Exploring Smallholder Land-Use Decisions through Serious Games: A case study of an Amazonian landscape in Peru

doi:10.21203/rs.3.rs-7547063/v1

Background

Tropical landscapes are largely shaped by the daily decisions of smallholder farmers. Public institutions and private companies increasingly aim to shape agricultural landscapes by influencing farmer decisions. Farmer land-use decision-making is complex and highly dynamic, especially in tropical smallholder systems. The use of serious games has been proposed as a promising research approach to understand these processes. However, it is often unclear how land-use decisions in such simulated settings relate to real life. In this paper, we explore how decisions in games made by farmers relate to their real-life decisions.

Methods

We draw from a case study in Madre de Dios, Peru, where we interviewed farmers to design a game representing key components of the local farming system and external interventions. The game was played during 20 sessions with 103 farmers. We explored the game’s credibility, salience and legitimacy by comparing farmer decisions in the game, including deforestation, crop choice, and participation in support programs, with those made in real life.

Results

Results show significant relations between real-life data and game decisions. The degree to which farmers receive support in real life predicts preference for institutional support in the game, and crop choices in real life resemble those in the game. Levels of support and crop choice in real life also correlate with the amount of forest cleared in the game. Overall, farmers found the game a good representation of their reality, which allowed them to discuss complex land-use decisions. We conclude that serious games can be credible, salient and legitimate tools to explore farmer decision-making around land use, allowing exploration of the impact of present and future interventions.

Agricultural expansion and deforestation are social and behavioral processes as much as they are ecological processes. The sum of local stakeholder’s decisions is influenced by, and contributes to, rapid changes in the landscape (Garcia et al., 2020). Smallholder farmers play a central role in shaping agricultural landscapes, notably so in regions with high forest cover and insecure livelihoods. Their daily land-use decisions are the result of complex processes involving economic dynamics, fluctuating resources, and the influence of reference groups and external actors (Githinji et al., 2023, 2024). External actors, such as public and private institutions, often aim to influence smallholders’ decisions with the goals of both alleviating rural poverty and preserving forests (Pokorny et al., 2021; Speelman et al., 2014). This is typically done through rural extension programs that encourage farmers to adopt certain technologies and land use strategies (Jones, 1997; Sabogal et al., 2008). However, efforts to achieve win-win outcomes that address both social and ecological issues often lead to interventions that are incompatible with smallholders’ goals, causing low adoption and success rates (Jackson, 2012; Pokorny, 2013b). Further, the degree to which such external interventions succeed in influencing smallholders’ decisions, and therefore land use, is difficult to study, since processes underlying farmer decision-making are complex, dynamic and often poorly understood (Githinji et al., 2024; Rizzo et al., 2024).

Novel types of rural intervention are needed, including approaches that are participatory and inclusive to smallholders’ local knowledge and needs (Pokorny et al., 2005, 2013). In order to develop such approaches, the scientific methods used to understand smallholder perspectives must themselves be innovative and inclusive of farmers’ voices. One such method is serious gaming, centered on role playing to engage with real-life decision making processes, designed to support the understanding of social-ecological systems (Barreteau et al., 2022). Serious games have been applied in a wide range of scientific fields, including education, health, social sciences, psychology, informatics, and are increasingly used in interdisciplinary fields of study (Wilkinson, 2016). In the field of natural resource management, the approach has been used to engage with land users in collective agroecosystem design (Ryschawy et al., 2022; Speelman, García-Barrios, et al., 2014) and to stimulate social learning about the consequences of land-use choices (Bosma et al., 2020; Sari et al., 2024) and complex socio-ecological problems in general (Dahdouh-Guebas et al., 2022; Tanika et al., 2023; Wamucii et al., 2023). Serious gaming is also considered a promising approach for exploring decision-making processes, allowing for social learning among stakeholders and the emergence of coalitions for change in the real world (Rodela & Speelman, 2023; Speelman, García-Barrios, et al., 2014; Van Noordwijk et al., 2020). However, in order to develop games that are credible (based on current understanding), salient (actionable through governance), and legitimate (aligned with stakeholder interests) (Cash et al., 2002; Van Noordwijk et al., 2020), game design clearly must draw from the represented reality (Garcia et al., 2022). The hypothesis is that if a game effectively reflects reality, players will make decisions in the game similar to those they would make in real life, allowing for the exploration of real-world scenarios. Still, to our knowledge, there are no studies that explore how closely the land-use decisions made by players in simulated settings resemble those they make in real life, risking to undermine the validity of game-based scenario results (Jackson, 2012).

In this paper, we explore the question: how do decisions made by farmers in a serious game relate with their real-life decisions and conditions? We set out to design a serious game representing key components of the farming system and external interventions that influence it. We then test the game’s credibility, salience and legitimacy by playing it with smallholder farmers and analyzing the correlation between decisions made in the game, and those made in real life. We base our analysis on a case study from Madre de Dios, a department in the Peruvian Amazon, where agriculture is currently growing exponentially (BCRP, 2023; ComexPerú, 2023; Lagneaux et al., 2024), generating interest from a variety of actors, including the regional governmental and non-governmental institutions, who are aiming to intervene in the farming system to foster forest conservation and/or economic development. Based on the results, we reflect on the implications of using serious games to investigate land use in complex farming systems (Fig. 1).

2.1. Study site

The Peruvian department of Madre de Dios is located in the Amazonian lowlands, bordering Brazil and Bolivia. The area covers 85,301 km² with a population of around 140,000 inhabitants (INEI, 2018). As other areas on the first level of the forest transition curve (Dewi et al., 2017), Madre de Dios is characterized both by high forest cover (98%) and high rates of deforestation, especially in hotspots with higher population densities (24,500 ha of tree cover loss in 2023 alone) (Global Forest Watch, 2024). The main driver of deforestation is gold mining, followed by arable and livestock farming (Nicolau et al., 2019). While gold mining is the region’s most lucrative sector, it officially employs only around 5% of the active population, compared to 20% by the farming and fishing sector (INEI, 2019). The region is currently in a phase of social and environmental transition, with an exponential growth of farming activities due to the recently improved access to national markets, allowed by the pavement of the Interoceanic Highway in the 2010s (Lagneaux et al. 2024). The crop covering the largest area is maize (Zea mays), followed by banana (Musa sp.), rice (Oryza sativa), cacao (Theobroma cacao), and cassava (Manihot esculenta) (DRAMDD, 2022). Copoazú (Theobroma grandiflorum) and citrus trees (Citrus sp.) are also important perennial crops grown in Madre de Dios. The latest agricultural census reported an average agricultural unit of almost 100 hectares (INEI, 2013), however most farms have been parceled into smaller units ranging between 20 and 100 hectares (Moore, 2019). Farmers are mainly smallholders producing for both subsistence and markets, in farming systems that range from highly diverse home gardens to extensive pastures and monocultures (Moore, 2019; Peña Valdeiglesia & Alegre Orihuela, 2017). While previous studies have shown that institutional support programs for farmers mainly consist of governmental programs and NGOs promoting perennial crop production (Lagneaux et al., 2025), many questions remain regarding the specific effect that different types of interventions have on farmers’ land-use decisions.

