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Optimising Global Food System Model Based on Index Evaluation System

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Nowadays, the unstable factors of the global food system, such as climate change,economic recession, political reforms, etc., have caused controversies in food acquisition and distribution, which are not conducive to the establishment and improvement of food systems in some developing countries. Thus, with an attempt to optimise current food system, we proceed our work as follows.To begin with, we analysed the main factors affecting the global food system based on corresponding questionnaires and past research essays, and obtained 7 second-level indicators. In order to make the evaluation model easier to measure, we further elaborated 21 three-level indicators.Next, by establishing a hierarchical structure model, we use the Analytic Hierarchy Process to calculate the subjective weight of the first-level indicators to meet the main characteristics of the current food system that is oriented by efficiency and profitability. Afterwards, based on the mean square error calculation after data standardisation, we establish the evaluation model of the second-level index by determining the weight of the third-level index.Following this, we use Analytic Hierarchy Process again to re-optimise the weights of the first-level indicators, so that the optimised food system model is guided by sustainability and equity, and the optimised second-level indicator model is obtained and applied to one developed and one developing country. Ultimately, the grey forecast model is used to optimise the forecasting model by comparing the current grain system model with the optimised grain system one, by analysing the adaptability of the model, and by drawing conclusions.
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Problem Chosen
E
2021
MCM/ICM
Summary Sheet
Team Control Number
2107651
Optimizing Global Food System Model Based on Index
Evaluation System
Summary
Nowadays, the unstable factors of the global food system, such as climate change,
economic recession, political reforms, etc., have caused controversies in food acquisi-
tion and distribution, which are not conducive to the establishment and improvement
of food systems in some developing countries. Thus, with an attempt to optimize cur-
rent food system, we proceed our work as follows.
To begin with, we analyzed the main factors affecting the global food system
based on corresponding questionnaires and past research essays, and obtained 7 second-
level indicators. In order to make the evaluation model easier to measure, we further
elaborated 21 three-level indicators.
Next, by establishing a hierarchical structure model, we use the Analytic Hi-
erarchy Process to calculate the subjective weight of the first-level indicators to meet
the main characteristics of the current food system that is oriented by efficiency and
profitability.
Afterwards, based on the mean square error calculation after data standardiza-
tion, we establish the evaluation model of the second-level index by determining the
weight of the third-level index.
Following this, we use Analytic Hierarchy Process again to re-optimize the weights
of the first-level indicators, so that the optimized food system model is guided by sus-
tainability and equity, and the optimized second-level indicator model is obtained and
applied to one developed and one developing country.
Ultimately, the grey forecast model is used to optimize the forecasting model by
comparing the current grain system model with the optimized grain system one, by
analyzing the adaptability of the model, and by drawing conclusions.
Keywords: Analytic Hierarchy Process, Efficiency, Equity, Food System, Grey Model,
Indicators, Optimisation, Profitability, Root Mean Squared, Standardization, Sustain-
ability
Contents
1 Introduction 2
1.1 Problemrestatement .............................. 2
1.2 LiteratureReview................................ 2
1.3 ProblemAnalysis................................ 4
2 Preparation of the Models 5
2.1 Notations..................................... 5
2.2 IndicatorStatements .............................. 5
2.2.1 Quantity Safety Index Construction . . . . . . . . . . . . . . . . . 6
2.2.2 Construction of Economic Security Indicators . . . . . . . . . . . 6
2.2.3 Construction of ecological environment safety index . . . . . . . 7
3 The Models 7
3.1 Model Evaluating the Current Food System . . . . . . . . . . . . . . . . . 7
3.2 Models Evaluating Indicators of the First and Second Level . . . . . . . . 9
3.2.1 StandardisingData........................... 10
3.2.2 Determining Weights . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.3 Evaluation of the Second Level Indicators . . . . . . . . . . . . . . 11
3.2.4 Evaluation of the First Level Indicators . . . . . . . . . . . . . . . 12
3.3 OptimisedModels ............................... 12
3.4 The Comparsion between the Current Food System and the Optimized
FoodSystem................................... 13
3.5 TheForecastingModel............................. 14
3.6 The Application of the Optimized Food System Model . . . . . . . . . . 15
4 Strengths and Weaknesses 15
4.1 Strengths..................................... 15
4.2 Weaknesses ................................... 16
5 Conclusion 16
References 17
Appendix A: Pseudo Code for Determining Weights 18
Appendix B: Pseudo Code for Grey Model 18
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1 Introduction
1.1 Problem restatement
Recently, the decline trend of the number of hungry people in the world over
the past decade has ended, and the number of hungry people has risen again. Reports
showed over 2 billion people, either suffering from hunger or being affected by moder-
ate levels of food insecurity, are estimated to be food insecure (they do not have regular
access to safe, nutritious and sufficient food).
