Zimbabwe: Making Sense of Knowledge Life Cycles in African Agriculture

opinionBy Charles Dhewa

While African agriculture and local socio-economic development is anchored on knowledge, skills and ability to apply (practical wisdom), trust and relationships are fundamental in acquiring and applying knowledge.

Farmers can have knowledge but lack skills to convert it into practical outcomes like crops, meat, milk and honey.

The ability to mobilise resources, methods and navigate environmental challenges may be lacking due to poor understanding of existing knowledge life cycles. As opposed to starting with knowledge, most development interventions start at the skills level. This is revealed through excessive emphasis on skills training which does not adequately consider farmers’ ability to apply what they learn from outsiders.

On the other hand, agricultural markets are mainly interested in the outcome from combining knowledge, skills and abilities, expressed through volumes of commodities, profit, growth-orientation, etc. Emphasis on outputs by development interventions also tend to ignore the application of knowledge, skills and abilities to produce better outcomes such as improved livelihoods, income, better decision making processes, wealth creation and employment creation, among others.

Managing climate change requires new knowledge life cycles

Many African communities, policy makers and development agencies realise the absence of new knowledge life cycles that can manage a changing climate. A decrease in crop yields and quality have caught most knowledge workers off-guard. There is urgent need to accurately identify knowledge gaps and layer such knowledge appropriately before utilising it. Continuous updating can enable knowledge curves to stay up until they reach a common sense level. This effort can start as upgrading the same commodities through value addition — new varieties or new products. Consumers can assist in identifying, sharing, classifying and utilising knowledge related to different commodities.

Where do we start when identifying and building a knowledge life cycle?

A sensible starting point is data gathering. Such data has to be contextualised and converted into information which answers questions like who, what, where and when? Answering such questions generates usable knowledge (how and why?) Some of the insights can show why agricultural commodities behave the way they do on the market. When this awareness reaches its peak, it becomes wisdom and actors can either go back to start looking for more knowledge elements.

It is also important to figure out the source of building a knowledge cycle. If it’s about the consumer, the process boils down to creating awareness and knowledge about commodities. This is where it becomes critical to classify knowledge by consumer class and niche markets.

The next step is satisfying knowledge needs embedded in commodities such as crop varieties, fruit type and quality, etc. Ultimately this translates into customer loyalty or specific niche markets. Given that consumer tastes and preferences continue to change, they can signal the need for new knowledge life cycles in terms of new tastes, preferences, levels of income, etc. As a result, the need to develop new knowledge cycles becomes apparent. It means carefully monitoring the changing consumer needs and consumption patterns.

Mapping knowledge life cycles along agricultural value chains such as production, marketing, processing and consumption is very important. This will avoid cases where knowledge life cycles at production level gets to the peak while consumption level knowledge life cycles are still very low and unstructured. In such situations, you don’t just remove a crop variety out of circulation without consulting consumers or the market. On the other hand, processors can end up having their own variety which has nothing to do with knowledge existing at production levels. The prevalence of different knowledge life cycles compels farmers, informal markets, formal markets and processors to work together in order to harmonise their different knowledge life cycles.

In agricultural communities, whose role is it to take stock or follow knowledge life cycle trends?

At the moment, it’s not very clear because every value chain actor is barely aware of the knowledge life cycle associated with his/her commodity. That is why the market often rejects commodities whose production did not take into account knowledge life cycles in the market. Agricultural performance in developing countries is being hindered by the prevalence of disintegrated knowledge life cycles. Characterising farmers can reveal their knowledge life cycles in relation to skills and ability to apply. A new farmer is at a different knowledge life cycle while some farmers have already reached their peak and now want to be assisted in fetching new knowledge life cycles. Farmers in the medium level may be on a growth path informed by their own knowledge life cycles.

Limitations of sporadic consultancy in gathering information in agricultural markets

Besides the fact that most of the details on the ground will have changed by the time a report is produced, the following are additional limitations:

The sampling is not usually properly structured. It’s easy to talk to the wrong people when those knowledgeable are known and available but perhaps unwilling to participate. Sporadic consultancy often resort to random sampling which means they can spend time talking to the wrong people. Transporters and other observers may have more knowledge than traders who are close to the action. Enough time needs to be spent on figuring out the criteria on who to interview.

Consultants cannot build confidence, trust and relationships immediately to be able to gather reliable evidence.

Focus on a few value chains is not meaningful given the inter-dependence between commodities in informal markets. If you focus on tomato and maize in isolation at the exclusion of fruits, you miss the entire picture because wild fruits affect the performance of tomatoes and maize in the market. You would rather conduct a comprehensive survey covering the entire market over at least two weeks.

