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<br />condition was then defined for six irrigation activities
<br />which include variations in frequency of water
<br />application as well as partial and full sprinkler
<br />systems. Available to each of the above combinations
<br />was a number of management activities. These
<br />activities were options open to the manager which he
<br />might employ, at a cost, in the face of rising salinity to
<br />mitigate the detrimental influence upon net returns.
<br />These activities include ditcb lining, land leveling,
<br />deep plowing, tiling, special bedding practices, and
<br />leaching irrigations. Various combinations of crops
<br />were defined to allow more than one crop on each acre
<br />per year. The program was then run for six salinity
<br />levels from 900 to 1,400 mg/I with the difference in the
<br />value of the objective function indicative of the
<br />damage associated witb the salinity change.
<br />
<br />c")
<br />o
<br />N
<br />.....t
<br />.....
<br />C.J1
<br />
<br />Model Constnlnts
<br />
<br />The number of acres available for crop production
<br />Was Umited to the available land including double
<br />cropping and excluding tbe historical pattern of fallow
<br />land. The quantity of water available for crop use had
<br />an upper limit associated with the water rights.
<br />Various categories of labor were constrained or
<br />simply accounted for to provide labor use information.
<br />Fertilizer rows were utilized as well as rows for new
<br />capital investment. Existing management improve.
<br />ments such as land presently tilled were inserted as
<br />data in the model. In order to restrict the production
<br />
<br />of high valued specialty crops, constraints were
<br />applied to total production of each commodity whicb
<br />serves as a proxy for the magnitude of market
<br />demand.
<br />
<br />The decrease in net profit available to farmers as
<br />a, result of salinity impacts was estimated through
<br />repeated running of the linear programming model.
<br />
<br />Results-Imperial Valley
<br />
<br />In order to indicate the predictive ability of the
<br />model, a comparison of selected factors is given in
<br />Table 14. The approximation of the existing situation
<br />by using 900 mg/I shows a very good correlation
<br />between historical trend and model results.
<br />
<br />Table 15 shows, on a crop.by.crop basis, a
<br />comparison between actual data and model results for
<br />yields, acres, and production for the Imperial Valley.
<br />
<br />Table 14. Selected factor comparison historic and LP
<br />Model 900 mg/!.
<br />
<br />Factor Historic L.P. Model
<br />
<br />Water Use. Acre. Feet 2,838,558 2,692,167
<br />Gross Output - Dollars 284,242,000 269,822,804
<br />Sprinkler to Establish
<br />Stand. Acres 56,600 69,973
<br />Full-Time Sprinkler ~ Acres 0 0
<br />
<br />Table 15. Comparison of actual conditions for Imperial Valley in 1974 with LP Model80lution at 900 mg/!.
<br />
<br />Crop
<br />
<br />Historic
<br />Yield
<br />
<br />Confidence Model Historic Model
<br />Interval Production Production Acres
<br />
<br />Asparagus 1.53 Tons
<br />
<br />Alfalfa 7.45 Tons
<br />
<br />Watermelon 9.80 Tons
<br />
<br />Tomato 7.68 Tons
<br />
<br />Onion 13.70 Tons
<br />
<br />CaIrot 14.00 Tons
<br />
<br />Cantaloupe 5.88 Tons
<br />
<br />Sugar Beets 22.00 Tons
<br />
<br />Sorghum
<br />
<br />2,25 Tons
<br />
<br />Barley
<br />
<br />1.90 Tons
<br />
<br />Wheat
<br />
<br />2.14 Tons
<br />
<br />Cotton
<br />
<br />2;43 Bales
<br />
<br />Lettuce
<br />
<br />10.83 Tons
<br />
<br />:1:0.16 4,533 6,568 2,963
<br />:I: 2,035
<br />
<br />:1:0.33 1,072,288 1,203,934 150,726
<br />:1:131,646
<br />
<br />:I: 1.42 29,846 25,777 3,046
<br />:I: 4,G68
<br />
<br />:1:2.85 19,018 16,951 2,529
<br />:I: 2,068
<br />
<br />:1:2.41 81,752 64,846 5,967
<br />:1:16,906
<br />
<br />:1:3.42 67,254 56,462 4,804
<br />:!:lO,792
<br />
<br />:1:0.59 77,504 61,866 14,028
<br />:1:15,638
<br />
<br />:1:3.36 1,459,281 1,615,143 66,331
<br />:1:155,862
<br />
<br />:1:0.27 91,101 100,934 67,736
<br />:I: 14,048
<br />
<br />:!:0.21 52,606 95,500 27,687
<br />:!: 42,894
<br />
<br />:1:0.29 131,182 125,191 61,300
<br />:!: 80,945
<br />
<br />:!:0.80 100,182 74,722 41,199
<br />:!: 25,460
<br />
<br />:!: 1.01 6,411,159 515,815 59,202
<br />:!:125,345
<br />
<br />14
<br />
<br />Historic
<br />Acres
<br />
<br />4,170
<br />
<br />176,051
<br />
<br />3,192
<br />
<br />2,401
<br />
<br />4,231
<br />
<br />4,657
<br />
<br />10,567
<br />
<br />69,193
<br />
<br />50,417
<br />
<br />51,766
<br />
<br />51,477
<br />
<br />36,625
<br />
<br />42,771
<br />
<br />1974 1974 1974
<br />Yield Production Acre
<br />
<br />
<br />1.63 7,500 4,600
<br />
<br />9.00 1,089,000 121,000
<br />
<br />7.25 29,000 4,000
<br />
<br />12.93 38,800 3,000
<br />
<br />12.00 36,000 3,000
<br />
<br />18.86 111,300 5,900
<br />
<br />7.53 62,500 8,300
<br />
<br />26.80 1,742,000 65,000
<br />
<br />2.30 74,000 32,000
<br />
<br />2.14 12,000 5,600
<br />
<br />2.53 263,000 104,000
<br />
<br />2.38 215,800 87,000
<br />
<br />1165 571,000 49,000
<br />
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