Ecology and SocietyEcology and Society
 E&S Home > Vol. 22, No. 2 > Art. 37
The following is the established format for referencing this article:
García-Barrios, L., J. Cruz-Morales, J. Vandermeer, and I. Perfecto. 2017. The Azteca Chess experience: learning how to share concepts of ecological complexity with small coffee farmers. Ecology and Society 22(2):37.
https://doi.org/10.5751/ES-09184-220237
Research

The Azteca Chess experience: learning how to share concepts of ecological complexity with small coffee farmers

1El Colegio de la Frontera Sur, Mexico, 2Universidad Autónoma de Chapingo, Campus Chiapas, México, 3Dept. of Ecology and Evolutionary Biology, University of Michigan, 4School of Natural Resources and Environment, University of Michigan

ABSTRACT

Small-scale coffee farmers understand certain complex ecological processes, and successfully navigate some of the challenges emerging from the ecological complexity on their farms. It is generally thought that scientific knowledge is able to complement farmers’ knowledge. However, for this collaboration to be fruitful, the gap between the knowledge frameworks of both farmers and scientists will need to be closed. We report on the learning results of 14 workshops held in Chiapas, Mexico during 2015 in which 117 small-scale coffee farmers of all genders (30% women) and ages who had little schooling were exposed by researchers to a natural history narrative, a multispecies network representation, a board game, and a series of graphical quizzes, all related to a nine-species complex ecological network with potential for autonomous control of the ongoing and devastating coffee rust epidemic that was affecting them. Farmers’ retention and understanding of direct and indirect bilateral interactions among organisms was assessed with different methods to elucidate the effect of adding Azteca Chess gaming sessions to a detailed and very graphical lecture. Evaluation methods that were better adapted to farmers’ conditions improved learning scores and showed statistically significant age effect (players older than 40 had lower retention scores) and gaming effect (lower retention of interactions included in the lecture but not in the game). The combination of lecture and game sessions helped participants better understand cascades of trait-mediated interactions. Participants’ debriefings confirmed qualitatively that they learned that beneficial organisms and interactions occur on their farms, and that gaming was enjoyable, motivating, and critical to grasp complex interactions. Many of the farmers concluded that the outcome of these interactions is not unique and not always in favor of rust control but is context dependent. Many concluded that there are feasible things they can do on their farms, derived from what they learned, to favor potential autonomous pest control.
Key words: autonomous pest control; coffee farmers; ecological complexity game; educational board-game; farmer scientist interaction; learning complexity; shade coffee; trait-mediated interactions

INTRODUCTION

Small-scale shade-coffee producers and community ecologists studying coffee farms share a familiarity with the ecological complexity of diverse agroecosystems. The convergence and complementarity of farmer and academic “ecological knowledge frameworks” is an active field of inquiry with potential to inform research agendas and production processes in landscapes composed of small coffee farms (Vandermeer and Perfecto 2013). In the past three decades, there has been an acknowledgment that the standard top-down “transfer of technology” approach to agricultural research is frequently inadequate, and a transition to participatory-action research became common (Kindon et al. 2007, Blackstock et al. 2010). One of the most conspicuous of these programs is based on the idea of “farmer-to-farmer” knowledge-sharing (Holt-Giménez 2006), evidenced in the experiences of Farmer Field Schools (Damtie et al. 2011). Such knowledge-sharing implicitly and sometimes explicitly deals with complex social and ecological networks. However, most frequently, the ecological complexity has been reduced to simple and generalized recommendations—for example, integrated pest management techniques or nature-friendly and organic production. For coffee farmers, this translates into farmers exchanging knowledge concerning direct applications of materials or techniques aimed at very specific consequences—soil amendments, shade management, and judicious use of herbicides, fungicides, and insecticides (Damtie et al. 2011).

Farmer-to-farmer and farmer-to-scientist experiences in coffee farms have frequently focused on biological pest control (Jarquin et al. 2006). Most cases thus far explored have involved a single pest and one or two (sometimes exogenous) natural enemies (e.g., Gómez et al. 2012). Results of producing and liberating natural enemies into the field, commonly referred to as “classical biological control,” have been modest because of ecological and logistic reasons but also, we argue, due to a knowledge and communication gap between scientists and farmers (Segura et al. 2004, Jarquin et al. 2005). The challenges of building common ecological knowledge increase significantly when dealing with more sophisticated programs of pest management (e.g., Lewis et al. 1997), such as so called “autonomous pest control” (Vandermeer et al. 2010). By their very nature, these more sophisticated programs acknowledge the existence of complex interactions involving many species. In their management practices, experienced farmers deal effectively with certain complex ecological processes, but most pay little attention to, or have little knowledge of, the behavior of the many small inconspicuous organisms that may be key to the operation of autonomous pest control, let alone those forces that indirectly, but significantly, relate to pest outbreaks (Perfecto and Vandermeer 2015). Conversely, ecologists committed to unraveling the details of pest management in coffee frequently lack the knowledge and tools to develop effective ways of incorporating their frameworks and findings into farmer-to-farmer and other participatory experiences in ways that help farmers (a) continuously update their management strategies based on a deep understanding of the specific case, and (b) develop better insights about the ecological complexity of other social-ecological processes that they must navigate (García-Barrios et al. 2016).

