Towards An Initial Crop Portfolio in Food-Insecure Arab Micro-States with

Embryonic Agriculture: A Constrained Optimization Approach David G. Raboy1,3 and Syed Abul Basher2, 3

Acknowledgments

We would like to thank the Qatar Statistics Authority for providing unpublished data. Similarly we owe a debt of gratitude to the Qatar Customs and Ports authority which furnished us with micro data on a shipment-by-shipment basis from 2005 through 2010. The International Center for Agricultural Research in Dry Areas (ICARDA) also supplied important data based on farms primarily in Syria. Finally we would like to thank the (two) referees and the editor for very constructive comments.

1 (corresponding author) Senior Policy Fellow, School of Public Policy, George Mason University, Email: draboy@gmu.edu; phone +1 703-919-1098; Home Mailing Address: 1003 Echols Street SE, Vienna VA 22180 USA 2 Senior Research Fellow, Fikra Research & Policy, PO Box 2664, Doha, Qatar. Email: syed.basher@gmail.com 3 When this research was initiated and the first Working Paper produced, both authors were seconded for fixed terms to the Qatar National Food Security Program (QNFSP) and Dr. Raboy co-authored the initial working paper. Most of the research was conducted after Drs. Raboy and Basher left QFNSP and Dr. Basher was not involved in the research until after departing from QNFSP.

Towards An Initial Crop Portfolio in Food-Insecure Arab Micro-States

with Embryonic Agriculture: A Constrained Optimization Approach Abstract

Recent periods of high and volatile food prices have prompted several import-dependent

Arab micro-states to consider at least some domestic production, among other proposals,

as a way of mitigating extremely volatile food prices, which even in rich micro-states can

have adverse health and economic effects and probably represent the most serious

manifestation of food insecurity to such countries. Using Qatar as a case study, which is

almost entirely import-dependent and faces exceptionally volatile food prices, we employ

non-linear mathematical programing and analogs to measures of volatility in the financial

sector to develop a model to guide the constitution of a crop portfolio for the inaugural

year for domestic production, supplemented by storage, which is optimal in the sense of

achieving the most dampening effect on price volatility. We conclude that the desired

result cannot be achieved for grains through domestic production and that strategic storage

must play a crucial role. However, a meaningful target for volatility reduction, well short

of self-sufficiency, can be achieved for all other raw products through an obtainable level

of domestic production.

Keywords: Food price volatility, Strategic storage, Domestic production, Market

concentration, Non-linear programming, Arab micro-states.

JEL Codes: C60, L40, Q18.

Although the food crisis of 2007–2008 was a global one, arid countries, particularly those

in the Arab region, were especially hard hit. Whereas the literature associated with the

2007–2008 food crisis emphasized supply disruptions, much contemporary analysis

considers the detrimental effects of price volatility (FAO 2011; World Bank 2010).

Resulting proposals have emanated from multilateral organizations, NGOs and the

countries themselves. These proposals are basically contained in four pillars—import

diversification, hedging through forward contracting or similar measures (virtual storage),

strategic storage, and domestic production. While still primarily stressing secure ways to

source from outside, some observers advocate greater reliance on domestic production as a

shield from external price volatility (World Bank 2009).

This article focuses solely on price volatility and is designed to illustrate an approach to a

planning function for an almost totally import-dependent arid Arab micro-state that

considers the four pillars previously mentioned. As a case study we consider the State of

Qatar, characterized by extremely high price volatility for imports in virtually every

agricultural commodity group. With the highest per capita GDP in the world, Qatar seems

a curious choice for our case study. Why should anyone, including the Qataris, be

concerned with food security as they devote billions of US dollars to projects like

sponsoring the FIFA World Cup? It is beyond the scope of this article to defend Qatar’s

policies—a fairly lengthy separate article would be required, but we do believe that there

are reasons for the Qataris to care about food security, especially extreme price volatility.1

At bottom, however, our decision to use Qatar as a case study is purely practical—Qatar is

2

an extremely good source of data for our empirical approach which illustrates the price

volatility across the full range of agricultural imports.

Small-scale arid countries common in the Arab world, with little purchasing power and

that are almost entirely dependent on imports, are particularly vulnerable to price volatility

and may be extra motivated to attempt at least some domestic production. This is because

scale issues make strategies such as import diversification difficult (Streeten 1994). For

example, in the case of Qatar almost all corn is imported from Argentina, and no corn is

currently grown domestically. An entire year’s demand wouldn’t even fill the smallest

model of bulk carrier. Virtual storage is problematic and risky due to countries’ relative

strengths and the specter of contractual opportunism. Unlike their large counterparts, small

countries burdens are linked to a combination of indivisible fixed costs and diseconomies

of scale, causing high costs in both the public and private sectors (IMF 2013).

Therefore in this article we consider that due to scale issues, import diversification is not a

viable remedy to price volatility and, similarly, virtual storage is extremely risky. The

major options are strategic storage and some level of domestic production. The challenges

facing micro-states considering launching embryonic domestic production are daunting—

water shortage, insufficient arable land, underdeveloped infrastructure, and supply-chain

mechanisms among others. These problems are exacerbated by scale issues that confront

Arab micro states such as the majority of those in the Gulf Cooperation Council (GCC)

countries.2,3

3

As stated this article does not opine on the efficacy, or desirability, of domestic production

in Arab micro states, but rather considers that in some states decisions have been made to

develop at least some domestic production, and in others production already exists at a

non-trivial level. But if there is to be domestic production, planning is required, especially

if the goal is to achieve maximum reduction in price volatility.

In the United Arab Emirates (UAE) some 70,000 hectares are under cultivation (latest

figures—2009) producing about 2 million tonnes of agricultural products, including 1.6

million tonnes of field crops, Aover 170,000 tonnes of vegetables and almost 280,000

tonnes of fruit. In addition, the UAE has developed livestock with over 4 million head of

sheep, goats, and cattle (UAE 2012). Lately, Ministers and other policy leaders have been

strongly stressing the need for a national food security program. The UAE Food Security

Project has been developed by the Abu Dhabi Food Control Authority (2013).

The State of Qatar has embarked on a national initiative to develop some level of domestic

production for food security purposes. The Qatar National Food Security Program

(QNFSP) envisions employing industrial scale solar plants to power desalination facilities

dedicated to agricultural irrigation. Coupled with high-tech production methods such as

hydroponics, QNFSP proposes to start what would effectively be a new agricultural sector

to augment increased strategic storage and rationalization of imports.4 An innovative pilot

project was completed in 2012 and produced its first crop in 2013. Its yields exceeded

expectations, and uncontemplated positive externalities were discovered. According to

Science Magazine: “A pilot plant built by the Sahara Forest Project (SFP) produced 75

4

kilograms of vegetables per square meter in three crops annually, comparable to

commercial farms in Europe, while consuming only sunlight and seawater” (Clery 2013).5

Kuwait has developed agriculture in certain sectors. The latest data (2006–2007) show

684,000 tonnes of production of vegetables and field crops, 37,000 tonnes of meat and

over 200,000 tonnes of eggs and dairy items (IMF 2011). Much of the meat production is

in poultry.

As of now the micro states of the Gulf with embryonic or emerging agricultural sectors

that are focusing on food security issues lack robust planning strategies, resources are

being wasted and the crop portfolios lack economic logic; in particular as concerns price

volatility—the most important manifestation of food security to these micro states. The

narrow focus of this article is to derive a model that can be used by policymakers in Arab

micro states to develop incentives and other policies to prod crop allocation towards an

optimal initial or modified crop portfolio geared towards mitigating price volatility.

An analysis of micro-data in Qatar demonstrates that extreme price volatility exists for

virtually all raw agricultural products imported into the country (Basher et al. 2013).

