The Maize Value Chain in Zambia:
Dynamics and Resilience Towards
Production Shocks
By Conrad Steinhilber
1 This paper is based on a more extensive master thesis by the same author. To request
more details concerning the work, please email: Conrad.Steinhilber@gmx.de
Abstract
Zambia is a country whose food security largely depends on maize. In the light of expected
structural maize deficits and the likely occurrence of shock events, the aim of this paper is
to test the resilience of the Zambian maize value chain towards production shocks in terms
of food supply security, and to find policy recommendations on how to increase this
resilience. To that end, I devised and applied a new framework for measuring resilience
properties using System Dynamics, which relies on comparing the development of food
security indicators between the base run and the respective shock scenario run of a
simulation model. Results show that the value chain is quite resilient towards floods and
exchange rate shocks, moderately vulnerable towards changes in fertilizer subsidy
programmes, and very vulnerable towards droughts, especially prolonged ones. In general,
the resilience of the value chain towards one-time shocks is good due to the existence of
maize buffer stocks that people can consume when production is low. However, the value
chain is not very resilient if faced with two or more different shocks, as buffer stocks are
quickly depleted and maize demand cannot be serviced any more. The resilience properties
are also strongly affected by demand adjustments of consumers in response to changing
maize availability, and moderately affected by the distribution of maize in between the
informal and formal value chain and the storage policies of FRA. The observed resilience
properties can endogenously be improved using smart long-term maize storage policies
that exploit surplus production years.
1. Introduction
1.1 Overview
Zambia is a society built on maize. Since the colonial period, a large part of the
government's legitimacy has depended on its ability to fulfil the implicit social contract with
its constituency that requires it to ensure a sufficient maize supply for the population
(Jayne & Jones, 1997). And the importance of maize has not waned over the last decades:
even today, maize still accounts for over 50% of the calories consumed by an average
Zambian citizen (FAO, 2015b), whereby especially the poorest strata of society depend on
maize over-proportionally (Nicole Mason & Jayne, 2009). This might not be a problem per
say would Zambians live in a state of food security - but unfortunately, the opposite is true.
FAO's (2014b) food security indicators show that Zambia has only been able to supply
around 90% of the calories its citizens need to live a healthy and active life over the last
years; and Gerber (2015) expects a structural maize deficit to emerge in Zambia in the near
future. On top of this generally tense situation, weak political and economic institutions,
bad infrastructure, heavy reliance on one main staple food crop and high exposition to
sudden and strong changes in its climatic, political and economic environment leave Zambia
in in a position of high vulnerability on many levels (Bertelsmann Transformation Index,
2012; Stockholm Resilience Centre, 2012; UNDP, 2014).
Given the central role of maize in Zambia, the population’s access to maize can legitimately
be seen as a proxy for overall food security in Zambia. Furthermore, several sources suggest
that the generally weak political and economic institutions, poor infrastructure, heavy
reliance on one main staple food crop and an exposition to sudden and strong changes in its
climatic, political and economic environment, that it shares with most of its Sub-Saharan
African neighbours, leave Zambia in in a position of high vulnerability on many levels
(Bertelsmann Transformation Index, 2012; Stockholm Resilience Centre, 2012; UNDP,
2014) . I will therefore adopt a resilience perspective and explore what happens to the
maize supply in Zambia when it is faced with production shocks. I want to find out how
resilient the mechanisms that bring the maize “from farm to fork”, i.e. the maize value chain,
are towards production shocks - and what can be done within the political and economic
boundaries of Zambia to enhance those resilience properties.
To that end, I will devise a framework that enables the quantitative measurement of
resilience with a System Dynamics (SD) simulation model. The completion of this case study
will also serve as a test of the usefulness and feasibility of the resilience framework devised,
and contributes to resilience research at large by exploring the usefulness and applicability
of the concept for specific case studies (Janssen & Anderies, 2013).
1.2 Problem Background
To get a clearer picture of the food security situation in Zambia, it is useful to look at FAO’s
indicators. The Prevalence of Undernourishment (PoU), denoting the probability that a
randomly selected individual from the reference population consumes less than her calorie
requirement for an active, healthy life was as high as 48.3% for the last measurement
period of 2012-13 (FAO, 2014). This means that every second Zambian you would meet by
chance is undernourished - a clear sign that food security is still a big issue in Zambia.
But in how far is this lack of food security attributable to availability problems? Looking at
the ADESA, we see that it has been fluctuating around 90% in the last years, meaning
Zambia could only supply 90% of the calories that its citizens need for a healthy lifestyle.
Moreover, Gerber (2015) expects that Zambia will enter a structural maize deficit in the
near future due to stagnating harvests and growing population, making the supply situation
even worse than it has been in the relatively good last harvest years.
Hence, we can conclude that food insecurity is an on-going problem in Zambia, that not only
stems from an inefficient distribution of food in the society (access dimension), but also
from the fact that the most basic requirement of sufficient availability of food is not fulfilled.
Following the rule that the most basic problems need to be addressed first, my work will
therefore focus on the problems of availability of food in Zambia.
However, since modelling all the value chains for all crops consumed in Zambia is not
feasible, it makes sense to find a proxy for food security. The obvious choice for this is
maize, since the long history of strong government support for maize (Zulu, Jayne, &
Beaver, 2007), coupled with deeply rooted cultural perceptions and traditions, made and
sustained maize as the single overwhelmingly important staple food crop in Zambia. Even
in spite of recent shifts towards a greater crop variety, maize still accounts for over 50% of
calories consumed in Zambia as of 2011 (FAO, 2015b) and is cultivated by 80% of farmers
in Zambia (Zulu et al., 2007).
Furthermore, Mason & Jayne, (2009) have found that there is a strong correlation in Zambia
between low income and an above-average reliance on maize as the main source of calorific
supply. This means that especially the poorest, who are typically also the most vulnerable
to shocks of all kinds, depend on maize over-proportionally for satisfying their basic
calorific needs. As food insecurity is mainly a problem for the poorest strata of society, this
means that maize is especially important to maintain food security for all of the population
in Zambia.
From the above information, it becomes clear that there can be no adequate food supply for
Zambians at large without a sufficient supply of maize. Since the value chain represents the
very mechanisms that actually get the maize from the farms scattered around the country’s
remote places to the non-subsistence consumers in the (semi-) urbanized regions, it’s role
is obviously decisive: no distribution of food from producers to consumers can take place
without a functioning value chain.
Field research suggests that food value chains are crucial when trying to address
inadequate availability of food, as in Sub-Saharan Africa, post-harvest losses typically
number around 10-23% of the original production (Hodges & Bernard, 2014). Bou
Schreiber (2015) shows that this problem is especially pervasive in Zambia: the main maize
purchaser FRA (Food Reserve Agency) uses mostly inadequate storage facilities, leading to
heavy annual grain losses that become even worse when the system is shocked out of its
equilibrium state, e.g. by unexpected bumper harvests.
2. Methodological Framework
Resilience is a very broad topic that has received increasing attention in various disciplines
over the last years. Yet, the use in manifold contexts and relative novelty of the concept
contributes to a lack of conceptual clarity and the existence of many different competing
definitions of resilience (Carpenter & Brock, 2008; Henry & Emmanuel Ramirez-Marquez,
2012). However, the concept is of course not used completely arbitrarily and therefore does
have a certain core that is widely agreed upon. Olsson, Jerneck, Thoren, Persson, & O’Byrne
(2015: p. 1) define this core as the agreement that “resilience is concerned with the ability [of
a system] to cope with stress or, more precisely, to return to some form of normal condition
after a period of stress.”
In their endeavour to further clarify the nature of resilience discourses, Olsson et al. (2015)
reviewed the systems thinking literature and concluded that there are essentially two types
of definitions of resilience in use:
1. Resilience as “bouncing back”
This definition stresses the quality of a system to withstand a disturbance and to
recover from it, while preserving its structure. Insofar, resilience is seen as the
ability of a system to resist forced structural change, while maintaining and/or
recovering its central functions. (See also: Dalziell & Mcmanus, 2004)
2. Resilience as “bouncing back and transforming”
This definition, contrary to the first one, understands the resilience of a system as
depending on the ability to change its structure. The idea is that when a system is
faced with a disturbance, it not only bounces back by recovering important
functions, but also transforms its structure in a way that makes it better adapted to
cope with the new environment.
These two lines of thought are therefore somewhat contradictory in how they view the role
of preserving a system’s structure. Adopting the second view also creates further questions
about the identity of a system: how big can the structural change be, so that the
transformed system is still considered a “smart adaptation” of the original system, and
when does the change become so big that the system essentially loses its identity and can
thus be considered to have broken down and succumbed to the disturbance?
Summing up, we can conclude that there are competing, even partially contradictory,
definitions of resilience and none can per se be said to be superior to the others. However,
to achieve conceptual clarity in my research, it is imperative to choose a clear definition.
Since I want to find out how the supply of maize changes in relation to production shocks, I
will have to look at short-to medium term changes that take place in a set of parameters
within a few months to years. It is very unlikely that the basic structure of maize value chain
will change significantly within this relatively short time, so that the component of
structural change that is central to the second definition is not relevant. As methodology
choices should be made according to their usefulness in reaching the research objectives, I
will therefore adopt the “bounce back” definition of resilience. “Vulnerability” will denote
the absence of resilience, or propensity for high impacts of a shock on a parameter.
However, it remains to make this still rather vague concept measurable. Henry & Emmanuel
Ramirez-Marquez (2012) propose a framework that operationalizes resilience as a function
of time, which is well suited for application in simulation models. They remark that
resilience always has to be understood as resilience of a certain function in the system
against a certain shock event. This makes sense, as function A of a system might not be
affected by a shock, while function B might break down completely - but when faced with a
different shock, the system might be able to maintain function B, but not function A.
Henry & Emmanuel Ramirez-Marquez' (2012) framework therefore requires to specify the
central functions of a system that one wants to evaluate, which they call “figures of merit”
(FOM). The development of these FOM is then simulated in a no-shock base run and a shock
scenario. The extent of change in the trajectory of the FOM between the base run and the
shock scenario then indicates how resilient the system is in terms of that specific FOM to
that specific shock. A graphic representation of this idea can be seen in figure 1.
