THE STATE OF AGRICULTURAL SECTOR MODELLING AND
POSSIBILITIES OF FURTHER DEVELOPMENT BASING ON SIMILARITY
BUNKÓCZI, LÁSZLÓ, PITLIK, LÁSZLÓ
Institute of Methodology, St.Stephens University, H-2103 Gödöllő, Hungary
KEYWORDS: sector modelling, simulation, automation, quality assurance, similarity
Since 1980 the regional agricultural analyses basing to statistical data assets (with doubtful
authenticity at national level also) were raised to formal grade within the EU, and from these
the most voluminous and complex version is the agricultural sector modelling. In the next
pages a “critical” interpretation of an EU-project (CAPRI1: Common Agricultural Policy -
Regional Impact Analysis) - closed in the beginning of 2007 - and the usage possibilities of
some results from the general modelling and futurology at sector modelling will be presented.
The aim of the study is to point out, that only in that case, when the idea of consistency is
well and detailed drafted can the modelling awaited to increase it’s authenticity and the
efficiency of it’s construction, as without these the social benefit of the models - because of
the relative high labour need and the uncheckable accuracy problems - can be questioned…
Justification of the topic choose (motivation)
The authors in the last decade took part in the largest (at national and EU level too)
agricultural sector modelling projects in basing and testing (SPELGR, PIT, IDARA, CAPRI –
cf.: http://miau.gau.hu). The model-development steps themselves are connected to the
Institute of Agricultural Policy at the Bonn University (IAP - http://www.agp.uni-bonn.de/)
and to their projects. The basing process can be seen as constructing the Economic Accounts
of the Agriculture (EAA) and national and regional level forecasts (e.g.: expected yields) both
defined as model input. In the frames of the testing process the task was to find arguments for
Common Agricultural Policy - Regional Impact Analysis
and against (cf. with consistency criteria) validating the datasets outcome as result of the
Other researches (connected to Prime Minister’s Office and to PhD works) running parallel
with sector modelling approached and examined the anomalies of data assets management
and the possibilities of the automation of modelling (OTKA F030664, T049013).
The occasion for publishing the experiences and recognitions gathered along years is the
closing of the CAPRI project, as all project-closing equals with the appointment of new
research directions also.
The aims of László Bunkóczi`s PhD research is to explore the anomalies of agricultural
sector modelling, to draw solution suggestions, to change the present rather intuitive based
forecasting practise with a consistent, plausible and checkable (either dynamic) future
generating, and thus in this study his task is to evaluate the CAPRI model.
In the centre of László Pitlik`s researches there is the exploration of the possibilities of
automatic knowledge acquisition basing on measured data. Testing the recognised general
knowledge affects the agricultural sector modelling also, as the most complex consistency
idea is drawn within the questions of the economics of the agriculture up till now. The
suggested similarity based analyses gives the methodological frames to automate the present
intuitive forecasting practise.
Short overview of the agricultural sector modelling (source: How the EU`s agricultural
policy is made, planned University note, made by the support of DAAD:
- SIMONA: Behind the abbreviation there is the simulation and monitoring model of
the former German Democratic Republic (GDR). The model was born before the
joining process of the former GDR to the EU, after the assignment of the German
allied agricultural government and had aims in agricultural sector modelling.
- QUISS: The model constructed in the seventies was known as a pioneer project of
the German modelling, and that’s why it was better used in a groping and in an
experimental way rather than a device for direct economy political consultancy. The
obtained experiences were built into the latter SPEL, RAUMIS, and CAPRI
applications. The aim of the model was to make quantitative analyses in regional
level and by farm types and to give information about the agriculture.