Ianchovichina et al. (2012) establish that high commodity price volatility exerts an

asymmetric effect on domestic prices, whereby only price increases are transmitted,

resulting in consumer uncertainty and inflation, along with volatility. Further Basher et al.

(2013) calculate that concentration by country of origin is extremely high, with all but a

very few commodities between 2008 and 2009 contained in the “highly concentrated”

range as measured by the HHI statistic, and that many approach monopoly levels (U.S.

5

Department of Justice 2010). This dominance by large monopoly or oligopoly suppliers

over micro states contributes to price volatility, as well as generally higher prices.

Our objective in this article is to analytically describe an initial crop portfolio in Qatar,

comprised of 21 commodities, for domestic production at a level that when influenced by

market-based incentives that serve to stabilize prices at remunerative prices for farmers,

coupled with such things as fixed-price long-term contracting, as described in Raboy et al.

(2013), will have the most inhibiting effect on price volatility. Strategic storage will prove

to be another import tool in combating price volatility.

Note that this objective does not address the myriad of other items that would have to be

accounted for as a country develops an overarching food policy. Our model does not

address consumer preferences, nutrition, substitutability, food safety and other factors that

would need to be included in a complete planning model. As price volatility is probably

the most serious food security issue for Qatar, however, and may contribute to ubiquitous

and serious health issues (see footnote 1), an exercise which isolates an analytical approach

to the issue is necessary. Indeed, for empirical reasons the analysis must be separate from

other food security issues to avoid distortion of results and allow separability of effects.

The outcome of our analysis can then be added to the other components necessary to

construct an overarching food-security strategy.

Our primary tool for assessing crop priorities as regards our price volatility goal is a

constrained optimization model based on non-linear mathematical programing. The

difficulties associated with non-linear mathematical programming are well known, and

6

those associated with our model will be discussed in the data and simulations sections. The

technical characteristics of this type of model allow astute construction of an objective

function isolated to the task. Nonlinear mathematical programming models can be

notoriously unstable, however, sometimes producing nonsensical outcomes deemed

“optimal.” Because over identification is not an issue, the objective function can be

matched with a series of algorithms that construct constraints (sometimes in substantial

numbers) on the function that when used with a light hand, can produce stable and useful

results.

When one thinks of a “portfolio” of crops, where in this case, the portfolio is based on

levels of product specific volatility, one logically is drawn to the literature and techniques

that emanate from financial economics. In fact, our model is heavily based on analogs to

financial portfolio theory tools to put a social value on the diminution of import price

volatility. It is not claimed that these are exact values but are sufficient to allow a rank

ordering in the optimization process.

The rest of the article is organized as follows. The next subsection describes the basic

nature of the constrained optimization problem. Following that, a subsection provides a

literature survey on the use of mathematical programming in agriculture. The second

section illustrates the objective function, while the third section describes the model’s

constraints. The penultimate section lists data sources, explains model functioning,

describes simulations and produces model results. The final Section adds conclusions and

suggestions for future research.

7

The Constrained Optimization Problem

Emerging agricultural sectors require direction so that everyone doesn’t grow tomatoes or

focus on crops where price volatility is not an issue. We believe that our mathematical

programming optimization approach, with its substantial reliance on methodologies

derived in the financial sector, is a good tool to address this one component of food

security.

It is hoped that the crop portfolio allocation can be achieved without resorting to command

and control government actions but rather by carefully designed incentives that least result

in inefficiency and perverse incentives. These same incentives would stabilize the prices of

domestic goods, guarding against domestic idiosyncratic price volatility that dwarfs world

prices (Raboy et al. 2013). Essential to price stability for domestic production will be

sufficient scale so that farmers are not just price takers, and tools such as long term

contracts with price components.

There is nothing in this exercise, however, that suggests that a feasible model solution will

allow all 21 crops to be produced in the portfolio in the quantities suggested by a relatively

unconstrained objective function. Topographical issues, extreme required expenditures on

water, and other factors may result in model solutions that substantially limit certain crops

from any reasonable outcome. This would preclude domestic production from ameliorating

food security for these crops and special emphasis would have to be placed on such things

as strategic storage.

Literature Survey

8

The novelty in this article is the way financial analogs enter the objective function to

address price volatility. The use of mathematical programing in agricultural situations is

nothing new. Starting with Heady and Candler (1960), programming models became

widely used in the economic analysis of agricultural decisions. These models could be

constructed from minimal data and the structure was well suited to resource, environmental

or policy constraints (Howitt 1995).

Constrained optimization models have been used in developing countries to address

conflicting goals. Policy decisions can be facilitated by showing the consequences of

alternative policies through optimization outcomes (Hazell and Norton 1986). One of the

earliest programming models constructed for a developing country is CHAC6 for planning

the Mexican agriculture sector. CHAC is a sector-wide constrained optimization model

containing supply and demand for 33 principal short-cycle crops in Mexico, organized

spatially in four major regions and 20 sub-regions. All together, the initial model contained

over 2300 alternative production vectors, including over 200 for corn. CHAC is an annual

model which may be solved for any given cropping cycle. The model is based on data for

1968, with the resources endowments of that year entered as constraints. As a tool for

policymakers, CHAC was designed to address several policy inquiries.

The Egyptian constrained optimization model HAPY7 was constructed to assess whether

the availability of water from Nile could become a constraint on national growth by 1990

(Hazell and Norton 1986). Since agriculture consumes about 95% of the Nile waters, the

immediate goals of HAPY were to estimate agriculture’s needs for water under different

9

scenarios, to estimate the marginal productivity of water in agriculture, and to evaluate

alternative investment in irrigation.

Other relevant models include the TASM I and TASM II models for Turkey (Le-Si et al.

1982; Norton and Gencaga 1985), the Philippine MAAGAP model of Kunkel et al. (1978),

the Tunisian model of Condos and Cappi (1976), the Brazilian model of Kutcher and

Scandizzo (1981), the Central American model (MOCA) of Cappi et al. (1978), and the

Malaysian model (TIGER) of Bell et al. (1982). See Hazell and Norton (1986) for further

information on these models.

With regards to the Middle East, Charnes et al. (1989) used a dynamic goal programming

model for planning joint collaboration between Egypt, Kuwait, Saudi Arabia and Sudan in

agriculture to achieve self-sufficiency in food production in the Middle East. The input-

output relationship governing the agriculture production and the food industry technology

is used as the constraint of the model.

More recently, employing a linear programming model of Iranian agriculture and a four-

sector error-correction model of the Iranian economy, Salami et al. (2009) examined the

macro-economic effects of a severe drought on the economy of Iran. Freier et al. (2011)

employed an augmented mathematical land use decision model to quantify the economic

and ecological impacts of droughts in a medium-scale Moroccan pastoral agroecosystem.

Khanna et al. (2013) used a constrained optimization model to examine the impact of the

blend mandate in India on the agricultural sector and its implications for food prices in

India.

10

THE FOOD SECURITY OBJECTIVE FUNCTION

This illustrative exercise employs a utility maximization process, assuming that other than

food security (price volatility) issues domestic commodities and imports are perfect

substitutes.8 The goal of the model is to influence the crop portfolio in the initial crop year

of operation—by definition this results in a static portfolio as crop mixes cannot be altered

within a crop year once planted. This implies that total consumption shares of each

commodity are fixed. As such, total utility is strictly a function of the proportion of

domestic production (and product released from strategic storage) in consumption for each

commodity which yields the anti-volatility security benefits in the objective function.9

Dynamic models which require elasticities and cross-elascticities to be measured within

the portfolio are appropriate for multi-year crop models, but such issues are not within the

scope of this article which is addressed solely to the crucial start-up function of initial crop

portfolio allocation.