2.2. Game design

The game developed in this study is called CHAKRAS¹ (CHoices in Amazonia for Knowledge and Research on Agricultural Systems); it is as a tool to explore farmers’ land-use decisions and the influence of external interventions on such decisions. The game is part of a larger category of games dealing with external agencies’ effects on farmers’ decisions (Van Noordwijk et al. 2020).

The CHAKRAS game was designed based on the companion modeling approach, using the Actors, Resources, Dynamic and Interactions (ARDI) method (Etienne et al., 2011). We identified key components of the Madre de Dios farm system and external interventions that influence it by carrying out 57 surveys with smallholder farmers. Farmers were identified randomly during field visits to villages along the Interoceanic Highway, within a 150-kilometer stretch both North and South-West of the city of Puerto Maldonado, where most of the region’s agricultural activities are concentrated (Figure 2). The interviewer and assistants asked residents to identify local farmers, and some interviews came through referrals from neighbors or relatives, while others took place during meetings of the AGROBOSQUE cooperative and a small farmers’ association in Montegrande. During each individual interview, we asked farmers about the main components of their farming system, and how components were influencing each other (see Annex 1 for full questionnaire). During these guided conversations, the interviewer drew individual Fuzzy-Cognitive Maps (FCM) on a sheet of paper, representing all the mentioned components and their interactions. FCMs are a tool allowing to elicit local stakeholders’ perception and mental model of their social-ecological systems (Özesmi & Özesmi, 2004). Through the analysis of the FCMs, we extracted key components of the farming system (Actors and Resources, from the ARDI framework) and the relationships between components (Dynamics and Interactions) (see Lagneaux et al., 2025, for details). We included the most mentioned components across all interviews into an element of the game, either as a rule (e.g., expenses were represented with an amount that had to be paid by all players at the end of each round), or an element of the board (e.g. secondary and primary forest plots were represented visually). The systems components (Actors and Resources) most often mentioned related to sales and income, crop production, and elements that influence crop production (internal or external to the farm) such as weed control, drought and institutional support.

2.3. Game development and testing

A first draft version of the CHAKRAS game was created in early 2023 (Figure 3). In order to calibrate the game’s mechanics (number of initial coins per player, costs, prices, etc.) and to simplify game rules to make the game more accessible, six test-rounds were performed between the 17^th and 19^th of March, 2023, with a total of 25 participants, including field technicians and representatives of various local NGOs, graduates in agricultural sciences, and researchers working on the farming system in the Peruvian Amazon. Through several game testing sessions, the game was ultimately simplified by reducing the number of components from 30 to 24. The six elements that were removed during trial rounds included pest and wild animal attack and control, home-consumption (in the game, all products were sold), animal production, and access to the market (idem) (Table 3).

Table 3: List of farming system (sub)components mentioned during individual interviews with smallholder farmers in Madre de Dios (n=57) ordered from most to least mentioned and the associated representation in the CHAKRAS game.

	Component	#	Game element
1	Income	48	Financial resources (coins)
2	Temporary crop production	46	Annual crops
	Banana	35	Banana
	Cassava	23	Cassava
	Maize	33	Maize
	Rice	13	Rice
3	Market sale	46	Sales of crops
4	Weed control	45	Clearing of land before planting + labor resources needed on each cultivated plot
5	Perennial crop production	45	Perennial crops
	Cacao	34	Cacao
	Copoazú	15	Copoazú
	Citrus	20	Citrus
6	Fertilization	43	Removal of primary forest allows for increased income from harvest in following round
7	Governmental institutions	41	Governmental support in round 3
8	Forest	41	6 plots of primary forest per farm
	Wood	13	Wood can be found in primary forest plots and harvested for coins at the beginning of round 2
	NTFP extraction	17	Brazil-nut can be found in primary forest plots and harvested for coins at the beginning of round 2
9	Weeds	38	Plot converts back into secondary forest when left bare
10	Expenses/inversion	38	Cost of living at the end of each round
11	Drought	35	Drought in round 3
12	Farm (general)	31	Board
13	Bank loan	29	Loan available from the agrarian bank
14	Hire labor	28	Labor resources can be rented
15	Expenses (home)	27	Cost of living at the end of each round + inflation in round 4
16	Labor	26	Labor resources (available per player or for rent)
17	Farmers’ associations	23	Players need a majority vote (“creating an association”) in order to receive support from the NGO and government
18	NGOs	21	NGO support in round 2
19	Rain/floods	20	Lack thereof (drought) in round 3
20	Soil (quality)	19	Crop productivity declines with multiple rounds of harvest
21	Neighbors	19	Other players
22	Slash-and-burn	19	Clearing secondary and primary forest plots
23	Fire	18	Risk of fire represented with a “fire dice” causing a 1/3 risk of burning a bordering plot when clearing land
24	Pruning	18	Labor resources needed on each cultivated plot

Players play the role of farmers who make decisions about their individual farm area, represented on the gameboard. Each round in the game represents a cropping season. Between the rounds, interventions by external actors were included to represent the three main types actors who intervene in the local farming system: governmental institutions, NGOs and private companies (see Lagneaux et al., 2025). At the beginning of round 2, a generic NGO promoting agroforestry is introduced, representing the typical approach of NGOs who aim to influence farmers in Madre de Dios. At the beginning of round 3, a generic governmental institution with a cacao-promoting program mimics one of the programs local farmers are the most familiar with in Madre de Dios (see Lagneaux et al., 2025). Agro-industrial companies in Madre de Dios are less common, therefore we included a generic private intervention (beginning round 4) encountered during fieldwork and believed to have the potential to become more important in the future, despite few farmers having benefited from it in real life: a maize seed company. To go further in the exploration of farmers’ preferences around land use and support program preference, we asked farmers to choose between two available interventions at the end of round 5: one that most farmers already interacted with in real life, namely a papaya company offering to rent land; and one that, at the time of the study, no farmers in Madre de Dios had been involved with, namely a carbon project offering to pay for the preservation of primary forest. Note that, while they had not participated in carbon projects, many farmers had heard of the growing carbon credit market and the theoretical financial value of standing forests (see Annex 1 for a complete overview of game rules and game mechanics).

