Agricultural Output Aggregation at a

Crossroads?Insights from the Interface of Mathematics, Agricultural Modernization and Indigenous Knowledge, 1961-2005Modernization and Indigenous Knowledge, 1961-2005

by Moyo, D.Z., Edriss, A.K., & Moyo, B.H.Z.

Introduction

• Measurement of agric. GDP or output is almost always in monetary terms; local currency & PPP dollars, for example.

• Central question: need this always be the case?– Is there analytical power as it were in alternatives like tonne and wheat

unit measurement.

– Should we be searching for even more alternatives?

• Paper, in brief, engages debate about tonnage, wheat unit and I$ output aggregation/measurement.output aggregation/measurement.– Debate seems to be quietening down.

• Question; Key contribution = No! The debate need not quiet down.– and demonstrates that engaging this debate (further) can be

worthwhile.

– Also demonstrates use of IK to expand the frontiers of maths/science.

• 2 parts: resilience modelling and actual output.

Presentation Outline

1. Introduction

2. Methodologya. Data & data sources

b. MPP/MVP vs agricultural resilience

3. Results/findingsa. Resilience modellinga. Resilience modelling

b. Output correlation coefficients

c. Output graphs (superimposed)

4. Discussiona. Questions

b. Contributions to answers

5. Summary & Conclusion

Data & data sources

• All data from FAOSTAT, 124 countries, 1961-

2005 (FAO, 2011).

• Key data includes output aggregated in tonnes,

wheat units and I$.

• Zone demarcation: agric tractors + chem.

Fertilisers: HIs, Intermediates & LIs.

MPP/MVP measurement of agricultural resilience

Results: Agric Resilience, Wheat Units

010

040

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

New Zealand

020

040

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

Malawi

020

040

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

Paraguay

Results: Agric Resilience, Tonnes

010

020

030

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

New Zealand

010

020

030

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

Malawi

Fitted values Fitted values

020

030

0

20 25 30 35 40 45Time variable

% MPP of land % land

Fitted values

Paraguay

Results: Agric Resilience, I$

010

020

0

20 25 30 35 40 45Time variable

% MVP of land % land

Fitted values

New Zealand

010

020

030

0

20 25 30 35 40 45Time variable

% MVP of land % land

Fitted values

Malawi

050

200

20 25 30 35 40 45Time variable

% MVP of land % land

Fitted values

Paraguay

Results: PW Correlation Coefficients

• Tables 4.7-4.9: Table 4.7 Pairwise correlation coefficients for agricultural output measured in international dollars (PPP), wheat units (W) and tonnes (T), high external input using countries, 1961-2005 Country name TW p-value TPPP p-value WPPP p-value

Albania 0.9210* 0.0000 0.9947* 0.0000 0.8851* 0.0000

Austria 0.9301* 0.0000 0.9510* 0.0000 0.8987* 0.0000 Bahamas 0.9484* 0.0000 0.8922* 0.0000 0.8325* 0.0000

Barbados 0.9982* 0.0000 0.9177* 0.0000 0.9043* 0.0000 Belize 0.9695* 0.0000 0.9808* 0.0000 0.9040* 0.0000

Bulgaria 0.8139* 0.0000 0.9782* 0.0000 0.7612* 0.0000 Canada 0.9214* 0.0000 0.9845* 0.0000 0.8951* 0.0000

Cuba 0.9865* 0.0000 0.9301* 0.0000 0.8584* 0.0000

Cyprus 0.0652 0.6703 0.8850* 0.0000 -0.3846* 0.0091 Democratic People's Republic of

Korea 0.9873* 0.0000 0.9969* 0.0000 0.9941* 0.0000

Egypt 0.9983* 0.0000 0.9961* 0.0000 0.9967* 0.0000

Fiji 0.9763* 0.0000 0.9219* 0.0000 0.9154* 0.0000 France 0.9670* 0.0000 0.9807* 0.0000 0.9709* 0.0000

Germany 0.9773* 0.0000 0.7148* 0.0000 0.6703* 0.0000 Greece 0.9842* 0.0000 0.9954* 0.0000 0.9783* 0.0000

Hungary 0.8237* 0.0000 0.9893* 0.0000 0.8084* 0.0000

India 0.9986* 0.0000 0.9948* 0.0000 0.9933* 0.0000 Israel 0.2921* 0.0516 0.9735* 0.0000 0.2098 0.1666

Italy 0.8133* 0.0000 0.9751* 0.0000 0.8176* 0.0000 Jamaica 0.7183* 0.0000 0.2831* 0.0595 -0.4476* 0.0020

Lebanon 0.9878* 0.0000 0.9979* 0.0000 0.9841* 0.0000 Lebanon 0.9878* 0.0000 0.9979* 0.0000 0.9841* 0.0000 Mauritius 0.9634* 0.0000 0.7897* 0.0000 0.5979* 0.0000

New Zealand 0.9785* 0.0000 0.9842* 0.0000 0.9933* 0.0000

Norway 0.5774* 0.0000 0.4963* 0.0005 0.4791* 0.0009 Pakistan 0.9976* 0.0000 0.9963* 0.0000 0.9911* 0.0000

