Indigenous peoples have contributed disproportionately little to recent climate change through their typically low-carbon lifeways, and yet many Indigenous peoples are currently experiencing disproportionately large impacts on their ecosystems and cultures (Raygorodetsky 2017). These impacts include the potential loss of culturally essential species (Grah and Beaulieu 2013), higher health risks (Doyle et al. 2014), infrastructure damage (Cochran et al. 2014), lessened availability of traditional foods (Lynn et al. 2014), declining water quantity (Cozzetto et al. 2014) and quality (Patrick 2018), and higher economic vulnerability (Gautam et al. 2014). Indigenous groups are typically highly aware of the complex impacts of climate change, and some have been since precolonial times (Aryal et al. 2016, Nursey-Bray et al. 2019, Simonetti 2019).
In the face of these disproportionate impacts, some Indigenous communities are crafting their own strategies to adapt (Gautam et al. 2014, Patrick 2018, Mashizha 2019, Nyahunda and Tirivangasi 2019), in some cases even shaping the policies that constrain them in developing their own adaptation strategies (Maldonado et al. 2014, Voggesser et al. 2014). For climate adaptation plans to be effective and appropriate, Indigenous people need to be deeply involved in their development at all scales: regionally, nationally, and globally (Cochran et al. 2014). Ideally, the state and other stakeholders then play a supportive role for communities engaging in their own culturally grounded adaptation actions (Richards et al. 2019). We refer to this strategy as “climate adaptation sovereignty,” an elaboration of “climate sovereignty” (Smith 2017), which is intended to emphasize self-determination in identifying, adapting to, and rectifying climate impacts, all in ways appropriate to Indigenous territories and cultures. In the context of this paper, we focus on the idea that Indigenous peoples have the right to develop their own solutions and practices for climate change adaptation, and as part of this sovereignty, they have the right to define what success and sustainability look like for themselves.
These community-based climate adaptation plans are best grounded in the community’s own knowledge of their system (Davidson-Hunt et al. 2012, Turner and Spalding 2013); however, assessing successful climate adaptation using traditional Indigenous knowledge systems (IKS) may be difficult. Within Indigenous communities there may be a range of levels of awareness of the potential impacts of climate variability (Herman-Mercer et al. 2016, Hossain and Paul 2019) and differences in understanding its causes (Boillat and Berkes 2013, Ahmed and Atiqul Haq 2019). Nevertheless, Indigenous groups are observing rapid transformations in the last few decades in the form of changing weather and timing of seasonal plant and animal behaviors (Cochran et al. 2014, Raygorodetsky 2017, Shaibu et al. 2019). Some Indigenous people are finding that their traditional climate indicators no longer work to predict, for example, when to plant, as climate change shifts systems away from historical patterns (Roncoli et al. 2002); this can erode faith in Indigenous knowledge systems to predict weather (Nyahunda and Tirivangasi 2019). There is therefore concern that the typically long-term accumulation methods of IKS may be impaired by the accelerating pace of climate change (Ebhuoma and Simatele 2019).
Many Indigenous groups have become interested in combining traditional knowledge with Western scientific knowledge, when this is done appropriately (Roncoli et al. 2002) and builds on existing Indigenous knowledge (Mapfumo et al. 2016). IKS may benefit from contemporary techniques such as community-based modeling, particularly when exploring the uncertain potential future behavior of social-ecological systems (d'Aquino and Bah 2014). Integrating participatory research with climate models can therefore help enhance adaptive capacity, potentially connecting traditional knowledge with a new generation of Indigenous practitioners as well as outsider climate modeling researchers to develop appropriate adaptation strategies (Valdivia et al. 2010). Modeling potential system impacts with communities can produce knowledge that has the richness of place-based knowledge, but also the advantages of the potential to scale up results (Ford et al. 2019). Agent-based models (ABMs) created using a participatory process (Voinov and Bousquet 2010, Étienne 2013, Barreteau et al. 2017) can be used to respectfully combine local knowledge with Western scientific knowledge and thereby better represent social-ecological systems (Müller et al. 2007, Castellani et al. 2019). ABMs can integrate knowledge with widely varying quantification and can be used to explore how systems may respond to interventions and changes in underlying system drivers (Spies et al. 2017), including possible behaviors under novel conditions. Community-based ABMs can therefore be useful tools for the development of Indigenous-led climate adaptation strategies.
