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RESEARCH PROGRAMS ON
Climate Change,Agriculture andFood Security
Integrated Systemsfor the HumidTropics
Roots, Tubersand Bananas
Yield Gap Analysis and Crop Modeling WorkshopNairobi, Kenya
POTATO YIELD GAP ANALYSIS: A REVIEW
International Potato CenterSub-program: Production Systems and Environment
POTATO YIELD GAP ANALYSIS:A REVIEW
Masai Lodge, 24-29 June 2013
D. Harahagazwe, R. Quiroz, B. Condori, C. Barreda and F. de Mendiburu
GYGA Workshop, Kenya 2012
WHY YIELD GAP ANALYSIS MATTERS?
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
• Business as usual will not meet projected global food demand in the coming years due to various factors
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
• 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..)
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)
POTATO PRODUCTION AND PRODUCTIVITY IN SSA
Source: FAOSTAT, 2013
Annual Production in SSAEastern and Central Africa
Year
1960 1970 1980 1990 2000 2010 2020
Ann
ual P
rodu
ctio
n (x
1000
t)
0
500
1000
1500
2000
2500
3000
3500
Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda
Source: D. Harahagazwe (FAOSTAT datasets)
Annual Production in Southern Africa
Year
1960 1970 1980 1990 2000 2010 2020
Ann
ual P
rodu
ctio
n (x
1000
t)
0
1000
2000
3000
4000
Angola Madagascar Malawi Mozambique
West Africa
Year
1960 1970 1980 1990 2000 2010 2020
Ann
ual P
rodu
ctio
n (x
1000
t)
0
200
400
600
800
1000
1200
Nigeria
Source: D. Harahagazwe (FAOSTAT datasets)
Annual Production in ECA region
Year
1960 1970 1980 1990 2000 2010
Ann
ual P
rodu
ctio
n (x
1000
t)
0
2000
4000
6000
8000
Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda
Source: D. Harahagazwe (FAOSTAT datasets)
YIELD GAP CONCEPT
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)
Conceptual framework of various Yg(Source: Lobell et al., 2009)
YGF<YGE<YGM
• 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
Yield gaps estimated at 2 levels
•Local focus (site-based approach)
•Upscaling approach (region, national, global)
Assessment of Yp and Yg (Lobell et al., 2009)
3 methods:1)Model simulations2)Field experiments and yield
contests3)Historical maximum farmer
yields
Attributes of Best Crop Models used in Yg analysis (van Ittersum et al., 2013)
Daily step simulationFlexibility to simulate management practicesSimulation of fundamental physiological processesCrop specificityMinimum requirement of crop “genetic” coefficientsValidation against data from field crops that approach
Yp (Yw)User friendlyFull documentation of model parameterization and
availability
But the best assessment of Yg SHOULD BE an integration of (Lobell et al., 2009):
a) Remote sensingb) Geospatial analysisc) Simulation models,d) Field experiments and e) On-farm validation
POTENTIAL YIELD
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”.
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/ ).
Soils
Climate
Germplasm
CO2
Weeds
Crop Traits
Diseases
Radiation
Temperature
Water
Pests
Nutrients
Potential yield (Yp)
Attainable yield
Actual yield (Ya)
Dry Matt
er Yield
Defining factors
Limiting factors
Reducing factors
Hierarchy of Yield Drivers and Associated Yield Levels
Source: R. Quiroz (Modified from Penning de Vries & Rabbinge, 1995)
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
ACTUAL YIELD
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
Actual Potato Yield at Global LevelSource: D. Harahagazwe (datasets from Monfreda et al., 2008)
ZOOMING IN – AFRICA (Source: D. Harahagazwe, datasets from Monfreda et al., 2008)
Tuber Yield in SSAEastern and Central Africa
Year
1960 1970 1980 1990 2000 2010 2020
Tube
r Yie
ld (t
.ha-1
)
0
5
10
15
20
25
Burundi DR Congo Ethiopia Kenya Rwanda Tanzania Uganda
Source: D. Harahagazwe (FAOSTAT datasets)
Southern and West Africa
Year
1960 1970 1980 1990 2000 2010 2020
Tube
r Yie
ld (t
.ha-1
)
2
4
6
8
10
12
14
16
18
Angola Madagascar Malawi Mozambique Nigeria
Source: D. Harahagazwe (FAOSTAT datasets)
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
EXAMPLE OF YIELD GAP
Potential Yield, Attainable Yield and Actual YieldEx: Ndinamagara (Cruza 148) Gisozi, 2007
Potential Yield Actual Yield
Fres
h Tu
ber Y
ield
(t.h
a-1)
0
10
20
30
40
50
44
3
Yield Gap (41 t.ha-1)
Yield Gap Fraction (0.93)
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 agro-climatic 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.
• 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.
ASANTE SANA!
THANKS A LOT!
MERCI BEAUCOUP!
MUCHAS GRACIAS!
MUITO OBRIGADO!
MURAKOZE!
