Poster prepared by Bekele Hundie, A. Kimaro, M. Swamila, S. Lyimo, Yangole, P. Lukuman, V. Afari-Sefa, F. Ngulu, J. Kihara, A. Abass, B. Bachwenkizi, M. Bekunda, I. Hoeschle-Zeledon for the Tropentag 2015 Conference on Management of land use systems for enhanced food security—Conflicts, controversies and resolutions, Berlin, 16-18 September 2015 Indicative results from cost-benefit-analysis of Africa RISING technologies in Tanzania show that almost all of the technologies being tested by the project are better than the base technologies (farmers’ traditional practices). The analysis was done by looking at three economic indicators; the gross margin (Tanzania Shillings/ha) (GM), benefit-cost–ratio (BCR) and returns to labor (TZS/person day) (RL).

1. Cost-Benefit Analysis of Africa RISING Technologies in Tanzania:
Summary of Results
B.H.Kotu1, A. Kimaro2, M. Swamila2, S. Lyimo3, Yangole3, P. Lukuman4, V. Afari-Sefa4, F. Ngulu1, J. Kihara5, A. Abass1, B. Bachwenkizi1, M.
Bekunda1, I. Hoeschle-Zeledon1
1IITA, 2ICRAF, 3SARI, 4AVRDC, 5CIAT
Contact: b.kotu@cgiar.org
The Africa Research In Sustainable Intensification for the Next Generation (Africa RISING) program comprises three research-for-
development projects supported by the United States Agency for International Development as part of the U.S. government’s Feed the
Future initiative.
Through action research and development partnerships, Africa RISING will create opportunities for smallholder farm households to move out
of hunger and poverty through sustainably intensified farming systems that improve food, nutrition, and income security, particularly for
women and children, and conserve or enhance the natural resource base.
The three projects are led by the International Institute of Tropical Agriculture (in West Africa and East and Southern Africa) and the
International Livestock Research Institute (in the Ethiopian Highlands). The International Food Policy Research Institute leads an
associated project on monitoring, evaluation and impact assessment.
www.africa-rising.net
1. Introduction
This paper provides a summary of cost benefit
analyses conducted on various agricultural
technologies being tested by Africa RISING
Program (AR) in Tanzania. The overall objective
of the analyses is to assess the profitability of
agricultural technologies from individual farmers’
point of view. The studies try to answer two main
research questions:
• Are the technologies better than the base
technologies? (a relative assessment)
• How much profitable the technologies are? (an
absolute assessment)
We considered 59 technologies under trial in
Babati and Kongwa-Kiteto AR research zones.
Eleven technologies are used as base
technologies to assess the performance of the
AR technologies. The base technologies
constitute farmers traditional practices. The
technologies are being evaluated by biological
scientists with regards to their contribution to
productivity improvement or reduce loss among
several crops, namely: maize, pigeon pea,
African eggplant, Amaranths, and tomato)
3. Data Collection and Analysis
A total of 1400 data observations from 11
separate agronomic trials were considered. We
used both biological and economic data which
include grain yield, grain prices, variable input
costs, and land cost. Yield data were collected
from agronomic trials. We used mean market
output prices for 2014 which were collected from
secondary sources. Costs of labor, land, and
draft power were estimated from Tanzania AR
baseline data for the target crops while costs of
commercial inputs (seeds, and fertilizers) were
collected from through key informant interviews.
We computed three economic indicators i.e.
gross margin (TZS/ha) (GM), benefit-cost –
ratio (BCR) ,and returns to labor (TZS/person
day) (RL). We conducted sensitivity analysis
with respect to output price changes, input
price changes, and wage rate changes.
4. Results
Results show that almost all of the AR
technologies are either as good as the base
technologies or better in terms of the three
economic indicators (Table 1). The mean BCR
ranges from 0.8 to 7. The grand mean is 1.7
indicating that economic returns of the
technologies are on average higher by 70%
than the breakeven point. The mean RL is
9097 TZS/personday which is also higher
than the average daily wage rate in the study
areas (i.e. 3596 TZS per day) as well as the
official minimum wage rate in Tanzania for
agricultural activities (i.e. 3846.5TZS per day).
There are apparent differences among the
three categories of technologies. High value
crops (HVC) technologies are better than soil
fertility management (SFM) technologies as
well as postharvest (PH) technologies in terms
all the three indicators used in our analysis.
Similarly, PH technologies are better than SFM
in terms of gross margin and BCR. These
differences are statistically significant at least
at 5% level. However, the latter two categories
are not different in terms of returns to labor.
Most of the technologies have positive benefits
(Figure 2). The degree of change apparently
varies among the technology categories as
one moves across the profit thresholds. For
instance, most of the SFM technologies could
yield 50% or less profit. In contrast, HVC
technologies mostly exceed 50%. One of the
three PH technologies have a profitability level
which is greater than 200%.
Figure 2: No. of AR technologies by profit levels
Benefits are more sensitive to changes in
output prices than to changes in input prices
and wage rates (Figure 3). This appears to be
similar across the three technology types.
However, SFM technologies are more sensitive
to changes than the other two categories of
technologies.
Figure 3: Sensitivity of profits of AR technologies
Conclusion
More than one-half of the technologies are
better than the base technologies in terms of
profits. Profit levels are more sensitive to
changes in output prices than changes in input
prices or wage rates. The results are indicative
but not conclusive as we used only a one-year
data for most of the technologies. Moreover,
benefits have been considered from individual
farmers’ point of view but not from society’s
point of view.
Figure 1: Location of the study areas
Table 1: AR technologies compared to base technologies
Acknowledgement
Africa RISING is supported by USAID as part
the Feed the Future Initiative of the US
Government. The authors are grateful to the
donor for the financial support.
Lower Similar Higher
Gross Margin (GM) 1 14 33
Benefit Cost Ratio (BCR) 1 21 26
Returns to Labor (RL) 1 29 18
0
5
10
15
20
25
30
35
40
45
50
>=0 >=50% >=100% >=150% >=200%
Total
SFM
HVC
PH
Profitability
No.oftechnologies
-80
-60
-40
-20
0
20
40
60
80
OP↑ OP↓ IP↑ IP↓ Wage↑ Wage↓
SFM
HVC
PH
%change
2. The study area