Land Management and Technology Adoption in Eastern Uganda ...
Transcript of Land Management and Technology Adoption in Eastern Uganda ...
Land Management and Technology Adoption in Eastern Uganda.
A Farm-Based Bio-Economic Modeling Approach.
By: Johannes Woelcke
Contribution to Final GTZ-Project Report “Policies for Improved Land Management in Uganda”
Zentrum für Entwicklungsforschung Center for Development Research
Universität Bonn
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1. Introduction Under the regimes of Idi Amin (1971-79) and Milton Obote (1980-1985) Uganda’s economy
plunged into a prolonged crisis with negative real GDP growth rates (Baffoe, 2000). In 1987
the Ugandan Government under Musevini introduced an Economy Recovery Program in
cooperation with the IMF (International Monetary Fund) and World Bank aiming at market
liberalization, privatization and decentralization. Although, these reforms have positive
impacts on Ugandan economy (GDP real growth has averaged 6 per cent per annum), the
productivity in the agricultural sector has either stagnated or declined (APSEC, 2000). The
agricultural sector is the mainstay of Uganda’s economy accounting for 43 % of the GDP, 85
% of the value of exports and providing 80 % of employment (FAO, 1999). Land degradation
is assumed to be a major factor contributing to declining agricultural productivity, poverty
and food insecurity. It was estimated that soil nutrient losses were among the highest in sub-
Saharan Africa in the early 80s and average annual nutrient losses were predicted to reach 85
kg/ha of N, P2O5, K2O by the year 2000 (Stoorvogel and Smaling 1990). Recent studies in
eastern and central Uganda have given high negative nutrient balances for most of the
cropping systems (Wortmann and Kaizzi, 1998).
The “critical triangle of development goals” by Vosti and Reardon (1997) implies that it is a
major objective for researchers and politicians to find technologies, institutions, and policies
and to make the three goals of growth, poverty alleviation, and sustainability more
compatible. It is obvious that the three goals are compatible in the long run. Sustaining the
natural resource base will help agricultural productivity growth and this will lead to poverty
alleviation. In the short run there might be trade-offs among the three goals taking into
account the short-term perspective of the individual farmer to satisfy the basic needs of the
household. Farmers need to have the incentive and the capacity for a sustainable
intensification of agriculture. Several factors such as policies, technologies, institutions,
population pressure, and agro-climatic conditions can affect the links between sustainability,
growth and poverty alleviation by influencing the choices of households and communities.
These factors have the potential to increase the compatibility of the three objectives.
Addressing these issues of sustainable intensification of agriculture, the Ugandan Government
has published a “Plan for Modernization of Agriculture” in 2000 as part of the “Poverty
Eradication Action Plan (PMA)” with the vision of “poverty eradication through a profitable,
competitive, sustainable and dynamic agricultural and agro-industrial sector.” The priority
areas for action are: improving access to rural finance, improving access to markets, research
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and technology development, sustainable natural resource utilization, and new ways of
management and better education in agriculture for farm households.
2. Research Objectives
The proximate causes of land degradation (e.g. very low use of inorganic fertilizers and
limited use of organic inputs, declining fallow periods, deforestation, crop production on
steep slopes with limited investments in terraces or other conservation measures) are
relatively well known, but the core of the land degradation problem is of economic nature.
Poor rural households in developing countries have to cope with a situation where land
productivity and therefore household income are stagnant or declining. Financial constraints
and imperfect market conditions are leading to livelihood strategies that contribute to nutrient
depletion since the majority of farm households does not perceive sustainable intensification
of agriculture as a suitable strategy (Barbier, 1997). The majority of rural households depend
on agricultural production as their main source of income, but the importance of off-farm
incomes increases as the average farm size declines. Consequently, labor, land, and cash
constraints are limiting the ability to invest in land improvements. It is an important and
difficult task to design effective policy strategies, which make environmentally sound
technologies affordable and adoptable for the farmers, including poor farmers. Some studies
(Feder, Just and Zilberman, 1985) have been conducted analysing the determinants, which
influence the adoption of technology (e.g. farm size, tenure, age, education and risk). How
farm households react to alternative policy strategies and how the adoption of a technology
affects the environment and the productivity simultaneously is less clear though.
Consideration of the problem presented above led to the following research objectives:
1. Improve understanding of key economic determinants affecting land management
decisions at the farm household level;
2. Analyze the likely impacts of land use policies aiming at more productive, sustainable,
and poverty-reducing land management in Uganda.
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With respect to research objective 1 the following hypothesis will be considered:
• Labor shortages, capital constraints, imperfect capital markets, distorted input and
output prices, transaction and information costs are the most binding factors affecting
land use practices and adoption of new technologies.
With respect to research objective 2 the following scenarios will be developed:
• Policy and institutional interventions mentioned as priority areas in the PMA
(development of local credit markets, promotion of improved technologies, labor
exchange institutions etc.), which affect farmers` choices of land management
practices.
• Potential impacts of promoted technologies on household welfare and sustainability
indicator.
3. Integrated Approach to Bio-Economic Modeling
Figure 1 illustrates the concept of the Integrated Approach to Bio-Economic Modeling in
Eastern Uganda. A model can be defined as a representation of an actual phenomenon to
explain real world systems. In order to depict the links between the problem statement, the
real world situation, and the final bio-economic modeling approach, the modeling process is
divided into four stages: the modeled system, the conceptual model, the representational
model and the computational model (Parrott and Kok, 2000). Each model step is addressing
specific objectives, reaching from the overall description of the study area and its comparative
advantages to the assessment of the likely impacts of relevant policy options at the farm
household level. Furthermore, the stepwise approach illustrates the concept of the sampling
procedure, which includes: a listing of the households in the study area, community surveys
(conducted by International Food Policy Research Institute, IFPRI) of both villages under
consideration, and two interconnected comprehensive household/plot level surveys. After
identification of the predominant development domains in Uganda, IFPRI selected
communities representing these domains for the community surveys. Two villages in Iganga
District, which were representing the development domain of interest and which were
captured by the community surveys were selected for this study.
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Figure 1: Integrated Approach to Bio-Economic Modeling
Modelled System • Development Pathways/ 1. Overall description of Development Domain Farming Systems study area (IFPRI)
2. Comparative advantages 3. Identification of potential
pathways analysed Stratified Random Sampling
Conceptual Model • Farm Households within 1. Identification/Description Community Surveys
Socio-Economic + Agro- of external factors (IFPRI) Ecological Environment 2. Identification of economic/
ecological constraints
represented Stratified Random Sampling Representational Model • Farm Household Models 1. Identification of represent. Household Survey 1
(Singh) household types 2. Capturing differences in resources/wealth endowm./
encode economic opportunities Principal Component/ Cluster Analysis Computational Model • Mathematical Progr./ 1. Understanding of household Household Survey 2
Neural Networks decision-making process Nutrient Balances 2. Economic/ecological impact of promoted technologies 3. Impact of relevant policy options
Aggregation Procedure
Modeling
Process
Objectives Sampling Procedure
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A census was conducted in both villages to get a complete list of the households, which
served as a sampling frame. For the first household survey stratified random sampling was
performed, in order to include the correct proportion of households conducting farm trials in
cooperation with CIAT (International Center for Tropical Agriculture) and Africa 2000
Network (A2N). After the identification of representative household types, households most
closely to the cluster centers were selected for the second household/plot level survey.
3.1 Farming Systems and Development Domains in the Study Area (Modelled System)
The concepts of farming systems and development domains help to define the overall socio-
economic and agro-ecological environment of the particular study area. Development
domains are characterized by their population density, market access, and agricultural
potential (Pender et al., 2001). Since different domains have different impacts on land
management, productivity, natural resource conditions, and welfare outcomes, comparative
advantages can be identified. These comparative advantages in turn lead to constraints and
opportunities for a sustainable development and to the identification of potential policy
measures.
Iganga District is located in the Lake Victoria Basin in Eastern Uganda. Iganga town is about
120 km north-east from Kampala (capital city of Uganda) and about 100 km away from
Busia, a important trading center in Kenya. The district belongs to the “Intensive Banana-
Coffee Lake Shore System” (Bashaasha, 2000) with high agricultural potential, high
population density, and high market access (Pender et al., 2001). Primary goal of production
is home consumption for the majority of the farm households. Traditional food crops are:
maize, bananas, sweet potatoes, cassava, beans, millet, sorghum, and Irish potatoes. The
traditional cash crops are coffee and cotton. The location of the district has an altitude of
1070-1161 meters above sea level and covers an area of about 11.113 km 2. The bimodal
rainfalls are varying from 1250 to 2200 mm per annum (Esilaba et al., 2001). Orthic
Ferralsols are the predominant soils in Iganga District (FAO, 1977). Magada and Buyemba,
the villages under consideration, are part of Imanyiro sub-county, which is located at 00 35´N,
32029´E.
In the last two decades the process of market liberalization and decentralization has
influenced land management at the farm level in Uganda in many different ways. Subsidies
were abolished and farmers are not assured of government sourced input supplies any more.
Additionally, minimum prices for the commodities are not guaranteed as in the past. Prices
are low and fluctuating, but markets have been developed for the majority of the products.
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Another consequence of the economic and policy reforms is the shifting of responsibilities for
extension services to the district level. Many NGOs have therefore intensified their efforts to
advice farm households on agriculture-related issues. Numerous NGOs are particularly active
in Iganga District. Different organizations, e.g. CIAT, A2N or Sasakawa Global 2000, are
promoting various types of technologies helping to overcome the interrelated problems of
land degradation, poverty and food insecurity. Their main objective is to encourage the
adoption and diffusion of soil productivity enhancing technologies (e.g. inorganic fertlizer,
organic fertilizer and soil and water conservation methods).
Considering the characteristics mentioned above, Iganga District falls into the category of a
program-induced development pathway with high population density, high market access, and
high agricultural potential. Taking into account the comparative advantages, a potential
profitable pathway involves intensive production of high value perishable crops, perennial
crops or livestock, or development of off-farm activities. The fact that agriculture in Iganga
District is predominantly a low-intensity, low-input and low-output system reveals that farm
households have not realized yet the comparative advantages of their region. These
considerations lead to another interesting research problem, which has to be addressed in this
study:
Why is agriculture production in a region belonging to a development pathway with high
population density, high market access and high agricultural potential predominantly
characterized by low intensity, low-input and low output systems?
3.2 Farm Households within their Socio-Economic and Agro-Ecological Environment
(Conceptual Model)
The farm households as the decision-making entities within their socio-economic and agro-
ecological environment can be defined as the conceptual framework (Upton, 1996; Ruben et
al., 1997). The agro-ecological and socio-economic environments are considered to be the
most important external factors determining farm household decision-making. The agro-
ecological environment (soil quality, climatic conditions etc.) defines the potential
agricultural production activities from which the households can select. On the other hand the
socio-economic environment (markets, service, and infrastructure) gives incentives or
disincentives to select from these activities. Policy interventions lead to changes in the socio-
economic environment resulting in different (dis)incentives for the farm households. The final
outcome of the decision making process of the household is reflected in the production
pattern, productivity, social well being of the household, and the impact on sustainability.
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Therefore, the farm household framework can be used to assess the implications of different
policy measures for crop and technology choice, production, market exchange, labor use, and
farm household welfare. Differences in risk behaviour (Roe and Graham-Tomasi, 1986),
market failures or missing markets (de Janry et al., 1991), and inter-temporal choice
(Fafchamps, 1993) can be taken into account as well.
IFPRI selected communities for the community surveys using a stratified random sample of
the identified development domains in Uganda. Out of the communities captured by IFPRI,
Magada and Buyemba were selected for this study representing the pathway defined above.
Therefore, mainly IFPRI survey data were used to characterize the socio-economic and agro-
ecological environment the farm households are facing in Magada and Buyemba.
Asked for the area that has the most positive impact on life since 1990, representatives of the
villages reported that security and peace improved substantially giving the safety needed for
non-restrictive social and economic activities.
The market access is relatively high for both villages, with trading centers for basic
commodities and inputs being away 1,5 km. Iganga town, one of the major towns in Eastern
Uganda, is about 20 km away from Magada and about 30 km from Buyemba. Moreover, the
relatively short distance to Kampala, Mbale and Busia presents attractive trading
opportunities.
The nearest tarmac road is about 10 km away from Magada and 20 km from Buyemba. The
centers of both villages are crossed by seasonal mud roads. The distances to the next primary
and secondary school are 500 m and 5 km respectively in Buyemba, and 800m and 2 km in
Magada. The next health center is 500 m away from Buyemba’s center and 300 m from
Magada’s center.
The majority of the households is selling their products at the farm gate to middle men in the
villages. Farmers are complaining about the low and fluctuating level of output prices. High
transaction costs (caused by lack of information) and imperfect competition are the
determining factors. The middle men are selling the products to local buyers in trading centers
and Iganga town. Part of this amount is sold to traders from Kenya or Mbale, Busia and
Kampala. Low level of agricultural input use was justified with high prices, especially for
inorganic fertilizer. Inefficiency in procurement, high transportation costs, and absence of
competitive pressure are leading to unreasonably high fertilizer prices (IFDC, 1999).
The labor markets can be characterized by a declining importance of labor exchange and an
increasing importance of hiring labor within the last 10 years. Increased off-farm
commitments were mentioned as the main determining factor, although off-farm employment
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opportunities – especially on a permanent basis - are limited. Labor exchange is a traditional
form of labor acquisition, where men or women with the same level of education form
working groups, based on the idea of overcoming labor constraints of its members. It is not
common among group members to pay for labor, occasionally labor is paid in-kind. The wage
rate for one hour of hired labor can be set at around 500 USh on average1. Factor markets of
livestock exchange for land preparation do not exist.
The average farm size declined from 1990 to 2000, whereas the proportion of cultivated land
increased in comparison to other land use categories such as fallow, grazing areas, and natural
forest/woodland. The consequence is a declining availability of cropland with declining
fallow periods. The most important methods of obtaining access to agricultural land are
getting land as a gift from relatives, purchase, and fixed rental. The price for land with annual
crops is three times higher in 1999 than it was in 1990. As indicated above, the dominant land
tenure system is freehold.
A fast growing population is identified as the main reason for the developments on the land
market with its increasing prices and declining land availability. The number of households
increased by 8 % per year from 1990 to 2000 in Buyemba, where a fast growing population
within the village, and in-migration due to the attractiveness of the trading center were
indicated as the major factors.
The access to credit is limited. One micro-finance institution is mentioned as the only formal
source. The Entandikwa fund provided by the Ugandan Government was stopped in 1997
because of bad repayment moral. Informal sources are used more frequently, it was reported
that 50 % of the households used relatives and friends as a credit source in 1999, 25 %
borrowed from traders, and 10 % from money lenders.
The perceived changes in resource conditions are a major deterioration for most natural
resource items, such as soil fertility, soil moisture holding capacity, soil erosion, and quality
of natural water sources. A major decrease in yield levels since 1990 was reported for main
crops, such as maize, millet, and bananas. For other crops a minor decrease or stagnation was
identified as the yield trend.
Summing up, market imperfections (e.g. low output prices, high input prices and missing
credit markets), declining soil fertility, prolonged dry seasons, as well as pests and diseases
were identified as major constraints for agricultural production.
1 1 US $ are 1776 USh (exchange rate from 9.4.2002).
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3.3 Farm Households (Representational Model)
Farm household models offer a promising perspective for the analysis of production and
consumption decisions at the farm level (Singh et al., 1986). Farm households are considered
to be the central decision makers regarding agricultural production. Individual farmers have to
decide which commodities to produce in which quantities, by which method, and in which
seasonal time periods. It is the objective of the farmers to maximize their utility, which
deviates from pure profit maximizing behaviour in many cases. For example, leisure and the
provision of enough food for household consumption are important goals, which have to be
taken into account, too. The decision-making procedure is subject to physical and financial
constraints (e.g. acres of land, days of labor and limited credit availability). Linkages between
production and consumption decisions, characteristic for farm households operating under
imperfect markets, have to be included. Due to the possibility of analysing both, production
and consumptions decisions, the farm household model approach represents a useful starting
point for the analysis of the effectiveness of economic policy instruments supposed to
enhance a sustainable intensification of land management.
The main objective of the first survey was to identify representative household types of the
selected two villages in Iganga District. Agricultural producers differ in their wealth,
economic opportunities, and resource endowments. Therefore, the investigation of producer
response to policy changes requires the identification of typical farm households and their
inclusion in the modelling approach (Hazell and Norton, 1980). The procedure of stratified
random sampling was performed in order to reflect the proportion of non-trial households and
trial-households in the sampling universe.
A listing done in 2000 indicated that 44 out of 608 households are conducting agricultural
technology trials in cooperation with CIAT and Africa 2000 Network. 11 different
technologies are promoted aiming at a sustainable intensification of agriculture, including
inorganic fertilizer (NPK), farmyard manure, trenches, green manure, and improved fallow.
Out of the first strata of 564 non-trial farmers 62 were selected randomly. Out of the second
strata of 44 trial farmers 5 were selected randomly for the subsequent analyses for
identification of representative household types. The other 39 trial farmers were also covered
in order to collect reliable data for technical coefficients of different technologies.
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A Principal Component Analysis with a subsequent Cluster Analysis are used to select
representative household types in the study region. The objectives of the Principal
Component Analysis were:
1. to analyse the structure of correlation among variables by defining a common set of
underlying factors,
2. to differentiate relevant from irrelevant variables for the subsequent Cluster Analysis
(variables which are not distinctive across the households can be eliminated).
3. the subsequent Cluster Analysis can be conducted with uncorrelated factor scores
(Backhaus, 1994). A Factor Analysis reduces highly correlated variables to one factor.
If highly correlated variables are used for the subsequent Cluster Analysis, some
characteristics have a stronger impact than others when it comes to the clustering of
objects.
