This document outlines the important steps in conducting social science research on agricultural pests and their control methods. It discusses estimating the extent and intensity of Striga infestation through farmer surveys, expert opinion, and georeferenced observations. It also covers testing control technologies on-station and on-farm through experiments, and analyzing the economics of different control methods using partial budgeting and marginal rate of return analysis. Farmer evaluation of technologies is also an important step through scoring and ordinal regression. The document provides examples for each step specific to research on Striga in Africa.

1. Economic Analysis of Striga
and its Control Methods
Hugo De Groote
International Maize and Wheat Improvement Center
(CIMMYT)
Renewed Strategies for Striga Workshop
ILRI, Nairobi, November 28-29 2017

2. Striga economics
• Striga is a parasitic weed that attacks cereal crops, retarding
plant growth, resulting in stunted and withered plants.
• As with other pests, many control technologies are available
and technically effective, but few are adopted, why?
• Social scientists can help answer that question by:
– Analyzing if technologies fit the system, are feasible and
economical
– Provide economic analysis (published, on-farm research is rare)
– Economic analysis over time with appropriate (high) discounting
rate
• Currently:
– Few social scientists are involved in Striga/agricultural pests
research
– Benefits are often overstated and costs underestimated
– In the larger Striga research community, economic analysis is not
well established
– The same goes for many other pest problems (storage, FAW, …)
• In this presentation:
– An outline pf the important steps in social science research of pests
and its control measures
– For each step: the principles are explained, and illustrated with
examples

3. Steps in economic analysis of Striga
1. Estimating the extent of the problem (area infested)
2. Estimating the intensity of the problem (infestation
levels, damage and crop loss)
3. Testing pest control methods on farm
4. Economic analysis of control methods
5. Farmer evaluation of control methods
6. Modeling and econometric analysis
7. Impact assessment

4. 1. Estimating the extent of the problem
– Methods:
• Assess if Striga is a problem in a
particular area (PRAs)
• Estimation of area estimated,
through
• Georeferenced observation:
– Direct observation: often preferred by
biological scientists, but expensive,
high variation and usually once in time
– Farmers’ observations: farmers can
recall several years, and give a fairly
good indicator
– Expert opinion

5. 1.1. Estimation the extent - PRAs
• Group discussions, combined with transects, village mapping,,
• Participants rank general constraints, followed by ranking of pest problems
• In Kenya, PRAs in different AEZs found Striga was the first pest problem in
the Moist Transitional zone, but was not mentioned elsewhere (stemborers
and weevils were)
Pests rank 1 rank 2 rank 3 rank 4 rank 5
Moist transitional stem borer weevils squirrels
Moist mid-altitudes striga weevils stem borer termites rodents
Dry mid-altitudes weevils stem borer chaffer grubs termites
Dry transitiona; weevils chaffer grubs stem borer termites squirrels
Highland tropics stem borer weevils cutworms rodents
Lowland tropics rodents stem borer weevils beetles storage moths

6. 1.2. Georeferenced
observations
– Direct estimation: difficult
and expensive
– Farmers’ observations
– Kenya:
• Clear limit 1500-1600 m
• 212,000 ha of maize
• 6 million people
– Tanzania: data never
analyzed
– West Africa ?
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Kisii
North
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Buret
Bomet
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Gishu
Lake Victoria
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Lake Victoria
Moist mid-altitude zone
Districts
Major roads
Striga presence
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De Groote, H., Wangare, L., Kanampiu, F., Odendo, M., Diallo, A., Karaya, H., & Friesen, D. (2008). The potential of a
herbicide resistant maize technology for Striga control in Africa. Agricultural Systems, 97(1-2), 83-94.

