1. Farmer-led innovation:
Takes into account farmer’s needs and aspirations
Based on good understanding of local situation
Is embedded in the social networks of farmers
Supported by organisations / people that work in collaborative ways with farmers and learn as they go
Research: what are the gaps and opportunities in support network around farmers?
It should be clear to you that we take a farmer-central perspective in our programme: this means that we value the role of farmers as co-producers of knowledge. We believe that agricultural support services should be close to farmers and that they should have a stake also in shaping how new innovations look. This kind of approach is different to the ‘top-down’ view of agricultural extension; it rather reconstructs innovations and the learning environment from below; trying to explore gaps and opportunities between farmers and the supporting actors.
“Process by which people develop new and better ways of doing things using their own resources and their own initiative”

2. Collaborative learning
Linear extension no more sufficient: innovation processes are dynamic and interactive, also unpredictable: demand-driven approaches may require a range of innovation support services. Not a menu of options! Depending on the problem at hand: it may be necessary to co-develop a new technology (e.g. better variety that is suited to local context) but innovation may also be about removing policy obstacles – may be a combination also.
Needs diagnosis – is also about exploring what is there: we had very nice presentation yesterday showing how innovation may be very diverse; it responds to a local situation but is not always fitting perfectly with what support actors are providing.
o Technical innovation
o Livelihood innovation: engage in entirely new livelihood (e.g. driver is land scarcity)
o Organizational innovation (sponsorships, joint funds for boreholes, farmer associations and rice partnerships that are based on compensation in kind; types of credit union.
Intermediation is finding a match between supply of services and demand. Especially: work to match demand and supply; our project is trying to do this; but requires also that you build on existing structures.
Arrows: show that agricultural innovation processes are dynamic; require a flexible, matching process with different services being offered (especially when challenges are related: food security plus intensification plus different interests of parties like COCOBOD and forestry commission).
LP specific: process of collaborative learning in which experts who add knowledge on the topic of the intended innovation team up with local farmers who bring understanding of the local situation, demand and implementation potentials.
Monitoring: not only to meet your performance indicators but also more general about how organisations collect information about farmers and how they then use monitoring of their impact or effectiveness as input for redesigning the services you provide. In this way you get a learning cycle: learn – report - adjust
to achieve a learning organisation: organisations that have systems in place to allow for good ‘demand articulation’ and have flexible systems.
Source: Kilelu et al. 2014

3. Intermediary roles in agricultural innovation
Articulate needs of farmers
Make knowledge accessible
Facilitate experimentation with farmers
Resolve conflicts
Coordinate a network
Build linkages across people and org’s: ‘Bridging organisations’
Advocacy / creating awareness
Influence policy makers, make new policies
More?
Different types of intermediaries: can be farmers who support farmer-to-farmer learning, can be scientists who collect data on local context and bring in new insights, may be value chain actors who develop new technologies and ‘good practices’ with farmers; facilitation of farmer field schools. In terms of our scientific evidence: bring in heterogeneity of farmers (interactions between groups) and participatory scenario development (‘business as usual; environmental prospects), role of local change makers (not the same as evidence based).
Yesterday discussed also in terms of people who are in touch with farmers but also linking tohigher-level institutions.
Roles may be articulating needs, building new linkages across groups of actors; making knowledge accessible, facilitate experimentation and mediation of conflict.
With this I want to show that there are many roles to fulfil: perhaps you will also recognise yourselves already doing one of these things: this means that you might actually be acting as an intermediary but are not consciously doing so. It also means that around this table there might be great innovation potential when we start combining these roles and putting our heads together our a challenge that we are facing from our respective positions.
We are also aware that such systems may be very intensive to manage: there have been science-led programmes like the CoS innovation platforms that had a very interesting approach to identifying a locally-relevant technology or policy innovation thru diagnostic studies; but we also know that these projects tended to collapse when the support from scientific institutions was pulled back.

4. Our focus: Linking levels (cross-level learning)
Value chains: A blind spot?
Research questions:
What are local interactions (socio-natural-VC) , farmer needs and locally-based innovations / opportunities? (not only technical or commodity focused…)
What are relevant actors and scales?
Action research:
connect levels
Strong linkages to existing programmes
Consumers
Manufacturers
Traders / processors
Connecting levels
Value chain and commodity focus: will look very much at one product and how it travels in the value chain and how support can be given to enhance quality and bulk of a certain produce and improving trust relationships between actors in the value chain; vertical relations. This implies a blind spot for seeing farmers in their environment and looking at the different interactions locally but also beyond the local that influence their livelihoods.
Through our learning platform process: we have started to focus on the district level, seeing that those actors closest to farmers actually perform important support roles and are much closer to farmers; they could also potentially fulfil a stronger role which is not foreseen in the classic VC approach. New actors into focus like LBCs who also engage in extension, provide livelihood security.
Usually there is a national innovation platform that coordinates more local Ips; national actors will then be there for experimentation (local sites) and national levels to influence policy etc.
Building relations is a process: understanding interactions and scope for innovation.
Multi-level learning: circulate information, material, lessons learned and knowledge from one level to the other; set up new relationships, try to stimulate learning across levels. Purpose was clear yesterday with also giving some of you a place yesterday in the panel.
You are intermediaries can also have a bridging function: ensuring that the district level is connected upwards.
Dynamic exchange: roles and responsibilities may change: platform has to adjust to this. P NP VC P SA VC

5. Preliminary research results

6. Mapping of collaborative learning and exploring (mis)matches
Goal: mapping agricultural learning with farmers at district level Kwaebibirem and Ahafo Ano North Districts / N = 31 (institutional actors)
Themes
Direct relationship with farmers and other organisations
Recent innovations – who developed? Were farmers involved?
How data is collected on farmers and scale of data management
Are farmers aspirations and preferences are taken into account?
How district level actors support farmer-to-farmer learning
If / how district-level actors can influence ‘higher-up’
31 organisations / representatives of spoken to:
I also understand that we would have to include farmers later on to see how they expereince these services.

