Food Security And Agricultural Sustainability (evor)

Food Security and Agricultural Sustainability: During the 60’s it was expected that in the near future, there would not be enough food to cope with the growing population. During the late 70’s, global food production grew to a level that was able to cope with the increasing population. Since then, the focus has been shifted towards the problems of insufficient food supplies in major parts of the world and concerns over intensive farming. Due to this, there has been an increasing amount of interest into long term food security due to problems such as land degradation. As a result of the need to target levels of pollution and sustainable farming, the UN introduced a programme titled Agenda 21. This however, was seen as too general for local scales and lead to the Brundtland report. This ensured that the later generations would be able to meet there own farming needs whilst still working towards improvement. The Food and Agricultural Organisation (FAO) of the UN have also offered a more specific description to lead towards sustainable farming that is more socially acceptable. A major issue is conflict management, between land degradation and productivity rise. In various practices the Total Factor Productivity (TFP) is used as a yardstick for comparison; however there are others. To gain a better understanding of the socio-economic aspects of land use and climate change, more research has been determined as necessary into1: • • • • • •

Agricultural impacts in developing regions Influence of climate scenarios on water availability in sensitive areas Socio-economic impacts of changes in human health Socio-economic impacts of environmentally induced migration Impacts of extreme weather events based on risk assessment Socio-economic impacts of changes in ecosystems and biodiversity

As the quality of the soil is one of the main contributing factors to the yield of the crop, it is important at maintain it at a reasonable level. The condition of the soil is improved by actively coping with soil pollution through regulatory and market measures. It is also improved by seeking feasible solutions for clean up areas e.g. implementing the bronwfield policy to repair damaged areas..

Rough set analysis as a tool for Meta-Analysis: Meta analysis uses previous research findings to look for any links which may be transferred to other, unexplored cases. The statistics of this process is well developed, especially when using quantitative data. Since the data used for this example was soft data, the standard techniques could not be implemented. To

compensate for this, a fairly new method was used for identifying patterns in qualitative data. This is known as rough set theory. Since rough set theory is in the relatively early stages, there are some problems: • • • • • •

Representation if uncertain or imprecise knowledge Evaluation of quality of available information with respect to consistency and presence or absence of repetitive data patterns Identification and evaluation of data dependencies Approximate pattern classification Reasoning with uncertainty Information-preserving data reduction

In this study, rough set theory was used to determine whether there are factors that systematically affect the variation in productivity in agricultural and agrofood sector. Decision rules are used to determine the importance of information. If attributes occur very frequently, it exerts more dominance on that decision rule. If the attribute occurs in every instance, it is seen as a dominant critical success factor.

Application: Two tables are shown in the document listing the productivity growth in the farm sector for OECD’s, and the growth in the productivity growth in the agro-food sector. The data from these tables then undergoes a rough set analysis. This is undertaken by classifying the codification for attributes and decision variables. The codes for these variables are shown in tables 3 and 4. Tables 5 and 6 present the results. Table 1 Studies of productivity growth in the farm sector of the OECD countries N 1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1

Input -0.10 -0.10 -0.10 -1.06 0.01 1.05 -0.40 2.40 1.40

Outpu t 1.10 1.90 2.50 1.17 2.37 3.57 2.60 0.20 2.60

1.90

Switzerland Switzerland Switzerland

Country Australia Australia Canada Canada Canada Canada Canada Canada Canada Canada

Time Period 1971 1981 1971 1990 1981 1990 1962 1970 1962 1978 1970 1978 1962 1971 1962 1990 1971 1981

TFP 1.20 2.00 2.60 2.23 2.36 2.52 3.00 -2.20 1.20

-0.50

1981

1990

-2.40

-1.20

1.10

1973

1981

2.30

-1.10 -1.10

0.90 0.00

1973 1981

1988 1988

2.00 1.10

3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3 6 3 7 3 8 3 9 4

U.S.

0.88

1.85

1953

1957

0.97

U.S.

0.25

2.92

1957

1960

2.67

U.S.

0.24

1.90

1960

1969

1.66

U.S.

0.68

1.06

1969

1973

0.38

U.S.

0.72

2.75

1973

1979

2.03

U.S.

0.50

2.20

1973

1988

1.70

U.S.

0.08

1.21

1962

1970

1.13

U.S.

0.31

1.72

1962

1978

1.41

U.S.

0.55

2.22

1970

1978

1.67

U.S.

0.14

1.66

1950

1960

1.52

U.S.

0.17

1.76

1950

1982

1.59

U.S.

0.05

0.84

1960

1970

0.79

U.S.

0.34

2.60

1970

1982

2.26

-0.30

1.60

1967

1987

1.90

France

0.10

2.30

1967

1987

2.20

The Netherlands

1.40

4.00

1967

1987

2.60

Belgium

-0.20

1.80

1967

1987

2.00

Luxembourg

-1.90

1.80

1967

1987

3.70

U.K.