7. 1.3. Expert opinion, extrapolate
Region
Country
% maize area
in striga
Maize area (1000 ha)
Striga infested area
(1000 ha)
Maize production (1000
tons)
East Ethiopia 5 1,410 70 2,744
East Kenya 5 1,665 83 2,138
East Tanzania 12 2,000 240 3,230
East Uganda 11 750 83 1,350
East Other 311 20 271
Subtotal 8 6,135 496 9,734
South Malawi 20 1,538 308 1,733
South Mozambique 10 1,312 131 1,437
South South Africa 1 3,204 32 9,965
South Zimbabwe 10 1,200 120 550
South Other 708 965 1,315
Subtotal 17 7,962 1,556 15,000
West Benin 18 714 129 843
West Burkina Faso 10 380 38 481
West Cameroon 13 504 66 966
West Congo (DRC) 13 1,483 185 1,155
West Côte d'Ivoire 7 1,000 65 910
West Ghana 20 733 143 1,158
West Nigeria 22 4,466 983 4,779
West other 929 105 1,034
Subtotal 17 10,209 1,713 11,326
Total 15 24,306 3,636 36,060
De Groote, H., Wangare, L., Kanampiu, F., Odendo, M., Diallo, A., Karaya, H., & Friesen, D. (2008). The potential of a
herbicide resistant maize technology for Striga control in Africa. Agricultural Systems, 97(1-2), 83-94.

8. 2. Estimating intensity (crop loss)
2.1. Expert opinion
• indirect estimation
• Expert opinion
– Area in Africa: 15%
– Crop loss: 30-50%
• Extrapolate
– Value of maize in SSA: US$
3.5 billion (sorghum about
1/3)
– Value of maize in striga
area: US$ 500 million
– Value of crop loss: US$ 250
– 500 million
De Groote, H., 2007. Striga economics. In: Ejeta, G. and J. Gressel (eds.), Integrating new technologies for Striga
control: Towards ending the witch-hunt. World Scientific Publishing, Singapore, pp. 265-280.

9. 2.2. Farmers’estimates
district area (ha) current prodn (kg/hh) crop loss (%)
Vihiga 0.48 416 55
Siaya 1.05 579 35
Rachuonyo 0.99 526 60
Homabay 1.3 635 57
Bondo 0.41 239 72
Kisumu 1.44 929 41
Total 0.98 552 53
● Farmer survey – representative sample
● Compare current yield with estimated yield if striga was not
present
● Express crop loss as % of the potential
● Results from Kenya: 50% crop loss
● Confirmed in different surveys
De Groote, H., Wangare, L., Kanampiu, F., Odendo, M., Diallo, A., Karaya, H., & Friesen, D. (2008). The potential of a herbicide resistant maize
technology for Striga control in Africa. Agricultural Systems, 97(1-2), 83-94.

10. 2.3. Estimating crop loss
directly
• Classic way to estimate crop loss from pest
problems is to compare yield of treated vs.
untreated plots
• For Striga, complications
– it is hard to control Striga 100%
– Need to go on-farm, highly variable, therefore
costly
• Alternative:
– Compare selected infested and uninfested (Kim,
2006), but there are many confounding factors, no
random assignment
• Modeling:
– Link yield to infestation level (regression)
– Average crop loss is the difference of yield at 0
infestation and at average infestation (for linear
regression)
– But simple models assume no factors common to
yield and probability of striga infestation

11. 3. Testing control methods
– Continuum of testing: on-station, on-farm/researcher managed, on-
farm/farmer managed, farmers’ own experiments: decreased
control, increasingly realistic
– On-station: usually great, not that very interesting
– Example: herbicide-coated maize seed (IR maize)
0
500
1000
1500
2000
2500
3000
3500
4000
control herbicide coating
fertilizer level
maizeyield(kg/ha
no fertilizer
30 kg N, 77kg P
60 kg N, 77 kg P
3.1. On-farm, researcher managed:
- In fields heavily infested with Striga
- IR-miaze: strong yield increase
- No effect of fertilizer
- Maybe:
- soil fertility was high, but not
realized with the Striga (although
contrary some literature)
- sample size to small
- farms not representative
De Groote, H., Wangare, L., & Kanampiu, F. (2007). Evaluating the use of herbicide-coated imidazolinone-
resistant (IR) maize seeds to control Striga in farmers’ fields in Kenya. Crop Protection, 26, 1496–1506.