- DAPS: The predecessor of the latter SPEL and CAPRI models. Sectoral like (not
regional). In it’s name there are hints for dynamic analyses and for it’s forecasting
- RAUMIS: The regional models (RAUMIS model family) have a higher resolution
than the EU and World trade (WATSIM model), and these models supply the data,
for more detailed (farm group level) analyses (DIES). Demo data for RAUMIS
(from 1991) can be reached under this URL: http://www.agp.uni-
- DIES: The DIES model basis on the data of the Farm Accountancy Data Network
(basically accountancy), but functionally in it’s methodology it concerns to the EAA
- PIT: In the frame of the PIT project, the first not formal, but SPEL methodology
based database was created between 1997 and 1999 for the assignment of the EU,
and on the base of it the ex-post analyses of the Hungarian agricultural development
was made (Köckler, 1999). The PIT database is the starting point for the simulation
module of the IDARA project. http://interm.gtk.gau.hu/spelgr/
- SPEL – Sektorales Produktions und Einkommens des Landwirtschaft (1996-1999)
- IDARA – Integrated Development of Agricultural and Rural Areas (2000-2003)
- CAPRI – Common Agricultural Policy - Regional Impact Analysis (2003-2007)
MATERIAL AND METHODOLOGY
The base material for the critical analysis is from the CAPRI - closing in the beginning of
2007- project’s databases, model runs and methodology. For the analysing of the possibilities
of automating partially the sector models, the experiences of the case studies - from wider and
wider area (more than hundred independent area) - of an own developed methodology were
used. The process forming these new similarity based solutions for online service is supported
by the INNOCSEKK 156/2006 project (c.f. my-x.hu).
Data content: The „material” of the analyses is the data. For the first step it’s necessary to
overview the basics of data assets management from agricultural sector modelling:
What kind of data assets is handled by a national level agricultural sector-model (cf.
- For starting database it’s perfect, quite good as an example and makes possible the
settlement in the direction of a column (sectoral) and in a row (national product
production, consumption, export import).
- More dozen plants and about one dozen animal husbandry sector or main product.
- Decades long time series (averages) for the countries of the EU and from other
There are four kinds of model (cf. SPEL):
- Short time forecasting model / simulation: After the central hypotheses of the SFSS,
the most important decisions concerning the production are already made. The
production is in progress. Thus the most important exogenous variables can be defined
(estimated) by experts (not on a statistical base). Only the substitutable resources and
products (diverse fodders) have to be calculated by the model in an endogenous way.
- Middle long forecasting model / simulation – simple: The MFSS1 is a device for
political analyses and serves as a support tool for decision preparation along dialogues
between politicians and decision preparatory. MFSS1
- Middle long forecasting model / simulation „complicated”: MFSS2
What contains a regional model (CAPRI vs. SPEL)?
- COCO (Consistent and Complete) starting database for each present EU member state
- for their NUTS II regions and for more than 1.000 farm types (CAPREG), and
- for large regions of the World,
- modelling environment load for:
- Age divided groups for animals
o Egg. bull/cow, heifer/young bull
o Diverse feed demand, diverse manure,
- general welfare indices (GDP, GNP), and
Used data sources: EAA and FADN
The limits of the data assets:
- Using FADN data, theoretically it would be possible to run a simulation for a specified
single farm – supposing, that an FADN farm is fully representative – but it’s not.
Because of the earlier item had to create farm types by NUTS II regions, which
behaves almost the same way, has almost the same size, with similar activities and
quantity of outputs etc. In case of certain regions 10 farm types aren’t enough but at
another place 3 is enough.
- After the datasets supplied by the national statistical offices it’s not possible to create a
consistent area balance of a country, that could be used the clearly see the inner logic of
a country’s, region’s land use. The 5-10% error only for the arable land questions what
we optimise in the end. When the land as a limited resource can’t be settled as basing a
model, how it can be expected, that the animal stocks and trade strategies built on the
ratio of sown area from a model, to be realistic? When it’s about a relative small sector,
the more dangerous is the disturbance caused by the gross area…
The inner model of CAPRI
There’s only one model/scenario in a highlighted position:
- BASELINE or standard run for 2013.
Beside it there are countless possibilities with the:
- Scenario databases and program files.
Comparing to SPEL here there’s no short-, mid- or long time model/forecast. Here is given
a forecast/model run for 8 years in the future, which is made under the „ceteris paribus”
principle where the environment and all the affecting factors are constant. Here come the
scenario runs where it’s possible to make a model run with different settings/factors which
could give answer for that question what would happen if something new measure is
introduced (e.g.: customs) or the way of subsidy were changed. The result is the difference
between the constant and planned agricultural policies, which in this form, in the absence of
future statistical data can never been checked.