-0.20

1.60

1967

1987

1.80

Ireland

0.80

2.60

1967

1987

1.80

Italy

0.80

1.80

1974

1987

1.00

Denmark

0.70

2.60

1974

1987

1.90

Germany

-0.40

1.30

1973

1989

1.70

France

-0.50

1.90

1973

1989

2.40

Italy

-0.60

1.70

1973

1989

2.30

0.40 0.00

3.10 1.20

1973 1973

1989 1989

2.70 1.20

Germany

The Netherlands Belgium/Luxemb

0 4 1 4 2 4 3 4 4 4 5 4 6 4 7 4 8 4 9 5 0 5 1 5 2 5 3 5 4 5 5 5 6

ourg U.K.

-0.20

1.50

1973

1989

1.70

0.90

2.30

1973

1989

1.40

Denmark

-0.10

2.30

1973

1989

2.40

Germany

0.00

1.70

1965

1985

1.70

France

0.10

1.80

1965

1985

1.70

-0.60

1.60

1965

1985

2.20

2.60

4.20

1965

1985

1.60

0.00

2.20

1965

1985

2.20

-0.30

2.20

1965

1985

2.50

0.90

2.90

1965

1985

2.00

-0.10

1.50

1965

1985

1.60

The Netherlands

1.40

3.60

1950

1960

2.20

The Netherlands

0.10

3.80

1960

1970

3.70

The Netherlands

1.10

4.40

1970

1980

3.30

The Netherlands

-0.50

2.40

1980

1988

2.90

The Netherlands

0.60

3.60

1950

1988

3.00

Ireland

Italy The Netherlands Belgium/Luxemb ourg U.K. Ireland Denmark

Table 2 Studies of productivity growth in the agro-food sectors of the OECD countries

A N 1 2 3 4 5 6

Country Canada Canada Canada Canada Canada Canada

Sector Coverage Food Beverage Manufacturing Food and Beverage Manufacture Food and Beverage

7 Canada 8 Canada

Food and Beverage Food

Method Index number Index number Index number Index number Index number Econometric cost function Econometric production function Econometric cost function

Time Period 61 86 61 86 61 86 62 85 62 85 61 82 61 61

82 79

9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3 3 4 3 5 3

Canada

Food and Beverage

Index number

62

77

Canada

Econometric cost function Econometric production function

62

75

Australia

Food and Beverage Food, Beverage and Tobacco

76

90

UK

Food

Input-output

54

63

UK

Food

Input-output

68

74

UK

Agriculture

Input-output

79

84

UK

Agriculture

Input-output

54

63

UK

Agriculture

Input-output

68

74

UK

Agriculture

79

84

UK

Food

79

86

UK

Drink

79

86

UK

Manufacturing

Input-output Econometric production function Econometric production function Econometric production function

79

86

US

Food

Index number

58

82

US

Food

Index number

58

72

US

Index number Cobb-Douglas production function

72

82

Australia

Food Food, Beverage and Tobacco

76

90

US

Food

Index number

50

77

US

Food

Index number

50

72

US

Food

72

77

60

85

60

85

60

85

60

85

60

85

60

85

Japan

Food

Index number Solow's growth method Solow's growth method Solow's growth method Solow's growth method Solow's growth method Solow's growth method

Italy

Food

Labour productivity

70

80

Italy Australia

Food Food

Labour productivity Labour productivity

53 80

63 85

Italy Canada Germany

Food Food Food

UK

Food

US

Food

accounting accounting accounting accounting accounting accounting

6 3 7 3 8 3 9 4 0 4 1 4 2 4 3 4 4 4 5 4 6 4 7 4 8 4 9 5 0

Austria

Food

Labour productivity

80

85

Belgium

Food

Labour productivity

80

85

Canada

Food

Labour productivity

80

85

Denmark

Food

Labour productivity

80

85

Finland

Food

Labour productivity

80

85

France

Food

Labour productivity

80

85

Germany

Food

Labour productivity

80

85

Ireland

Food

Labour productivity

80

85

Italy

Food

Labour productivity

80

85

Japan Netherlan ds

Food

Labour productivity

80

85

Food

Labour productivity

80

85

Norway

Food

Labour productivity

80

85

Sweden

Food

Labour productivity

80

85

UK

Food

Labour productivity

80

85

Table 3 Codification of variables of studies related to the farm sector Condition Attributes: A1 - Country Other Canada Australia USA Large European UK Countries Germany Italy France Small European Denmark Countries Finland Netherlan ds Sweden Norway Belgium

Code

Code 1 1 1 2 2 2 2 3 3 3 3 3 3

A2 Input

A3 Output

Less than -0.5 Between -0.4 and -0.1 Between 0.0 and 0.4 Between 0.5 and 0.9 More than 1.0

1 2 3 4 5 Code 1

Less than 1.5 Between 1.6 and 2.6 More than 2.5

2 3

Time period: Code A4 - Starting Point

Before 1960 1970 1980

1 2 3

Code A5 Length

10 Years 20 Years 30 Years

1 2 3

Decision Variables: Total Factor Productivity

Less than 1.5 Between 1.6 and 2.5 More than 2.5

Code 1 2 3

Table 4 Codification of variables of studies related to the agro-food sector Condition Attributes: A1 - Country Other Canada Australia USA Asia Japan Large European UK Countries Germany