12. 3.2. Testing on-farm,
farmer managed
District Striga counts/m2 Nitrogen (kg/ha) maize yield (kg/ha)
IR maize control IR maize control IR maize control
Bondo 3.68 10.75 * 43.3 7 ** 1701.5 631.7 *
Vihiga 0.43 1.67 ** 26.7 5.8 *** 831.1 276.4
Rachuonyo 9.17 8.15 0 0 1157.2 824.7
Total 4.27 6.93 23.2 4.4 ** 1291.1 599.1 *
 Large, representative sample in 3 districts
 Simple design:
 compare IR maize to farmer’s variety
 All other factors farmers’ choice
 But: visit by NGO officials, and farmers gave
preferential treatment to IR
 IR did not work in 1 district, likely heavy rain
De Groote, H., Wangare, L., & Kanampiu, F. (2007). Evaluating the use of herbicide-coated imidazolinone-resistant (IR)
maize seeds to control Striga in farmers’ fields in Kenya. Crop Protection, 26, 1496–1506.

13. 4. Economic analysis of control (on-farm, 2002)
control
herbicide (30
g/ha)
no fert. fert no fert. fert
Benefits Average yield(kg/ha) 985 1644 3663 3575
Gross field benefits($) 199 333 741 723
Costs Cost of herbicide($/ha) 0 0 4 4
Cost of fertilizer($/ha) 0 125 0 125
Cost of labour to apply
fertilizer($/ha) 0 4 0 4
Total costs that vary($/ha) 0 129 4 133
Net benefits($/ha) 199 204 737 590
Analysis Extra benefit 133 542 524
Extra cost 129 4 133
MRR 0.04 135.4 2.9
• Partial budget analysis
• Marginal analysis (MRR)
De Groote, H., Wangare, L., & Kanampiu, F. (2007). Evaluating the use of herbicide-coated imidazolinone-
resistant (IR) maize seeds to control Striga in farmers’ fields in Kenya. Crop Protection, 26, 1496–1506.

14. 5. Farmer
evaluation
5.1. Scoring
– Establish selection criteria
– Scoring 1 (v. poor)-5 (v good)
– Ordinal regression
Cropping
system Treatment
Maize
variety
Fertiliz
er
Coefficient
(log-odds
ratio)
exp(coefficien
t)
Push-pull
1 IR Yes 2.62 *** 13.7
2 IR No 2.39 *** 10.9
3 Local Yes 1.95 *** 7.0
4 Local No 2.09 *** 8.1
Maize -
soybean
5 IR Yes 1.53 *** 4.6
6 IR No 0.62 *** 1.9
7 Local Yes 0.89 *** 2.4
8 Local No 0.86 *** 2.4
16 Local No (redundant)
De Groote, H., Rutto, E., Odhiambo, G., Kanampiu, F., Khan, Z., Coe, R., & Vanlauwe, B. (2010). Participatory
evaluation of integrated pest and soil fertility management options using ordered categorical data analysis.
Agricultural Systems, 103, 233-244.

15. 5.2. Farmer evaluation: contingent valuation
(to estimate the market for a technology)
• Farmers are explained a new
technology
• Contingent on there being a
market, how much would they
want to pay
• Ex. IR maize, Kenya:
• farmers interested, would buy
3.67 kg each,
• Would extrapolate to a market
of 1,735 to 2,332 tons in
Kenya
Demand for herbicide resistant maize seed
0
1
2
3
4
5
6
0 100 200 300 400 500
Seed price (KSh/kg)
Demand(kg/farmer)
De Groote, H., Wangare, L., Kanampiu, F., Odendo, M., Diallo, A., Karaya, H., & Friesen, D. (2008).
The potential of a herbicide resistant maize technology for Striga control in Africa.
Agricultural Systems, 97(1-2), 83-94.

16. 6. Modeling and Econometrics
• Conventional economic analysis production function:
• Recommendations are calculated as the input level where marginal
benefit equals marginal costs:
• In pest control
• The benefit of the control measure is calculated in crop loss abated
• Therefore, we need two equations
– effect of the pest on production,
» with all the compounding factors:
– how the control measure controls the pest
• And careful design and analysis
– Further complication:
• Most Striga control measures take several seasons
• Therefore, we need to accumulate benefits and costs over time,
with the appropriate discount factor:
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17. 6.1.Economic analysis over time
Western Kenya: PP, rotations, IR maize
0 200 400 600 800 1000
Mono, local, no fert
Mono, IR, no fert
Mono, local, fert
Mono, IR, fert
Monocropping
Crot, local, no fert
Crot, IR, no fert
Crot, local, fert
Crot, IR, fert
Crotalaria rotation
Soy, local, no fert
Soy, IR, no fert
Soy, local, fert
Soy, IR, fert
Soybean rotation
PP, local, no fert
PP, IR, no fert
PP, local, fert
PP, IR, fert.
Push Pull system
US$/ha/year
Revenue (US$/ha/2 seasons)
Maize ($0.20/kg)
Napier ($0.075/kg)
Desmodium ($0.033/kg)
Soybean ($0.526)
-200
-100
0
100
200
300
400
500
600
S1 S2 S4
Profit(US$/ha/season)
Season (1-6)
Profit (US$/season)
Push-pull
Soybean
Crotalaria
Mono-cropping
S3
S5 S6
S3
S5 S6
De Groote, H., Vanlauwe, B., Rutto, E., Odhiambo, G. D., Kanampiu, F., & Khan, Z. R. (2010). Economic analysis of
different options in integrated pest and soil fertility management in maize systems of Western Kenya.
Agricultural Economics, 41(5), 471-482.

