economic impact of soil salinity in agriculture. a case study of ... · economic impact of soil...

4gricultural Systems 41 (1993) 369 386

Economic Impact of Soil Salinity in Agriculture. A Case Study of Bardenas Area, Spain

S. Zekr i*

Departamento de Economia Agraria, Universidad de Cordoba, C6rdoba, Spain

&

L. M. Alb i su

Unidad de Economia Agraria, Servicio de lnvestigaci6n Agraria, Diputaci6n General de Aragon, Zaragoza, Spain

A BS TRA CT

This study analyses the economic damage resulting from high soil saliniO' levels in the irrigated lands' of Bardenas I in Zaragoza, Spain. The S T E M method, an interactive multi-objective programming technique, is" used to represent the current situation with the corresponding salinity levels. The approach developed is then used to simuhtte the Jimlre situation. An economic analysis is carried out to demonstrate the Ji, asibility of salt-affected soil reclamation.

I N T R O D U C T I O N

Salt accumulation in the soil is one of the most serious problems facing agriculture in arid and semi-arid regions. This accumulation is the conse- quence of both crop evapotranspiration and saline irrigation water. Quite often, the situation is aggravated by additional contributions of salt from the soil and/or from shallow ground water. The effects of salinity are expressed through an overall decline in yield and substitution of sensitive

*Present address: Departement d'Economie Rurale, ESAM. 112l Mograne, Tunisia.

369 Agricultural Systems 0308-521X/93/$06.00 ~ 1993 Elsevier Science Publishers Ltd, England. Printed in Great Britain

370 S. Zekri, L. M. Albisu

crops by resistant ones, which are generally less valued. In the long run, the secondary effects of salinisation of rivers and aquifers (ground water) need to be taken into account (Yaron, 1981 ).

The case study presented here concerns Bardenas I, an area of 56 760 ha of irrigated land situated north of Zaragoza and south of Navarra in Spain. More than 20% of this area (12 000 ha) is affected by salination. Of this 9500 ha are totally unproductive because of the high salinity con- centration in the top soil. The irrigation water is of excellent quality with an electric conductivity of 0.3-0.4 dS/m (Ayers & Westcot, 1987). The principal sources of salinity are salts arising from meteorisation~ in combination with secondary salinisation in areas lacking natural drainage.

The objectives of this study are: (i) representation of the current situation with the corresponding soil salinity levels; (ii) simulation of a future situation without the effects of salinity; (iii) estimation of soil reclamation costs and benefits; and, (iv) economic feasibility of the land reclamation project.

The method of analysis used is the STEM method (Benayoun et al., 1971) - - an interactive multi-objective programming technique offering the advantages associated with multi-objective methods. In real life, the Decision Maker (DM) is usually interested in seeking an optimal compromise between several objectives, many of which can be in conflict. Furthermore, an interactive approach does not make any general assumptions on the DM's utility function. That is, the interactive methods operate in an iterative way by moving from one efficient solution to another, according to the local preferences of the DM. In addition, the STEM method is relatively easy to use compared to other interactive methods, and demands information which can easily be provided by the DM.

REPRESENTATION OF THE CURRENT SITUATION

Two linear multi-objective models are used to reflect present salinity levels, one for Navarra and the other for Zaragoza. The following four objectives are considered: (i) maximisation of total Gross Margin (GM) representing profitability of the farm business to the farmers, (ii) maximisation of labour used in order to determine the maximum potential of employment that can be generated in the farm sector under the current conditions, farming being the main activity in the region, (iii) minimisation of labour seasonality to spread the use of labour evenly during the

Economic impact o f soil salinity in agriculture in Spain 371

year to avoid periods of unemployment - - mathematically, this objective is expressed in terms of mean-absolute deviations using Hazell's method (1971) as modified by Romero et al. (1987); and (iv) minimisation of risk expressed after Berbel's method (1988), which is a combination of Target Motad (Tauer, 1983) and Atwood inequality (1985). This method gives the probability of failure. That is, the probability of GM falling below a target level g.