Import prices in Qatar reflect the intrinsic private value of a good, but also includes the

welfare diminishing components that affect food security. These welfare diminishing

components of import price include the effects of price volatility, which is our focus. But

scale issues also significantly distort prices. For small countries, purchasing-scale issues

are typically a major source of food-security difficulties, as they are systemic. For example

the c.i.f. prices of Argentine corn imports in Qatar are about US$200 more than the generic

Argentine fob price, and 55% higher than EU c.i.f. prices (EUROSTAT 2010; Qatar

Statistics Authority 2007) of Argentine corn delivered from the same Argentine origin port

11

to European North Sea (ARAG) ports. Explanations can include logistics issues, strategic

behavior, etc..

A detailed analysis at both origin and point of importation is necessary to isolate such

things as rent-seeking behavior and idiosyncratic logistics costs. It is beyond the scope of

this article to analyze such distortions. As a result, the objective function is limited to the

role of price-volatility avoidance, and associated origin concentration, also related to

generally higher prices and a contributor to volatility, may be addressed in a series of

constraints.

The objective function is defined by:

Max i

I

iiQU ∑

=

=1ρ (1)

Where:

Qi = the quantity produced domestically of each commodity i, as determined by the

solution of the model;

ρi = the value per unit of domestic production of commodity i of the positive social value

of avoiding some level of price volatility through domestic production, a non-linear

function to be described below. ρi will be different for each commodity which sets up the

optimization format. Computationally it was more efficient to maximize implicit premiums

from price volatility avoidance than to adopt a minimization strategy. It should be noted

12

that well planned use of strategic storage also produces ρs, as will be discussed in the

simulation section.

Price Volatility and Portfolio Theory

The relative values of mitigating price volatility among commodity imports can be

quantified, not exactly but sufficiently for purposes of rank ordering in the model, based on

analogs to financial portfolio theory. Measurement of volatility and concepts of “risk

premiums” for food import price volatility can apply in ways equivalent to financial-

instrument risk as long as agricultural products are viewed in isolation—not as commodity

assets in a financial portfolio.

For example Minot (2014) states that to measure agricultural price volatility, instead of

using the standard coefficient of variation (cv), one can employ unconditional volatility, a

tool used widely in the financial markets, which is calculated as the standard deviation in

rates of return, typically calculated as the proportionate change in prices from one period to

another. Also GARCH models are used, as in finance, where variability may change over

time (Minot 2014).

In order to produce a social value, rather than just a measure of volatility, we employ

another analog from finance theory, a modified version of the Capital Asset Pricing Model

(CAPM) (Lintner 1964; Sharpe 1964) to measure price-volatility avoidance premiums.

Economists have applied CAPM to commodities, but these analyses compared commodity

returns in a financial portfolio setting to stock market and riskless returns in bond markets

13

(Bodie and Rosansky 1980; Dusak 1973) or considered commodity volatility in another

financial setting (Erb and Harvey 2006).

Our purpose is different: to compare price volatility of single agricultural imports to that of

a market risk premium equal to the difference in percentage price changes in a market

basket of imports in a defined market and those of a world index of agricultural

commodities that represents relatively low price volatility (the closest analogue to a

riskless asset). In our model, each component relates solely to the dynamics of agricultural

products, but there is always an analog to a model of financial assets.

Import price volatility, for any commodity i, is reflected in the expected percentage change

in price:

( ) ( )FMfE ii −+=∆ β (2)

Where:

E(Δi) = expected percentage change in the price of agricultural import i;

_M = a long term arithmetic average of percentage changes in a broad market basket of

Qatari agricultural imports;

_F = a long term arithmetic average (Brealy et al. 2008) of percentage changes in a world

food index that is considered to be of low risk;

f = the expected annual percentage change in the low-risk food index; and

14

βi = represents import–specific price volatility for a single commodity relative to the

market risk premium defined as the difference in the Qatari import basket and the riskless

representation of food commodities.

The social value import-price-volatility-avoidance premium from domestic production is

represented by ρ.

( )[ ]iii FMF ωβρ ,−= , the social value premium for import i, a non-linear function of

commodity-specific price volatility and the domestic-production share in consumption of

commodity i, ωi.

Objective Function Form

The price-volatility-avoidance social value premium expression, a function of ωi, must be

non-linear to satisfy diminishing marginal social utility. There is no empirical analysis on

food-security-related transformations from import dependency to domestic production

which would serve to inform the structure of the objective function. We experimented with

quite a few function forms—quadratic, etc. We decided to settle on one functional form for

the objective function that satisfied the microeconomic theoretic requirements but was also

the most computationally efficient. Admittedly, this choice was ad hoc, but no other forms

proved either theoretically or empirically superior. As a test on the reasonableness of our

procedure we tested various functional forms for empirical results, and found no material

difference among the options. Other forms tested included partial-quadratic, and even

linear.

15

Diminishing marginal utility manifests as follows. A logical assumption is that there is

little social value from domestic production until some threshold is reached regarding its

share of consumption. Once this threshold is reached, as the domestic-production share

increases, social value should first increase rapidly, and then increase at a diminishing rate

as the domestic-production share approaches unity—in this zone the objective function

will be concave to the origin. The percentage maximum price-volatility social premium is

reached when ωi = 1. A functional form that satisfies these parameters is, for each

commodity, to set:

ρi = 0 for ii T≤ω ; ( )FMiii −= βωρ )10(log10 for ii T>ω (3)

Where Ti = the threshold at which the domestic-production share of consumption begins to

produce social utility through price volatility mitigation.10 Graphically ρi takes the form

displayed in Figure 1. [place Figure 1 approximately here]

Substituting (3) into (1), and understanding that no social premiums are generated when

ii T≤ω , the objective function becomes:

∑=

=I

iU

1( )FMQ iii −βω )10(log10 for ii T>ω (4)

CONSTRAINTS

Non-linear mathematical programing optimization models are quite ornery. Typically

mathematical programming models are over identified—the constraints far exceed the

16

variables in the objective function. At some values, constraints may even conflict. Often it

is difficult to even produce a solution within the feasibility region, much less an optimal

result. Feasibility implies there is a space where all of the constraints can be satisfied. In

nonlinear cases, the objective function may wander in and out of a feasibility region,

leading to the illusion of optima. With a large number of constraints, and if one key

constraint is “tight,” the solutions for most of the variables in the objective function may

just satisfy the constraints with only a few variables having slack and therefore displaying

interior solutions (Williams 2013). These are optimal, given the strict model construction.

They are not, however, as elegant as calculus solutions employing the Lagrange Multiplier

method, which may be appropriate for theoretical or simple applied analysis, but rarely

suitable for real-world problems.

Some of the constraints described below are strict physical ones. Others are based on a set

of budgets and costs and therefore strictly binding because once a facility is budgeted and

built for a certain capacity, that capacity cannot be changed in our time frame. There may

be some cultural constraints with some elasticity.

Costs: In this article, we only include the costs to the government. This is logical because

the exercise is creating social benefits. As described below there may be direct

government expenditures, or those relating to Public Private Partnerships. We have

included the major categories, but clearly outside our narrow realm there will be many

more government costs.

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A potentially important set of constraints are those that attempt to reduce the level of

imports when concentration from a set of countries of origin is very high. These will be

discussed below but will most likely have to be factored into incentives as per Raboy et al.

(2013). Use of such constraints will vary from simulation to simulation depending on the

functioning of the model.

Land availability: There are at least four sub constraints associated with the land

constraint. The first is a physical constraint which reflects the amount of potential arable

land. The physical land constraint is precise. All the land will have to be reclaimed at a

cost. We assume that the government considers land to be a public good and pays for all

reclamation costs for all land planned to be reclaimed. Miscalculations are possible,

however, so there exists several ways that this constraint can be binding. First, the cost of

renovation may be understated and the budget may not be able to reclaim all arable land.