2.4. Game implementation

The CHAKRAS game was played in 20 game sessions (see locations in Figure 2), each lasting about three hours and including 4 to 6 participants. In total, 103 stakeholders participated in the game sessions. All participants were farmers identified during field visits between April and May 2023. We used convenience sampling, by identifying farmers during random village visits, sometimes using key contacts of association and cooperative leaders, and snowball sampling, by inviting previous player’s colleagues and neighbors. In most sessions, players knew each other, they were neighbors and/or family members, however in some sessions players did not previously know each other. In three sessions, the game was played with the board members of a cacao cooperative (AGROBOSQUE and COPAIDI), and in one session the members of the board of the Puerto Maldonado Agricultural Fair played. Two sessions were played with members of indigenous communities in their territories (Infierno and El Pilar). In these session local leaders selected participants.

Each game session started with a short pre-game survey about players’ demographics, farm size, level of support by external actors (e.g., NGOs, regional government programs, etc.) and crop choices (see questionnaire in Annex 2). Demographic data was included, based on the hypothesis that variables such as age, gender or regions of origin could influence players’ decisions. Participants were then briefed on game rules. During the game, the facilitator was careful to avoid emitting value-judgement on players’ land-use decisions and/or preferences, to prevent biasing farmers’ choices. After playing the game, a debriefing session was led by the facilitator to discuss how farmers felt during the game, revisit key events (such as in the case of conflicts, invasions between neighbors, or alliances between players), and discuss similarities and differences between the game and reality, what players learned, and which interventions they preferred (see debriefing guide in Annex 3).

2.5. Data collection and data analysis

Data about players was collected pre-game, during a short interview. During the game, all data about player’s decisions was collected, throughout all rounds, using audio recording of the game session, photos of the board at the end of each round, and written notes taken by the research team throughout the game. The research team was composed of five people, one to facilitate the game and four to collect data and help players access the game tokens for coins or crops.

We analyzed individual and collective decisions made by players during the game and whether these decisions (“game data”) were related to players’ land-use decisions in real life (“life data”). To assess legitimacy, we complemented these quantitative analyses with the qualitative feedback of players on the game’s validity, provided during post-game discussions.

Life data. Demographic and land-use data collected during pre-game surveys included a player’s age, gender, region of origin (Amazonian, Andean or Coastal), total land size, size of cultivated land, size of secondary forest, size of primary forest, type of main crop (perennial or annual), proportion of perennial crops among managed crops, support level (see criteria in Table 1), and general opinion on support projects (good, neutral or bad).

Game data. Recorded data on decisions made by players during the game included the number of deforested plots per round (for both primary and secondary forest), number of plots of annual and perennial crops installed per round (which we call “crop choice”), decision about whether or not to receive support from a given external intervention, which we call “vote” (i.e., NGO support, governmental support, papaya rental and carbon credits), size of the loan taken out and whether or not the loan was paid back.

Table 1: Support levels, along with the criteria used to attribute support levels to farmers. If a farmer receives various types of support, the highest of the support levels is considered to define support level.

Support level	Criteria
1	No support received over the last 5 years
2	Received support from a governmental institution
3	Received support from an NGO
4	Is part of a cooperative

We calculated the significance of relationships between game data and life data by calculating p-values with various statistical tools, based on whether the data was continuous, categorical with only two categories (e.g., yes or no), or categorical with more than two categories (Table 2). The p-values were calculated with R Studio (R Core Team, 2023; RStudio Team, 2023).

Table 2: Statistical methods used to explore significance of relationship between game data and life data, based on the type of data available (continuous or categorical, with only two or more categories).

Life data Game data		Categorical data (only 2)	Categorical data (more than 2)	Continuous data
Life data Game data		Gender Type of main crop	Support level Region of origin Opinion on support	Age Proportion of perennial crops Land sizes
Categorical data	Intervention votes Loan paid back	Chi-squared test of independence	Fisher's exact test on contingency table	Student’s T-test
Continuous data	Number of deforested plots Crop choice Size of loan	Student’s T-test	ANOVA model	Correlation coefficient

We only considered correlation to be of statistical significance when the p-value of the Pearson correlation test was below 0.05. We considered the correlation to be negligible when the correlation coefficient was below 0.1, and weak when it was between 0.1 and 0.3.

3.1. Rules of the CHAKRAS game

The CHAKRAS game, designed based on an analysis of the Madre de Dios farming system, consists of a hexagonal board divided into six sections, representing six farms, each made of nine plots: three secondary forest plots and six primary forest plots (Figure 4). Each player starts with the same area of land, with the same number of economic resources (a total of 10 coins each) and with a specific and randomly allocated number of labor resources (between one and four small humanoid figurines). Economic resources can be used to clear forest, buy seeds and seedlings and/or hire additional labor resources. Players can choose to replace each plot of forested land with one of eight different land uses, namely i) maize, ii) rice, iii) banana, iv) cassava, v) cacao, vi) copoazú, vii) citrus and viii) timber trees. Annual crops (maize, rice, banana and cassava) are cheaper to plant, and can be harvested once, one round after planting. Perennial crops (cacao, copoazú and citrus) are more expensive to plant, and can be harvested each round, starting two rounds after planting. Timber trees can be added to a plot that already contains perennial crops (creating an agroforestry system) and can be harvested for wood after three rounds. Crops can only be planted on bare land (after clearing forest by fire or harvesting an annual crop) and clearing land poses a risk of fire, represented by a six-sided die with a 1/3 probability of burning a bordering plot. Planting on a plot that previously contained primary forest leads to an increase in harvest for one round, to represent increase in fertility from burned biomass. Consecutive planting of annual crops on the same plot will lead to a decrease in harvest (minus one coin per harvested plot per round), to represent fertility loss. If a cleared plot is left unplanted for an entire round, it regrows into secondary forest and fertility is returned. Players can choose to take out a loan for investment or if they are no longer able to pay the yearly maintenance fee (“cost of living”, proportional to the number of a player’s labor resources). Players are allowed to pay back the loan, along with interest rates, in various parts. It is considered that the loan has not been paid back if the loan, along with interest rates, has not been paid by the end of the 5^th round (the end of the game). Detailed rules are available in Annex 1.

One game session consists of five rounds. However, players are unaware of how many rounds will be played, to avoid influencing their decisions at the end of the game. Players are told the rules at the beginning of the first round. Starting at round two, each round starts with the moderator sharing two new pieces of information, consisting of external support interventions (by an NGO, the regional government, or various private companies) and unexpected events such as drought and inflation (Table 4: New information shared with players at the beginning of each round. Each news only applies to one round and support interventions can only be used in the round it is made available.).

