By Mihaela Tabacaru
We all know it: no measure is better than its implementation.
Sustainable agriculture in Africa has never been an easy task to achieve. Food security, meaning access to sufficient, safe and nutritious food for people’s needs and preferences at all times, is still on the goals, not achievements list. Yet well-planned and well-targeted investment in small-scale farming could help countries feed themselves and reduce their dependency on outside assistance. Policies, however, often fail.
Let`s take one example: Zambia. Zambia is among the most food insecure countries in the world, held back by a lack of access to fertilizer, seed, transport, markets and other social infrastructure. Although a fertilizer support system is in place for small-scale farmers, it is often an unstable and unreliable source of nutrients for their crops.
The devil is in the implementation. Well intended policies and technological innovations often fail to produce large-scale effects for small-scale farmers because they operate in a complex system that comprises interaction and interdependence between nature and human decisions. Each year, farmers need to decide what to spend their money on. For example, they need to choose between activities that increase food production immediately, like the application of fertilizers, or activities that increase long term soil fertility but require farmers to tolerate smaller harvests today. The complexity of the trade-off arises from the dynamic and interlinked nature of farm decisions: while budget allocation decisions are of the farmers` decision domain, the outcomes of these decisions, such as food production, are not.
Complex systems – unintuitive, yet understandable
Complex systems like the ones ensuring food security for the farmers in Zambia deceive our human intuition about what is right to do and when. We humans are wired to think short term, even if we know it’s bad for us. Just think about our (mis)use of credit cards: “buy now” is important, payment is in the future. The glass is (half)-full now, we can think about the empty part later.
Complex systems are messy, have many stakeholders, a web of interactions, and a lot of different meanings about what is right to do when. Solving problems in such a systems requires a lot of coordination, working together, negotiating meaning, and accounting for long-term consequences of common actions.
One of the crucial components for solving problems together is understanding problems together , or so called socially shared metacognition: “a goal-directed, consensual, and complementary regulation of joint cognitive processes in the collaborative learning context”. In other words, making sense of the problem together. Socially shared metacognition emerges when group members make their thinking visible and ask questions requiring an explanation or a rationale. Based on these explanations and rationales, the group discusses whether or not they select a new approach or a new strategy for proceeding in problem solving. Translated to complex systems, successful policy implementation requires stakeholder involvement and collaborative problem solving. Understanding and managing complexity calls for a coordinated effort.
One of the most important stakeholders in our example, often ignored, are the farmers themselves, who have the best picture of their challenges, and in-depth knowledge of their food system, and what works best for them . The solution seems obvious: ask the farmers. The challenge: how do you talk to farmers, who have low to no formal education, about policies in agricultural systems and its interconnections, issues that are particularly unintuitive, even for well educated policy makers or politicians?
Researchers in system dynamics (SD), a model based analysis and policy design methodology that focuses on complex interdisciplinary problems, believe the key to solving the puzzle of this issues is NOT teaching, but involving. Kopainsky et al.`s research shows that farmers do not lack information about crops and seeds, investment and how to plant. What they lack are ways to collectively create a language for expressing and acting on that knowledge: weighing and positioning the pieces of knowledge against each other, how they interconnect, and how each year`s choice of investments leads to a path. Or another. Using simple analogies and co-creation, the researchers have managed to elicit useful insights that have the potential of having a long term impact on the decision making habits of the farmers.
The key to securing food for the farmers is not more information, but activating and complementing the knowledge they already have through a well designed process.
Co-creating understanding of the food system: a water glass analogy
The process the researchers used is based on SD modelling. A well known process for policy analysis and design, the SD field has a long tradition in facilitating learning about complex systems through the use of system diagrams and computer simulation models. SD has a lot to offer when it comes to looking at systems, by increasing the learner’s` ability to, among other, understand how the behavior of a system arises from the interaction of its agents over time, discover feedback processes underlying the visible patterns, recognize delays and understand their impact . But the methodology, even if applied in a participatory way in group settings, relies on computer simulation and quite abstract reasoning.
Understanding the allocation, accumulation and depletion of farmers` resources (e.g. their budget or their food stocks) was crucial to understanding the system, and creating the common language needed to influence it. A big question for Kopainsky and her colleagues was therefore how theycould bring this complex modelling method to farmers. The novelty? Instead of using a computer, they used water glasses to represent accumulations or stocks. The method Kopainsky and her colleagues used, can be applied to other systems than food systems.
Method: Collectively creating an understanding of the (food) system – step by step
Start by discussing key terms & issues for the theme at hand (in this case food security and money), using a variety of representations for the terms: text labels, pictures, physical objects. Use enough time on this to allow good inputs.