Then a lower amount of land becomes a binding constraint. Sufficient funds could be

allocated, in which case the amount of land becomes a strict constraint. Third, the budget

for land reclamation may be adequate, but results in the master budget being exceeded, say

due to cost overruns on solar/desalination plants. This would force some decisions to be

made, one of which might include less arable land. Homogeneity of arable land, based on

existing analysis of Qatar (DAWR 2005) is assumed. New research in progress, but

incomplete (e.g., De Pauw 2010) may indicate that arid, potentially arable land is

heterogeneous, requiring the model to incorporate integer programming, reducing

flexibility which effectively diminishes available land. If none of the financial plans are

violated than the simple land constraint can be expressed as ∑=

≤I

iii LlQ

1where: li = land

18

requirement per unit of commodity i (reciprocal of yield); and L = total land that can be

cultivated.

Water availability: Water is a key constraint, but it is a financial one, not a physical one.

In the case of a country like Qatar, should the food policy concept be implemented, the

constraint will be defined by the capacity of solar desalination facilities, a function of costs

that the country will be willing to bear. If, however, budgeting errors are made or

engineering mistakes results in less than expected capacity, water becomes a physical

constraint as plant capacity is fixed. It is assumed that the government does not pay for the

water system outright, but rather participates in a Private Public Partnership (PPP) as is the

case with large solar generating facilities in the U.S., Canada, and the EU. Whether

through a capital grant, loan guarantees, feed-in tariffs or some other system we assume

that during the start-up year Qatar pays 25% in present value or direct expenditure of the

cost of solar plants, desalination facilities, and associated infrastructure. The physical

water, defined by final system capacity is, constraint is:∑=

≤I

iii WwQ

1where: wi = water

requirement per unit of commodity i; and W = total available water for agriculture. Even

with the government participating in a PPP, the costs associated with the water system are

by far the largest.

Crop subsidy constraint: As described in Raboy et al. (2013) most crops in the portfolio

will require some government support in order to be competitive with imports. A system

was designed that minimally interfered with the market and avoided perverse incentives.

Using corn as an example, Raboy et al. (2013) calculated that an incentive on the order of

19

24% of import price would be required. Grain would require more support than, say,

produce, but in this article to be conservative we use a figure that is 24% of all actual

economic costs for each commodity and total expenditure is determined by the model

simulations. The exceptions are dates and fodder where Qatar is almost self-sufficient and

no subsidy is offered. We consider that the government conducts estimates of production

levels and then sets a budget for crop subsidies which is a financial constraint on the

model. As opposed to the other components of the overall budget which are tied to

construction of the water system and reclamation of land, however, if the crop-subsidy

budget is exceeded, the government could agree to a supplementary budget.

Overall Budget: All the sub-budgets sum to an overall budget. Miscalculations in separate

budgets could lead to a number that surpasses the overall budget. There may or may not be

any contingency in the overall budget; if not the budget could be a binding one that forces

budget cuts in the components of the system. This is relevant as each sub-budget is under

the control of a different government agency, but one assumes that the master budget is set

by the Ministry of Finance. The Ministry of Finance sets the overall budget based on the

inputs of the other agencies.

The Import Origin Constraint: As stated before, lack of import diversification results in

higher prices and contributes to price volatility, especially when a micro-state is

transacting with a large exporter. Whereas there is substantial empirical literature regarding

prices and concentration within a country it is a highly controversial subject and there is

little to inform policy as regards concentration from country of origin. Further any price or

volatility effects would depend on the proportion of imports in total consumption. A

20

country’s imports of a product may come from only one country, but if the proportion of

imports relative to the complete domestic market is small, then import concentration is

relatively unimportant.

Evidence from Sapienza (2002), Allain et al. (2013), Salinger (1990) and others have

found that increased concentration can lead to price increases in the 1 to 7% range, and

deconcentration can lead to lower prices.

Rather than include deconcentration effects in the objective function, as such effects are

illusive even in full domestic markets, our methodology sets a series of constraints, one for

each product, that states the amount of domestic production required to reduce imports and

import concentration enough to plausibly have an effect on price and price volatility. These

constraints may or may not be used or altered depending on the functioning of the model

during a particular simulation. It is an iterative process. Designing the maximum

constraints is a difficult task and many approaches have merit--here we rely on regulatory

structures in, for example, the U.S. and the empirical research that have informed those

structures. As described in the data section we start with the HHIs calculated solely for

import origin in Basher et al. (2013). We then prorate them to account for existing

domestic production as calculated by Qatar Statistics Authority (2009). The tentative

constraint is defined as the amount of domestic production required to reduce the adjusted

origin HHI to a target related to the “highly concentrated” designation as stated by the

Antitrust Division of the U.S. Department of Justice (2010). The DOJ defines highly

concentrated as an HHI of 2,500. But to reach this, some commodities, where there is

currently no domestic production and origin HHIs are near the monopoly level of 10000,

21

would have to reach domestic production shares of as high as 76%. This would completely

disrupt any meaning in the model’s output, indeed feasible solutions would probably be

impossible.

Further, certain crops which would otherwise have to be produced sufficiently to produce

large shares of consumption have low βs, implying that forcing the model to produce large

shares of crops facing little price volatility would not be an optimizing strategy. The

resulting portfolio would not be the one that maximized price volatility mitigation, but one

where a rigidly derived constraint diverted scarce resources from production of crops with

high βs.

Cultural Constraints: Qatar is almost self-sufficient in dates which are important

culturally, yet given its water intensity, an unconstrained model would almost certainly

result in a very small quantity of domestic production. This would most likely not be

acceptable to Qataris. Therefore, we set a constraint that requires a minimum of date

consumption to be produced in Qatar, but this minimum will be below existing levels to

allow flexibility in the model. Although not as culturally sensitive, Qatari produces the

majority of its fodder, and there is a cultural dynamic at play there as well. We therefore

require a certain percentage of fodder be produced domestically. This illustrates one of the

unfortunate conflicts that exist in this type of exercise. Both dates and fodders are very

water intensive, second only to grains, and the cultural constraint will divert a lot of

production from other commodities. But both dates and fodder have low βs, clearly

because Qatar is almost self-sufficient in these two and importers will not have the market

power that leads to volatility. So this cultural constraint, based on very deep feelings, will

22

have a noticeable effect on the model outcome, and the deviation from the volatility-

reducing optimal portfolio.

IMPLEMENTATION AND CASE STUDY

The mathematical formulation of our model will result in a large number of equations and

variables. In the presented form, the optimization problem takes the structure of a non-

linear mathematical program, which can be solved by various mathematical programming

software packages such as Lindo, GAMS or MATLAB. Future research can consider the

use of a mixed-integer, non-linear program, should land prove to be sufficiently

heterogeneous. Our model solves for the levels and allocation of 21 agricultural products

that produce the greatest social value associated with volatility reduction, subject to 47

constraints, 63 non-linear elements, 71 formulas and 333 coefficients.

Data

Data derive from several sources, primarily Qatar, but also Syria and other sources. C.I.F.

import prices were provided by Qatar Statistics Authority (2009) and food indices were

obtained from FAO (2009). Percentage changes in the latter were projected over one year.

The import market basket is calculated from Qatar data and is constructed as follows.

Fixed value weights based on import proportions for each of 45 key commodities, the

weights of which sum to 1, are established for the years 2006-2008, from official data

(Qatar Statistics Authority 2009). The final weights employed are the average annual

weights over the 3-year period. The Qatar Food Market Basket (QFMB) is defined as the

product of these fixed weights and c.i.f. import prices for each of the included

23

commodities, summed for each month. The QFMB is calculated on a monthly basis over

the period February 2004 through December 2008.

