate causes and research assistance during this project. underlying driving forces of tropical deforestation: tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical LITERATURE CITED locations. BioScience 52(2):143-150. http://dx.doi.org/10.1641/0006-3568 Agrawal, A., A. Chhatre, and E. R. Gerber. 2015. Motivational (2002)052[0143:PCAUDF]2.0.CO;2 crowding in sustainable development interventions. American Political Science Review 109(3):470-487. http://dx.doi.org/10.1017/ Gelcich, S., M. J. Kaiser, J. C. Castilla, and G. Edwards-Jones. S0003055415000209 2008. Engagement in co-management of marine benthic Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ resources influences environmental perceptions of artisanal Pelletier, L. G., K. M. Tuson, I. Green-Demers, K. Noels, and A. fishers. Environmental Conservation 35(1):36-45. http://dx.doi. M. Beaton. 1998. Why are you doing things for the environment? org/10.1017/S0376892908004475 The motivation toward the environment scale (MTES). Journal of Applied Social Psychology 28(5):437-468. http://dx.doi. Greiner, R. 2015. Motivations and attitudes influence farmers’ org/10.1111/j.1559-1816.1998.tb01714.x willingness to participate in biodiversity conservation contracts. Agricultural Systems 137:154-165. http://dx.doi.org/10.1016/j. Pfaff, A. S. P. 1999. What drives deforestation in the Brazilian agsy.2015.04.005 Amazon? Evidence from satellite and socioeconomic data. Journal of Environmental Economics and Management 37 Greiner, R., L. Patterson, and O. Miller. 2009. Motivations, risk (1):26-43. perceptions and adoption of conservation practices by farmers. Agricultural Systems 99(2-3):86-104. http://dx.doi.org/10.1016/j. Phalan, B., M. Onial, A. Balmford, and R. E. Green. 2011. agsy.2008.10.003 Reconciling food production and biodiversity conservation: land sharing and land sparing compared. Science 333(6047):1289-1291. Hair, J. F., Jr., W. C. Black, B. J. Babin, and R. E. Anderson. 2009. http://dx.doi.org/10.1126/science.1208742 Multivariate data analysis. 7th ed. Pearson Education, Harlow, Essex, UK. Phelps, J., L. R. Carrasco, E. L. Webb, L. P. Koh, and U. Pascual. 2013. Agricultural intensification escalates future conservation Kaimowitz, D., and A. Angelsen. 1998. Economic models of costs. Proceedings of the National Academy of Sciences of the tropical deforestation: a review. Center of International Forestry United States of America 110(19):7601-7606. http://dx.doi. Research (CIFOR), Bogor, Indonesia. org/10.1073/pnas.1220070110 Lambin, E. F., B. L. Turner, H. J. Geist, S. B. Agbola, A. Angelsen, Price, J. C., and Z. Leviston. 2014. Predicting pro-environmental J. W. Bruce, O. T. Coomes, R. Dirzo, G. Fischer, C. Folke, P. S. agricultural practices: the social, psychological and contextual George, K. Homewood, J. Imbernon, R. Leemans, X. Li, E. F. influences on land management. Journal of Rural Studies Moran, M. Mortimore, P. S. Ramakrishnan, J. F. Richards, H. 34:65-78. http://dx.doi.org/10.1016/j.jrurstud.2013.10.001 Skånes, W. Steffen, G. D. Stone, U. Svedin, T. A. Veldkamp, C. Vogel, and J. Xu. 2001. The causes of land-use and land-cover Rico García-Amado, L., M. Ruiz Pérez, and S. Barrasa García. change: moving beyond the myths. Global Environmental Change 2013. Motivation for conservation: assessing integrated 11(4):261-269. http://dx.doi.org/10.1016/S0959-3780(01)00007-3 conservation and development projects and payments for environmental services in La Sepultura Biosphere Reserve, Leblois, A., O. Damette, and J. Wolfersberger. 2017. What has Chiapas, Mexico. Ecological Economics 89:92-100. http://dx.doi. driven deforestation in developing countries since the 2000s? org/10.1016/j.ecolecon.2013.02.002 Evidence from new remote-sensing data. World Development 92:82-102. http://dx.doi.org/10.1016/j.worlddev.2016.11.012 Robalino, J. A., and A. Pfaff. 