Figure 1: Hunger Map, Adapted from FAO, 2020.
Despite the current global food system in a fraction of developed countries
works well, but the food systems in most of the countries’ are not objective, and still
advocate for economic and social policies to counteract the effects of adverse economic
cycles when they arrive.
Hence, we decided to create a sustainable food evaluation system, which con-
centrates on Quantity Safety Index, Economic Security Index, Ecological and Environ-
mental Safety Index, and Grain Quality and Safety Index.
1.2 Literature Review
In the increasingly globalized food systems, challenges result from interactions
across different scales and levels. The integrated actions should be taken by governors
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and innovators in local, national, regional, even global levels, by both public and pri-
vate actors, and across multiple levels- not only in agriculture, but also in trade, policy,
health, environment, gender norms, education, transport and infrastructure, etc. It
requires a synergetic merging rather than those only based on relatively cheaply and
efficiently.
The Food and Agriculture Organization of the United Nations (FAO) proposed
in 2014 that the development of the food system needs to consider the value generated
by the three aspects of economy, society and environment. (Figure 1).
Figure 2: Source: Adapted from FAO, 2014.
In terms of economy, if the activities carried out by each food system can bring
benefits or economic value-added to stakeholders, such as consumer food improve-
ment, workers’ wages, government taxes, corporate profits, etc.
In terms of society, it is necessary to consider activities such as food distribution
for disadvantaged groups classified by gender, age, race, etc., to promote balanced so-
cial development, such as nutrition and health, traditional activities, and animal wel-
fare.
In terms of environment, by ensuring that there is no ecological damage to the
surrounding natural environment by food system activities, and taking biodiversity,
water quality, soil, healthy growth of animals and plants, carbon footprint, water foot-
print... as our considerations.
The food system wheel framework was proposed by FAO, which includes poverty
reduction, food security and nutrition (Figure 1). According to the three levels of sus-
tainability: economic, social, and environmental. The structure of the system con-
sists of core system, societal elements and natural elements. The core system includes
production, aggregation, processing, distribution, consumption, waste disposal, and a
layer of services supporting the flow.
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Figure 3: food system wheel Framework
1.3 Problem Analysis
Food security system is complex. For most countries, the risk index of food
system on various aspects or links are not always consistent. There may exists relative
unsafe factors in a national food system, which may show high security on some levels,
while in other levels show low security. The inconsistency of security situation on
food system of different levels makes people hard to conclude an overall judgment of
a national food security situation.
Our article starts with levels of efficiency, profitability, sustainability, analysis of
the three dimensions of economic, environmental and social impacts, and equity, and
gets the relevant factors that affect the agricultural system (three-level indicators), and
then collect the data in 22 randomly selected countries, such as Argentina, Australia,
Brazil, China, etc. from 2000 to 2018. The Grain production volatility rate, Grain sown
area, etc., and 21 other three-level indicators data in the two countries are cleaned and
standardized (positive and negative indicators), and the weight values of the second
level indicators of the food system are adjusted respectively. We evaluated the current
food system with high efficiency and high profit as the orientation, used a combination
of analytic hierarchy process and entropy method to calculate the weight of the food,
increased the weight of the first-level index of efficiency and profitability, established
an optimized evaluation model, and brought it into some countries for calculation.
a) We conducted a comprehensive analysis of the four levels of economy, environ-
ment, food security and food quality to analyze the factors that affect the food sys-
tem.
b) We quantified economic, social, natural factors, and obtained four first-level indi-
cators, seven second-level indicators, and 21 third-level indicators.
c) We use the analytic hierarchy process and entropy method to analyze the weight of
the four aspects of the current food system.