Henry & Emmanuel Ramirez-Marquez (2012) further differentiate between two features of
resilience in their framework:
The initial vulnerability is determined by how strong the initial impact of a system
disturbance is on the FOM. In figure 1, this would be the drop in the scenario graphs
occurring between times seven and eight. The FOM in scenarios 1 and 2 drops by 6,5
units, while the FOM in scenario 3 only drops by 4,5 units. The initial vulnerability of
the FOM against the shock in scenario 3 would thus be smaller.
The adaptive capacity is determined by how fast the FOM recovers from the shock
by returning towards its original base run trajectory. The FOM shows the lowest
adaptive capacity towards the shock in scenario 1, as it takes the longest time to
return to the base run trajectory. While the FOM in shock scenarios 2 an 3 actually
rise by a total of 6,5 units throughout the first six time steps after the onset of the
shock, the FOM in scenario 1 only grows by 3,8 units in the same time.
FOM development under different shock scenarios
18
16
== No-shock
147 scenario
12 4 = Shock
10 4 scenario 1
"Shock
8 scenario 2
6 5 == Shock
scenario 3
4
2
0 —
12 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Figure 1:Development of FOM under different shock scenarios
The overall resilience of the system in terms of the FOM under consideration is therefore
measured as the integral between the base run and the respective shock scenario run curve
of the FOM. This framework will be the basis for measuring resilience in my analysis.
The choice of an FOM for my analysis is guided by the insights we have gained so far: that
there is already not enough calorific supply in Zambia to fulfil the population’s needs for an
active and healthy life, and that maize plays a decisive role in ensuring this supply. With
these two guidelines in mind, I created a new indicator that is based on the ADESA and will
serve as the central FOM for my resilience analysis. ADESA is an indicator that measures the
availability of food (DES) in relation to the demand. While the availability is solely
determined by the total amount of maize in the market, and does not account for problems
concerning the access dimension, demand is determined by the Average Dietary Energy
Requirements (ADER). The ADESA calculated as a ratio using the following equation:
DES/ADER. So if for example, the demand for food per person is 3000 kcal/day, but only
2000 kcal/day can be consumed due to a lack of availability, the ADESA takes the value of
0,67.
The indicator I intend to use as the FOM for my resilience analysis is called “Adjusted
Dietary Energy Supply with Maize” (ADESM). It is a modification of the ADESA in four
respects: firstly, since we are just looking at the maize sector it is only concerned with
maize instead of all food sources. Secondly, I am looking at the population of Zambia as a
whole and therefore aggregate all individuals’ ADER to one national demand. Thirdly, my
model includes feedback loops that alter demand as a result of changes in availability. The
demand in the model is thus adjusted to represent parts of the access dimension as well.
Fourthly, the availability of maize (equivalent to the DES) is measured in terms of
consumption. This rests on the simple and plausible assumption that consumers will
consume whatever amount they demand when that maize is available to them.
Like the ADESA, the ADESM is measured as a ratio between zero and one, with a value of
one representing a full servicing of the adjusted demand, and zero indicating that no
consumption takes place at all. Hence, the ADESM is calculated using the following formula:
Maize consumption
ADESM = —————_______
Adjusted demand for maize
3. The Model
3.1 Scope
Kaplinsky & Morris (2001: p. 4) define a value chain as ,,the full range of activities which are
required to bring a product or service (..) through the different phases of production
(involving a combination of physical transformation and the input of various producer
services), delivery to final c 's, and final disposal after use“. In the case of maize meal,
it makes sense to focus on maize, as it is the central raw material required to produce maize
meal (along with the labour and capital needed to refine it). I thus define the start of the
value chain as being the production of raw maize by farmers in Zambia. Adopting this
product-based view makes it clear that the end of the value chain then has to be the
consumption of the maize meal by consumers.
The length of the simulation is from 2004 to 2020, with 2004 - 2014 being the reference
data period. Furthermore, it is important that the model runs in months in order to be able
to reflect the strong seasonality of the Zambian maize market: the green harvest comes in in
March and April, followed by the main harvest in May and June. While private buyers start
purchasing maize from smallholder farmers right away when the harvest becomes
available, FRA only purchases maize in the official government-announced marketing
season, which usually lasts from June/July until the end of September or beginning of
October (Nyanga, 2015b). While maize is usually well available in the months following the
harvest (the “plenty season”), supplies dry up later in the year (Jayne et al. 2009). This
happens either because the harvest was too small to service domestic demand, or because
large FRA purchases have locked up all the remaining maize in the formal value chain, so
that consumers in the informal chain are devoid of supply. The time following the harvest
(roughly April - October) is therefore called the “plenty season”, while the time later in the
marketing year (roughly November - March) is seen as the “lean season”.
Since my aim is to understand the dynamics of the maize value chain in Zambia, it seems
trivial to say that the geographical boundary of my analysis will be Zambia as a political,
economic and geographic entity. However, this has important implications for the model.
While export and import decisions are made and financed according to internal dynamics,
and are thus represented in the model, food aid is administered by external agents and thus
not portrayed. This is because food aid constitutes a transfer of goods from other
geographical areas and political entities that is ultimately at donor discretion and cannot be
influenced by actors from Zambia. Since the aim is to find out what the resilience properties
of the maize value chain in Zambia are, and what can be done in Zambia to enhance those,
looking at food aid does not help answering the question and is thus excluded.
3.2 Model Overview
Reduction demand
informal due to limited
availability
ts)
Consumer demand
informal original
Spillover of demand from
7 informal to formal value
Consumer demand chain
informal adjusted Q +
Gap between demand
and supply for grain
Gap between demand
+ and supply informal
+
‘Consumption
informal
informal retailing [Consumer] Hammer milling © -coasamer]
informal informal OO
grain storage litical storage]
A
Comercial farmer
Availability of grain harvest
for private traders 7
” | +
+ | ———~ Commercial Milling and Consumption” +
mblage [Mitters |__ Teiling ‘Consumer | foal
Farmer storagi storage Ssorage
Smaffholder Consumer demand
arves! formal original
FRA storage oS) -
FRA purchases ales
purchases FRA sales Gap between demand *
; and supply formal
Desired FRA _2
reserves
Figure 2: Value chain model overview
The value chain begins with the production of maize by smallholder farmers. They store
their maize in the form of grain and then sell it (often via assemblers acting as middlemen)
to either informal grain retailers, commercial millers, or the FRA. Since FRA usually buys at
above-market prices (N. M. Mason & Myers, 2011), I assume that farmers prefer to sell to
them and reserve as much grain as possible for FRA sales - only the remainder is sold to
private buyers.
From there, the maize value chain is effectively split in two: there is the more formalized
avenue (which I will call formal value chain henceforth) shown at the bottom in figure 2,
involving farmers selling either directly or through small-scale assemblers to large
commercial millers, these millers milling and refining the grain to different types of meal,
selling it to retailers and these retailers then selling their bags of maize meal to consumers,
who finally consume the product. The FRA is also involved in this avenue, as it purchases
large amounts of grain from farmers, then selling it only to large commercial millers later in
the year (N. M. Mason & Myers, 2011).
Note that commercial farmers, who in recent years accounted for about only 8% of the
production (see appendix C.3), just like FRA, only sell to larger commercial millers in the
formal value chain, and not to small-scale informal grain retailers or consumers. Since
retailing takes maximally only a few days and retailers have not been observed to exhibit
significant strategic behaviour, I subsumed retailing into one flow with milling (which is
also done quickly in comparison to the time step of a month). A developed wholesale sector
is generally lacking and therefore also not represented in the model(Nyanga, 2015a).
However, there is also the less formalized avenue visible at the top of figure 2 (henceforth
the informal value chain), which is formed by consumers buying grain directly from
farmers (in rural settings), or from informal grain retailers that purchase grain from small
maize assemblers or farmers. These consumers then bring their grain to small hammer
mills throughout the country to transform it into whole grain maize meal, which is then
taken home and eventually consumed by themselves (Leathers, 1999: chapter 5). Here as
well, a developed wholesale sector is missing.
Both of these chains work like a classical supply chain with a backwards-induced feedback
structure, so that downstream demand drives upstream demand. Upstream actors not only
adjust their orders according to changing demand from downstream consumers, but also
adapt their inventory upwards (according to: coverage time * orders) to be able to cover a
longer timespan of orders with the new higher volume from their inventory. The structure
is thus prone to produce a bullwhip effect when changes in demand occur, as increases in
demand are amplified with every upstream stage.
There are three main feedback loops in the model. The first one (C1) alters informal
demand in response to availability. The counteracting loop C1 represents how informal
consumers adjust their demand downwards in times of limited availability, and ensuing
high prices for maize. Note that this downward adjustment is limited by a lower bound that
corresponds to the minimum dietary energy requirements by FAO (2014): people would
take any measures to prevent going below this threshold that means starving.
Loop C2 shows that, when not enough maize is available in the informal value chain to
satisfy even the adjusted lower demand, the informal consumers will have to resort to
buying the more expensive roller meal from the formal value chain: this reduces demand in
the informal value chain and increases demand in the formal value chain. Furthermore, a
certain fraction of consumers starts buying other carbohydrates as maize substitutes when
maize grain becomes unavailable. This spill over of demand happens progressively stronger
in the lean season as grain in the informal value chain becomes scarcer; and the process
leads to fluctuations and an ensuing bullwhip effect in the formal value chain. When maize
becomes available in the informal value chain again with the next harvest, the demand
returns to the level of the original informal demand.
The link between the informal customer demand and the consumption is only represented
as a dotted line because the consumption only depends on the demand in terms of its upper
bound. However, the gap between demand and consumption is actually is caused by the
lack of maize supply coming through the flows from upstream. The loop involving this link
is therefore not really a reinforcing loop and | thus did not label it accordingly.