The Zimbabwe Agro-Pastoral Management Model (ZAPMM; Eitzel et al. 2018) is an ABM originally developed in partnership between Zimbabwean nongovernmental organization The Muonde Trust and allied outsider researchers. Muonde is engaged in developing, supporting, and spreading Indigenous innovations in their part of rural Zimbabwe (and beyond), and ZAPMM was intended to facilitate community discussions regarding management interventions and climate change. Initial academic research on ZAPMM focused on quantitative and qualitative validation of the model (Eitzel et al. 2020) and though it was useful to the community, the original version used only a single set of definitions of system sustainability (out of necessity because of the complexity of the model and scope of evaluating and validating it). In the spirit of Indigenous climate adaptation sovereignty, with this study we extend the analysis of ZAPMM to investigate a wider range of sustainability definitions inspired by further conversation with Muonde. We ask, via the model, how definitions of sustainability affect the assessment of Muonde’s Indigenous climate adaptations.
ZAPMM was intended to represent the agro-pastoral system in Mazvihwa Communal Area, Midlands Province, south-central Zimbabwe. Mazvihwa is classified in the lowest-potential agricultural zone of the country, and has a semiarid climate with highly variable within-year and between-year rainfall. Farmer-pastoralists living in the Communal Area have survived despite these conditions using a variety of strategies to manage livestock, crops, and woodland grazing areas. They have historically been able to maintain large livestock herds in this grazing area, which also holds importance as a source of medicines, wild foods, and spiritual significance. Over time, however, local land use choices have decreased the amount of woodland grazing area in favor of increasing agricultural production (Fig. 1).
The Muonde Trust is a local nongovernmental organization governed and staffed by people from around Mazvihwa. The community-based research team currently includes approximately 30 individuals from a range of clans and backgrounds, with more women members than men. This team has been developing and promoting a variety of Indigenous innovations that use agro-ecological principles to increase agricultural productivity (Appendix 1). Through community-based research, Muonde seeks to answer questions regarding the consequences of both existing management techniques as well as newly developed interventions on the sustainability of their agro-ecosystem.
The Muonde research team has been recording data on a variety of aspects of their agro-pastoral system, including many of these management interventions, over multiple decades. From the 1980s through the 2010s, the team has conducted a variety of semistructured and open-ended interviews and surveys to collect information on farming, animal husbandry, and ecological restoration practices (Wilson 1990). The team has also measured growth rates of woodland trees after clear-cutting and coppicing, fencing consumption rates by termites, amounts of fencing material used, and other factors influencing the sustainability of different system elements. In addition, outsider researchers have partnered with the community-based research team to assist with field measurements and interviews, as well as analysis of aerial imagery. The team has also archived rainfall data. See Eitzel et al. (2020) for a detailed description of these data sources.
ZAPMM is the result of a modeling process intended to provide discussion support for Muonde and the local community to determine how much land should be allocated to arable production and how much land to leave as woodland grazing area. Initial stages of model construction involved Muonde’s cofounders and a team of outsider researchers, with outsider-driven technical implementation but collaborative model design and calibration using Muonde’s archive of community-based data. We then held workshops with the whole Muonde research team in small and large groups to collaboratively verify and discuss the model. Ultimately the model was adapted and structurally validated through this process: it contained all the important aspects of the system with appropriate causal mechanisms (Qudrat-Ullah 2012). In addition, the model was practically validated as a useful tool for Muonde to discuss land use planning with local leaders (Saam 2019). We also attempted to behaviorally validate the model by directly comparing the harvests and livestock numbers with Muonde’s data (Barlas 1989), and found that while harvests matched relatively well, livestock numbers tended to be much lower than in the actual system (Eitzel et al. 2020). We take this difference into account in the below analyses.
