1. Yield Gap Analysis and Crop Modeling Workshop
Nairobi, Kenya
RESEARCH PROGRAMS ON
Climate Change,
Agriculture and
Food Security
POTATO YIELD GAP ANALYSIS: A REVIEW
Integrated Systems
for the Humid
Tropics
Roots,
Tubers
and Bananas
International Potato Center
Sub-program: Production Systems and Environment

2. POTATO YIELD GAP ANALYSIS:
A REVIEW
Masai Lodge, 24-29 June 2013
D. Harahagazwe, R. Quiroz, B. Condori,
C. Barreda and F. de Mendiburu

3. GYGA Workshop, Kenya 2012

4. WHY YIELD GAP ANALYSIS
MATTERS?

5. WHY YIELD GAP ANALYSIS MATTERS?
• SSA will account for one half of the world population
increment by 2050
• Continued increased demand for agricultural products
(food, feed and biofuels):
– agricultural food demand is expected to increase by 50%
by 2050 (Tilman et al., 2001)
– The feed grain demand in developing countries is expected
to increase by 84% by 2020 (1997’s baseline – Delgado et
al., 1999)
• Unfortunately the maximum possible yields achieved in
farmers’ fields might level off or even decline in many
regions over the next few decades (Lobell et al., 2009)
– plateau theory

6. • Business as usual will not meet
projected global food demand in
the coming years due to various
factors

7. Three broad options to face the global
food demand (Licker et al., 2010):
–Expand the area of croplands at the
expense of other ecosystems;
–Increase the yields on the existing
croplands (i.e. closing the yield gaps)
–Reallocate current agricultural
production to more productive uses

8. • 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 research
R&D as it is a good management decision tool
for improved resource-use efficiency (land,
fertilizers, water, etc..)

9. Examples of yield gaps at global level
(Neumann et al., 2010)
Based on frontier yield (source:
– Wheat:
36 %
– Rice: 36%
– Maize: 50 % (c. 80% in
Africa)

10. POTATO PRODUCTION AND
PRODUCTIVITY IN SSA
Source: FAOSTAT, 2013

11. Annual Production in SSA
Eastern and Central Africa
3500
Annual Production (x1000 t)
3000
Burundi
DR Congo
Ethiopia
Kenya
Rwanda
Tanzania
Uganda
2500
2000
1500
1000
500
0
1960
1970
1980
1990
2000
2010
2020
Year
Source: D. Harahagazwe (FAOSTAT datasets)

12. Annual Production in Southern Africa
Annual Production (x1000t)
4000
Angola
Madagascar
Malawi
Mozambique
3000
2000
1000
0
1960
1970
1980
1990
Year
2000
2010
2020

13. West Africa
1200
Annual Production (x1000t)
1000
Nigeria
800
600
400
200
0
1960
1970
1980
1990
2000
2010
2020
Year
Source: D. Harahagazwe (FAOSTAT datasets)

14. Annual Production in ECA region
Annual Production (x1000t)
8000
6000
Burundi
DR Congo
Ethiopia
Kenya
Rwanda
Tanzania
Uganda
4000
2000
0
1960
1970
1980
1990
2000
2010
Year
Source: D. Harahagazwe (FAOSTAT datasets)

15. YIELD GAP CONCEPT

16. Yield Gap
•Yg = Yp – Ya
• “The difference between Yp and
average farmers’ yields over some
specified spatial and temporal scale
of interest” (Lobell et al., 2009)

17. Conceptual framework of various Yg
(Source: Lobell et al., 2009)
YGF<YGE<YGM

18. • Yg can be defined and measured in a variety of
ways: Lack of consistency in Yg analysis in
literature
• Normally developed countries have low yield
gaps for some crops like maize, wheat, potato
and rapeseed (Licker et al., 2010)
• Yield gaps across Africa are on the higher end
of the spectrum for many crops

19. Yield gaps estimated at 2
levels
•Local focus (site-based
approach)
•Upscaling approach
(region, national, global)

20. 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

21. Attributes of Best Crop Models used
in Yg analysis (van Ittersum et al., 2013)
 Daily step simulation
 Flexibility to simulate management practices
 Simulation of fundamental physiological processes
 Crop specificity
 Minimum requirement of crop “genetic” coefficients
Validation against data from field crops that
approach Yp (Yw)
User friendly
Full documentation of model parameterization
and availability

22. But the best assessment of Yg SHOULD
BE an integration of (Lobell et al.,
2009):
a)
b)
c)
d)
e)
Remote sensing
Geospatial analysis
Simulation models,
Field experiments and
On-farm validation

23. POTENTIAL YIELD

24. Yield Potential vs. Potential Yield
Definition 1 (Evans and Fischer, 1999):
Yield potential: “yield of a cultivar when grown in
environments to which it is adapted, with nutrients
and water non-limiting and with pests, diseases,
weeds, lodging, and other stresses effectively
controlled”.
Potential yield: “the maximum yield which could be
reached by a crop in given environments, as
determined, for example, by simulation models
with plausible physiological and agronomic
assumptions”.

