Climate change poses a substantial threat to food security in Sub-Sahara African countries. Maize is the most important staple crop in Kenya, but its yields have stagnated, and the production per capita has decreased in the last decades. Climate smart agriculture technologies become more important to sustainably increase agricultural productivity and adapt to climate change. However, it is important to first evaluate these technologies with farmers to learn about their needs. Therefore, participatory evaluations of five drought tolerant maize varieties were conducted in Makueni County, Kenya. OLS and ordinal regressions are used to find out which characteristics farmers value in maize varieties, how the evaluations compare to observed grain yields, and whether the adoption of the improved varieties has an effect on farmers’ income. Results show that farmers’ perceptions of varieties are quite complex. Farmers do not only value yield, but a broad spectrum of different characteristics. Yield becomes more important to farmers in the end-season, while plant development and stress resistance play a more important role in the mid-season. Drought resistance is crucial to all farmers in both seasons, which is also confirmed by a follow-up survey. All varieties get significantly better scores on-station than on- farm, which corresponds to yield data that was collected at harvest. The difference of generated yield between on-station and on-farm is higher in a year with little rainfall, matching the farmer evaluations. Overall, women seem to score more nuanced than men, who score quite similarly across the varieties. Tego WE1101 is the least liked variety by farmers, but it yielded most in the trials. DH02, the variety that is liked best on-station, had the lowest yield both on-station and on- farm. Overall, farmers’ scores do not reflect yield data well, indicating farmers’ complex perceptions of varieties beyond yields. Calculations show that farmers in Makueni County could increase their income by 70% with the adoption of the improved maize varieties from this trial. The analysis shows the importance of participatory evaluations for understanding farmers’ perceptions. It is important for breeders to take farmers' perceptions into consideration when developing new varieties. This is crucial to increase the adoption rate of improved maize varieties and contribute to more food security and a better economic situation for smallholders in Kenya.

1. Participatory evaluation of five drought tolerant
maize varieties in the semi-arid regions of Kenya
Florian Neubauer1, Hugo De Groote2, Bernard Munyua2, James Njeru2, Michael Ndegwa2
1 Georg-August-University Göttingen; Contact: florian.cp.neubauer@gmail.com
2 International Maize and Wheat Improvement Centre (CIMMYT)
30th International Conference of Agricultural Economists
Vancouver, Canada, July 28 – August 2, 2018

2. Overview
• Motivation
• Trial description
• Results
• Conclusion

3. Motivation
• Problem: Climate change and declining maize
production per capita in Kenya
• Literature gap:
– Farmers’ perception of varieties
– comparison to observed yield
• Questions:
– Which characteristics do farmers favor in maize?
– Which DT varieties do well in Makueni County?
– What effect does the adoption of DT maize have on
farmer’s income?

5. Drought tolerant maize varieties
Drought
TEGO
WE1101
DH02 DH04 KDV1 Duma43
Days until maturity 125-135 70-100 80-120 75-90 132
Average yield
(t/ha)
4-5 4 5 2-4 3-5
Type Hybrid Hybrid Hybrid OPV Hybrid
Distributor Dryland
Seed
Ltd.
Kenya
Seed
Company
Kenya
Seed
Company
Dryland
Seed Ltd.
Seed Co
Ltd.

6. All maize (5)
x
1 bean (KAT B1)
All maize (5)
x
1 PP (KAT 60/8)
All beans (4)
x
1 maize
(DH02)
2 PP
x
DH02
Trial set up

7. Questionnaire

8. Descriptive statistics
0%
20%
40%
60%
80%
100%
Purchased
improved maize
seed
Purchased
fertilizer
No
Yes
Female
75%
Male
25%
0
100
200
300
400
2016 - Mid 2016 - End 2017 - Mid 2017 - End
Female Male

9. a
coefficients of OLS regression, dependent variable: overall evaluation
***
***
***
***
***
***
***
***
***
***
***
-0,02 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16
Number of cobs
Yield
Drought resistance
Early maturing
Height
Barrenness level
Tillers development
Biomass
Cob size
Stalk borer resistance
Germination / Crop stand
Lodging resistance
Foliar disease resistance
Good tip cover
Stalk thickness
Importance of characteristics in overall evaluation in mid
season, for womena
Women

10. a
coefficients of OLS regression, dependent variable: overall evaluation
***
***
***
***
***
***
***
***
***
***
***
***
***
***
**
***
***
**
**
***
-0,02 0 0,02 0,04 0,06 0,08 0,1 0,12 0,14 0,16
Number of cobs
Yield
Drought resistance
Early maturing
Height
Barrenness level
Tillers development
Biomass
Cob size
Stalk borer resistance
Germination / Crop stand
Lodging resistance
Foliar disease resistance
Good tip cover
Stalk thickness
Importance of characteristics in overall evaluation in mid
season, for women and mena
Women Men