Berbel's method estimates the probability of failure; that Pr, the probability of GM falling below a given target level g. The Atwood inequality is expressed as follows:

P A D P r ( G M < g) < - - (1)

n( t - g )

where Pr(E) = G M = g =

P A D =

17 =

t =

Probability of event, E Gross Margin for the farm Gross Margin target level determined by the DM Sum of partial absolute deviations Number of years included in the analysis Parameter with respect to which the negative deviations are measured; t has no economic significance; t > g

In this case the value of g is considered as 140 000 ptas/ha. The value of t is determined by a parametric analysis looking for a value generating a P A D always slightly greater than zero, while maximising G M . Stated differently, t is slightly greater than the maximin value (Berbel, 1990).

The model

The soil salinity levels and extensions were adapted from two maps. The one representing the Bardenas I irrigated area corresponding to Navarra has a scale of 1:25000. The map representing Bardenas I area of Zaragoza has a scale of 1:50000. The model includes 15 crops dis- tributed in three or four soil types. The soil conditions differ only in their electric conductivities. Crop acreages are adopted from MAPA (11984-6). Crop budgets were calculated using the data of Cavero & Delgado (1984) and Montero & Fando (1981). Labour use expressed in monthly hours per hectare for each crop are compiled from the above sources. Crop prices are real 1982 7 averages, expressed in 1987 ptas. Crop yields are averages, for non saline soils, for the same period.

Mathematically, the specifications of objectives and constraints are listed below:

372 s. Zekri. L. M. Albisu

Objectives

Subject to

21

Max ~ GMs i = 1

where GMt

Li S l~l +

S,,,

t

21

Max ~ L, i = l

12

Min ~ (S,,, + + S,,,) m 1

6

Min ~ Nj j = l

- - 21

x~ + x~ + x~ + x~ + ~ x~ < Sl i - - l l

X2 + )(5 + X~ + Xm < $2

X3 + )(6 < $3

18 18 18

i = 11 i II i = 11

F(II) - X.,,i,,i < X i < Xm.x, i = 1; 7: 9; 14; 15; 16

~ (c,,,, c,)x,. + s,,, - s,,,+ = o

6

Gm U X~ + Ni >- ti = 1; 2; . . .; 21 =1

21

£Xi=A = 19

= Average Gross Margin of Crop i = Labour use in hours/ha/year for crop i = Monthly labour mean-positive deviation = Monthly labour mean-negative deviation = Gross margin negative deviation from t = Parameter

(2)

(3)

(4)

{5)

(6)

{7)

(8)

(9)

Economic #npact o f soil salinity in agriculture in Spain 373

C io/

x , =

G m U =

S i =

m a x i ~--

f'mini ~ A = / --

i =

Labour in hours/ha for crop i and month m Monthly average of labour in hours/ha for crop i Area for crop i Gross margin for crop i in year j Soil type Maximum area for crop i Minimum area for crop i Average for tree crops area during 1984 through 1986 Indicates the year, j = 1 corresponds to 1982 . . . . . j -- 6 :1987 Corresponds to the crop (1 -- wheat in soil S~; 2 -- wheat in soil $2; 3 = wheat in $3; 4 = barley in S~; 5 = barley in $2; 6 = barley in $3; 7 = corn in S~; 8 = corn in $2; 9 = alfalfa in S~; 10 = alfalfa in $2; 11 = cauliflower; 12 = pea: 13 = (French) beans: 14 = potatoes; 15 = red pepper; 16 = tomatoes; 17 -- onions; 18 = artichokes; 19 -- apple; 20 -- pear; 21 -- peach)

Constraints (2), (3) and (4) refer to the kind of crop to be grown in each of the soil types. Constraints (5) and (6) impose upper and lower bounds for vegetable crops as well as for wheat, corn and alfalfa. The objective of these constraints is to have a solution close to the current situation in the area. Constraints (7) and (8) permit the measurement of labour seasonality and risk respectively. Finally constraint (9) refers to the area dedicated to tree crops. There is little change from year to year in tree cropped area, that is why an equal sign has been imposed. Crop rotation constraints are unnecessary here since only 5% of the area is cultivated with vegetable crops. The crop rotation constraints for corn, wheat and alfalfa are redundant, and hence are omitted.

