This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.

1. Farming with organic fertilizer: crop-livestock integration for
sustainable resource management in Ethiopia
Hailemariam Teklewold Belayneh, University of Gothenburg, Dpt of Economics. E-mail:
hailemariam.teklewold@economics.gu.se Tel: +46-76-262-7814
ABSTRACT
In the mixed farming system where low soil organic matter content and soil nutrient depletion
stifles crop productivity and livestock feed availability, the emphasis on intensification focusing
crop-livestock integration is important for harnessing nutrient recycling. This study suggests an
econometric model to estimate the increase of organic fertilizer production due to livestock
technology adoption and the crossover effect of crop production, identify the critical factors
constrained the taking up of livestock technology and suggest policy recommendations towards
the strengthening of crop-livestock synergies aiming to sustainable use of natural resources.
Analysis is made on a cross section of 493 farm households in the central highlands of Ethiopia.
The result shows that adoption of crossbred-cow technology depends positively on nearness of
the farm households to the extension service and access to complementary inputs and
negatively on her risk preference. Adopting crossbreeding technology induces an expected
increase of farm household’s organic fertilizer production of 3.93 tons. The positive cross over
effect of crop technology on organic fertilizer production is significantly higher for crossbred-
cow technology adopter than the effect on non-adopter. Hence, crop-livestock integration as
explained by the product-product relationship is strong due to joint application of crop and
livestock technologies.

2. Motivation
Soil fertility depletion is one of the problem causing declining
food production in developing countries
(Amare etal 2005; Girmay etal, 2008).
In Ethiopia:
National estimates of input, extraction and balance:
• Inorganic: 8.5 N kg/ha and 9.8 P kg/ha
• Organic: 29 N kg/ha and 7.2 P kg/ha
Extraction: harvested crop, erosion, leaching, etc
• Balance: -122 N kg/ha and -13 P kg/ha
- negative soil nutrients balance
- mining of the soil

3. This is mainly due to constraints:
– Inorganic fertilizers (DAP and urea) = 14% area
• due to continuous rise of prices (Croppenstedt et al (2003); Amare
et al (2005); Teklewold et al 2006).
– Organic fertilizers (manure) = 15% area
• Allocation problem: source of energy and selling
(Mekonnen and Kohlin, 2008; Girmay etal 2008; Amare etal,
2005; Fitsum etal 1999; Erkossa and Teklewold (2009) ).
• Availability constraints: the past few decades have
witnessed:
• Increasing population densities
• Decreasing availabilities of arable land
• Conversion of grazing land for cultivation
• Declining of soil fertility
• Traditional production system (limited intensification)

4. • The problem:
– Low livestock productivity and dwindling of
manure supply
– Low agriculture productivity (partly due to little
organic fertilizer produced)
– Interventions do not take into account crop-
livestock interactions.

5. The questions:
• How can the system of crop-livestock are
further integrated for organic fertilizer in terms
of:
• technological,
• managerial and
• institutional considerations?

6. Fig. 1. Farm yard manure under crop-livestock system
Farm
Yard
Manure
Farm land
(size,
productivity)
Technology
Biomass
(Straw)
Traction
Grazing
land
Farm
inputs
Market
(cash, off-
farm)
Livestock
Crop
(Grain)
Household
(consumption,
labor)

7. Objectives of the study:
• suggest an econometric model to estimate the effect of the
non-allocable livestock technology adoption and the crossover
effect of crop technology on FYM production,
• identify the critical factors constrained the taking up of
livestock technology and
• suggest policy recommendations towards the strengthening of
crop-livestock synergies aiming to sustainable use of natural
resources in the mixed crop-livestock system

8. • H1: average farm household FYM production is higher for
livestock technology adopters (those owned cross-bred cow)
than non-adopter farm households:
• H2: the cross over effect of crop technology (area covered with
modern crop seeds) on FYM production is higher for those
farm households who integrate livestock technology in their
farming system than the non-adopters
• H3: livestock technology adoption influenced
+ by access to other complementary inputs
+ by effective communication
• the innovation-diffusion model or transfer-of-technology
(Roger, 1962)
- by risk aversion behavior
( ) ( )u
m
b
m yy Ε>Ε

9. Method of analysis:
- endogenous switching regression
Livestock
Technology
Adopters
Non-Adopters
( ) ( )( ) u
mi
b
mimi yhyhy α−+α= 1
bibbi
b
mi uXy +β′=
uiuui
u
mi uXy +β′=

10. The data and study areas
• The data for this study originates from a farm
household survey conducted by the EIAR in
2006.
• conducted in three different zones of the
central highlands of Ethiopia.
• The total sample consists of 491 farm
households.
• Multi-stage random sampling technique was
employed for selecting districts and
households from each area.

