International Food Policy Research Institute (IFPRI) and Ethiopian Development Research Institute (EDRI) in collaboration with Ethiopian Economics Association (EEA). Eleventh International Conference on Ethiopian Economy. July 18-20, 2013

1. ETHIOPIAN DEVELOPMENT
RESEARCH INSTITUTE
Smallholder demand for and access to
chemical fertilizers in Ethiopia
Guush Berhane, Alemayehu Seyoum Taffesse, Fanaye
Taddesse, Catherine Ragasa and Thomas Woldu
IFPRI ESSP-II
Ethiopian Economic Association Conference
July 18, 2013, Addis Ababa
1

2. Introduction
• With the increased focus given to it, the performance of Ethiopias
agriculutre sector, e.g., in terms of output levels, has improved significantly.
• Despite the progress in recent years (in terms of production levels),
intesification and productivity levels are still far below desired levels
• Only 30-40 % of farmers apply fertilizer (ATA-IFPRI)
• …. Still, apply much below recommended rates
• On the contrary, there is substantial increase in the volume of fertilizer
imported, … with significant carry-over to the next year (average of 275
thousand tons annually between 2009-2010) (ATA-IFPRI) – leading to
arguebly significant inefficiencies nationally.

3. Introduction
• Implying that there is substantial challenge in the supply-side of the fertilizer
value chain.
• With limited scope to expand cultivable area and ever increasing population
pressures, increasing productivity and transformaing agriculture becomes
even a much higher order policy agenda for Ethiopia today than ever!
• Very high commitment and desire of policy makers to increase not just
production but also productivity through intensification.
• Manifested by the continued commitment and desire to transform the
sector, and particulalry to ‘food production’ as a main national food security
pillar.

4. Introduction
• The discussion on the supply side (fertilizer delivey chain) is slowly taking off
(taking new shape recently). However, evidence on fertilizer use and
intensity … demand …. Particulalry vis-à-vis its profitability remains unclear.
• Particulalry, with ever increasing fertilizer price and volatile moisture
(climate change), evidence is limited as to what extent addressing supply
side market failures (e.g., credit constraints, late delivery, susbtantial
variations in distance to delivery centers, etc.,) can promote fertilizer take-up
• Of interest to study is, controlling for access problems, what explains
demand for fertilizer?
• What sort of interventions could be of relevance to promoting fertilizer at
the “LAST MILE”, given prices and environmental settings?

5. Introduction
• Why do some households, e.g., facing similar agro-climatic conditions and
relative prices with others adopt and excel, while others don’t?
• A number of studies point to differences in risk profiles, moisture stress,
market access, profitability, intensitification and endowments …
• Further understanding of this issue - using large dataset, covering the major
food basket woredas of the country would mean providing better evidence
to policy that can help exploit the potential – with enormous implications to
national food security.
• More pragmatically, such studies would be useful inputs to minimizing
current national supply – demand mismatchs and avoiding carry-over
inefficiencies; i.e., contribute to framing the forcasting of national fertilizer
demand on a sound micro evidence !

6. Data and Method
• Data
• Comes from a recent household and community surveys conducted in four
regions of Ethiopia – a baseline survey for the AGP (CSA & IFPRI).
• The AGP is one of unique programs focusing on the (high) agricultural
production potential woredas of the country (covers 93 of Ethiopia’s 450
highland woredas)
• Covering around 8000 farm households from 304 communities (EAs),
containing a wealth of household- and plot- level data!
• Although only represetative at the AGP woreda level, it is a unique and very
recent data covering such diverse but most potential production zones
• … notice that most micro datasets so far – understandably - tend to over
sample the low potenial wordas in the country!

7. Data and Method
• Method – use Cragg’s Double-Hurdle Model
• We have cross-sectional data - we cannot exploit time-demension of
observations to deal with potential challenges
• FERTILIZER DEMAND with many zero values for more than one reason!
• It has been tested time and again that censored regressions like Tobit alone
are not suitable to estimate demand in the face of many constraints – NOT
just price - conditioning the decision. Given prices,
– Farmers who want it and get it
– Farmers who want it but don’t get it because of supply constraints
– Farmers who don’t want it because it is not profitable at current prices
• Noting this, we resort to estimating fertilizer demand in two stages,
implemeting the Cragg’s (1971) Double-Hurdle Model – as adapted to
fertilizer demand!

