The document discusses an analysis of potato yield gaps in Sub-Saharan Africa (SSA) using participatory modeling, highlighting the importance of the crop in the region and the low yields compared to potential production. It outlines methods for assessing yield gaps and presents initial findings from workshops and regional experiments involving multiple countries. The study aims to develop tools for potato growers to better understand and achieve higher yields through community collaboration and scientific analysis.

1. Potato yield gap analysis in SSA
through participatory modeling:
Optimizing the value of historical
breeding trial data
Dieudonné Harahagazwe et al.
International Potato Center
19th EAPR Conference, Brussels, Belgium
7 July 2014

2. Presentation Outline
1. Potato crop in the African context
2. The concept of yield gap
3. Work conducted so far and
preliminary results
4. What next

3. Is potato an important Crop in SSA?
Year
1960 1970 1980 1990 2000 2010
AnnualProduction(x1000t)
0
2000
4000
6000
8000
Burundi
DR Congo
Ethiopia
Kenya
Rwanda
Tanzania
Uganda
Source: D. Harahagazwe (FAOSTAT datasets)
• Since 2005 developing
world (including Africa)
produces more
potatoes than
developed world
• But this increase is
mainly due to acreage
increase as yields are
still low (< 10 t/ha)
• Ex: Belgium with
80,000 ha of potato
per year produces
more than half of total
production of 7 ECA
countries in this
graphic

4. Yield Gap, the Concept
The difference between Yp and average farmers’ yields
over some specified spatial and temporal scale of
interest (Lobell et al., 2009)
Yg = Potential yield (Yp) – Actual yield (Ya)

5. Yp analysis provides a measure of untapped
food production capacity
Also, knowledge of yield gaps (importance,
magnitudes and causes) helps in better
orienting investments in agricultural R&D as it
is a good management & decision tool for
improved resource-use efficiency (land,
fertilizers, water, etc..)

6. Examples of yield gaps at global
level (Neumann et al., 2010)
• Wheat and Rice: 36%
• Maize: 50 % (c. 80% in Africa)

7. Assessment of Yp and Yg
(Lobell et al., 2009)
3 methods:
1) Model simulations
2) Field experiments and yield contests
3) Historical maximum farmer yields

8. Participatory Potato Yield Gap
Analysis in SSA through a
Community of Practice (CoP)
Through
Partnership between CIP
and NARS and University
of Dschang (Cameroon)
And Funding from

9. Members of the CoP and co-authors
R. Quiroz, D. Harahagazwe, B.
Condori, C. Barreda, F. de
Mendiburu, A. Amele, D. Anthony,
E. Atieno, A. Bararyenya, A. A.
Byarugaba, P. Demo, J. Guerrero,
B. Kowalski, C. Lung'aho, V.
Mares, D. Mbiri, G. Mulugeta, B.
Nasona, A. Ngugi, J. Njeru, B.
Ochieng, J. Onditi, M. Parker, J. M.
Randrianaivoarivony, E. Schulte-
Geldermann, C. M. Tankou, G.
Woldegiorgis and A. WorkuCoP
0 1 2
Yearsofexperience
0
5
10
15
20
25
30
35
Mean

10. Objective of the study
Develop methods and tools that could be used to
determine what potato growers in developing
countries in general and in SSA in particular are
losing and/or could achieve.

11. Yg estimated at 2 levels
Local focus (phase 1): site & season - based
approach
Going to scale (phase 2): temporal and spatial
dimensions

12. Participating Countries

13. Approach: Community of Practices
Workshops Regional Field
Experiments
ICT(e-mail
exchange, Skype
meetings, phone
calls, SMS, etc..)

14. Group Photo - First Potato Yield Gap Workshop
Kenya, June 2013
• Participants introduced to concepts, methods and tools
• Participants provide feedback on the tools developed

15. Partial View - Second Potato Yield Gap Workshop
Ethiopia, October 2013
• Tools updated and utilized by participants
• First yield gaps estimated at site level
• Work plan for regional experiments co-developed

16. Parameter Estimator, a
routine in Solanum Model

17. Free download:
http://inrm.cip.cgiar.org/home/downmod.htm

18. Key Findings
Site
R
wegura
Fongo-TongoM
ulungu
Adet1
Adet2
TigoniKabukuKabete1Kabete2Antsirabe
Sussundenga
Kuru
KalengyereSuyianM
arigat
Bem
beke
TuberYield(t/ha)
0
10
20
30
40
50
60
70
Yield Simulated on Potential conditions
Yield Observed in experiment
Average yield

19. Yield Type
TuberYield(t/ha)
0
10
20
30
40
50
60
70
Yield Simulated on Potential conditions
Yield Observed in experiment
Average yield
Yield gap

20. Way forward
Regional experiments: going on in 8 countries
(Burundi, Cameroon, Democratic Republic of
Congo, Ethiopia, Kenya, Mozambique,
Tanzania and Uganda)
Model validation
Multi-year analysis versus projected climate
change
Spatial analysis

21. References
FAOSTAT. 2013. URL: http://faostat3.fao.org/home/index.html
GYGA. 2013. Global Yield Gap Atlas web site. URL: http://www.yieldgap.org/
Lobell, D.B., Cassman, K.G., Field, C.B. 2009. Crop Yield gaps: their
importance, magnitudes, and causes. Ann. Rev. Environ. Resour. 34, 179-
204.
Neumann, K., Verburg, P.H., Stehfest, E., Müller, C. 2010. The yield gap of
global grain production: a spatial analysis. Agric. Syst. 103, 316-326.
Tilman, D., Fargione, J., Wolf, B., D’Antonio, C., Dobson, A., Howarth, R.,
Schindler, D., Schlesinger, W.H., Simberloff, D. & Swackhammer, D. 2001.
Forecasting agriculturally driven global environmental change. Science, 292,
281-284.
Licker et. Al. 2010. Mind the gap: how do climate and agricultural
management explain the ‘yield gap’ of croplands around the world? Global
Ecol. Biogeogr. (19) 769 – 782.

22. THANK YOU