Monthly market risk premiums are equal to the difference in monthly percentage changes

in the QFMB and the FAO Food Index. These are averaged over the period March 2004

through March 2008 to produce the long-run annual market risk premium. The market risk

premium is 7.2%. Figure 2 compares the QFMB to the FAO Food Index over the sample

period. [Figure 2 Approximately here]

Adjusted HHIs targeted by constraints are based on Basher et al. (2013) and the U.S.

Department of Justice (2010). Target HHIs and the amount of required domestic

production for each product are presented in Table 1. [Please put Table 1 approximately

here] Previously the iterative procedure by which these constraints may be used was

discussed.

Estimation of βs occurs as follows. First, the “excess return” method is used:

( ) ( ) εβ +−+=−∆ )FMcfE i (5)

Where ε will enter into an explicit conditional variance model as follows:

It is well established that when substantial volatility exists in the dependent variable, as is

the case with percentage changes in import prices for small countries, OLS is inappropriate

for estimating βs in an equation such as (4) due to both autoregression and

heteroskedasticity. Rather the variance of the dependent variable must itself be modeled,

using the class of conditional variance models. Generalized Autoregressive Conditional

24

Heteroskedasticity models (GARCH) are better suited for our estimation (Bollerslev 1986;

Taylor 1986). There are many versions of GARCH models including GARCH-M (Engle et

al. 1987) which places a variance-related parameter in the regression equation, IGARCH

(Engle and Bollerslev 1986) which constrains the variance-equation parameters, and

EGARCH (Nelson 1991) which assigns specific functional forms to the conditional

variance equation. In addition, GARCH models do not assume that residuals are normally

distributed. The distribution can be normal, follow a Student’s t, or a generalized error

distribution (GED). An article that summarizes the GARCH variants and best mirrors our

methodology is Kaiser (1996).

For each commodity we conduct estimation for the price-volatility CAPMs using all of the

following models—OLS, OLS-AR, GARCH (1,1), GARCH-M, IGARCH, and EGARCH,

and we also test each of the three error-distributions described above for the GARCH

models, leading to 14 separate estimations for each commodity. Following Kaiser (1996),

the best choice of model is based on the log-likelihood and the Akaike Information

Criterion (AIC) (Bozdogan 2000), both of which are comparison methods, but the latter is

not capable of test statistics. The magnitude in differences in both log-likelihood and AIC

gave us confidence that our choices of best models were appropriate—those with the

obviously lowest AIC. Table 2 provides our statistical results for β estimation. [place

Table 2 approximately here]

Note that our βs were based on monthly data. The results reported in Basher et al. (2013)

suggest that weighted-averaging diminishes real volatility as the volatility based on

25

coefficients of variation with micro data seem much higher than that in our CAPM

estimations.

Results were precise for most commodities. Extrapolations were required for some

commodities where missing data due to erratic trade hindered estimation. In one case

where Qatar is almost self-sufficient, thin import data resulted in statistical insignificance.

Finally, in some thinly traded fruits and vegetables, βs less than one were assumed.

The ‘land constraint’ requires two sets of inputs: total available arable land and land

requirements for each commodity. The former is based on Qatar data: The Atlas of Soils

for the State of Qatar (DAWR 2005), Malik and Al-Khanji (2009), and De Pauw (2010).

Arable land measures, especially in arid climates may differ. Land suitability for

agriculture is first divided into two categories—“S” (suitable) and “N” non-suitable. It is

then sub-classified as follows: S1 (highly suitable), S2 (moderately suitable), S3

(marginally suitable), N1 (currently not suitable—“land having severe limitations that

preclude the given type of use, but can be improved by specific management”), and N2

(permanently not suitable). (DAWR 200). There is very little S2 land, all on farms in

Qatar and no S1 land. 7,638.3 hectares of S3 land exist on farms but are not under

cultivation. Cultivation on farms occupies 6,839.4 hectares. (De Pauw 2010). Outside of

farms it has been estimated that there are 24,974 hectares of S3 land that could be

reclaimed. (DAWR 2005).

Some, taking a long view, have included N1 land as potentially arable, but for our

purposes considering available land for a startup, it is not credible to include any N1 land

26

in the land constraint. N1 land is considered currently unsuitable due to a combination of

some or all of salinity, drainage, and physical problems—issues that would be very

expensive to solve and would take considerable time.

Therefore our land constraint is defined as any land in Qatar up to the S3 level or slightly

less than 40,000 hectares (rounded up to 40,000 hectares). This is may be overstated

because there are crops being grown currently on farms in Qatar in small amounts that are

not in our 21-crop portfolio but may still occupy land on farms currently under cultivation.

These other fruits and vegetables sum to about 25,000 tonnes of production. (Mazid and

Aw-Hassan 2010).

Land requirements for each commodity, the reciprocal of yields, derive from Syrian (De

Pauw 2010) and UAE data (UAE Ministry of Economy 2006).The cost of land reclamation

is provided by Arab Qatari Company for Agricultural Production,11 and estimated at

US$23,688.19/ha.

Water requirements for each crop are also based on Syrian farms (Mazid and Aw-Hassan

2010; Oweis 2010). Water costs derive from several sources, including DLR (2007) Kahra

Maa (2010), and DAWR (2009). Based on these data and calculations by the authors,

capital expenditure of US$1 billion in the integrated water system will yield 21 million m3

of water per year. Under our PPP assumption this implies US$250 million in direct

government expenditure or equivalent PV for the annual level just mentioned. Water and

land intensity, as well as total consumption and imports for each commodity, are provided

in Table 3. [Place Table 3 approximately here]

27

Crop subsidy costs are based on Raboy et al. (2013). The total budget is an estimate of the

sum of all the budget components.

Total consumption figures originate from two sources, and are derived from Qatari and

Moroccan data (Guerouali 2010). Some crops are used primarily for human consumption.

Other crops, like fodder, barley, and corn, are components of animal feed. In livestock

production, the overwhelming amount of land and water are associated with feed.

Accordingly in this exercise we include in total consumption the “virtual grains” that

would be employed in the production of animal products imported into Qatar, but not the

dairy or animal products themselves, a matter left for future research. Clearly Qatar would

not be self-sufficient in meat so the level of virtual grain is set at 50%. Total consumption

of raw agricultural commodities destined for human consumption is based on Qatar

Statistics Authority (2009b). Total consumption of grains used for animal feed (virtual

grain in meat and dairy imports) comes from Guerouali (2010).

Simulations and Discussion

In this article we provide the results of simulations relating only to two anti-price-volatility

policies. The first is limited to domestic production and the second includes strategic

storage. This is sufficient to represent the possibility of achieving the goals described in

this article and the key question addressed, beyond the empirical results, of whether the

goals can be achieved at all, and if so must strategic storage be a component of any food

security strategy, and at what level.

28

The first simulation relating to the first policy (no storage) attempts to achieve the levels of

domestic production necessary to hit the target adjusted-origin HHIs listed in Table 1 and

then reach an optimum in the objective function. No storage is included to achieve this

goal. Government expenditure is budgeted as follows: The design calls for reclamation of

all 40,000 hectares of land, requiring a direct government expenditure of US$10.7 million.

Expenditures on capacity for the solar/desalination facility are realized as substantial, and

the model is run repeatedly, starting at a design capacity of 310,000,000m3 with associated

PPP costs of US$3.7 billion. Crop subsidies are budgeted at $40 million in government

outlays, based on predicted production. The total budget for the system, without any

contingency, is, in the first, lowest-cost iteration, set at US$3.8 billion.

The model is run with the planning constraints just mentioned. This initial iteration is

infeasible—almost no constraints are met. Only wheat, barley, and corn, are grown, and

barley doesn’t meet its target.12

The primary culprit producing the infeasibility is water which reached its constraint of

310,000,000m3 before anything but grain could be produced. Land was not a problem as

there was at least some slack in the constraint.