Characterisation of farmers and other actors can inform this process better. New farmers and those who have been farming for more than 20 years control their losses differently. Commodity characterisation is also very important. For instance, sorting necessities from high value commodities.

Losing a leaf from a vegetable bundle that costs 50c is different from losing one green pepper from a $3 pack of pepper which translates to 30 percent loss. Looking at volume and value is critical because it does not mean that high volume commodities also have high value on the market. That is why economic losses should be considered in addition to physical losses. Gathering information should not be once-off. Losses in summer when it’s humid and muddy are different from losses in winter when the environment is cold and the same with spring when it’s very hot.

Importance of tracking tools rather than once-off surveys

There is need for tools that can be used to track commodity volumes, losses, etc. Such instruments can consistently inform better than consultants who come into the market once in a blue moon. Also critical is choice of commodities — is so important. Losses of non-perishable commodities like maize or sorghum can be easily managed. Losses for these commodities are too insignificant. Besides being much easier to store, by-products can be used to feed livestock and that is a critical part of value addition. It is also not helpful to focus on pre-harvest and post-harvest because agricultural production now happens throughout the year.

Starting with a baseline

Getting into the market first to establish a baseline before introducing a tracking mechanism is very important. The next important step is building the capacity of local resource persons or institutions to gather evidence. The main advantage here is that these locals can contribute knowledge they have gathered over time. Once-off consultants do not have time to build the necessary confidence, trust and relationships important for reliable evidence gathering. There is also need for feedback mechanisms such that after gathering evidence and observing practices in the market, traders and other actors are brought together to validate findings from which recommendations for improvement can then be crafted.

Knowledge life cycles in the financial sector

The financial sector in developing countries is also struggling to understand knowledge life cycles in its industry. For instance, banks are still interested in the amount of loans disbursed and repayments rather than the outcome of those loans in terms of improving lives.

Financial institutions are stuck with old knowledge life cycles which have been over-used and over-copied. More than 200 MFIs use the same models like group lending, based on knowledge life cycles that have outlived their usefulness.

Financial inclusion means going back to develop new knowledge life cycles — identifying knowledge gaps, new customers, utilisation, etc. This should inform loan cycles based on knowledge-driven models. At the moment there is too much imitation. If banks ask people what else they want to know about banks, they will be told.

That will inform tailor-making of financial products as opposed to the conventional one-size fits all. Mechanisms, methodologies and infrastructure for tracking knowledge life cycles for each agricultural commodity are completely absent. Almost everyone now knows how to produce maize, tomatoes, chickens, small grains, goats, leafy vegetables and cattle. This is translating to gluts of the same commodities on the market. Communities stop growing when the knowledge life cycle stagnates. The young generation needs new knowledge life cycles. New farmers need their own knowledge life cycles in line with their context, capacity and resources.

ICTs can be used to identify knowledge gaps, mobilise appropriate knowledge and classify it in ways that reveal different knowledge life cycles. However, it is not useful to adapt ICTs just for disseminating price information without understanding the knowledge life cycle foundation like volumes of commodities, sources, varieties and frequency of supply. People need reasons that inform the price in terms of why the prices are what they are. Trends and volumes can inform more than just prices. The impact of climate change may not be expressed by price alone.

Helping people, communities and organisations to identify and build knowledge life cycles eMKambo has started rolling out master-classes whose themes include helping farmers, communities and organisations to identify and create their knowledge life cycles.

Most enterprises focus on the production life cycle (revenue and volumes traded) yet behind the production life cycle is the knowledge life cycle that should be updated continuously. What happens on the consumer side signals competitiveness and shows the extent to which knowledge is now common. That pushes the production life cycle.

The capacity to identify key elements of an effective knowledge-management strategy can no longer be over-emphasised. Most blue chip enterprises are collapsing because their knowledge cycles have reached their ceiling and cannot adjust to the new agile economy. Too many copy-cats are increasing competition.

Charles Dhewa is a proactive knowledge management specialist and chief executive officer of Knowledge Transfer Africa (Pvt) (www.knowledgetransafrica.com whose flagship eMKambo (www.emkambo.co.zw has a presence in more than 20 agricultural markets in Zimbabwe.

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Zimbabwe: Making Sense of Knowledge Life Cycles in African Agriculture

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Posted by on Sep 14 2017. Filed under Agriculture. You can follow any responses to this entry through the RSS 2.0. Both comments and pings are currently closed.

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