ZAPMM was written in NetLogo (Wilensky 1999), representing the scale of Mudhomori village in Mazvihwa (600 hectares in size), broken down into a 50 x 50 grid of NetLogo patches; each patch is therefore 0.24 ha. Model runs lasted at most 60 calendar years (the length of our rainfall data time-series), with a discrete 8-hour time step to allow for management actions to happen several times within a day. The Indigenous innovations of interest to Muonde most directly impact three system components, which we represented in our model as “cows” (NetLogo agents, including both male and female animals and representing livestock in general), “crops” (NetLogo patches, including any type of crop), and “woodland” (namely savannah; also NetLogo patches). In the model, these entities interact in the following ways: cows plough crops, woodland provides fencing material for crops, and cows eat crops and woodland (Fig. 2). Cows also reproduce according to a simple two-stage population model (adults and calves) with a constant probability of reproduction for each adult cow agent in a single model time step.
Both outsider researchers and the Muonde team were concerned with possible impacts of climate change on Mazvihwa’s agro-pastoral system, so we modeled two rainfall scenarios: one using the historical yearly rainfall data time-series (“historical”), and one drawing from a zero-truncated normal distribution with the same mean as the historical rainfall data and 1.5 times the standard deviation (“high-variation”), representing the potential for increased year-to-year variation in rainfall predicted by climate models downscaled for Southern Africa (Shongwe et al. 2009, Jury 2013).
The Indigenous innovations included in the model are listed in Table 1 and numbered in Figure 2. They are implemented in the model through an interface designed as a computer-mediated roleplay, whereby the user explores the impacts of possible management decisions made by farmers and local leaders in the real system. Intervention 1, “proportion crops,” was the central question driving the creation of the model, while interventions 3-5 (“preserve forest,” “crop innovations,” and “stone walls”) represent recent innovations promoted by Muonde, and interventions 6-8 (“move cows,” “subsidize cows,” and “store grain”) are management strategies historically employed by farmers in Mazvihwa. (See Eitzel et al. 2020 for analysis of intervention 2, “spatial configuration,” which we do not address here.)
The model also conserves biomass and energy across trophic levels, with metabolic efficiency losses from producer to consumer, energy densities of different kinds of biomass, and a required minimal biological maintenance energy for cows (Molden 2013). We use a linear relationship between rainfall and plant growth (as observed in these Southern African ecosystems; Rutherford 1978) for both crops and woodland with a nonzero intercept for crops. After an initialization period for the simulation to move past any transient behavior dependent on initial conditions, we track several metrics during each model calendar year: number of cows, amount of crop harvested (in metric tons), and amount of woodland biomass (in metric tons).
We used NetLogo’s BehaviorSpace functionality to explore a range of combinations of management choices; see Eitzel et al. (2020) for the details and results of these parameter sweeps. To explore how definitions of sustainability change the way we view ZAPMM’s results, we analyzed two specific outcomes for a given model run: (1) system persistence for all 60 years and (2) average annualized harvest. Average annual harvest is included as a measure of sustainability at the suggestion of one of Muonde’s founders, who pointed out that food sovereignty in the context of a weak national economy is central for this community, while their challenge is to achieve this without compromising the long-term persistence of their system. We defined persistence as a set minimum amount of cows, woodland, and harvest at the end of every model year; we calculated average annualized harvest by dividing total accumulated harvest by the number of years before the modeled system dropped below any of the persistence thresholds (if it did so).
Average annualized harvest was therefore a shorter term measure of sustainability: a particular run could maximize harvest at the expense of livestock numbers or woodland biomass and only last a few years but with potentially excellent harvest, resulting in a value of “not persistent” and a high annual harvest for that run. In contrast, persistence was a longer term measure of sustainability: a model run might last all 60 calendar years with cows, crops, and woodland above the persistence thresholds, while the average harvest over that time might be correspondingly lower (representing a classic resilience trade-off).
From a climate adaptation sovereignty perspective, the people of Mazvihwa should define their own persistence thresholds: what constitutes “enough” harvest, cows, or woodland for a village the size of Mudhomori (approximately 100 households in 2013). Through interviews with the Muonde research team, we established minimum thresholds of 50 cows, 48 metric tons of harvest, and enough woodland biomass to replace Mudhomori’s current amount of brushwood fencing (280 metric tons of woodland biomass). However, we know from the team’s historical data that in recent decades the system has had years of zero harvest and years with as few as five cows in Mudhomori village. In the interest of exploring the sensitivity of our model’s results to the definition of these persistence thresholds, we allowed the minima to range from the Muonde team’s thresholds down to “biologically-based” minima: two cows (in order to reproduce), one adult woodland tree as a seed source (0.02 metric tons), and enough crop harvest to reseed a field (0.06 metric tons). Eitzel et al. (2020) used these biologically minimal thresholds and give details on the calculations of these minima. Both the average annual harvest and the persistence model outcomes depend on these threshold definitions: for example, if the thresholds are high, then the model will not persist very long, and the harvest will not have enough time to accumulate.
