Bio- economic models integrating agronomic, environmental and economic issues with agricultural use of water Flichman G. in

Flichman G.

in

Dupuy B. (ed.).

Aspects économiques de la gestion de l' eau dans le bassin méditerranéen Bari : CIHEAM

Options Méditerranéennes : Série A. Séminaires Méditerranéens; n. 31 1997

pages 327-336

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--- Flichman G. B io-econ omic models in tegratin g agron omic, en viron men tal an d econ omic issu es with agricu ltu ral u se of water. In : D upuy B. (ed.). Aspects économiques de la gestion de l'eau dans le bassin méditerranéen . Bari : CIHEAM, 1997. p. 327-336 (Options Méditerranéennes : Série A.

Séminaires Méditerranéens; n. 31)

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G. Flichman

SUMMARY

-

This paper presents a framework that permits an integrated analysis of a complex

system in which intervene biophysical, socio-economic and policy components. A short introduction deals with the origin of this methodology, that implies a multidisciplinary approach, including soil

science, agronomy, crop physiology and economics. A review of the different ways by which economists study the technical dimension of production is presented, making a comparison

between the approach based on the use of econometric production functions and the engineering production functions approach. The advantages and limitations of both orientations will be briefly developed, in order to point the suitability of applying the engineering production function approach when analysing agricultural water use issues.

A schematic description of bio-economic models that integrate agronomic simulation models with mathematical programming models is presented. The specific case of POLEN project, realised in collaboration between IAM- Bari and IAM-Montpellier Institutes with a group of European research teams is showed as an application of this approach. New methodological developments, trying to ameliorate the performance of these models will be showed.

Key words: Simulation Models, mathematical programming, bio-economic modelling, production functions, irrigation.

RESUME: Cette communication a pour objectif de présenter un cadre de travail qui permet

l'analyse intégrée d'un système complexe où participent de composants biophysiques, socio- économiques et politiques. Dans l'introduction nous développons l'origine de cette méthodologie, qui implique une approche multidisciplinaire: pédologie, agronomie, physiologie de plantes et économie. Une révision de la manière dont les économistes saisissent la dimension technique de la production est présentée en comparant les estimations économétriques de fonctions de

production et l'approche de la fonction de production dite "d'ingénieur". Les avantages et les

inconvénients de ces deux orientations sont développés, pour montrer comment l'approche qui utilise les fonctions de production "d'ingénieur" est la plus adaptée, concernant les aspects

éconorniques de l'utilisation agricole de l'eau.

Options Méditerranéennes, Sér. A /n"31, 1997 Séminaires Méditerranéens

328

Une description schématique des modèles bio-économiques qui peuvent se construire à travers l'intégration de modèles de simulation agronomique avec modèles de programmation mathéma- tiques est présentée. Le cas spécifique du projet POLEN, développé en collaboration entre I'IAM- Bari et I'IAM-Montpellier, avec un groupe d'équipes de recherche européen est exposé comme

exemple. Des nouvelles approches méthodologiques, qui tentent d'améliorer la performance de ces modèles seront aussi discutées.

Mots-clés: modèles de simulation, programmation mathématique, modélisation bio-économique, fonctions de production, irrigation.

An economic to

a to analyse the techni-

cal options an eco-

nomics, these technical sented by what is called

duction functions to be a statistical estimation of the input-output

senting the possible combinations of duction that can be used in to obtain

the output. of to a

function. is that when we have to deal with many inputs and

simultaneously, the analysis becomes much

We deal with this a multi-discipli-

type a

methodological the

technology development. now possible to build and policy components. the develop-

ment of the

that made feasible bio-economic

modelling. These al-

with climate, soil and tillage (including in a

way the effects on soil and

This was developed

in

U.S.A. and We have been

al, 1985) in has been in

in

Economic models that

hom biophysical models called "bio-

senting technology in an economic model can be in a substantial way by the use of simulated data obtained a biophysical model

in bioeconomic associated with techniques, such as soil sion pollution of

As we have mentioned, if we dealing with the

economic dimension of it is

to get a detailed of the available set of techniques. to have

Bio-economic models integrating agronomic,

environmental and economic issues with agricultural use ofwater 329

h c t i o n s on

-

A limited number of observations the estimation of the

to be than the

-

is aleatory character

of samples

- the individual

techniques that the basic data the estimations of the

influenced by the present structure of relative

prices. the

in the

that

moment is quite low.