11. ***
***
***
***
***
***
***
***
***
***
***
***
***
**
**
*
***
*
***
***
-0,02 0,02 0,06 0,1 0,14 0,18 0,22 0,26
Yield
Drought resistance
Height
Number of cobs
Tillers development
Barrenness level
Germination / Crop stand
Cob size
Good tip cover
Lodging resistance
Stalk borer resistance
Stalk thickness
Biomass
Early maturing
Foliar disease resistance
Importance of characteristics in overall evaluation in end
season, by men and womena
Women Men
a
coefficients of OLS regression, dependent variable: overall evaluation

12. Variety On-Station E D C B A A+B
DH04 0 8% 20% 32% 29% 10% 39%
DH04 1 3% 10% 24% 40% 22% 62%
DH02 0 8% 21% 33% 29% 10% 39%
DH02 1 2% 7% 18% 41% 32% 73%
Duma43 0 6% 16% 31% 34% 14% 47%
Duma43 1 3% 8% 21% 41% 27% 68%
KDV1 0 5% 15% 29% 35% 15% 51%
KDV1 1 3% 10% 24% 40% 23% 63%
Tego 0 7% 19% 32% 31% 12% 42%
Tego 1 7% 19% 32% 31% 11% 42%
Predicted Probabilities of the scores on-station vs. on-
farm

13. Variety On-Station E D C B A A+B
DH04 0 8% 20% 32% 29% 10% 39%
DH04 1 3% 10% 24% 40% 22% 62%
DH02 0 8% 21% 33% 29% 10% 39%
DH02 1 2% 7% 18% 41% 32% 73%
Duma43 0 6% 16% 31% 34% 14% 47%
Duma43 1 3% 8% 21% 41% 27% 68%
KDV1 0 5% 15% 29% 35% 15% 51%
KDV1 1 3% 10% 24% 40% 23% 63%
Tego 0 7% 19% 32% 31% 12% 42%
Tego 1 7% 19% 32% 31% 11% 42%
Predicted Probabilities of the scores on-station vs. on-
farm

14. Variety On-Station E D C B A A+B
DH04 0 8% 20% 32% 29% 10% 39%
DH04 1 3% 10% 24% 40% 22% 62%
DH02 0 8% 21% 33% 29% 10% 39%
DH02 1 2% 7% 18% 41% 32% 73%
Duma43 0 6% 16% 31% 34% 14% 47%
Duma43 1 3% 8% 21% 41% 27% 68%
KDV1 0 5% 15% 29% 35% 15% 51%
KDV1 1 3% 10% 24% 40% 23% 63%
Tego 0 7% 19% 32% 31% 12% 42%
Tego 1 7% 19% 32% 31% 11% 42%
Predicted Probabilities of the scores on-station vs. on-
farm

15. Variety On-Station E D C B A A+B
DH04 0 8% 20% 32% 29% 10% 39%
DH04 1 3% 10% 24% 40% 22% 62%
DH02 0 8% 21% 33% 29% 10% 39%
DH02 1 2% 7% 18% 41% 32% 73%
Duma43 0 6% 16% 31% 34% 14% 47%
Duma43 1 3% 8% 21% 41% 27% 68%
KDV1 0 5% 15% 29% 35% 15% 51%
KDV1 1 3% 10% 24% 40% 23% 63%
Tego 0 7% 19% 32% 31% 12% 42%
Tego 1 7% 19% 32% 31% 11% 42%
Predicted Probabilities of the scores on-station vs. on-
farm

16. DH04
DH04
DH02
DH02
Duma43
Duma43
KDV1
KDV1
Tego
Tego
0
1000
2000
3000
4000
5000
6000
7000
8000
On-station On-farm
DH04
DH04
DH02
DH02
Duma43
Duma43
KDV1
KDV1
Tego
Tego
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
On-station On-farm
Average yield in kg/ha
Predicted Probabilities
(“positive” score)

17. Predicted Probabilities
on-farm (“positive” score)DH04
DH04
DH02
DH02
Duma43
Duma43
KDV1
KDV1
Tego
Tego
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
0,9
1
2016 2017
DH04 DH02 Duma43 KDV1 Tego
DH04
DH04
DH02
DH02
Duma43
Duma43
KDV1
KDV1
Tego
Tego
0
1000
2000
3000
4000
5000
6000
7000
8000
2016 2017
DH04 DH02 Duma43 KDV1 Tego
Average yield on-farm,
kg/ha
2016: good rainfall; 2017: drought

18. Impact assessment (revenue side)
• Average yields on-farm: 2.2 t/ha
• Rural HH survey 2013: 1.3 t/ha
• US$ 580 vs. US$ 345
à 1.7 times higher revenue

19. Conclusion
• Farmers’ perceptions of varieties are complex
• Evaluations do not match yield data well
• Important to improve yield on-farm
• Overall, DT maize significantly improves farmers’
revenue
• DT maize varieties essential for climate change
adaptation

20. Thank you
for your
interest!
A special thanks to CIMMYT and
the CGIAR Research Programs
MAIZE and CCAFS for their
financial support