The STEM method proceeds in two phases: a calculation phase and a decision phase (Romero & Rehman, 1989). The first step of the calculation phase consists of constructing the 'pay-off" matrix. This is done by optimising, separately, each of the four objectives subjected to the set of constraints. The second step calculates the normalising weights. The last step in the calculation phase is the computat ion of the first solution nearest to the ideal point. This task is accomplished by solving the following linear programming problem:

M I N D subject to X ~ F fli [Z* Zi] < D if the objective is maximised /3i [Zi - Z*] < D if the objective is minimised

where D is the distance between the achievable and the ideal values of the objectives. With the minimisation of D we are seeking the solution

374 S. Zekri. L. M. Albisu

nearest to the ideal point, in a minimax sense. That is, we are minimising the maximum deviation from among the individual deviations. F is the feasible region in decision space defined by the constraints given in II. Z* is the ideal of objective Z~, that is, the optimum value of Z~. Z,~ is the anti-ideal of objective Zi, which is the worst value of objective Z;, when the other objectives are optimised./3; is the normalising weight for objective Z; defined as:

a; /3;-

4

; = 1

where

Z*- Z*i c~i [E au 2 ] 1/2, if the objective is maximised.

z*

Z , ; - Z i "~ ] 1/2, a; [E a,j- if the objective is minimised.

Z , ;

a;j being the coefficient representing the contribution of crop j in the i-th objective. As the objectives are measured in different units normalisation is neces- sary to make them commensurable.

The decision phase starts by presenting to the DM the first optimal solution together with the pay-off matrix. After comparing this solution with the ideal vector, the DM may decide that:

(a) the achievement levels for all the objectives are satisfactory. Thus the solution obtained is the best compromise and the process ends; or,

(b) none of the objectives achieves a satisfactory level, then, another method should be used (Cohon, 1978); or,

(c) some of the objectives are satisfactory. In this case, the DM will indicate which attributes could be worsened, in order to improve the others. The DM must also indicate the maximum degradation possible before the satisfactory level of an attribute becomes unsat- isfactory. This leads to the calculation of new weights and an im- position of additional constraints on the problem. The method proceeds until the DM is satisfied with a given solution.

In this case, at the presentation of the first optimal solution together with the pay-off matrix, the area planner (DM) considered it satisfactory and the procedure ended. It is worth mentioning that the first solution

Economic impact of soil salinity in agriculture in Spain 375

generated by the STEM method always coincided with the best-compromise solution when the metric L~ (i.e. P -- 0c) (see Romero & Rehman, 1989) is used and the objectives are normalised in the way proposed by the authors of the STEM.

The results of both models are as follows. In Table 1 the solution is presented in both variable space and objective space. Corn covers the major part of the area with 54.75%. Barley comes in second order with 16-44%, followed by wheat and alfalfa with 12.31% and 10.73% respectively. In the objective space the first row refers to the values of each objective achieved in this solution. The second row gives the ideal values.

TABLE l First STEM Optimal Solution of the Current Situation in Zaragoza

Area (%) Area (ha)

Decision Variable Space X I Wheat in S* I 12.31 4 747 X~ Wheat in S 2 X 3 Wheat in S~ X 4 Barley in S~ 11.63 4 485 X 5 Barley in S~ 3.72 1 483 X 6 Barley in S 3 1.09 423 X v Corn in S I 54.75 21 114 X~ Corn in S 2 X9 Alfalfa in S~ 10.73 4 138 Xl0 Alfalfa in S~ Xll Cauliflower 0.57 220

XI2 Pea XI3 French beans X~4 Potatoes 0-26 100.5 X~5 Red pepper 1-76 679 X~6 Tomatoes 1.13 436 Xl7 Onions 1.22 470.5 X~ Artichoke Xl9 Apple X2o Pear - - - - X21 Peach 0.81 3l l

Objectives G M L PAD S ( Ptas/ha ) (h/ha/year) (h/ha/year)

Multiple Objective Space Solution 164 541 90-8 2.204 57.77 Ideal 164 662 94-37 0-281 45.671 Anti-ideal 155 500 79.54 23.86 66-64

