Agricultural production's impact on water and energy demand

Resources

and Energy

13 (1991) 307-321.

North-Holland

Agricultural production’s impact water and energy demand A choice modeling Timothy

on

approach*

N. Cason

University of Southern California, Los Angeles, CA 90089, USA

Robert

T. Uhlaner

Quantum Consulting Inc., Berkeley, CA 94704, USA Received June 1990. final version

received January

1991

In addition to consummg large quantities of water, irrigation pumping requires a significant amount of energy. Furthermore, crop and irrigation technology decisions have a direct impact on the demand for these resources. This paper develops an empirical model of crop and irrigation technology choice and presents estimates for a number of western states. The model demonstrates that growers react to relative price changes in a manner consistent with protitmaximizing theory. Costs are more important than revenues in inducing growers to alter their production choices. An example demonstrates how the model can assess the impact of new irrigation technologies and aid in water and energy conservation planning.

1. Introduction This paper develops a probabilistic discrete choice model of the farmer’s decision regarding crop and irrigation technology choice and presents results from a multinomial logit model estimated for a number of western states. This research is motivated by an interest in irrigation’s use of scarce resources - in particular, water and electricity - that are directly affected by irrigation technology decisions. The model produces estimates consistent across regions and demonstrates that farmers react to economic incentives (i.e., relative price changes) in a way that is both measurable and consistent with profit-maximizing theory. Costs are shown to be more important than *Thts study was funded in part by the Electric Power Research Institute (RP2340-2). The authors thank Ray Squitieri, Bruce Smith, David Zilberman, Phil Hummel and Gary Casterline and an anonymous referee for helpful comments. Craig Furline provided excellent research assistance. 0165~572/91/$03.50

CI 1991-Elsevier

Science

Publishers

B.V. All rights

reserved

308

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

revenues in inducing farmers to alter their production choices. An example demonstrates how the model can assess the impact of new technologies and aid in water and energy conservation planning. Irrigation technology decisions have a direct impact on both water and energy demand. For example, in drought-stricken California over 80 percent of the state’s water is used in agricultural production. Expanding irrigated acreage has resulted in significant declines in the water table and reductions in water quality in many areas.’ Because government is not eager to subsidize large water projects and environmentalists are lighting dam projects, future growth must be met by reallocating existing water resources. Devising methods for improving irrigation efficiency is thus a priority in developing water policy for many regions. Promoting new irrigation technologies is a frequently-used method for improving overall efficiency, since new technologies (such as drip systems) are very efficient. Furthermore, electricity for irrigation pumping is important for planning purposes of many rural electric cooperatives; it is not uncommon for electric utilities serving agricultural intensive areas to have irrigation demand account for over 50 percent of sales and over 75 percent of system peak. Because of their direct impact on water and energy demand, irrigation technology decisions (including energy conservation measures) are clearly important for utility forecasting and planning. Most earlier studies on technology selection use an engineering approach. In the engineering approach, the profits of each alternative technology are calculated to determine the circumstances under which each technology is most desirable. This paper employs an econometric approach and uses 14 years of historical data to explain and predict the factors affecting crop and technology choice. Our approach is similar to that of Caswell and Zilberman (1985), who use a binomial logit specification with aggregated data from California’s Central Valley to explain irrigation technology adoption. Extending their approach, we model technology and crop choices as a joint decision. We estimate our model using an annual time series, in contrast to Caswell and Zilberman, who use a cross-section of 98 subregions of counties for one period. Acreage attributes such as soil type, slope, and drainage, proximity to surface water sources and depth to ground water constrain the irrigation technology choice set. These considerations are important at the micro level of an individual plot on a specific farm, and each factor will influence the probable costs and returns associated with the alternative technologies under consideration. The farmer combines factual information with an informed judgement regarding future events outside his or her control. For example, ‘See, for example, Mapp (1988) for a discussion of the implications the Texas High Plains (which has recently reversed this trend and irrigated farming) and elsewhere.

of irrigated agriculture witnessed a reduction

in in

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

309

seasonal rainfall and the future price of the crop may be regarded as uncertain from the producer’s perspective. The econometric model assumes that producers make choices to maximize expected utility through farm profits to capture all of these factors. Section 2 describes the form of the multinomial logit model applied to this problem. Section 3 details the assumptions and data used to estimate the model. The estimation results are presented in section 4. We apply our model in section 5 to an example that analyzes the impact of a new irrigation technology. Determining the adoption of new technologies is a problem concerning electric utilities, environmentalists, and regulators. Section 6 summarizes the findings.