We illustrate the practical importance of Indigenous climate adaptation sovereignty by comparing the results when preferring maximum harvest versus maximum persistence, or for different persistence threshold definitions. For the biologically minimal persistence thresholds, we test the sensitivity of the average annual harvest variable to all the same model parameters we examined for persistence in Eitzel et al. (2020), using the same methods (see Appendix 2 for details of the generalized additive model used to test sensitivity). Also for biologically minimal thresholds, to assess how the two outcomes (persistence and average annual harvest) traded off for different proportions of crops, we averaged over other interventions and divided the model runs into bins of proportion-crops, graphically representing them for both historical (as a baseline) and high-variation rainfall scenarios.
We can order each combination of the six categorical management interventions (numbered 3–8 in Table 1) by their degree of success based on either definition (harvest or persistence). We assessed the practical importance of using the community’s definitions by examining how different the two rankings are, i.e., how much the definition matters in suggesting which intervention combinations are “best.” We calculated the average persistence and annual harvest for all simulations in each of the 64 possible combinations of these interventions and ranked each combination in terms of highest to lowest persistence, and highest to lowest average annual harvest. We compared the “best” combinations to each other, and also calculated Kendall’s Tau for the two lists. Tau is typically used as a nonparametric test of correlation (ranging from 1 for two identical lists to -1 for reversed lists), so a significant Tau means that the two lists are more similarly ordered than a random ordering. We know that our two outcomes are correlated (because the way they are constructed depends on each other), so we expect Tau to be significantly different than 0. We also use Tau to understand how different the lists are from each other by examining the effect size, asking how our Tau value compares with lists that are only slightly different from each other, e.g., a list with each consecutive pair of items swapped. These analyses were performed in R (R Core Team 2018).
To explore the sensitivity of both outcomes (persistence and average harvest) to different definitions of persistence varying between the biological minima up to Muonde’s minima, we used a script in Python (Python Software Foundation 2018) to post-process the model outputs of our parameter sweep. No new NetLogo code was created for this analysis, but rather we randomly selected a persistence threshold independently for cows, crops, and woodland for each of our model runs, and used them to determine whether each model run had persisted all 60 calendar years and what the average annual harvest was for the duration it persisted. We did this 10 times and aggregated the results to average over possible variation in the procedure. We then graphically examined how the evaluation of the most sustainable crop proportion depended on each threshold.
For biologically minimal thresholds (as in Eitzel et al. 2020), the relationship between model persistence and average annual harvest is largely inverse (Fig. 3). Using only the persistence definition, the system is more sustainable for very low proportions of crops and produces very low average harvest, while the harvest definition points to success at very high crop proportions, which result in zero models persisting all 60 model years. There is a compromise at a threshold around 10 metric tons of annual harvest, where persistence can range from hardly any models persisting up to almost 20% of models persisting; this corresponds to around 50–60% proportion-crops. Thus choosing to use either or both measures of sustainability would suggest a different optimal crop proportion.
Like crop proportion, the most sustainable combinations of management interventions were different according to the two different outcome variables (for biologically minimal thresholds). For both rainfall scenarios, the top-ranked intervention for one sustainability definition was lower-ranked for the other definition (Table 2; see Appendix 3 for full tables with all 64 possible combinations, ranked in order by either harvest or persistence.) These results align with a comparison of the single-variable sensitivity analysis results for annual harvest (Appendix 2) and persistence (Eitzel et al. 2020): storing grain had the biggest positive effect on sustainability regardless of the definition of sustainability, but crop innovations and stone walls were unhelpful for persistence and helpful for average annual harvest, while preserving forest and moving cows to better grazing were helpful for persistence and unhelpful for harvest.