*S i = Soil type

376 S. Zekri, L. ,14. Alhisu

Finally the anti-ideal or nadir points are given in row three. The GM value achieved in this solution is 164 541 ptas/ha and is almost the ideal value 164 662 ptas/ha. The difference is very small, 121 ptas. The deviation between the achieved value of labour objective and its ideal is also quite small. This is due to the fact that the two objectives GM and labour are complementary. That is, when one objective is maximised the other almost reaches its ideal value. Stated differently, crops with high values of GM are also highly labour intensive. Little conflict exists between the objectives GM and risk, as the risk objective reaches a value not different from its ideal when the objective GM is optimised. More than 83% of the area is cropped with corn, wheat and barley, for which prices are protected. Yields generally vary very little. Only approx. 5% of

TABLE 2 First STEM Optimal Solution of the Current Situation in Navarra

Area ('!,,) Area (ha)

Decision Variable Space

X~ Wheat 5.03 425 X, Wheat in _hi5 " X 4 Barley 16.13 1 362 Xs Barley in 5n t 397 336 X7 Corn 56.56 4776 Xs Corn in 5n I X,~ Alfallh 7"2 608 Xio Alfalfa in 5n I Xl~ Cauliflower 3-15 266 X~_, Pea X13 French beans X14 Potatoes 0.8 67.5 Xi5 Red pepper 1.11 93.5 Xi6 Tomatoes 0.75 63.5 XI; Onions 1.06 89.5 Xts Artichoke 1-25 105-5 XI~ Apple X_, 0 Pear 0.63 53.5 X_, I Peach 2-33 197

Ot~jectives G M L P A D S ( Ptas/ha ) (h/ha/year) (h/ha/year)

Solution Ideal Anti-ideal

192 337 119.26 4.342 60.71 198 425 137.22 0.01 46.058 175 000 91.2 51.483 114-088

:' 5n~ = Soil type


the area is cropped with vegetable crops for which the variability in both prices and yields is very important. Labour seasonality reaches an inter- mediate value between its ideal and anti-ideal.

Table 2 gives the results for the current situation for that part of Bar- denas I which is situated in Navarra. The GM achieved is higher than what is achieved in Zaragoza. More than 8% of the area is cropped with vegetable crops, which represents about twice the percentage of vegetable crops in Zaragoza. In this case the difference between the ideal and the achieved values for GM is 8088 ptas/ha. The achieved value for the labour objective is nearer to its ideal than to its anti-ideal. The deviation with respect to the ideal is of 17-36 h/ha/year. The achieved value for the risk objective is much closer to the ideal than to the anti-ideal. Finally, the labour seasonality objective achieves a value of 60.71 h/ha/year with a deviation of 14.652 h/ha/year with respect to its ideal value.

Table 3 summarises the results for all of the Bardenas | irrigated area in both Zaragoza and Navarra. Column 1 is no more than the present irrigated areas of both Zaragoza (Bardenas I) and Navarra (Bardenas I) and the sum of these two parts.

The total Gross Margin in Bardenas I is of 7969 million ptas/year. The labour used is of 2347 man-work units/year, considering that one man- work unit corresponds to 1920 h/year. The sum of the deviations with respect to the labour-mean; that is, the labour seasonality, amounts to 1427 man-work units/year.

The calculation of the probability of failure to reach a GM less than 140 000 ptas/ha is based on expression (1). The results seem incoherent with those postulated above (that the larger the area cropped with vegetable crops the more risky is the alternative). The vegetable crops are the crops with higher prices and yields variability. Corn, wheat and barley have government established-prices, and yields change very little. However, the problem arises from eqn (1) and not from the postulates. More specifically, parameter t is at the origin of this inconsistency. The parameter t must be taken as a value slightly higher than the maximin (Berbel, 1990) to avoid inefficient solutions in a multi-criteria problem. By doing so, the maximin is different for different sets of constraints. Keeping g, the disaster level, constant in expression (1) is no longer valid for the comparison of the results when one or more constraints are changed, causing t to change. More research is needed to clarify the empirical use of expression (1) and to determine parameter t.

The probability of failure to reach a GM under 140 000 ptas/ha for the whole area of Bardenas I is computed by weighting the probabilities of Zaragoza and Navarra by their percentage of irrigated area in each part to the total of Bardenas I.


~ v

L)

~~

e

o ~:~

~.~

N

c"~l e'q ~ !