4. data can be reduced for the subsequent Cluster Analysis (Hair, 1998).
One side effect of this multivariate data analysis is certainly the associated loss of statistical
information.
Various variables, captured in the first survey, are used as inputs for numerous Principal
Component Analyses. Because of their correlation structure and their relevance the following
8 variables were selected for the final Principal Component Analysis:
time of adoption (of improved cassava variety) compared to opinion leader, number of
inorganic fertilizers/agrochemicals, number of trial types conducted, number of different
types of training, values of residence buildings and other structures of the household, values
of radios, perceived walking time to output market, and percentage of quantity disposed on
total production. Different measures (e.g. Kaiser-Meyer-Olkin Measure of sampling
adequacy, Bartlett Test of Sphericity) indicate that this set of variables is appropriate for a
Principal Component Analysis (see table A 5 in appendix). The most common criteria for the
number of extracted factors is the eigenvalue. Three factors have an eigenvalue greater than 1,
explaining together 67 % of the variance (see A5). Table 1 illustrates the rotated component
matrix with the factor loadings. The factors could be titled as: innovativeness, household
assets, and market orientation.
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Table 1: Principal Component Analysis
Rotated Component Matrix a
,90 ,19 ,19
,88 ,06 ,23
,84 ,20 -,10
-,51 ,25 ,26
-,04 ,84 -,04
,35 ,77 -,10
,00 ,07 ,73
-,08 ,22 -,68
number of inorganicfertilizer/agrochemicalnumber of trial typesconductednumber of differenttypes of trainingtime of adoptioncompared to opinionleadervalues of residencebuildings and otherstructures of thehouseholdvalue of radioswalking time outputmarket(minutes)% of quantity sold ontotal production
1 2 3Component
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Rotation converged in 6 iterations.a.
Source: own calculations Computed factor scores are used as the input for the subsequent Cluster Analysis. The main
reasons for performing this type of multivariate analysis are 1) identification of homogenous
household groups and 2) to provide a criterion for the selection of households for the in-depth
interviews in the second household survey. Regarding the clustering algorithm, a combination
of hierarchical and non-hierarchical methods was chosen in order to fine-tune the results and
to have a validity check. For the hierarchical clustering the similarity measure chosen is the
commonly used Squared Euclidean Distance, which is computed by the following formula:
2
1
2 )( jk
p
kikij
xxD −= ∑=
,
where
Dij
2 = squared distance between object i and j ,
xik = value of kth variable for the ith object,
xjk= value of the kth variable for the jth object,
p = number of variables.
Regarding the clustering technique, Ward’s Method was applied, which belongs to the
agglomerative methods. In the agglomerative methods each object starts as its own cluster. In
subsequent steps the two closest clusters (or objects) are combined into a new aggregate
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cluster. The Ward’s Method forms clusters by maximizing within-clusters homogeneity.
Within-group sum of squares is used as the measure for homogeneity. Therefore, Ward’s
Method tries to minimize the total within-clusters sum of squares (Sharma; 1996).
Non-hierarchical methods have some advantages over hierarchical, e.g. the results are less
susceptible to outliers in the data, the distance measure used, and the inclusion of irrelevant
variables (Hair et al. 1998). Since these benefits are only realized with specified seed points, a
combination of both methods was chosen. Furthermore, the hierarchical analysis determines
the appropriate number of clusters. After the initial seed points are selected (these are the
cluster centres of the hierarchical method), all objects (households) within a pre-specified
threshold distance are included in the resulting cluster. Then for each cluster new centres are
computed and the objects are assigned again. Objects may be reassigned if they are closer to a
new cluster centre. No standard procedure for the number of clusters to be formed exists. A
simple example for a stopping rule is to look on large increases in the agglomeration
coefficient. A large increase indicates that two very different clusters are being merged. Table
A 6 in the appendix illustrates the calculated percentage change in the coefficient for 10 to 2
clusters. There is a large percentage increase when the number of clusters is reduced from 4 to
3 clusters. Therefore, forming 4 clusters seems to be an appropriate solution.
Finally, the following four clusters were identified (see Figure 3): subsistence farm
households (30 %), semi-subsistence farm households (52 %), commercial farm households
(10 %), and innovative trial farm households (7 %).
Figure 2: Identified Farm-Household Groups
52%30%
10% 7%Semi-Subsistence Farm-HouseholdsSubsistence Farm-Households
Commercial Farm-Households
Innovative Trial Farm-Households
Source: own calculations (based own survey 2000)
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beans
maize
cassava s.pot. coffee
bananasgnuts
other
0
10
20
30
40
50
%land
Figure 3: Average proportion of cultivated land under major crop types
Source: own survey 2000
Descriptive statistics provide information on household characteristics, household assets (see
appendix A 1 – A 4), major crop types (Figure 3), main differences between the different farm
household groups (see Table 2), major information sources for applied technologies (Figure
4), and reasons for non-adoption of technologies (Figure 7).
The average farm size in the villages is about 2.3 ha; the dominant type of tenure is freehold.2
The main crops grown in the area are maize, beans, cassava, sweet potatoes, bananas, coffee,
fruits, and vegetables (Figure 2). The majority of the farms have few or no livestock and the
mean numbers are 1 bovine livestock, 2 goats and 5 chickens per farm. Agricultural
production can be characterized by low levels of fertilizer application and poor agronomic
practices based on rain-fed agriculture with hand hoe cultivation.
The innovative trial farm households belong to the early adopters of a mosaic resistant
cassava variety. They are (by definition) the only group, which is conducting trials in
cooperation with CIAT. These households apply the highest number of inorganic fertilizers
and other agrochemicals, and they are the only group with a reasonable number of different
types of agricultural training.
The commercial farm households achieve the highest mean values for the following variables:
value of residence and other structures of the household, value of agricultural equipment per
person involved in farming, total value of agricultural production, value of agricultural
production per acre cultivated land, and quantity sold on total agricultural production.
Interesting is the fact that they belong to the late adopters of the mosaic resistant cassava
variety. There are two explanations: 1) cassava is not important as a cash crop and therefore
not of major interest for commercial farm households 2) wealthier households are excluded
from the communication process of the average farm households. Subsistence and semi-
2 The average proportion of land under freehold status is 72 % (see appendix A 4).
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subsistence farm households attain relatively low mean values for the following variables:
years of schooling of household head, value of household assets, quantity sold of total
agricultural production, value of agricultural production (total and per acre cultivated land),
and number of inorganic fertilizers and other agrochemicals applied. Furthermore, the
subsistence farm households face very long walking times to the next output market and
belong to the group of late adopters.
Table 2: Characteristics of the Identified Clusters
Semi-Subsistence Farm Households
Subsistence Farm Households
Commercial Farm Households
Innovative Trial Farm Households
Household Characteristics Years of schooling head 4.4 5.5 12.4 7.7 Years of schooling wife 4.3 3.3 8.1 5.6 Number of different types of training (since 1990)
0.7 0.3 1.0 4.2
Household Assets Value of residence and other structures (103USh)
837 1267 7601 1951
Value of radios (103USh) 22 16 74 43 Value of agricultural equipment per person involved in farming (USh)
5261 4358 9739 5778
Crop Production Total value of agricultural production (103USh)
833 455 1635 1066
Value of agricultural production per acre cultivated land (103USh)
182 182 224 207
Quantity sold on total production (%)
52 23 64 35
Perceived walking time to output market (minutes)
45 142 64 81
Intensity of land use3 1.2 0.9 1.4 1.1 Labor-land ratio4 131 260 165 159 Innovativeness Time of adoption (improved cassava variety) compared to opinion leader (years)
0.7 4.6 5.8 -2
Time of adoption (improved cassava variety) compared to personal network (%)
0.41 0.66 0.73 0.33
Number of technologies adopted last 10 years
5 5 6 8
Number of trial types conducted
0 0 0 7
Source: own survey 2000
3 intensity of land use is defined as ratio between land area cultivated last 12 months and total land size 4 labor-land ratio is defined as ratio between labor use on farm (person days) and cultivated land size
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Peers and friends are after CIAT the most important information sources for applied
technologies. Figure 4 illustrates the enormous impact NGOs apparently can have on the
livelihood strategies of farm households. Shifting the responsibilities for extension services to
the district level within the decentralization process does not seem to work properly yet in the
study region. Restructuring the Ministry of Agriculture, Animal Industries and Fisheries
(MAAIF) involved a drastic reduction in the number of staff. Selecting innovative households
and opinion leaders for the CIAT technology trials seems to be a promising strategy for a
widespread diffusion of innovations (Esilaba et al., 2001). The theory on diffusion of
innovations emphasises the importance of social networks for the adoption and diffusion of
innovations (Rogers 1995, Valente 1995, Berger 2001).
CIAT/A2N
DFI
Peers/Fr.
SG RelativesExtension
Other
0
20
40
60
Frequency
Figure 4: Major information sources for applied technologies
Abbreviations: DFI=District Farm Institute; SG=Sasakawa Global 2000
Source: own survey 2000
Rogers (1995) pointed out that typically the cumulative S-shaped adopter distribution closely
approaches normality. The normal frequency distribution has several characteristics that are
useful in classifying adopters. Mean values and standard deviations are used to classify
adopters in the following four categories: early adopters, early majority, late majority and
laggards. Valente (1995 and 1996) introduced a method to estimate thresholds of adoption,
empirically based on the concept of social networks. Empirically estimated thresholds of
individual farm households to adopt new technologies will help to illustrate the diffusion
patterns of promoted technologies. Figure 5 confirms Rogers’ classification of adopter
categories for a mosaic resistant cassava variety in the study area.
The African Cassava Mosaic Virus (ACM-V) has had a devastating effect on cassava, one of
the major food crops in the region. Therefore, in the middle of the 90s the International
Institute of Tropical Agriculture (IITA) and the National Agricultural Research Organization
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(NARO) initiated a cassava multiplication programme for multiplying ACM-V resistant
cassava varieties in the district (Vredeseilanden-Coopibo-Uganda, 1998).
Insufficient awareness, unavailability, and high input costs are the most important reasons for
the non-adoption of new technologies (Figure 6). These figures are an indicator for the
importance of well-functioning markets and social networks for the adoption and diffusion of
innovation.
time of adoption compared to social system
5,04,03,02,01,00,0-1,0-2,0-3,0
Figure 5: Time of Adoption (Cassava)
Freq
uenc
y
20
10
0
Source: own survey 2000
new
insuf.aware
not available
input costs
other
0
50
100
150
Frq.
Figure 6: Reasons for non-adoption of technologies
Source: own survey 2000
3.4 Bio-Economic Household Model (Computational Model)
The main objective of the second household/plot-level survey was to provide useful input for
the calibration of the programming matrices, including data for the estimation of input-output
coefficients and farm income analysis. Altogether 20 households were interviewed in-depth.
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Out of each cluster at least three households were chosen, which are most closely to the
cluster center. Additionally, 7 farm households out of the 44 trial farmers were captured with
respect to the type of trials they are conducting in order to collect sufficient data for analyzing
the effects of promoted technologies. Besides, CIAT provided the trial data of 4 seasons in
2000 and 2001, and soil data of their test farmers used for yield estimation.
Furthermore, 110 composite soil samples for 0-20 cm and 20-40 cm were taken, and 61 plots
were measured to provide more specific data for the subsequent modelling work. Compasses
were used for measuring plot angles, the distances were measured by tapes. Supplementary,
intensive cooperation with local technical experts and collection of secondary data allow a
modelling work reflecting the “real world situation”.
Bio-economic models combine socio-economic factors influencing farmers’ objectives and
constraints with biophysical factors affecting production possibilities and the impacts of land
management practices (Barbier 1996 and 1997; Kruseman et al. 1997). These models may
identify the optimal level of technology adoption and the impact on incomes and natural
resource conditions under a changing socio-economic environment. Implemented as multi-
agent systems (Berger 2001) outcomes from agent-agent and agent-environment interactions
could be captured as well, e.g. diffusion of innovations together with the evolution of farm
incomes and natural resource conditions over time. The bio-economic modelling approach
developed for this study consists of three major components: mathematical programming
model to reflect the farm household decision-making process under certain constraints,
artificial neural networks as a yield estimator, and nutrient balances as a sustainability
indicator. The results of the yield estimator and the calculation of nutrient balances are then
incorporated into the programming model. In the following each component and the
integration into one modelling approach will be described.
3.4.1 Artificial Neural Networks as a Yield Estimator
Artificial Neural Networks (ANN) belong to a new research area called artificial intelligence.
ANN attempts to mimic the human brain and its structure to develop a processing strategy. As
in the human brain, multiple parallel processing units are engaged in pattern recognition.
There are many different types of ANN with different applications available, which are
described for example in Bishop (1998). As far as the author knows neural networks were not
used for yield estimation purposes before. Park and Vlek (2002) examined the possibility of
predicting soil property distribution with ANN. The main reasons why this type of model was
preferred for yield estimation instead of a deterministic simulation model were:
19
• their enormous data requirements, which cannot be satisfied in the study region
• it is frequently quoted that many deterministic models are just working at the calibrated
sites (Beven et al., 1989 and Hoosbeek et al., 1992)
• their limited capacity of estimating the impact of many different technologies.
Traditional statistical approaches, e.g. General Linear Model (GLM) and multiple regressions,
have several limitations to model complex and non-linear dynamics in crop yield. The main
differences between GLM and multiple regression analysis are that for the GLM the
distribution of the dependent variable does not have to be continuous, and GLM allows linear
combinations of multiple dependent variables (see Park and Vlek, 2002). The modeling
accuracy (R2=0.75) of the ANN yield estimator is promising.5 Comparing the results for yield
estimations of the ANN with GLM (Figure 7) led to the conclusion that non-linearity should
be taken into account while estimating the impact of soil characteristics and different
technology options on yields.
Figure 7: Comparison of Modelling Accuracy ANN - GLM
0,00,10,2
0,30,40,50,6
0,70,80,9
1,0
SY ASY CY ACY GY AGY TB ATB
Yield Index
R2
ANN
GLM
Abbreviations: SY=stover yield, ASY=adjusted stover yield; CY=cob yield; ACY=adjusted cob yield;
GY=grain yield; AGY=adjusted grain yield; TB=total biomass; ATB=adjusted total biomass
Figure 7 illustrates that ANN achieves a higher modeling accuracy not only for grain yield,
but for all other yield indices considered as well, e.g. stover yield and cob yield. The main
limitations of ANN are its data dependency and the absence of any statistical inference tests
for model weights of overall model fit.
5 For ANN output reports see appendix A 8.
20
1124 CIAT farm trial data, collected during 4 seasons in 2000 and 2001 in Magada and
Buyemba were used for the yield estimation. 25 farm households participated in these maize-
trials. Additionally, eleven basic soil attributes were measured for one sample per farmer.
Therefore, the following yield function was estimated with the ANN:
Y = f (PH, OM, N, P, K, Na, Mg, Sand, Clay, Silt, FYM, GM, BRP, MRP, BB, Pre-Pac, TSP,
Nfert, NP, NPK),
where
Y= maize yield level
PH=soil-ph, OM=organic matter, N=nitrogen content of soil, P=phosphorus content of soil,
K=potassium content of soil, Na=sodium content of soil, Mg=magnesium content of soil,
Sand=sand content of soil, Clay=clay content of soil, Silt=silt content of soil,
FYM=farmyard manure trial, GM=green manure trial, BRP=Busumbu rock phosphate trial,
MRP= Minjingu rock-phosphate trial, BB= Busumbu Blend trial (90% Busumbu rock
phosphate, 10% TSP), Pre-Pac= Pre-Pac (rock phosphate, urea and rhizobia), TSP=Triple-
Super-Phosphate trial, Nfert=Urea-trial, NP=Urea+Triple-Super-Phosphate trial,
NPK=Urea+Triple-Super-Phosphate+Muriate of Potash trial
During the process of building a network, the type of neural network model, the number of
nodes, the type of activation function, number of hidden layers, and the learning rule have to
be determined. The neural network type applied in this study is the most commonly used
multiplayer perceptron model. The ANN is a sequential arrangement of three basic types of
layers: input layer, output layer, and hidden layer. These layers consist of small processing
units called nodes, which are analogous to the neuron of a human brain. Nodes are receiving
input values and creating output values. The incoming data is processed by creating a
summated value in which each input value is multiplied by its respective weight. The summed
value is processed by an activation function to create output, which is sent to the next node in
the system. The activation function chosen here is the commonly used hyperbolic tangent
function in order to introduce non-linearity. An output node receives an input value and
calculates an output value. This value is the final one, it is not sent to another node. In order to
represent more complex relationships than just a one-to-one relationship from input to output
a third type of layer (hidden layer) is introduced, which is located in between the input and
output layer.
21
The main difference in comparison to other multivariate techniques is the ability of the neural
network to “learn”. The created output values are compared with the actual values. If there is
a certain difference, the error in the output value is calculated and then distributed backward
through the system. This common form of training is called “backpropagation”. During the
modeling process the number of nodes and hidden layers were changed. The best performance
(lowest Mean Square Error and highest R2 (0,75)) was achieved with 21 input nodes, 8 output
nodes and one hidden layer with 25 nodes. ANN analyses were conducted using the software
NeuroDimension 3.0 (Principe et al., 2000).
Figure 8: ANN Model Output -Impact of technology options on maize yield
-60%-40%-20%
0%20%40%60%80%
100%120%140%
FYM MRP BRP TSP Blend Pre GM Nfert NP NPK
chan
ge
of
yiel
d soil1soil2soil3soil4soil5soil6
Source: ANN model output (based on CIAT trial data 2000 and 2001)
The ANN output desired for the programming matrix is to quantify the impact of different
land management practices and soil classes on the yield. Therefore, the CIAT soil data and
the soil data collected during the second household/plot level survey were used for clustering.