Poland 0.5402* 0.0001 0.8410* 0.0000 0.6440* 0.0000 Portugal 0.7196* 0.0000 0.4129* 0.0048 -0.0287 0.8517

Republic of Korea 0.1444 0.3440 0.9255* 0.0000 -0.2173 0.1516 Romania 0.8109* 0.0000 0.9635* 0.0000 0.8441* 0.0000

Saint Kitts and Nevis 0.9996* 0.0000 0.9988* 0.0000 0.9973* 0.0000

Saint Lucia 0.9810* 0.0000 0.9903* 0.0000 0.9648* 0.0000

Saint Vincent and the Grenadines 0.9704* 0.0000 0.8780* 0.0000 0.9122* 0.0000

Spain 0.8141* 0.0000 0.9931* 0.0000 0.7964* 0.0000

Sri Lanka 0.9905* 0.0000 0.9909* 0.0000 0.9822* 0.0000 Sweden 0.9208* 0.0000 0.5829* 0.0000 0.7757* 0.0000

Switzerland 0.9002* 0.0000 0.6067* 0.0000 0.4703* 0.0011 Thailand 0.9934* 0.0000 0.9892* 0.0000 0.9916* 0.0000

Turkey 0.9889* 0.0000 0.9987* 0.0000 0.9928* 0.0000

United Kingdom 0.8691* 0.0000 0.8727* 0.0000 0.9590* 0.0000

United States of America 0.9716* 0.0000 0.9988* 0.0000 0.9738* 0.0000

Viet Nam 0.9952* 0.0000 0.9994* 0.0000 0.9960* 0.0000

TW = correlation between tonnage and wheat unit output; TPPP = correlation between tonnage and international dollar output; WPPP = correlation between wheat unit and internal dollar output; Colour coding denotes variations in magnitude and statistical significance of association; * denotes statistical significance at 10% significance level.

Results: Aggreg. Output, HIs

6.0e

+06

8.0e

+06

1.0e

+07

Out

put (

I$)

1.0e

+07

1.5e

+07

2.0e

+07

Out

put (

tons

)

2.0e

+07

4.0e

+07

6.0e

+07

Out

put (

whe

at u

nits

)

New Zealand

4.0e

+06

6.0e

+06Out

put (

I$)

5.0e

+06

1.0e

+07

Out

put (

tons

)

02.

0e+

07O

utpu

t (w

heat

uni

ts)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)Output (I$)

Results: Aggreg. Output, HIs

2000

0040

0000

6000

0080

0000

1.0e

+06

Out

put

(I$)

5000

001.

0e+0

61.

5e+0

62.

0e+

062.

5e+

06O

utpu

t (to

ns)

2.0e

+06

3.0e

+06

4.0e

+06

5.0e

+06

6.0e

+06

7.0e

+06

Out

put

(whe

at u

nits

)0 10 20 30 40 50

Time var

Output (wheat units) Output (tons)

Output (I$)

Albania

2.5e

+06

3.0e

+06

3.5e

+06

4.0e

+06

4.5e

+06

Out

put

(I$)

5.0e

+06

6.0e

+06

7.0e

+06

8.0e

+06

9.0e

+06

1.0e

+07

Out

put (

tons

)

1.0e

+07

1.5e

+07

2.0e

+07

2.5e

+07

3.0e

+07

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Austria

1000

015

000

2000

025

000

3000

0O

utpu

t (I$

)

050

00010

000015

0000

Out

put (

tons

)

1000

0015

0000

2000

002500

0030

0000

Out

put (

whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Bahamas

4.0e

+06

6.0e

+068.0e

+061.0e

+07

Out

put (

I$)

5.0e

+06

1.0e

+071.5e

+07

2.0e

+07

Out

put (

tons

)

02.0

e+074.0e

+076.0e

+07

Out

put (

whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

New Zealand

Results: Aggreg. Output, Intermediates

1.5e

+06

2.0e

+06

2.5e

+06

Out

put (

I$)

6.0e

+06

8.0e

+06

1.0e

+07

1.2e

+07

Out

put (

tons

)

1.5e

+07

2.0e

+07

2.5e

+07

Out

put (

whe

at u

nits

)

Malawi

5000

001.

0e+

06Out

put (

I$)

2.0e

+06

4.0e

+06

6.0e

+06

Out

put (

tons

)

5.0e

+06

1.0e

+07

Out

put (

whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)Output (I$)

Results: Aggreg. Output, Intermediates

2000

0040

0000

6000

0080

0000

1.0e

+06

Out

put

(I$)

5000

001.

0e+

061.

5e+0

62.

0e+

062.

