Poster by Monica K. Kansiime, Piet Van Asten and Koen Sneyers for the ISPC Science Forum 2016, Addis Ababa, 12-14 April 2016

1. Introduction
Targeting agricultural policy interventions in East Africa farming systems
Monica K. Kansiime1,2 , Piet Van Asten3 and Koen Sneyers4
1Horn Economic and Social Policy Institute (HESPI), P. O. Box 2692-1250, Addis Ababa, Ethiopia;
2CAB International, P. O. Box 633-00621, Nairobi, Kenya;
3International Institute for Tropical Agriculture (IITA), P. O. Box 7878 Kampala, Uganda;
4ZOA Uganda, P. O. Box 28154, Kampala, Uganda;
Results
Conclusions
Agriculture development is considered the engine for economic growth in Sub Saharan Africa. However, productivity in the sector lags considerably behind that of other
continents, and the region’s potential. East African countries (Kenya, Uganda and Tanzania) still remain below the 6% per annum growth rate targeted by the CAADP.
Programmes aimed at improving agricultural productivity in the region have often generated weak responses, attributed to failure to take into consideration the vast diversity of
farming systems. Better knowledge of farm diversity and farm efficiency is important in understanding processes driving agricultural productivity and for targeting policy
interventions for enhanced sustainable production and resource use efficiency (Tittonell et al., 2011). Building on farm typology work, this paper provides empirical evidence on
the links between farm diversity and resource use efficiency. This also contributes to identification of intervention options to address agricultural production taking cognizance of
farm heterogeneity and livelihood strategies.
Data used and sources
The study used a dataset collected by IITA and Zoa Uganda
from 500 households in West Nile zone in Uganda. The zone
covers an altitude range from 600m to 1700m, and annual
rainfall range from 800mm to 1500mm. Soils are good to
moderate rated. There are four contrasting agro-ecologies in the
zone, which represent about 70% of the key landscape features
in East Africa (Garrity et al., 2012), which helped to illustrate the
diversity of smallholder production systems in East Africa on one
hand, and efficiency of different farm types on the other.
Farm types and livelihood strategies
Households exhibited diversified livelihood strategies
including crop and livestock sales, trading and off farm
employment. Average farm size per district varied between
2.4 to 4.8 hectares. Farming was dominated by crop
production. Livestock units ranged between one and three.
At least 86% of the households named crop production as
their primary activity, though its contribution to total
household incomes was less than 50% in all the districts
except Nebbi.
Over 75% of the variability between farms was explained
by four principal components – income from sale of crops
(33%), % of farm land under cash crops (22%), earning
from trade (12%) and % of farm land under food crop
(10%).
At 60% coefficient of similarity, we identified three distinct
farm types - Farm-specialised, Diversified and Off-farm
specialised. Significant (p<0.01) differences across farm
types were observed for, proportion of income from
farming, farmed area and land use patterns, confirming
these as good indicators for distinguishing between farm
types (Tab. 2)
Table 2: Characteristics of the observed farm types in
the study districts
• Significant diversity within smallholder farming
systems exists, largely explained by differences in
income sources, size of farmland and land use
patterns.
• Households of different farm types were
distributed within and across districts, implying
that geographical stratification is not sufficient to
understand heterogeneities of rural households,
but rather farm specific characteristics.
• Diversification of income sources holds potential
to reduce risks at household level, but has
negative effects on farm efficiency.
• Commercial orientation of farms showed positive
effects on farm resource use efficiency.
Supporting farmers to access markets would go a
long way in achieving production efficiency.