Some observations are offered, and then the crux issue is discussed. The results should be

interpreted, not as a solution where the results can be taken literally, but rather as the

quantities that brought the model closest to feasibility before it determined that further

iterations would never result in an outcome in the feasibility zone, nor advance towards

that zone. As such it is not an equilibrium solution but an “I give up, I did the best I could”

29

result. Still the non-equilibrium results are worthy of comment. Domestically-produced

grains have the highest social value from diminishing import-price volatility of all products

in the portfolio, as demonstrated by their βs, so the model algorithm naturally moves them

first towards their goals. But they are also the most land and water intensive crops by far

and so, in the first iteration, the water constraint became binding with only grains being

produced. Note, some reclaimed land went unused—there was slack in the land constraint.

What would happen if the water constraint were substantially relaxed, albeit at great

expense?

In the next iteration regarding the domestic-production-only target, therefore, water

capacity is increased to 400,000,000m3. The PPP cost is US$4.8 billion, meaning that total

public and private costs for the solar/desalination system and associated infrastructure is

over US$19 billion. This still does not produce a feasible solution. The grains and four

vegetables meet their targets, but there is no production of anything else, in violation of

binding constraints relating to origin HHI target reductions. The four vegetables are

tomatoes, cucumbers, peppers, and peppers. The former three have very high social values

from the high price volatility that exists in import prices and green beans have very low

water requirements. For these reasons, along with grains these four vegetables receive the

model’s attention. But none of the results should be taken literally as the model ceases to

operate when it finds that there is no action it can take to get closer to feasibility.

When one reviews the results, however, one recognizes the existence of a more

fundamental problem with the policies represented by the model. To produce the

outcomes presented by even this infeasable iteration would require over 500 more hectares

30

than the 40,000 hectares of arable land in Qatar as illustrated in the data section. In other

words no amount of expenditure on water or anything else could create a feasible solution

as long as the anti-import-price policy is the one represented in the model—purely based

on domestic production. The land constraint, at least in the initial period of operation is

physically binding, not financially so. It is not possible to relax it.

This indicates that if a country like Qatar decided to domestically produce grains, which

has shown to be agronomically feasible, at the levels necessary to achieve a meaningful

dampening of import-price volatility, it would sacrifice any similar opportunity with all

other crops, and would have to cease to produce dates and forage, in which it is almost

self-sufficient, as well as tomatoes and cucumbers, where production levels are significant.

It would solely produce grains, and would be 100% import dependent on all other

commodities.

In designing the scenarios for the second policy option (including strategic and market

dictated storage) it is clear that the origin HHI targets cannot be met for all products solely

through domestic production. A plan must be developed that includes the other leg of

QNFSP’s Master Plan, storage, in the most efficient way that addresses the import

volatility issue. The crops chosen for storage would logically be the most land and water

intensive if produced domestically, but also must be capable of long-term storage. From

the first simulation we know that grains cannot be produced in Qatar, at least at the levels

necessary to meet the goals we have prescribed. Further, a storage strategy involving

grains addresses the scale issue and effectively combats the origin concentration problem

in a key area—one resulting from lack of scale.

31

A strategy could be developed as follows. Some small amount of grain would still be

grown in Qatar as a buffer, and to develop human capital. Then storage facilities would be

built that would be large enough to contain the deficit relative to target levels, plus a

strategic amount equal to, say, a year’s consumption which provides insurance against

export curtailment and other food security disruptions. The plan would be to wait until

world grain prices were low, and then hire a large bulk carrier or carriers. At this point, as

Qatar is going to purchase a substantial amount of grain, it has gained purchasing power

which incents exporting countries to compete for the business. Qatar no longer has to act

like a micro-state. Qatar locks in the large shipments of grain at the low price, and then

fills its storage facilities. Grain is released as the market dictates at a low, stable price.13 A

brief discussion of storage is included at the end of this article.

As to domestic production, we produce one of many possible, qualitatively similar

scenarios. The plan sets the minimum domestic production level for wheat, corn, and

barley at 5% of consumption each and caps them at 10%. The deficit to meet the origin

HHI target and also provide for a year of strategic storage will be imported as per the

strategy discussed above. Providing for the deficit this way will produce price-volatility

reducing social premiums.

The plan calls for reclamation of all 40,000 hectares at a budget just shy of about US$10

million. This will probably result in excess land, considered a buffer. There is a significant

change in planning as regards water, which substantially reduces both private and public

costs substantially. The planned capacity for the solar/desalination facilities is

206,000,000 at a PPP cost to the Qatari government of US$2.5 billion. US$40million is

32

still budgeted for crop subsidies, some of which may defray storage costs. The total budget

(before considering storage capital costs) is approximately $2.6 billion. The actual budget

may change due to the final level of crop subsidies.

The reduction-of-HHI constraints were never guarantors of a scientifically determined

level of price-volatility diminution. But they served as reasonable guidelines. Other

information should influence these constraints, however. For example, from Table 1 we

see that under a strict reading of the HHI rule fully 60% of the consumption of green beans

should be produced domestically although Table 1 also demonstrates that almost 20% is

already produced domestically. More important, from Table 2 the β for green beans is less

than half of the market β which by definition is 1. This means that the import price

volatility associated with green beans is very low. There is no reason to artificially

increase its level of domestic production through a large minimum-production-requirement

constraint. Indeed that would perversely transfer resources from commodities with higher

social contributions. Nine of the 21 commodities in our portfolio have βs substantially less

than 1, establishing that imports of these commodities are not contributing to price

volatility, indeed these import prices are much less volatile than the overall import index

(QFMB). Further, 5 of the nine have levels of domestic production 10% or higher and two

provide Qatar with close to self-sufficiency.

A nation initiating an agricultural sector should be well diversified, but the policy

emphasis should be on those things, such as import-price volatility, that are national issues.

In addition, nonlinear mathematical programming models work best when there are some

boundary conditions, especially when objective function components of some entities may

33

have very large first derivatives. For low-import-price-volatility commodities, as

determined by βs that round to .5 or below, a separate boundary constraint rule applies,

regardless of the HHI-origin calculation. For 7 of the 9—onions, carrots, green beans,

squash, broccoli, pumpkins, and melons—the minimum boundary condition is 5% of

production. The maximum boundary is 10%

For high import-price-volatility commodities, the minimum production constraints are

approximations of those in Table 1, but don’t exceed 40% as that is viewed as not realistic.

Commodities with substantial levels of domestic production have minimum boundaries set

close to existing production levels plus add-factors where necessary to more closely

approximate the constraints derived in Table 1, if such constraints are relevant (in

cucumbers there is no add factor because Table 1 does not call for any remedial production

with respect to origin HHIs). Upper boundaries on all of these are production no more

than 50% of consumption.

One final addition. Dates have extreme cultural importance in Qatar which has social

value and must be recognized. As of now Qatar produces over 90% of its dates. They are

also one of the most water intensive crops in the portfolio. To provide flexibility in the

model, but recognize a cultural reality that could sink a policy proposal, we limit date

production to 50% of production. Similarly fodder production is historic and almost

iconic. Qatar is usually self-sufficient in fodder but it, too, is very water intensive.

Accordingly we bound fodder production at between 30 and 50% of consumption.