We report on the results of a series of workshops held in the Sierra Madre de Chiapas, Mexico during 2015 in which small-scale coffee farmers of all genders (30% women) and ages who had little schooling were exposed to (a) a natural history narrative, (b) a multispecies network representation and graphical analysis, and (c) a board game, all related to a complex ecological network with potential for autonomous control of the ongoing and devastating coffee rust epidemics (Hemileia vastatrix) affecting them. Pre- and post-game graphical quizzes were administered to evaluate the learning process that occurred through these activities. The basic ecological interactions of the nine-species network have been extensively researched empirically and theoretically by Perfecto and Vandermeer, with collaboration from other scholars and farmers (Vandermeer et al. 2010, Perfecto et al. 2014) as part of a broader coffee agroecology project (Perfecto and Vandermeer 2015 ). The game Azteca Chess—designed by the authors and named after the key ant species Azteca sericeasur—has been successfully tested with students for playability, engagement, and learning (García-Barrios et al. 2016).

Our objectives are to (a) share our workshops and “gaming methodology” with small-scale farmers as a resource to be added to more comprehensive and long-term field-based educational processes, (b) explain why and how our network-learning evaluation tools had to be better adapted to farmers’ conditions during the process, (c) describe (in their and our terms) what farmers learned from this experience, and (d) define to what extent the gaming sessions made a difference in being able to understand and remember the network’s interactions and deduce some of its consequences for autonomous pest management.

METHODS

The Azteca autonomous pest management network

We provide a stylized and brief description of the Azteca network. For more details and ecological depth, see Vandermeer et al. (2010), Perfecto and Vandermeer (2015), and García-Barrios et al. (2016). Fig. 1 depicts a network of empirically verified interactions occurring among organisms on shade-coffee bushes. This is the network that has the potential to aid in the control of the coffee rust fungus. The sessile coffee scale insect, Coccus viridis, serves as alternative prey/host to the white halo fungus Lecanicilium lecanii, and thus facilitates the latter’s predatory action on the coffee rust. However, the scale is heavily consumed by the adults and larvae of a lady beetle, Azya orbigera, and by a group of parasitoid wasps (in Fig. 1 represented by the “scale-killer wasp”). But a tree-nesting ant (Azteca sericeasur; Azteca hereafter) protects scales in exchange for the honey dew they extrude. Azteca patrols scale colonies and scares away all but the beetle’s larva, which is protected from the ants by the waxy filaments that cover its body. The beetle larva is controlled by parasitoid wasps (mainly Homalotylus shaviakane, plus other wasps in the family Encyrtidae; in Fig. 1 represented by the “larvae-killer wasp”). This complementary ant/wasp protection of the scale is compromised by (a) the ant itself, since the Azteca ant does not discriminate and also scares away larvae-killer-wasps, and (b) the parasitoid fly, Pseudacteon lascinosus (in Fig. 1 represented by the “head-hunting phorid fly”), that attacks Azteca ants when they are moving and triggers a temporary halt in the patrolling activity of the ant as a defense mechanism. The reduced activity of the ant when the parasitoid flies are present provides a window of opportunity for the adult lady beetle to oviposit under the scales (and also eat scales), and for the scale-killer and larvae-killer wasps to approach their hosts. Thus, we see two main types of interaction: predatory (X consumes Y), and trait-mediated (Z’s behavior modifies X’s behavior, which changes X’s predatory efficiency over Y). Various levels of interaction occur. In the first level, X harms Y by eating, parasitizing, or scaring it; in the second level, Z benefits Y by harming X, an enemy of Y; and finally, a higher level that is represented by the cascading effects from levels 1 and 2. The predatory and trait-mediated interactions that cascade through different pathways from the fly to the coffee scale can ultimately lead to one of two attractors (local exuberant coffee scale colonies and local coffee scale extinction). The theoretical and empirical relations between coffee scale and rust dynamics are even more complex, and are explored in Vandermeer et al. (2014) and McCook and Vandermeer (2015).

Azteca Chess

The features and rules of this board game are thoroughly described in García-Barrios et al. (2016). We explore its use as a learning and communication tool between farmers and researchers. Nevertheless, in Appendix 1, we provide a graphic summary of the game and links to the manual, for the reader’s convenience. In Fig. 2, we display the hexagonal-cell game board, which highly stylizes a transversal section of a coffee bush. It exposes the initial spatial display of tokens representing different organisms.

First set of workshops

Two sets of workshops with coffee farmers were organized: five in the Sepultura Reserve in April and nine in Sepultura and Tacaná reserves in July–August 2015 (Fig. 3).

LGB and JCM had conducted previous work in the region and were trusted by many of the participants. In the first workshops, family members of all genders and ages were invited by public broadcast to a lecture that addressed the rust problem, followed by Azteca Chess training, a game tournament with prizes, and a small dinner followed by a reflection session. With a limit of 16 players per workshop, a total of 72 people aged 12–70 and with a deficient rural primary school level attended and played, and 67 completed all quizzes. Workshops lasted 4 hours.