Loop C3 represents FRA’s policy to release its reserves when there is a supply shortage in
the formal value chain in order to stabilize prices and supply. The feedback from FRA sales
to consumption is implicit in the flow structure bringing the maize from the FRA to
consumers - the more maize FRA releases; the more is of course available to consumers in
the long run for consumption. Note, however, that this feedback loop is limited by the
amount of FRA’s reserves - once they have released everything in their storage, the loop
cannot unfold any more impact.
I further want to draw attention to the importance of FRA’s purchasing decisions for the
behaviour of the value chain. The more maize FRA purchases, the fewer maize is available
for private traders to buy from smallholder farmers - and the fewer they can buy, the
smaller becomes the supply with maize for the informal value chain. This again has effects
on when loops C1 and C3 become active.
3.3 Assumptions
Since any model is an abstraction of reality, it always has to rely on a set of assumptions
that allow a simplification of the real world’s complexity, and my model is no exception. The
most important assumptions are therefore listed below.
Prices
Since information about prices was not available in sufficient detail and quality, the impact
of prices is represented indirectly through preferences of consumers or producers to
buy/sell from certain actors that have consistently cheaper/higher prices, or through loop
C1 where scarcity acts as a proxy for price development
Post-harvest losses
Losses of maize in the value chain were only modelled if they exceed a certain significance
level, which is again determined by the duration and quality of storage. Losses are most
significant in FRA storages, where up to 50% of stored maize can be lost due to
inappropriate storage per year (Bou Schreiber, 2015).
Exports
Maize exports through FRA are often driven by strongly discretionary political decisions
rather than market dynamics, and are thus represented as external. Furthermore, I assume
that only big commercial farmers and FRA have the means to export and market their maize
abroad. However, the government is assumed to only allow exports in years where
domestic maize production significantly exceeds demand.
Imports
I assume that private maize imports are negligible, as they are generally discouraged and
hindered through discretionary government interventions, inefficient bureaucratic
processes and the fear of having to compete with subsidized FRA maize on the domestic
market (Dorosh, Dradri, & Haggblade, 2009). In my model, all imports thus run through
FRA, which is the agency charged with carrying out external maize trade operations for the
government.
FRA stock management
In order to fulfil its mandate to guard against price fluctuations by keeping maize buffer
stocks, FRA is assumed to try to accumulate carryover stocks of maize every year. However,
in years of structural maize deficits, they will always release those stocks to stabilize the
maize market and support the consumers as much as necessary.
Capacity constraints
The model does not feature capacity constraints since even in years with extreme bumper
harvests (such as 2014) and ensuing high volumes of maize handled, no capacity problems
have been observed.
Consumer behaviour in the formal value chain
Consumers in the formal value chain generally belong to the wealthier strata of society and
are thus assumed to be able to pay higher prices, as maize gets scarcer later in the year.
Furthermore, it has been observed that maize prices in the formal value chain are not very
elastic towards changes in availability (Nicole Mason & Jayne, 2009). Therefore, I assume
that consumption patterns do not change in relation to changes in maize supply.
3.4 Subsistence Sector
Subsistence production is maize that is produced and then consumed by the producing
farmers themselves. It never enters the market and the value chain and is thus not relevant
for my analysis. However, subsistence production is still important for my model insofar it
satisfies a certain share of national demand. Fluctuations in the subsistence demand thus
simply mean for my model that those 75% of smallholders who produce mainly for
subsistence (Zulu et al., 2007) were able to satisfy their own demand for maize to a greater
or lesser degree than in the previous year. Corollary, their need to top up their own maize
stocks with externally purchased maize just becomes lower or higher by the same amount
that subsistence production fluctuates. The subsistence production is thus simply deducted
from total demand (including a mark-down for losses occurring in storage and milling) to
yield the demand for marketed maize.
3.5 External Inputs
Concerning the external inputs to my model, the reference period 2004 - 2014 was based
on data, while for the development from 2015 - 2020, the following assumptions were
made.
Population
Zambia's population is expected to grow steadily and strongly over the next years
(UNDESA, 2012), leading to parallel increases in demand for maize.
Maize production
The data for the base
run of my _ model
comes from the base
run of Gerber's (2015)
production model.
While he only splits
his data in “sold” and
“not sold” (ice.
subsistence)
production, I further
distinguish between
Million metric tons per year
Maize Production Base Run
A
Bop
NOR
0,8
0,6 5
==SH Surplus
== Commercial
Figure 3: Maize production inputs from data and Gerber's base run
sold production from smallholders and commercial farmers. To this end, I assume that the
relation of 8% commercial and 92% smallholder production of the total sold production
14
will stay constant for the next years. The development of the resulting yearly maize
production that is fed into my model can be seen in figure 3.
Maize imports
For simplicity, and because I am interested in the endogenous development of the maize
sector in Zambia without external influences, I assume that the trend of the last three years
holds and no maize is imported.
FRA storage capacity
According to Bou Schreiber (2015), FRA plans to heavily invest in increasing storage
capacity, so that FRA expects to have 250.000 tons of silo and 1.200.000 tons of shed
capacity by 2020.
FRA purchase policy
In order to save money and prevent high storage losses, I assume that FRA scales back on
their heavy purchase volume from 2010 - 2014 and regress towards the historical mean of
buying around 15% of the yearly traded harvest.
3.6 Validation
Even thought the lack of a reference mode of behaviour graph made it impossible to
compare the model’s output against it, high confidence in the model was established by
subjecting it to a series of tests, as proposed by Barlas (1996). It passed structure and
parameter confirmation tests, dimensional consistency test, direct extreme condition test,
behaviour sensitivity test, qualitative features analysis, extreme condition test and
integration error tests. For the sake of keeping the paper short, | will not go into detail
about this, for more information feel free to contact the author.
4. Results
4.1 Base Run Behaviour
An analysis of the behaviour of the base run should be based on the evaluation of the
behaviour of the central variable, which is the ADESM. Since the model runs in months from
January 2004 on, the time units on the graphs should be understood in the following way:
month 1 is the beginning (January) of 2004, month 2 is February 2004, month 13 is January
2005... and so forth up to month 204, which is December 2020.
The graphs showing the ADESM always display a value range between 0 and 1.
® 1: ADESM
a 19 al rT
\. i! hh
1 of
1 0.
,00 51,75 102,50 153,25 204,01
Page 1 Months
2 ‘ADESM Base Run
Figure 4: Behaviour of ADESM in base run
The first thing that you will notice when looking a the ADESM’s behaviour in figure 4 is the
strong seasonality: every year, with the exception of summer 2013 to summer 2015
(months 112-136), the ADESM fluctuates throughout the year. That has to do with the
seasonality of the maize harvest: while there is some green harvest coming in every year in
March and April, it only has a share of 7.5% of the total production. The main harvest then
becomes available in May and June, which is when you see the ADESM spiking up. This
behaviour reflects the typical alternation between the “plenty season” and “lean season” in
Zambia (Nicole Mason & Jayne, 2009), where maize is well available in the months
following the harvest and becoming very scarce in the months at the end of the marketing
year. Since the first and last years of the simulation are years of structural maize deficit, the
domestic maize consumption is used up after a few months following the harvest and the
ADESM thus drops to zero. Note that Zambia did receive food aid in those years that most
probably prevented the maize supply from completely drying up, but since we are focusing
on the internal dynamics of maize production these are neglected, as discussed in section
Bile
The increasing size of the drops in the ADESM in the lean seasons of 2018-2020 (months
169-204) is because Zambia experiences a roughly steady yearly production from 2015 on,
while the population gradually grows. The gap between production and demand therefore
becomes increasingly bigger.
A little exception to that general seasonality pattern is the smaller spike that the ADESM
shows from zero to about 0,6 in the months 23-24 (November-December 2005). This is due
to the incoming harvest by commercial farmers that I assume to start selling in the height of
the lean season in order to receive better prices. However, as their share of the total traded
production drops over the following years, the impact of their production on the ADESM
declines as well.
The reader will further notice that the ADESM often stagnates at a level of 0,84. This
happens when the supply of maize grain for consumers in the informal value chain has
dried up, making them lower their consumption and eventually start purchasing
commercial roller meal from the formal value chain. The fact that this behaviour occurs
even in years of surplus production, such as 2010-12 (months 73-96), is due to
dysfunctional FRA policies.
A good year to explain the dynamics behind this phenomenon is 2010 (months 73-84):
while this year actually features a good harvest that exceeds demand by a great margin,
FRA purchases such vast amounts of maize (878.750 tons out of the total yearly
smallholder production of 1.062.010 tons), that not much is left to purchase for private
buyers. Millers, via small commercial maize assemblers, and informal grain retailers then
compete for this relatively small amount of maize grain available to private buyers. Thus,
their demand cannot be satisfied, which ultimately leads to supply shortages in the informal
value chain.
@ 1: FRA purchases 2: Commerc...e assemblage 3: Informal grain retailing 4: ADESM
y 5200004 %
4a: os Ry
— i _——
1
3 260000]
4a: 0
3 eth
z 0 ¢ 3S
— - T 1
3,00 76,00 79,00 82,00 85,0
Page 1 Months
2 ‘Smallholder maize purchases
Figure 5: Smallholder grain sales
In figure 5, we can see how this logic translates into behaviour: “commercial maize
assemblage” and “informal grain retailing” reach zero around month 80. This means that all
the maize that smallholders have not contracted to FRA and that was thus available for
private buyers has been sold. As a result, the “non FRA smallholder sales switch” (reflecting
the fact that farmers will reserve the rest of their maize for the more profitable FRA
purchases) turns to zero. The flows of informal grain retailing and commercial maize
assemblage therefore also drop to zero at that time. This means that the inflow of maize to
the informal value chain stops and consumers respond by gradually lowering their monthly
consumption, which makes ADESM gradually approach 0,84 as a response.
The problem with FRA’s purchase and sales policies is not just that they often dry up the
informal market, but also that they purchase much more than they can sell or want to store
as security stocks. This leads to long residence times of the maize in their storages and
ensuing high losses. Hence, at the end of the marketing year, they are faced with a bad
choice: either let their excess maize rot away with exponentially rising loss ratios (cf. figure
6), or export it under unfavourable terms of trade.”