Finally, six different soil classes were identified (see A 6)6. The soil characteristics of the
different classes and the technology options mentioned above were tested against the trained
network to receive the desired output. Figure 8 illustrates the impact of different technology
options on maize yield in comparison to the control plots without any treatment as received
from the ANN model. The ANN estimates for yield response under BRP, Blend, NP and NPK
treatments are quite positive with an average yield increase of around 40%. For TSP the
average increase was 26% and for MRP 16%. The other trials (FYM, Pre-Pac, GM and NP)
had on average nearly no impact on the yield in comparison to the control plots.
6 Hierarchical clustering with Ward’s Method as cluster method and Squared Euclidean Distance for interval measurements was chosen.
22
Figure 9: Experimental data: Impact of technologies on maize yield
-10%
0%
10%
20%
30%
40%
FYM MRP BRP TSP Blend Pre-Pac GM Nfert NP NPK
ch
ang
e o
f yi
eld
Source: own calculations (based on CIAT trial data 2000 and 2001)
ANN model results and descriptive analysis of experimental data received from CIAT (Figure
9) indicate similar effect of the promoted technologies on yield. The only major difference
can be observed for the impact of Blend. As mentioned above, the ANN model indicates a
significant positive impact of this technology on maize yield, whereas, the average impact
received from the experimental data is very low (2 %). This is a surprising result since the
experimental data show a positive impact of BRP as well (nearly 40 %), and Blend consists of
90 % BRP and 10 % TSP. One possible explanation for the differences of the results for
Blend received from the model and from the experimental data is, that for the latter data
source the impact of different soil classes is neglected. As Figure 8 illustrates the ANN model
indicates a low impact of Blend on soil class 2 (10 %) as well.
Apparently, the impacts of the selected technology options on maize yield are not as high as
one might expect from trials conducted at research stations under the control of scientists.
Therefore, the method of on-farm trials allows to estimate the incentives for the farmers to
adopt a specific technology more realistically.
3.4.2 Nutrient Balances as Sustainability Indicator
Nutrient balances for nitrogen (N), phosphorus (P) and potassium (K) were selected as a
sustainability indicator. The appropriateness of nutrient balances as an indicator for soil
productivity and sustainability assessment was discussed in the literature frequently (Lynam
et al., 1998). Among other aspects Lynam et al. (1998) criticized that farm household
decision-making exclusively based on nutrient balances leaves out any economic
consideration. There are cases where farmers consciously exploit nutrient stocks to invest in
23
capital that leads to sustainable development in the long run. The integration of nutrient
balances and economic considerations in a mathematical programming approach can help to
overcome this problem. Additionally, it is very difficult to quantify the feedback effects of
nutrient losses on the development of the yield level, taking into account the complexity of
nutrient dynamics in the soil. But nutrient balances can at least serve as an indicator of soil
productivity in the future considering the total nutrient stock in the soil.
The calculations are based on the concept of the study “Nutrient Balances and Expected
Effects of Alternative Practices in Farming Systems of Uganda” (Wortmann and Kaizzi,
1998). This study was carried out in Imanyiro Sub-county, to which also Magada and
Buyemba belong. Own data collection (especially the soil samples) helped to adjust the
nutrient balances for the households under consideration. The nutrients-removing factors
captured in the programming matrix are: erosion, harvest of the main product, harvest of the
crop residues, leaching, and denitrification. The nutrients-adding factors are: inorganic
fertilizers, farmyard manure, green manure, biological nitrogen fixation, and atmospheric
deposition. Nutrient losses through erosion are based on the Universal Soil Loss Equation
(USLE). The rainfall factor R was set at 4007, the topographic factor SL was estimated
according to Renard et al. (1994) for cropped land of moderate ratio of rill to inter-rill erosion,
and for fallow using scales for rangeland. The slope gradient was assumed to be 4 %,
according to a study carried out in Magada by the Center for Development Research. Since
the input-output coefficients of the programming matrix were calculated on the basis of one
hectar, assuming a quadratic plot shape, slope length was set at 100 meters. According to
Wortmann and Kaizzi (1998) the crop factor C ranged from 0.2 to 0.4 (0.2 for banana and
coffee intercrop associations, 0.3 for annual intercrop associations and sole crop bananas and
coffee, 0.4 for annual sole crops) and the erosion management factor P was set at 1 unless the
farmers were attempting to control erosion. The soil factor K was set at 0.04 (Barber et al.,
1979) and the nutrient enrichment factor of the runoff was assumed to be 1.5. The calculation
of N-losses through erosion is based on the organic matter content received from the analysed
soil samples. Extractable P was received from the soil analysis, total P calculation is based on
the method developed by van den Bosch (1997). K nutrient losses through erosion is based on
total K as calculated by Stoorvogel and Smaling (1990). Secondary data were used to quantify
nutrient contents of harvested products, residues, and fertilizers (Stoorvogel and Smaling,
1990; Defoer et al., 2000; Wortmann and Kaizzi, 1998).
7 Compare Wortmann and Kaizzi (1998).
24
3.4.3 Mathematical Programming
Currently available bio-economic models are based on mathematical programming
approaches. A mathematical programming model helps to find the farm plan (defined by a set
of activity levels) that maximizes the objective function, but which does not violate any of the
fixed resource constraints, or involve any negative activity levels.
This model type offers great possibilities to formulate a wide range of actual and potential
activities and to determine their relative attractiveness. Advanced techniques offer the
possibility to reflect farmers ̀ behaviour realistically, e.g. the inclusion of risk aversion and
household food requirements in the objective function and constraints (Hazell and Norton,
1985).
Two extreme prototypes of agricultural programming models were defined by Hanf (1989).
The “simultaneous equilibrium approach” maximizes a common sectoral utility function and
assumes a perfect market mechanism. Secondly, the “representative independent farm model”
that can be defined as an independently computed farm model representing a certain farm
type. Computational results are added up to regional results. Hanf (1989) concludes that the
latter model should be chosen, if the sector development is characterized by 1) imperfect
markets, 2) behavior other than pure profit maximization and 3) adjustment processes.
Therefore, a model approach similar to this type seems to be an appropriate choice taking into
account the conditions in the study area. The approach offers the possibility to analyse the
behaviour of the individual farmer. Programming models are able to simulate adjustment of
land use under changing conditions. Therefore, they are an appropriate approach to analyze
the choice among alternative activities and technologies and to assess the impacts of
alternative policies in the short and long run.
Brandes (1985) criticized linear programming methods in the sense that as a consequence of
compensating errors and due to the temptation of manipulation, the model builder could give
the impression that his model reflects reality. An important weakness of these conventional
simulation models, apart from the aggregation error, is that they do not explicitly capture the
interactions between the farm households and therefore neglect transaction and information
costs.
The mathematical programming model computes the optimal production and consumption
plans based on a lexicographic utility concept: the households first satisfy their nutrition goals
before maximizing the household income, subject to financial, technical and sustainability
constraints. Nutrient requirements and consumption preferences that the households
articulated during the in-depth interviews are included. The farm household decision-making
25
problem is captured through mixed-integer programming consisting of 507 variables and 201
constraints. The inclusion of risk aversion in the objective function (e.g. by MOTAD or
quadratic risk programming, Hazell and Norton 1980) was neglected since district agricultural
annual reports did not indicate a high variability of yields of main crops during the last 10
years (e.g. mean yield value for maize: 1467 kg/ha, standard dev.: 130 kg/ha). Of course the
yield variability at the disaggregated farm household level could be different, but
corresponding time series data were not available. Discussions with extension officers
confirmed that the variation of yields does not have a significant impact on the planning
process of farm households. Data from the second household/plot-level survey were used to
calculate the variable costs per hectare of land for crop activities and per livestock unit. Gross
returns of the production activities were not captured in the objective function directly, in
order to give the household the option either to sell or to consume the produced commodities.
The market or farm gate price of the products appears as the objective function value in the
respective selling activity. Production expenses, which were not included in the variable
costs, such as hiring in labor, hiring in tractor, borrowing credits etc. were accounted through
the objective function values of the respective activities.
Activities included in the applied programming approach
The main activities captured are: crop production, livestock production, consumption and
selling of agricultural products, permanent off-farm employment, hiring in/hiring out
temporary labor, labor exchange, labor transfer, hiring tractor, investment activities,
borrowing credit, and technology options based on CIAT farm trials.
Regarding crop production activities, two growing seasons, six different soil types, different
cropping methods (mono-cropping, inter-cropping), and different technology levels
(improved seeds, local seeds, different seed rates etc.) are taken into account. Crops under
consideration are: maize, beans, sweet potatoes, cassava, groundnuts, millet, sorghum, coffee,
bananas, tomatoes, onions and passion fruits. For livestock production activities the focus is
on local and exotic cows. Small stock production activities (e.g. chicken, porkers and goats)
were neglected due to lack of reliable data at the farm household level.
The model allows for the option to sell the whole amount produced of a specific crop, to use it
for home consumption or to choose a combination of both. The prices for agricultural inputs
and outputs were taken directly from the household survey. Besides, if necessary, secondary
data (APSEC, 2000) were used.
26
Households are offered to opt for permanent off-farm activities as a binary activity, if the
interview indicated relevance. Furthermore, temporary off-farm activities are divided into 5
different time periods throughout one year. For the same time periods casual labor can be
hired in. The division into 5 periods guarantees that enough labor is available for farming
activities which have to be carried out at fixed points of time. A price difference between the
activities of hiring in and hiring out labor reflects control costs for the time labor is hired out.
Labor activities were divided into male and female labor, since certain agricultural production
activities are traditionally only carried out by men (e.g. land clearing), others only by women
(e.g. cultivation of sweet potatoes). Transfer activities make sure that labor is provided for
production activities, which are carried out by both, male and female worker, and only adult
labor (older than 16 years) is hired out. Labor exchange was captured as an activity although
its importance is declining. As mentioned above, the labor market changed during the last 10
years from labor exchange oriented to labor hiring oriented. Scenarios with two different
labor sources could help to understand the impact of the changes in the labor market on land
management practices. Constraints guarantee that only as much labor is imported by labor
exchange as labor is exported by the farm household. There appears no objective function
value for the labor exchange activities. Its value for the household is reflected by the shadow
prices, which are calculated within the sensitivity analyses.
Because of their policy and practical relevance in Iganga District, the following investment
activities were chosen: treadle pump for irrigation, walking tractor, and zero-grazing unit.
Data on investment costs, repair and maintenance costs and technical details were received
from AETRI (Agricultural Engineering and Appropriate Technology Research Institute under
NARO). Investment costs and costs for repair and maintenance were annualized.
Financial constraints are often mentioned as one of the major constraints for the adoption of
modern agricultural technologies. Therefore, two different types of credit schemes were
considered explicitly: micro finance schemes operated by commercial banks and micro-
finance institutions (e.g. Finca, Pride Africa). Credits for investments and for annual
production costs were differentiated for each scheme. Different credit conditions, such as
credit limits, gestation periods and interest rates, were accounted for through respective
formulation of activities and constraints of the programming matrix.
Finally, different technology options based on CIAT farm trials are captured as additional
production activities (see 3.4.1). Application rates and input prices were received either from
CIAT/A2N or collected from markets.
27
The FAO is conducting a small scale irrigation project in cooperation with the Ugandan
Ministry of Agriculture, Animal Industry and Fisheries in seven districts, among others in
Iganga’s neighbour district Jinja.8 Gross margin calculations received from project officials
were used to capture the economic impact of irrigation for farm households. Irrigated crops
were limited to high value crops, such as clonal coffee, tomatoes, onions, and passion fruits.
Labor requirements for irrigation with treadle pumps were received by interviewing AETRI
staff. Irrigation is restricted to the dry seasons (December – March and July – September) in
the model. A2N is already promoting small scale irrigation with treadle pumps in neighbour
villages.
Constraints included in the applied approach
The main resources and constraints considered are: total land area, crop rotation, labor,
nutrient requirements of household members, consumption preferences, capital constraints
(including credit limits), and nutrient balances as a sustainable indicator.
Land is the basic resource of production. The land related production activities are expressed
on a per hectare basis. It is taken into account that a certain percentage of the total land area is
needed for fallow. The available land resources are divided into six soil classes as mentioned
above. Discussions with soil scientists from ZEF reveal the importance of considering each
soil parameter for an identified soil class. One parameter might give the impression of a fertile
soil, whereas, other values are very low or even reach toxic levels (compare appendix A 9).
Soil class 1 is characterized by average values for ph (5.7), organic matter content (2.7 %) and
potassium content (22 mg/100 g soil). Outstanding is the low phosphorus content (3.3 ppm).
The ph-value of soil class 2 is a little bit lower (5.2), the phosphorus content is higher (4.3
ppm) and the sand content is quite high as well(71 %). Soil class 3 is characterized by a
relatively low ph-value (4.2), whereas for soil class 4 a relatively high ph-value is indicated.
Furthermore, the calcium content is extremely high on soil 4 (160 mg/100 g soil). A very high
phosphorus content is reported for soil 5. Soil 6 can be characterized by a low ph value of 4.2,
a low organic matter content of 1.4 % and an extreme high sand content of 81 %.
Crop rotation constraints make sure that the cultivation of a specific crop does not exceed a
certain percentage of the total land area. The fact, that continuous cultivation of one crop on
the same plot would lead to nutrient mining, pest and diseases is realized by the farmers. The
crop rotation constraints were formulated with the help of local extension officers.
8 Project title: Small Scale Irrigation Technology Demonstration, Project Symbol: TCP/UGA/8821
28
Labor is a major factor of production in the less mechanized and less developed agriculture in
the study area. Labor requirements by various production activities and labor availability
during an agricultural year jointly determine the resource allocation and productivity of a farm
household. Because of the uneven distribution of land and household members among
different farm households, the labor availability per unit of land varies across farms.
Therefore, theses farms have different degrees of work participation on the farms and in the
rural labor markets. The production period of 12 months was divided into 5 time periods to
make sure that different critical time spans during an agricultural year are captured with
respect to their labor requirements. If seasonality of resource use is ignored, it is likely that
the model solution obtained will be unrealistic by requiring more resources (here labor) in
some periods than are available (Hazell and Norton, 1980). Furthermore, labor was divided
into three age groups to capture their different productivity levels (child labor between 14-16
years is treated as equivalent to 80 % of adult labor, adult labor above 55 years is treated as
equivalent to 50 % of adult labor between 17 and 55 years). It is assumed that men and
women with primary activity farming and no off-farm activity are working 6,5 hours per day
in agricultural production. If they have an off-farm activity as a secondary activity, a
minimum of 4 hours is spent on agricultural production and a maximum of up to 4 hours on
off-farm activities. Children are working 4 hours per day on the farm, their availability is
restricted to the school holidays. Adults above 55 years are working not more than 4 hours per
day on the farm.
The priority of the farm households across all household groups is to satisfy the basic needs of
the household members, especially the food requirements. Most crops are used at least partly
for own consumption, the surplus is sold. Only coffee, fruits and vegetables can be considered
as traditional cash crops. The seasonal nutrient requirements of the household members are
introduced as a constraint in the programming matrix. Average estimates for each type of
consumer unit were worked out to obtain seasonal consumption requirements of the
households. The differences due to age, sex , and activity level are also captured (Latham,
1997). To reflect consumption priorities, the amounts of different crops consumed are
considered as well.
Another major constraint of the farm households in the study area is the amount of capital
available for agricultural production. Two types of capital requirements are differentiated:
capital required in the first year for investment activities, and capital required for annual
operational production costs. To calculate the available amount for the first type, investment
costs of investments still used were added up and to the sum 10 % were added as a tolerance
29
level. For the latter type annual production costs were calculated and again 10 % were added
as a tolerance level. Of course, these capital constraints could be relaxed by credit activities in
the scenarios.
Programming models for each identified household type (subsistence farm households, semi-
subsistence farm households, trial farm households and commercial farm households) were
calibrated. Model validation was conducted through measuring the association of model
solutions with observed values as suggested by Mc Carl and Apland (1986). The model
results were regressed on observed values, where a perfect association would be indicated by
an intercept of 0 and a slope of 1. The received values for R2 are 0.95. 0.99, 0.89, 0.94, the
received values for the intercept are: 0.04, -0.01, -0.03 and 0.06, the received slope values are:
0.96, 1.01, 1.02 and 0.83. Therefore, the “goodness-of-fit” test is indicating sufficient
association between real world data and model results. The programming models were solved
using the software Premium Solver Platform V3.5 for Excel (Frontline Systems, Inc.).
In the following first simulation results on the impact of different soil classes on land
productivity and labor intensity will be discussed. The four households selected for
representing the four identified clusters are having their plots on soil class 1, where nearly
50% of the samples included in the soil clustering belong to. The following two diagrams
(Figure 10 and 11) reflect the simulation results for land productivity and labor intensity
under the current land use practices for the four different household types.
The highest productivity is achieved on soil class 2 by all household types, followed by soil
classes1 and 3. The productivities on soil classes 4 to 6 are comparatively low. On soil 6 the
semi-subsistence farm household for instance just achieved 25 % of the productivity achieved
on soil 1. The highest productivity on each soil class is indicated for the commercial
household, followed by the trial farm household. The major factor contributing to higher
productivities of these farm household types is the cultivation of improved seeds and the use
of agrochemicals. On the first three soil classes the semi-subsistence household achieved
higher productivities than the subsistence farm households. On soil classes 3-6 both
household types had to reduce their consumption preferences, the subsistence household more
drastically than the semi-subsistence. On soil class 6 the subsistence farm household is no
longer able to satisfy the calorie requirements of its members. The recommended amount has
to be reduced to 70 %, which has serious consequences for food security.