18. Marginal rate of return over time
RankTechnology Cost MRR Comments
Mean St. dev.
1 Push-pull, IR, no fertilizer 1275 444 652 1.13 MRR low
2 Push-pull, local maize, no fertilizer 1172 521 560 Dominating
3 Push-pull, local maize,fertilizer 1098 444 983 2.43 MRR high
4 Push-pull, IR, fertilizer 1082 505 1071 1.87 MRR low
5 Soybeans, local maize, fertilizer 353 504 976 0.14 MRR low
6 Soybeans, IR, fertizer 317 385 1019 0.02 MRR low
7 Soybeans, local maize, no fertilizer 309 383 658 Base level
8 Soybeans, IR, no fertilizer 198 369 700 (1st profitable
9 Monocrop, IR, fertilizer -41 258 1156
Profit (discounted, U
De Groote, H., Rutto, E., Odhiambo, G., Kanampiu, F., Khan, Z., Coe, R., & Vanlauwe, B. (2010). Participatory
evaluation of integrated pest and soil fertility management options using ordered categorical data analysis.
Agricultural Systems, 103, 233-244.

19. 6.2. Modeling: example (2004 on-farm trials)
Model 1 (without striga) Model 2 (with striga)
Variables (differences) B Std. Error p B Std. Error p
(Constant) 485.5 281.3 0.09 369.5 252.8 0.152
N/ha 6.9 4.9 0.16 7.0 4.3 0.113
Striga count/ha -48.6 14.5 0.002
R2 0.05 0.27
N 40 40
● Combined effect of germplasm and striga control: 485 kg/ha
● Adding Striga to model: Split: 252 kg/ha germplasm, 49 x 4= 200 kg/ha from striga control
● Need to adjust design (add soil fertility and seed bank) and proper econometrics (2 stage
estimation)
● Dep. Variable: Yield difference between IR and control :
● Independent: differences in Nitrogen application and Striga counts
De Groote, H., Wangare, L., & Kanampiu, F. (2007). Evaluating the use of herbicide-coated imidazolinone-
resistant (IR) maize seeds to control Striga in farmers’ fields in Kenya. Crop Protection, 26, 1496–1506.

20. 7. Impact assessment
• Ex ante:
– Economic Surplus: compares value of increased
production, adjusted for price reduction and consumer
benefits, with development costs
– Poverty analysis:
• overlay poverty map with Striga map (Kenya: 61% poverty)
• Include poverty indicators in surveys, farmer evaluation,
adoption studies
• Ex post
– Adoption studies
• Level of adoption
• Factors that influence adoption (logistic regression)
– Redo the above analysis

21. 8. Conclusions
• Economic analysis in Striga research
– Seminal research was conducted in Kenya
– Methods were developed and applied to IR maize, crop rotation, push pull
• Unfortunately,
– donors and leading research organizations lost interest
• Research did not extend to other countries, and no adoption or impact
studies were conducted
• Way forward: incorporate social science and economic analysis,
following these steps:
– Estimating the extent (area) and intensity (crop loss) of Striga
– Test control methods on-farm, with decreasing researcher involvement but farmer
more involved in experiment and design, follow-up
– Proper economic analysis of control methods (partial budget, MRR)
– Farmer evaluation of control methods
– Modeling and econometric analysis: operationalize conceptual framework, estimate
the relations
– Impact assesment
– To be added: poverty analysis

22. Thank you
for your
interest!