During a 45-minute PowerPoint-supported lecture, we:

  1. presented the basics of the rust’s biology and data on regional damage caused to coffee producers in the region;

  2. explained and practiced with farmers a “happy-face grammar” to establish the notion and graphical representation of first- and second-level ecological interactions. For this purpose, we used the simplest network: the rabbit harms [angry face—first level] a crop; the fox harms [angry face—first level] the rabbit; the fox benefits [happy face—second level] the crop; and

  3. constructed step-by-step—and thoroughly explained—the natural history and network representation of each element in Fig. 1. At intervals, volunteer participants were asked to come up to the stage and use the grammar to follow the interaction paths down to the scale and rust to deduce if a given organism had the potential to indirectly benefit or harm the farmer (Fig. 4). The lecture’s goal was to familiarize farmers with the diversity of organisms involved, their elaborate behaviors and choices, their types and levels of interactions, and their ultimate effect on scales and, potentially, on rust.

Immediately after the lecture, participants were asked without previous notice to answer a graphical, semi-open quiz “A” in 7 minutes. The same procedure was repeated after the gaming sessions (approximately 3 hours later). Quiz A (Figs. 5 and 6) explored mainly retention (memorization) and understanding, the first two of Bloom’s taxonomy of learning levels (Anderson et al. 2001). Players were asked to connect beneficial and harmful faces departing from each organism to one or more organisms affected by such behaviors. To obtain retention scores per player, we counted only matches with 14 of the 18 bilateral interactions presented in Fig. 1. The 14 were selected to make the average interaction levels of the nine “in the game” interactions and the five “not in the game” interactions as similar as possible.

Comparing pre- and post-gaming quiz scores does not immediately yield the gaming effect because the difference in scores results from the lecture’s learning effect minus the forgetting effect plus the positive effect of quizzes on countering the forgetting effect (Roediger and Karpicke 2006) plus the gaming effect itself. Comparison of pre- versus post-gaming quiz scores only allows knowing if all post-lecture activities were able to counter or even overcompensate the lecture-forgetting effect. We will call it the post-lecture effect. To isolate the gaming effect, two performance scores (0–10 scale) were calculated per player: one for the nine “in the game” interactions and another for the five “not in the game” interactions. This was done separately for the pre- and post-game quiz A. Fifty-six of the respondents were younger than 41, and 11 were between 41 and 68 years of age. We noticed that the second age group more commonly struggled to follow the workshop activities and to do the quizzes, so in all statistical analysis, age group was considered as an additional explanatory factor. The pooled set of “in the game” and “not in the game” scores obtained in the pre- and post-game A quizzes was subject to a three-way Univariate General Linear Model analysis; i.e., a UGLIM (pre/post quiz x “in the game?” x age group). Additionally, paired t tests were performed to compare pre- versus post-game sets of scores for each age group. All tests were performed in SPSS (Version 16, 2007, SPSS Inc., Chicago, Illinois, USA).

Participants were trained in Azteca Chess by LGB through a 20-minute lecture and a step-by-step hands-on demonstration, followed by personalized clarifications during the game by JCM and LGB. Each participant played three times (Fig. 7). Post-game collective reflections were conducted by JCM. They were not tape recorded; only notes were made on paper.

Second set of workshops

A different set of 50 players from the Sepultura and Tacaná subregions participated in these workshops, again conducted by LGB and JCM. Interesting findings, inconclusive results, and perceived weakness of the learning evaluation process motivated six changes in our learning evaluation tools, which were decided by all authors:

First, the Azteca network and game-training lectures—and the game itself—were unaltered, but the nine workshop groups were limited to six players each to allow people to perform three supervised post-game quizzes through question and answer (Q&A) dialogues with a researcher. Groups were formed by open invitation made by a local contact-farmer in each community. Participants were informed they were considered to be working collaborators who were exploring a learning method, so each received a 1-day rural salary of US$10. Fifty participants completed all activities. Half were aged 12–40 and half were 41–68, again with a deficient primary school level as average schooling. Participants played three times in rotation with others. Workshops lasted 4–5 hours.

Second, before starting the workshop, each player was interviewed briefly by JCM about his/her knowledge of species dwelling on coffee bushes, their interactions, and their effect on pest problems.

Third, the semi-open “A” quiz was substituted with a closed “B” quiz (Figs. 8 and 9) that was meant to capture in a step-by-step, unambiguous, and less overwhelming way the same information, as well as to familiarize the participant with responding quizzes. The player held the graphical quiz, and the researcher asked them to decide, for each of 16 bilateral interactions, (a) if it was included in the game, (b) if species X benefited or harmed species Y, and how (to distinguish knowing from guessing). Researchers were extremely careful not to hint, approve, or disapprove responses. Two performance scores (0–10 scale) were calculated per player: one for the 10 “in the game” interactions and another for the six “not in the game” interactions. The pooled set of scores was subject to a two-way UGLIM (age group x “in the game?”) to reveal the game effect.

To test if this quiz “B” procedure was better able to capture the possible gaming effect than was quiz “A”, we pooled the two workshop set data and performed a three-way UGLIM, comparing scores from post-game quizzes “A” and “B”.

Additionally, the frequency of players correctly answering each of the 16 bilateral interactions was calculated and arc-sin transformed. A linear regression model with dummy variables was used to explore how such frequencies were affected by age group (0 = young; 1 = older), “in the game?” (0 = no; 1 = yes), level (1, 2), and type of interaction (predatory = 0; trait-mediated = 1).