® 1: Accumulated yearly losses FRA 2: FRA maize in permanent storage 3: FRA maize in slabs
i 660004
a 1000000 Sf
2
33000 a
50000077 fo
14
2) " on el /
3 on!
3,00 76,75 80,50 84,25 88,0
Page 1 Months
ebie
2 FRA maize stocks
Figure 6: FRA storage losses
An exception to the constantly recurring mismatch between demand and supply described
so far is the time between summer 2013 and summer 2015 (months 113-140), where the
ADESM in figure 5 nearly constantly displays a value of 1, indicating the full satisfaction of
maize demand. This is due to the fact that in 2013 and 2014, harvests were very good and
FRA purchases sufficiently low, so that the available purchase volume for private traders
was high enough to service the demand from the informal value chain throughout the
whole year.
As you can see, the key to understanding the model behaviour really is the distribution of
the smallholder maize sales: they determine if the maize goes into the formal or informal
value chain, or is locked up at FRA’s storage facilities and lost to pests. Once the maize has
entered one of the value chains from the smallholder stocks, or is sold by FRA, it is
processed and consumed according to the rules explained in chapter 3.2. However, since
the relative distribution of smallholder maize sales between the value chains is not only
2 These are due to the fact that FRA usually purchases maize at an above-market price that they cannot
demand when selling maize abroad. They therefore often sell at a loss and it is just a matter of choosing the
lesser bad for them in that situation.
19
determined by supply, but also the demand for smallholder maize in the form of the DAR
informal grain retailing and the miller’s demand driving the commercial assemblage flow.
Summing up, the following can be used as a rule of thumb for understanding the dynamics
behind the fluctuations in our key parameter ADESM: it usually rises in May as the new
harvest comes in, staying at a plateau value of one that indicates the full servicing of
demand in both value chains. The grain supply then dries up either because the harvest was
simply too small to satisfy total demand, or because FRA has locked up large amounts of
maize in the formal value chain. ADESM then falls to a value around 0,84 indicating that
people who would prefer to consume grain have to reduce their daily consumption and
eventually resort to buying expensive maize meal. The time when this shift occurs depends
on how much smallholder maize was channelled into the informal value chain. This in turn
depends on the availability of maize in the formal and informal value chain in the preceding
marketing year: if the supply gap in the informal value chain was greater than in the formal
chain, the initial demand to refill stocks at the beginning of the marketing year is higher and
the informal value chain therefore attracts relatively more maize.
If the total harvest in the current marketing year was smaller than total yearly demand, the
supplies in the formal value chain eventually also dry up, leaving the ADESM to fall to zero.
If it was a surplus year, ADESM stays at 0,84. As the next main harvest comes in in May, the
cycle begins again. Only when the difference between smallholder surplus harvest and FRA
purchases is big enough to allow private traders and grain retailers to satisfy demand for
grain all year round, the ADESM stays at 1 throughout the whole year.
The sales by commercial farmers later in the year and the incoming green harvest in March
and April do bring some relief in the lean season, but they generally are rather insignificant
due to their small size in comparison to total yearly harvest and demand. Furthermore, as
commercial farmers only sell to the formal value chain, their production does not help to
reduce the gap in demand in the informal value chain, and thus will not change the value of
the ADESM if it is at 0,84.
One more important determinant of the ADESM’s behaviour is the existence of carryover
maize stocks from last year. If the current year shows a structural maize deficit, the
20
consumption of stocks that have been accumulated in a better preceding year can stabilize
the ADESM.
4.2 Production Shock Scenarios
It was shown that the most relevant adverse impacts on the maize production would come
from sudden changes (shocks) in the following parameters:
* Cultivated land
¢ Exposure to water
¢ Fertilizer Use
The most likely and relevant scenarios significantly altering these variables were found to
be the following:
Exchange rate shocks
Such shocks reduce the purchasing power of the Kwacha in relation to the US Dollar, the
currency in which fertilizer is internationally traded - which again increases the average
price for fertilizer for Zambian farmers, as most fertilizer is imported. I assume a dynamic
response of the economy; insofar steady high prices for imported fertilizer will make
domestic production more attractive and thus reduce import rates.
Floods
Large floods cause significant losses in the cultivated land area.
Fertilizer subsidy shocks
Cuts in the fertilizer subsidy program for smallholder farmers reduce the amount of
fertilizer that farmers can purchase and use.
Droughts
Droughts lead to a loss of cultivated area and lower yields due to a lack of rain and strong
heat. A scale with 5 different drought strengths was used for different scenarios.
21
4.3 Simulation Results for the Different Scenarios
Following the methodology laid out in chapter 2, I will measure the resilience of the value
chain in terms of the integral between the curves for the ADESM in the base run and the
ADESM in the respective shock scenario runs. The maximal impact of a shock would thus be
that the ADESM goes to zero for all the six years, or 72 months, simulated into the future.
This would lead to an integral of 60 between the base run’s ADESM and the shocked run’s
ADESM. The minimum difference is of course 0 when there are no adverse effects on the
ADESM in the scenario run. Using this range as a yardstick, we can thus analyse the
resilience of the value chain towards the different production shock scenarios in chapter 5.
The simulation results are ized in figure 8 below.
Scenario Description of shock ADESM integral
final value
1 Permanent Increase in Kwacha Value Towards US Dollar 4,86
by 35%
2 Permanent Increase Kwacha Value Towards US Dollar by 8,49
50%
3 Flood Loss of Cultivated Area by 10% in 2015 0,18
4 Flood Loss of Cultivated Area by 20% in 2015 0,45
5) Flood Loss of Cultivated Area by 30% in 2015 1,44
6 Flood Loss of Cultivated Area by 20% in two consecutive 1,95
years (2015-16)
Z Extreme 3-year Drought (2015-17) 20,08
8 Extreme 2-year Drought (2015-16) 12,44
9 Extreme 1-year Drought (2015) 6,17
10 Severe 2-year Drought (2015-16) 10,33
11 Severe 1-year Drought (2015) 5,02
12 Moderate 2-year Drought (2015-16) 6,71
22
13 Moderate 1-year Drought (2015) 2519
14 Extreme followed by Severe Drought (2015-16) 11,41
15 Steady Fertilizer Subsidies of 1500 Kwacha/Person/Year 0
16 Fertilizer Subsidies Permanently Cut in Half to 750 4,64
Kwacha per Person and Year
17 Fertilizer Subsidies Permanently Abandoned 13,88
18 Flood Loss of Cultivated Area by 20% and Zero Subsidies 3,96
in 2015, followed by Subsidies Cut-in-Half to 750
Kwacha/Person/Year in 2016
19 Flood Loss of Cultivated Area by 20% in 2015 and 2017, 18,48
as well as Permanently Abandoned Fertilizer Subsidies
20 Severe Droughts in 2015 and 2017 and Extreme Drought 19,77
in 2016, as well as Reduced Fertilizer Subsidies in 2015
and 2018 (750 Kwacha/Person/Year) and No Fertilizer
Subsidies in 2016-17
21 Severe Droughts in 2015 and 2017 and Extreme Drought 26,31
in 2016, as well as Permanently Abandoned Fertilizer
Subsidies
22 Extreme Droughts in 3 consecutive years (2015-17), as 27,41
well as Permanently Abandoned Fertilizer Subsidies
Figure 7: Overview of production shock scenarios
5. Scenario and Resilience Analysis
Using the final value of the integral between the scenario and base run ADESM as a metric,
we can evaluate the relative resilience of the value chain towards the different production
shock scenarios. In order to keep the analysis as concise and informative as possible, I will
group the scenarios according to the nature of the shock scenario and evaluate the
resilience of the value chain to the different types of shock scenarios.
23
5.1 Exchange Rate Shock Scenarios (No. 1-2)
While currency shocks do have a significant impact on the ADESM, their accumulated effect
is less pronounced in the medium to long term compared to the fertilizer subsidy and
drought scenarios. In terms of our methodological framework, the initial vulnerability is not
very high, but the permanence of the change undermines the adaptive capacity, so that in
the third year, the maize buffer stocks (i.e. stocks maize stocks that are carried over from
one year to the next to act as a security buffer in case of a shock) are depleted and the
structural maize (production to demand) deficit that is growing bigger over the years,
cannot be compensated any more. The ADESM therefore then breaks down to zero in 2017
(months 156 - 167) and the integral surges up.
® 1: Integral between ADESM base and scenario run
Vi 54
|
ia
1 2,54
de"
33,00 150,75 168,50 186,25 204,01
Page 1 Months
2 Integral between ADESM for base and scenario run
Figure 8: ADESM integral for scenario 1
However, due to the dynamic market response I assume, the producers compensate for the
more expensive fertilizer imports by raising domestic production. This leads to decreasing
marginal yearly impacts of the changed exchange rate value on the ADESM, as can be see by
the decreasing growth of the integral. The adaptive capacity thus becomes stronger again
over time.
24
5.2 Flood Loss Scenarios (No. 3-6)
The maize value chain in Zambia is quite resilient towards flood losses of cultivated area, as
the relatively small maximal value of 1,88 for the impact of the flood scenarios shows. As
can be seen by comparing scenarios 4 (one-year flood) and 6 (two-year flood), the adverse
effects on the food supply rise exponentially when floods occur in two consecutive years:
one year with 20% area loss has an effect of only 0,45, while the same event occurring in
two consecutive years has an effect of 1,95. The impact is thus more than four times as high
when the same flood loss shock is repeated in a consecutive year.
To investigate the reasons for this increasing impact, it we need to look at the change of
maize supply over the flood years (2015-16). Maize can be supplied to consumers either
from fresh production of the current year or from carryover stocks that were accumulated
over the last years. Comparing the graphs for SC 6 and the base run in figure 9, we can see
that the difference between yearly maize production in both actually becomes smaller in
the second shock year of 2016. Changes in the production for the current year can therefore
not explain the increasing impact.