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d) We use the analytic hierarchy process to re-optimize the weights corresponding to
these four first-level indicators in order to revise the model in terms of sustainability
and fairness.
e) We construct the Grey model to predict the trend of important indicators in a de-
veloping country and a developed country.
f) We have proved that the model has a certain degree of adaptability, but it needs to
be improved.
2 Preparation of the Models
2.1 Notations
The primary notations used in this paper are listed in the following table.
Table 1: Notations
Symbol Definition
QSI Quantity Safety Index
ESI Economic Security Index
EESI Ecological Environmental Safety Index
FS Food Supply
FA Food Access
FU Food Utilization
EPS Economic and Political Stability
Y1Grain Production Volatility Rate (%)
Y2Grain Sown Area (/1000ha)
Y3Grain Yield per Unit Area(Million Tonnes/ha)
Y4Per Capita Food Consumption(Million Tonnes/Ca put)
W1Financial Expenditure for Food Production(USD$)
W2Food Price(USD$)
W3Net Profit of Planting Staple Food(USD$)
Z1Chemical Fertilizer Application Amount per Unit of Cultivated Land(kg/ha)
Z2Proportion of Crops Affected(%)
X1Average Value of Food Production (I$per Caput)
X2Average Protein Supply(gr/caput/day)
X3Average Supply of Protein of Animal Origin(gr/caput/day)
X4Average Protein Supply(%)
X5Number of People Undernourished, 3 Years Average(Million)
X6Gross Domestic Product per Capita (in purchasing power equivalent)(Constant 2017 International$)
X7Percentage of Children Under 5 Years of Age Who Are Stunted(%)
X8Percentage of Children Under 5 Years of Age Affected by Wasting(%)
X9Percentage of People Using Safely Managed Drinking Water Services(%)
X10 Per Capita Food Production Variability(Constant 2004 2006 Thousand International$per Capita)
X11 Per Capita Food Supply Variability(kcal/caput/day)
X12 Political Stability and Absence of Violence/Terrorism(Index)
2.2 Indicator Statements
In order to be able to analyze the current global food system more comprehen-
sively, we introduced four levels of food security coefficient index based on the data
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from AMIS agricultural market information system. One of the first-level indicators is
efficiency, representing both solid and basic foundation of national food security, and
the remaining three indicators are profitability, sustainability and equity.
Under the above indicators, we developed seven second-level indicators: Quan-
tity Safety Index, Economic Security Index, Ecological and Environmental Safety Indi-
cator, Food Supply, Food Access, Food Utilization, and Economic and Political Stabil-
ity.
The Quantity Safety Index of efficiency indicator is set by establishing an abso-
lute quantity index system including grain production volatility rate, grain sown area,
grain yield per unit area, per capita food consumption. The Economic Security Index
includes financial expenditure for food production, food price, net profit of planting
staple food. Ecological and Environmental Safety Index consists of 2 indicators, which
are chemical fertilizer application amount per unit of cultivated land and proportion
of crops affected. Food Supply includes per capita grain output, per capita protein
supply,per capita animal source protein supply and adequacy rate of dietary energy
supply. Food Access includes degree of food shortage and per capita domestic pro-
duction planting. Food Utilization includes the proportion of short children under 5
years old, percentage of children under 5 years of age affected by waste, proportion
of the population with clean water. Economic and Political Stability includes the per
capita food production variability, per capita food supply variability and political sta-
bility and non-violence.
2.2.1 Quantity Safety Index Construction
We physically characterize the Quantity safety index that food production is
more susceptible to economic and social factors, such as climate and markets, which
often exhibit certain fluctuations.
Grain production volatility rate: Set as one of the important indicators to mea-
sure the stability of grain production. The volatility rate of grain production is
Rt= (YtY0
t)/Y0
t(1)
where Ytrepresents the total grain production in year t, and Y0
trepresents the 5-year
moving average of grain production.
Grain sown area: Set as the basis of grain quantity security. The larger the grain
sown area is, the higher the degree of security of grain quantity is.
Grain yield per unit area: Reflects the development of grain science and technol-
ogy. The higher the grain yield per unit area, the higher the degree of security of grain
quantity.
Per capita food consumption: Only by ensuring the food security of individual
subjects can the overall food security be realized. The per capita grain possession index
can not only reflect the stability of the total amount of grain, but also reflect the grain
supply capacity with the growth of population.