Cod e 1 1 1 2 3 3

Italy France Denmark Finland Netherla nds Sweden Norway Belgium

Small European Countries

A2 - Sector Coverage

A3 - Method Used

4 4 4 4

Food Beverages Manufacturing Food and Beverages Food, Beverages and Tobacco Agriculture

Index number Econometric cost function Econometric production function Input-output Cobb-Douglas production function Solow growth accounting method Labour productivity

Time Period: Co de A4 - Starting Point

3 3 4 4

19 50 19 60 19

1 2 3

Cod e 1 2 3 4 5 6

Cod e 1 2 3 4 5 6 7

70 19 80

4 Cod e

A5 Length

10 Years 20 Years 30 Years

1 2 3

Decision Variables: Average Percentage Change

Less than 1% Between 1% and 2% More than 2%

Cod e 1 2 3

Table 5 Information system of the studies related to the farm sector after codification Object s 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

A1 1 1 1 1 1 1 1 1 1 1 3 3 3 1 1 1 1 1 1 1 1

A2 2 2 2 1 3 5 2 5 5 5 1 1 1 4 3 3 4 4 4 3 3

A3 1 2 2 1 2 3 3 1 3 1 1 1 1 2 3 2 1 3 2 1 2

A4 2 2 3 1 1 2 1 1 2 3 2 2 3 1 1 1 2 2 2 1 1

A5 1 2 1 1 2 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1

D 1 2 3 2 2 3 3 2 1 2 2 2 2 3 3 3 1 2 2 1 1

22 1 4 23 1 3 24 1 3 25 1 3 26 1 3 27 2 2 28 2 3 29 3 5 30 3 2 31 3 1 32 2 2 33 3 4 34 2 4 35 3 4 36 2 2 37 2 1 38 2 1 39 3 3 40 3 3 41 2 2 42 3 4 43 3 2 44 2 2 45 2 3 46 2 1 47 3 5 48 3 3 49 2 2 50 3 4 51 3 2 52 3 3 53 3 3 54 3 5 55 3 1 56 3 4 Table 6 Information system the codification Object s 1 2 3 4 5 6 7

A1 1 1 1 1 1 1 1

A2 1 2 3 4 3 4 4

2 2 1 2 2 1 1 1 2 1 3 1 1 1 1 1 3 2 1 2 3 1 1 3 2 1 2 2 3 1 2 3 2 1 2 2 2 1 2 3 2 1 2 2 3 1 2 2 2 2 1 3 3 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2 2 1 2 2 2 1 2 2 3 1 2 1 2 1 2 2 2 1 2 2 3 1 2 2 1 1 2 2 3 2 1 2 3 1 1 3 3 2 1 3 2 3 1 3 3 1 3 3 of the studies related to the agro-food sector after

A3 1 1 1 1 1 2 3

A4 2 2 2 2 2 2 2

A5 3 3 3 3 3 3 3

D 1 1 2 1 1 1 1

8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 2 1 2 2 1 3 2 2 1 4 4 1 4 4 2 2 4 2 3 4 4 4 2

1 4 4 5 1 1 6 6 6 6 1 2 3 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 1 2 3 4 4 4 4 4 4 3 3 3 1 1 1 5 1 1 1 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

2 2 2 3 1 2 3 1 2 3 3 3 3 1 1 3 3 1 1 3 2 2 2 2 2 2 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

2 2 2 2 1 1 1 1 1 1 1 1 1 3 2 2 2 3 3 1 3 3 3 3 3 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 2 2 1 2 1 2 4 4 4 1 1 1 1 1 1 1 3 2 3 4 3 3 4 4 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2

Table 7 Accuracy and quality of classification of the decision variable for the farm sector Class of decision Class of Lower Upper decision Accuracy of approximation approximation dependant approximati number of variable on number of objects objects 1 0.500 7 14 2 1.000 31 31 3 0.611 11 18 Accuracy of approximation : 0.777 Quality of approximation : 0.875 Core of attributes : A1, A2, A3, A4, A5 Table 8 Accuracy and quality of classification of the decision variable for agrofood sector Class of decision Lower approximation

Class of decision Accuracy of dependant approximati variable on number of objects 1 0.5862 17 2 0.4167 10 3 0.1667 1 4 0.6250 5 Accuracy of classification : 0.4925 Quality of classification : 0.6600 Core of attributes : A1, A2, A3, A4

Upper approximation number of objects 29 24 6 8

Results: To utilise these results there is a need to develop a classification algorithm, to then allow for predictions to be made. The algorithm may also be used for the classification of new values. It was noted that not all of the rules are equally important or reliable. In the case study there were 23 exact rules and 3 approximate rules for the Total Food Productivity and 13 exact rules and 5 approximate rules for the agro-food sector. As many of the rules are based only on observations, the quality can be quite low. To decide which rules were most relevant, they were compared using a strength method to simplify the decision table to a few rules.

Conculsion: This paper shows the potential for rough set theory to allow for future predictions of growth in the farm sector. This could be applied to allow for predictions of

yields of crops for the year to provide a better insight into the amount of food being produced, and thus the amount needed to import/ available to export.

1: Van Ierland and Klaasen (1996)