Fourth, to explore if players recognized trait-mediated interactions in the game, the researcher offered the player a board with tokens and asked them to make the proper moves to show (a) how an ant scares away a beetle, (b) how an ant scares away a wasp, (c) how a fly favors a beetle by killing a contiguous ant, and (d) how a fly’s mere presence deters ants from approaching a beetle. All except (c) are trait-mediated interactions.

Fifth, high-level cascades along the Azteca network were explored qualitatively and briefly with farmers during the lecture (e.g., the fly harms the ant that harms the beetle that harms the scale that benefits the fungus that harms the rust). We developed a post-game hands-on quiz (the cascade quiz) (Fig. 10) in which six medium-sized cards of the sequence (fly–ant–larvae-killer wasp–larval beetle–scale–rust) were presented to the player as an initial condition. We then changed the current state of the network by increasing the fly’s population (the size of the card) and asked the player to propagate the consequences on other species’ populations by changing, if necessary, the size of the subsequent cards (smaller and bigger card were available). Players were asked the reason for each change they made to discriminate correct answers from guessing. We registered how many steps the player propagated correctly along the sequence and built the frequency distribution of correct steps for all players. Before taking the cascade quiz, the farmer practiced with a similar story using fox–rabbit–plant cards. As with the Lotka-Volterra dynamical model, the Azteca network exhibits complicated oscillatory behaviors due to its many predator–prey interactions, which were not discussed with the farmers. In this exercise, we obviously avoided such complications and focused on a very stylized form of cascading quantitative effects in a single time step.

Sixth, after players were personally interviewed for quizzes, we held a collective reflection session about the workshop, the game, and the practical implications of the experience. Unlike the first set of workshops, all reflection sessions were recorded and transcribed. Thirty-five farmer quotes were selected for this paper.

RESULTS

First set of workshops

The five tournament sessions were successful in that all 72 participants engaged in the lecture and game, and 67 completed both “A” quizzes and participated in reflection sessions.

The average pre- and post-game quiz scores, which measured retention of bilateral interactions, were both low (around 50% correct answers). Table 1 shows there was a significant age group effect (younger did better than older) but no gaming effect, as measured both by the pre- versus post-game score comparison and the “in” or “not in” the game comparison. The younger age group performed slightly better than the older group. For the younger group paired t tests, “in the game” post-game scores were marginally higher (5.73; p = 0.09) than “in the game” pre-game scores (5.32). This first set of gaming experiences revealed an important age group effect, but only slight and statistically marginal post-lecture effects and gaming effects that could not counter the effect of partially forgetting the lecture details after 3 hours.

Participants were surprised to know how many small organism live and actually interact on a coffee bush, were interested in the fact that these animals act pro or against coffee rust control both directly and indirectly, and were amazed and amused by some of the insects’ elaborate behaviors. Overall, only one-fifth of participants recalled and/or could graphically represent 60% or more of the direct and indirect relations in the Azteca network. Yet in all debriefings and reflection sessions, players commented positively on more general aspects of the learning experience. They concluded that insecticides should be applied carefully or not at all to avoid killing beneficial insects. Finally, they considered that learning the game was challenging at first, but later it was fun and rewarding, and some noted that the game complemented the lecture or was absolutely necessary to understand the interactions.

Second set of workshops

A different group of 50 farmers from the Tacaná and Sepultura subregions participated in this second set of workshops. While more than 80% of players could list, before the workshop, five or more vertebrates and invertebrates that could be found on their coffee bushes, 75% were unaware of some of the interactions, and the same proportion did not know that some interactions can control pests. Interestingly, unawareness was somewhat lower in the younger group. Thirteen percent of young players and twenty percent of older players spontaneously said that most species found on coffee bushes are pests.

The average player had 73% correct answers in the single quiz applied post-game (i.e., average score = 7.3). Sixty percent of players got a score between 8 and 10 (i.e., good to excellent). Table 2 shows significant age group and gaming effects on quiz “B” performance. Younger players got higher scores than older players (8.1 versus 6.5; p < 0.0001), and “in the game” scores were higher than “not in the game” scores (8.4 versus 6.1; p < 0.0001)

Table 3 compares the post-game quiz scores of the two sets of players that were exposed to quiz “A” and quiz “B”, respectively. It confirms significant age group effects across both workshop sets and a significant effect of the second set of workshops on capturing the gaming effect (see the “workshop x gaming” interaction effect). Thus, with the new and unambiguous quiz “B” format, filled through a step-by-step Q&A interaction between player and researcher, the effect of the game and of age group on the player’s capacity to retain and understand network interactions became starkly apparent. Fig. 11 graphically summarizes the various statistical trends presented, and the positive effect of the quiz “B” procedure.

Figs. 12 and 13 show, for the younger and the older group, respectively, the frequency of players who recalled each of the 16 bilateral interactions in the lecture. According to the linear regression model (Table 4), the frequency of correct answers to a given interaction was significantly increased by being an “in the game” interaction, was reduced by being the answer of an older player, and was not influenced by the interaction’s level (1 or 2) and type (trait-mediated or predatory). In other words, the frequency of correct answers did not change significantly by being a higher level interaction or a trait-mediated interaction.