2.2.2 Construction of Economic Security Indicators
The subjects related to food economic security include the government, farmers
and consumers. On the one hand, food economic security focuses on people’s ability
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to obtain food economically, and the solution is that people can "afford" food, while
farmers can get reasonable income from growing food.
Financial expenditure for food production: According to the proportion of grain
sown area in total sown area.
Food price:Food price reflects the basic supply and demand of grain, and has a
negative correlation with food security. The higher the food price, the lower the degree
of food security.
Net profit of planting staple food: The level of food profit directly affects the
direct income of farmers, and affects the enthusiasm of farmers.
2.2.3 Construction of ecological environment safety index
Chemical fertilizer application amount of cultivated land: A large amount of
chemical fertilizer may lead to soil acidification and soil pollution, seriously affect the
farmland ecosystem and threaten the food ecological environment security
Proportion of crops affected: Diseases, pests and natural disasters reflect the
results of the interaction between species in the food farmland ecosystem and between
crops and climatic conditions,
Rd=Sd/S100% (2)
where Bfis the financial expenditure on grain production, fis the national financial
expenditure on agriculture, forestry and water, Sfis the sown area of grain, and Sis
the total sown area.
3 The Models
3.1 Model Evaluating the Current Food System
Firstly, we use Analytic Hierarchy Process(AHP)to calculate subjective weights.
The AHP is mainly used to judge, compare and evaluate some complex problems that
are difficult to use quantitative analysis. It’s a multi-objective decision analysis method
that combines quantitative analysis and quanlitative analysis.
The method AHP can divide the more complex problem and ask each of the
smaller factors to answer. These small parts of the decomposed factors are combined
according to a certain upper and lower relationship to build an orderly ladder struc-
ture.
Determine the importance of each factor in each level to the factors of the upper
level, that is, the weight value, and finally combine these weight values to obtain the
combined weight value of the lowest level to the highest level. The main steps of AHP
are as follows:
Step1. Establishment of hierarchical structure model.
After conducting a certain investigation and understanding of the problem to be
solved, the factors that affect its development can be divided into different levels,
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generally target level, criterion level and scheme level. In our model, we add a
sub-criteria level to the model structure. The criterion level and the sub-criteria
level are the direct factors that affect the target level. The scheme level is the
primary factor affecting the criterion level and the indirect factor affecting the
criterion level and the indirection factor affecting the target level. The change of
the criterion level determines the trend of the target level and also dominates the
plan level.
Step2. Judge the construction of the matrix.
According to the establishment of the structural model in step 1, the class level
between the factors is determined. In this model we use the 1-5 scale method to
express the importance of the factors in the same level relative to the previous
level, and then construct a judgement matrix to express the degree importance
or unimportance. For example, 5 means extremely important, and 1/5 means
extremely unimportant.
Step3. Weight value and consistency check.
Firstly, we calculate the eigenvalues and the corresponding eigenvectors of the
judgment matrix, and find the eigenvector corresponding to the largest eigen-
value of the matrix. The consistency indicator is expressed by C.I. The larger the
C.I, the worse the consistency of the judgment matrix. On the contrary, the bet-
ter the consistency effect. In order to understand whether the consistency of the
judgment matrix is satisfactory, the consistency ration CR=C.I/R.I is introduced.
If C.R is less than 0.1, the consistency result is satisfactory.
Step4. Total weight calculation and consistency check.
According to the explanation of the principle of analytic hierarchy process and
the evaluation index system of the above-mentioned food system, taking the food sys-
tem as the research object, the hierarchical structure model shown in Figure 1 is ob-
tained.
Figure 4: Hierarchical model
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We use A to represent the judgement matrix of the criterion layer efficiency, prof-
itability, sustainability, and equity for the target layer, and take the criterion layer’s
judgement on the scheme layer as an example to calculate the weight value.
A=
tar get l ayer E f f iciency Pro f itability Sustain ability Equity
E f f iciency 1 10/9 2 5/2
Pro f itability 9/10 1 5/2 10/3
Sustainability 1/2 2/5 1 10/9
Equity 2/5 3/10 9/10 1
AHP uses the sum-product method to calculate the subjective weight value of
the criterion layer indicator relative to the target layer. The weight vector is obtained
through the calculation of eigenvectors and eigenvalues. The calculated results need
to be tested to determine the quality of the matrix.
