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Africa RISING ESA Project Partners Meeting
1 April 2021
Virtual via Ms TEAMS
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  1. A. Kimaro, ICRAF
  2. B. Zemadim, ICRISAT
  3. C. Azzarri, IFPRI
  4. C. Thierfelder, CIMMYT
  5. D. Mgalla, IITA
  6. E. Swai, TARI-Hombolo
  7. F. Kizito, IITA
  8. F. Muthoni, IITA
  9. G. Fischer, IITA
  10. I. Dominick, WorldVeg
  11. I. Hoeschle-Zeledon, IITA
  12. J. Groot, WUR
  13. J. Kihara, Bioversity-CIAT
  14. J. Manda, IITA
  15. J. Odhong, IITA
  16. L. Claessens, IITA
  17. M. Bekunda, IITA
  18. M. Mutenje, IITA (consultant)
  19. P. Okori, ICRISAT
  20. R. Chikowo, MSU


  • Average and distributional impacts of soil and water conservation technologies in Tanzania - Dr. J. Manda (IITA)
  • Technology targeting-linking recommendation domains and farm typology data - Dr. Jeroen Groot (WUR)

Average and distributional impacts of soil and water conservation technologies in Tanzania - Dr. J. Manda (IITA)

  • Download presentation from link in the title above.


  • Irmgard. For high-level indicators on the SWT adaptation, I wonder whether one can determine the impact at a higher level if you have only considered adopting the technologies from one season while there are other seasons.
  • Julius. We assessed adoption in the number of years that farmers have been using the technology. The findings show, most of them used the technology for not less than one year, and the average was around two to three years. Even when we had defined adoption differently, we could still find similar effects for example assessed farmers who had used the technologies in more than two years, and the result is still very similar to those who used the technology in two years.
  • Irmgard. Okay, then in the publication, these similarities/differences should be apparent.
  • Julius. Okay, maybe we can make a note of that and say it does not make much difference.
  • Irmgard. Julius, you said you used house food insecurity access queso? To my understanding, that is a scale to measure household access to food security. I wonder if it should not read the household food insecurity assess scale?
  • Julius. We will counter-check it. But all those presented measures access of food security.
  • Regis. What else are these farmers who are adopting SWT doing ? because there are many positively related variables. It could be the same farmers who are using SWT are also using fertilizers or other technologies. Did you try to disentangle that?
  • Julius. In terms of fertilizer, we found out at least 10% are using fertilizers. For the impact pathway, we are developing two papers, the one I presented is at the household level, there is another study at plot level where we are accounting for other inputs like fertilizers and manure to see if they also contribute to the yield. If we account for this in the regression, then all different effects remaining should be due to the SWT.
  • Regis. Okay, Julius, in the background, I suggest you introduce the farming systems context because, to me, it seems like it is a water-limited system and not a limited nutrient system.
  • Julius. I will share with you the manuscript incases of further inputs.
  • Gundula. I found it interesting that the sex of the household had is essential. However, I found it misleading in a certain way because it is less an indirect indicator may be for adult labor in the family, income, land tenure. After all, in the male-headed household, the situation is very different to the female-headed home not only sex but also the implications of the household can be the factor.
  • Julius. Yes, Gundula, you are right. From the result I shared, there are so many other things that can be controlled at the plot level, looking at the land ownership and land management ownership, which could capture more information about gender dynamics.
  • Carlo. It is unclear to me why ARBES data which is available since 2015 but not used. This data has valid counterfactuals groups. We should have used (ARBES) data to look at the technologies that, in the meantime, farmers had to adapt, and not looking at new farmers that we do not have the baseline data. The new farmers selected in 2020 do not have baseline data such as their initial characteristics.

Technology targeting-linking recommendation domains and farm typology data - Dr. Jeroen Groot (WUR)

  • Download presentation from link in the title above.


  • Regis. One of the conclusions/recommendations is you could predict the use of technologies by 70%-80% accuracy based on the presented variables. I also like to emphasize that the prediction of technologies' use is highly linked to locations /agroecology. I thought that would be the first thing to consider. We should consider the functional typologies which could be easier to apply. The details discussed are interesting, but how do we take it at the operational level?.
  • Jeroen. In principles, the idea would be you fill in those twelve variables for a farm, and it pops up with priorities for the list of crops, agroecology, farm, and other technologies follows. It is not mainly on predicting the use but also on getting the idea on the suitability for context, depending on the agroecology conditions, socioeconomic conditions, and household features. But I agree it should be packed in a way that It can be easy to use. We also invasion it could simply use on mobile phones.
  • Irmgard. Jeroen, the technology that you have selected and the ranges of adoption you found are interesting. I wonder if there is also a connection between the technologies which would influence the range. For example, if one has a high probability of adapting fertilizer, this could be linked to an increased likelihood of using improved variates.
  • Jeroen. Yes, there are some interactions like that, particularly the ones related to livestock and cropping systems and locations. I agree that there could be a correlation.
  • Irmgard. We get good results from your analysis Jeroen, but how do we use this in our research and our recommendations in practical terms?
  • Jeroen. In principle, it is simple in a sense; if you have an overview of which technologies are used, where they are used, and we have quate information within the farmers' project of the farmers…
  • Irmgard. So, it depends on other team members to use this information, which has been an issue for many years. It's nice to produce a publication, but that is not enough.
  • Jeroen. Yes, it is true. If we mobilize the data we have ( the overview of where technologies are and use by who) and link it to farm features, we can start applying it.
  • Mateete. Relating to ARBES, it came late and could not help us, and the product of ARBES that Jeroen presents also came late and could not help. We are also not able to validate from what Jeroen presented. We can write the paper, but we won't have time for validation.
  • Carlo. About the ARBES data. We have had the data six-year ahead. So, the typologies results are helpful, especially in phase two, because we scale out the technologies.

Mateete. We had already selected farmers before the ARBES. There was no way we could run away from the farmers that we have chosen and start afresh.

  • Carol. In phase two, we could still use the data because the data could tell us where the scale-out should happen if we could consult the field researchers, a field researcher, a chief scientist, and Jeroen. We could have matched the expert knowledge on the fields about the farmers' and communities' with the objective say their knowledge, and Jeroen could compare with the biophysical data set.
  • Mateete. The other point is about scaling Carlo is, AR is a research project, and at least with ESA project toward scaling, we have been working with our developing partners. We cannot impose what we get from our research because they have their programs and objectives. Also, we are not going to take these development partners to only our farmers.
  • Comment. Jonathan. Regarding to the practical application of the Farm Typology results. I see other projects develop tools and applications to make things like site-specific fertilizer recommendations, which allow farmers to enter some descriptors and suggest to farmers what to use. I think to leave a legacy from the effort made in Farm Typology research, it is my idea that we should consider developing a tool and enter some characteristics with a list of validated technologies from the project that could give specific priority leads.
  • Jeroen. It is a good idea.