Table 4: New information shared with players at the beginning of each round. Each news only applies to one round and support interventions can only be used in the round it is made available.

Round	Information 1	Information 2
1	Basic rules are shared, e.g., plots, crops, coins, labor resources, cost of life.
2	Players can receive one coin for each of their primary forest plots that contains Brazil-nut and/or timber.	An NGO is offering support. If a majority of players votes in favor, each player can receive free seedlings for one plot of a perennial crop (either cacao, copoazú or citrus) mixed with timber.
3	A drought is causing harvests to decrease (-1 coin) for one round.	The regional government is offering support. If a majority of players vote in favor, each player can receive free seedlings for one plot of cacao.
4	Rising inflation is causing both labor costs and cost of living to double (x2 coins).	A private company is offering support. All players can receive free maize seeds for an unlimited number of plots.
5	Inflation remains.	Players can vote between one of two land-use opportunities: either they can rent out one or multiple plots to papaya farmers for one coin per plot, or commit to protect (never clear) one or multiple primary forest plots for a carbon credit program for one coin per plot. Players are allowed to not vote. Note that players are not aware this is the last round when they are offered this choice.

3.2. Overview of decisions made in the game

Players made a wide range of land-use decisions, ranging between the deforestation of only one secondary and one primary forest plot, to large scale deforestation, as in the case of a player who removed all the primary forest on his board. Interestingly, the number of primary and secondary forest plots that players chose to clear was not correlated with overall financial balance, defined as the number of coins minus any remaining debt at the end of the game. This suggests that, despite a broad range of deforestation choices, the game design did not incentivize or reward farmers for either preserving or clearing forest plots (Figure 5).

Most of the forested plots that farmers removed during the game were secondary forest (62% of all forest plots removed) and they were mostly removed during the first round. After the first round, deforestation (of both primary and secondary forest) decreased steadily (Figure 6). Notably, players preferred to clear land close to the highway, and only rarely cleared plots closer to the center of the board.

Across all game sessions, the crops that were planted most often were annual crops (60% of the crops chosen by players) with maize being the crop planted the most often overall. Among perennials, timber (planted mostly with cacao) was the most popular choice, possibly because it could be combined with other perennials (Figure 7).

Crop choice varied widely across different rounds. Annual crops (especially maize) were planted most extensively during the first round, while perennial crops (especially cacao) and timber (for agroforestry) were planted in largest amounts during the second and third round respectively. In round 4, maize was planted in much higher proportion than other crops, and in round 5, timber was chosen the most often (Figure 8).

These patterns of choice suggest a strong influence of external interventions, such as in the case of the NGO offering free agroforestry seedlings in round 2, the regional government offering cacao seedlings in round 3, and the private company offering free maize seeds in round 4. In round 5, the increase in timber plantations could be attributed to the fact that players sold the timber they had planted in round 2 and decided to repeat the experience by planting more.

Across all 20 game sessions, the external support most often chosen by players was that of the NGO. The second most popular support was that of the maize company, followed by the governmental project. When asked to choose between the carbon project, the papaya rental or none, despite similar economic benefits, farmers strongly preferred the carbon project over papaya rental (Figure 9). Note that at the time of this study, there is no carbon project in Madre de Dios providing farmers with payments for forest preservation. During the debriefing discussions, farmers in favor of the carbon initiative tended to express environmental considerations: “We would not do it for money, it would be to be able to preserve forest and continue to breathe” (a farmer in session 8) while those who opposed it feared it would take away their right to continue farming (“If this becomes a reality, there wouldn’t be farmers anymore!” – a farmer in session 9). Overall, most farmers saw the carbon project as a desirable approach that would help them protect forests.

In the case of the NGO support, players could choose to receive seedlings for either a cacao, copoazú, or citrus plot. Players mostly chose cacao (31%), followed by citrus (23%) and copoazú (17%).

3.3. Comparing real-life demographics and farm management with decisions in the game

Age had a mild but significant negative correlation with the number of annual crops planted, suggesting that younger players were more likely to choose annual over perennial crops (Table 5). This could be related to younger farmers’ need to produce quick returns on investment, as they may have limited resources to engage in longer-term investment for perennial plantations. During the debriefing, a player summarized it as following: “In real life, we plant short-term crops first, and only later we plant pure long-term crops to avoid deforesting too much” (session 20). Players’ genders, regions of origin, and opinions about support programs were not significantly correlated with decisions in the game ((Table 5).

The support level received by players in real life seemed to be the most important predictor for game strategy (Table 5). Farmers who were in favor of governmental support (round 2) and who voted for the carbon project over papaya land rental (round 5) were significantly more likely to have received higher levels of support (including NGO support and cooperative membership) in real life (Figure 10). The same was true for players who cleared fewer primary forest plots in the game (Figure 11). Support level in real life was also significantly related to the amount of money players decided to borrow: players who took out higher loans in the game were the ones who had received governmental support in real life (Figure 11). An explanation could be that AGROIDEAS, a program of the Ministry of Agriculture that many players were involved with, includes the granting of loans as a support strategy.

Farmers’ land use, and specifically their preference for growing annual or perennial crops in real life, was also significantly related to various of their decisions in the game. Players who mentioned a perennial crop as their primary crop in real life voted more often in favor of governmental support (round 3), to deforest less primary forest, and to plant fewer annual and more perennial crops in the game. Players who mentioned a higher proportion of perennial crops out of all the crops they mentioned growing in real life also voted more often in favor of governmental support in the game and were more likely to choose the carbon project over papaya. The proportion of perennial crops in real life was mildly negatively correlated with annual crop choice in the game and mildly positively correlated with perennial crop choice in the game (Table 5).

While in the game, all players managed the same farm size, real life farm sizes differed significantly between players. Farmers with larger cultivated lands in real life were more likely to be in favor of governmental support in the game. Interestingly, farmers who paid back their loan in the game also had a higher farm size on average. Finally, players who chose carbon had a much larger area of primary forest on their farms than players who chose papaya (11 ha of primary forest in real life versus 3,3 ha on average). Total land surface and secondary forest area in real life did not significantly affect players’ decisions in the game (Table 5).

Interestingly, farmers who, in real life, had formal land ownership (i.e., land title) planted more agroforestry in the game (2.1 plots on average) compared to farmers with unstable land tenure (i.e., rental or possession certificate) (1.3 plots on average).

Table 5: Correlation coefficients (grey cells) and p-values (white cells) for all the comparative analyses between players’ characteristics in real life, and their decisions in the game. The correlation coefficients and p-values that show significance are highlighted in bold font. This is also the case for correlation coefficients, whose p-values were calculated to identify significant correlation.