1) Calculate the eigenvalues and eigenvectors of the comparison matrix: Solve the
largest eigenvalue of the matrix A: λmax =4.019, the corresponding eigenvector is:
A0=1.380 1.487 0.617 0.516
2) Consistency check:
Since the order of matrix A is 4, the random consistency indicator of the query
data is R.I=0.89, then the calculation of C.I is:
C.I= (λmax 4)/(41)0.006
Therefore, the consistency ratio indicator is CR=C.I/R.I much less than 0.1. It can
be seen that the criterion layer is consistent with the matrix of the target layer, that
is, the construction of the comparison matrix A is reasonable and feasible. In order to
understand the calculation results more clearly and intuitively, list the table.
Figure 5: The weight value of the criterion layer to the scheme layer
3.2 Models Evaluating Indicators of the First and Second Level
First of all, the index system should be introduced for evaluations of efficiency,
profitability, sustainability, and equity. The index system is listed in the following table.
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Table 2: Index System for Evaluation of Food System
Indicators of the First Level Indicators of the Second Level Indicators of the Third Level Indicators Tropism
QSI QSI Y1-
Y2+
Y3+
Y4+
ESI ESI W1-
W2+
W3+
EESI EESI Z1-
Z2-
FSI FS X1+
X2+
X3+
X4+
FA X5-
X6+
FU X7-
X8-
X9+
EPS X10 -
X11 -
X12 +
3.2.1 Standardising Data
In the standardising procedure, the standardisation method using the range of
data is proceeded. For positive indicators, the standardisation formula is stated as
follows.
~
x1= (~
xmin{~
x}
~
1)/(max{~
x} − min{~
x})
For negative indicators, the formula is stated as follows.
~
x1= (max{~
x}
~
1~
x)/(max{~
x} − min{~
x})
where
~
x1
is the standardised vector of
~
x
based on the range of
~
x
Also,
~
x
contains all data in a particular indicator of the third level, and the order of data does
not matter. For example, suppose an indicator of the third level is X1,
~
x
contains all data of average values of food production from 2000 to 2018 in the 22
selected countries.
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3.2.2 Determining Weights
In a particular indicator of the third level,
~
x1
contains all standardised data, and the Root Mean Squared value, denoted as
could be calculated. The formula is stated as follows.
=s(~
x1mean{~
x1}
~
1)T(~
x1mean{~
x1}
~
1)
22
where 22 is the number of our selected countries.
After all Root Mean Squared values are calculated, the weights could be deter-
mined immediately. For example, X1through X4are all indicators of the third level cor-
responding to the same indicator of the second level FS, and their Root Mean Squared
values are calculated as 0.2353, 0.2737, 0.2653, and 0.2215. The weights can be calcu-
lated by putting one of the Root Mean Squared values at a time on the numerator, and
putting the sum of them on the denominator. Hence, the weights are 0.2363, 0.2749,
0.2664, and 0.2224. For the same reason, weights of other indicators of the third level
could be calculated.
3.2.3 Evaluation of the Second Level Indicators
Since all of the weights are determined, the evaluation of indicators of the second
level could be determined immediately. For example, the weights of X1through X4
corresponding to the same indicator of the second level FS are 0.2363, 0.2749, 0.2664,
and 0.2224. The model is then constructed as follows.
FS =0.2363X1+0.2749X2+0.2664X3+0.2224X4
The full model is constructed as follows.
QSI =0.2269Y1+0.3306Y2+0.1970Y3+0.2455Y4(3)
ESI =0.1793W1+0.4065W2+0.4142W3(4)
EESI =0.8810Z1+0.1190Z2(5)
FS =0.2363X1+0.2749X2+0.2664X3+0.2224X4(6)
FA =0.7422X5+0.2578X6(7)
FU =0.2855X7+0.2362X8+0.4783X9(8)
EPS =0.3221X10 +0.2348X11 +0.4431X12 (9)
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3.2.4 Evaluation of the First Level Indicators
Since for efficiency, profitability, and sustainability, the indicators of the first and
second levels are the same, it is not necessary for us to evaluate the corresponding first
level indicators, since we have evaluated before. However, for equity, the indicator of
the first level is FSI, and the formula is given as follows.