7. 1. Interactions with farmers and other parties
Interactions with farmers based on mutual trust (farmers may walk out or not show up)
Farmers in groups receive extension easier than others. Cocoa (FBS) has more functional groups than oil palm.
Not many platforms existing at District level – exception is RELC annual joint planning (OPRI coordinated - Kwaebiberem)
Collaboration MOFA-COCOBOD on diverse livelihoods (e.g. beekeeping) and health measures
Formats found: field visits / on-site trainings (NR), house visits (NR), meetings with FGs (e.g. FBS training), farmer forum. Farmers expect T&T / something – hard for AEAs to engage them without this.
Raymond: “We normally deal with groups, if you are not in a group then we don’t attend to you”. Forming groups in cocoa may have to do with access to inputs. He also explaiend you need to deliver on your promises. Roll call at meetings: if farmers lose interest, you can no longer do your work effectively. “When you visit their farms, let them know that you are all coming to learn – it all depends on how you conduct yourself” (109). AEAs are also vulnerable to central planning mistake”s: if inputs are delivered late, they suffer the brunt and risk losing their ‘clients’.
Group approach: farmer groups trained one by one in 3 year cycle: when good agricultural practices have been obtained; move on to the next group.
Vida: In our kind of work we need to get motorbikes; MOFA – AEAs might only get trainings selectively; once in the two years; some are invited and then supposed to hand over knowledge to others.
Partnerships may be there at higher levels: round-tables etc. – not part of my researh.

8. 2. Understanding of local situation
Main focus: Yield, farm size and land ownership
Public extension: Plot-level cultivation monitoring happens on project-basis, not continuously (when money dries up…)
Community development: project requests gathered per community with PCs; selection at regional level (e.g. AGL programme manager)
MOFA district planning: crop suitability for a district; determines what should be grown (statistics)
Possible mismatch between data collected and ‘good’ understanding of local situation; shift in focus on diversified livelihoods (e.g. plantain-cocoa) may change this.
Emphasize that statistics on VC / prod is not the same as understanding of local understanding.
Loyalty PCs: pass books showing where sold – registration of farmers to get loyalty; bulk COCOBOD: quality control is interested in total amounts sold per PC per district;
Cultivation monitoring example: JICA Japanese rice growing project; additional data on application of fertilizer, weed management, soil prepation etc.
FBS - COCOBOD: shifts to more agro-ecological / LEI approach – what lessons learnt on productivity and profitability?
Many projects collect information on farmers. Data collected is good for goals of value chain actors (loyalty for LBCs, bulk for COCOBOD etc.) but unclear if / how it can support innovation services.

9. 3. What room for learning from farmers?
‘Knowledge transfer’ approach in public and private extension. Farmer-led innovation was not mentioned. Focus on low adoption rates, ‘modified knowledge’ seen as imperfect implementation of best practices
Extension officers and PCs are close to farmers (could be bridging actors) but the reporting and monitoring structure is not conducive to learning;
Centralised chain of communication structures with several links; (Sub) district collects data – aggregation at higher levels
Private sector has demonstration plots (brings knowledge to farmers, facilitates extension); public sector works with conventional extension and has logistic constraints.
Basically about mismatches in collaborative learning.
Question is about transfer of knowledge versus more collaborative learning; has to do with whether farmer innovation is considered relevant and whether attitudes amongst VC chain actors and support actors are open to other ways of farming.
Experimentation facilities is very dependent on sector: MOFA struggles whereas other actors can develop these fields.
Text box: Via page 103;
Missing in my research: role of CRIG and their practices in adjusting to farmer’s needs.

10. 4. Peer-to-peer learning
CHED: Whatsapp platform for extension officers at district and national
Train the trainer (extension)
Lead farmers do extension – often without compensation
Trend: lead farmer and farmer-based decision-making structures are being formalised and farmers / leaders get more responsibilities:
What are the main considerations for choosing this method?
District level farmer representative, works within a structure of zones of which each has a chairman and representative: have meetings to jointly formulate grievances to COCOBOD; in case of Kade: grievnaces voiced were cocoa price, fertilizer not being applied – call to have it subsidised (user pays);
more responsibilities for training, doing administrative tasks and being community liaison (distribution inputs?)
Interesting question yesterday: chief farmers do not have a reporting task (Q came whether they need to report to COCBOD – become part of reporting system).

11. Conclusions: challenges and opportunities
Resources MOFA and effectiveness public extension
Top-down structure in organisations: are local agents heard?
Data collecting and reporting are not conducive to understanding local situation
Is there learning from earlier intervention approaches (‘from ‘best practices’ to ‘best fit’)?
Opportunities:
Focus on diverse livelihoods and LEI agriculture (COCOBOD) : can bridge your work as support actors
Experimentation with lead farmers: greater stake for farmers and farmers representatives to not only be part of giving training but also ensuring co-development of extension
Validation
Collaborative learning?
What were lessons learnt from earlier extension and experiments with farmer fields schools etc. (learning organisations)? Why lead farmer now so widespread?
How important is the district level actually when it comes to collaborative learning? On what basis are operational units defined? What is the link to the landscape level?
What purpose does the data collected on farmers serve?
New collective structures (e.g. cabbage) not formalised and ‘off the extension radar’
Can experimentation be improved?