		Player’s characteristics
		Gender		Age		Region of origin		Support level		Main crops		Perennial proportion		Project opinion		Land area		Cultivated area		Secondary forest area		Primary forest area
Game choices	NGO vote		0.07		0.34		0.39		0.39		0.76		0.90		0.90		0.31		0.03 *		0.38		0.44
	Government vote		0.50		0.94		0.09		0.02 *		0.03 *		0.02 *		0.08		0.39		0.73		0.57		0.83
	Land rental vote		0.30		0.24		0.16		0.004 **		0.07		0.0007 ***		0.68		0.84		0.14		0.11		0.002 **
	Secondary forest removal		0.25		0.019		0.48		0.61		0.38		-0.038		0.12		-0.108		-0.010		0.083		-0.157
	Primary forest removal		0.51		-0.200		0.33		0.006 **		0.03 *		-0.161		0.87		-0.067		0.126		-0.008		-0.140
	Short term crop choice		0.15		-0.272 **		0.95		0.06		0.03 *		-0.232 *		0.09		-0.117		-0.007		-0.016		-0.123
	Perennial crop choice		0.50		0.050		0.98		0.19		0.009 **		0.243 *		0.30		-0.128		0.086		-0.106		-0.138
	Paying back of loans		0.32		0.13		0.16		0.20		0.44		0.11		0.70		0.73		0.0097 **		0.533		0.9691
	Amount of loan taken out		0.72		0.192		0.11		0.03 *		0.67		0.048		0.18		0.051		0.039		0.193		-0.132
p<0.05; p<0.01; **p<0.001

3.4. Qualitative feedback on the game’s credibility, legitimacy and salience

During most (all except one) post-game discussions, farmers reported that the game resembled their reality. For some farmers “nothing was missing” (session 1) in the game, while others specified the ways in which the game resembled their experience of farming. Farmers perceived the game to be a good representation of reality regarding the kinds of intervening institutions and programs represented (“The projects are the same in real life as in the game” (session 5), or, “NGOs are like this in real life, we have lived this!” (session 6), and it was said that the game represented well the economic difficulties faced in real life: “Profitability in the game is as in real life” (session 5), and “These five rounds, this is how we suffer. It is all work and investment” (session 11). The game represented very real struggles to pay back loans (“Loans are the same [in real life], most of us cannot pay them back. We cannot pay back because of high costs” session 11) or to hire labor (“[The game] represents reality in that people do not want to work because we pay them little and the work is hard. But if you harvest more than 3 or 4 hectares you need extra labor” session 9). Several players commented that they had, in real life too, lost parts of their crops to fire.

Players commented that their land use decisions in the game resembled real life, for example by starting with planting annual crops and only later planting perennial crops. One player said, “The game is as in real life, each one played with the same ambitions as in real life, [some are] conformist and relaxed, others are more ambitious” (session 7). This expressed similarity between real-life choices and choices in the game confirms our findings of correlations between real-life crop choice and game crop choice (see section 4.3.2) and was further demonstrated in a per-session analysis of strategies: a community dedicated to cassava plantations in real life planted the most cassava out of all the game session (39 plots compared to 4 plots on average in other sessions). Game sessions with members of cacao cooperative board members resulted in a 1,5 times higher number of cacao plots compared to other sessions.

Players also commented on some important differences between the game and reality. The most important distinctions related to finances: loans are more difficult to receive in reality than in the game. A player explained: “To receive a loan, they ask you to be part of an association, you have to be registered” (session 5). In addition, the market is not as predictable as in the game, with many products (including bananas, citrus and rice) suffering from recurring low seasonal demand. In general, prices go down when a lot of farmers are selling products at the same time, which typically happens right after maize harvesting season. One player commented: “In reality, our greatest problem is the market. Copoazú for example produces well, but they give you a low price for it” (session 14). Several players noted that, in reality, they mix various crops on a single plot, including successional systems that alternate maize or cassava, bananas and cacao. Some players wished the game would also include livestock. Regarding external interventions, players had often had real-life experiences with NGOs, the regional government, and papaya farmers, but most had not heard about private companies offering maize seeds and knew of carbon projects only in theory. In general, players commented that they felt more supported by external institutions in the game than in real life: “If we had all the support of the game, including loans, we could have a better income” (session 18).

Overall, the CHAKRAS game was credible, based on a systemic analysis of the farming system; salient, through the representation of interventions that influence farmers in real life; and considered legitimate by the players, who perceived that the game aligned with their views.

3.5. Implications for the use of serious games to investigate land use

One of the main issues noted elsewhere for serious games is a lack of clarity about the degree to which players’ decisions in the game can be extrapolated to their decisions in real life (Garcia & Speelman, 2017; Jackson, 2012). Previous agricultural studies using serious games have concluded, based on post-game discussions, that farmers’ decisions in the game do resemble those they make in life (see Castella et al., 2005; Fisher et al., 2012; Kong et al., 2021; Novotny et al., 2024; Speelman, García-Barrios, et al., 2014b). In this study, we have gone a step further by showing that farmers’ decisions in the game relate to their real-life conditions and choices, in ways not always visible while playing and not expressed orally during post-game conversations.

We argue that serious games, when designed in a way that mimics the local social-ecological system, lead stakeholders to make similar decisions than those they (think they) would make in real life. The feedback we received from players during the discussions, and the significant relations between players’ real-life and in-game decisions, may not have been possible without the meticulous design steps we took. Building a robust model of the social-ecological system of interest seems to be an important prerequisite for the successful use of a serious game to explore decision-making processes. As described by Garcia et al. (2022), a game that allows for accurate representation of decision-making should feel “real” to the players who interact with it.

We converge with previous studies (e.g., Alpuche Álvarez et al., 2024) in stating that one of the main strengths of serious games is the ability to reveal decision-making patterns that may not be observed or mentioned in traditional questionnaire-based research. We believe that serious games have the potential to enrich traditional research structures (often consisting in a lineal relationship between researcher and subject) by turning the researcher into an external observer, while the research subject interacts not with a scientist but rather with (a representation of) their own system (Speelman et al., 2017). By removing the researcher from the center of action, farmers can be freed from a sense of expectations and ideas about what they believe is expected from them. In return, researchers can gain new insight by watching what stakeholders do, rather than hearing what they have to say about it (Castella et al., 2005).