Most players did not have trouble distinguishing in the quiz which bilateral interactions were included in the game (92% of answers were correct). Table 5 shows that almost every young person could translate the most relevant trait-mediated level 1 interactions into token moves on the board, while only two-thirds could do so for the trait-mediated level 2 interactions. Older players followed the same pattern but at lower frequencies.

After the game, 64% of players were able to elucidate the cascading effects of a change (sudden fly increase) on a six-level interaction pathway, while 25% did not understand the exercise and/or could not go beyond [more fly -> less ant]. Performance was not significantly related to age group.

In Appendix 2, we present 35 selected farmer quotes on the different topics addressed during post-game reflection sessions. Here, we reproduce five:

1. “With the game I understood a bit better, because with words only, I get drowsy—that’s the truth—every time I attend a workshop. In the game, as I look at the board and move the tokens, and as I see how the good and the bad animals eat each other, I really get to understand which helps me and which doesn’t.”

2. “On the wall we only see [PowerPoint] figures. But once in the game, it’s as if we were seeing it in reality: each animal did his act, defending his life, giving life to others he helps. It benefits our minds because we have to think, we have to analyze what we are about to do; it clears our mind because in our mind everything is so tangled, but once our mind can focus on how we are going to deal with the situation…so yes, it was great!”

3. [The youngest player 12 years old; already a farmer]: “The fly helps the beetle by killing the ant. The fly also helps the beetle by hovering above the ants so that they can’t approach; this way of helping the beetle is more difficult to perceive. The beetle is freed from the ant and now has no problem to eat the scales, and without these, the rust can better reproduce. Now, the wasp kills the larva beetle so the rust won’t reproduce, but if ants become abundant again, they frighten the wasp and it flees from the coffee bush.”

4. [Researcher]: “Which outcome do you expect more frequently on a coffee bush: the scale population flourish and rust is somewhat controlled; scale population remains low; scale population is eliminated and rust flourishes.” Answers: (1) “It depends on what animals are there: for example, if the fly is absent the beetle is busted; it depends.” (2) “When there are more ants and wasps, scales increase, but ants are in danger also, so sometimes they win and sometimes they lose. Sometimes they are more abundant and sometimes less.”

5. “We are very aggressive with ants and all animals, but today we are learning about them, and I will take to my family the message that dear God sent us today through you. You came today to awaken our belief that our coffee farms are not 100% lost to rust: We have defenders, ants that are struggling for us. We are very rude with them, but starting today, we are going to give ants a little bit of freedom; we won’t mess with them, just let them be there.”

DISCUSSION AND CONCLUSIONS

Farmer Field Schools (FFS) and other “farmer to farmer” learning experiences with scientists’ involvement have contributed to strengthening the capacity of legions of rural people to develop and share with others control strategies that seek to promote comprehensive knowledge and adaptive management. Yet, results are mixed, such learning is not easy to evaluate, and many challenges remain (Henk van den Berg: FFS evaluation 2004 report for the Global IPM Facility). The situation described is similar for small-scale coffee farm experiences (Damtie et al. 2011). In Sierra Madre de Chiapas coffee farms, important efforts have been made by researchers to (1) identify the relevant insects and fungus (e.g., Barrera 2008), (2) explore pest versus natural enemy bilateral interactions (e.g., Gómez et al. 2012, Jackson et al. 2012), and (3) describe farmers’ knowledge and develop coffee FFS and participatory biological control programs (e.g., Segura et al. 2004, Jarquín et al. 2006, Barrera 2008). The ongoing rust epidemics are exhibiting processes that invite the actors involved to gain an even broader and shared understanding of the complexity of autonomous pest control, emerging from cascading, multispecies trait-mediated interactions.

The coffee rust pandemic that started in 2012 in the Sierra Madre de Chiapas has been devastating for coffee farmers. Many resorted to heavy use of fungicides during the acute phase and are partially or totally substituting varieties of Coffea arabica, which are rust susceptible, high quality, and shade-tolerant, with varieties that are rust resistant but reportedly of lower quality. Some are planting the resistant (thus far) species Coffea canephora (also known as robusta), well known to be of low quality. Others are waiting for the epidemics to pass and are substituting dead coffee plants with the same or less susceptible C. arabica varieties (Valencia, personal communication). While short-term responses alleviate the crises generated by the rust epidemic, in the long term it is strategic to pay more attention to complex networks of species that interact with each other and keep pests under control. It is important to convey that autonomous pest management is not a simple recipe or a “magic bullet” but rather a complex, context-dependent process (Lewis et al. 1997, Vandermeer et al. 2014) that can be embraced and explored adaptively through long-term building of a collective agroecological culture among the different actors involved.

Small-scale coffee farmers have sophisticated ecological knowledge about many processes occurring in their farms (Vandermeer and Perfecto 2013, Valencia et al. 2015), but as the literature reports and our preworkshop survey confirms, they know their shade-coffee farms thrive with life but pay little attention to many organisms unless their harm is significant (Segura 2004, Jarquín 2005, 2006, López-del-Toro et al. 2009); they are rarely aware that some pesky organisms (e.g., ants and scales) and their inconspicuous ecological associates indirectly exert potential autonomous control over rust and other coffee pests (Perfecto and Vandermeer 2015). Furthermore, they generally lack a framework for learning about subtle ecological processes that would improve pest control at broad spatial and temporal scales (Rebaudo and Dangles 2015).