34

Table 5 presents the inputs and results of the illustrative simulation for the second policy

option which includes storage. This time we are able to achieve an optimal result,

maximizing the dampening of price volatility, given the many constraints we have

imposed. There is a reduction in the total budget due to actual crop subsidies less than

budget. A buffer of over 30,000 hectares that are not needed to achieve the optimal initial

portfolio will have been reclaimed, but will prove useful in the future. [Table 5

Approximately here]

Wheat, corn, and barley have the highest rates of import-price volatility of any of the 21

commodities in our portfolio. In raw tonnage imported they dwarf the other 18

commodities. Therefore it is essential to Qatar to hit its HHI-origin target through storage

and the purchasing strategy previously discussed. To make up the origin target deficit

storage will have to be built for about 160,000 tonnes of grain. The deficit for wheat is

31,000 tonnes, 54,000 tonnes for corn, and 72,000 tonnes for barley.14 A year’s worth of

safety storage of the three grains is on the order of 550,000 tonnes. So, including an extra

buffer of 20,000 tonnes the strategy in this second major policy option will require

construction of storage facilities capable of storing 730,000 tonnes of grain. The total

capital cost of building the numerous facilities required to store this amount of grain ranges

from US$31 million and US$55 million, depending on the type and configurations of

storage facilities (Dhuyvetter et al. 2007–unit conversions by authors).15 This could be

financed through a PPP mechanism to enhance privatization or via direct government

expenditure.

CONCLUDING REMARKS

35

We have introduced a new approach to guiding policy decisions related to embryonic

domestic agricultural production to enhance food security in small arid countries; a

characteristic that describes many Arab ones. The sole focus of this article has been the

measurement of social premiums from domestic production and strategic storage which

partially avoid agricultural import-price volatility. These results are included in the

objective function of a constrained optimization exercise, and motivated by the many

constraints.

The exercise was a narrow one. It focused strictly on diminishing food price volatility and

was limited to guiding a proposal for a crop portfolio in the initial year of operation of a

food security program. This is but one component of an overarching food security

strategy, but it is an important one. The list of other tasks is daunting—designing an

efficient distribution chain, producing a food safety program that is up to international

standards, designing attractive PPP models to attract direct foreign investment, extension

programs, developing contracts that encourage stability, accounting for food preferences,

nutrition, elasticities and cross-elasticities, etc. Separate analyses and models will be

required for many on these, and it is the combination of results and the judgment of

experts, informed by these results, that will define the optimal crop portfolio. Extreme

food-import-price volatility is sufficiently damaging to Qatar, however, that our opinion

holds that it demands separate treatment.

Solely with respect to our narrow model a brief, non-inclusive list of future research

projects is as follows:

36

1. Meat and dairy products could be explicitly included in the commodity

list—requiring an animal-feed model which relates the demand for grains and fodder, as

well as their composition, to domestic-production levels of poultry, meat and dairy

products.

2. The possibility of land heterogeneity must be explored to determine

whether a mixed-nonlinear-integer modeling technique is required.

3. In this article water requirements are point estimates but other research

has estimated requirements for at least some crops as convex curves. Should this research

cover a wide range of commodities, the results should be incorporated into the model.

4. Hedonic models employing micro-data should be used to measure such

items as the effects of logistics anomalies and rent seeking behavior, as well as other issues

that could affect price volatility so that associated avoidance premiums can be added

explicitly to the objective function and/or represented in further constraints.

5. New technologies, such as the pilot project described earlier must be

incorporated to input accurate land and water intensities for each crop, as well as to ensure

that the costs employed are accurate and up to date.

37

1 Qatar’s income is extremely skewed, even among Qatari nationals, and the country

depends on middle-class expatriates for virtually all services; from construction to

engineering to medical services to financial services, education, etc. Household budgeting

is a key economic issue for most families. The price volatility in virtually all commodities

imported into Qatar is, itself, unpredictable (Basher et al. 2013), resulting in chronic

insecurity when forming household budget strategies of which food is a major component.

The inability to formulate probabilities or patterns in volatility to inform choices has been

simulated in neuroeconomic experiments addressing decision making under uncertainty

(Pushkarskaya et al. 2010; Huettel et al. 2005; Platt and Huettel 2008). There is substantial

evidence from the experimental literature that during decision making different areas of the

brain are stimulated depending on the level of uncertainty. When family-budget decision-

making occurs under extreme uncertainty such that the formation of expectations upon

which to base choices becomes essentially impossible a priori, qualitatively different brain

functions are most likely invoked. If the neuroeconomic experimental results properly

represent this budget dilemma, then as in the experiments, the sympathetic nervous system

(“flight or fight”) is activated and stress-related neurotransmitters and hormones (cortisol)

are released (Hsu et al. 2005; Chritchley et al. 2001). This would occur on a regular

basis—during every shopping trip for produce, and since this is a regular activity, this

stressor would be ubiquitous and exist over long periods of time. There is substantial

evidence that chronic stress, resulting in constantly elevated cortisol levels, leads to the

type of health issues witnessed intensively in Qatar, such as obesity, type-two diabetes,

38

hypertension and mental health issues out of line with other countries in similar economic

situations (Everly and Lating 2013; Kopp and Réthelyi 2003; Joëls and Baram 2009). This

is not to say that a causal relationship between food price volatility and health issues in

Qatar has been established, or even tested, but a plausible hypothesis exists that endemic

food price volatility may be a contributor to Qatar’s extremely serious health issues. If the

hypothesis were sustained it would indicate economic implications for productivity,

diversification possibilities, innovation, etc. This suggests a very important subject for a

national research agenda.

2 Some have proposed greater cooperation in purchasing among GCC states to achieve the

economies of purchasing power scale necessary to mitigate import price volatility and

other food security dilemmas that result from lack of scale. It is beyond the purview of this

article to analyze this possibility, and heretofore cooperation has proved illusive, but it is

surely a desirable topic for further research.

3 These include countries such as Bahrain, Kuwait, Oman, Qatar and the United Arab

Emirates (UAE), all having miniscule populations. According to Streeten (1993), a country

is considered to be “small” if its population were less than 10 million people. Save for the

UAE, the Arab micro states we consider have populations much smaller, in the 2 to 3

million range and therefore we refer to them as micro states.

4 At this writing QNFSP’s Master Plan has been completed and QNFSP has been

disbanded with the plan and necessary personnel transferred to the Ministry Business &

Trade with ultimate responsibility resting with the Minister. It is unclear which portions of

the Master Plan will be initially implemented. In this article we base our analysis on the

final QNFSP Master Plan. 39

5 For a detailed description of the pilot project see The Sahara Forest Project,

downloadable at www.saharaforestproject.com .

6 CHAC takes its name from a rain god of the Maya.

7 HAPY is an early Egyptian god of the harvest.

8 As there is currently almost no domestic production in Qatar currently, there is no basis

to conclude otherwise. Several crop cycles and the evolution of strategies in Qatar that

decide whether or not to invest in Klein-Leffler type premiums will be necessary to assess

this assumption, as will be the response of competing importers. This is also

computationally convenient when defining an optimization problem solely with respect to

price volatility. It should be noted that there is a small proportion of imports, tomatoes and

lettuce from the Netherlands for example, that are of a much superior quality to other

imports and enjoy substantial Klein-Leffler premiums. The quantities are sufficiently

small as to not affect our analysis.

9 Diminution of volatility is considered to be a positive social benefit and therefore the

objective function maximizes the elimination of volatility rather than being presented as a

minimization problem. This makes the calculations in the other parts of the model more

tractable.

10 On an historic note, Bernoulli (1738) described his utility function as logarithmic.

11 Established in 1989, the Arab Qatari Company for Agricultural Production, is one of the

largest producers in Qatar. Website: http://www.aaaid.org/english/Arab_Qatari_Co.htm

12 Note that since all the information is given in the text, and only three crops were

produced, no portfolio table is provided for this first iteration, but is available from the

authors. 40

13 A similar counter-cyclical strategic storage policies to protect consumers from very high

and volatile food prices in the Middle East and North Africa region is also advocated by

Larson et al. (2014).