VI VI VI

q"~ ' ~ 0",

Economic impact of soil sal#tity in agriculture in Spain 379

Table 4 represents the monthly distribution of labour expressed in man-work units. The percentage of mean-deviation is shown in the second row. In April and August the use of labour is almost equal to the mean. June represents the month in which the use of labour is highest and is more than twice the mean. In January the use of labour comes to its lowest value, only 10% of the mean. The use of the mean as a target with respect to which deviations are minimised presents two inconvenient factors: (a) both positive and negative deviations are penalised. As the DM is interested in maximising the labour use at the regional level, the minimisation of the positive deviations becomes senseless; (b) the mean does not represent a real target for the DM, and besides the mean varies with each combination of crops, thus making comparisons between alter- natives difficult. A better way of minimising labour seasonality is to minimise the negative deviations with respect to the target level of agricultural employment (Berbel, 1989). Two problems make the use of this approach difficult when planning at regional level: first, the planned area rarely (if ever) coincides with the administrative divisions for which the statistics are available; second, the determination of the target level of agricultural employment at a regional level must also include agricultural unemployment, that is the number of employed people from the farm sector plus the unemployed people available for work at the farm sector. Presently, this distinction of unemployed people by activities does not exist.

TABLE 4 Employment Distribution and Percentage of Mean-deviation

Month Man Work Units Deviations (%)

January 19 90 February 98 -49 March 38 80 April 197 + 1 May 386 +97 June 430 + 119 July 172 12 August 188 September 359 +83 October 309 +58 November 119 -39 December 32 84

Total 2 347

380 s. Zekri. L. M. Albisu

SIMULATION OF THE F U T U R E SITUATION

For the simulation of the future situation, the salinity problem is considered solved. In fact, various studies have demonstrated the possibility of reclamation of saline soils. The experimental results of soil reclamation, at the farm level in the Bardenas area, presented by Beltr/m (1978) , served as a basis for this work.

The model used in this section has made some modifications to the one used for the representation of the current situation. In this case, and during the first three years after the reclamation, and during the period of leaching, the reclaimed soils are sown solely to barley. Barley has high tolerance salinity. Subsequent to the barley storing period, reclaimed soils could be cultivated for any of the existing crops in the area. Hence, two models are required to simulate the future situation. The first model represents the annual situation for years one to three. The second one represents the annual situation of the period of the 4th to the 15th year. The planning horizon considered is 15 years. The total area, that is, reclaimed plus the present irrigated area is of 56 760 ha.

Additional constraints occur due to the amount of disposable irrigation water available from April to September. A sensitivity analysis test, in the second model, has been carried out with a 10% decrease and a 10% increase in prices, as well as a Right Hand Side (RHS) sensitivity analysis. The RHS sensitivity analysis refers to the upper bounds for alfalfa and vegetable crops which has been changed from 10% and 5% to 20% and 10% respectively.

The results are shown in Tables 5 and 6. Table 5 gives the results under each of the above mentioned situations. The probability of having a G M less than 140 000 ptas/ha during the first three years of the project is very high, 36%. This is due to the fact that 21% of the area must be cultivated with barley during the leaching period. Barley has a gross margin of only 54 921 ptas/ha. The highest G M is obtained in the case where the upper bounds for alfalfa and vegetable crops are of 20% and 10% of the area respectively. As the objectives G M and labour are complementary, the labour use reaches its highest value under the same conditions.

Table 6 refers to monthly water disposable and required from April to September under the existing constraints as well as under the projected ones (see rows 2 and 3, respectively). It is worth mentioning here that there is a problem with monthly water distribution; that is, July puts a constraint on the amount of cultivation that can occur, in order to have 10% of the area cropped with vegetable crops and 20% with alfalfa. Water shortage in July seems to arise from the canal capacity rather than from the amount of water available in the dam.

Economic impact q/'soil salinity in agriculture in Spain 38

0

D.

,o

b~

,.o o

r~

o

0

0

d

a~

2 @

c~

.~ %

~.~

~-~

,,.o a., r,~ 0

VI

O0 i'~" I~" I

;< ~'~ ~ r ~ .

v/

~ 0

. ~

O = ~ v

< 2 ~

0

S

._=

,m

©

©

o

0

u,)

&.) r~

0

o

.~ d

"S . I N

o

~ ~ ~

. ,1

[,,.