In our case, a first small step in providing such a framework to farmers was to explore with them the learning effects of combining (1) a natural history narrative of the Azteca ant network (as a temporal surrogate of long-term field observation and experimentation by farmers themselves), (2) a basic training in acknowledging and analyzing multilevel indirect interactions in network diagrams, and (3) a board game that mobilizes such multilevel interactions and reveals the resulting network’s qualitative attractors (i.e., local Azteca network persistence or extinction).

Ecological dynamics are complex and difficult to share (Leiba et al. 2012), more so with people who have no formal training. In the process, we had to learn what works for small-scale coffee farmers and what needs to be further adapted. Most reports on educational games assume that learning has taken place given that these methods and tools are problem-solving oriented, interactive, and motivating, and require players to focus, think, collaborate, and be creative. These claims are frequently consistent with players’ self-evaluations (Etienne 2014). Very few studies statistically compare learning methods and/or pre- and post-game specific knowledge (e.g., Cushman-Roisin et al. 2000, Speelman and García-Barrios 2009, Loula et al. 2014).

The first set of workshops revealed an important age group effect on quiz scores, favoring younger players, but only slight and statistically marginal differences between pre- and post-game quizzes. Overall, the average quiz score was significantly lower than the average score of high school urban students (García-Barrios et al. 2016)—and only 20% of participants recalled and/or could graphically represent 60% or more of the direct and indirect interactions. Results suggested that either the learning evaluation tool was not adequate or that forgetting the interactions as presented in the lecture was not prevented by the subsequent activities. We observed that the open-ended quiz “A” for recalling and reconstructing bilateral interactions was prone to different interpretations, and/or it created challenges that not all players could deal with when left alone to work with this tool. We decided to modify, adapt, and expand our evaluation procedures to avoid overwhelming farmers and to probe and better understand their learning.

In the resultant, second set of workshops, quiz “B” addressed explicitly and systematically the interactions to be recalled. Scores improved significantly for both age groups, and the effect of the game and of age group on the player’s capacity to retain and understand network interactions became starkly apparent. Regression results suggest that the workshop and game might have countered the difficulty of grasping and retaining level 2 and trait-mediated interactions. Most players did not have trouble distinguishing which bilateral interactions were included in the game, and almost every young player could translate the most relevant trait-mediated level 1 interactions into token moves on the board, while only two-thirds could do so for the trait-mediated level 2 interaction. Older players followed the same pattern but at lower frequencies. After the game, two-thirds of players (both younger and older) were able to elucidate the cascading effects of a population change.

Farmers’ comments during reflection sessions confirmed qualitatively that participants learned that potentially beneficial organisms and interactions occur on their farms, and that gaming was enjoyable, motivating, and critical to grasp complex interactions. Many of the farmers concluded that the outcome of these interactions is not unique and not always in favor of rust control, but is context dependent. Most saw that there are feasible actions derived from what was learned (tolerate ants and keep the trees they use to nest, tolerate scales, reduce pesticides, pay more attention to small organisms and their behaviors, etc.). Farmers also gave researchers insights on how they learn; how they sometimes struggle with lectures, gaming rules, and quizzes; how more practice and time could allow them to master the topic; and how field visits would consolidate the learning process. Overall, the effects of learning and evaluation tools displayed in the second set of workshops show that a significant proportion of small-scale coffee farmers were capable of dealing with a complex ecological interaction network, and deriving general lessons, changes in attitudes, and potential actions. The general learning experiences will probably persist in participants’ minds, even as the details might fade away. Any actions that farmers might take as a result of this experience are not part of our study’s framework, but the literature reports a significant effect of coffee farmer learning on their subsequent actions (Damtie 2011). To better define these actions and their actual pest control capacity, some farmers requested future discussion and work in the field about specific conditions and managements that could foster a significant effect of white halo fungus over coffee rust in their farms. As stated in the Introduction, these are context-dependent, open questions which need—and offer the opportunity for—collaborative on-farm research.

Diniz et al. (2015) report that it is unusual and difficult to involve farmers in multilevel interaction network analyses, and Mani et al. (2013) discusses how financial worries partially impair poor farmer’s capacities for such complex cognitive task. Therefore, it is encouraging that younger farmer participants performed very well with the B quiz, and as well as outstanding urban students did in a previous set of workshops using the A quiz (García-Barrios et al. 2016). Azteca workshops need to be further adapted, and complemented with field visits, more so for older participants who struggled with this learning approach.

We are confident that farmers and their allies will become interested in Azteca workshops, both for understanding this specific network but mainly for a better appreciation of the complexity of agroecosytem ecological networks (Benitez et al. 2014, Perfecto and Vandermeer 2015). We expect others to significantly contribute to further adapting these workshops and to incorporating them into broader participatory research and learning experiences in small-scale coffee farmer territories. The kind of abilities Azteca workshops seek to promote in small-scale farmers (observing subtle elements and processes, conceiving and integrating their networked interactions, and mobilizing the latter through game simulations) might be a stepping stone toward even more ambitious goals such as empowered and effective small-scale farmers’ participation in multiactor social-ecological analysis and decision-making processes (e.g., Etienne et al. 2011, d’Aquino and Bah 2013, Diniz et al. 2015).