30
Figure 10: Land Productivity (Gross Output/ ha/ year)
0
200
400
600
800
1000
1200
1400
1600
soil1 soil2 soil3 soil4 soil5 soil6
productiv 10^3 Ush
SubSemiTrialCom
Source: Bio-economic model output
Figure 11: Labor Intensity
0
500
1000
1500
2000
2500
Sub Semi Trial Com
h/h
a an
d y
ear
soil1
soil2
soil3
soil4
soil5
soil6
Source: Bio-economic model output
Figure 11 reveals the labor intensity for the different household types on different soil classes.
The labor intensity of the subsistence household is increasing on soils 4-6. These soil classes
are less productive leading to lower crop yields. To satisfy the household consumption
preferences a certain amount of specific crops have to be produced. To achieve the desired
output level of one crop, the area under this crop has to be expanded on less productive soils.
This leads to changing production patterns, since consequently the area under other crops will
have to be reduced. If the production activities, which are expanded, are more labor intensive
than the ones which are reduced, consequently overall labour intensity increases. In the case
of the subsistence farm household the food crop production activities are comparatively more
labour intensive. Therefore, the necessary expansion of their production on less productive
31
soils contributes to increasing labor intensities on soils 4-6. Similar conclusions can be drawn
for the semi-subsistence household, though labor intensity is not increasing as significant as
for the subsistence farm household. For the trial farm household and commercial farm
household the constraints of satisfying consumption preferences are not as binding on most
soil classes as for the household types discussed before due to higher overall productivity.
These household types sell a larger proportion of their produced goods. The development of
labor intensity on soils 1-4 follows standard economic theory of factor allocation. Marginal
productivity is increasing and consequently labor intensity is increasing as well and vice
versa. On soils 5 and 6 land productivity is so low that the goal of farm households of
satisfying basic needs is determining the production structure and therefore labor allocation.
Only a minor proportion of produced goods is sold. The consequences are increasing labor
intensities although land productivities are decreasing.
4. Agricultural Policy Scenarios
The selection of relevant policy scenarios is based on the “Priority Areas for Action” defined
in the Plan for Modernization of Agriculture (PMA) published by the Ugandan Government
in 2000. The priority areas for action are: improving access to rural finance, improving access
to markets, research and technology development, sustainable natural resource utilization, and
management and education for agriculture. The PMA was composed in line with the economy
wide decentralization, privatization and liberalization policy in Uganda by redefining the roles
of the private sector and the central and local governments. The role of the public sector in
agriculture should be reduced substantially, focusing mainly on research, extension, and
regulatory functions. The Government is committed not to be involved in the import or
distribution of agricultural inputs, nor to subsidize inputs or to be involved in direct provision
of rural financial services. The Government “would like to see all activities connected with
agricultural production, processing, trading, supply of inputs, exports and imports be carried
out entirely by the private sector” (FAO, 1999). Therefore, the selected policy scenarios
neglect direct market interventions concerning output price policies (e.g. taxes, subsidies,
fixed prices), input policies (e.g. subsidies, input delivery systems), marketing policies (e.g.
monopoly parastatals, trader licensing) and credit policies (e.g. credit provision by the state).
The focal point of the model scenarios is rather to which extent and how the market
environment could be improved to provide sufficient incentives to reach growth and
sustainability goals simultaneously.
32
The objectives of the scenarios, run with the developed integrated bio-economic model
approach, can be divided into four steps:
1. Explore the feasibility of reaching socio-economic goals of farm households
(satisfying basic needs of its members and income maximization) and sustainability
goals (non-negative nutrient balances) simultaneously under the current constraints.
2. Explore whether the relaxation of technical constraints (introduction of new
technologies) and capital constraints (provision of credit) could contribute to achieve
the goals defined above.
3. Illustrate to which extent improvements of the market environment (reflected by
reduction of distortion of input and output prices) in combination with provision of
credit and promotion of alternative forms of labor acquisition could contribute to a
significant increase of household welfare and nutrient balances. Furthermore, the
potential means leading to a reduction of market imperfections will be discussed.
4. Illustrate the economic and ecological impacts of the intensive production of high
value crops, since this pathway could be identified as a potential profitable pathway in
a development domain with high agricultural potential, high market access and high
population density. Furthermore, the transformation of subsistence to commercial
farming was mentioned as one major objective in the PMA.
Each single objective has to be seen in the context of the research question, why agricultural
production in a region belonging to a development pathway of high population density, high
market access and high agricultural potential is predominantly characterized by a low
intensity system.
Scenarios #1:Binding constraints and feasibility of non-negative nutrient balances
In the following it is discussed for the identified household types whether, under the current
constraints, the socio-economic goals of farm households to satisfy basic needs of its
members and to maximize the income and the sustainability goals of non-negative nutrient
balances could be reached simultaneously. Additionally, it is explored whether the relaxation
of technical constraints (by introduction of new technologies promoted by CIAT) and capital
constraints (by provision of credit) could contribute to reach this goal. For this purpose the
developed bio-economic model is used to run different scenarios for all household types (the
scenarios a - f are defined in tables 3 to 6). The technologies included are the ones captured in
the ANN yield estimator. The objective function value of the model (Total Gross Margin,
33
TGM) is used as an indicator for household welfare and the nutrient balances as sustainability
indicator.
Under current constraints the subsistence farm household type can satisfy the basic household
needs on the first three soil classes associated with high negative nutrient balances (scenario
a). As expected from previous results the highest Total Gross Margin (TGM) is achieved on
soil class 2. On soil classes 4 – 6 no feasible solution can be attained since the consumption
requirements cannot be fulfilled on the less productive soils. On soil classes 4 and 5 the
consumption preferences have to be reduced partially and on soil class 6 the recommended
nutrient requirements of the household members have to be reduced to 70 % of the original
value. The introduction of the sustainability constraint of non-negative nutrient balances
(scenario b) leads to no feasible solutions indicating that this sustainability goal cannot be
achieved with the current land management practices.
Tab. 3: Subsistence Farm Household Type: Feasibility of socio-economic and sustainability goals
Scenario Soil 1
Soil 2 Soil 3 Soil 4 Soil 5 Soil 6
a. current constraints TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1299 -28 -8 -39
1742 -37 -9 -54
1193 -28 -9 -40
# not feasible
# not feasible
# not feasible
b. + sustainability constraint TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
c. + new technologies TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1310 -29 -6 -40
1776 -40 -2 -57
1194 -28 -8 -40
# not feasible
# not feasible
# not feasible
d. +sustainability constraint +new technologies
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
e. +sustainability constraint +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
f. +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1356 -39 +12 -51
1822 -60 +35 -77
1195 -29 -8 -41
# not feasible
# not feasible
# not feasible
Source: own calculations (model results)
34
The introduction of new technologies promoted by CIAT (scenario c) leads just to a slight
increase of the TGM (e.g. + 0.8 % on soil 1) with nearly no improvement of the nutrient
balances, since the adoption of these technologies is not profitable under current market
conditions. Therefore, the introduction of new technologies alone cannot lead to the
achievement of the sustainability goal (scenario d). Even with the additional provision of
credit (scenario e), the subsistence farm household type is not able to achieve non-negative
nutrient balances. The last scenario type is neglecting the sustainability constraint and is
focusing on the introduction of new technologies coupled with the provision of credit.
Comparing the results with the baseline scenario a, TGM is increasing by 4 % on soil 1, but
the nutrient balances for nitrogen and potassium are becoming more negative (-39 % and –31
% respectively). Profitable adoption of rock phosphate contributes to increasing P-balances.
Tab. 4: Semi-Subsistence Farm Household Type: Feasibility of socio-economic and sustainability goals
Source: own calculations (model results)
Scenario Soil 1
Soil 2 Soil 3 Soil 4 Soil 5 Soil 6
a. current constraints TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1490 -52 -12 -62
1886 -65 -14 -82
1405 -51 -12 -63
# not feasible
# not feasible
# not feasible
b. + sustainability constraint TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
c. + new technologies TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1524 -66 +24 -83
1886 -65 -14 -82
1405 -51 -12 -63
# not feasible
# not feasible
# not feasible
d. +sustainability constraint +new technologies
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
e. +sustainability constraint +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
1730 +8 +69 0
1130 +6 +56 0
# not feasible
# not feasible
# not feasible
f. +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
1633 -79 +59 -94
1945 -64 -8 -78
1413 -48 -3 -59
# not feasible
# not feasible
# not feasible
35
The scenario results for the semi-subsistence farm household type are in some aspects
different to the ones for the subsistence farm household type. Under the current constraints
(scenario a) the nutrient requirements of the household members can be satisfied on each soil
class, although the consumption preferences have to be reduced partially on soils 4 – 6.
Again, high negative nutrient balances on each soil class can be observed. With current land
management practices non-negative nutrient balances cannot be achieved (scenario b). The
introduction of new technologies (scenario c) is leading to a slight increase of the TGM (2 %)
on soil 1 associated with higher negative nutrient balances (nutrient balance for nitrogen is
decreasing by 27 %). The new technologies alone cannot contribute to the achievement of the
sustainability goal (scenario d), whereas the combined introduction of new technologies and
provision of credit (scenario e) can reach this goal on soil 2 and 3. The introduction of new
technologies and provision of credit while neglecting the sustainability constraint (scenario f)
is leading to an increase of the TGM by 12 % on soil 2 and 25 % on soil 3, while the nitrogen
balance is decreasing from + 8 kg/ha to –64 kg/ha on soil 2 and from +6 kg/ha to –48 kg/ha.
These adverse effects can be interpreted as an indicator for a trade-off between economic and
ecological goals.
Just as the semi-subsistence farm household type, the trial farm household type can fulfil the
nutrient requirements of its members on each soil class. Table 5 indicates very high negative
nutrient balances in the baseline scenario a. The consumption preferences cannot be fulfilled
on soils 4 – 6, but a minor reduction of preferences could contribute to a feasible solution
(scenario a). As discussed for the two household types before, the defined sustainability goal
of non-negative nutrient balances cannot be achieved with current land management practices
and the introduction of new technologies alone (scenarios b – d). Scenario c indicates that the
introduction of new technologies alone cannot increase TGM significantly. Only on soil 1 the
phosphorus balance is improving due to the adoption of rock phosphate. At the same time the
balances for N and K are declining. With the simultaneous introduction of new technologies,
provision of credit, and sustainability constraints, the only feasible solution is achieved on soil
3 (scenario e). Neglecting the sustainability constraint (scenario f) is leading to an increase of
TGM on soil 3 by 39 %, but the nutrient balances are decreasing again (for example nitrogen
from +8 kg/ha to –41 kg/ha).
36
Tab. 5: Trial Farm Household Type: Feasibility of socio-economic and sustainability goals
Source: own calculations (model results)
The commercial farm household can fulfil the nutrient requirements and consumption
preferences of its members on each soil type in scenario a. The nutrient balances indicate very
high negative values. As for the other three household types, the sustainability goal cannot be
achieved with current land management practices (scenario b). The introduction of new
technologies (scenario c) is contributing to a slight increase of the TGM on soils 1, 2, 5 and 6
(e.g. on soil 1 by 8 %), whereas, the nutrient balances for nitrogen and potassium are
decreasing due higher nutrient losses through harvested products. The adoption of rock
phosphate is leading to a significant improvement of the phosphorus balance (on soil 2 for
example from – 20 kg/ha to +18 kg/ha).9 The sustainability goal can be reached on soil
9 Negative environmental impacts of high positive nutrient balances (eutrophication) have be taken into account.
Scenario Soil 1
Soil 2 Soil 3 Soil 4 Soil 5 Soil 6
a. current constraints TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
2395 -43 -11 -47
2907 -71 -17 -79
2306 -41 -11 -47
# not feasible
# not feasible
# not feasible
b. + sustainability constraint TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
c. +new technologies TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
2452 -60 +21 -69
2915 -75 -8 -84
2306 -41 -11 -47
# not feasible
# not feasible
# not feasible
d. +sustainability constraint +new technologies
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
e. +sustainability constraint +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
1735 +8 +50 0
# not feasible
# not feasible
# not feasible
f. +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
2575 -69 +35 -81
3024 -77 -5 -86
2418 -41 -10 -47
# not feasible
# not feasible
# not feasible
37
classes 2 –6 by introducing the new technologies (scenario d). Comparing the results of the
technology scenarios with and without the sustainability constraint (scenarios d + c) reveals
again a trade-off between economic and ecological goals. Neglecting the sustainability
constraint is leading to an increase of the TGM by 26 % on soil 2 and an increase of the
nitrogen balance from –83 kg/ha to 0 kg/ha. Similar trade-offs could be observed when
comparing the scenarios where new technologies are introduced and credits are provided; in
one case with, in the other without the sustainability constraint (scenarios e + f).
Tab. 6: Commercial Farm Household Type: Feasibility of socio-economic and sustainability goals
Source: own calculation (model results)
To sum up, the results of the scenarios reveal difficulties in achieving the goal of non-
negative nutrient balances, especially for the subsistence, semi-subsistence, and trial farm
household types. Current land management practices are leading to high negative nutrient
Scenario Soil 1
Soil 2 Soil 3 Soil 4 Soil 5 Soil 6
a. current constraints TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
4800 -77 -15 -71
6108 -110 -20 -111
4555 -74 -14 -72
3387 -27 -6 -20
3298 -26 -6 -20
2393 -26 -5 -18
b. + sustainability constraint TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
# not feasible
c. + new technologies TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
5204 -96 +58 -100
6157 -83 +18 -81
4555 -75 -14 -72
3387 -26 -2 -22
3307 -25 -1 -18
2402 -22 -4 -15
d. +sustainability constraint +new technologies
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
# not feasible
4882 0 +54 0
3739 0 +26 0
2773 0 +17 0
2887 0 +15 0
1079 0 +16 0
e. +sustainability constraint +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
3373 0 +47 0
5316 0 +65 0
3794 0 +53 0
2885 0 +17 0
2957 0 +16 0
1764 0 +16 0
f. +new technologies +credit
TGM (103 USh) N-Balance (kg/ha) P-Balance (kg/ha) K-Balance (kg/ha)
5223 -104 +56 -106
6199 -67 +51 -58
4555 -74 -14 -73
3387 -27 -6 -20
3307 -25 -1 -18
2402 -22 -4 -15
38
balances. Due to higher yields and consequently higher nutrient losses through harvested
products and stover, the value of the nutrient balances for the commercial farm household
type is higher negative than for example the nutrient balance value for the subsistence farm
household type. In most cases, neither the relaxation of technical constraints (by introduction
of new technologies), nor the relaxation of capital constraints (by the provision of credit)
could contribute to reach this sustainability goal. The main reason for this result is the non-
profitability of the promoted technologies under current market conditions resulting in low
adoption. Consumption preferences have to be reduced partially for the first three household
types on less productive soils. The subsistence farm households could not even satisfy the
recommended nutrient requirements of its household members on one soil class. For the
commercial farm household it is feasible to fulfil non-negative nutrient balances with the
introduction of new technologies in most cases, and with the additional provision of credit in
any case. This household type faced no problems to satisfy the basic needs of its household
members. Moreover, the scenarios illustrated a trade-off between the goals of improving
household welfare and of achieving ecological goals under the current market environment.
Since the sustainability goal of non-negative nutrient balances could not be reached in many
cases and direct market intervention were excluded as suitable policy measures for Uganda,
the subsequent scenarios deal with the potential of market improvements to contribute to a
significant increase of household welfare and nutrient balances simultaneously.
Scenarios #2: Economic and ecological impacts of promoted technologies under market
improvements
Before the scenario results will be discussed, the market environment in Uganda and in the
study region will be described, the potential extent to which input and output prices could
change will be examined, and potential policy measures to reach these changes will be
discussed.
As already indicated before the market environment in Iganga District, as in most parts of
Uganda, is far from being perfect. High transaction costs, high transportation costs, and
imperfect competition are leading to a low level of output prices and unreasonably high input
prices. The marketing chain for agricultural products in Iganga District involves middle men
in the villages, local buyers in trading centers and Iganga town, and traders from Kenya and
Mbale, Busia and Kampala (see figure 12). The price offered to the farm households by the
middle men depends on the price set by the local buyers in the town or trading centers, which
in turn is determined by the price offered by the foreign buyers. A study carried out by
39
Vredeseilanden-Coopibo-Uganda (1998) indicated a mark-up of 60 % between the price
received by the farmer and the price retailers were offering for Iganga District. Own survey
data indicated even higher price differences between farmer, wholesaler and retailer. In 2001
the price mark-up of maize between farmer and wholesaler was 62 %, and between farmer
and retailer 212 % (see figure 13). Farmers do not have the bargaining power when selling
their products at the farm gate, because in many cases they do not know the price offered at
other levels of the marketing chain.
Figure 12: Marketing chain for agricultural produce in Iganga District
Source: Vredeseilanden-Coopibo-Uganda (1998)
LOCAL BUYERS IN TRADING
CENTRES AND IGANGA TOWN
TRADERS IN
KENYA
TRADERS IN
MBALE; BUSIA;
KAMPALA
FARMERS
MIDDLE MEN IN VILLAGES
40
Figure 13: Price mark-ups of maize in the marketing chain 1998-2002
0%50%
100%150%200%
250%300%350%400%450%
1998 1999 2000 2001 2002
price mark-up farmer -wholesaler [%]
price mark-up farmer -retailer [%]
Source: own survey data (interview with Iganga District Cooperative and Marketing Officer)
These illustrations reveal the impact reduced transaction costs could have on the increase of
agricultural product prices received by the farmers. An essential step would be to improve
market transparency by providing farm households relevant agricultural information. One
option would be to implement a market information system (MIS) as suggested by IFDC for
the input markets (IFDC 1999). This MIS could collect, analyze, and publish information on
the development of output prices, input prices and other relevant information. The next step
would be the identification of appropriate channels to spread this information to the farm
households (see figure 14). Two possible channels could be taken into account: 1) mass media
(especially radio) and 2) extension workers via opinion leaders within the villages acting as
channel agents. Mass media (radio) could either spread the information direct to the
households or via opinion leader. 66 % of the surveyed households reported that they own a
radio, whereas other potential information sources like newspapers or telephone are only used
by 15 % and 6 % respectively. Of course these information sources could be important in the
medium to long run, but in the short run radio seems to be an appropriate mean to reach rural
households. On the other hand the issue of maintaining the radios (e.g. batteries), and the
perception and usage of the radio as an important information source have to be taken into
account as well. The latter aspect could be subject to public campaigns and part of the work of
extension services.