The principles of the CAPRI model:
a 3 year average – or an average year - is created from the starting database (2002-
a future state is created for 8-years, which can be interpreted as the average of the
target 3 year (minimising the impacts of weather),
expert opinions are used basing on future linear trends visualised to define the future
yields, the inner/international supply and demand,
along creating the future state, the goal (function-?) is always to maximize the income
of the given region,
among affecting factors the inner and outer supply and demand are taken into account,
for calculating prices an iterative method is used which supposes that there is/will be
always equation on the market between Supply and Demand,
contains constraints, which has to be taken in a forced way into account by the model,
not to reach extreme results (market balance, production, prices, production value,
After this description it can be summarized, that in case of all plants the crossing point of
the supply and demand quantities - curves (?) - is made in an iterative way where the
curves/functions are made by linear trends, and the goal is among all steps to maximize the
region’s income taking into account the constraints.
Other modules of CAPRI:
- Complex modelling of husbandry:
o Divided age group of animals,
o Diverse feed demand and environment load by species, for genders and age
o Remark about the quality of data: On the base of the regional pig substance
statistics the authors didn’t manage to find that kind of parameter
combination to validate all the biologic and technologic expectations in all
the counties and to deduce the national indexes from the indexes of counties
(e.g. mass of cutting) (http://miau.gau.hu/miau/78/pig_consi.xls).
- Model for environmental load:
o Nascent ammonia, methane, carbon dioxide, N2O,
o nascent manure,
o used fertiliser.
- Modelling indices reflecting general developed state of the economy and society:
- All the routines are executed under the GAMS program language. The extension is
- Storing the data is under the GAMS data exchange format: *.gdx – freeware
- User interface:
o Java based User Interface(s)
o Results under XML base under Explorer – „old”, or in the „new” version:
1. The critical characterization of sector modelling:
The tasks of the initialisation and testing of sector modelling are to ask for the principles of
data assets management and to ensure the interpretation of the model run results.
PARTIAL EXAMINATION OF THE RESULTS`S OF A CAPRI MODEL RUN
Examining the values for 2013, the following part of the table which is highlighted for the
Sector 2002 2013 change
(1.000 ha) (1.000 ha) (%)
Other oilseeds 37,87 1,87 -95,06%
Pulses 27,84 0,12 -99,59%
Sugar beet 57,88 246,98 326,75%
Other industrial plants 2,68 0,06 -97,66%
All land cultivated 6.886,71 6.627,8 -3,76%
Production areas and their changes 2002-2013 CAPRI model run (baseline) results (date:
27.10.2006. work material)
After this table, assuming unchained agricultural policy, for the average year of 2013 on
the base of the 2002 year, there will be 246,88 thousand hectares of sugar beet instead of
57,88 thousand hectare in Hungary beside unchained population and market tendencies. Of
course starting out from the average year of 2002, it neither can be taken into account, that in
2006 the sugar factories in Kaba and in Kaposvár, before it in 2005 in Hatvan and before that
in Selyp was closed, and nor that the EU Commission in the autumn of 2006 began again
reviewing the necessity of the sugar beet based sugar production within the EU, instead of the
higher sugar contained sugar reed, though that shall be imported. Not taking into account the
topical events - as a BASELINE run shall not know them – though raises the question: if the
production area is forecasted by trends, how it can be, that the value is positioned into that
height which is more times higher than the chronological maximum, without specifying the
demographic and market demand which could impact this huge increase.
Also conspicuous from the listed numbers the 95,06% decrease of the other oils seeds in a
period, when the renewable energy resources can be identified as general strategical aims.
After the information of the moment of the analyses: the government could agree with the
„national” oil company (MOL), and it seems that the factory in the region of Nagykunság -
delivered in 2002 - now – after 5 years - will be able to start with bio-diesel production
(extruding and etherisation) what is made from rape seeds. Reflecting to this agreement this
95% decrease is unintelligible, where the latest information means only the realisation of the
known strategy, and not a cardinal strategy change. There’s the question again: how this
minimal value under the chronological minimum (1.870 ha) can be deducted and what kind of
powers lead to this point.
Other significant industrial product’s production area - like the maize - doesn’t change in a
significant measure after CAPRI BASELINE run. Starting out from the chronological data a
significant change is not necessary also, and examining the bio-alcoholic project’s plans,
perhaps the reduction of the area of maize won’t be not needed to reduce, but this isn’t sure
for the wheat.
Not referencing for the topical-political plans which affect the agriculture’s future, it’s
important to see clearly, that in case of trend based forecasts using the whole chronological
time series it’s hardly likely to predict 320% growth or 95% decrease. Whether only the data
of the last three years are used for an 8-year long forecast, than a drastical 20% year/year
change (along 3 years) may cause that less than 100% decrease or more than 100% increase.