Comparing the full ranked lists, the Kendall’s Tau value was 0.43 for the historical rainfall scenario and 0.47 for the high-variation rainfall scenario, which is an intermediate level of correlation (that is, the two lists are still quite different from each other). For comparison, our lists’ Kendall’s Tau is smaller than Tau for lists with consecutive items swapped relative to the original (0.97) and for lists with each element swapped 16 positions away (0.49; see Appendix 3 Table A3.5 for more examples). The rankings of intervention combinations from our two measures of sustainability success are more different than these examples, though they are significantly more similar than two random ranked lists (p < 0.001), as expected.
As requirements for persistence became more stringent, fewer and fewer models were able to meet these criteria; at Muonde’s desired persistence thresholds, few if any models persist (see Appendix 4 for additional discussion of which thresholds are most responsible for this effect). This is likely due to ZAPMM’s omission of many additional Indigenous adaptations, and the fact that quantitative validation indicated that it produced cow counts much lower than the real system. For model runs that do persist, those with proportion-crops set to intermediate values tend to have higher persistence, with largest values in the range of 30–50%. Proportion-crops otherwise has little interdependence with cow or woodland thresholds in terms of their collective effect on persistence, though there is slightly higher persistence for lower proportion-crops as the cow threshold is raised (more woodland is needed to sustain a larger cow population). The harvest threshold does have a predictable effect: as the threshold becomes higher, models with lower proportion-crops will not be able to generate enough harvest and these become automatically not persistent (Fig. 4).
Across all results, higher variation rainfall results in worse outcomes, regardless of the definition of sustainability. The patterns described above hold for both historical and higher variation rainfall (Figs. 3-4, Table 2).
We examined two different ways to expand sustainability definitions in ZAPMM: comparing persistence with average annual harvest, and altering minimum persistence threshold values. We asked what the model has to say about ideal crop proportions and combinations of other management interventions. The spirit of ZAPMM was always to generate discussion and create connections between what the model is able to represent and what is locally understood to be happening in the real agro-pastoral system in Mazvihwa. We therefore offer first a discussion of the model’s outcomes, and then offer historical context for our sustainability definitions and discuss a wider range of adaptations employed in Mazvihwa.
One clear finding from the model is that intermediate crop proportions enhance persistence while balancing the need for adequate harvest (visible in both Figs. 3 and 4). This is a key point for Muonde, addressing their initial concern regarding community land use planning to constrain ongoing conversion of woodland to fields. Notably, only examining the behavior of the model for biologically minimal persistence thresholds (as we did in Eitzel et al. 2020) did not reveal this pattern. And even for biologically minimal thresholds, using different definitions of sustainability (persistence and harvest) highlight different combinations of categorical management interventions as successful. Agriculture-focused interventions like crop innovations contribute to higher harvest, and a wider variety of interventions including preserving forest contribute to higher persistence.
High-variability scenarios are systematically worse in both outcomes (see Appendix 4 Fig. A4.1), reinforcing concerns that climate change will worsen the difficulty of choosing between different definitions of sustainability: the only way to get similar persistence in the high-variability scenario is to be willing to accept lower average annual harvest (Fig. 3). The model also indicates that more interventions may be necessary to achieve a persistence level similar to the historical case (See Appendix 3, Tables A3.1 and A3.3: the best persistence, 41.04%, corresponds to five interventions in the high-variation case, and for a similar persistence level in the historical case, 41.00%, only three interventions are needed). Because higher rainfall variability due to climate change worsens outcomes, it becomes increasingly critical to consider multiple ways of assessing and enhancing sustainability.
The actual minima in the community’s dataset indicate that there have historically been many fewer livestock than Muonde’s desired threshold (the minimum was 5; Muonde’s desired minimum threshold was 50), and that the lowest harvest was lower than their desired threshold (there were drought years with no harvest; Muonde’s threshold was 48 metric tons), so in reality, the community in Mazvihwa has been obliged to sustain their system with lower thresholds than their stated model goals (by drawing on external resources). During this time period, the agro-ecosystem’ resources have been drawn down as well, which the research team and local farmers have observed in a variety of ways (for example, the amount of land set aside for woodland grazing has been steadily declining). There have been extremely difficult times for the community as well (long droughts, need for outside aid, high mortality due to the AIDS epidemic, and political and economic instability). So, though Muonde’s persistence thresholds are higher than the system’s historical minima, these thresholds reflect the community defining for itself what they need to thrive, not just to survive, setting their goals for their future higher than the way they have functioned in the past.