41
Figure 14: Agricultural Information Network
The second potential channel is based on social interactions between farm households. Above
it was mentioned that relatives and friends are very important information sources for the
adoption of new technologies. Analyses carried out by CIAT in the study region confirmed
that social interactions played a key role for the diffusion of innovation. Based on these
findings, extension agents could spread relevant agricultural information via identified
opinion leaders. Certain opinion leaders are of special importance, taking into account that 50
% of the surveyed households had at least one person belonging to the group of trial farm
households in their communication network with whom they discuss issues related to
agriculture.10 The identification of opinion leaders could be a task carried out by extension
agents as well.11 Since restructuring the MAAIF involved a drastic reduction in the number of
staff, the selection of opinion leaders for spreading information to individual farm households
seems to be a cost-effective and promising strategy. One critical aspect following this strategy
is certainly the identification of appropriate opinion leaders providing information for all
social and religious groups.
Inefficiency in procurement, high transportation costs, and absence of competitive pressure
are leading to unreasonably high input prices, especially fertilizer prices (IFDC, 1999). The
fertilizer market in Uganda is in a very early stage of development. The total fertilizer use is
estimated to be approximately 12000 tons in 1999. There are only four fertilizer
importers/wholesalers. The linkages with traders in Kenya, a natural place for importing
10 Which is an enormous proportion if one takes into account that trial farm households account only for 7 % of all households in both villages.
Market
Information
System
Mass Media
(Radio)
Opinion Leader
(Change Agent)
Household
Household
Household Extension
Agent
42
fertilizers in Uganda, are surprisingly few. Since the market liberalization policy was
implemented, government policy is to remain the import of fertilizer entirely in the private
sector. Figure 15 illustrates the potential levels to which fertilizer prices could be reduced by
improving the market environment and marketing chain. In the Soil Fertility Initiative
Concept Paper by FAO (1999), it was reported that at the end of 1998 the average price for
fertilizer landed in Mombasa (Kenya) was US$ 250 per ton and freight to Kampala was about
100 US$ per ton. Further US$ 50 were added due to clearance at the border, trans-loading,
storage and import charges. Therefore, the total cost c.i.f. Kampala was about 400 US$, which
is very high in comparison to Kenya and other neighbouring countries. It is estimated that
c.i.f. price Kampala would fall by a quarter only by rising the volumes to levels, which would
justify shiploads and trainloads (FAO, 1999). The majority of the fertilizer is delivered to
stockists in 50 kg bags. The fertilizers are repacked into smaller units of 5 kg and 1 kg leading
to a price increase of 100 %. Combining both effects (economies of scale in transportation
and neglecting the costs of repacking the fertilizers) could optimally result in a fertilizer price,
which is 37.5 % of the current prices. A further input price decrease could be attained through
increased competition on the fertilizer market. The FAO (1999) compared the price build-up
for small fertilizer retailers with the price build-up for small sugar or soap powder retailers,
both supplied by stockists. The market for sugar or washing powder is a more mature and
higher volume market in Uganda. A comparison of the price build-ups shows that fertilizer
prices could additionally decrease when the competition and the volumes on the fertilizer
market would increase.12 Taking into account the high transportation costs and the high mark-
up of retailers, there is a huge potential to reduce the fertilizer price substantially. An
alternative P-fertilizer is Busumbu Rock-Phosphate with deposits near Tororo. The P-content
is low in comparison to TSP, but the price should also be far lower. The “International Mining
and Development Ltd.”, a Canadian based company, is at the exploratory stage of a
commercial investment in Busumbu.13
11 For the identification of opinion leaders extension agents could collaborate with experienced NGO staff or researchers, who applied similar approaches (as for example CIAT/A2N in the study region). 12 The impact of increased competition on the fertilizer price cannot be quantified exactly. 13 Rock phosphate is not available on the fertilizer market in Uganda yet. Due to uncertainty about its availability in the future rock phosphate is not included in the scenarios if not explicitly mentioned.
43
100% 75
% 50% 37,5
% ??%
0%
25%
50%
75%
100%
% of current input price
Scenario1 Scenario2 Scenario3 Scenario4 Scenario5
Figure 15: Potential levels of fertilizer price reduction
minimum input price achievable [%] maximum input price reduction achievable [%]
Source: based on FAO (1999)
Scenarios #2.1: Economic and ecological impact of decreasing fertilizer prices
This type of scenarios focuses on the economic and ecological impact of the stepwise
reduction of fertilizer prices. Sensitivity analysis was chosen to reflect at which critical
parameter values (fertilizer prices) a significant improvement of nutrient balances could be
achieved for the different household types. The price changes assumed in the scenarios should
be compared with the potential price reductions discussed above. It should be emphasized
here that the following analyses refer to soil class 1 only and that the new technology option
are just available for maize. The plots of the four representative households belong to this soil
cluster, as well as nearly 50 % of the soil samples included in the soil clustering. Figures 8
and 10 reveal that the soil productivity and impact of the technologies on yield are different
for the other soil classes.
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5
Potentials for input price reduction
• none (current situation)
• economies of scale transport
• marketing efficiency
• economies of scale transport
• marketing efficiency
• economies of scale transport
• marketing efficiency
• increased competition
Min. input price achievable (% of current price)
100 %
75 %
50 %
37,5 %
??
44
Figure 16: Subsistence Farm Household: Sensitivity Analysis Fertilizer Price Reduction
-50
0
50
100
150
100 75 50 40 37,5 30 20 10 5 0
% of current price
TGM 10^4 Ush
kg/ha NPK
TGM NPK
Source: model results
The diagrams for each household type (Figures 16 –19) illustrate the development of the
TGM as an economic indicator and the nutrient balances as a sustainability indicator with
stepwise reduced fertilizer prices. The tables below the diagrams indicate the development of
reduced costs for selected farming activities, and the area on which certain fertilizers were
adopted. The reduced costs are an indicator for the profitability of farming activities. They
reveal by which sum the costs of an economic activity have to be reduced before it enters the
optimal solution, meaning before the household adopts this activity. Figure 16 illustrates that
the subsistence farm household is far from adopting fertilizers at the current prices, e.g. the
reduced costs for maize with NPK-fertilizer application is 313*103 USh (season 1). In the
following the reduced costs are further decreasing with reduced fertilizer prices. When a price
reduction of 37.5 % of the current price is reached, the subsistence farm household starts to
adopt NP fertilizers on 0.02 ha. The increase of the TGM and the improvement of nutrient
balances are negligible. The fertilizer prices have to be reduced up to 10 % of the current
prices before NPK is adopted on 0.07 ha. Assuming that fertilizers would be available for
14 Abbreviations: Maize+N1=nitrogen fertilizer application on maize in season 1 etc.
% of current price
100 75 50 40 37.5 30 20 10 5 0
Reduced costs (103 USh)14 Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
182 211 313 188 252 364
139 124 193 144 165 244
95 37 73
101 79
123
78 2
25 83 44 75
78 0
19 79 35 63
84 0
11 66 9
27
85 0 2
74 0 5
89 4 0
89 4 0
97 7 0
97 7 0
131 22 0
131 22 0
Area adopted (ha)
0 0 0 0 NP1 0.02
NP1 0.02
NP1 0.02 NP2 0.03
NPK1 0.03
NPK2 0.04
NPK1 0.05
NPK2 0.06
NPK1 0.14
NPK2 0.14
45
free, the subsistence household could adopt NPK on 0.28 ha, leading to a positive nutrient
balance for phosphorus (+2 kg/ha). The balances for nitrogen and potassium are just
improving slightly in comparison to the current price situation, from –29 kg/ha to –22 kg/ha
and from –40 kg/ha to –33 kg/ha respectively. Figure 16 shows that the reduction of fertilizer
prices has nearly no impact on the TGM. Free available fertilizers would increase this
household welfare indicator by only 2 % in comparison to a situation with current prices.
Figure 17: Semi-Subsistence Farm Household: Sensitivity Analysis Fertilizer Price Reduction
-100
-50
0
50
100
150
200
100 75 50 40 37,5 30 20 10 5 0
% of current price
TGM 10^4 Ush
kg/ha NPK
TGMNPK
Source: model results
The semi-subsistence farm household starts to adopt NP (0.08 ha) not until 10 % of current
fertilizer prices are reached (see figure 17). The reduced costs indicate that the
competitiveness of farming activities, including the adoption of fertilizers, is very low due to
non-profitability until this tremendous price reduction is attained. When the fertilizer price
amounts to 5% of the current price, positive nutrient balances for nitrogen (+16 kg/ha) and
phosphorus (+76 kg/ha) could be achieved. The balance for potassium would still be negative,
but the high negative balance of –63 kg/ha could be reduced to –8 kg/ha. Under this price
scenario NPK-fertilizer could be adopted on 1.59 ha and NP on 0.13 ha. The TGM increased
% of current price
100 75 50 40 37.5 30 20 10 5 0
Reduced costs (103 USh) Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
505 833
1154 464 792
1114
392 606 841 351 566 801
279 380 528 238 340 488
234 290 403 193 249 363
222 267 372 182 227 331
189 200 288 148 159 237
143 109 153 103 69
112
72 3
20 69 0
17
118 12 0
94 0 0
151 26 0
127 14 0
Area adopted (ha)
0 0 0 0 0 0 0 NP2 0.08
NPK1 0.76 NP2 0.13
NPK2 0.83
NPK1 0.76
NPK2 0.96
46
by 5.5 % in comparison to the baseline scenario. A fertilizer price of 0 would lead to a further
slight increase of the TGM, the nutrient balances for nitrogen and phosphorus would be
positive, the nutrient balances for potassium would be slightly negative (-2 kg/ha). The
overall impact of fertilizer price reduction on TGM of the semi-subsistence farm household is
very modest again.
Figure 18: Trial Farm Household: Sensitivity Analysis Fertilizer Price Reduction
-60
-40
-20
0
20
40
60
100 75 50 40 37,5 30 20 15 10 5 0
% of current price
TGM 10^5 Ush
kg/ha NPK
TGMNPK
Source: model results
The fertilizer prices have to decrease to 15 % of the original value before the trial farm
household starts to adopt NPK-fertilizer on 0.13 ha (see figure 18). The increase of TGM and
the improvements of the nutrient balances are negligible. With 10 % and 5 % of the original
prices the adoption of NPK-fertilizer could increase to 0.25 ha and 1.21 ha respectively. The
latter price decrease could lead to substantial improvements of nutrient balances. In
comparison to the baseline scenario with current prices, the balance for nitrogen could
increase from –43 kg/ha to –7 kg/ha, the balances for phosphorus could increase from –11
kg/ha to +46 kg/ha and for potassium from –47 kg/ha to –18 kg/ha. The increase of the TGM
would be modest (1 %). Free available fertilizers would not provide sufficient incentives to
expand the area on which fertilizer are applied in comparison to a price decrease to 5 % of the
original price. Only the TGM would increase from 2420*103 USh to 2449*103 USh.
15 Reduced costs are not presented since the applied software does not provide a sensitivity report when mixed integer programming is used.
% of current price15
100 75 50 40 37.5 30 20 15 10 5 0
Area adopted(ha)
0 0 0 0 0 0 0 NPK1 0.13
NKP1 0.13
NPK2 0.12
NPK1 0.61
NPK2 0.6
NPK1 0.61
NPK2 0.6
47
Figure 19: Commercial Farm Household: Sensitivity Analysis Fertilizer Price Reduction
-100-80-60-40-20
020406080
100
100 75 50 40 37,5 30 20 15 10 5 0
% of current price
TGM 10^5 Ush
kg/ha NPK
TGMNPK
Source: model results
The commercial farm household type adopts NP-fertilizer on 0.62 ha when the input prices
decrease to 30 % of the current value (see figure 19). The phosphorus balance would increase
from –15 kg/ha to 0 kg/ha, the nitrogen balance would increase slightly, whereas the
potassium balance would decrease slightly. The next significant change could occur with a
fertilizer price decrease to 20 % of the current price. The adoption of NP-fertilizer could
increase to 2.68 ha, leading to a highly positive phosphorus balance (47 kg/ha) and an
improvement of the nitrogen balance from –77 kg/ha in the baseline scenario to –21 kg/ha.
For the potassium balance a modest decrease could be observed from –71 kg/ha to –81 kg/ha.
A further fertilizer price decrease to 10 % of the current value would lead to an adoption pf
NPK-fertilizer on 1.84 ha and NP-fertilizer on 1.66 ha. This scenario would result in an
increase of the TGM of 3.6 % and N-, P-, K-balances of –11 kg/ha, +65 kg/ha and –48 kg/ha
respectively. NPK-fertilizer adoption would jump up to 4.03 ha with a price decrease to 5 %
of the original value. The result of this adoption would be a significant improvement of the
% of current price
100 75 50 40 37.5 30 20 15 10 5 0
Reduced costs (103 USh) Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
213 255 368 189 238 346
170 168 248 143 149 224
127 81
128 100 62
103
109 47 80 83 28 55
105 38 68 78 19 43
95 16 37 71 0
16
95 0 9
79 0
11
105 0 1
87 0 6
116 4 0
94 0 1
133 11 0
110 7 0
149 17 0
127 14 0
Area adopted (ha)
0 0 0 0 0 NP1 0.62
NP1 1.58 NP2
1.1
NP1 1.76 NP2 1.36
NPK1 1.84 NP2 1.66
NPK1 1.84
NPK2 2.19
NPK1 1.84
NPK2 2.19
48
potassium balance (-8 kg/ha). Assuming that the fertilizers would be available for free, would
not change the fertilizer adoption and nutrient balances in comparison to the latest scenario.
The TGM would increase by 7.7 % compared to the baseline scenario, the increase of the
TGM with 5 % of the current fertilizer prices would be 5.7 %.
To summarize, the sensitivity analysis of fertilizer price reduction illustrated that a significant
adoption of fertilizers contributing to a substantial increase of the nutrient balances could be
achieved only with a tremendous reduction of the current fertilizer prices. High reduced costs
in the baseline scenarios revealed that each household type is far from adopting new
technologies, because of their non-profitability in the current situation. The reduced costs are
too high and thus only slight decreases of current fertilizer prices would probably not lead to
an adoption of improved practices. The prices have to be reduced at least to 37.5 % of the
current value before adoption starts. A significant adoption cannot be expected before a
decrease of 10 % - 5 % of the current price is reached. This is very difficult to achieve with
policy measures aiming at reduction of transaction costs and improvements of the market
environment only (as discussed above). At the same only a very modest increase of the TGM
could be observed for each household type. Therefore, policy options focusing only on the
reduction of fertilizer prices would probably not be a promising strategy targeted on a
sustainable intensification of agriculture.
Scenarios #2.2: Economic and ecological impact of increasing agricultural output prices
The next type of scenarios focuses on the impact of stepwise increased agricultural product
prices on household welfare (TGM), production structure, and sustainability indicator
(nutrient balances). The price scenarios used in this sensitivity analysis should be compared
with the results of the discussion about the potentials for an increase of output prices. Figure
20 shows that for the subsistence farm household type nutrient balances for nitrogen,
phosphorus and potassium are nearly constant with increasing prices, even up to 100 % of the
current values. The main reasons are that there is no adoption of new fertilizer technologies
and that the production structure is stable due to the low market orientation of the household
type under consideration. Although the output prices are increasing, the reduced costs (see
appendix A 10) indicate that the subsistence household is far from adopting farming activities
involving the application of new fertilizer. The reduced costs for NPK-fertilizer for example
are decreasing from 313*103 USh in the baseline scenario to 246*103 USh with an output
price increase of 70 %, before the costs increase again. Changing output prices are leading to
a change of the relative competitiveness of farming activities. Therefore, the production
49
structure is changing with a price increase of 80 %, where intercropped coffee and bananas
are reduced from 0.76 ha to 0.65 ha and the area under improved maize increases from 0.07
ha to 0.28 ha. The cultivation of improved maize is of special interest in this context.
Regarding the adoption of this improved variety the model results are different from the
observed values. The households reported that only local maize is cultivated, whereas the
model indicates an adoption of an improved maize variety on 0.07 ha in two seasons.
Anyway, the model indicates that the adoption of the improved variety would become more
profitable with an output price increase of 80 %.
Figure 20: Subsistence Farm Household: Sensitivity Analysis Output Prices
-100
-50
0
50
100
150
200
0 10 20 30 40 50 60 70 80 90 100
output price increase %
TGM 10^4 Ush
kg/ha NPK
TGMNPK
Abbreviations: Maize l/Beans l=local maize variety intercropped with local beans variety; Maize i: improved maize variety; Sweet Pot Bu=sweet potatoes (variety Bunduguza); Maize l/Gnuts=local maize variety intercropped with groundnuts; Coffee l/Ban=local coffee variety intercropped with cooking bananas. % of outprice increase
0 10 20 30 40 50 60 70 80 90 100
Production structure (ha) Maize l/Beans Maize i Sweet Pot Bu Cassava Maize l/Gnuts Coffee l/Ban
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.07 0.85 0.1 0.05 0.76
1.2 0.28 0.85 0.1 0.05 0.65
1.2 0.28 0.85 0.1 0.05 0.65
1.2 0.28 0.85 0.1 0.05 0.65
Source: model results
Increasing the agricultural output prices by 50 % and 100 % could lead to a raise of TGM by
11.5 % and 23 % respectively. The inclusion of rock phosphate would lead to an adoption of
0.07 ha Busumbu Rock Phosphate and therefore to a slight improvement of the phosphorus
balance (from –8kg/ha to –6 kg/ha). A further output price increase would not provide
sufficient incentives for expanding the area under rock phosphate. As already pointed out for
the subsistence household, the semi-subsistence farm household cannot profitably adopt the
promoted fertilizer technologies just by increasing the agricultural output prices (see figure
50
21). Since sustainable land management practices are not adopted, the nutrient balances are
not increasing either. A change of the production structure is observed when the output price
is increased by 70 %. The area under improved maize would jump from 0.21 ha up to 0.37 ha,
simultaneously the area under intercropped improved maize and cassava and the area under
intercropped coffee and bananas decreases. This changing production structure leads to a
slight deterioration of nutrient balances.