Total yearly maize production under 20% flood loss
2.050
2.000 |
1.950 4
1.900 >
1.850 —
1.800
1.750 / f ==="2-yr flood (SC 6)
1.700
1.650 VA J
| <a
1.600 T T T
2015 2016 2017 2018 2019 2020
==Base Run
== 1-yr flood (SC 4)
Thousand tons of maize
Figure 9: Total yearly maize production in scenario 4
The answer can be found in the difference of the total maize that is stored throughout the
value chain: for this parameter, the difference between scenario 6 and the base run
25
becomes much bigger in 2016, as buffer stocks had to be used up in order to maintain a
sufficient supply in 2015 (see figure 9).
Maize stored in value chain at the end of february
1.200 \
\
800 Run
== 1-yr flood (SC 4)
Thousand tons of maize
== 2-yr flood (SC 6)
200
eee
2015 2016 2017 2018 2019 2020
Figure 10: Total maize stored in value chain at the end of February in scenario 4
Since 2016 is a year with just enough production to prevent the ADESM from dropping to
zero, stored maize stocks are consumed and reach virtually zero at the beginning of the
2017 marketing season. 2017, however, is a year with an even worse supply-to-demand
ratio where buffer stocks would be needed even more. As these are now depleted, the low
production can - other than in the base run and scenario 4, which feature enough buffer
stocks, not be offset and the ADESM drops to zero, as can be seen in figure 11a.
B® 1: ADESM Base Run Reference: ‘2: ADESM @ 1: Integral between ADESM base and scenario run
1 1 i y nT 1 #
; VV. ie
2. Ina,
3 o. | 1 1
1
A | : : : 1 a ;
133,00 15075 768.50 1ee25 204,00 733,00, T5075 Toe50 Te025 2040
age 1 Months Page 1 Months
2 Comparison ADESM scenario run to base run 2 Integral between ADESM for base and scenario run
‘igure 11a: ADESM comparison scenario 4 Figure 11b: ADESM Integral scenario 4
26
This drop of course leads to a strong increase in the integral between the scenario and base
run ADESM (cf. figure 11b), therefore explaining the big difference between the two- and
one-year flood scenarios.
These observations lead to an interesting conclusion: the initial vulnerability of the value
chain to the flood-induced area losses is not that high, but there is a certain threshold of
time with consecutive shocks, after which the system becomes very vulnerable to any
further perturbation in the production due to the depletion of buffer stocks. We can thus
attribute the resilience properties towards production shocks to two main factors: the
change in yearly production itself, and the ability to buffer the effects of production shocks
through carryover maize stocks from the preceding years.
The behaviour observed and described in the last paragraphs can be generalized across all
the scenarios simulated: while differences between the base run and scenario ADESM
normally are greatest in the years of the actual shock events, there usually is a lasting
adverse effect buffer maize stocks. Looking at these stocks and the current production is the
key to understanding the development of our resilience indicators.
The reader should note that in some scenarios, production lags behind in the years
following the shock by a small margin, e.g. the drought scenarios; while in other scenarios
yearly production actually overtakes the base run reference production due to a
compensation response. The latter is the case for the flood loss scenarios. However, these
responses are caused by dynamics in the production sector, are thus external and I
therefore will not expand on this topic.
While there is not much that actors in the value chain can do to change the production
output of maize, the finding about the buffer stocks is interesting in terms of my research
question of how resilience properties can be enhanced. If it was possible to accumulate
higher buffer stocks in the value chain, the impact of shock events could be mitigated and
the resilience properties thereby ameliorated. This will be discussed in more detail in
section 6.2.
27
5.3 Drought Scenarios (No. 7 — 14)
Drought scenarios have the highest impact of all the single-shock scenarios. In the case of
three consecutive extreme droughts in scenario 7, the final integral value of 20,08 shows a
substantial impact, which amounts to more than a third of the integral value that a complete
loss of supply would cause. We can thus conclude that the value chain is very vulnerable to
drought scenarios, mainly because the adverse effects of droughts on maize production are
very substantial compared to other scenarios.
Marginal impact on ADESM Integral per year of consecutive
extreme drought
Consecutive drought years
Figure 12: Marginal impact of consecutive extreme drought years on ADESM integral
Just like the flood scenarios, drought scenarios show an increasing marginal yearly impact
on our resilience indicator. This is due to the same reasons as discussed for the flood
scenarios, namely the progressively depleted buffer stocks. However, since the overall loss
in yearly production is much higher in these scenarios, the effect of change in current
production is so great that the effect of the buffer stock development is relatively less
important. This can be seen by the small relative growth in marginal impact compared to
the flood loss scenarios, displayed in figure 12.
5.4 Fertilizer Subsidy Scenarios (No. 15 -17)
The fertilizer subsidy shock scenarios are different from the other classes of shocks, as the
system faces a permanent change without a built-in compensation response like in the
exchange rate shock scenarios. Subsidies are cut in half (scenario 16), or abandoned
completely (scenario 17) in 2015 and then stay that way all through to 2020. This leads to
production constantly being around 6,5% lower every year compared to the base run in
28
scenario 16 and around 19,5% lower in scenario 17 throughout all six years. Looking at the
graphs for scenario 16, we can see how this translates into changing our resilience
indicators.
@ 1: ADESM Base Run Reference 2: ADESM 3: Total maize on store
io 1
2000000 a
al 1
od
1000000
6 J N\
33,00 150,75 168,50 186,25 204,
Page 1 Months
2 Comparison ADESM scenario run to base run
Figure 13: Comparison of ADESM and maize stocks scenario 16
# 1: integral betwe...e and scenario run 2: Yearly non subsistence demand 3: Yearly SH surplus production
54
1600000
2,8
e000] 7
4 |
Of
33,00 150,75 168,50 186,25 204,0
Page 1 Months
2 Integral between ADESM for base and scenario run
Figure 14: Development of ADESM integral against yearly demand/production scenario 16
29
The logic behind the behaviour of the resilience indicators is similar to the one explained in
the preceding sections: there is only a relatively small excess original demand? (i.e. demand
exceeding production) in 2015, so that the production deficit can be buffered by the
consumption of carryover stocks. The size of the carryover stocks is represented by the
local minima of the purple line in figure 13. In 2016, however, the difference between
production and original demand rises and the buffer stocks are now lower than the year
before. The growing gap in 2016 cannot be redeemed by consuming the already reduced
buffer stocks and the ADESM drops to zero later in the 2016-17 marketing year. The
permanently low production and the rising population lead to an ever-growing gap in
demand vs. production that does not allow carryover stocks to be built up. This leads to the
breakdowns in ADESM becoming progressively bigger in every consecutive year’s lean
season. The only reason why the growth of the integral slows down in 2019-20 (months
181-204) is that the base run also performs worse over time.
While the initial vulnerability is quite low, as indicated by the shallow initial growth of the
integral, due to the permanence of the effect, the adaptive capacity of the system is
undermined as buffer stocks are depleted. The shock effects therefore accumulate to a
significant level in the long run. If the shocks were only to occur in one or two consecutive
years, the impact on the ADESM would be comparatively small, probably comparable to
what we have seen for the flood loss scenarios. I therefore conclude that resilience of the
value chain towards shocks in the fertilizer subsidies is relatively high compared to other
shock types when they feature the same number of impact years.
Before moving on to discuss the combined scenarios, | would like to draw the reader's
attention to a phenomenon that is important in understanding the resilience analysis. There
is effectively a “threshold” behaviour for the ADESM in my model: since there are only
effectively two compensation mechanisms in terms of demand adjustment when maize
becomes scarce (eating less per day and changing to other carbohydrate sources), the
ADESM either stays at 0,84 where both mechanisms are at play and the consumption is
sufficiently reduced to not exceed supply - or it collapses to zero very quickly as all maize
stores in the value chain are depleted. Whenever this sometimes-fine threshold is crossed
3 » Original demand“ refers to the demand before it is adjusted for dynamic consumer responses to scarcity
30
and the ADESM thus falls to zero in the scenario run, but just manages to stay at around
0,84 in the base run, the integral surges up.
5.5 Combined Scenarios (No. 18-22)
The combined scenarios have - except for scenario 18 - a very strong impact on the
ADESM. The impact of the combined scenarios reflects what we have found out about the
resilience of the value chain to the different single-shock scenarios: the lower the resilience
of the value chain is to the single shocks that make up the combined scenario, the greater is
the impact of the combined scenario as well. The underlying dynamics of the translation of
production shocks to changes in the ADESM are essentially the same as described in the
preceding sections and | will thus not go into detail about them again.
An interesting observation is that the combined scenarios have a lower impact on the
ADESM than the sum of the two single-shock scenarios. For example, the 3-year extreme
drought leads to a final value of the integral of 20,08 and the abandonment of fertilizer
subsidies to an integral of 13,88. Yet, the impact of the combined shock scenario 22,
featuring both of these developments, does not amount to an integral value of 33,96, but
instead only 27,41. The reason for this is that the production sector shows a decreasing
marginal impact on yearly maize production when shocks are added up.
5.6 Conclusions Resilience and Scenario Analysis
To close this part of my analysis, | want to sum up the most central findings from this
chapter:
* The value chain is quite resilient towards flood events causing loss of cultivated
area, as well as towards exchange rate shocks.
¢ The value chain is moderately resilient towards fertilizer subsidy shocks. The
moderately strong effect of these scenarios is mostly attributable to the permanence
of the change. The effect can be expected to be rather small when assuming that the
shock only lasts one or two seasons, as the initial vulnerability of the value chain
towards fertilizer subsidy shocks was shown to be low.
* The value chain is vulnerable towards a prolonged drought. While a drought
lasting only one year still has only limited impact and its effects on the ADESM can
31
be mitigated through the consumption of carryover stocks, already a second
consecutive medium to extreme drought year depletes the buffer stocks and unfolds
increasingly strong impacts on the maize supply.
Even though there is a decreasing impact on the ADESM when combining two
shocks, the value chain is generally very vulnerable towards a combination of shocks
hitting it simultaneously or consecutively.