2012. Contagious development: neighbor interactions in deforestation. Journal of Development Lynne, G. D., J. S. Shonkwiler, and L. R. Rola. 1988. Attitudes Economics 97(2):427-436. http://dx.doi.org/10.1016/j.jdeveco.2011.06.003 and farmer conservation behavior. American Journal of Agricultural Economics 70(1):12-19. http://dx.doi.org/10.2307/1241971 Robinson, B. E., M. B. Holland, and L. Naughton-Treves. 2014. Does secure land tenure save forests? A meta-analysis of the Mayer, F., and G. Gereffi. 2010. Regulation and economic relationship between land tenure and tropical deforestation. globalization: prospects and limits of private governance. Global Environmental Change 29:281-293. http://dx.doi. Business and Politics 12(3):1-25. org/10.1016/j.gloenvcha.2013.05.012 Meyfroidt, P., E. F. Lambin, K.-H. Erb, and T. W. Hertel. 2013. Rode, J., E. Gómez-Baggethun, and T. Krause. 2015. Motivation Globalization of land use: distant drivers of land change and crowding by economic incentives in conservation policy: a review geographic displacement of land use. Current Opinion in of the empirical evidence. Ecological Economics 117:270-282. Environmental Sustainability 5(5):438-444. http://dx.doi.org/10.1016/ http://dx.doi.org/10.1016/j.ecolecon.2014.11.019 j.cosust.2013.04.003 Rudel, T. K., R. Defries, G. P. Asner, and W. F. Laurance. 2009. Moller, A. C., R. M. Ryan, and E. L. Deci. 2006. Self- Changing drivers of deforestation and new opportunities for determination theory and public policy: improving the quality of conservation. Conservation Biology 23(6):1396-1405. http://dx. consumer decisions without using coercion. Journal of Public doi.org/10.1111/j.1523-1739.2009.01332.x Policy and Marketing 25(1):104–116. https://doi.org/10.1509/ jppm.25.1.104 Rudel, T., and J. Roper. 1997. The paths to rain forest destruction: crossnational patterns of tropical deforestation, 1975-1990. Moros, L., M. A. Vélez, and E. Corbera. 2017. Payments for World Development 25(1):53-65. http://dx.doi.org/10.1016/ ecosystem services and motivational crowding in Colombia’s S0305-750X(96)00086-1 Amazon Piedmont. Ecological Economics, in press. http://dx.doi. org/10.1016/j.ecolecon.2017.11.032 Ruiz-Mallén, I., C. Schunko, E. Corbera, M. Rös, and V. Reyes- García. 2015. Meanings, drivers, and motivations for community- Narloch, U., U. Pascual, and A. G. Drucker. 2012. Collective based conservation in Latin America. Ecology and Society 20 action dynamics under external rewards: experimental insights (3):33. http://dx.doi.org/10.5751/ES-07733-200333 from Andean farming communities. World Development 40 (10):2096-2107. http://dx.doi.org/10.1016/j.worlddev.2012.03.014 Ryan, R. M., and E. L. Deci. 2000. Intrinsic and extrinsic motivations: classic definitions and new directions. Contemporary Ecology and Society 24(1): 4 https://www.ecologyandsociety.org/vol24/iss1/art4/ Educational Psychology 25(1):54-67. http://dx.doi.org/10.1006/ ceps.1999.1020 Ryan, R. L., D. L. Erickson, and R. De Young. 2003. Farmers’ motivations for adopting conservation practices along riparian zones in a mid-western agricultural watershed. Journal of Environmental Planning and Management 46(1):19-37. http://dx. doi.org/10.1080/713676702 Salk, C., M.-C. Lopez, and G. Wong. 2017. Simple incentives and group dependence for successful payments for ecosystem services programs: evidence from an experimental game in rural Lao PDR. Conservation Letters 10(4):414-421. http://dx.doi.org/10.1111/ conl.12277 Souto, T., J. L. Deichmann, C. Núñez, and A. Alonso. 2014. Classifying conservation targets based on the origin of motivation: implications over the success of community-based conservation projects. Biodiversity and Conservation 23 (5):1331-1337. http://dx.doi.org/10.1007/s10531-014-0659-9 Steg, L., and C. Vlek. 2009. Encouraging pro-environmental behaviour: an integrative review and research agenda. Journal of Environmental Psychology 29(3):309-317. http://dx.doi.org/10.1016/ j.jenvp.2008.10.004 Stern, P. C. 2000. New environmental theories: toward a coherent theory of environmentally significant behavior. Journal of Social Issues 56(3):407-424. http://dx.doi.org/10.1111/0022-4537.00175 Turner, B. L., II, E. F. Lambin, and A. Reenberg. 