We must also recognize how colonial history relates to our definitions of sustainability. Requiring a certain amount of grazing area to be sustainable (part of our persistence definition) is related to the idea of a system’s livestock carrying capacity, which has potential negative connotations in Mazvihwa. Farmers have historically managed to maintain livestock populations well above what has been thought of by scientists as the carrying capacity of the system, and in fact numbers have continued to increase over time despite apparent system degradation, e.g., in soil, vegetation biomass, and wetlands. After evaluating the system to be above its carrying capacity, the Rhodesian government required farmers to sell animals at low prices while allowing white ranchers to buy the animals at a profit (Scoones 1990), a practice that is painfully remembered by the people of Mazvihwa.
In addition to this top-down and potentially inaccurate assessment of carrying capacity and unjust method of adjustment, the Rhodesian government was also responsible in the first place for the concentration of people into “Native Reserves” with low agricultural potential. Overcrowding in these areas put heavy pressure on the agricultural productivity of the ecosystem, which led in turn to top-down government land use planning, an intervention that eroded Indigenous governance systems around balancing individual and community needs for woodland and stymied Indigenous agricultural innovations and adaptation. This legacy explains Muonde’s focus on reclaiming community agency in pushing the system toward greater harvest while moderating the risk of collapse. This trade-off between persistence and harvest is therefore of great interest, as is the insight that an intermediate proportion of cropland may strike a balance between the two.
Several of the historical and recent management strategies employed in Mazvihwa help to smooth over year-to-year variation, potentially increasing resilience of the system to shocks. First, farmers have historically stored harvests, allowing one good year’s bumper crop to get the community through multiple years of little harvest. Muonde is also encouraging local farmers to cultivate drought-adapted Indigenous small grains (sorghum, millet) that allow greater harvest in dry years and store better than other crops. And Muonde’s water harvesting techniques can help to buffer the community against both within-year and between-year variation in rainfall (see Appendix 1 for more detail). These strategies reflect a classic definition of resilience: that the system can recover to a given state after a shock, for example, a drought. Sustainability, from this perspective, is about defining what state is desirable and then ensuring that the system will recover from shocks and return to that state (Carpenter et al. 2001). And resilience can be defined as more than simply the tendency of a system to return to its initial state after a shock: it can be conceptualized as including human and system agency to adapt and even transform in the face of ongoing disturbance (Galappaththi et al. 2019). Resilience as transformation is a core purpose of Muonde’s work.
Because models are necessarily incomplete representations of systems, and our work is intended to support the Indigenous adaptation in Mazvihwa, we complement the model by sharing some of the additional strategies the community has used both traditionally and recently. Traditionally, after harvest is complete, animals are allowed to graze on the crop remnants (e.g., Müller et al. 2007), relieving some of the pressure on woodland vegetation and provisioning livestock in the off-harvest season. Muonde’s recent woodland restoration projects, which include grazing areas, sacred forests or rambotemwa, and “key resources” like vegetated ditches that grow faster in dry years (Scoones 1989), can provide more grazing for livestock but also yield wild food as well as spiritual and medicinal benefits (Lunga and Musarurwa 2016, Woittiez et al. 2013). As the postindependence government opens up some of the land formerly held by commercial ranches and mining companies for resettlement, some farmers have moved into these nearby areas to take advantage of new resources. Families may take on small jobs (“piece-work”), pan for gold, or find other sources of income like burning wood for charcoal. In addition, there are many groups and local institutions that support community members in difficult times, including women’s garden associations, churches, and nongovernmental organizations in addition to Muonde (Eitzel et al. 2016). People in Mazvihwa have also engaged in labor migration, with family members moving to big cities in Zimbabwe and neighboring countries to find work and send funds home. Some of these strategies are seen as undesirable “coping” within this society but they reflect the ingenuity and flexibility of the community.