8. 8
Fertilizer Use (= demand) %
Did you use fertilizer in the last (Belg and) Meher (2002/3 E.C)? (y=1) 34.0
Did you ever use fertilizer in the last 5 yrs (2006-2010)? (y=1) 60.0
% of hh that used fertilizer in 1998 E.C. (2006) 72.0
% of hh that used fertilizer in 1999 73.4
% of hh that used fertilizer in 2000 78.9
% of hh that used fertilizer in 2001 83.6
% of hh that used fertilizer in 2002 E.C. (2010) 89.6
If used fertilizer around the end of the last 5 years, was it on credit?
(y=1) 24.0
Descriptives

9. 9
Hurdles to fertilizer access
Order of importance to HHs (%)
1st 2nd 3rd
Supply problem (shortage) 16.0 10.8 16.3
Late arrival 12.0 22.8 18.3
High price 39.4 32.3 32.9
Lack of credit 9.9 28.4 25.9
No Problem/Not relevant for me 16.9 2.3 1.9
Others 5.8 3.5 4.6
# of respondents that answered these
Qs.=6,897
Descriptives

10. 10
Hurdles to using credit %
No need for loan 46.4
No one available to get a loan from 40.9
Tried to get a loan but was refused 5.8
Afraid that I cannot pay back … risk 3.1
Interest rates too high 1.9
Expected to be rejected, so did not try 0.9
I have no assets for collateral 0.7
afraid of losing collateral 0.3
Others 0.04
Number respondents that answered these questions 7,189
Descriptives

11. 11
Access to fertilizer (=available, timely, credit) %
If you wanted to purchase fertilizer, did you have the choice
from whom and where you could purchase it? (Y=1) 35.7
Availability of fertilizer before the start of planting season 40.6
What was the method of payment?
Cash 67.5
Free? 17.5
Credit 9.9
partialy in cash and partialy in credit 4.9
partially free partially in credit 0.2
Descriptives

12. What is in the model?
• The DH model has two parts: Access and Demand Equations
• Determinants of probability of access ( acees defined as availability, timing,
credit)
– Zonal dummies
– Availability of supply centers at the EA
– Education and age to capture experience and knowledge
– Land and livestock to capture wealth effects
• Determinants of use and intensity of use of fertilizer – (demand)
– Household characteristics (age, hhsize, education, literacy …)
– Wealth indicators (Wealth quitiles, land, livestock, …)
– Plot characteristics (soil quality, irrigation, manure use, improved seed…)
– EA characteristics (distance to market, coops, VSLs, …)
– Extension service (DA visits, FTC visits, specific advices, …)
– Proxy for fertilizer VCR
– Zone dummies

13. 13
Description of Variable Mean Med Sd Min Max
Dependent
Vars
Access to fertilizer 0.58 1 0.49 0.00 1
Log of amount (kg) of UREA used 1.35 0.00 1.84 0.00 8.29
Log of amount (kg) of DAP used 2.05 2.20 2.07 0.00 8.87
log of amount (kg) of ALL fertilizer
used 2.34 2.77 2.24 0.00 8.99
Fertilizer
Access
Finacial institution available (Y=1) 0.89 1 0.31 0.00 1
RUSACOs available (y=1) 0.43 0 0.50 0.00 1
Tropical livestock units 3.54 2.60 4.05 0.00 59.68
Age squared 2067.61 1600.00 1484.54 225.00 9604.00
Both Access
and Use
Age 42.90 40.00 15.07 15.00 98.00
Cultivated land (ha) 1.28 0.9 1.27 0.00 16.24
Education years (>4) 0.14 0.00 0.35 0.00 1.00
Vars. used in access/demand regression

14. 14
Vars. used in access/demand regression
Fertilizer
Use
Description of Variable Mean Med Sd Min Max
cattle (count) 4.11 3.00 4.94 0.00 84.00
Oxen (count) 1.05 1.00 1.37 0.00 31.00
Output price*land cultivated (mean) 2.27 1.67 2.30 0.00 48.90
Household size 4.83 5.00 2.15 1.00 17.00
Householld size sqr 27.96 25.00 23.91 1.00 289.00
Litracy 0.41 0 0.49 0 1
Land is not fertile (on average) 0.11 0 0.31 0 1
Improved seed use (at least in one plot) 0.22 0 0.41 0 1
Irrigation use (at least in one plot) 0.06 0 0.23 0 1
Use of manure (at least in one plot) 0.56 1.00 0.50 0 1
DA vist in last meher (y=1) 0.34 0.00 0.47 0 1
Farmer visited FTC (y=1) 0.07 0.00 0.26 0 1
Farmer was advised on fertilizer use in last
meher (y=1) 0.43 0.00 0.49 0 1
Distance of household to market (km) 12.13 9.00 11.41 0.00 65.00
Wealth quintile2 0.19 0 0.39 0 1
Wealth quintile3 0.21 0 0.40 0 1
Wealth quintile4 0.21 0 0.41 0 1
Wealth quintile5 0.21 0 0.41 0 1