Household characteristics Farm
specialised
(N=204)
Diversified
farms
(N=176)
Off-farm
specialised
(N=120)
Pearson
Chi square
P-value
Contribution to total cash revenue (%)
Cropping 78.46 (1.93) 22.84 (1.54) 27.16 (2.07) 792.101*** 0.000
Livestock 1.53 (0.34) 14.49 (1.71) 8.84 (1.43) 310.289*** 0.018
Petty trade 10.26 (1.51) 27.35 (2.29) 20.70 (2.51) 406.124 0.253
Non-skilled off-farm 2.49 (0.51) 14.63 (1.85) 4.21 (1.07) 289.708 0.173
Formal employment 1.73 (0.46) 0.79 (0.40) 24.84 (3.20) 180.046*** 0.000
Remittance 2.14 (0.48) 4.29 (1.08) 6.21 (1.43) 213.292* 0.118
Natural capital
Total land owned (ha) 4.02 (0.17) 3.14 (0.20) 3.17 (0.15) 128.861*** 0.003
Total livestock units 1.47 (0.13) 1.95 (0.24) 1.83 (0.20) 499.53 0.220
Land use types
Total farmed land (ha) 3.60 (0.14) 2.73 (0.15) 3.06 (0.15) 145.272*** 0.001
Land_food crop (%) 30.61 (2.17) 6.96 (1.12) 8.81 (1.33) 193.5*** 0.000
Land_duo-purpose crops
(%)
28.64 (2.01) 12.31 (1.31) 57.25 (3.09) 277.021*** 0.000
Land_cash crops (%) 30.94 (2.14) 66.86 (2.29) 26.02 (2.80) 306.668*** 0.000
HH social factors
Household size (#) 6.97 6.53 7.13 35.389*** 0.018
Labour (# fulltime on farm) 2.5 2.4 2.2 24.763 0.419
Extension access (%) 92.23 83.91 79.49 11.066*** 0.004
Membership to groups (%) 85.78 89.20 82.50 0.778 0.678
Livelihood outcomes
Total cash revenue ǂ 4.43 (0.63) 1.75 (0.14) 4.03 (0.58) 806.911 0.312
Net income ǂ 4.11(0.63) 1.42(0.14) 3.74(0.58) 920.598 0.277
Table 3: Parameter estimates of stochastic
production frontier and technical inefficiency
***, **, * indicates statistical significance at 1%, 5% and 10% respectively
Figures in parentheses are standard error.
Ln VOP Farm
Specialised
(N=204)
Diversifie
d farms
(N=176)
Off-farm
specialised
(N=120)
Overall
sample
(N=500)
Stochastic production frontier
Constant 4.436
(0.396)
4.734***
(0.448)
6.269***
(0.541)
4.666***
(0.2597)
Ln FARMED 0.613***
(0.077)
0.766***
(0.083)
0.811***
(0.092)
0.615***
(0.047)
Ln LAB 0.314***
(0.070)
0.246***
(0.078)
-0.076
(0.091)
0.257***
(0.045)
Ln FERT 0.029***
(0.016)
0.039*
(0.021)
0.008
(0.029)
0.019*
(0.010)
Ln SEED -0.015**
(0.009)
-0.010
(0.009)
0.004
(0.011)
-0.008
(0.005)
Farm type (Diversified) -0.020
(0.023)
Farm type (Off-farm specialised) -0.015
(0.026)
Variance parameters
σv 0.147 0.168 0.232 0.176
σu 0.327 0.298 0.003 0.289
σ2 0.128 0.117 0.054 0.114
λ = σu/σv 2.222*** 1.768*** 0.012 1.647***
Log likelihood 1.517 -0.466 4.889 34.088
Wald χ2 (6) 139.83 176.38 97.02 345.55
Mean TE (%) 85.4 86.7 85.9 85.4
Data analysis
Farm typology analysis was used to group farms, based on key
drivers of diversity in farming practices and livelihood activities.
We used principal component analysis to identify the factors
explaining the most variability between the farms pooled across
districts, and hierarchical clustering to identify farm groups
based on the identified factors.
Stochastic Frontier Production function model was used to
estimate technical efficiency of the identified farm types. We
adopted Battese and Coelli (1995) general stochastic production
function frontier model for this analysis (eq.1):
yi= f(xi; β) + exp(vi – ui), i = 1, 2, 3, ...n (1)
Where, yi denotes the production of the ith farm (i = 1, 2,…, n); xi
is a (1 x k) vector of functions of input quantities used by the ith
farm; β is a (k x 1) vector of unknown parameters to be
estimated. vi is a random error having zero mean, which is
associated with random factors, not under the control of the
farm. The model is such that the possible production, yi
∗
is
bounded above by the stochastic quantity, 𝑦𝑖
∗
(eq.2).
𝑦𝑖
∗
= f(𝑥𝑖; β) + exp(𝑣𝑖), i = 1, 2, 3, ...n (2)
Technical Efficiency is thus defined as the ratio of the observed
output (yi) to the corresponding frontier output yi* = exp (-ui).
Technical inefficiency effects were defined as;
ui = ziδ + wi (3)
Where zi is a (p x 1) vector of explanatory variables associated
with the technical inefficiency effects; δ is a (1 x p) vector of
parameters to be estimated, and wi is the random variable
defined by the truncation of the normal distribution with mean 0
and variance σu2. One-step MLE was used to simultaneously
estimate the production function, and the inefficiency model
Presence of technical inefficiency was tested using log likelihood
in the half-normal model λ = σu/σv. Data were analysed using
STATA 12 statistical package. Table 1 shows the distribution of
the variables used in the models.