0

I

, .0

a~

<

0", t '~

A ~ O 0

O~

oo

oo ~

{3,o oo

oo

~ b

B

.,.; ,...,

©

m

£ e..,

S

0

0

E

.g

.£

0

*


ESTIMATION OF SOIL RECLAMATION COSTS

To reclaim the saline soils, the installation of a drainage system is needed. Clay pipes are inserted at a depth of 1.5 m. The interval between pipes is 20 m. Application of gypsum is recommended for soil having low hydraulic conductivity (Beltrfin, 1978). The drainage system cost is estimated at 148 184 ptas/ha. The cost of gypsum is 17 500 ptas/ha. As the data given in the maps is insufficient to determine which area needs gypsum, we considered that the gypsum is to be applied to the whole area. Hence, the total cost per hectare is 165 684 ptas (at 1987 prices). The annual maintenance costs are considered to be 2050 ptas/ha. As the area to be reclaimed is 11 948 ha, the total cost is 1979 million ptas. The annual maintenance costs for the area are 24.5 million ptas.

ESTIMATION OF THE BENEFITS OF SOIL R E C L A M A T I O N

The agricultural benefits arise from the elimination of the salinity problem. These benefits are estimated using almost the same procedure developed by Moore et al. (1974). The difference is due to the fact that we used a multi-objective programming approach rather than a single objective one. This permits taking into account employment generation, risk and seasonality of labour associated to the salinity problem. The benefits are given by the difference between the two gross margins and the two labour uses of the models representing the current and the future situations. The results, including the sensitivity analysis test, are shown in Table 7.

The benefits of soil reclamation amounts to 520 million ptas per year and the generation of 40 jobs, during the first three years of the project. The maximum benefits take place if the present cropped areas of alfalfa

TABLE 7 Benefits of Soil Reclamation

Agricultural Real average prices Sensitivity analysis Benefits (1987) 4th 15th year

1st 3rd 4th 15th lO%priee lO%priee R.H.S. )'ear year deerease increase test

Million of ptas. 520 per year

Total employment 40 generation (man-work units)

1 622 1 417 1 851 2 128

461 444 437 799


,<

~d

e<

i

~ I ~ .

t"q , ~ o'5 ~ tr5

tt~ tt~

tt~ e¢~

0"~ tg5

tt~

tt')

r q e~ (D

,.D

y=

tt~

2

©

e-

e"

>

2

ee~ tt~

0

>

Z y.


and vegetable crops are duplicated, generating 799 jobs and a benefit of 2128 million ptas per year.

ECONOMIC FEASIBILITY OF THE RECLAMATION PROJECT

The model uses market prices. The area studied is quite small compared to the whole irrigated area in Spain. Only direct costs and benefits are included. For example, down stream water salinization is not considered. This is chiefly because the impacts are not received in the same area. On the other hand, we considered that secondary benefits represent no real gain to society (Gardner & Young, 1985). The discount rate used is 9%, and a 15-year planning horizon has been adopted.

The net present value (NPV) is 8019 million ptas, and the employment generation is of 461 jobs at the end of the fourth year of the project.

A sensitivity analysis test has been carried out for the discount rate and the costs. We considered that only 30% of the area to be reclaimed needs gypsum. On the other hand, the pipe cost is reduced by 50%. So that the total cost of the project is, in this case, 1355 million ptas.

The combination of all the factors taking part in the computat ion of the NPV and the internal rate of return (IRR) are shown below. Table 8 gives the NPV, the IRR and the Benefit-Cost ratio (B/C) under different assumptions. The annual benefits during the first three years are assumed constant. The NPV ranges from 5350 to 1l 541 million ptas. The IRR is very high and ranges from 40.84')'0 to 63.5% The B/C varies between 2.7 and 8.51.

CONCLUSIONS

The benefits resulting from the land reclamation project are considerable. They vary between 11 541 million ptas, with 799 jobs generated, and 5350 million ptas, and 444 new jobs. These great differences are due in part, to a lack of a more precise information on soil conditions which in- fluence drainage costs and the extent of the reclaimed area. The evolu- tion of salinity has been taken into account by considering two distinct periods (years 1 to 3; and years 4 to 15) in the planning horizon.