ZAPMM was built to support Indigenous innovation and knowledge in Mazvihwa. It was designed to spark discussion rather than to prescribe particular management strategies, a fortunate aspect of the process, given that different definitions would have yielded different prescriptions. We discovered that broadening our definitions of sustainability was also instrumental in enabling the model to answer the principal community question (what proportion of land to allocate to agriculture) as well as the ancillary question of what other interventions were most effective. Although a typical view of sustainability would emphasize overall long-term persistence, key for the community are questions of how much they need in each aspect of their system in order to thrive. When we can build the model with attention to these local definitions (especially harvest), the relevant trade-offs with persistence actually become clearer. This means that the model can help the community to debate what proportion of their land area should be dedicated to crops, regaining responsibility for something that has grown uncontrollably without community coordination and planning since the retreat of local government from land use planning.
Along those lines, the Muonde Trust has run community workshops with local farmers and leaders using the model as a discussion tool to generate new thinking about collective action in making local land use decisions. Based on these workshops, Muonde’s leaders have proposed to local decision makers a plan to negotiate land use rights more flexibly, allowing farmers to recultivate currently fallow land rather than cutting down woodland to create more crop fields, and they have already begun piloting this policy. In addition, they are writing a biocultural protocol protecting the sacred forests (rambotemwa) and have formed a Rambotemwa Protection Committee. They have begun hosting restoration festivals in which community members and leaders plant seedlings from Muonde’s nursery in parts of the rambotemwa that have been degraded. Future work could explore how the model was used to support these community discussions with decision makers to coordinate land use decisions in order to balance harvests with other values in the system.
Collective action such as these discussions about land use planning and local forest protection, when based on traditional norms in local and Indigenous groups, can be key to coping with the impacts of climate change (Nyima and Hopping 2019) and restoring the resilience of degraded social-ecological systems (Lansing 2007). Farming adaptations to climate change can be derived from traditional Indigenous knowledge, and a key part of sustainability at the local level is the exchange of this knowledge among smallholder farmers (Aniah et al. 2019), making Muonde’s farmer-to-farmer training programs particularly important. Work like Muonde’s is essential in a place like Mazvihwa, where scarce resources and authority made disjointed by colonialism have meant that collective planning has been difficult. Integrating Indigenous values, governance, and knowledge into policies may allow systems that have become maladapted in the face of climate change to escape the historically dependent trajectory they are on (Parsons et al. 2019). Our modeling process and exploration of sustainability definitions has helped Muonde to reach out to local leaders and community members and to generate discussion about how best to plan for land use, reinforcing Indigenous climate adaptation sovereignty through new creation of knowledge and collective self-determination.
We gratefully acknowledge the Santa Fe Institute for hosting the initial collaborations during their 2015 Complex Systems Summer School, as well as affiliates Stephen Guerin, Andrew Berdahl, Joshua Epstein, and fellow student Juan Carlos Castilla for advice in our initial modeling efforts, and Isaac Ullah and Matthew Potts for additional advice. Tallinn University of Technology gave us time on their high-performance computing cluster for our initial BehaviorSpace parameter sweeps, and supported A. Veski's travel to the Summer School. This work would be impossible without the many dedicated members of the Muonde Trust who have gathered data over the last 35 years, and we are especially grateful to those who participated in our model development workshops: Handsome Madyakuseni, Austen Mugiya, Tatenda Simbini Moyo, Britain Hove, Nehemiah Hove, Khaniziwe Chakavanda , Simon Ndhlovu, Sikhangezile Madzore, Innocent Ndlovu, Blessed Chikunya, Maria Fundu, Lucia Dube, Guilter Shumba, Ndakaziva Hove, Sarah Tobaiwa, Moses Ndhlovu, Adnomore Chirindira, Oliver Chikamba, Cephas Ndhlovu, Esther Banda, Egness Masocha, Abraham Ndhlovu, Princess Moyo, Godknows Chinguo, Nenero Hove, Hosea Ndlovu, Valising Mutombo, Beulah Ngwenya, Ruth Munhundagwa, Vonai Ngwenya, Nyengeterai Ngandu, Saori Ogura, Alejandra Cano. Finally, we thank Trevor Caughlin for comments on a draft of the manuscript. Publication costs, M.V. Eitzel's salary and travel, and the community-based workshops were supported by the United States National Science Foundation under Award Number 1415130. NSF had no involvement in study design; collection, analysis, and interpretation of data; writing of the paper; or the decision to submit for publication.
The data/code that support the findings of this study are openly available in CoMSES.net at https://doi.org/10.25937/ta23-sn46.
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