Thus from this point of view it wouldn’t be allowable to work only with that 3 years long
As the length of the period for forecasting was given as a subjective expert opinion, arises
the question logically, how can the given data (all time series) be processed in a unified
methodology, in that way that the expectable change of the production levels (as real
endogenous variables) shall come into being in the form of the most consistent estimation.
This will be described in a separate chapter and the methodological general potential of the
similarity analyses will be presented.
The 4% reduction of all cultivated area can’t be said to be significant, but in this case we
should hold in front of our eyes the principles of balanced statements and have to specify also
where this out falling area appears and why do we have to count with out falling areas from
an economical point of view.
2. The possibilities of utilisation of the similarity analysis for improving the authenticity
and the efficiency of sector modelling:
The similarity analysis always starts out from an object-attribute matrix. Anything can be
an object, or an attribute (X, Y). Along the analysis the next general questions can be
- can it be shown out, and whether yes, in case of which objects there is illogical
state (inconsistency) by the examined phenomena (Y)?
- for which kind of rule set (expert system) can the relation set between X and Y
attributes inducted (cf. ceteris paribus, yield function)?
- how figures the value of Y (simulated, and forecasted) on the base of arbitrary X-
- which X-es are in tight relation with Y, and which attributes becomes noise
(perturbation) on the base of the analyses?
- what kind and how strong field of forces are behind the inconsistencies?
- what is the best practice (cf. genetical potential)?
- how big insecurity can be assigned to each outcome of an expert system (cf. CNF)?
- can the change of time and the space be deducted through the change of
Translating it into the vocabulary of the agricultural sector modelling:
- Can some disproportions be shown out in case of prices, yields, costs deduced on
the base of the environmental and inner attributes of the objects (e.g.: regions,
forms of enterprises)?
- How the discrete yield functions of given products look like (how big and what
kind of combinatorial space can describe them) and how figures the ceteris paribus
- What kind of values (e.g.: rain, yield, soil attributes) could be measured on those
areas where no measuring station exists, but more relevant environmental factor is
known beside the measured and environmental data of the measuring station?
- What kind of financial, accountancy and environmental indexes are in tight relation
with another one? (e.g.: http://miau.gau.hu/miau/104/fb_dipl.doc)
- Which factors lead and with how big impact to the evolving of migration, yield,
bio-diversity and soil load?
- How big is the genetical potential in case of plant and animal products?
- What kind of doubtfulness debits the entire yield estimations?
- Whether discrete estimations show or not the passing by of the forecasted period?
One of the most important inputs of the agricultural sector modelling is to specify the
expectable yields for the planned period (e.g.: after 8 years) on the base of expert estimations.
Nowadays the experts wish to make it by choosing an arbitrary long period from the given
time series, fitting on it a trend and reading the result from this trend (in other words: fully
subjective). These unproved opinions may conflict with each other in cases of more plant and
animal products, and solving these inconsistencies can be assured through only by fine-tuning
of these subjective opinions iteratively.
Instead of these arbitrary, inaccurate, unproved and time spending process it’s possible to
work out the next method: Basing on the relative long time series, let’s choose that kind of
space-time (e.g.: country, year) objects, where the examined period has passed several (e.g.:
on the base of the data 1973-1978 (X), Y should be the yield change between 1978-1986). For
the out forming object-attribute matrixes is valid automatically the COCO methodology
(Component-based Object Comparison for Objectivity). As the result of it, it can be
determined, to which consequence sample pattern (e.g.: quick growing, slow growing,
stagnation, slow decrease, quick decrease) can the topical (last) pattern rated most likely. The
consequence sample patterns are not else than valued estimations certainly, so in this way it’s
possible to give answer without guessing (!) for the question what can be expected for e.g. 8
years later, and the answer is based on a rule set which is valid for more hundred objects…
This is not else than special yield function estimation for key-press. These kind of
examinations were made in the frame of the IDARA project and as a separate research task
(cf. pig stock forecasts) (http://miau.gau.hu/miau/search2.php3?string=idara, and
Beside estimating the input data, the similarity analyses is able to show in case of outputs
(e.g.: prices, production areas, number of animals) -estimated by any methodology – which
object hangs out from the row supposing simple, additive or multiplicative relation. In this
way the anomalies (sugar beat, rape etc.) being explored by expert view and listed above, can
be derived automatically as error/suspicion lists on the base of the data assets.