Figure 21: Semi-Subsistence Farm Household: Sensitivity Analysis Output Prices
-100
-50
0
50
100
150
200
250
0 10 20 30 40 50 60 70 80 90 100
output price increase %
TGM 10^4 Ush
kg/ha NPK
TGMNPK
Source: model results
In comparison to the baseline scenario TGM would increase by 23 % with an output price
increase of 50 %, and by 47 % with an output price increase of 100 %. If rock phosphate
would be included, it could be profitably adopted on 0.89 ha under current price conditions,
which would lead to an improvement of the phosphorus balance from –12 kg/ha to +24 kg/ha.
Another increase of the area under rock phosphate (1.08 ha) would be achieved by raising the
output price up to 80 % of the current values.
The production structure of the trial farm household type is less stable compared to the
household types already discussed, when output prices are increased stepwise. As we have
seen for the other two household types already, improved maize can be adopted profitably
with increasing agricultural product prices. Figure 22 shows that the relative competitiveness
% of outprice increase
0 10 20 30 40 50 60 70 80 90 100
Production structure (ha) Maize I Maize i/Cassava Sweet Pot Bu Millet Sorghum Coffee l/Ban
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.21 0.75 0.33 0.21 0.11 0.25
0.37 0.73 0.33 0.21 0.11 0.2
0.37 0.73 0.33 0.21 0.11 0.2
0.37 0.73 0.33 0.21 0.11 0.2
0.37 0.73 0.33 0.21 0.11 0.2
51
of the sweet potato variety Silk is rising, leading to an increase of the cultivated area. In the
baseline scenario only 0.19 ha are under Silk, whereas a price increase of 30 % to 100 %
could result in a cultivation of 1.12 ha. Another interesting aspect is the decreasing area under
local coffee and the simultaneous increase of the area under bananas.
Figure 22: Trial Farm Household: Sensitivity Analysis Output Prices
-80
-60
-40
-20
0
20
40
0 10 20 30 40 50 60 70 80 90 100
outprice increase %
TGM 10^5 Ush
kg/ha NPK
TGMNPK
Source: model results
Under current price conditions local coffee is planted on 0.55 ha. An output price increase of
10 % and more would affect the relative profitability of coffee cultivation negatively,
resulting in an exclusion of this farming activity. Instead, banana cultivation becomes
economically more attractive and can be adopted profitably on 0.10 ha when the agricultural
product prices increase by 10 %. Again, the relative competitiveness of farming activities
involving the application of fertilizers is not increasing sufficiently for an adoption of these
technologies. Therefore, no improvement of the nutrient balances can be expected from rising
the level of output prices only. Rather the contrary is the case: the changing production
structure affects the nutrient balances negatively, e.g. the balance for nitrogen in the baseline
scenario is –43 kg/ha and is decreasing with a price increase of 30 % to –57 kg/ha. One factor
% of outprice increase
0 10 20 30 40 50 60 70 80 90 100
Production structure (ha) Maize I Maize i/Beans Sweet Pot Bu Sweet Pot S Cassava Maize i/Cassava Gnuts I Coffee l Ban Cow local
0 0.54 0.30 0.19 0.32 0 0.17 0.55 0 1
0 0.54 0.30 0.17 0.60 0 0.17 0.17 0.10 1
0 0.54 0.30 0.82 0.36 0 0.17 0.0 0.13 1
0.09 0.54 0.30 1.12 0.19 0.02 0.17 0 0.15 1
0.09 0.54 0.30 1.12 0.19 0.02 0.17 0 0.15 1
0.09 0.54 0.30 1.12 0.19 0.02 0.17 0 0.15 1
0.09 0.54 0.30 1.12 0.19 0.02 0.17 0 0.15 1
0.11 0.54 0.30 1.12 0.21 0 0.17 0 0.15 1
0.11 0.54 0.30 1.12 0.21 0 0.17 0 0.15 1
0.11 0.54 0.30 1.12 0.21 0 0.17 0 0.15 1
0.11 0.54 0.30 1.12 0.21 0 0.17 0 0.15 1
52
contributing to this negative impact is the relatively high nutrient extraction caused by sweet
potatoes. It is obvious that TGM is rising with increasing product prices, e.g. 50 % output
price increase results in a TGM increase of 17 %. Including rock phosphate in the selectable
technology options would lead to a significant improvement of the phosphorus balance. The
adoption would increase from 0.85 ha under current prices to 1.3 ha with a price increase of
50 %, and to 1.45 ha with a price increase of 100 %. At the same time the phosphorus balance
improves from –11 kg/ha under current prices when no rock phosphate is provided, to +21
kg/ha when it is selectable, to 37 kg/ha and 42 kg/ha with a 50 % and 100 % price increase
respectively.
Figure 23: Commercial Farm Household:Sensitivity Analysis Output Prices
-100-80-60-40-20
020406080
100
0 10 20 30 40 50 60 70 80 90 100
output price increase %
TGM 10^5 Ush
kg/ha NPK
TGMNPK
% of outprice increase
0 10 20 30 40 50 60 70 80 90 100
Production structure (ha) Maize I Maize I/Beans Maize I/Cassava Sweet Pot Bu Sweet Pot S Gnuts I Coffee Clonal Cow exotic
1.90 0.43 0.77 0.56 0.43 0.24 0.22 1
3.93 0.43 0.02 0.56 0.44 0.24 0.22 1
3.39 0.43 0.55 0.56 0.98 0.24 0.22 1
3.92 0.43 0.02 0.56 1.51 0.24 0.22 1
3.04 0.43 0.20 0.56 2.05 0.24 0.22 1
3.04 0.43 0.20 0.56 2.05 0.24 0.22 1
3.04 0.43 0.20 0.56 2.05 0.24 0.22 1
3.09 0.43 0.31 0.56 2.32 0.24 0.22 1
3.09 0.43 0.31 0.56 2.32 0.24 0.22 1
3.47 0.43 0.12 0.56 2.32 0.24 0.22 1
3.47 0.43 0.12 0.56 2.32 0.24 0.22 1
Source: model results
The production structure of the commercial farm household reveals its relatively high degree
of market orientation (see figure 23). A quite big proportion of the cultivated land is under
improved maize, the sweet potato variety Silk, and clonal coffee. Furthermore, the household
already adopted an exotic cow for milk production. The production structure is subject to
several changes when output prices are increased stepwise. Especially, the cultivated area
under improved maize and sweet potato Silk is significantly increasing. The area under clonal
coffee and the numbers of exotic cows is constant. Again, the reduced costs of farming
53
activities involving fertilizer application are not decreasing sufficiently by changing output
prices only. Thus, their adoption is not profitable yet. Changing production patterns are
leading to a slight decrease of the nutrient balances for nitrogen, phosphorus and potassium.
An increase of output prices of 50 % rises TGM by 26 %. The inclusion of rock phosphate as
an additional technology option is leading to an adoption of Busumbu Rock Phosphate on
4.03 ha in the baseline scenario. Consequently, the nutrient balance for phosphorus is
increasing from –16 kg/ha to +59 kg/ha. A price increase for agricultural products does not
result in a further increase of the adoption of rock phosphate.
This sensitivity analysis reveals that increased agricultural product prices alone do not lead to
a profitable adoption of new fertilizer technologies. Therefore, the nutrient balances remain
highly negative or even decrease further when output prices are increasing due to changes of
the production structure. The production structure of the subsistence and semi-subsistence
household type is relatively stable due to a low degree of market orientation, whereas the
changes for the trial farm household and commercial farm household are significant. For each
household type the cultivation of improved maize becomes more profitable, and the trial farm
and the commercial farm household type are expanding the cultivated area under the sweet
potato variety Silk. As expected, TGM increases with increasing output prices, e.g. with a
price increase of 50 % TGM increases 11.5 % – 26 %.
Scenarios #2.3: Introduction of credit, improvement of price relations, promotion of labor
exchange
The sensitivity analyses discussed above indicate that neither a sole decrease of fertilizer
prices to a realistic degree, nor the sole increase of agricultural output prices could probably
contribute to the simultaneous achievement of sustainability and economic goals. Therefore,
in the following it is examined whether combining both price effects and additional measures
like provision of credit or the promotion of alternative forms of labor acquisition could
potentially improve the current situation which is characterized by serious nutrient depletion.
For this purpose, sensitivity analyses of simultaneously decreasing fertilizer prices and
increasing agricultural product prices are conducted to identify promising price relations with
low reduced costs for farming activities involving the application of fertilizers. Shadow prices
of resource constraints help to identify most binding factors affecting the adoption of new
technologies. The following figures 24 – 27 illustrate the potential consequences of promoted
agricultural technologies and reduction of market failures on household welfare and
sustainability criteria for the different household types. The reduction of market failures is
54
reflected by simultaneously decreasing input prices and increasing output prices. Provision of
credit is included for assessing the impact of capital constraints on the adoption of new
technologies. Another constraint frequently quoted as a major factor prohibiting the adoption
of new technologies is the labor constraint. Labor exchange, a traditional form of labor
acquisition in the study region, was included in the scenarios to investigate whether it would
be an appropriate option to overcome the problem of labor shortages. The tables below the
diagrams define the scenarios considered and indicate the area on which a certain technology
is adopted.
The constraints for credit provision differ among the farm household types. Collaterals are the
fundamental prerequisite for getting a loan. The access to credit from microfinance institutes
like Pride Africa is restricted to households with regular off-farm income, since the repayment
of the loan is split into successive periods. The model divides the repayment of loans received
from microfinance institutes into periods of 4 months. The duration of commercial loans is 12
months. The maximum loan a household can receive is determined mainly by the level of off-
farm income and other collaterals. This maximum amount differs among the different
household types.16 The interest rate for a loan from a microfinance institute is set at 20 %. The
interest rate for a loan from a commercial bank is set at 22 %. Households with a low wealth
status – like subsistence farm households – would probably face serious problems of applying
successfully for loans from microfinance institutes as well as from commercial banks. Despite
these problems, this household type is included in the credit scenarios to illustrate the
potential benefits of a widespread provision of credit, including poor farmer.
Scenario 1 in figure 24 illustrates the situation for the subsistence farm household type under
current price relations. Under these conditions there is no adoption of fertilizer technologies
and the nutrient balances are highly negative (nitrogen: -29 kg/ha, phosphorus: -8 kg/ha,
potassium: -40 kg/ha). A price relation that allows the subsistence farm household to
profitably adopt NP-fertilizer (0.02 ha) is given when the input price is reduced to 40 % of the
current value and the output prices are increased by 50 %. The TGM increases by 12 % in
comparison to the scenario with current price relations (scenario 1), but the increase of the
nutrient balances is not significant. Providing credit would increase the area on which NP-
fertilizer is applied slightly (to 0.05 ha), contributing to a further modest improvement of the
nitrogen and phosphorus balances. Scenarios 4 and 5 compare the impact of credit provision
when the fertilizer prices are reduced to 37.5 % of the current prices and the agricultural
product prices are increased by 50 %. Without credit NP-fertilizer are profitable adopted only
16 Determination of maximum loan and down payment is based on expert knowledge.
55
on 0.03 ha. With the provision of credit the subsistence farm household type might adopt this
technology on 0.73 ha, leading to a positive phosphorus balance (+17 kg/ha) and a significant
improvement of the nitrogen balance (-11 kg/ha). The balance of potassium is decreasing
further though from –40 kg/ha to –49 kg/ha. Regarding the goal of reducing the high negative
values of nutrient balances, the most favourable situation is achieved when the fertilizer prices
are reduced at least to 20 % of the current prices, output prices are increased by 50 % and
credit is provided. The household can adopt NPK-fertilizer on 0.73 ha. In comparison to
scenario 1 TGM increases by 16 % and the N-, P-, K-balances are increasing from –29 kg/ha
to –12 kg/ha, -8 kg/ha to +17 kg/ha and –40 kg/ha to –23 kg/ha respectively.
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Characteristics • current
conditions • trial tech
• trial tech • input
price: 40%
• output price: +50%
• trial tech • input
price: 40%
• output price: +50%
• credit
• trial tech • input
price: 37.5%
• output price: +50%
• trial tech • input
price: 37.5%
• output price: +50%
• credit
• trial tech • input
price: 20%
• output price: +50%
• trial tech • input
price: 20%
• output price: +50%
• credit Area adopted (ha) 0 NP
0.02 NP 0.05
NP 0.03
NP 0.73
NP 0.05
NPK 0.73
Source: model results
Figure 24: Subsistence Farm Household: Scenar ios on combined ef fects
-90
-40
10
60
110
160
Scen1 Scen2 Scen3 Scen4 Scen5 Scen6 Scen7
TGM 10^4 Ush
kg/ha N P K
TGMNPK
56
A price relation which in combination with the provision of credit leads to a profitable
fertilizer adoption by the semi-subsistence farm household type is given when the input price
is reduced to 30 % of the current value and 10 % are added to the current product price
(scenario 3). NP-fertilizer can be adopted on 0.21 ha contributing to improvements of the
nitrogen and phosphorus balance (for N from –52 kg/ha to –44 kg/ha and for P from –12
kg/ha to –2 kg/ha). TGM rises by 5 %. Without the provision of credit NP-fertilizer adoption
is not profitable. NPK-fertilizer adoption becomes profitable when the input prices are
decreased to 25 % of the current price, the ouput prices are increased by 40 %, and credit is
provided.
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Characteristics • current
conditions • trial tech
• trial tech • input
price: 30%
• output price: +10%
• trial tech • input
price: 30%
• output price: +10%
• credit
• trial tech • input
price: 25%
• output price: +40%
• trial tech • input
price: 25%
• output price: +40%
• credit
• trial tech • input
price: 25%
• output price: +50%
• credit
• trial tech • input
price: 25%
• output price: +50%
• credit • labour
exchange Area adopted (ha) 0 0 NP
0.21 0 NPK
1.28 NPK 1.28
NPK 1.72
Source: model results
The application of NPK-fertilizer on 1.28 ha is significantly increasing the nutrient balances
(nitrogen: -2 kg/ha, phosphorus: +53 kg/ha, potassium –17 kg/ha.). Again, scenario 4
illustrates that fertilizer adoption is not profitable with same price relations but without the
provision of credit. Hence, a high shadow price for capital is indicating the profitability of
improved access to credit provision. Scenarios 6 and 7 explore the effects of a further input
price reduction (25 % of current price), a further output price increase (50 %), credit
F i g u r e 2 5 : S e m i - S u b s i s t e n c e F a r m H o u s e h o l d : S c e n a r i o s o n c o m b i n e d e f f e c t s
- 1 0 0
- 5 0
0
5 0
1 0 0
1 5 0
2 0 0
S c e n 1 S c e n 2 S c e n 3 S c e n 4 S c e n 5 S c e n 6 S c e n 7
T G M 1 0 ^ 4 U s h
k g / h a
N P K
T G MNPK
57
provision, and an additional option for labor exchange in scenario 7. The results of scenario 6
are nearly the same as for scenario 5. The only change is an additional increase of TGM by 5
%. Labor exchange has the potential to increase the area under NPK-fertilizer application
from 1.28 ha to 1.72 ha. This can lead to positive balances for N (+18 kg/ha) and P (+85
kg/ha), and a significant improvement of the K-balance (-2 kg/ha). Simultaneously, in
scenarios 6 and 7 TGM would rise by 26 % and 27 % respectively.