In general, the resilience of the value chain towards a one-time shock (only
occurring in one production season) is quite good and it exhibits a low initial
vulnerability. However, as soon as it is faced with consecutive shocks, the adaptive
capacity quickly wears off as buffer stocks are soon depleted after one or maximum
two years, and the impacts on the ADESM become very significant.
There are two main determinants for the effect that a shock has on the ADESM in the
value chain: the change in the current year’s production, and the availability of
carryover stocks that can act as a buffer. The policy analysis in the next chapter will
therefore focus on how buffer stocks can be used to enhance the resilience
properties of the value chain.
5.7 Impacts of Model Structure on Resilience Properties
Trying to keep the analysis of the different resilience responses of the model as concise as
possible, I focused on the most important impact factors that actually change in between
the scenarios and therefore explain the differences observed. These are, as we learned in
the preceding sections, the current year’s maize production and buffer stocks. The model
structure itself did not change across the scenarios, wherefore | did not explicitly mention
its effects on the ADESM in the previous sections.
However, the feedback structure of the value chain model naturally has a significant
influence on the results of the resilience analysis. Running different “structural scenarios”
by turning switches and loops on and off revealed the following:
The demand spill-over loop (C2) represents a very strong mechanism to cope with
food insecurity. The fact that consumers change to other crops, as well as the fact
that informal consumers buy around 20% less maize meal (due to higher prices)
than grain when being forced to change does, significantly lowers overall demand.
32
This reduces pressure on the maize market and leads to longer coverage of demand
with existing maize volumes. The importance of this mechanism can be seen by the
fact that the ADESM integral for scenario 16 would rise by 232% if one deactivated
this loop.
Less important, but still significant is loop C1 that represents how consumers in the
informal value chain reduce their demand in times of dwindling grain supply / rising
prices. By reducing demand, this loops has similarly beneficial effects on the ADESM
as loop C2. Even though the lowered consumption leads to an ADESM of around 0,84
instead of 1 (and thus a rising integral), the fact that consumers can eat maize at a
reduced level for longer before the supply breaks down completely leads to a
smaller overall integral - meaning that resilience is higher due to the effects of C1.
Another important coping mechanism that lowers the ADESM integral for a given
shock scenario is loop C3: FRA’s decision to offload buffer stocks in years of maize
deficit helps to stabilize the maize supply and thus food security. However, the
strength of this loop’s impact depends on the size of FRA’s reserves: i.e. if the
preceding year’s maize production was so bad already that FRA offloaded most of its
reserves, very little impact can be achieved through this mechanism.
6. Policy Analysis
After having evaluated the resilience properties of the value chain in chapter 5, in the
following section I want to explore how the can endogenously be improved. With results
showing that buffer maize stocks play a crucial role in determining the resilience of the
value chain towards production shocks, I designed policy interventions that aim to promote
the creation of those stocks.
6.1 Policies Under Base Run Assumptions
The policy I want to test is rather straightforward and relies on existing structures that are
already well established. Namely, I want to see what happened if FRA would try to fulfil its
original mandate: increasing food security for Zambians by buying and keeping strategic
maize reserves as buffer stocks. The stocks would only be released in times of maize
33
deficits. To test the effect of such storage policies under different circumstances, I simulated
them in a low-impact scenario with permanent change (scenario 16), as well as a high-
impact scenario featuring a shock of limited duration (scenario 8).
The results show that in an environment of consecutive structural maize deficit years, it
does simply not make sense to accumulate maize stocks in one year and release it in
another, since all one achieves with that policy is to improve the food security situation in
one year by worsening it in another. This policy has no significant positive effect on the
resilience metrics and is furthermore hardly economically feasible: it is hard to imagine that
people would be okay with taking maize out of the market in a deficit year for the sake of
storing for eventual future use in worse years.
6.2 Policies Under Changed Scenario Assumptions
Having thus established that policies relying on storing domestically produced maize are
not promising in an environment of constant structural maize deficits, | want to explore the
effects of storage policies in an environment with occasional bumper harvests - which, as
we know from historical data, do regularly occur in Zambia (cf. appendix A.1).
To investigate the effects of the policy, I will use a new scenario (No. 23), which features
base run production in 2015-16, then a high production year in 2017, followed by two
shock years in 2018-19 and base run production in 2020 again.
The policy that I want to test is for FRA to accumulate large buffer stocks in years with
bumper harvests and lock up the excess production (the amount of yearly production that
exceeds yearly demand) in their storages with the intention to keep these stocks constant at
that level, unless they need to release it in case of emergency. An emergency is defined as a
time when the ADESM would fall below a value of 0,8 without policy intervention.
In the case of scenario 23, this policy will cause FRA to keep 515.000 tons of their purchases
in the bumper harvest year of 2017 as a strategic reserve, and then release 390.000 tons in
the first shock year of 2018 to keep the ADESM over 0,8. This leaves them with 125.000
tons to spend in the second year. The uneven distribution over the two drought years is due
to the assumption that they cannot foresee the second drought coming and need to keep the
ADESM from collapsing to less than 0,8 in the first year. This assumption seems credible, as
34
it would hardly be justifiable for FRA to not release these emergency relief stocks in the
first drought year, just by pointing at the vague possibility of second shock coming up next
year. Running the simulation with and without the policy intervention, we get the following
results for our resilience indicator:
@ 1: integral ADESM with policy 2: ADESM Integral without policy
4 8,405
3 f
y
y 4.204
Ke
4 F
2: 0,00 fmm 1
33,00 15075 768,50 Tees 204.0
age 1 Months
5 Integral between ADESM for base and scenario run
Figure 15: Integral between ADESM for base and scenario run in scenario 23
Looking at figure 15, we can see that the ADESM performs significantly better when the
policy is in place. The “no policy” run of the scenario featured a final integral between the
scenario ADESM and the base run ADESM of 8,39 - while the integral in the “with policy”
run only amounted 5,56. This is a reduction by more than one third. Note that the total
production over the years is exactly the same in both runs; the only change is the policy of
FRA to store bigger amounts of maize in surplus years and not export the excess maize at
the end of the respective surplus year. We can thus conclude that an intelligent storage
policy by FRA that exploits the frequent occurrence of surplus harvest years can
significantly enhance the resilience of the value chain to production shocks - without any
exogenous help or inputs from outside Zambia.
6.3 Feasibility of the Proposed Policy
To conclude the policy analysis part, I want to address the feasibility of the proposed policy.
While I have argued that it is neither desirable nor politically feasible to accumulate maize
buffer stocks in years of structural maize deficit, the policy proposed and tested in section
6.2 appears to be useful and feasible, as I will show in this section. Possible frictions that
35
might hinder the implementation of the proposed policy can arise from political opposition,
storage capacity problems leading to high losses, and funding shortfalls for FRA. I will
discuss these problems in turn below.
Looking at political pressures that always have a big influence on the decisions taken in
politically controlled organizations like FRA, I can see no reason why an accumulation of
excess maize in surplus years should trigger political resistance or public outrage.
Loss of maize in FRA storages is actually a very valid concern when trying to implement a
policy that requires storing large amounts of maize for long times, potentially over years.
While FRA does possess large shed capacities, maize stored in sheds is subject to excessive
losses after just a few months of residence time: after one year, we can expect a loss ratio of
more than 50% and after 1,5 years even more than 80% (cf. appendix C.3). However, Bou
Schreiber (2015) expects FRA to keep on increasing their silo construction so that by the
end of 2019, they will have silo capacities of nearly 250.000 tons. This means that a great
portion of the maize can be stored in a way that produces almost no significant losses (less
than 3% even after 1,5 years of storage time). Furthermore, if FRA keeps up a steady flow
of maize through their storage by mixing and selling maize from last year while stocking up
fresh maize, they can limit the residence time and thus the loss ratio to reasonable amounts,
even in the sheds. | therefore believe that the storage loss problem can be adequately
addressed and will ultimately not hinder the implementation of the proposed policy.
The biggest threat for implementation is in my opinion the fact that FRA would need steady
and significant funding over a long time in order to properly execute the storage policy
proposed. Funding for FRA has been fluctuating quite a lot over the decades and was
always subject to often-arbitrary discretionary political decisions (N. Mason, 2011).
Furthermore, funding a storage programme will not immediately bring benefits that can be
presented to the electorate and there are opportunity costs of allocating funds to the
proposed policy, since that money then cannot be used for other, maybe more popular
programmes like consumer price subsidies or other poverty reduction measures. I
therefore see a real danger that policymakers might, especially in pre-election periods, re-
allocate the funds from FRA’s buffer stock programme to other measures that reap instant
4 For details on the storage loss ratios, see appendix C.3
36
benefits for the population. However, in the end the funding decisions depend on the
government’s will to follow through with a policy and it therefore can work if there is
sufficient political will to do it.
6.4 Change in FRA’s Sales Policy
Another change in policy I strongly want to suggest is that FRA should start selling maize to
grain retailers supplying the informal value chain. The current policy of just selling to big
commercial millers actually “locks up” maize in the formal value chain, which is eventually
often either exported under unfavourable terms or lost in inappropriate FRA storages,
while customers in the informal value chain at the same time cannot satisfy their demand
for cheap grain and have to reduce consumption. This obviously inefficient policy leads to
the ADESM taking on a value of around 0,84 instead of 1 even in surplus years, as can be
seen for the years 2010-12 (months 73 - 96) in figure 16.
This problem can easily be avoided by a simple policy change requiring FRA to open their
sales to grain retailers as well. This policy would actually help them fulfil their original
mandate - increasing food security for the Zambian population as a whole - much better
and more efficient. To illustrate the effects of this policy, I simulated the ADESM for the
months 73-96 with and without the proposed FRA sales policy. The results in figure 16
show that the performance of the ADESM would have been enhanced significantly.
@ 1: ADESM Base Run Reference 2: ADESM with new FRA sales policy
ere 2 Teo
4
at 0,504
:
a °,
3,00 79,00 85,00 91,00 97.0
Page 1 Months
2 Comparison ADESM scenario run to base run
Figure 16: Comparison ADESM development of old and proposed FRA sales policy
37
Concerning feasibility of this policy change, I do not see any big obstacles to
implementation. Since FRA has maize storages all over the country, FRA officials could just
go there during a number of fixed sales days and administer the exchange of maize against
money - much like they do when they buy maize grain from smallholders, just the other
way around. Moreover, such a policy change can be expected to be popular in the electorate,
as helps the majority of consumers to gain better access to their preferred form of maize.
