Groundnut in Kenya and Tanzania

Happy Daudi, CIMMYT, Kenya

1. Why did you use tricot?

Tricot gives us quality and robust data and is more cost effective than other methods for on farm testing. Tricot also involves more farmers for better results.

2. What lessons did you learn using tricot?

We learned that the willingness of farmers is important to succeed and when you come with a new and an effective approach, they are more willing to participate. We also learned that if you want to involve many farmers, you need a lot of seed and can be a limitation, especially for crops with low multiplication rates. The ClimMob software language is only in English, and this can increase the costs because we have to hire translators. Lastly, we learned that tricot helps improve our engagement with the network of farmers that participate.

3. How has tricot helped in decision making?

Tricot data helped us write a proposal for a varietal release. Normally the farmer’s perspective is not well represented, but by collecting feedback from farmers via tricot we were able to develop a good product profile for groundnut.

4. Are there other insights that you gained from the tricot approach?

With tricot, we can estimate on farm genetic gain, which is important for research and breeding, and before tricot we could only do this with on station data which was very different. Tricot also helped us group varieties according to market segment; for example, in Tanzania, farmers selection traits are different from zone to zone.

5. What can you advise people who want to use tricot.

I would advise that people must organize and plan well. Don’t worry about the software – it is easy to learn it. However, you need to multiply enough seeds for the trials so it is important to see how many farmers you can give seeds to with what you have. Keep in mind the trial needs to be according to the growing season as many farmers do not have irrigation.

6. Do you think the tricot approach is appropriate for variety evaluation and release?

Yes, it is especially useful for breeders. They can get more preference information than other approaches because tricot involves many farmers, who are the ones who would adopt the released varieties. Tricot also allows to extract more information on genetic gain than other approaches and it is useful for grouping data according to the market segment.

7. What challenges need to be addressed?

The software needs to be better at harmonizing quantitative data. Currently the methodology is more focused on qualitative data (ranking) but breeders need quantitative data a well (e.g. for yield).