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Characteristics • current
conditions • trial tech
• trial tech • input
price: 37.5%
• output price: +50%
• trial tech • input
price: 37.5%
• output price: +50%
• credit
• trial tech • input
price: 25%
• output price: +60%
• trial tech • input
price: 25%
• output price: +60%
• credit
• trial tech • input
price: 25%
• output price: +60%
• credit • labour
exchange
• trial tech • input
price: 10%
• output price: +20%
• credit
Area adopted (ha) 0 NP 0.02
NP 0.11
NP 0.13
NPK 0.27
NPK 1.3
NPK 1.3
Source: model results
Focusing on the trial farm household type a price relation which can lead to a first adoption of
NP-fertilizers on 0.02 ha is given when the output price is reduced to 37.5 % and the
agricultural product price rises by 50 %, but only if credit is provided additionally. Because of
the small area on which NP-fertilizer is applied the changes of nutrient balances are
negligible, whereas TGM increases by 17 %. Additional provision of credit could lead to an
application of NP-fertilizer on 0.11 ha and an investment into another local cow. The
following scenarios 4 – 6 are based on an input price decrease to 25 % of the original value
Figure 26 : Tr ia l Farm Household: Scenar ios on combined e f fec ts
-70
-50
-30
-10
10
30
50
S c e n 1 S c e n 2 S c e n 3 S c e n 4 S c e n 5 S c e n 6 S c e n 7
T G M 10^5 Ush
kg/ha N P K
T G MNPK
58
and an ouput price increase by 60 %. Credit is provided in scenarios 5 and 6. In scenario 6 the
option for labor exchange is added. In Scenario 4 (without credit and labor exchange) NP-
fertilizer can be adopted profitably on 0.13 ha. The changes of nutrient balances are
negligible, TGM rises by 21 %. When credit is provided additionally, NPK-fertilizer can be
applied on 0.27 ha, which leads to an increased nutrient balance for phosphorus. The balances
for nitrogen and potassium are decreasing in comparison to scenario 1 from –43 kg/ha to –48
kg/ha and –47 kg/ha to –52 kg/ha respectively, due to changing production patterns. A
significant increase for all nutrient balances can be observed when the trial farm household
type is given the option for labor exchange. The shadow price in the time periods where the
trial farm household is acquiring labor by exchange is 500 USh, which is quite high in
comparison to other periods where the shadow prices are around 200 USh. The nitrogen
balance jumps up to –6 kg/ha, the phosphorus balances reaches a value of +50 kg/ha and the
potassium balances increases to –18 kg/ha. Simultaneously, TGM rises by 29 %. To achieve
the same values for the nutrient balances without labor exchange, the fertilizer prices have to
be reduced to 10 % of the current price, and the output prices have to increase at least by 10 %
(scenario 7). A fertilizer price decrease to 40 % and a simultaneous rise of the product price
by 50 % leads to a profitable adoption of NP-fertilizer by the commercial farm household
type only if credit is provided additionally. The adoption of this fertilizer type is increasing
the nitrogen and phosphorus balances in comparison to scenario 1 (from –77 kg/ha to –53
kg/ha and from –15 kg/ha to +22 kg/ha respectively). At the same time TGM rises by 27 %.
NP-fertilizer can be adopted profitably on 3.66 ha when input prices are reduced to 30 % of
the current value, output prices are increased by 50 %, and credit is provided. Consequently,
nutrient balances for N and P are increasing significantly (N-balance: -17 kg/ha, P-balance:
+65 kg/ha), whereas the balance for K reaches even higher negative values than in scenario 1
(K-balance: -98 kg/ha). Again, this price relation cannot lead to an adoption if credit is not
provided. High shadow prices for capital indicate that the economic situation of the household
can be improved substantially if access to credit is improved. To achieve a profitable adoption
of NPK-fertilizer (on 3.95 ha), fertilizer prices have to be reduced at least to 20 % of the
current price, output prices have to increase by 50 %, and credit has to be provided. This price
constellation cannot lead to a profitable adoption of NPK-fertilizer without credit. Scenario 6
illustrates that without credit NP-fertilizer can be adopted profitably on 1.37 ha.
59
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 6 Scenario 7 Characteristics • current
conditions • trial tech
• trial tech • input
price: 40%
• output price: +50%
• trial tech • input
price: 40%
• output price: +50%
• credit
• trial tech • input
price: 30%
• output price: +50%
• trial tech • input
price: 30%
• output price: +50%
• credit
• trial tech • input
price: 20%
• output price: +50%
• trial tech • input
price: 20%
• output price: +50%
• credit Area adopted (ha) 0 0 NP
1.76 0 NP
3.66 NP 1.37
NPK 3.95
Source: model results
The scenarios revealed that improved input-ouput price relations in combination with
provision of credit and alternative forms of labor acquisition (labor exchange) can contribute
to a significant increase of nutrient balances and TGM simultaneously. The sensitivity
analyses focusing exclusively on input price reduction (scenarios #2.1) illustrate that fertilizer
prices have to be reduced to 10 % - 5 % of the current prices before a major increase of the N-
, P- and K-balances can be achieved. To achieve input price reductions to this extent by
improvements of the market environment alone, seems to be very problematic though.
Combining fertilizer price decrease with ouptut price increase, the provision of credit and
labor exchange can lead to significant improvements of nutrient balances when fertilizer
prices are reduced to at least 25 % - 20 % of the current prices. Referring to the discussion
about the potential level of fertilizer price reduction, even a price reduction to this extent is
difficult to achieve. Economies of scale in transport, improvements in the marketing chain,
and increased competition have to be attained simultaneously to reach input prices of
comparable levels.
Figure 27: Commercia l Farm Household: Scenarios on combined ef fects
-120-100
-80-60-40-20
020406080
Scen1 Scen2 Scen3 Scen4 Scen5 Scen6 Scen7
TGM 10^5 U s h
kg/ha N P K
TGMNPK
60
The scenarios revealed that at the same time the agricultural product prices have to increase
by about 50 %. This level might be realistic to attain by improving the bargaining power of
farm households. Generally, it was illustrated that a substantial improvement of the socio-
economic environment is urgently needed to give farmers sufficient incentives to adopt more
sustainable land management practices.
Scenarios #2.4: Economic and ecological impact of the intensive production of high value
crops
A potential profitable pathway involves intensive production of high value crops and
perennial crops due to the characteristics of high agricultural potential, high market access,
and high population density in Iganga District. Therefore, this type of scenarios deals with the
introduction of vegetables and fruits (onions, tomatoes and passion fruits), and investments in
supplementary technologies. Simultaneously, the introduction of credit for operational and
investment costs will be considered. At the moment, only households that belong to the
commercial and trial farm household groups are cultivating vegetables or fruits. Increased
productivity and the transformation of subsistence or semi-subsistence farming systems to
more commercially oriented systems belong to the major objectives of the Plan for
Modernization of Agriculture (PMA). Additionally, it is proposed in the PMA to minimize
over-reliance on rain-fed agriculture, and to promote irrigation technology for high value
crops for specialized markets. Prolonged dry seasons – mentioned by many farmers as the
major problem of agricultural production in the study area – might prevent farm households
from investing in expensive inputs as long as economic returns are not guaranteed due to
potential droughts. For each household type the potential consequences of the defined
scenarios on economic and ecological indicators will be illustrated. The introduction of
vegetables and fruits contributes to a slight increase of the TGM of the subsistence farm
household type (4 %) due to a change of the production structure (see appendix A 11 a).
Onions can be profitably adopted on 0.04 ha. Other economic indicators like labor intensity,
labor productivity, land productivity, capital intensity and capital productivity, for which very
low values are given in the baseline scenario, are increasing to a modest degree as well.17
Nutrient balances are decreasing slightly with the introduction of vegetables.
61
Tab. 7: Introduction of high value crops: Subsistence Farm Household Type Base Scenario 1
• Veg.+Fruits • Trial tech
Scenario 2 • Veg+Fruits • Credit op.cost • Trial tech
Scenario 3 • Veg+Fruits • Credit op.cost • Credit inv.cost • Trial tech
TGM (103USh) 1299 1356 2166 2403 Labor Intensity (h/ha) 1205 1224 1508 1551 Labor Productivity (103USh/h)18
0.29 0.3 0.5 0.66
Land Productivity (USh/ha)19
349 374 752 1025
Capital Intensity (103USh/ha)
259 265 347 494
Capital Productivity (USh/USh)20
1.34 1.41 2.17 2.07
Nutrient Balances21 N -28 -30 -45 -47 P -8 -9 -14 -13 K -39 -41 -51 -55 Source: own calculations (model results)
In the study region, the adoption of vegetables and fruits is not associated with application of
fertilizer so far. Therefore, lack of data makes it impossible to run scenarios with the
combined introduction of vegetables or fruits and fertilizer. In scenarios with the introduction
of treadle pumps for irrigation, fertilizers are applied on the irrigated area. In scenario 2 credit
for operational costs is provided in addition to the introduction of vegetables and fruits. The
major changes of the production structure are the increased proportion of onions and
decreased proportion of intercropped coffee-bananas. The economic indicators are increasing,
TGM rises by 67 %. At the same time a substantial decline of the nutrient balances is
observed, when vegetables/fruits are introduced without application of fertilizers. The
nitrogen balance decreases by more than 60 %. The additional provision of credit for
investments leads to a further rise of TGM (by 85 % in comparison to the baseline scenario).
This credit type is used for investment in a treadle pump for irrigation. The cultivated area
under onions is declining, whereas tomatoes (irrigated) are introduced. The nutrient balances
of nitrogen, phosphorus and potassium decrease to –47 kg/ha, -13 kg/ha and –55 kg/ha
respectively.
Comparing the production structure of the semi-subsistence farm household type (see figures
28-30) in the baseline scenario and in scenario 1, the proportion of improved maize
17 Factors explaining why farm households do not produce vegetables and fruits in the baseline scenario will be discussed in the conclusions. 18 Labor productivity refers to total gross output per labor hour 19 Land productivity refers to total gross output per ha agricultural land 20 Capital productivity refers to total gross output per USh used
62
intercropped with cassava is decreasing from 53 % to 2 % of the cultivated land, whereas
onions are introduced on 25 % in scenario 1. Improved maize mono-cropping (7 %) is
substituted by local maize mono-cropping (28 %). In the third scenario credit is used for
investment in a treadle pump for irrigation leading to an additional change of the production
structure. Tomatoes (irrigated) are introduced on 17 % of the cultivated land and passion
fruits on 10 %. BRP is applied on improved maize on 7 % of the cultivated land. The
economic indicators TGM, labor productivity, land productivity, and capital productivity are
increasing progressively with the introduction of vegetables and fruits in the first scenario,
additional provision of credits for operational costs in the second plus provision of credits for
investment costs in the third scenario. TGM is increasing by more than 100 % from the
baseline scenario to the third scenario. Land productivity is nearly four times higher.
Tab. 8: Introduction of high value crops: Semi-Subsistence Farm Household Type
Base Scenario 1 • Veg.+Fruits
Scenario 2 • Veg+Fruits • Credit op.cost
Scenario 3 • Veg+Fruits • Credit op.cost • Credit inv.cost
TGM (103USh) 1490 2205 2987 3280 Labor Intensity (h/ha) 1207 1610 1902 1925 Labor Productivity (103USh/h)
0,41 0,6 0,79 0,99
Land Productivity (USh/ha)
498 967 1493 1919
Capital Intensity (103USh/ha)
272 369 559 758
Capital Productivity (USh/USh)
1,83 2,62 2,67 2,53
Nutrient Balances N -52 -63 -84 -83 P -12 -14 -2 -4 K -62 -69 -87 -87 Source: own calculations (model results)
The negative values of nutrient balances of N and K are increasing, whereas K is slightly
decreasing when vegetables, fruits and credits were introduced. In the baseline scenario the
balance for N was –52 kg/ha, for P –12 kg/ha and for K –62 kg/ha. In scenario three –83
kg/ha for N, -4 kg/ha for P and –87 kg/ha for K.
21 Nutrient balances are measured in kg per ha and year
63
Figure 28: Production Structure (Baseline Scenario)
Maize i7%
Maize i/Cs53%
Spot12%
Millet+Sorg11%
Coffee/Ban17%
Figure 29: Production Structure (Scenario 1)
Maize l28%
Maize i/Cs2%
Spot12%Millet+Sorg
11%
Coffee/Ban14%
Coffee8%
Onions25%
Figure 30: Production Structure (Scenario 3)
Maize i/Cs2%
Spot11%
Millet+Sorg11%
Coffee/Ban14%
Maize i BRP7%
Onions28%
Passion10%
Tomatoes (irrigated)
17%
64
The production structure of the trial farm household is changing with the introduction of
vegetables/fruits in scenario 1 (see appendix 11 b) as well. Onions are introduced on 1.36 ha
and passion fruits on 0.02 ha. At the same time the cultivation of local coffee is not profitable
any more. With the introduction of treadle pumps in scenario 3, tomatoes (irrigated) are
introduced on 0.86 ha and the production of onions declines to 0.5 ha.
Tab. 9: Introduction of high value crops: Trial Farm Household Type
Base Scenario 1 • Veg.+Fruits
Scenario 2 • Veg+Fruits • Credit op.cost
Scenario 3 • Veg+Fruits • Credit op.cost • Credit inv.cost
TGM (103USh) 2395 3696 3909 5241 Labor Intensity (h/ha) 1186 1786 1786 1927 Labor Productivity (103USh/h)
0,56 0,86 1,02 1,32
Land Productivity (USh/ha)
662 1528 1817 2542
Capital Intensity (103USh/ha)
499 994 1173 1600
Capital Productivity (USh/USh)
1,33 1,54 1,55 1,59
Nutrient Balances N -43 -79 -79 -84 P -11 -25 -25 -25 K -47 -74 -74 -87
Source: own calculations (model results)
The introduction of vegetables and fruits (scenario 1) and the additional provision of credits
for operational and investment costs (scenarios 2 and 3) contribute to significant increases of
all economic indicators. TGM for example increases from scenario 1 to scenario 3 by 54 %,
63 % and 119 % in comparison to the baseline scenario. At the same time labor productivity
increases by 54 %, 82 %, and 136 % respectively. This positive development of the economic
indicators is again associated with substantial decreases of nutrient balances. The balance for
nitrogen for example declines by nearly 100 % in comparison to the baseline scenario.
As discussed above the commercial farm households have already in the baseline scenario
high negative nutrient balances. These balances for nitrogen and potassium become even
more negative with introduction of vegetables/fruits, credits, and irrigation pumps. The
nitrogen balance is decreasing from –77 kg/ha (baseline scenario) to –97 kg/ha (scenario 3).
The balance for phosphorus is increasing from –15 kg/ha to +15 kg/ha (scenario 2) due to the
application of rock phosphate on 2.35 ha. In scenario 3 the area on which rock phosphate is
applied declines to 0.24 ha contributing to a decrease of the P-balance to –12 kg/ha. The
production structure of the commercial farm household is changing with the introduction of
65
vegetables/fruits (see appendix A 11 c). Onions are introduced on 2.88 ha and passion fruit is
profitably adopted on 0.61 ha. The production of clonal coffee is neglected in the farm
management plan in scenario 1, whereas in the baseline scenario it is cultivated on 0.22 ha.
The provision of credit and the following investment in pumps for irrigation leads to the
adoption of tomatoes (irrigated) on 1.5 ha, an increase of the area under passion fruit (1.44 ha)
and a decline of the area under onions to 1.38 ha. As for the other households before, TGM is
increasing (by 83 % in scenario 3) simultaneously with other economic indicators. Land
productivity is increasing by 94 %, 122 % and 179 % from scenarios 1 -– 3 in comparison to
the baseline scenario.
Tab. 10: Introduction of high value crops (Commercial Farm Household Type) Base Scenario 1
• Veg.+Fruits Scenario 2 • Veg+Fruits • Credit op.cost
Scenario 3 • Veg+Fruits • Credit op.cost • Credit inv.cost
TGM (103USh) 4800 7888 8079 8781 Labor Intensity (h/ha) 1365 1605 1907 1974 Labor Productivity (103USh/h)
0,68 1,12 1,09 1,3
Land Productivity (USh/ha)
925 1799 2057 2578
Capital Intensity (103USh/ha)
705 1013 1145 1457
Capital Productivity (USh/USh)
1,3 1,78 1,8 1,77
Nutrient Balances N -77 -86 -116 -97 P -15 -18 +15 -12 K -71 -71 -104 -85
Source: own calculation (model results)
The scenarios on economic and ecological impacts of the production of high value crops –
identified before as a promising strategy for the development pathway with high population
density, high market access and high agricultural potential – reveal to a certain degree trade-
offs between economic and sustainability goals. Economic indicators are increasing
substantially while nutrient balances are decreasing in comparison to the current situation,
which is already characterized by serious nutrient depletion. It should be emphasized again,
that due to the lack of data it was not possible to run scenarios with fertilizer application on
vegetables without irrigation. This option should be taken into account seriously when
promoting sustainable intensification of agricultural. Moreover, the scenarios illustrated that
irrigation in combination with application of fertilizers is highly profitable. Therefore, a
potentially profitable strategy would be not to restrict the promotion of irrigation to high value
66
crops such as vegetables and fruits, but also to extend irrigation on crops like maize. Due to
very high yields on irrigated plots, the nutrient extraction through harvested products cannot
be balanced through the amount of fertilizer applied.
5. Conclusions
Current land management practices in the study region can be characterized by low land,
labor and capital productivity for the majority of the farm households leading to poor
economic performances and food insecurity in some cases. These characteristics of low
intensity agricultural production systems reveal that potentially profitable pathways involving
intensive production of high value crops are not realized yet. In addition, highly negative
nutrient balances raise the concern of declining soil productivity in the future. Assuming that
the mass of the topsoil is 3 * 106 kg/ha and the average N content of identified soil class 1 is
0.12 %, the annual decrease of 52 kg/ha (annual N loss of the semi-subsistence household
type in the baseline scenario) is equivalent to 1.4 % of the N-stock. With a constant relative
loss rate of 1.4 % per year, soil N content will be half the present value after 50 years.
Scenario results illustrated that non-negative nutrient balances are not feasible with current
land management practices. Even with the simultaneous introduction of promoted technology
options and credit provision the achievement of this sustainability goal was not feasible in
many cases – especially for the subsistence, semi-subsistence, and trial farm household type -
due to non-profitability. The reasons for this non-profitability are unreasonably high fertilizer
prices and very low agricultural product prices due to market imperfections. Additionally, the
impacts of the promoted technologies on yield are modest in many cases, as the ANN-model
indicated. Yield data were available only for 4 seasons (2 years). Long-term trials might give
higher average yields for these technologies. Moreover, the fertilizer technologies were tested
as on-farm trials. Trials under the permanent control of researchers at research stations might
have led to more positive results. Further nutrient depletion might lead to increasing yield
impacts as well. Sensitivity analyses on input price reduction illustrated that fertilizer prices
have to decline extremely (to 10 % - 5 % of the current prices) before fertilizer application
becomes profitable to an extent, which can contribute to substantial improvements of nutrient
balances. The discussion about the potential impacts of market improvements on fertilizer
price decreases indicate that it is very problematic to reach price reductions of this level.