If the knowledge framework gap between ecologists and farmers regarding complex agroecological issues is to be reduced in both directions, farmers’ interests and capacities to better understand the ecology of their farms should not be preconceived, overestimated, or underestimated. As researchers, we need to go further in our dialogue with farmers, be sensitive to cultural differences in dealing with complex processes (Strohschneider 2002), and learn how to facilitate learning in ways that empower small-scale coffee farmers, both to understand and take action in their own fields and to allow them to fully participate in both mainstream and critical multiactor deliberations and decisions about complex social-ecological processes that strongly affect them (García-Barrios et al. 2015).

RESPONSES TO THIS ARTICLE

Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.

ACKNOWLEDGMENTS

We thank workshop participants from the Sepultura and Tacaná territories (Sierra Madre de Chiapas), and Baldemar Zacarías Mejía, Benigno Gómez Gómez ,and Gustavo López Bautista for their enthusiastic collaboration. Workshops were supported by an NSF/OPUS grant 1144923 to I. Perfecto: Ecology and Complexity of the Coffee Farm, and by ECOSUR´s special grant to L. García: Family Agriculture. We thank two anonymous reviewers for their very useful suggestions.

LITERATURE CITED

Anderson, L., D. Krathwohl, P. Airasian, K. Cruikshank, R. Mayer, P. Pintrich, J. Raths, and M. Wittrock. 2001. A taxonomy for learning, teaching, and assessing: a revision of Bloom’s taxonomy of educational objectives. Pearson, Allyn and Bacon, New York, USA.

Barrera, J. 2008. Coffee pests and their management. Pages 961–998 in J. Capinera, editor. Encyclopedia of entomology. Second edition. Springer.

Benítez, M., J. Fornoni, L. García-Barrios, and R. López. 2014. Networks in agroecology. Pages 64–77 in M. Benitez, O.,Miramontes, and A. Valiente-Banuet, editors. Frontiers in ecology, evolution and complexity. Editora C3-CopIt-arXives, Mexico City, Mexico.

Blackstock, K. L., J. Ingram, R. Burton, K. M. Brown, and B. Slee. 2010. Understanding and influencing behaviour change by farmers to improve water quality. Science of the Total Environment 408:5631–5638. http://dx.doi.org/10.1016/j.scitotenv.2009.04.029

Cushman-Roisin, B., N. Rice, III, and M. A. Moldaver. 2000. A simulation tool for industrial ecology: creating a board game. Journal of Industrial Ecology 3:131–144. http://dx.doi.org/10.1162/108819899569601

Damtie, B., R. Skarripai, and G. Adugna. 2011. Effectiveness of farmer field school model. Farmer field school model. Lambert Academic Publishing, Kentucky, USA.

d’Aquino, P., and A. Bah. 2013. A participatory modeling process to capture indigenous ways of adaptability to uncertainty: outputs from an experiment in West African drylands. Ecology and Society 18(4):16. http://dx.doi.org/10.5751/es-05876-180416

Diniz, F. H., K. Kok, M. A. Hoogstra-Klein, and B. Arts. 2015. Mapping future changes in livelihood security and environmental sustainability based on perceptions of small farmers in the Brazilian Amazon. Ecology and Society 20(2):26. http://dx.doi.org/10.5751/es-07286-200226

Etienne, M., editor. 2014. Companion modelling: a participatory approach to support sustainable development. Springer, Berlin, Germany and Éditions Quæ, Versailles, France.

Etienne, M., D. R. Du Toit, and S. Pollard. 2011. ARDI: a co-construction method for participatory modeling in natural resources management. Ecology and Society 16(1):44. http://dx.doi.org/10.5751/es-03748-160144

García-Barrios, L., R. García-Barrios, J. Cruz-Morales, and J. Smith. 2015. When death approaches: reverting or exploiting emergent inequity in a complex land-use table-board game. Ecology and Society 20(2):13. http://dx.doi.org/10.5751/ES-07372-200213

García-Barrios, L., I. Perfecto, and J. Vandermeer. 2016. Azteca chess: gamifying a complex ecological process of autonomous pest control in shade coffee. Agriculture, Ecosystems, & Environment 232:190–198. http://dx.doi.org/10.1016/j.agee.2016.08.014

Gómez, J., J. Barrera, P. Liedo, and J. Valle. 2012. Influence of age and diet on the performance of Cephalonomia stephanoderis (Hymenoptera, Bethylidae) a parasitoid of the coffee berry borer, Hypothenemus hampei (Coleoptera, Curculionidae). Revista Brasileira de Entomología 56(1):95–100. http://dx.doi.org/10.1590/s0085-56262012005000017

Holt-Giménez, E. 2006. Campesino a campesino: voices from Latin America’s farmer to farmer movement for sustainable agriculture. Food First Books, Oakland, California, USA.

Jackson, D., K. Zemenick, and G. Huerta. 2012. Occurrence in the soil and dispersal of Lecanicillium lecanii, a fungal pathogen of the green coffee scale (Coccus viridis) and coffee rust (Hemileia vastatrix). Tropical and Subtropical Agroecosystems 15:389–401.

Jarquin, R., F. Barrera, F. Guharay, and L. García-Barrios. 2006. Manejo integrado de Hypothenemus hampei bajo dos modelos de capacitación y difusión en México. Manejo Integrado de Plagas y Agroecologia 78:36–45.