In this study, we set out to examine the degree to which decisions made by farmers in a Serious Game setting relate with real-life decisions. We designed the CHAKRAS game based on an analysis of the farming system in Madre de Dios, the Peruvian Amazon, and assessed its credibility, salience and legitimacy through multiple game sessions with farmers. We showed that the game, made credible through a meticulous design process, and salient through the representation of institutional interventions, was recognized by farmers to be a legitimate representation of their realities. Our results further demonstrate this by showing that the degree to which farmers receive support and their crop choices in real life both relate to decisions they make while playing the CHAKRAS game. In addition, farmers’ crop choice in the game was strongly related to the various institutional support programs represented in the game, including those of an NGO, the government, and a private company. Considering the current expansion of the agricultural frontier in Madre de Dios, and the observed influence of external interventions on farmers’ decisions, we argue that actors and policies involved in the design of such interventions will play a key role in the future of Amazonian people and forests. Novel approaches to rural interventions are needed, based on innovative scientific methods. We conclude that serious games, like the one developed for this study, are useful to simulate the influences of interventions on farmers’ decision-making processes, particularly due to the fact that farmers’ decisions and preferences in the game resemble their decisions in real life. As serious games show potential for application in intervention design, future research could explore the social learning outcomes of serious games, for example the influence of playing the CHAKRAS game on farmers’ opinions, which are themselves likely to inform real-life decisions.

Acknowledgments

Authors acknowledge all the farmers who have participated in this study. We also thank the participants of the six game trial sessions carried out in Puerto Maldonado. We are especially grateful to the many individuals who have helped us contact farmers, including Percy Rojas and Yesmi Hualla Zevallos, and the local institutions who have facilitated various game sessions, namely RONAP, AIDER, the AGROBOSQUE cooperative, and Camino Verde Tambopata. Special thanks to Elisabeth Cuadros Bolaños, Armando Ferensk Vega Lopez, Jhon Farfán Ccama and Leo Mougel for their assistance and support in carrying out interviews, organizing fieldwork and facilitating the game sessions. We thank Robin Van Loon for his valuable assistance with language editing.

Funding : This study was funded by the SESAM program of Wageningen University (INREF) and the Foundation of German Business (SDW, Stiftung der Deutschen Wirtschaft).

Conflicts of interest: Authors declare no conflicts of interest

Ethics approval: This study was approved by the Research Ethics Committee for review of non-medical studies of Wageningen University & Research (WUR-REC). The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

Consent to participate: Verbal informed consent was obtained from all individual participants included in the study. The participant’s data was anonymized after all game sessions were carried out.

Consent for publication: The authors affirm that research participants provided informed consent for publication of anonymized data.

Availability of data and material: The datasets generated during and/or analyzed during the current study are available in the DANS Data Station Life Sciences repository, under https://doi.org/10.17026/LS/Y92GKQ and https://doi.org/10.17026/LS/NDB8RL (Lagneaux, 2025b, 2025a).

Code availability: Not applicable

Authors' contributions: Conceptualization, E.L., E.S., K.D., D.C-C.; Methodology, E.L. and E.S.; Investigation, E.L. and T.A.; Writing – Original Draft, E.L.; Writing –Review & Editing, E.L., E.S., K.D., D.C-C., T.A.; Funding Acquisition, E.S. and E.L.; Supervision, E.S., K.D., and D.C-C.