25. Definition 2 (GYGA project):
Yield potential = Potential yield:
“yield of a crop cultivar when grown with
water and nutrients non-limiting and biotic
stress effectively controlled”(van Ittersum
et al., 2013 - GYGA group
http://www.yieldgap.org/ ).

26. Hierarchy of Yield Drivers and Associated Yield Levels
Crop Traits
Germplasm
Defining factors
Potential yield (Yp)
CO2
Dry Matter Yield
Radiation
Limiting factors
Attainable yield
Climate
Temperatu
re
Reducing factors
Water
Actual yield (Ya)
Nutrients
Soils
Weeds
Pests
Source: R. Quiroz (Modified from Penning de Vries & Rabbinge, 1995)
Diseases

27. Measuring yield potential: a mission
impossible?
• A concept rather than a quantity: quid
estimation? – perfection! (Lobell et al., 2009)
• Well-managed field studies in which all growth
factors are eliminated
• Replicated over a number of years and sites to
obtain a reliable average Yp
• Representative of the dominant cropping system
in the region of interest (planting date, spacing,
cultivar maturity, etc..)
Source: GYGA, 2012

28. ACTUAL YIELD

29. Actual Yield (Ya)
(Source: van Ittersum et al., 2013)
• Working definition:
“The yield actually achieved in a farmer’s
field”
• Time and space dimension:
– The average yield (in space and time)
achieved by farmers in the region under the
most widely used management

30. Actual Potato Yield at Global Level
Source: D. Harahagazwe (datasets from Monfreda et al., 2008)

31. ZOOMING IN – AFRICA
(Source: D. Harahagazwe, datasets from Monfreda et al., 2008)

32. Tuber Yield in SSA
Eastern and Central Africa
25
Burundi
DR Congo
Ethiopia
Kenya
Rwanda
Tanzania
Uganda
Tuber Yield (t.ha-1)
20
15
10
5
0
1960
1970
1980
1990
Year
2000
2010
2020
Source: D. Harahagazwe (FAOSTAT datasets)

33. Southern and West Africa
18
Angola
Madagascar
Malawi
Mozambique
Nigeria
16
-1
Tuber Yield (t.ha )
14
12
10
8
6
4
2
1960
1970
1980
1990
2000
2010
2020
Year
Source: D. Harahagazwe (FAOSTAT datasets)

34. Sources of Actual Yields
• Preferably at site level (as defined by selected
weather station and dominant soil types):
mean and spatial/temporal variation
• High quality sub-national data (county,
district, village, municipality level)
• Last option (coarse resolutions): Global
gridded yield datasets/maps like Monfreda et
al., 2008 (best available global crop yield
datasets) or SPAM
Source: GYGA, 2012

35. EXAMPLE OF YIELD GAP

36. Potential Yield, Attainable Yield and Actual Yield
Ex: Ndinamagara (Cruza 148) Gisozi, 2007
50
Fresh Tuber Yield (t.ha-1)
44
40
30
Yield Gap (41 t.ha-1)
Yield Gap Fraction (0.93)
20
10
3
0
Potential Yield
Actual Yield

37. REFERENCES
•
•
•
•
•
•
•
•
FAOSTAT. 2013. URL: http://faostat3.fao.org/home/index.html
Evans, L. T. and Fischer, R. A. 1999. Yield Potential: Its Definition, Measurement, and
Significance. Crop Sci. 39 (6) 1544-1551.
Ittersum, M. K. van, Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. A. and Hochman, Z. 2013.
Yield gap analysis with local to global relevance-A review. Field Crops Research 143, 4-17.
GYGA. 2012. Global Yield Gap and water Productivity Atlas (GYGA) Workshop Training
Materials. 6-8 June 201, Naivasha, Kenya.
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.
Van Wart, J., Van Bussel, L.G.J., Wolf, J., Licker, R., Grassini, P., Nelson, A., Boogaard, H.,
Gerber, J., Muelle, N.D., Classens, L., Cassman, K.G., Van Ittersum, M.K. 2013. Use of agroclimatic zones to upscale simulated crop yield potential. Field Crops Res. 143. 44-55.
Monfreda, C., Ramankutty, N., Foley, J.A. 2008. Farming the planet: 2. geographic distribution
of crop areas, yields, physiological types, and net primary production in the year 2000. Global
Biogeochem. Cy. 22, 1-19.

38. • MapSpaM. SPAM data Download. URL:
http://mapspam.info/download/ accessed on 19 June 2013
• 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. Forecasting agriculturally driven global
environmental change. Science, 292, 281-284.

39. ASANTE SANA!
THANKS A LOT!
MERCI BEAUCOUP!
MUCHAS GRACIAS!
MUITO OBRIGADO!
MURAKOZE!