FSI =FS ·FA +FA ·FU +FU ·EPS +EPS ·FS
2(10)
3.3 Optimised Models
First of all, we use Analytic Hierarchy Process(AHP)to optimize the weights of
the first level indicators so that the optimized model is oriented towards sustainability
and equality. Repeat the steps described in the above mentioned, and use B to repre-
sent the judgement matrix of the criterion layer efficiency, profitability, sustainability,
and equity for the target layer, and take the criterion layer’s judgement on the scheme
layer as an example to calculate the weight value.
B=
tar get l ayer E f f iciency Pro f itability Sustain ability Equity
E f f iciency 1 10/9 1/3 1/4
Pro f itability 9/10 1 1/3 1/4
Sustainability 3 3 1 2/3
Equity 4 4 3/2 1
We use AHP to obtain the calculated results which are as follows:
1) Calculate the eigenvalues and eigenvectors of the comparison matrix: Solve the
largest eigenvalue of the matrix B: λmax =4.003, the corresponding eigenvector is:
B0=0.455 0.432 1.290 1.824
2) Consistency check:
Since the order of matrix B is 4, the random consistency indicator of the query
data is R.I=0.89, then the calculation of C.I is:
C.I= (λmax 4)/(41)0.001
Therefore, the consistency ratio indicator is CR=C.I/R.I much less than 0.1. It
can be seen that the criterion layer is consistent with the matrix of the target layer, that
is, the construction of the comparison matrix B is reasonable and feasible. In order
to understand the calculation results more clearly and intuitively, a table is listed as
follows.
It can be obtained that the weight distribution of the first-level indicator meets
the optimization goal. Optimize the three-level indicators evaluation model by
AHP, the optimized full model can be constructed as follows:
QSI =0.3154Y1+0.2932Y2+0.2366Y3+0.1548Y4(11)
ESI =0.3958W1+0.3593W2+0.2449W3(12)
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Figure 6: The weight value of the criterion layer to the scheme layer
EESI =0.5882Z1+0.4118Z2(13)
FS =0.2907X1+0.2853X2+0.2460X3+0.1780X4(14)
FA =0.6250X5+0.3750X6(15)
FU =0.2744X7+0.2009X8+0.5247X9(16)
EPS =0.2027X10 +0.1795X11 +0.X12 (17)
3.4 The Comparsion between the Current Food System and the Op-
timized Food System
Compared with the current food system, the optimized food system is mainly
reflected by the changes in the indicators of the optimization system:
From the current food system with efficiency and profitability as the main orien-
tations, it has shifted to the optimized food system with sustainability and equity
as the main orientation.
The optimized food system has made policy adjustments. The rapid develop-
ment of industrialization and urbanization is closely related to economic con-
struction. Economic construction will inevitably occupy arable land, which will
lead to land shortage and drastically reduce the land used for food production.
Therefore, the optimized food system advocates saving land and taking the road
of new industrialization and urbanization.
The optimized food system proposes a new working mode.
1) The unsustainable problem of agricultural production resource utilization is very
prominent. In order to increase grain production, excessive chemical fertilizers
are generally used everywhere. If things go on like this, it will cause soil com-
paction, destroy soil physical and chemical properties, reduce soil organic matter
content, imbalance the proportion of soil nutrients, low fertilizer utilization, and
continuously reduce the effect of increasing grain production. Therefore, reduc-
ing the amount of fertilizer application is the first action.
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2) Ensure stable food production capacity. Under the current situation of reduced
grain production in a certain place in a country, the government can use re-
serves and increase imports without significantly impacting the international
grain market, thereby achieving a balance between domestic food supply and
demand. However, as the country has experienced consecutive years of unfa-
vorable weather that has reduced its grain storage capacity to the limit, and the
international grain market is also tight, it is necessary to have sufficient grain pro-
duction capacity available in the country to achieve the sustainable development
of the grain system.
3.5 The Forecasting Model
In order to calculate the realization time of the optimized food system model, we
use the Grey Prediction Model to predict it.
