1. 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.
2. 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.
3. Presentation Outline
1. Introduction
2. Methodology
a. Data & data sources
b. MPP/MVP vs agricultural resilience
3. Results/findings
a. Resilience modellinga. Resilience modelling
b. Output correlation coefficients
c. Output graphs (superimposed)
4. Discussion
a. Questions
b. Contributions to answers
5. Summary & Conclusion
4. Data & data sources
• All data from FAOSTAT, 124 countries, 1961-
2005 (FAO, 2011).
• Key data includes output aggregated in tonnes,
wheat units and I$.
• Zone demarcation: agric tractors + chem.
Fertilisers: HIs, Intermediates & LIs.
6. Results: Agric Resilience, Wheat Units
0100400
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
New Zealand
0200400
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
Malawi
0200400
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
Paraguay
7. Results: Agric Resilience, Tonnes
0100200300
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
New Zealand
0100200300
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
Malawi
Fitted values Fitted values
0200300
20 25 30 35 40 45
Time variable
% MPP of land % land
Fitted values
Paraguay
8. Results: Agric Resilience, I$
0100200
20 25 30 35 40 45
Time variable
% MVP of land % land
Fitted values
New Zealand
0100200300
20 25 30 35 40 45
Time variable
% MVP of land % land
Fitted values
Malawi
050200
20 25 30 35 40 45
Time variable
% MVP of land % land
Fitted values
Paraguay
9. 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* 0.0000Lebanon 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 theGrenadines 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.
10. Results: Aggreg. Output, HIs
6.0e+068.0e+061.0e+07
Output(I$)
1.0e+071.5e+072.0e+07
Output(tons)
2.0e+074.0e+076.0e+07
Output(wheatunits)
New Zealand
4.0e+066.0e+06
Output(I$)
5.0e+061.0e+07
Output(tons)
02.0e+07
Output(wheatunits)
0 10 20 30 40 50
Time var
Output (wheat units) Output (tons)
Output (I$)
11. Results: Aggreg. Output, HIs
2000004000006000008000001.0e+06
Output(I$)
5000001.0e+061.5e+062.0e+062.5e+06
Output(tons)
2.0e+063.0e+064.0e+065.0e+066.0e+067.0e+06
Output(wheatunits) 0 10 20 30 40 50
Time var
Output (wheat units) Output (tons)
Output (I$)
Albania
2.5e+063.0e+063.5e+064.0e+064.5e+06
Output(I$)
5.0e+066.0e+067.0e+068.0e+069.0e+061.0e+07
Output(tons)
1.0e+071.5e+072.0e+072.5e+073.0e+07
Output(wheatunits)
0 10 20 30 40 50
Time var
Output (wheat units) Output (tons)
Output (I$)
Austria
1000015000200002500030000
Output(I$)
050000100000150000
Output(tons)
100000150000200000250000300000
Output(wheatunits)
0 10 20 30 40 50
Time var
Output (wheat units) Output (tons)
Output (I$)
Bahamas
4.0e+066.0e+068.0e+061.0e+07
Output(I$)
5.0e+061.0e+071.5e+072.0e+07
Output(tons)
02.0e+074.0e+076.0e+07
Output(wheatunits)
0 10 20 30 40 50
Time var
Output (wheat units) Output (tons)
Output (I$)
New Zealand
16. 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. mensuration
approaches 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?
17. 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 thee.g. rationality of man as a producer and the
multiplicity of agricultural outputs produced, even
at the country level.
18. 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 othermodelling 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.
19. 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.
20. 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 theiii. (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 reductionist
and simplistic. Contrast with cases where models are, rather
superficially, virtually exclusively used to explain reality.