- Ask participants (farmers) to elaborate on how these key terms/issues are related. The outcome: a draft map of interdependencies, that reinforce or hinder each other, also referred to as a causal loop diagram.
- Focus first on desired behavior, what we want to see, e.g. the reinforcing mechanism: more food harvested- more food sales – more revenue from sales – increased affordability allowing cultivating more land – increased production – even more food and so on.
- Discuss disturbances that prevent this desired mechanism from playing out: brainstorm on factors that can drain the water glasses.
- The above discussion provides the basis for collecting options available to participants to re-fill the water glasses or to start filling them up
- Reflect on the behavior that these processes give rise to.
- Spend enough time evaluating the direct/indirect, desired/undesired consequences of the available options.
- Finish with a discussion on what participants could do themselves with this new knowledge to improve their situation( food security and livelihoods) in a sustainable way
Half-full or half-empty water glass?
The water glass was used as an analogy for the resources farmers operate with, like money/budget and harvested food. The discussion between the researchers and farmers revolved around budget allocation decisions (how to spend their money), and the likely outcomes of their decision in the short and long term (does that money come back to me in the end, or does it deplete my water glass even more). Any action that increased the money stock, like for example “piece work” (doing paid work for others) increased the money stock and would add water in their glass. Any action that decreased the stock (e.g. buying fertilizers) would empty a bit the glass.
Reinforcing mechanism elicited by Zambian farmers
The results were very encouraging: participants ended up critically discussing how the government, particularly with its fertiliser subsidies, affects the working of reinforcing feedback loops between food production and income. Their insights covered how budget allocations impacts production, the importance of planning and record keeping in order to measure the impact of your decisions, and even differentiating between concepts such as yield and production. Their conclusion was simple: Plan and decide together!
A particularly good example was the reflection on piece work: before the workshops, there was wide agreement among participants that piece work is important for meeting short term money and food needs; after the workshops, participants were much more differentiated in their reasoning about piece work. The short term money and food needs are real and cannot simply be ignored. However, participants had become acutely aware of the need for careful timing of piece work: they had realized that it was absolutely key that they first weeded their own land before they went to help their neighbors. This strategy would allow them to reach both short- as well as long-term objectives. In the short run, they would be able to make some money to cope with immediate money and food needs. and in the long run, they would not harm their own harvest because they would have taken good care of their land
This is no little thing: stakeholder involvement has been proven difficult in many other domains, and it is especially challenging when the level of complexity of the problem meets the relatively low level of formal education of the farmers. The farmers came themselves to the conclusion that short term thinking, or firefighting, is detrimental in the long run, that, for example, by doing work for others, they are not weeding their own crops, thus endangering their resource, “the water glass of food” for themselves, ending up not filling it as they thought of in the beginning. The same glass was half-full or half-empty depending on how they used the water.
Why does the water analogy work?
The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming seemed to make for a well-balanced toolbox. Causal loop diagrams can be thought of as sentences that are constructed by identifying the key variables in a system (the “nouns”) and indicating the causal relationships between them via links (the “verbs”). By linking together several loops, one can create a concise story about a particular problem or issue. The toolbox provided incentives for the participants to engage through familiar items from their day-to-day practice while at the same time posing conceptual challenges that need to be resolved in the group.
Furthermore, Kopainsky`s work shows that understanding a relatively small number of system concepts was enough to allow for a broad range of systemic considerations, like understanding the causal relations between elements in the system and closing the loop of causality (virtuous and vicious circles of growing food), the concept of stock levels, reinforcement and self-regulation, cross-impact, and how it all depends on initial states (how much water one has in the glass to begin with). Keeping it simple was, in this case, also enough.
The “participatory SD” method Kopainsky and her colleagues used, seems to be well adaptable to support a learning-by-doing approach for this particular audience. But farmers did more than doing: they reflected on their doing. Research shows that learning is augmented if one deliberately focuses on thinking about the actions one has been doing. Reflecting after completing a task has been shown to significantly enhance the learning process, even more than doing more of the same thing.
Instead of conclusion
Being prepared for and resilient to uncertainty means that farmers need to better understand the food and farming system as a system – with all its complexity and feedback structures .
Participatory SD seems to be unlocking modelling to a relatively new audience and strengthens local communities through shared systems learning, networking and an increased focus on local governance and empowerment.
Complex systems need not be complicated systems if implementation measures are carefully adapted to the users`needs. The devil is actually in the knowledge about the system.
 “Innovative schools – teaching and learning in the digital era” (2015)
 Tuike Iiskala (2015), Socially shared metacognitive regulation during collaborative learning rpocesses in student dyads and small groups, PhD dissertation University of Turkue, Finland, available at https://www.utupub.fi/bitstream/handle/10024/113800/AnnalesB407IiskalaDiss.pdf?sequence=4&isAllowed=y
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