14 Authors’ calculations based on the simulation output reported in Table 5.

15 It is beyond the scope of this article to discuss the different storage options in terms of

the size of individual units, type of construction or configuration. This is a subject for a

separate optimization exercise.

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Figure 1: Graph of Utility Function for Domestic Production of a Single Product Commodity

Utility

0% Domestic Production Share of Consumption 100%

Utility Max

Social Food-Security Value

T%

-20.0%

-15.0%

-10.0%

-5.0%

0.0%

5.0%

10.0%

15.0%

3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11

2004 2005 2006 2007 2008

MO

NTH

LY %

CHA

NG

E IN

INDI

CES

Figure 2: FAO Food Index Monthly % Changes vs. QFMB Monthly % Changes

FAO FOOD PRICE INDEX QFMB

Table 1: Calculation of domestic minimum production from HHI calculations

Commodity Import %

Origin HHI (Unadjusted)

Prorated Origin

HHI

HHI Reduction to Hit 2700

Target

Required Domestic

Production Share

Wheat 99.99% 3427 3427 727 26.96% Maize 99.39% 6557 6517 3817 30.89% Barley 99.61% 9971 9933 7233 42.52% Potatoes 99.64% 8583 8552 5852 76.50% Onions 86.18% 3448 2971 271 16.48% Tomatoes (hydroponics) 70.61% 7068 4991 2291 47.86% Carrots 89.05% 3491 3109 409 20.22% Cucumber (hydroponics) 34.73% 6238 2166 0 0.00% Beans (hydroponics) 88.18% 7245 6389 3689 60.73% Lettuce and Cabbage 98.33% 3605 3545 845 29.07% Peppers 89.28% 4596 4103 1403 37.46% Squash 99.97% 4416 4415 1715 41.41% Broccoli 100.00% 4650 4650 1950 41.16% Pumpkins 100.00% 7461 7461 4761 69.00% Oranges 100.00% 4297 4297 1597 39.96% Mandarins 100.00% 3150 3150 450 21.21% Grapefruit 100.00% 3384 3384 684 26.15% Grapes (hydroponics) 99.31% 2438 2421 0 0% Mellons 83.32% 4878 4065 1365 3% Dates 9.48% 4975 472 0 0.00% Alfalfa, Rhodes 0.00% 3426 0 0 0.00% Source: Authors’ calculations.

Table 2: Agricultural commodity price volatility βs

Commodity β Standard Error Probability Error

Distribution Distribution Probability*

Wheat 2.300 assumed nss Maize 2.524 0.015 0.0000 Normal Barley 2.300 assumed nss Potatoes 2.224 0.137 0.0000 Normal Onions 0.196 0.007 0.0000 GED 0.0000 Tomatoes 1.685 0.264 0.0000 Normal Carrots 0.470 0.108 0.0015 GED 0.0003 Cucumber 1.100 assumed NSS nss Green Beans 0.447 0.025 0.0000 GED 0.0000 Lettuce and Cabbage 0.923 0.311 0.0030 Student's t 0.0000 Peppers 1.586 0.252 0.0000 Normal Squash 0.080 0.000 0.0000 Normal Broccoli 0.266 0.092 0.0039 GED 0.0000 Pumpkins 0.300 assumed nss Oranges 1.295 0.264 0.0000 GED 0.0000 Mandarins 1.636 0.513 0.0014 Normal Grapefruit 4.309 0.620 0.0000 Normal Grapes 3.954 1.147 0.0006 Normal Melons 0.522 0.134 0.0001 GED 0.0000 Dates 0.401 0.082 0.0000 GED 0.0000 Alfalfa, Rhodes Grass and Berseem 0.625 0.042 0.0000 GED 0.0000 Source: Authors’ calculations. *Tests for non-Normal (Gaussian) distributions.

Table 3: Land/water Requirements for Domestic Production, Domestic Consumption and Imports

Commodity Land: Hectares/Tonne

Water: m3/tonne

Total Consumption

(tonnes) Imports

(tonnes)

Wheat 0.2 2,316.5 139,838 139,829 Maize 0.190476 1,478.8 210,304 209,031 Barley 0.25 1,660.1 193,029 192,284 Potatoes 0.041667 299.6 12,064 12,020 Onions 0.025 250.1 37,062 31,940 Tomatoes (hydroponics) 0 92.8 40,374 28,508 Carrots 0.028571 103.3 7,683 6,842 Cucumber (hydroponics) 0 88.9 13,166 4,572 Beans (hydroponics) 0 35.4 1,396 1,231 Lettuce and Cabbage 0.014286 54.1 16,764 16,484 Peppers 0.049334 257.7 5,410 4,830 Squash 0.029542 85.1 3,236 3,235 Broccoli 0.026976 117.3 5,931 5,931 Pumpkins 0.027181 121.7 2,206 2,206 Oranges 0.028571 274.3 17,690 17,690 Mandarins 0.033333 310.1 2,964 2,964 Grapefruit 0.02 243.6 6,253 6,253 Grapes (hydroponics) 0 881.7 3,042 3,021 Mellons 0.033333 99.0 13,564 11,302 Dates 0.083333 1,389.4 23,659 2,243 Alfalfa, Rhodes 0.013333 1,613.8 342,007 0 Source: Authors’ calculations.

Table 4: Inputs and portfolio results from simulation of an iteration of the domestic production only policy--INFEASIBLE

Quantity Required Design

Capacity

Implied Cost if Actual Usage Was Equal to

Capacity

A priori Budgeted Amounts

Land (ha) 40,534* 40,000 Land Cost $9,601,809* $9,544,000 Water (m3) 322,355,562 400,000,000 Water PPP Cost $3,836,031,184 $4,760,000,000 Crop Subsidy Cost $24,511,980 $40,000,000 TOTAL $3,870,144,973 $4,809,544,000

Crop Portfolio from

Model Solution (tonnes)

Commodity Wheat 37,699 Maize 64,968 Barley 82,080 Potatoes 0* Onions 0* Tomatoes (hydroponics) 19,320 Carrots 0* Cucumber (hydroponics) 3,950 Beans (hydroponics) 848 Lettuce and Cabbage 0* Peppers 2,027 Squash 0* Broccoli 0* Pumpkins 0* Oranges 0* Mandarins 0* Grapefruit 0* Grapes (hydroponics) 0* Melons 0* Dates 0* Alfalfa, Rhodes 0* Source: Authors’ calculations. xxxx* Indicates violation of a constraint.

Table 5: Inputs and portfolio results from simulation of an iteration regarding a policy including strategic storage--OPTIMAL

Quantity Actually Used Design

Capacity

Implied Cost if Actual Usage Was Equal to

Capacity

A priori Budgeted Amounts

Land (ha) 8,612 40,000 Land Cost $2,039,913 $9,544,000 Water (m3) 206,000,000 206,000,000 Water PPP Cost $2,451,400,000 $2,451,400,000 Crop Subsidy Cost $7,481,520 $40,000,000 TOTAL $2,460,920,433 $2,500,944,000

Optimal Crop

Portfolio from Model Solution (tonnes)*

Commodity Wheat 13,984 Maize 21,030 Barley 19,303 Potatoes 9,462 Onions 8,860 Tomatoes (hydroponics) 20,550 Carrots 2,046 Cucumber (hydroponics) 3,950 Beans (hydroponics) 1,396 Lettuce and Cabbage 5,672 Peppers 2,233 Squash 1,452 Broccoli 2,813 Pumpkins 1,569 Oranges 7,645 Mandarins 806 Grapefruit 2,188 Grapes (hydroponics) 139 Melons 5,525 Dates 16,556 Alfalfa, Rhodes 110,805 Source: Authors’ calculations. *Portfolio includes domestic production only.