5e+

06O

utpu

t (t

ons)

2.0e

+06

3.0e

+06

4.0e

+06

5.0e

+06

6.0e

+06

7.0e

+06

Out

put

(whe

at u

nits

)0 10 20 30 40 50

Time var

Output (wheat units) Output (tons)

Output (I$)

Algeria

1.5e

+07

2.0e

+07

2.5e

+07

3.0e

+07

3.5e

+07

4.0e

+07

Out

put

(I$)

4.0e

+07

6.0e

+07

8.0e

+07

1.0e

+08

1.2e

+08

1.4e

+08

Out

put

(ton

s)

1.0e

+08

1.5e

+08

2.0e

+08

2.5e

+08

3.0e

+08

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Argentina

1.0e

+071.5e

+072.0e

+072.5e

+07

Out

put

(I$)

2.0e

+07

4.0e

+07

6.0e

+07

8.0e

+07

1.0e

+08

Out

put (

tons

)

5.0e

+071.0e

+081.5e

+082.0e

+08

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Australia

5000

001.

0e+

061.

5e+

062.

0e+

062.

5e+

06O

utpu

t (I

$)

2.0e

+06

4.0e

+06

6.0e

+06

8.0e

+06

1.0e

+07

1.2e

+07

Out

put (

tons

)

5.0e

+06

1.0e

+07

1.5e

+07

2.0e

+07

2.5e

+07

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Malawi

Results: Aggreg. Output, LIs

2.0e

+06

3.0e

+06

4.0e

+06

Out

put (

I$)

5.0e

+06

1.0e

+07

1.5e

+07

Out

put (

tons

)

2.0e

+07

3.0e

+07

Out

put (

whe

at u

nits

)

Paraguay

01.

0e+

062.

0e+

06O

utpu

t (I$

)

05.

0e+

06O

utpu

t (to

ns)

01.

0e+

07O

utpu

t (w

heat

uni

ts)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)Output (I$)

Results: Aggreg. Output, LIs

5000

001.

0e+

061.

5e+

062.

0e+

062.

5e+

06O

utpu

t (I

$)

2.0e

+06

4.0e

+06

6.0e

+06

8.0e

+06

1.0e

+07

Out

put

(ton

s)

1.0e

+07

1.5e

+07

2.0e

+07

2.5e

+07

3.0e

+07

Out

put

(whe

at u

nits

)0 10 20 30 40 50

Time var

Output (wheat units) Output (tons)

Output (I$)

Angola

050

00001.0e

+061.5e

+06

Out

put

(I$)

1.0e

+06

1.5e

+06

2.0e

+06

2.5e

+06

3.0e

+06

Out

put

(ton

s)

2.0e

+06

4.0e

+06

6.0e

+06

8.0e

+06

1.0e

+07

1.2e

+07

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Yemen

2000

0040

0000

6000

0080

0000

1.0e

+06

1.2e

+06

Out

put

(I$)

1.0e

+06

2.0e

+06

3.0e

+06

4.0e

+06

5.0e

+06

6.0e

+06

Out

put

(ton

s)

05.0

e+061.0e

+071.5e

+07

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Zambia

01.0e

+06

2.0e

+06

3.0e

+06

4.0e

+06

Out

put

(I$)

05.0

e+061.0e

+071.5e

+07

Out

put

(ton

s)

01.0

e+072.0e

+07

3.0e

+07

Out

put

(whe

at u

nits

)

0 10 20 30 40 50Time var

Output (wheat units) Output (tons)

Output (I$)

Paraguay

Poking questions

• Key questions are: Why should/(are) the results be so? What explains them? What do we learn?

• e.g. peculiarity(ies) associated with LIs and intermediates: careful thought and further investigation regarding appropr. mensurationinvestigation regarding appropr. mensurationapproaches and methods in theses areas?

• Association levels: TPPP, TW, WPPP: Might output correlation values be a measure or an indicator or a form of metaphorical representation of resilience?

Making sense of the findings (in part?)

1. Not just mensuration theory, but also the

strong possibility that there are important

livelihood elements at play.

� At macro and micro levels.

� e.g. rationality of man as a producer and the � e.g. rationality of man as a producer and the

multiplicity of agricultural outputs produced, even

at the country level.

Making sense of the findings (in part?)

2. Science has not yet explored and discovered everything, and we need not act as if it has.

� vs starting point.

� Reality ought to mould our models, when modelling is necessary and useful, not the other modelling is necessary and useful, not the other way round.

� A firm understanding of reality should be the main or key thing, the compelling force, as opposed to being satisfied with ‘beautiful’ models and theories.

� vs agricultural development impasse.

Summary & Conclusion

• Nature of the discourse on agricultural output aggregation so far makes for good theoretical progress in that it forewarns us of perceived or conceived potential pitfall areas.

• However falls short of overtly specifying the • However falls short of overtly specifying the theoretical assumptions/premises that must first hold.

– & appears to have been readily accepted by many with little question.

• Paper offers empirical insights into the realism of the posited concerns.

Summary & Conclusion

• Results: more research into:i. more specific specifications of when the pitfall

concerns would hold.

ii. conditions (for example, why and how) that allow for strong correlations.

iii. (possible) implications/interpretation in terms of the iii. (possible) implications/interpretation in terms of the underlying livelihood structures, strategies, systems and dynamics.

iv. implications for economics and research.i. e.g. reality must mould models and theories, and do so only

when modelling is necessary in as much as it is reductionistand simplistic. Contrast with cases where models are, rather superficially, virtually exclusively used to explain reality.

Thank you