- the techno-

logical progress measurement

this

if

the past

the it will

be difficult to the technical

the

functions, we should obtain technical coefficients of

hom

tion not data

adjusted to "a

tions. A it is almost im-

possible to obtain all the

is not done in to obtain input data

economic models. is to

the influence on

soil the influence of in the

as the This is the case, even in the stations exist. We have been able to

using an

model. As the model we have

used is its limits -Steduto et

al, 1985-, but it is still tool that if

This

We on

changes in the

tion well as on tion, soil

a kind duction function. We have used it

et

this in implying a

count. Even if

in

be used in

is the most suitable tool.

the possibility of simulating

the of with the

is

We may contend that ogy in

- fol-

lowing the function ap-

is complemented with simulated data, the techniques that not actually used ex

330 Flichman

the pollution levels.

is to make at

between what is usually called positive, analytical applied economics, it

- -

as positive models

-

based on the use of - as

We will not discuss the fact that

els it is to

fm that

to in an the technical

and the in which the

-

model specification - and if we h c t i o n to be

in to situation, and not to

advice an agent on the best way to

mode1 can be built in to to

of a model implies the need of getting this

and validated. That means, the model should be

able to the in

to allow us to that

impact of these changes on the system. At used to

11). This fact induced the idea upon We model as positive, if it is built with

the of situation to be

able to of

events. Anyway, a positive model of this type can be used to help decision making in

in

willbe able to to these de-

mand

The essential specificities of bio-economic positive models may be synthesised as follows :

-

of technical specification.

-

ⁱⁿ

simulation results of bio-physical models.

-

to calculate negative externalities associated to each solution of the both tem-

An

nalities is the level of soil This means that it is possible to

to nitrate

- The high detail of technical Specification

characterising the ag-

can be applied

at is

cult to use them dealing with big na- tional levels. Of this is just

in data manipu- lation.

- an interdisciplinary

approach. This is the most difficult to

in

that has its in discipline. This

minimum

in We met this

when we wanted to

just

We

model, in to the

in this

Bio-economic models integrating agronomic,

environmental and economic issues wifh agricultural use of wafer 33 1

Scheme of a positive bio-economic model

SimulatiÓns. Results:Yields, Nitrate Pollution, Pesticide Pollution, Erosion. For each

soil type REAL AND PO- TENTIAL TECHNIQUES

I

^ARE

ater Price and 0th Input Prices : seeds, fertilisers, phytosanitary

Availability of land, water, irrigation equip-

ment, other capital goods and labour

Risk Aversion

Agricultural Equipment

I

Mathematical Programming Model

I

Policy Scenarios

Results in terms of impacts: income,

environmental impacts socio-economic

Results in terms of

erosion cropping pattern,

pesticide pollution, soil production levels,

nitrate pollution, choice of techniques

water demand

. ..

332 G, Flichman

in to analyse the impacts of

in

methodology was employed in all the case studies.

The objective of this the impact on the pat- of technological choice, the level of input use,

the as

La in

Andalucía in Spain and Alentejo in

of

the on in the

case of

the effects of the is

- uses simulated data

the technical coef- ficients used have been obtained simu-

pollution index used is the summation of all flow and

-

is a recursive model. Optimisation is

each an in-

fluence on the in

of yields associated with schemes as in

goods, financial flows, etc.