1. The critical characterisation of agricultural sector modelling:
The equation, and it’s deduction between Supply and Demand in the model is made across
the way as it was built into the model, like „the classical and well behaving market” as it is
represented by the Marshall-cross, where the equation may happen, but only in that case when
the supplied more expensive product neither produced nor taken to the market on the Supply
The Marshall-cross (source: own figure)
In case of Hungary for wheat and crops the following scheme is more proper to utilize:
105 EUR/t E=?
80 EUR/t D mills
2 million t 5 million t
The evolving Demand (D) and Supply (S) „curves” after intervention purchases (source:
The produced about 5 million ton soft-wheat’s price since 2004 has been determined not
by the equation of Supply and Demand, than the guaranteed intervention buying ups. Thus
those mills/trading and exporting companies that were used to the earlier equilibrium price
which was between 20-22 thousand HUF, are not any more able to buy any gram of soft
wheat in this price as non of the producers sells the wheat cheaper as the guaranteed 105
EUR/ton price. Nonetheless if the country needs only 2 million tons of soft wheat, and the
105 EUR/ton price is given for it buy the mills, there will be plus 3 million tons of soft wheat
which is produced inside the country and will stay inside too. From this point the market
equilibrium can be interpreted only up till 2 million tons of soft wheat and not for the resting
3 million tons, which could lead to introduce the idea of surplus and overproduction.
An other important ad after the Marshall-crosses nice archy demand and supply curves,
that in reality all the points of these two lines have to be the same up till 2 million tons and
can not be interpreted the meaning of the archiness as the producers are willing to sell small
quantity at low price only while the buyers - counter to it - willing to buy it at high price only,
big amount of product the producers willing to sell at high price only while the buyers willing
to buy it at low price.
2. The problems of the application of the similarity analyses:
The universality of similarity analyses wears it’s own critics also:
- the equation can be a vision made by an over learning model…
- the cause of the inconsistency can be the elemental lack of data …
- the estimations for one Y may conflict with each other, so in case of more factors
assuring consistency comes up as a separate task…
- in case of relative low number of objects more solutions with the same fitting may
Solving the upcoming problems leads back to the area of intuitive model control-
mechanisms. But it’s not all the same, at which level human intelligence is needed there,
where the human knowledge may have impacts transferred into source code, or there, where
the experiments of constructing the next source code are taken place. Implicitly (following the
logic of chess-automats) all the knowledge that can be algorithmic has to be programmed, and
only exploring the new knowledge items shall be left under the rules of human heuristics…
Over viewing the problems of sector modelling and the potential solutions for it.
In case of a statical model it’s impossible to gain out the results for the inner 7 years when
the forecasted range is 8 years. Contrary to this, nothing blocks the developers to move back
yearly the starting year to form out an 8 year long model or the outlook of the main model run
only for 5-6-7 years. With these actions dynamical results can be gained, which could certify
the future with showing the path leading to there.
The BASELINE delivered by CAPRI for 2013 – or the results of a scenario run, can be
examined only as a future state and not as a process with all the milestones leading to the end
For evaluating the goodness of modelling a similar complicated problem these cardinal
points are figuring out in front of us:
- In case of dynamic processes a long time forecast may get authentic only by getting
known the steps toward till the end point. Because of the conscious static future state
of the CAPRI, only with executing model runs for the earlier years and generating
future states for the 4-5-6-7th year could make indirectly dynamic the model and
authenticate the basically known result for the 8th year.
- The change potential of the yields (in year/year) of the Hungarian plant production
excludes the possibility of using linear trend curves for short time – but in case of
longer forecasting they can be used. In this case yields weren’t examined, but the
forecasted drastically change of production areas makes the forecast/model run
- A future state is credible if it’s - at least - inside itself consistent and plausible. E.g.:
the all cultivated area stays the same for each year, or the change can be followed and
known why it happens – but without this, the consistency can’t be measured at all.
CAPRI tries to assure the consistency and plausibility with a relative numerous constraints
– but may be, it seems that there are some problems either with the not enough quantity of
constraints, or with the starting 3 years for forecasting or perhaps with the incidentally
2. The general principles of the similarity analysis affecting the sector modelling:
Against the potential problems the shown methodology of similarity analysis (COCO) is
able to efficiently evoke the expert intuition, to execute the automatically induction with a
high precision, so it results time savings and more accuracy in the modelling.
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