Grey prediction is a kind of prediction between black and white. One part is
known and the other part is unknown. There is an uncertain relationship between sys-
tem factors. Grey prediction is to identify the degree of difference in the development
trend between the various factors of the system by calculating the correlation between
the factors. Its core system is the Grey Model (GM), which is a method of model-
ing the original data by accumulating or substracting or means, etc. to generate an
approximate exponential law.
The steps can be shown as follows:
Step1 First calculate the level ratio. When the level ratio is in the interval
(e2
n+1,e2
n+2)
it means that the data is suitable for model construction.
Step2 If the original value does not pass the level ratio test, you can pass the "trans-
lation conversion", that is, add the "translation conversion value" to the original
value, so that the new data meets the level ratio test and calculate based on the
data, and then calculate the predicted value and so on At the same time subtract
the’translation conversion value’.
Step3 Model construction calculates the development coefficient a, the grey effect b,
and calculates the post-test difference ratio C value.
Step4 Make predictions on the data.
Step5 Test the model, including relative error test, grade ratio deviation value test, etc.
The results of our optimized food system model can be followed in Table 3. :
Table 3: GM Model Construction Results
Development Coefficient a Grey Effect b Post-test Ratio c Value
0.0129 3.7625 0.2197
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It can be seen from the above table that the development coefficient a, the grey
effect b, and the post-test ratio C value are obtained after the model is constructed; the
post-test difference ratio C value is 0.220<=0.35, which means that the model accuracy
level is very good.
3.6 The Application of the Optimized Food System Model
We first try to apply our optimized food system model to one developed coun-
try - USA and one developing country - China. Since they are the most representative
countries in developed and developing countries so that the results are more convinc-
ing. The application steps are as follows:
Step1. Collect Data about each third-level indicator in China and USA.
Step2. Take the data into our optimized food system model.
Step3. Compare the two countries’ results.
Following the steps in above steps, we can get the results as follows:
Values China USA
QSI 20492.3447 9451.3055
ESI 155.8125 62.9929
EESI 122.7518 41.1487
FS 149.3325 267.9085
FA 3254.1201 20694.2349
FU 3.9448 52.8209
EPS 3.6451 9.2918
From the results, we can see that the the efficiency, profitability and sustainability
of the optimized food system model in China is much better than that of USA, but the
equality of the optimized food system model in USA is much better than that of China.
4 Strengths and Weaknesses
4.1 Strengths
The optimized food system model uses accurate and latest databases to guaran-
tee the reliability of the results. The results have high reference value and can be
applied in each country immediately.
From a long-term perspective, the current food system model does not have long-
term development conditions. The weakness of sustainability and equity will
lead to the deterioration of the entire system until it collapses. However, the
optimized food system model can mainly develop equity and sustainability. At
the same time, driving the development of the efficiency and profitability of the
national food system has the benefit of a win-win situation.
Team # 2107651 Page 16 of 18
Considering the changes in the environment, the optimized food system model
is extensible and can add new indicators to get accurate results.
4.2 Weaknesses
Not all indicators are used in the models. Consequently, there may be some de-
viations in the two models.
We suppose that all the countries will actively cooperate with interventions we
put forward, and neglect those passive countries. There may be some deviations
between the practical outcomes and the model we predicted.
5 Conclusion
To sum up, the instability of the world food system model is caused by multiple
factors, and multiple indicators need to be used for measurement and analysis. We
first establish an evaluation model of the current food system and classify multiple
indicators to meet the current situation where the current food system model is mainly
oriented on efficiency and profitability.
Although our model is somewhat subjective, we get the mean squared error by
standardizing the data, and the established secondary indicator model can effectively
express the current food system. At the same time, evaluation and optimization based
on evaluation models also reflect the change and improvement of the weight of food
system indicators to a certain extent. Our model shows that in optimizing the global
food system, sustainability and equity can promote global development in most as-
pects.
Team # 2107651 Page 17 of 18
References
[1] FAOSTAT. (2021). Retrieved 7 February 2021, from
FAO. 2014. Sustainable food value chain development – Guiding
principles. Rome.
[2] Cui Mingming & Nie Changhong. (2019). Research on the evolution of my coun-
try’s food security based on the index evaluation system. Bulletin of the Chinese
Academy of Sciences (08), 910-919. doi:10.16418/j.issn.1000-3045.2019.08.009.