11. • The data provides a unique opportunity for the analysis
requiring different:
– household characteristics,
– crop and livestock system components.
– Manure production and various utilization
• contains information such as:
– household and farm characteristics (sex, age, education,
labor, etc);
– social organization (cooperatives, associations);
– resource endowments (land, livestock, credit, off-farm,
etc);
– agricultural technologies (crops, livestock);
– information (extension, training) and
– farmer’s risk preference

12. Descriptive statistics
Variables Definition Non-binded Binded
Location
westshoa West shoa zone = 1 0.177 0.123
eastshoa East shoa zone = 1 0.316 0.247
Crop effect
improvedarea Modern crop variety grown (ha) 1.049 (0.747) 1.052 (0.764)
cultivated Cultivated land area (ha) 2.152 (1.528) 2.538 (1.876)
Livestock effect
traindairy Training on dairying = 1 0.049 0.300
privatgraz Private grazing land (ha) 0.477 (0.454) 0.656 (0.809)
comunalgraz Access to communal grazing land = 1 0.342 0.370
dungprice1 Price of FYM (Birr/100 kg) 64.992 (19.131) 66.471 (21.907)
TLU1 Livestock size (Tropical Livestock Unit) 5.823 (3.009) 7.815 (4.864)
coopmilk Cooperatives member = 1 0.083 0.207
zerograz Cut and feeding system = 1 0.034 0.097
concentrate concentarte feeding = 1 0.068 0.758
veternairy veterniary service = 1 0.075 0.722

13. . . . continued decriptive statistics
Variables Definition Non-binded Binded
Location
westshoa West shoa zone = 1 0.177 0.123
eastshoa East shoa zone = 1 0.316 0.247
Market/information
distanceda Distance to extension agent (hours) 0.515 (0.449) 0.452 (0.431)
offarm Off-farm work = 1 0.361 0.471
Household characterstics
age Age in years 45.748 (12.937) 46.590 (12.862)
sex Male = 1 0.865 0.907
education Years of education 3.297 (3.721) 4.991 (4.353)
adultEquvalent Family size (in adult equivalent) 4.565 (1.778) 4.843 (1.810)
equib Rotating credit and saving club = 1 0.421 0.467
Risk Farmer’s risk preference (Rank)
1=Neutral to prefering 0.165 0.211
2=Slight to neutral 0.098 0.154
3=Moderate 0.203 0.189
4=Intermediate 0.180 0.185
5=Severe 0.120 0.079
6=Extreme averse 0.233 0.181
N Number of cases 265 226

14. Switching regression: FYM production differentials
)()( uiuui
u
mibibbi
b
mi uxyuxy +β′=>+β′=
- It is found that:
- But the difference is due to:
- technological
- observed characterstics
Unconditional Non-binded Binded
Conditional on Conditional on
Adopter, Non-Adopter, Binding Non-binding Binding Non- Binding
8.98 (0.15) 5.05(0.07) 4.79 (0.11) 5.36 (0.10) 10.37 (0.26) 7.81 (0.14)
Average differences in FYM production under different regimes
3.93(0.17) 2.49 (0.18) 2.55 (0.29)
0.57(0.16) 5.58 (0.29)
5.01 (0.27)
Table 5. Average FYM production (ton/annum) under different regimes
( )b
myE ( )u
myE ( )0=hyE u
m( )1=hyE u
m ( )1=hyE b
m ( )0=hyE b
m
( ) ( )u
m
b
m yEyE − ( ) ( )00 =−= hyEhyE b
m
u
m
( ) ( )10 =−= hyEhyE u
m
u
m
( ) ( )01 =−= hyEhyE b
m
b
m
( ) ( )11 =−= hyEhyE u
m
b
m
( ) ( )01 =−= hyEhyE u
m
b
m

15. Estimation results - FYM equation
Variables
FYM equation
Binded Non-binded
Coef. Robust Std. Err. Coef. Robust Std. Err.
Constant 3.495*** 0.559 3.280*** 0.386
Sex -0.251** 0.106 0.014 0.090
Education 0.021** 0.010 -0.018* 0.010
adultEquvalent 0.015 0.022 0.024 0.023
Offarm -0.023 0.076 -0.155** 0.072
Cultivated -0.030 0.019 0.003 0.030
improvedarea 0.118** 0.046 0.014 0.039
Traindairy -0.074 0.092 -0.122 0.146
Privatgraz 0.066* 0.042 0.084 0.073
comunalgraz -0.061 0.087 -0.327*** 0.075
Dungprice 0.007*** 0.002 0.005** 0.002
TLU 0.049*** 0.007 0.029* 0.016
Zerograz 0.420** 0.186 0.222 0.172
E(y

16. Switching regression results – technology adoption
Variables
Switcher
Coef.
Robust Std. Err.
Constant -4.668*** 1.111
Age 0.086** 0.042
age2/1000 -0.847** 0.406
Offarm 0.482** 0.191
Traindairy 0.635** 0.309
Privatgraz 0.244* 0.146
Dungprice 0.019*** 0.005
TLU 0.083*** 0.028
Risk -0.109** 0.048
Distanceda -0.635*** 0.237
Concentrate 1.834*** 0.261
Veternairy 1.585*** 0.210

17. Conclusions and implications
• The major constraint in smallholder farms in many
developing countries is the negative soil nutrient
balance.
• Jointness of crops and livestock production is often
considered as an opportunity towards sustainable
agricultural production
– because of the associated organic matter and
nutrient recycling.
• In further support of the idea, this study employed an
endogenous switching model:
– To show joint intensification on technologies for increasing
crop-livestock synergies for sustainable soil mgt.

18. • It is clear that a considerable amount of component
research, along disciplinary lines has been
undertaken.
– however, many technological innovations have suffered
from limited uptake due to a wide range of cultural,
economic and technical reasons;
– and a lack of appreciation of the wider impacts of
technologies in the other system of components may often
be ignored
– this might be due to the underestimation by policy makers
and planners of the importance of farming system approach
that consider each system as an integral and significant
component of the mixed farming system, affecting the
sustainable management of resources.