- is treated in the model using a method that the

1983). We of

by climatic conditions that will affect yields and

(b) by

on

complete set that is

long

on yields and pollution.

of

built to

of

pectations. A in

the the level of subsidies

abilities to expected subsidies based on 3 to 5 in the

an analysis of the techni- cal, socio-economic and environmental aspects of the problem within a unified framework. The

with it possible

to associate the

with yields and potential levels of pollution. All

this, with

situation. The same level of

tion in ni-

to the all the

the

tain the optimal solutions fkom the economic point it is possi-

ble to the in

tential pollution.

can m the model in the aim of evaluating the "cost" in

losses caused by an the pol-

lution level and also calculate the

ing level

Definition of the crop production activities The is built out

obtained using the We de-

fined the dimensions of the

ties in to with the

of

to the name of one of the in that is the dimension. The second is

with each technical schedule applied to that soil is the dimension and the last one is the in the

in this

in to make the the model as "clean" as possible.

Bio-economic models integrating agronomic,

environmental and economic issues with agricultural use of water 333

Some of these technical coefficients come fiom in-

-

quantities of and applied put

This data obtained out of simulations of five

- that

sample of a (25

- principal

of these

sults is in the text as - The objective function: F m Net is

the calcula- tion the of the five

- tions. is annual. Capi-

and

tions in the on an annual basis.

MAX U (Xt, Yt)= EINETINCOME(Xtl Yt)]

-

A(Xtl Yt) (1

1

U ( ) is the utility level; Xt the set of

decisions in t, including allocated allocated

to so

on; Yt the set of

level; is the expected value

in equation 2, is net income; cp is the coefficient; and A(Xt,YJ is the sum of negative deviations of

(Xt, Yt)/,

the in

equation 6.

One element of the like this:

meaning 1 gown with technique 1, on soil 2, the YES).

NETINCOME (.) is composed of the following elements:

NETlNCOME(Xf, YJ = REVENUE(Xt;PJ+SUB§lDY(XJ

-

VARCOST(Xt)-FINANCOST(Yt, Yt-l)-FIXEDCOST§ (2)

is the t;

the sulting hom the

put) with to each

is the sum of all collected sub-

the is the

sum of all

the net financial costs dependent on financial deci- sions in t and t-l.

is subject to the follow- ing

- constraints: Equation 3

the simply means two

things: all land available is subject to the set the

allocated to a i in

t, has to be less than the of j

in t-l; its is to

Water constraint: Equation 4 define the means that the total of the used us subject to

in each

.Z(Xt*WATER(XJ) S WATA VAIL(f) (4)

334 G.

case of the of An-

dalucía), availability

been built, in to analyse the with al-

-

constraints : This is the simplest balance equation used in the

says that the sum of all is

less than the amount of avail-

able in the plus the amount

of seasonal is an

in

aXf*LASOR(xsI SFARMLAB + SEASON(f) ( 5 )

-

constraints: is to the next two equations:

NETINCOME(Xf, Yf;e,q,n) + DEV(e,q,n) = E[NET/NCOME(Xt, Yt)] (6) Equation 6 computes each combination of states of the negative deviations of actual net in- come fiom the expected value of net

(e,q,n)

instability. Subsidies instability e,

and can take values within the

(el, e2, 4). Yields instability is ac- which in turn can take 5 values, one each of the five consid-

in the (l) (q is high in

low in Lastly, in-

stability is by the n, which can

take two values (nl,

n3

one is optimistic and the

pessimistic. sum, 5 is

sented by (3 *5*2=30) equations, one each combination of "states of

-Xe Z; -Xn DEV(e, f,n) d ( X f , Yt) (7)

Equation 7, sums up all the negative deviations and makes them less than equal than A(XbYJ. The side of this equation, multiplied by the coefficient, in the objective function (equation 1) with a negative sign

equations, not deal with

specific technical and economical, ap- plicable to each

pollution is estimated in of total ni- losses in the calculates the losses

activity, and these values

associated the

activities. is then possible to llcountl' the potential pollution that is with each specific optimal solution of the model.