Methodology
Table 1: Model variables and their distribution
Farm diversity and resource use efficiency
Figures in parentheses are standard error
Statistical significant at the 0.01 (***), 0.05 (**), 0.1 (*) level o probability
ǂ In million UGX/year
References
Variable Explanation Mean SD Min Max
Production function variables
OUTPUT (y) Value of production (VOP)ǂ 1.71 0.92 0.06 6.57
FARMED Total farmed land (ha) 1.26 0.74 0.2 6.1
LAB Cost of labour inputsǂ 0.62 0.04 0.04 2.38
SEED Cost of purchased input seedǂ 0.05 0.09 0 1.08
FERT Cost of inorganic fertiliserǂ 0.00 0.02 0 0.25
Farm type 2 Dummy variable, Farm type 2 = 1, otherwise = 0
Farm type 3 Dummy variable, Farm type 3 = 1, otherwise = 0
Inefficiency model variables
TI score (ui) Technical inefficiency score 0.16 0.14 0.00 1.00
GENDER Sex of head of household (male =1, female =0) 0.86 0.35 0 1
FAMLAB Members (>18 years) full time on-farm labour 2.40 1.50 0 12
EXP Farming experience of household head in years 16.00 13.53 0 71
HHSIZE Total no. of people permanently living in the
HH
6.87 2.79 1 21
CRED Received agricultural credit (yes=1, no=0) 0.73 0.45 0 1
EXT Access to extension services (yes=1, no=0) 0.84 0.37 0 1
TLU* Tropical livestock units (TLUs) 1.72 2.49 0 24.77
OFFARM Off-farm income (% of total household income) 45.03 34.67 0 100
ORIENT Farm orientation (commercial=1, subsistence=0) 0.31 0.46 0 1
Technical Efficiency
Farm-specialised and Diversified farms exhibited technical
inefficiency in the use of labour and fertiliser, implying that
they could still achieve higher output from better use of
these inputs (Tab.3). The estimated value of λ = 1.647 for
the entire sample was significantly different from zero,
suggesting the existence of inefficiency effects for farmers in
West Nile.
Across the entire sample, gender of head of household,
household size, full time family labour and farm orientation
showed positive and significant effects on technical
efficiency (Tab. 4). Commercial orientation in farming
significantly reduced farm inefficiency for Farm-specialised
and Diversified farm types. Access to credit and extension
showed efficiency enhancing effects to Diversified and Farm
specialised farm types respectively. Off-farm income
significantly (p<0.05) diminished technical efficiency for
Farm-specialized farm types.
ǂ Figures are in million UGX/ha/year
* Tropical livestock units: sum of the animals with conversion factors; cattle (0.7), sheep and goats (0.1), pigs
(0.20), and poultry (0.01). Source: Bongers et al. (2015)
TI Score Farm
specialised
(N=204)
Diversified
farms
(N=176)
Off-farm
specialised
(N=120)
Overall sample
(N=500)
Coef. SE Coef. SE Coef. SE Coef. SE
GENDER -0.864* 0.486 -1.467** 0.634 33.419 2.722 -0.732** 0.317
HHSIZE -0.049 0.131 -0.156* 0.099 0.066 0.227 -0.092* 0.062
FAMLAB -0.139 0.233 -0.288 0.245 -1.514* 0.797 -0.262* 0.139
EXP 0.008 0.015 0.033* 0.018 0.046 0.042 0.018** 0.009
CRED 0.033 0.477 -1.502*** 0.610 -0.047 1.294 -0.352 0.279
EXT -0.903* 0.612 0.840 0.678 -0.183 1.349 -0.121 0.320
TLU -0.044 0.079 0.012 0.083 -1.196* 0.818 -0.024 0.050
ORIENT -3.568* 2.557 -1.035** 0.527 -1.533 2.241 -1.112*** 0.374
OFFARM 0.012** 0.006 0.008 0.007 0.002 0.013 0.007** 0.003
cons -2.959** 1.500 -1.137 1.255 -32.692 2.722 -1.387** 0.749
σv 0.173 0.014 0.175 0.016 0.198 0.015 0.176 0.013
Table 4: Factors affecting technical inefficiency
***, **, * indicates statistical significance at 1%, 5% and 10% respectively
Figures in parentheses are Standard Error
Garrity, D., Dixon, J. & Boffa, J. (2012).
Understanding African Farming Systems Science and
Policy Implications. Prepared for Food security in
Africa, Bridging research and Practice, Sydney, 29-30
November, 2012.
Battese, G.E. & Coelli, T.J. (1995). A model for
technical inefficiency effects in a stochastic frontier
production function for panel data. Empirical
Economics, 20:325-332.
Corresponding author address and email: CAB International, P. O. Box 633-
00621, Nairobi, Kenya, email: monkansiime@yahoo.co.uk