15. 15
Variables UREA+DAP UREA DAP
Coef. (RSE) Coef. (RSE) Coef. (RSE)
Finacial institution available ?(Y=1) 0.25**
(0.12)
0.11
(0.11)
0.42***
(0.11)
RUSACOs available? (y=1) 0.16**
(0.07)
0.22***
(0.07)
0.18***
(0.09)
Cultivated land (ha) 0.18***
(0.03)
0.09***
(0.03)
0.19***
(0.03)
Education years (>4) 0.27***
(0.09)
0.21**
(0.09)
0.28***
(0.09)
Age 0.03 ***
(0.01)
0.02
(0.01)
0.03***
(0.01)
Age squared -0.01***
(0.00)
0.00 *
(0.00)
0.00***
(0.00)
Tropical livestock units 0.01
(0.01)
0.02**
(0.01)
0.01
(0.01)
Results: Access to Fertilizer (supply, timing, credit)

16. 16
Results: Fertilizer Demand Conditional on Access
Variables UREA+DAP UREA DAP
Coef. (RSE) Coef. (RSE) Coef. (RSE)
Cultivated land (ha) 0.15***
(0.03)
0.10***
(0.04)
0.15***
(0.03)
Education years (>4) 0.02
(0.06)
0.10
(0.08)
-0.00
(0.06)
Age 0.00
(0.00)
0.00
(0.00)
0.00
(0.00)
cattle (count) 0.00
(0.01)
0.01
(0.01)
-0.01
(0.01)
Oxen (count) 0.09***
(0.03)
0.06**
(0.03)
0.12***
(0.03)
Output price*land cultivated (mean) 0.02
(0.02)
0.03
(0.02)
0.03**
(0.02)
Household size 0.03
(0.03)
0.03
(0.04)
0.03
(0.03)

17. 17
Use of manure (at least in one plot) 0.04
(0.05)
-0.10*
(0.06)
0.04
(0.05)
DA vist in last meher (y=1) 0.09*
(0.05)
0.07
(0.06)
0.09*
(0.05)
Farmer visited FTC (y=1) 0.14*
(0.08)
0.13
(0.09)
0.14*
(0.08)
Farmer was advised on fertilizer use
(y=1)
0.07
(0.05)
-0.04
(0.06)
0.06
(0.05)
Distance of household to market (km) 0.00
(0.00)
-0.00
(0.00)
0.01**
(0.00)
Wealth quintile2 0.33***
(0.08)
0.40***
(0.10)
0.24***
(0.08)
Wealth quintile3 0.42***
(0.09)
0.34***
(0.10)
0.43***
(0.09)
Wealth quintile4 0.55***
(0.09)
0.51***
(0.10)
0.55***
(0.09)
Wealth quintile5 0.73***
(0.10)
0.71***
(0.12)
0.68***
(0.10)
Results: Fertilizer Demand Conditional on Access

18. 18
Key results
• Access
– Significant zonal differences in fertilizer access – remotness matters! The
detail is interesting!
– Significnat EA level differences in access due to (non-) availability
cooperatives or other supply mechanisims still related to remotness NOT
just distance but also infrastructural
– Wealtheir, experiened and better educated households have significantly
better access
– Availability of financial institutions, RUSACOs, credit associations matter
significantly

19. 19
Key results
• Demand (intensity of use)
– Individual wealth differences as measured by wealth quintiles
– Size of land cultivated
– Owning oxen
– Extension services (cautiously!)
– Use improved seeds (cautiously!)
– Distance to market centers
– Sensitivity to the VCR proxy

20. 20
Key observations
• All of these determining factors may have important implications but in our
observation (also supported by this data) two critical issues stand strong
hindering access to and demand for fertilizer in Ethiopia:
• 1) Fertilizer supply chain
– improving the efficiency and timeliness of availability of fertilizer,
improving the nature and size of fertilizer retail or distributional
channels – one that is quick to respond to demand.
• 2) Credit
– No ad hoc solutions and non-market based approaches would help to
address this problem as credi itself is subject to risks and incentive
problems
– Stregnthening exisitng credit institutions and encouraging new ones to
flourish
– Well thought through credit products, perhaps with some insurance to
mitigate risks

21. 21
Thanks everyone!