The use of this methodology building models in allowed us to to all the cases. was also

possible to analyse the that dif-

have in the

changes the

mented. this model allows to make

it in

the policies of

level simultaneously (that is the case of devaluation, that affected the situation in some of the case-studies).

(l) of the climatic conditions of fiom data of long pe-

Bio-economic models integrating agronomic,

environmental and economic issues with agricultural use water 335

Effects of the by

BY

REGIONS S-W FRANCE (HAUTE GARONNE) LA BEAUCE

S-E ENGLAND (KENT)

EMILIA-ROMAGNA

ANDALUSIA

ALENTEJO

CROP PATTERN ++OILSEEDS --CEREALS

++CEREALS --OILSEEDS +OILSEEDS --CEREALS

++OILSEEDS

-- CEREALS

REFORM NEUTRAL

FARM INCOME ABSOLUTE AND RELATIVE

INCREASE ABSOLUTE AND RELATIVE DECREASE ABSOLUTE INCREASE RELATIVE DECREASE ABSOLUTE INCREASE RELATIVE DECREASE ABSOLUTE AND RELATIVE INCREASE ABSOLUTE AND RELATIVE DECREASE

NITRATE POLL --DECREASES

--DECREASES

-DECREASES

--DECREASES

-DECREASES

-DECREASES

l

IRRIGATION

INCREASE SURFACE, CONSTANT WATER USE DECREASES

NON EXISTING

NEUTRAL

NEUTRAL, DEPENDING ON CLIMATE NON EXISTING

I

close to what is going on

in the studied imple-

mentation.

to the impact of the

on in two South-

em An-

dalucía (Flichman et al, 1995). the case,

the the application of

an in the

the total Andalucía, the

seem to the of

gation, both in of

On the side, it the negative

Using a bio-economic model to

a much

in Andalucía than in SO

in the

taking into account the level of using

OF NEW

developing this is open the way new

aspects of as follows:

336 G.

- Level of the analysis. The model level has limitations policy analysis. is to develop models at

dealing with the

-

-

as speci-

fied in the

with the defined. A

will to new disciplines

in the

ics is

tant as well to ing

-

Blaskovic, Une analyse du système de production agricole socialisé dans la région continentale de la Croatie et les possibilités de réorganisation - Tentative d'utilisation des modèles. de Etudes

de et 1992, 129 p.

-

On Functions. Association of

13-14,1988

- de l'Agriculture. Economica, 1988.

-

Boussemart, J.P. Flichman, G. Jacquet, F. L'évaluation la de la un modèle bio-économique: une la et la de Toulouse. Communication présentée

dans les llémes 2 et 3 juin 1994 9

-

ha-Toulouse, 1994.

-

G., Vicien, C. in

socio-economic criteria f o r the analysis and the prevision land use and land evaluation. 12340 EN.

- G. A Using a Simulation as

Activities Agricultural Systems,

- Flichman, G. Analyse des possibilités d'expansion de l'offie de en Convention d'étude 1993.

- Flichman, G. of in Agriculture.

-

Flichman,G., Garrido, A., Varela Ortega, C. policy and technical choice : a analysis of

income soil use and and (L) 15 p.,

in : an economic perpective. Actes du 34ème l'EAAJ3

Association of Economists), 7-9 1994, (Esp.), Ed. Luis Albisu

Vauk 1995,380 p.

- Jacquet, Flichman, G. et efficacité 1988.

- N., G. Ciollaro, Santos Pereira : "Effect of changing schedules

- Steduto, P., Pocuca, V., Caliandro, A. An Evaluation of the simulation submodel of

wheat in with to be published in the

-

(1984). A modelling to the between

ods of limited To be published in

and soil 27 129 N. 44.