2007. The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences of the United States of America 104(52):20666-20671. http://dx.doi.org/10.1073/pnas.0704119104 Vásquez, T. 2015. Territorios, conflicto armado y política en el Caquetá: 1900-2010. Facultad de Ciencias Sociales, Universidad de los Andes, Bogotá, Colombia. Vélez, M. A., X. Rueda, L. Moros, A. Link, A. Guerrero, and J. S. Rodríguez. 2016. Paisajes productivos sostenibles: Diseño de un sistema de incentivos para la conservación en el piedemonte Amazónico. Universidad de los Andes and Patrimonio Natural, Bogotá, Colombia. APPENDIX 1. Questionnaire in Spanish (presented in the same order as Table 2) Biophysical and productive characteristics of the farm and productive uses of the land ¿Cuál es el tamaño de este predio? ¿Tiene café? (SI o NO) ¿Qué área tiene de café? unidades ¿A quién le vende el café? ¿Tiene caña? (SI o NO) ¿Qué área tiene de caña? ¿Tiene pastos? (SI o NO) ¿Qué área tiene de pastos? ¿Tiene otros cultivos? (SI o NO) ¿Qué área tiene de otros cultivos? ¿Tiene bosque? (SI o NO) ¿Qué área tiene de bosque? ¿Tiene rastrojo? (SI o NO) ¿Qué área tiene de rastrojo? ¿Tiene ganado? (SI o NO) ¿Cuántas cabezas? Socioeconomic characteristics of the household ¿Hace cuántos años vive en esta finca? ¿Cuál es la situación legal de ESTE predio? (Escoger una) • Tiene escritura y ESTÁ registrada en la oficina de registro • Tiene escritura pero NO está registrada en la oficina de registro • Tiene escritura pero NO SE si está registrada en la oficina de registro • Tiene escritura pero está en proceso de sucesión • Tiene promesa o papeles de compraventa pero no hay escritura • Tiene posesión pero NO se tiene papeles ¿Hasta qué año estudió? ¿Cuál es su edad? ¿Me puede decir cuántas personas viven en esta finca? Para cada persona que vive en la finca preguntar: Sexo Edad ¿Hasta qué año estudió? ¿Cuánto es el ingreso de su hogar mensualmente? (Mencionar que el hogar es la gente que vive en su casa y que el ingreso del hogar es la suma de todos los ingresos que puedan tener los miembros del hogar) ¿Le gustaría que sus hijos se dedicaran a las labores del campo? (si ya tienen una profesión: ¿Le hubiera gustado?) Social capital and social networks ¿Usted es miembro de alguna organización o asociación productiva? ( SI, NO, ¿Cuál?) Usted diría que la gente de esta vereda: • Se ayudan mucho • Se ayudan poco • No se ayudan Income sources ¿Qué cultivo le genera más ingresos a su hogar? (Seleccionar uno) • Café • Caña • Otro • Ninguno ¿Cuáles de estas actividades fueron fuentes de ingreso para su hogar en el último año? [Marcar solo las opciones que fueron fuente de ingreso] • Cultivos en mi predio para vender • Madera o leña en mi predio para vender • Madera o leña del bosque por fuera de mi predio para vender • Ganado en mi predio • Trabajo de jornalero en actividades agrícolas • Por cuenta propia: construcción/industria/comercio/servicios • Por salarios en construcción/servicios domésticos/industria/comercio /servicios/gobierno/educación • Pensiones • Plata que le mandan familiares o amigos desde afuera del país • Programas del gobierno • Otros Motivations Le voy a leer unas frases. Le pido que por favor me diga si está de acuerdo con esa frase usando la siguiente escala: 1 es NO, 2 es UN POCO, 3 es SI, DE ACUERDO, 4 es TOTALMENTE DE ACUERDO. Disfruto cuando no tumbo el bosque Soy del tipo de gente que no tumba el bosque Me arrepiento si tumbo el bosque Me siento culpable si tumbo el bosque La gente más cercana a mí se molestaría conmigo si tumbo el bosque Mis vecinos me criticarían si tumbo el bosque Yo cuido el bosque SOLO si me pagan por hacerlo Yo no tumbo el bosque por miedo a las multas que me pueda poner la autoridad ambiental No veo qué puedo ganar conservando el bosque Deforestation behavior ¿Usted o alguien de su hogar ha tumbado bosque desde que llegó a su predio? (SI o NO)