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

%

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8 6 5 3 2 1 1No.

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Profitability

ResultsFigure 1: No. of technologies at different profit levels

>=Br

eake

ven

>=50

%

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0%

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8 6 5 3 2 1 1No.

of

tech

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Profitability

SFMSFM

ResultsFigure 1: No. of technologies at different profit levels

>=Br

eake

ven

>=50

%

>=10

0%

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0%

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8 6 5 3 2 1 1No.

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tech

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es

Profitability

HVC

ResultsFigure 1: No. of technologies at different profit levels

>=Br

eake

ven

>=50

%

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0%

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8 6 5 3 2 1 1No.

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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

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%

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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.

..

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45

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86

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15

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Base caseInput price up (25%)Input price down (25%)

No.

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Profitability

Sensitivity AnalysisFigure 8: Effect of changes of wage rates on No. of profitable technologies

>=Br

eake

ven

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86 5

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44

25

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42 1 1 1

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14

7 64

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Base caseWage rate up (25%)Wage rate down(25%)

No.

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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

The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.

Thank Youb.kotu@cgiar.org