“The Cartesian Dream is all about prediction and control,” said Mario Giampietro of the Institute of Environmental Science & Technology, Autonomous University of Barcelona. “The COVID pandemic has shown that it’s not true. In emergencies answers are needed even when you don’t know everything. It’s not about having the right model, it’s whether the model you have is useful for the problem.”
“Complexity entails a big challenge for science: ‘all models (and data) are wrong, but some are useful’,” he added. “When dealing with complex issues we have to learn how to use models and data.”
And using models, data and practical tools is what Giampietro and his colleagues Aloisius Louie, Mathematical Biologist, Canada, Ansel Renner of the Institute of Environmental Science & Technology, Autonomous University of Barcelona and Richard Sikora of the Institute for Crop Science and Resource Conservation at the University of Bonn, hope to use to provide some answers to the challenges of transforming agriculture in southern Africa. The work builds on a STIAS long-term project on sustainable agriculture that resulted in the book Transforming Agriculture in Southern Africa: Constraints, Technologies, Policies and Processes.
Agricultural production in Africa is extremely diverse reflecting vastly different cultural contexts and environmental conditions, and the challenge of transforming it is highly problematic for governments, policy and decision makers.
The group explained that so far, we have largely witnessed a general failure of agricultural-development policies—in both developed and developing countries—resulting in farmers abandoning rural areas and, frequently, localised environmental degradation.
“This failure can be explained by the fact that conventional models based on reductionism cannot handle the matter,” said Giampietro. “For this reason, we propose the use of practical analytical tools based on complexity (complex not complicated!), capable of generating a robust diagnosis and anticipation of the problems we should expect in the development of rural Africa. The recipe we propose is a mixing of different domains related to theoretical complexity, metabolic principles in practice, and agriculture in practice, generating a new type of science that does not solve ‘problems’ (whose problems?), but rather helps communities and decision makers to better understand sets of situations, options and threats to be able to take better-informed decisions.”
The approach being developed by the group aims to help policy makers in Africa make better decisions in relation to the changing agricultural landscape as well as to develop capacity to deal with practical, important and urgent issues.
“The idea is to work with rather than against complexity,” said Giampietro.
Two types, neither sustainable
He explained that there are two types of agriculture with different functions and effects on society and the environment—currently neither is sustainable.
Low External Input Agriculture (LEIA) (subsistence agriculture) results in too-low productivity of land and labour, and High External Input Agriculture (HEIA) (industrial agriculture to feed the cities) entails high food costs, drastic reductions in the numbers of farmers and high environmental impact. The global movement towards HEIA is a forced consequence of economic and population growth.
Technology, machinery and fossil energy changed the relationship between agriculture and rural areas. In the pre-industrial era the rural population provided energy to rural production by means of labour—it was low input and did not, in relative terms, result in major environmental damage. But in the post-industrial period it’s high input (in seeds, machines, fertiliser, etc.) agriculture. New technologies mean increased production and falling prices. Larger farms mean fewer farmers and are not necessarily economically viable without subsidies.
“Agriculture at the moment is not sustainable,” said Giampietro. “The sword of Damocles lies over agriculture in Africa. We need a deliberation support system to Identify and illustrate the entanglements and help policy formulation across ministries.”
The group proposes that studying the factors associated with the sustainability of agriculture requires four lenses to explore the option space of: 1) viable food-production techniques; 2) desirable social practices; 3) compatible interactions with the environment; and, 4) stable financial and political processes.
They also stress that it’s important to move beyond silos caused by decision making within individual ministries (agriculture, social affairs, environment, finance, rural development) and to rather focus on addressing individual problems by unpacking the relationships between the factors.
“We propose a deliberation-support system in which the four lenses can be used, first individually to get relevant information, and then to integrate the different results in a coherent, transparent knowledge space,” explained Giampietro. “The system we have developed is based on relational analysis and can be used to guarantee a fair, informed deliberation: 1) in diagnostic mode, checking the situation of different agricultural systems; and, 2) in anticipation mode, running a series of ‘what if’ simulations exploring possible changes to the status quo, such as changes in population, demography, diet, technology, economic structure, and/or in climatic context.”
“We want to be able to answer questions about what production techniques and technologies you should use taking into account space, social practices, environmental pressures, and the technological, socio-economic and cultural contexts at a macro, farm and country level.”
He described it as an application that can be used to handle quantitative answers, tailored and calibrated to the specific situation in different countries.
“We are looking at relationships over vectors,” he explained. “Of course all numbers are useful but it’s about how they are used. We need to establish the relationship between different numbers. In relational analysis you look at the relationships over the functional and structural elements.”
“It’s about generating and structuring knowledge spaces and then flexibly interfacing with them using information dashboards,” added Renner. “When anticipating drivers of change such as increasing population and urbanisation, such a knowledge space and approach is needed to explore possible re-arrangements of agricultural paradigms. It’s essentially about mobilising the knowledge base and seeking possible solutions via many solutions.”
“We believe it could be a powerful way of helping policy makers make decisions,” said Sikora. “At least it may force policy makers to look at their decisions and hopefully change.”
“It’s a different way of using science,” continued Giampietro. “The expert helps to deliberate about what is important and to identify the areas in need of more analysis. It doesn’t solve problems but helps communities and decision makers to understand different situational options for effective decisions. It’s not a model but a set of interactions to which we can add new models and benchmarks.” He also emphasised that it cannot predict the future but can predict aspects of the system that will be affected by unpredictable events.
In discussion, the group highlighted that no tool can replace human decision making or influence power structures but it “allows governments to see the consequences of decisions and gives decision makers needed lag time to change structures in the system”.
They also pointed out that technology itself is not the central problem for agriculture, the real issues are the relationships between farms, households and land uses.
“People are leaving rural areas because rural life doesn’t allow them options,” said Giampietro. “We need to be discussing multifunctional use of rural areas. We need to start the discussion, give rural people the option of doing something else, make rural life more economically diversified, change the story.”
Michelle Galloway: Part-time media officer at STIAS
Photograph: Anton Jordaan