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Cost-Benefit Analysis of Africa RISING Technologies in
Tanzania: Summary of initial resultsBekele H. Kotu
Contributing scientists: A. Kimaro, M. SwamilaS. Lyimo,Yangole, V. Afari-Sefa, P. Lukuman, F.
Ngulu, J. Kihara, A. Abass, Beatrice, M. Bekunda, I. Hoeschle-Zeledon
Africa RISING East and Southern Africa Review and Planning Meeting, July 14-16, 2015, Malawi
The study This study aims to assess the profitability of agricultural
technologies under study by AR team in Tanzania Research questions
Are the technologies profitable? (Absolute Assessment) Are the technologies better than the base technologies in terms
of financial benefit? (Relative Assessment) We considered 59 technologies under trial in Tanzania
(both Babati and Kongwa-Kiteto research zones) 11 technologies are base technologies while the remaining
technologies are new ones
The study Designed to increase the productivity/loss of several
crops: maize, pigeon pea, eggplant, tomato, and amaranths
We have data for 2 years for 32 technologies, but only a one year data for the remaining technologies
The technologies can be categorized Soil Fertility Management (SFM)= 46 High Value Crops (HVC)=9 Postharvest Management (PH)= 4
The study More than 1400 data observations from 11 separate
experiments Used both biological and economic data These includeoGrain yieldsoOutput prices o Labor input and costs, land cost, draft power costs, costs of
commercial inputs (seeds, fertilizers and pesticides)
Analysis Computed three economic indicators
Gross margin (TZS/ha) Benefit-Cost –Ratio Returns to labor (TZS/persondays)
Conducted sensitivity analysis Output price changes Input price changes Wage rate changes
Results
Lower Similar Higher
Gross Margin (GM) 1 14 33
Benefit Cost Ratio (BCR) 1 21 26
Returns to Labor (RNLB) 1 29 18
Table 1: AR technologies Vs Base technologies
ResultsAR technology Base Technology T valueMean SD Mean SD
GM 4755819 23021498 1873409 12993707 3.588*BCR 1.7 1.5 1.2 2.5 5.338*
RNLB** 9081 9009 6497 55564 1.339
Table 2: Performance of the technologies
* Significant at 1% level**Average wage rate is 3596TZS/day
Results
GM BCR RNLB
Mean SD Mean SD Mean SD
SFM 480674a 542980 1.5b 0.6 8159b 5445
HVC 53474654b 63410261 4.3a 4.1 19590a 23520
PH* 11066c 11095 3.4b 3.1 15378b 13868
Table 3: Performance of the technologies, by type
*Based on changes in net benefit due to the new technologiesa,b,c: means with similar letters are not different from each other at 5% level of significance
ResultsFigure 1: No. of technologies at different profit levels
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
5046
29
8 6 5 3 2 1 1No.
of
tech
nolo
-gi
es
Profitability
ResultsFigure 1: No. of technologies at different profit levels
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
5046
29
8 6 5 3 2 1 1No.
of
tech
nolo
-gi
es
Profitability
SFMSFM
ResultsFigure 1: No. of technologies at different profit levels
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
5046
29
8 6 5 3 2 1 1No.
of
tech
nolo
-gi
es
Profitability
HVC
ResultsFigure 1: No. of technologies at different profit levels
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
5046
29
8 6 5 3 2 1 1No.
of
tech
nolo
-gi
es
Profitability
PH
Sensitivity AnalysisFigure 2: Sensitivity to output price, input price, and wage
OP↑ OP↓ IP↑ IP↓ Wage↑ Wage↓
-50-40-30-20-10
01020304050
GMBCRRNLB
%ch
ange
Sensitivity AnalysisFigure 3: Sensitivity by technology category (GM)
OP↑ OP↓ IP↑ IP↓ Wage↑ Wage↓
-80
-60
-40
-20
0
20
40
60
80
SFMHVCPH%
chan
ge
Sensitivity AnalysisFigure 4: Sensitivity by technology category (BCR)
OP↑ OP↓ IP↑ IP↓ Wage↑ Wage↓
-30
-20
-10
0
10
20
30
40
50
SFMHVCPH
%ch
ange
Sensitivity AnalysisFigure 5: Sensitivity by technology category (RNLB)
OP↑ OP↓ IP↑ IP↓ Wage↑ Wage↓
-60
-40
-20
0
20
40
60
SFMHVCPH
%ch
ange
Sensitivity AnalysisFigure 6: Effect of output price changes on No. of profitable technologies
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0% 0
10
20
30
40
50
60
46
29
86 5
3 2 1 1
48
42
21
7 6 64
2 1
34
11
64
2 1 1 1 1
Base caseOutput price up (25%)Output price down(25%)
No.
of
tech
nolo
gies
Profitability
Sensitivity AnalysisFigure 7: Effect of input price changes on # of profitable technologies
>=Br
eak.
..
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
50
29
45
23
86
42 1 1 1
47
38
15
7 64
2 2 1
Base caseInput price up (25%)Input price down (25%)
No.
of
tech
nolo
gies
Profitability
Sensitivity AnalysisFigure 8: Effect of changes of wage rates on No. of profitable technologies
>=Br
eake
ven
>=50
%
>=10
0%
>=15
0%
>=20
0%
>=25
0%
>=30
0%
>=35
0%
>=40
0%
0
10
20
30
40
5046
29
86 5
3 2 1 1
44
25
86
42 1 1 1
47
38
14
7 64
2 1 1
Base caseWage rate up (25%)Wage rate down(25%)
No.
of
Tec
hnol
ogie
s
Profitability
Sensitivity Analysis
≤1 ≤2 ≤3 ≤4 ≤5 ≤10 ≤50 ≤10005
101520253035404550
Output price down
Input price up
Wage rate up
No.
of
tech
nolo
gies
Elasticity of GM
Median =1.6
23
Figure 9: Number of technologies at various sensitivity level
Median =4.4
Median=2.1
4
29
Concluding remarks Most of the technologies are either better than or as profitable as the base
technologies Profits mostly lie between the breakeven point and the 100% threshold But profits can go even more than 100% for a few technologies Profits are more sensitive to changes in output prices than changes in input
prices or wage rates The level of sensitivity also varies among the technology categories, SFM being
the most sensitive ones The results are initial: we included a one year data for some of the technologies The study considers only economic parameters, and other advantages of the
technologies are not considered Moreover, profits have been considered from individual farmers’ point of view
but not from society’s point of view.
Africa Research in Sustainable Intensification for the Next Generation
africa-rising.net
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