7. Conclusion
7.1 Overview of Results
The general result was that the resilience of the value chain towards one-year shocks is
quite good, as the ADESM exhibits a low initial vulnerability due to the existence of buffer
stocks in the chain that can be consumed as a substitute for lacking fresh production.
However, as soon as the value chain is faced with several consecutive shocks, resilience is
low: the adaptive capacity quickly wears off as buffer stocks are soon depleted after 1-2
years, and the food supply breaks down. We furthermore found that there are two main
determinants for the effect that a shock has on the ADESM in the value chain: the change in
the current year’s production, and the availability of carryover stocks that can act as a
buffer.
Yet, as discussed in chapter 2, resilience can only be understood as resilience towards a
specific shock, and we therefore needed to disaggregate the results into the different types
of shocks. In doing so, we learned that the value chain is quite resilient towards exchange
rate shocks due to the dynamic response of the production system assumed, as well as
towards flood events causing a loss of cultivated area. In the case of shocks affecting the
fertilizer subsidies, the value chain is rather vulnerable when these changes become
permanent, but can be expected to show a relatively small vulnerability if the changes only
last 1-2 years.
However, the value chain is very vulnerable towards a prolonged drought. While a one-year
drought still has only limited impact and its effects on the ADESM and can be mitigated
38
through the consumption of carryover stocks; already a second consecutive medium to
extreme drought year depletes the buffer stocks and leads to increasingly strong impacts on
the maize supply. Finally, in the case of two different types of shocks hitting the value chain
simultaneously or consecutively, resilience has proven to be low. The adaptive capacities in
the form of buffer stocks are insufficient to alleviate the effects on the ADESM and the food
supply quickly falls to threateningly low levels - even though the marginal impact of a given
shock on the maize production decreases as shocks accumulate in a combined scenario.
Concerning the relation between the model’s structure and the resilience exhibited towards
production shocks by the value chain, we found out that the two consumption adjustment
loops have an especially strong impact on the resilience properties due to their direct
influence on the computation of the ADESM via changes in demand. The reduction of
demand in response to the changing availability of grain in the informal value chain, as well
as the behaviour of informal consumers to change to other crops and roller meal when
supply in the informal value chain dries up, both significantly improve the resilience in
response to a given shock. These coping mechanisms reduce demand for a given supply and
therefore improve the ratio that determines the ADESM.
Moreover, the non-FRA smallholder sales switch and the information feedback structure of
the informal and formal value chain were shown to affect the distribution of maize between
the two value chains. And since consumers respond differently to supply changes in the
informal value chain due to the two consumption adjustment loops, this distribution in turn
affects the ADESM. Furthermore, the FRA reserves switch structure and the feedback
structure of the value chain determine which actors build up how much storage stocks
throughout the value chain - which in turn influences the extent of buffer stocks available
to mitigate the impact of a given shock on the ADESM.
Analysing policies that can improve the resilience properties, we learned that the key to
endogenously improve performance was the creation and maintenance of buffer stocks.
However, it became clear that building up carryover stocks in an environment of
permanent structural maize deficits was neither desirable in terms of its effect on the
resilience metrics, nor politically feasible. Yet, | showed that a smart storage policy, using
FRA’s infrastructure and exploiting the frequent occurrence of surplus production years,
39
could significantly improve the resilience towards production shocks. Furthermore, the
feasibility of such a policy seemed promising under a few conditions, which were that FRA
keeps expanding its silo capacity as predicted by Bou Schreiber (2015), keeps a steady flow
of maize through its storages and that the storage programme is backed up by sufficient
political will. Lastly, I showed that the often inefficient distribution of maize in between the
formal and informal value chain, which can lead to supply shortages even in bumper
harvest years, could easily be remedied if FRA changed its sales policy in a way that also
allowed sales of maize into the informal value chain.
7.2 Discussion of Methodological Framework
Apart from the main goal of yielding insights about the structure, dynamics and resilience
properties of the maize value chain in Zambia, my work also served as a test for the
usefulness of my framework for quantified measurement of resilience in an SD simulation
model. I therefore shortly want to evaluate how the framework has performed.
Comparing using this framework to the “usual ways” of analysing the behaviour of SD
models by graphically comparing the development of a host of variables, I feel that the firm
focus on one metric helped to get a much clearer picture of the value chain’s capacity to
maintain a sufficient food supply in response to the different production shocks. Using the
ADESM integral as a metric, we received a fine relative scale that allowed comparing the
strengths of the impact between the different scenarios more precisely. Moreover, the
distinction between initial vulnerability and adaptive capacity helped to add further clarity
to the discussion of the shock responses.
The major drawback of using this method is probably the lack of an absolute scale - the
values of the integral only make sense in relation to each other and cannot be compared to
some form of general metric. The next step in developing a System Dynamics resilience
measurement framework would be to define an upper bound of a shock’s effect on the
integral of the FOM and compute the respective actual shock’s magnitude as a ratio of that.
This would allow the comparison of different system’s resilience towards a given shock.
40
7.3 Limitations and Areas for Further Work
Maize alone, as overwhelmingly important as it is for the food supply in Zambia, does not
determine the food security situation on its own. Even though agricultural productivity is
probably correlated between different crops, as their yields are determined by similar
parameters, one can imagine a year with a bad maize harvest and a good harvest for other
crops that may act as a substitute. In that case, a low ADESM for maize might not be so
much of a problem, as consumers could relatively easy change to other food sources. To
reflect the situation in Zambia more holistically, it would therefore be necessary to model
the value chains for other crops as well - something I unfortunately did not (yet) have the
time and resources to do. However, the literature I consulted suggested that the
distribution channels for other important crops in Zambia are structured in a similar way to
the maize value chain, so that future research could build on the basic model structure that
I carved out for maize, and adapt it to represent the value chains for other crops.
Another interesting avenue to expand this work would be to investigate the access
dimension in the model in greater detail. However, this would most probably require to
explicitly model prices. Since there is hardly enough comprehensive information about
prices at the different stages of the value chain, as well as their seasonal fluctuations that
drive the demand dynamics, further work in that direction would require field research.
Having only investigated the effects of production shocks, it would be interesting to also
look at the resilience of the value chain towards energy and transportation shocks. The
latter would require including spatial dimensions into the model, as the impact of shocks
affecting the transportation capacity of a given physical flow in the model would depend on
the distances covered in that link. A way to go about this could be to compute averages for
the distances maize typically travels from stage A to stage B in the value chain. This average
could then be used to model the degree of impact that the shocks disturbing the
transportation capacity of the flow would unfold. The means of transportation that are
typically used in that flow would probably also have to be accounted for in such an effect
variable. However, I did not find appropriate information about this in the secondary data
or literature, so that researchers looking at this phenomenon would probably need to go to
Zambia for first-hand data collection.
41
Bibliography
Barlas, Y. (1996). Formal aspects of model validity and validation in system dynamics. System
Dynamics Review, 12(3), 183-210. doi:10.1002/(SICI)1099-1727(199623)12:3<183::AID-
SDR103>3.0.CO;2-4
Bertelsmann Transformation Index. (2012). Zambia Country Report. Gitersloh.
Bou Schreiber, E. E. (2015). Maize Losses During Storage: A System Dynamics approach to the
Food Reserve Agency Case in Zambia. University of Bergen.
Carpenter, S. R., & Brock, W. a. (2008). Adaptive capacity and traps. Ecology and Society, 13(2).
doi:40
Chapoto, A., Chisanga, B., Kuteya, A., & Kabwe, S. (2015). Bumper Harvests a Curse or a
Blessing for Zambia : Lessons from the 2014 / 15 Maize Marketing Season, (93).
CSO Zambia. (2015). Database of the Central Statistical Office. Retrieved from
http://zambia.africadata.org
Dalziell, E. P., & Mcmanus, S. T. (2004). Resilience, Vulnerability, and Adaptive Capacity:
Implications for System Performance. Jnternational Forum for Engineering Decision
Making, 17. Retrieved from http://ir.canterbury.ac.nz/handle/10092/2809
Dorosh, P. a., Dradri, S., & Haggblade, S. (2009). Regional trade, government policy and food
security: Recent evidence from Zambia. Food Policy, 34(4), 350-366.
doi:10.1016/j.foodpol.2009.02.001
FAO. (2014). Food security Indicators. Rome. Retrieved from http://bit.ly/l4FRxGV
FAO. (2015a). FAOSTAT Databse. Retrieved from http://faostat3.fao.org/home/E
FAO. (2015b). Food Balance Sheet Zambia. Retrieved March 30, 2015, from
http://faostat.fao.org/site/368/DesktopDefault.aspx?PageID=368#ancor
Gerber, A. (2015). Agricultural Theory in System Dynamics. In Proceedings of the 33rd
International Conference of the System Dynamics Society. Cambridge, Mass.
Henry, D., & Emmanuel Ramirez-Marquez, J. (2012). Generic metrics and quantitative
approaches for system resilience as a function of time. Reliability Engineering & System
Safety, 99, 114-122. doi:10.1016/j.ress.2011.09.002
42
Hodges, R. (AHPLIS), & Bernard, M. (2014). APHLIS — Postharvest cereal losses in Sub-
Saharan Africa , their estimation , assessment and reduction. doi:10.2788/19582
Janssen, M., & Anderies, J. (2013). A multi-method approach to study robustness of social—
ecological systems: the case of small-scale irrigation systems. Journal of Institutional ....
Retrieved from http://journals.cambridge.org/abstract_S1744137413000180
Jayne, T. S., & Jones, S. (1997). Food marketing and pricing policy in Eastern and Southern
Africa: A survey. World Development, 25(9), 1505-1527. doi:10.1016/S0305-
750X(97)00049-1
Jayne, T. S., Mason, N., Myers, R., Ferris, J., Mather, D., Lenski, N., ... Boughton, D. (2009).