Alpuche Álvarez, Y. A., Jepsen, M. R., Müller, D., Rasmussen, L. V., & Sun, Z. (2024). Unraveling the complexity of land use change and path dependency in agri-environmental schemes for small farmers: A serious game approach. Land Use Policy, 139, 107067. https://doi.org/10.1016/j.landusepol.2024.107067
Barreteau, O., Abrami, G., Bonté, B., Bousquet, F., & Mathevet, R. (2022). Serious games. In R. Biggs, A. de Vos, R. Preiser, H. Clements, K. Maciejewski, & M. Schlüter (Eds.), The Routledge handbook of research methods for social-ecological systems (pp. 176–188). Routledge. https://library.oapen.org/bitstream/handle/20.500.12657/49560/9781000401516.pdf?sequence=1#page=209
BCRP. (2023). MADRE DE DIOS: Síntesis de Actividad Económica Febrero 2023. Banco Central de Reserva del Peru, Departamento de Estudios Económicos. https://www.bcrp.gob.pe/docs/Sucursales/Cusco/2023/sintesis-madre-de-dios-02-2023.pdf
Bosma, R. H., Ha, T. T. P., Hiep, T. Q., Phuong, N. T. H., Ligtenberg, A., Rodela, R., & Bregt, A. K. (2020). Changing opinion, knowledge, skill and behaviour of Vietnamese shrimp farmers by using serious board games. The Journal of Agricultural Education and Extension, 26(2), 203–221. https://doi.org/10.1080/1389224X.2019.1671205
Cash, D., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N., & Jäger, J. (2002). Salience, credibility, legitimacy and boundaries: Linking research, assessment and decision making. Assessment and Decision Making. http://dx.doi.org/10.2139/ssrn.372280
Castella, J.-C., Trung, T. N., & Boissau, S. (2005). Participatory Simulation of Land-Use Changes in the Northern Mountains of Vietnam: The Combined Use of an Agent-Based Model, a Role-Playing Game, and a Geographic Information System. Ecology and Society, 10(1). https://doi.org/10.5751/ES-01328-100127
ComexPerú. (2023). PRODUCCIÓN AGROPECUARIA RETROCEDIÓ UN 14.2% EN ABRIL. COMEXPERU - Sociedad de Comercio Exterior Del Perú. https://www.comexperu.org.pe/articulo/produccion-agropecuaria-retrocedio-un-142-en-abril
Dahdouh-Guebas, F., Mafaziya Nijamdeen, T. W. G. F., Hugé, J., Dahdouh-Guebas, Y., Di Nitto, D., Hamza, A. J., Kodikara Arachchilage, S., Koedam, N., Mancilla García, M., Mohamed, M. O. S., Mostert, L., Munga, C. N., Poti, M., Satyanarayana, B., Stiers, I., Van Puyvelde, K., Vanhove, M. P. M., Vande Velde, K., & Ratsimbazafy, H. A. (2022). The Mangal Play: A serious game to experience multi-stakeholder decision-making in complex mangrove social-ecological systems. Frontiers in Marine Science, 9. https://doi.org/10.3389/fmars.2022.909793
Dewi, S., Van Noordwijk, M., Zulkarnain, M. T., Dwiputra, A., Hyman, G., Prabhu, R., Gitz, V., & Nasi, R. (2017). Tropical forest-transition landscapes: A portfolio for studying people, tree crops and agro-ecological change in context. International Journal of Biodiversity Science, Ecosystem Services & Management, 13(1), 312–329.
DRAMDD. (2022). SUPERFICIE COSECHADA DE LOS PRINCIPALES CULTIVOS (ha.) AÑO 2022. Información Agrícola – DRAMDD. https://www.dramdd.gob.pe/informacion-agricola/
Etienne, M., Du Toit, D. R., & Pollard, S. (2011). ARDI: A Co-construction Method for Participatory Modeling in Natural Resources Management. Ecology and Society, 16(1). https://www.jstor.org/stable/26268857
Fisher, J., Farré, I., Dray, A., Khimashia, N., & Perez, P. (2012). Serious games to explore uncertainty of future farms. 16th ASA Conference, 14-18 October 2012, Armidale, Australia. https://www.agronomyaustraliaproceedings.org/images/sampledata/2012/8191_4_fisherj-serious-games-to-play.pdf
Garcia, C. A., Savilaakso, S., Verburg, R. W., Gutierrez, V., Wilson, S. J., Krug, C. B., Sassen, M., Robinson, B. E., Moersberger, H., Naimi, B., Rhemtulla, J. M., Dessard, H., Gond, V., Vermeulen, C., Trolliet, F., Oszwald, J., Quétier, F., Pietsch, S. A., Bastin, J.-F., … Waeber, P. O. (2020). The Global Forest Transition as a Human Affair. One Earth, 2(5), 417–428. https://doi.org/10.1016/j.oneear.2020.05.002
Garcia, C. A., Savilaakso, S., Verburg, R. W., Stoudmann, N., Fernbach, P., Sloman, S. A., Peterson, G. D., Araújo, M. B., Bastin, J.-F., Blaser, J., Boutinot, L., Crowther, T. W., Dessard, H., Dray, A., Francisco, S., Ghazoul, J., Feintrenie, L., Hainzelin, E., Kleinschroth, F., … Waeber, P. O. (2022). Strategy games to improve environmental policymaking. Nature Sustainability, 5(6), 464–471. https://doi.org/10.1038/s41893-022-00881-0
Garcia, C., & Speelman, E. N. (2017). Landscape Approaches, Wicked Problems and Role Playing Games (ForDev Working Paper No. 01). Tropenbos International ComMod Workshop. https://www.researchgate.net/profile/Claude-Garcia/publication/320170969_Landscape_Approaches_Wicked_Problems_and_Role_Playing_Games/links/59d26d02aca2721f4369ac08/Landscape-Approaches-Wicked-Problems-and-Role-Playing-Games.pdf
Githinji, M., van Noordwijk, M., Muthuri, C., Speelman, E. N., & Jan Hofstede, G. (2023). Farmer land-use decision-making from an instrumental and relational perspective. Current Opinion in Environmental Sustainability, 63, 101303. https://doi.org/10.1016/j.cosust.2023.101303
Githinji, M., van Noordwijk, M., Muthuri, C., Speelman, E. N., Kampen, J., & Hofstede, G. J. (2024). “You never farm alone”: Farmer land-use decisions influenced by social relations. Journal of Rural Studies, 108, 103284. https://doi.org/10.1016/j.jrurstud.2024.103284
Global Forest Watch. (2024). Madre de Dios, Peru Deforestation Rates & Statistics. https://www.globalforestwatch.org/dashboards/country/PER/18?category=forest-change
INEI. (2013). Resultados Definitivos. IV Censo Nacional Agropecuario 2012. Instituto Nacional de Estadística e Informática (INEI).
INEI. (2018). Censos 2017: En Madre de Dios viven 141 070 personas. Censos Nacionales 2017. https://censo2017.inei.gob.pe/censos-2017-en-madre-de-dios-viven-141-070-personas/
INEI. (2019). Evolución de los Indicadores de Empleo e Ingresos por Departamento, 2007—2018.
Jackson, C. (2012). Internal and External Validity in Experimental Games: A Social Reality Check. The European Journal of Development Research, 24(1), 71–88. https://doi.org/10.1057/ejdr.2011.47
Jones, G. (1997). Chapter 1—The history, development, and future of agricultural extension. In Burton E. Swanson, Robert P. Bentz, & Andrew J. Sofranko (Eds.), The history, development, and future of agricultural extension. Food and Agriculture Organization of the United Nations. https://www.fao.org/4/W5830E/w5830e00.htm
Kong, R., Castella, J.-C., Suos, V., Leng, V., Pat, S., Diepart, J.-C., Sen, R., & Tivet, F. (2021). Investigating farmers’ decision-making in adoption of conservation agriculture in the Northwestern uplands of Cambodia. Land Use Policy, 105, 105404. https://doi.org/10.1016/j.landusepol.2021.105404
Lagneaux, E. G. (2025a). Individual fuzzy-cognitive mapping surveys with smallholder farmers in Madre de Dios, Peru [Dataset]. DANS Data Station Life Sciences. https://doi.org/10.17026/LS/NDB8RL
Lagneaux, E. G. (2025b). Serious game data on land use among smallholder farmers in Madre de Dios, Peru [Dataset]. DANS Data Station Life Sciences. https://doi.org/10.17026/LS/Y92GKQ
Lagneaux, E. G., Callo-Concha, D., Speelman, E. N., & Descheemaeker, K. (2024). Panarchy to explore land use: A historical case study from the Peruvian Amazon. Sustainability Science, 19(4). https://doi.org/10.1007/s11625-024-01502-9