Jarquin, R., L. Jímenez, S J. F. Guharay, and F. Barrera. 2005. Aportes de productores y científicos al entendimiento de la agregación de la broca en cafetales de Chiapas, México. Manejo Integrado de Plagas y Agroecologia 75:77–82.

Kindon, S., R. Pain, and M. Kesby, editors. 2007. Participatory action research approaches and methods: connecting people, participation and place. Routledge.

Leiba, M., R. Zuzovsky, D. Mioduser, Y. Benayahu, and R. Nachmias. 2012. Learning about ecological systems by constructing qualitative models with DynaLearn. International Journal of E-Learning &Learning Objects 8:165–178.

Lewis, W. J., J. C. van Lenteren, S. C. Phatak, and J. H. Tumlinson. 1997. A total system approach to sustainable pest management. Proceedings of the National Academy of Sciences of the United States of America 94(23):12243–12248. http://dx.doi.org/10.1073/pnas.94.23.12243

López-del-Toro, P., E. Andersen, L. Barraza, and A. Estrada. 2009. Attitudes and knowledge of shade-coffee farmers towards vertebrates and their ecological functions. Tropical Conservation Science 2(3):299–318. http://dx.doi.org/10.1177/194008290900200303

Loula, A. C., L. N. de Castro, A. L. Apolinário Jr., P. L. B. de Rocha, M. Carneiro, V. Reis, R. Machado, C. Sepulveda, and C. El-Hani. 2014. Modeling a virtual world for the educational game Calangos. International Journal of Computer Games Technology Article ID382396. http://dx.doi.org/10.1155/2014/382396

Mani, A., S. Mullainathan, E. Shafir, and J. Zhao. 2013. Poverty impedes cognitive function. Science 341:976–980. http://dx.doi.org/10.1126/science.1238041

McCook, S., and J. Vandermeer. 2015. The big rust and the Red Queen: long-term perspectives on coffee rust research. Phytopathology 105(9):1164–1173. http://dx.doi.org/10.1094/phyto-04-15-0085-rvw

Perfecto, I., and J. Vandermeer. 2015. Coffee agroecology: a new approach to understanding agricultural biodiversity, ecosystem services and sustainable development. Earthscan, Routledge, New York, New York, USA.

Perfecto, I., J. Vandermeer, and S. M. Philpott. 2014. Complex ecological interactions in the coffee agroecosystem. Annual Review of Ecology, Evolution, and Systematics 45:137–158. http://dx.doi.org/10.1146/annurev-ecolsys-120213-091923

Rebaudo, F., and O. Dangles. 2015. Adaptive management in crop pest control in the face of climate variability: an agent-based modeling approach. Ecology and Society 20(2):18. http://dx.doi.org/10.5751/es-07511-200218

Roediger, III, H. L., and J. D. Karpicke. 2006. The power of testing memory. Basic research and implications for educational practice. Perspectives on Psychological Science 1(3):181–210. http://dx.doi.org/10.1111/j.1745-6916.2006.00012.x

Segura, H. R., J. F. Barrera, H. Morales, and A. Nazar. 2004. Farmers’ perceptions, knowledge, and management of coffee pests and diseases and their natural enemies in Chiapas, Mexico. Journal of Economic Entomology 97(5):1491–1499. http://dx.doi.org/10.1603/0022-0493-97.5.1491

Speelman, E. N., and L. E. García-Barrios. 2009. Agrodiversity v.2: an educational simulation tool to address some challenges for sustaining functional agrodiversity in agro-ecosystems. Ecological Modelling 221(6):911–918. http://dx.doi.org/10.1016/j.ecolmodel.2009.12.007

Strohschneider, S. 2002. Cultural factors in complex decision making. Online Readings in Psychology and Culture 4(1). http://dx.doi.org/10.9707/2307-0919.1030

Valencia, V., P. West, E. Sterling, L. García-Barrios, and S. Naeem. 2015. The use of farmers’ knowledge in coffee agroforestry management: implications for the conservation of tree biodiversity. Ecosphere 6(7):1–17. http://dx.doi.org/10.1890/es14-00428.1

Vandermeer, J., and I. Perfecto. 2013. Complex traditions: intersecting theoretical frameworks in agroecological research. Agroecology and Sustainable Food Systems 37:76–89.

Vandermeer, J., D. Jackson, and I. Perfecto. 2014 Qualitative dynamics of the coffee rust epidemic: educating intuition with theoretical ecology. BioScience 64(3):210–218. http://dx.doi.org/10.1093/biosci/bit034

Vandermeer, J., I. Perfecto, and S. Philpott. 2010. Ecological complexity and pest control in organic coffee production: uncovering an autonomous ecosystem service. BioScience 60:527–537. http://dx.doi.org/10.1525/bio.2010.60.7.8

Address of Correspondent:
Luís García-Barrios
Carretera Panamericana y Perisur s/n San Cristóbal de las Casas, Chiapas, Mexico
luis.garciabarrios@gmail.com
Jump to top
Table1  | Table2  | Table3  | Table4  | Table5  | Figure1  | Figure2  | Figure3  | Figure4  | Figure5  | Figure6  | Figure7  | Figure8  | Figure9  | Figure10  | Figure11  | Figure12  | Figure13  | Appendix1  | Appendix2