Sensitivity analyses show that increasing output prices can contribute to a significant
improvement of the household welfare, but simultaneous improvements of nutrient balances
67
cannot be expected. Increasing output prices might be achieved by strengthening the
bargaining power of farm households by improving the access to relevant information.
Capital and labor constraints prohibit farm household from the adoption of new technologies.
Isolated measures with marginal effects on input and output prices will probably not help to
reach the goals of agricultural growth, poverty alleviation, and sustainability. Rather
combined measures leading to significant improvements of the socio-economic environment
are needed to increase the chances of reaching the desired development goals. The next step
was to explore whether the combined effects of declining fertilizer prices to a realistic extent
and increasing output prices in combination with credit provision and alternative forms of
labor acquisition have the potential to reach economic and sustainability goals
simultaneously. Scenario results demonstrate the important role credit provision can play for
the adoption of new technologies. Labor exchange – as an alternative form of labor
acquisition – should be taken into account as well while promoting sustainable land
management practices. At the same time fertilizer price reduction to 25 % - 20 % of the
current prices and an agricultural product price increase by 50 % are needed to achieve
significant improvements of nutrient balances and household welfare. This extent of
decreasing input prices and increasing output prices might be difficult to achieve as well, but
combined effects of economies of scale and reduction of market failures could lead to
substantial price changes. The production of high value crops - identified as a potentially
profitable strategy in a study region belonging to a development pathway with high
population density, high market access and high agricultural potential – could lead to
substantial improvements of economic indicators. The impacts on nutrient balances need
further research, but cultivation without fertilizer application, as predominantly practiced in
the study region, contributes to trade-offs between economic and sustainability goals, since
the nutrient balances are decreasing to a significant extent.
This study concludes that the main reason for the fact that farm households do not realize the
potentials provided by a development domain with high population density, high market
access and high agricultural potential is the non-profitability of intensive agricultural
production activities under current socio-economic and agro-ecological conditions. The main
reasons for this non-profitability are market imperfections reflected by high transaction costs,
high transportation costs, and insufficient access to credit markets. At the same time the
modest impacts of promoted agricultural technologies on yields cannot cover the high input
prices.
68
The study reveals that further research is needed, both in socio-economic and agro-ecological
perspectives. The applied bio-economic model is an appropriate approach for identification of
the optimal level of technology adoption and the impact on incomes and natural resource
conditions for heterogeneous household agents in a changing socio-economic environment.
As discussed above, model solutions sometimes differ from the observed values, e.g. when
vegetables and fruits are introduced exogenously, farm households adopt the production of
these crops in contrast to the real world situation where these activities are not included in the
farm plan. This leads to the important research question what farm households prevent from
producing vegetables and fruits in the baseline scenario. An appropriate method to answer this
question might be multi-agent systems (MAS). The sampling procedure, the selection of
representative farm household types, and diffusion analyses described in previous sections
fulfill specific data requirements to extend the bio-economic model developed in this study to
a dynamic version implemented as a connected multi-agent system. This model type might be
an appropriate approach to forecast the diffusion of innovations together with the evolution of
farm incomes and natural resource conditions over time (Berger, 2001). Two variables affect
the adoption and diffusion of innovations. Firstly, the net benefit of adoption, which can be
“objectively” measured and is accounted for in the programming approach developed for this
study. Secondly, costs that relate to farmer’s managerial capacity. These costs are usually
referred to as adoption costs and include planning and information costs, socio-psychological
adjustment costs, temporary production losses as well as “subjective” risk premiums and
option values.22 In the MAS-approach developed by Berger (2001) a decision rule is
implemented which accounts for adoption thresholds based on social interactions in addition
to net benefit considerations. This is an interesting approach to consider at least part of the
adoption costs and explain why technologies normally do not diffuse as “smooth” as
predicted with approaches, which focus on net benefits only.
From the natural science perspective further research activities are needed to better
understand the impact of the altitude of negative nutrient balances on yield in the long run.
Scenarios on improved price relations, provision of credit and promotion of alternative forms
of labor acquisition indicated that the negative value of N balance of the subsistence farm
household type was reduced to 43 % of its original value, which is equivalent to a loss of 12
kg per ha and year. Assuming the mass of the topsoil defined above and N content of soil
class 1 with a constant N loss the soil N content will be half in 230 years. It has to be clarified
whether slightly negative nutrient balances really affect crop yields negatively in the long run.
22 Metcalfe (1988) illustrates how these two variables influence the diffusion process in the agricultural sector.
69
Therefore, the understanding of nutrient dynamics in the soil and the feedback effects
between nutrient depletion, soil nutrient content and yield level has to be improved.
70
Appendix A1:
Descriptive Statistics: Household Characteristics
1 17 7,85 3,61
1 11 3,60 2,16
20 80 45,97 17,5218 70 35,14 12,39
0 18 6,07 4,67
0 15 4,68 4,07
0 2 ,36 ,64
0 2 ,22 ,52
number of householdmembers
number ofhh-members involvedin farmingage of household headage of wife
years of schoolinghousehold headyears of schooling wife
number ofhh-membersparticipated in training(since1990)
number ofhh-membersparticipated inextension in 1999/2000
Min Max Mean Std. Dev.
A2:
Primary Activity Household Head
farming72%
wage worker
9%
trading10%
other9%
A3:
Primary Activity Wife
farming94%
other6%
71
A4:
Descriptive Statistics: Household Asset Land
,02 30,00 5,61 5,26
,20 2,25 1,08 ,60
0 100 79 23
0 100 34 45
0 100 11 31
0 100 39 45
0 100 72 43
0 100 20 39
total land size (ownedor operated, in acres)land use intensity a
% of upland soils ontotal land size% of land inherited% of land received asa gift% of land purchased% of land underfreehold status% of land undercustomary status
Min Max Mean Std. Dev.
ratio berween land area cultivated in 12 months and total land sizea.
A5: Test for appropriateness of Principal Component Analysis
KMO and Bartlett's Test
.652
186.87428
.000
Kaiser-Meyer-Olkin Measure of SamplingAdequacy.
Approx. Chi-SquaredfSig.
Bartlett's Test ofSphericity
A 6: Cluster Analysis: Determination of cluster numbers
Number of Clusters Agglomeration Coefficient
Percentage Change in Coefficient to Next Level
10 9
20.5 23.6
15.1 16.9
8 27.6 19.2 7 32.9 27.1 6 41.8 28.5 5 53.7 23.3 4 66.2 44.3 3 95.5 42.6 2 136.2 36.6 1 186.0 -
72
A 7: Cluster Analysis
Total Variance Explained
2,84 35,53 35,531,44 17,99 53,521,06 13,25 66,77,91 11,33 78,10,88 10,96 89,06,46 5,78 94,84,33 4,19 99,03,08 ,97 100,00
Component
12345678
Total% of
VarianceCumulative
%
Initial Eigenvalues
Extraction Method: Principal Component Analysis.
A 8: ANN Output Reports
Season 2000 A: Desired Output and Actual Network Output
0
1000
2000
3000
4000
5000
6000
7000
8000
1 38 75 112 149 186 223 260 297 334 371
Exemplar
Ou
tpu
t
Grain yield (kg/ha) Grain yield (kg/ha) Output
Performance Stover Yield Adjusted
stover yield Cob yield Adjusted cob
yield Grain yield Adjusted
grain yield MSE 1161011 13040654 192293 773572 675463 762806 NMSE 0,31 0,33 0,41 0,35 0,29 0,30 MAE 812 2372 236 569 617 650 Min Abs Error 1,42 15,92 0,36 2,61 4,36 1,99 Max Abs Error 4404 18661 3624 4733 3298 3508 r 0,83 0,82 0,77 0,81 0,84 0,84
73
Abbreviations: MSE=mean squared error; NMSE=normalized mean squared error (MSE/variance of desired output); MAE=mean absolute error; Min Abs Error=minimum absolute error; Max Abs Error=maximum absolute error; r=correlation coefficient
Season 2000 B: Desired Output and Actual Network Output
0100020003000400050006000700080009000
10000
1 35 69 103 137 171 205 239 273 307 341
Exemplar
Ou
tpu
t
Grain yield (kg/ha) Grain yield (kg/ha) Output
Performance Stover Yield Adjusted
stover yield Cob yield Adjusted cob
yield Grain yield Adjusted
grain yield MSE 3433696 16186099 1088779 4591298 1028244 2474502 NMSE 0,48 0,73 0,25 0,66 0,24 0,50 MAE 1118 2273 793 1374 770 1131 Min Abs Error 1,98 5,52 0,38 0,99 0,98 3,57 Max Abs Error 20980 41223 4308 14884 3956 8712 r 0,72 0,52 0,87 0,59 0,87 0,71
Season 2001 A: Desired Output and Actual Network Output
0
2000
4000
6000
8000
10000
12000
1 19 37 55 73 91 109 127 145 163 181
Exemplar
Ou
tpu
t
Grain yield (kg/ha) Grain yield (kg/ha) Output
Performance Stover Yield Adjusted
stover yield Cob yield Adjusted cob
yield Grain yield Adjusted
grain yield MSE 1313062 2300541 838583 1715369 3550533 31707294 NMSE 0,19 0,24 0,13 0,21 0,28 0,70 MAE 807 1073 704 1015 1193 2540
74
Min Abs Error 6,05 8,40 2,83 6,84 31,11 9,68 Max Abs Error 4815 5389 3758 4685 17274 65010 r 0,90 0,88 0,93 0,89 0,85 0,55
Season 2001 B: Desired Output and Actual Network Output
0100020003000400050006000700080009000
1 20 39 58 77 96 115 134 153 172 191
Exemplar
Ou
tpu
t
Grain yield (kg/ha) Grain yield (kg/ha) Output
Performance Stover Yield Adjusted
stover yield Cob yield Adjusted cob
yield Grain yield Adjusted
grain yield MSE 1039540 1685691 1568928 4222713 1785688 3241039 NMSE 0,51 0,50 0,28 0,52 0,23 0,29 MAE 788 992 892 1093 972 1374 Min Abs Error 14,99 8,26 19,15 3,38 2,17 19,90 Max Abs Error 3600 5320 9154 22410 8510 10391 r 0,74 0,76 0,87 0,74 0,89 0,86
75
A 9: Soil Classification
Soil Class 1
39 5,10 6,90 5,6641 ,435639 1,70 3,60 2,7231 ,382839 ,07 10,50 3,3146 2,503239 12,90 32,50 21,7821 4,5627
39 ,08 ,18 ,1203 2,585E-0214 4,38 7,58 6,0843 ,921339 37,34 124,30 74,8528 24,737014 17,21 41,57 30,7479 5,642339 47,48 75,84 64,9446 6,9893
39 13,24 43,24 25,3897 6,998539 6,20 18,20 9,6656 2,208314
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
Soil Class 2
18 4,50 5,80 5,3278 ,314018 1,70 2,50 2,0556 ,243118 ,45 11,40 4,2683 2,691418 9,80 20,40 14,3889 2,9478
18 ,06 ,13 9,667E-02 1,766E-024 3,20 4,15 3,7325 ,4055
18 25,90 72,36 49,9639 13,58514 13,30 19,04 16,2925 2,9987
18 49,48 80,56 71,3267 8,5671
18 9,24 39,24 19,5533 8,135818 5,28 15,64 9,1200 2,8892
4
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
Soil Class 3
4 3,90 4,90 4,2000 ,46904 2,70 5,00 3,5000 1,04244 ,97 7,14 4,4075 2,73034 11,40 18,90 14,2500 3,4723
4 ,13 ,25 ,1713 5,543E-021 4,74 4,74 4,7400 ,4 20,43 79,61 41,6600 28,05931 20,20 20,20 20,2000 ,4 54,92 77,48 65,2500 11,9252
4 13,24 34,52 21,5600 9,66004 9,28 21,64 13,1900 5,69371
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
76
Soil Class 4
14 5,60 7,00 6,1643 ,399214 2,70 4,70 3,6214 ,615414 ,22 16,70 7,3050 5,342114 25,50 47,50 34,4357 6,6609
14 ,08 ,24 ,1664 4,757E-023 7,23 8,65 8,0567 ,7382
14 65,32 268,02 160,3336 69,20283 25,85 34,50 30,4800 4,3571
14 45,48 81,48 59,2571 11,8709
14 9,24 47,24 29,9029 10,520714 6,20 21,28 10,8400 4,1181
3
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
Soil Class 5
3 5,60 6,00 5,7667 ,20823 2,60 3,20 2,8667 ,30553 22,20 27,80 24,1333 3,17703 24,10 32,90 29,7333 4,8911
3 ,10 ,16 ,1333 3,055E-021 5,92 5,92 5,9200 ,3 51,80 132,75 98,9233 42,08121 28,51 28,51 28,5100 ,3 52,56 77,48 66,5067 12,7233
3 17,24 34,88 25,1200 8,96903 5,28 12,56 8,3733 3,76111
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
Soil Class 6
5 4,00 4,50 4,2000 ,21215 1,30 1,50 1,3600 8,944E-025 ,45 2,68 1,2240 ,88345 ,47 3,30 1,6040 1,0862
5 ,04 ,06 5,200E-02 8,367E-035 ,24 1,07 ,5700 ,32895 6,76 10,14 8,5600 1,28495 6,07 9,23 7,6320 1,44495 76,92 84,92 81,3200 2,9665
5 8,52 12,52 10,5200 2,00005 6,56 10,56 8,1600 1,67335
PHOM (%)P (ppm)K (mg/100g soil)
N (%)Na (mg/100g soil)Ca (mg/100g soil)Mg (mg/100g soil)SAND (%)
CLAY (%)SILT (%)Valid N (listwise)
N Minimum Maximum Mean Std. Deviation
77
A 10: Reduced costs agricultural product price sensitivity analysis Subsistent Farm Household: Sensitivity Analysis Agricultural Product Price Increase % increase of current price
0 10 20 30 40 50 60 70 80 90 100
Reduced costs (103 USh) Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
182 211 313 188 252 364
181 203 304 186 239 348
180 195 294 185 226 332
180 188 284 184 214 316
179 180 275 182 201 300
178 172 266 182 188 284
178 164 256 180 175 268
177 157 247 179 162 253
201 196 303 203 198 304
265 315 470 267 317 471
330 434 637 332 437 639
Area adopted (ha)
0 0 0 0 0 0 0 0 0 0 0
Semi-subsistent Farm Household: Sensitivity Analysis Agricultural Product Price Increase % increase of current price
0 10 20 30 40 50 60 70 80 90 100
Reduced costs (103 USh) Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
505 832
1154 464 792
1114
534 886
1233 500 852
1199
563 939
1311 536 911
1284
593 992
1389 572 971
1369
622 1045 1468 608
1031 1454
651 1098 1546 644
1091 1539
680 1151 1624 679
1150 1624
702 1185 1674 704
1187 1676
718 1206 1706 720
1208 1708
735 1227 1737 737
1229 1739
751 1248 1769 753
1250 1771
Area adopted (ha)
0 0 0 0 0 0 0 0 0 0 0
Commercial Farm Household: Sensitivity Analysis Agricultural Product Price Increase % increase of current price
0 10 20 30 40 50 60 70 80 90 100
Reduced costs (103 USh) Maize+N1 Maize+NP1 Maize+NPK1 Maize+N2 Maize+NP2 Maize+NPK2
213 255 368 189 238 346
213 239 349 203 244 353
215 228 335 218 251 362
250 284 415 260 317 455
298 366 531 309 403 574
326 409 593 338 448 639
354 452 655 367 493 704
382 495 717 395 538 767
410 537 779 424 584 833
417 538 782 431 586 839
417 523 764 431 573 823
Area adopted (ha)
0 0 0 0 0 0 0 0 0 0 0
78
A 11 a: Production Structures Subsistence Farm Household Type
Production Structure (Baseline Scenario)
Maize l/ Groundnuts
1%
Cassava5%
Coffee/ Banana41%
Maize l/ Beans31%
Sweet Pot Bu22%
Production Structure (Scenario 1)
Coffee/ Banana40%
Maize l/ Groundnuts
1%Cassava5%
Maize l/ Beans 31%
Sweet Pot Bu22%
Onions1%
Production Structure (Scenario 3)
Maize l/ Beans31%
Coffee/ Banana
22%Sweet Pot Bu
22%
Onions6%
Tomato12%
Cassava5%
Maize BRP1%
Maize l/ Groundnuts
1%
79
A 11 b: Production Structures Trial Farm Household Type
Production Structure (Baseline Scenario)
Coffee l30%
Sweet Pot Silk5%
Groundnuts i6%
Sweet Pot Bu7%
Maize / Beans15%
Cassava18%
Maize i/ Cassava
19%
Production Structure of (Scenario 3)
Tomato irr30%
Maize i/ Beans18%Onions
17%
Cassava9%
Ground Nuts Improved
8%
Sweet Pot Bu9%
Coffee cl irr5%
Maize l/ Beans1%
Banana3%
Production Structure (Scenario 1)
Onions49%
Maize l/ Beans1%
Passion1%
Banana5%Ground Nuts i
8%Sweet Pot Bu
9%
Cassava9%
Maize i/ Beans18%
80
A 11 c: Production Structures Commercial Farm Household Type
Production Structure (Baseline Scenario)
Maize i/ Cassava
17%
Cassava32%
Maize i22%
Ground Nuts i3%
Sweet Pot Silk5%
Sweet Pot Bu11%
Coffee cl5%
Maize i/Beans5%
Production Structure (Scenario 1)
Onions42%
Passion18%
Ground Nuts i4%
Maize i/ Cassava
18%
Sweet Pot Bu8%
Maize l/ Beans5%
Maize BRP5%
Production Structure (Scenario 3)
Sweet Pot Bu7%
Cassava9%
Maize BRP6%
Groundnuts i3%Maize l/ Beans
4%Passion
36%
Tomato irr18%
Onions17%
81
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