Lagneaux, E. G., Descheemaeker, K., Speelman, E. N., & Callo-Concha, D. (2025). Farming Systems in the Peruvian Amazon: A Fuzzy Cognitive Mapping Study on the Impact of Interventions. Agricultural Systems.
Moore, T. (2019). Deforestation in Madre de Dios, its implications for first peoples. In Deforestation in Times of Climate Change (Chirif, A., p. 201). IWGIA. https://www.academia.edu/download/89023256/Peru_Deforestation_in_Times_of_Climate_Change_Dec_2019.pdf#page=202
Nicolau, A. P., Herndon, K., Flores-Anderson, A., & Griffin, R. (2019). A spatial pattern analysis of forest loss in the Madre de Dios region, Peru. Environmental Research Letters, 14(12), 124045. https://doi.org/10.1088/1748-9326/ab57c3
Novotny, I. P., Boul Lefeuvre, N., Attiogbé, K. S., Wouyo, A., Fousseni, F., Dray, A., & Waeber, P. O. (2024). Exploring farmer choices in Southern Togo: Utilizing a strategy game to understand decision-making in agricultural practices. Agricultural Systems, 218, 103960. https://doi.org/10.1016/j.agsy.2024.103960
Özesmi, U., & Özesmi, S. L. (2004). Ecological models based on people’s knowledge: A multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1–2), 43–64.
Peña Valdeiglesia, J., & Alegre Orihuela, J. C. (2017). Tipificación de prototipos de sistemas de producción agroforestal en la provincia de Tambopata, Madre de Dios. APORTE SANTIAGUINO, 10(2), Article 2. https://doi.org/10.32911/as.2017.v10.n2.166
Pokorny, B. (2013). Smallholders, forest management and rural development in the Amazon. Routledge. https://www.taylorfrancis.com/books/mono/10.4324/9780203073919/smallholders-forest-management-rural-development-amazon-benno-pokorny
Pokorny, B., Cayres, G., & Nunes, W. (2005). Participatory Extension as Basis for the Work of Rural Extension Services in the Amazon. Agriculture and Human Values, 22(4), 435–450. https://doi.org/10.1007/s10460-005-3398-4
Pokorny, B., de Jong, W., Godar, J., Pacheco, P., & Johnson, J. (2013). From large to small: Reorienting rural development policies in response to climate change, food security and poverty. Forest Policy and Economics, 36, 52–59. https://doi.org/10.1016/j.forpol.2013.02.009
Pokorny, B., Robiglio, V., Reyes, M., Vargas, R., & Patiño Carrera, C. F. (2021). The potential of agroforestry concessions to stabilize Amazonian forest frontiers: A case study on the economic and environmental robustness of informally settled small-scale cocoa farmers in Peru. Land Use Policy, 102, 105242. https://doi.org/10.1016/j.landusepol.2020.105242
R Core Team. (2023). R: A language and environment for statistical computing. [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
Rizzo, G., Migliore, G., Schifani, G., & Vecchio, R. (2024). Key factors influencing farmers’ adoption of sustainable innovations: A systematic literature review and research agenda. Organic Agriculture.
Rodela, R., & Speelman, E. N. (2023). Serious games in natural resource management: Steps toward assessment of their contextualized impacts. Current Opinion in Environmental Sustainability, 65, 101375. https://doi.org/10.1016/j.cosust.2023.101375
RStudio Team. (2023). RStudio: Integrated Development Environment for R. (Version 2023.03.1) [Computer software]. Posit Software, PBC. https://posit.co
Ryschawy, J., Grillot, M., Charmeau, A., Pelletier, A., Moraine, M., & Martin, G. (2022). A participatory approach based on the serious game Dynamix to co-design scenarios of crop-livestock integration among farms. Agricultural Systems, 201, 103414. https://doi.org/10.1016/j.agsy.2022.103414
Sabogal, C., Jong, W. de, Pokorny, B., & Louman, B. (2008). Manejo forestal comunitario en América Latina: Experiencias, lecciones aprendidas y retos para el futuro. Centro Agronómico Tropical de Investigación y Enseñanza (CATIE). https://repositorio.catie.ac.cr/handle/11554/2970
Sari, R. R., Tanika, L., Speelman, E. N., Saputra, D. D., Hakim, A. L., Rozendaal, D. M. A., Hairiah, K., & van Noordwijk, M. (2024). Farmer Options and Risks in Complex Ecological-Social systems: The FORCES game designed for agroforestry management of upper watersheds. Agricultural Systems, 213, 103782. https://doi.org/10.1016/j.agsy.2023.103782
Speelman, E. N., García-Barrios, L. E., Groot, J. C. J., & Tittonell, P. (2014). Gaming for smallholder participation in the design of more sustainable agricultural landscapes. Agricultural Systems, 126, 62–75. https://doi.org/10.1016/j.agsy.2013.09.002
Speelman, E. N., Groot, J. C. J., García-Barrios, L. E., Kok, K., van Keulen, H., & Tittonell, P. (2014). From coping to adaptation to economic and institutional change – Trajectories of change in land-use management and social organization in a Biosphere Reserve community, Mexico. Land Use Policy, 41, 31–44. https://doi.org/10.1016/j.landusepol.2014.04.014
Speelman, E. N., Van Noordwijk, M., & Garcia, C. (2017). Gaming to better manage complex natural resource landscapes. In S. Namirembe BL, M. Van Noordwijk, & P. Minang (Eds.), Co-investment in Ecosystem Services: Global Lessons from Payment and Incentive Schemes (World Agroforestry Center).
Tanika, L., Sari, R. R., Hakim, A. L., van Noordwijk, M., Peña-Claros, M., Leimona, B., Purwanto, E., & Speelman, E. N. (2023). The H 2 Ours game to explore Water Use, Resources and Sustainability: Connecting issues in two landscapes in Indonesia. Hydrology and Earth System Sciences Discussions, 2023, 1–38.
Van Noordwijk, M., Speelman, E., Hofstede, G. J., Farida, A., Abdurrahim, A. Y., Miccolis, A., Hakim, A. L., Wamucii, C. N., Lagneaux, E., Andreotti, F., Kimbowa, G., Assogba, G. G. C., Best, L., Tanika, L., Githinji, M., Rosero, P., Sari, R. R., Satnarain, U., Adiwibowo, S., … Teuling, A. J. (2020). Sustainable Agroforestry Landscape Management: Changing the Game. Land, 9(8), 243. https://doi.org/10.3390/land9080243
Wamucii, C., Van Oel, P., Teuling, A. J., Ligtenberg, A., Mwangi Gathenya, J., Hofstede, G. J., Van Noordwijk, M., & Speelman, E. N. (2023). Guiding community discussions on human-water-related challenges by serious gaming in the upper Ewaso Ng’iro river basin, Kenya. EGUsphere [Preprint]. https://doi.org/10.5194/egusphere-2023-2459
Wilkinson, P. (2016). A Brief History of Serious Games. In R. Dörner, S. Göbel, M. Kickmeier-Rust, M. Masuch, & K. Zweig (Eds.), Entertainment Computing and Serious Games (Vol. 9970, pp. 17–41). Springer International Publishing. https://doi.org/10.1007/978-3-319-46152-6_2

The name of the game comes from the Quechua word chakra, meaning farm, the most commonly used term for one’s farm employed by farmers in Madre de Dios, normally referring to a traditional, biodiverse family-managed farm system.

Supplementarymaterial.docx

Exploring Smallholder Land-Use Decisions through Serious Games: A case study of an Amazonian landscape in Peru

Status:

Version 1

Abstract

Background

Methods

Results

Figures

Introduction

Materials and methods

2.1. Study site

2.2. Game design

2.3. Game development and testing

2.4. Game implementation

2.5. Data collection and data analysis

Results and discussion

3.1. Rules of the CHAKRAS game

3.2. Overview of decisions made in the game

3.3. Comparing real-life demographics and farm management with decisions in the game

3.4. Qualitative feedback on the game’s credibility, legitimacy and salience

3.5. Implications for the use of serious games to investigate land use

Conclusion

Declarations

References

Footnotes

Supplementary Files

Status:

Version 1