[3] AMIS Agricultural Market Information System. (2021). Retrieved 8 February 2021,
from
[4] Maan Pu, Cai Jianming, Lin Jing, Guo Hua, Han Yan, Liao Liuwen. The temporal
and spatial evolution of the global food security pattern from 2000 to 2014 and its
influencing factors[J]. Acta Geographica Sinica, 2020, 75(02): 332-347 .
[5] 2019 - The State of Food Security and Nutrition in the World (SOFI): Safeguarding
against economic slowdowns and downturns | World Food Programme. (2019). Re-
trieved 7 February 2021, from
[6] Alexander Y. Prosekova, S. A. (2018). Food security: The challenge of the present.
Geoforum 91, pp. 73-77.
[7] Cheikh Mbow (Senegal), C. R. (n.d.). Food security.
[8] Egal, F. (2019, 10 3). Review of The State of Food Security and Nutrition in the
World. World Nutrition 2019, pp. 95-97.
Team # 2107651 Page 18 of 18
Appendix A: Pseudo Code for Determining Weights
xmin=min (min ( F oo dSecur i ty ) ) ;
xmax=max (max( Foo dS ecurit y ) ) ;
xrange=xmax−xmin ;
FoodS ecu rity = ( Fo odS ecu rit y −xmin)/ x ra ng e ;
[ xrow , x c ol ] = s i z e ( FoodSecurity ) ;
xrms=mean(s q r t (sum ( ( Foo dS ec uri ty mean(mean( F oodS ecur ity ) ) ) . ^ 2 ) / xrow ) ) ;
X= [ X , xrms ]
Appendix B: Pseudo Code for Grey Model
syms a b ;
c = [ a b ] ’ ;
A= [ 0 .368421053 0.5 7 8 9 4 7 368 0.473684 2 1 1
0 . 31 5 7 8 94 7 4 0 . 3 15 7 8 9 47 4 0 . 1 5 7 8 9 4 7 3 7 ] ;
B=cumsum(A) ;
n=length (A ) ;
for i = 1 : ( n−1 )
C ( i ) = ( B ( i )+ B ( i + 1 ) ) / 2 ;
end
D=A;D( 1 ) = [ ] ;
D=D’ ;
E=[−C; o nes ( 1 , n − 1 ) ] ;
c=inv ( E *E ’ ) *E*D;
c=c ’ ;
a=c ( 1 ) ; b=c ( 2 ) ;
F = [ ] ; F ( 1)=A ( 1 ) ;
for i = 2 : ( n+1 0 )
F ( i ) = (A( 1 ) − b/ a )/ exp ( a *( i − 1) ) + b/ a ;
end
G= [ ] ;G( 1 ) =A( 1 ) ;
for i = 2 : ( n+1 0 )
G( i ) = F ( i ) −F ( i − 1 ) ;
end
t1 = 2 0 0 0 : 2 0 1 8 ;
t2 = 2 0 0 0 : 2 0 1 8 ;
G, a , b
plot ( t 1 , A, ’ o , t2 , G)
xlabel ( ’ Year )
ylabel ( ’Indicator ’ )
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Article
The group of basic problems that determine the existence of mankind involves the surplus of food for some and the malnutrition of others. There is an opinion that ensuring food security is an integrated task of agriculture and political will, combined with the logistics of product delivery. Despite joint efforts and various UN programs to combat hunger, only short-term local results have been achieved. Food security, especially in the global sense, has not yet been implemented, and there are reasons for this. The analytical review presents evaluation of the achieved result and points out the activities that require adjustments.
Sustainable food value chain development -Guiding principles
  • Faostat
FAOSTAT. (2021). Retrieved 7 February 2021, from http://www.fao.org/faosta t/zh/data/BC FAO. 2014. Sustainable food value chain development -Guiding principles. Rome.
The temporal and spatial evolution of the global food security pattern from 2000 to 2014 and its influencing factors
  • Maan Pu
  • Cai Jianming
  • Lin Jing
  • Guo Hua
  • Han Yan
  • Liao Liuwen
Maan Pu, Cai Jianming, Lin Jing, Guo Hua, Han Yan, Liao Liuwen. The temporal and spatial evolution of the global food security pattern from 2000 to 2014 and its influencing factors[J]. Acta Geographica Sinica, 2020, 75(02): 332-347.