Patterns and Trends in Food Staple Markets in Eastern and Southern Africa: Toward the
Identification of Priority Investments and Strategies for Developing Markets and Promoting
Smatlholder Productivity Growth.
Kaplinsky, R., & Morris, Mi. (2001). A Handbook for Value Chain Research. Brighton.
Kuteya, A., Sitko, N., & Inn, K. (2014). Review of the effects of FRA on Zambia ’ s maize
market : High prices despite bumper harvests.
Leathers, H. 209); Transaction Costs Analysis of Maize and Cotton Marketing in Zambia and
Te inable Devel Publication Series. College Park, Maryland.
Mason, N. (2011). Marketing Boards, Fertilizer Subsidies, Prices, & Smallholder Behaviour:
Modelling & Policy Implications For Zambia. Michigan State University.
Mason, N., & Jayne, T. S. (2009). Staple Food Consumption Patterns in Urban Zambia: Results
from the 2007/2008 Urban Consumption Survey (Vol. 2009). Lusaka.
Mason, N. M., & Myers, R. J. (2011). The Effects of the Food Reserve Agency on Maize Market
Prices in Zambia (No. 60) (Vol. 2011). Lusaka.
Nyanga, P. H. (2015a). Personally Interviewed by Conrad Steinhilber, 18.05. Bergen.
Nyanga, P. H. (2015b). Personally Interviewed by Conrad Steinhilber, 27.04. Bergen.
Olsson, L., Jerneck, A., Thoren, H., Persson, J., & O’Byrne, D. (2015). Why resilience is
unappealing to social science: Theoretical and empirical investigations of the scientific use
of resilience. Science Advances, 1(4), €1400217—e1400217. doi:10.1126/sciadv.1400217
Stockholm Resilience Centre. (2012). Applying Resilience Thinking. Seven Principles for
Building Resilience in Social-Ecological Systems. Retrieved from
43
http://www.stockholmresilience.org/download/18.10119fe11455d3c557d6928/13981507997
90/SRC+Applying+Resilience+final.pdf
UNDESA. (2012). World Population Prospects: The 2012 Revision. Retrieved from
http://esa.un.org/wpp/unpp/panel_indicators.htm
UNDP. (2014). Human Development Report 2014. Sustaining Human Progress: Reducing
Vulnerabilities and Building Resilience. New York City.
Zulu, B., Jayne, T. S., & Beaver, M. (2007). Smallholder Household Maize Production and
Marketing Behaviour in Zambia and its Implications for Policy (Vol. 2007). Lusaka.
44
APPENDIX
A.1: Maize Production Data
Maize production in metric tons
Total maize Commercial Smallholder Smallholder Smallholder
production farmers total production surplus subsistence
2004 1213599 48579 1165021 343878 821143
2005 866187 65613 800574 178803 621771
2006 1424439 84960 1339479 564348 775131
2007 1366158 97099 1269059 647863 621196
2008 1211566 85578 1125988 522033 603955
2009 1887010 229893 1657117 613356 1043761
2010 2795483 331960 2463523 1062010 1401513
2011 3020380 233484 2786896 1663043 1123853
2012 2852687 220521 2632166 1362812 1269354
2013 2532800 195793 2337008 1215244 1121764
2014 3350671 259016 3091655 1550346 1541309
2015 1821490 66219 1755271 761513 993758
2016 1823090 64551 1758539 742338 1016201
2017 1843830 64258 1779572 738971 1040601
2018 1877020 64792 1812228 745113 1067115
2019 1917240 65768 1851472 756334 1095138
2020 1961300 66966 1894334 770112 1124222
Sources for maize production data:
¢ All data projections from 2015-2020 are based on simulations from Gerber (2015)
¢ Total maize production:
o 2004 - 2013: CSO Zambia (2015: data sheet "maize production timeline")
o 2014: Chapoto et al. (2015)
¢ Total smallholder production:
o 2004-2011: N. M. Mason & Myers (2011)
o 2012 - 2014: Triangulated from total maize production assuming a steady
relation between commercial and smallholder production
¢ Smallholder surplus:
o 2004-2011: N. M. Mason & Myers (2011)
o 2012-2014: Triangulated from other sources as: Smallholder surplus
= total maize traded (Kuteya, Sitko, & Inn, 2014) - commercial production
* Commercial farmers:
o 2004 - 2011: Triangulated from other sources as:
Commercial production = total production - total smallholder production
o 2012-2014:
Triangulated from total maize production assuming a steady relation
between commercial and smallholder production
¢ Smallholder subsistence:
o 2004 - 2012: Triangulated from other sources as:
SH subsistence = total smallholder production - smallholder surplus
o 2013: Triangulated from smallholder production assuming ratio of
subsistence consumption staying steady for two years.
o 2014: Triangulated from smallholder production with information about
subsistence ratio from (Chapoto, Chisanga, Kuteya, & Kabwe, 2015)
A.2: FRA Data
F.R.A. Parameter in metric tons
Yearly purchase Desiredreserves Shedcapacity Silocapacity Imports
2004 105279 0 539200 55720 9400
2005 78667 0 567020 55720 38950
2006 389510 0 566430 55270 119700
2007 396450 0 566410 55060 1458
2008 73876 0 566460 54960 1015
2009 198630 0 566480 54930 42027
2010 878570 131786 566480 54930 5704
2011 1579891 236984 590950 57370 290%:
2012 1044998 156750 652990 63540 0
2013 422391 63359 737320 71920 10)
2014 1031303 154695 833910 82460 0
2015 380757 76151 932780 99130 10)
2016 371169 74234 1026640 125610 0
2017 369485 73897 1111110 164140 0
2018 372556 74511 1179570 212660 0
2019 378167 75633 1204760 244410 10)
2020 385056 77011 1211838 248011 0
Sources for FRA Data:
¢ Yearly purchase:
2004 - 2010: N. M. Mason & Myers (2011)
2011 - 2013: Kuteya et al. (2014)
2014: Chapoto et al. (2015)
2015 - 2020: Assuming FRA wants to purchase 50% of smallholder surplus
oo 0 0
production
¢ Desired reserves:
Derived from the yearly purchase under the assumption that FRA wants to keep
20% of their yearly purchase as reserves.
¢ Silo capacity:
Bou Schreiber (2015)
¢ Shed capacity:
Bou Schreiber (2015)
¢ Imports:
o 2004-2014: FAO (2015a: Timeline under "Trade -> Crops & livestock ->
Zambia")
o 2015 - 2020: Assuming no imports take place
A.3: Storage Loss Data
Ac lated storage Loss in per cent
Residence Silo Loss Shed Loss Slab Loss
time (months)
1 0,00% 0,72% 5,72%
Z 0,28% 1,61% 6,61%
3 0,81% 1,83% 6,83%
4 0,82% 4,47% 9,47%
5 0,83% 6,50% 11,50%
6 1,36% 10,12% 15,12%
Z 1,66% 14,97% 19,97%
8 1,86% 20,61% 25,61%
9 2,04% 26,86% 31,86%
10 2,20% 34,09% 39,09%
11 2,34% 42,22% 47,22%
12 2,46% 51,23% 56,23%
13 2,56% 61,14% 66,14%
14 2,64% 71,93% 76,93%
15 2,70% 83,62% 88,62%
16 2,74% 83,62% 88,62%
17 2,76% 83,62% 88,62%
18 2,76% 83,62% 88,62%
Sources for storage loss data:
¢ All data from Bou Schreiber (2015)
A.4 Roller Meal to Consumer Made Hammer Meal Price Relation Data
Price August (ZMK/kg)
Lusaka Kitwe Mansa Kasama Mean
Breakfast meal (25 kg bag) 1391,0 1421,0 1505,0 1373,0 1422,50
Roller meal (25 kg bag) 915,0 975,0 1093,0 1000,0 995,75
Ratio breakfast of commercial meal 0,90 0,87 0,66 0,93 0,84
Composite commercial meal 1342,0 1362,2 1363,8 1348,4 1354,09
Consumer made meal (hammer 1063 942 910 941,00 956,50
mill)
Relation roller meal to grain price 1,42
Price February (ZMK/kg)
Lusaka Kitwe Mansa Kasama Mean
Breakfast meal (25 kg bag) 1536,0 1562,0 1750,0 1706,0 1638,50
Roller meal (25 kg bag) 1188,0 1261,0 1408,0 1408,0 1316,25
Ratio breakfast of commercial meal 0,92 0,88 0,62 0,96 0,84
Composite commercial meal 1506,9 1525,1 1620,1 1694,3 1586,62
Consumer made meal (hammer 1185 1138 1336 1455 1278,50
mill)
Relation roller meal to grain price 1,24
Source: Nicole Mason & Jayne (2009: tables 10 & 11)
Price relation values plotted over the year assuming steady change from lean to plenty
season yields this final relation:
Yearly Counter Price relation
127
1,24
1,42
12 1,30
oN
A.5 Urban Consumption of Meal Types Data
Commercial meal Hammer mill meal
Lusaka 0,899 0,101
Kitwe 0,855 0,145
Mansa 0,606 0,394
Kasama 0,317 0,683
Average 0,669 0,331
Source: Nicole Mason & Jayne (2009: tables 10-11)
Categories “consumer made maize meal via taking grain to grinding mill” and “maize meal
made at grinding mill and sold by a vendor/retailer” were aggregated to “hammer mill
meal” and the categories “samp” and “green maize” were excluded from the calculation to
obtain the final ratio visible in the table above.
A.6 Production Inputs for Scenario 23
Year Smallholder Ci ial bsi e Data taken from
surplus farmers Production scenario
2015 761513,4 66218,6 993758,0 Base Run
2016 761513,4 66218,6 993758,0 Base Run
2017 1370691,08 248154,7 1291558,5 High Production
2018 395346,1 34377,9 704086,0 Shock (SC8)
2019 363404,6 31600,4 691625,0 Shock (SC 8)
2020 754799,3 65634,7 1111506,0 Base Run