In the case of doubling the vegetable crops and alfalfa areas, there will be an increase in the gross margin and labour use, not only in the currently saline soils but also in the non-affected soils. The sole limiting factor, in this case, will be the availability of irrigation water in July and August. On the other hand present water consumption is very high,

Economic impact of soil salini o, in agriculture in Spain 385

9500 m3/ha, compared to the computed level of 7400 m3/ha. Currently, 120 Hm 3 of irrigation water is wasted; this has negative effects on salinisation of rivers and aquifers. However, these effects can be evaluated by studying the whole of Ebro river basin.

A C K N O W L E D G E M E N T S

Thanks are due to Dr. Carlos Romero and Dr. Julio Berbel for their comments on a preliminary draft of this paper. The English editing of Christine M6ndez is also gratefully acknowledged.

R E F E R E N C E S

Atwood, J. (1985). Lower Partial Moments and Safety. Amer. J. Agric. Econ., 67(4), 787-92.

Ayers, R. S. & Westcot, D. W. (1987). La Calidad del Agua en la Agricultura. Estudios FAO. Riego y Drenaje, 29, Rev. 1.

Beltrfin, M. (1978). Drainage and Land Reclamation of Salt-affected Soils. Bar- denas Area, Spain. Internat. Institute of Land Reclamation and Improve- ment, Publication no 24, Wageningen, Netherlands.

Benayoun, R. J., De Montgolfier, J. T., Tergny, J. & Latrotchevo, O. (1971). Linear Programming with Multiple Objective Functions: Step Method (STEM). Mathematical Prog., 1,366-75.

Berbel, J. (1988). Target Returns within Risk Programming Models: A Multi- objective Approach. J. Agric. Econ., 39(2), 263-70.

Berbel, J. (1989). Analysis of Protected Cropping: An Application of Multi- objective Programming Techniques to Spanish Horticulture. European Rev. Agric. Econ., 16(2), 203-16.

Berbel, J. (1990). A Comparison of Target MOTAD Efficient Sets and the Choice of Target. Canadian J. Agric. Econ., 38, 149-57.

Cavero, F. M. & Delgado, I. (1984). Secano y Regadio en Arag6n: Una Ori- entaci6n Cuantitativa. Anales INIA/Servicio de Economia y Sociologia Agrarias, 8, 141-73. Ministerio de Agricultura, Pesca y Alimentacion, Madrid.

Cohon, L. J. (1978). Multiobjective Programming and Planning. Academic Press, London.

Gardner, R. k. & Young, R. A. (1985). An Economic Evaluation of the Col- orado River Basin Salinity Control Program. Western J. Agric. Econ., lO(1), 1-12.

Hazell, P. B. (1971). A Linear Alternative to Quadratic and Semivariance Pro- gramming in Farm Planning under Uncertainty. Amer. J. Agric. Econ., 53, 53-62.

MAPA (1984, 1985, 1986). Superficies Ocupadas por los Cultivos Agricolas, Navarra y Zaragoza.


Montero, C. M. & Fando, E. U. (1981). Fichas de Presupuestos de Opciones Alternativas en Diferentes Orientaciones Productivas para Explotaciones de Regadio con Superficie de 10 ha en la Zona Norte de Zaragoza. Servicio de Extensi6n Agraria, Diputaci6n General de Arag6n, Zaragoza.

Moore, C. V., Snyder, J. H. & Sun, P. (1974). Effects of Colorado River Water Quality on Irrigated Agriculture. Water Resources Research, 10(2), 137 44.

Romero, C., Amador, F. & Barco, A. (1987). Multiple Objectives in Agricul- tural Planning: A Compromise Programming Application. Arner. J. Agric. Econ., 69(1), 78-86.

Romero, C. & Rehman, T. (1989). Multiple Criteria Decision Making Techniques for Agricultural Decisions. Elsevier, Amsterdam.

Tauer, E. M. (1983). Target MOTAD. Amer. J. Agric. Econ., 65(3), 606-10. Yaron, D. (1981). The Salinity Problem in Irrigation: An Introductory Review.

In Salinity in Irrigation and Water Resources, ed. D. Yaron. Marcel Dekker, Inc., New York, pp. 1 20.

economic impact of soil salinity in agriculture. a case study of ... · economic impact of soil...

Documents