2. A multinomial

logit model

We develop a structural model of acreage utilization by modeling farmers’ choices of crop and irrigation technology (crop/technology) alternatives.’ The number of acres planted with crop i and irrigated with technology j at time t, Aijf, is a function of the net value at time t, uiJt, yielded by each acre:

where uijt is an error term. This specification would suffer from two notable problems if estimated using ordinary least squares (OLS). First, ignoring the restriction that Aijt 20, because planting negative acreage is not possible, results in biased estimates. Second, eq. (1) assumes independence between the acreage planted with alternative {i, j) and all other crop/technology combinations. Since total utilized acreage remains relatively constant, changes in acreage utilization are shifts from one alternative to another. Therefore, changes in acreage utilization occurring in this way violates the independence assumption. The multinomial logit (MNL) model can account for these problems by imposing the appropriate restrictions. Formally, the two restrictions on the dependent variable, I&,~, are:

(2) and (3)

where TA, denotes *Dry-land

farming

the total acres at time t. is consldered

an Irrigation

‘technology’

throughout

the paper.

310

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

The MNL model estimates the probability that each alternative is selected as a function of its value. Value (aijt) is a linear combination of revenues and costs associated with cultivating a particular crop using a particular irrigation technology. Section 3 develops the explicit expression for uiJt. The farmer’s problem at time t is the maximize utility as follows:

max Y$, = UiJt+ eijt Ii, j)

(4)

where Y$ is the level of underlying latent utility associated with alternative {i,j> at time t. The expression eijt is a residual that captures the unobserved errors in perception and optimization by the farmer as well as unobserved variations in tastes. If the residuals eiJt are independently and identically distributed3 and are described by the type I extreme-value distribution with probability density function:

f(eijt)= ev C- e,jt- ev ( - eijt)l then the expected probability that the crop/technology selected is given by [see McFadden (1973)]

I

Prob(II&f, t) = exp(Uijt) C 1 ev (uijt). allr

(5)

alternative

{i, j} is

(6)

allj

The MNL functional is empirically robust. [See, for example, McFadden et al. (1977).] Section 4 uses the MNL model and maximum-likelihood techniques to estimate the relative weights of the revenue and cost variables that minimize the prediction error. Section 5 demonstrates the applicability of the model to assess the impact of new irrigation technologies. Since the model is specified using cost and revenue attributes associated with each crop/technology alternative, new crop/technology alternatives can be analyzed by defining them in terms of

3This t.i.d assumption for the unobserved error appears justified when different farmers over a large geographical area are making the optimization decisrons, although it is not appropriate when the same decision-maker is choosing crops and irrigation technologies for different plots of land. Since the regtons considered in this paper contain thousands of individual farmers, the i.i.d. assumption is justitied because the data and the decisions we model are aggregated over these several thousand decisions.

T.N. Cason and R.T. Vhlaner, Agriculture and water and energy demand

their expected costs and revenues. Using the estimated new alternatives’ adoption can be forecasted.

3. Assumptions,

311

model parameters,

the

data and methodology

A time series of acreage distributions is used to estimate the model described in eq. (6). In specifying the exact form of this model, we made the following four assumptions: choices made each year is large. Therefore, A.1. The number of individual the estimated probabilities are equivalent to realized proportions4 In this way, the observed acreage proportions can be used as the dependent variable. These proportions are all positive and sum to one, satisfying the restrictions of eqs. (2) amd (3). A.2. Farmers form expectations season using the previous season’s

of revenues values.

and

cost

for the upcoming

A.3. Labor, capital, fertilizer and water are sufficient to characterize the production inputs. The utilization of each input required for each crop/ technology production choice is constant over the 16year sample period.5 The amount spent on each input, however, is allowed to vary as the price of that input varies. of sprinkler and gravity irrigation technologies A.4. The distribution the total irrigated acreage for each crop is analyzed at the state level.6

across

Assumption A.3 deserves further discussion. The costs are broken up into four major categories: labor, C,; capital, C,; fertilizer, C,; and water, C,.

41f, for example, 1,090,000 acres are planted and each one has a 20 percent probability of having corn/center pivot selected, on average 200,000 acres will have corn/center pivot. This is a common assumption when adapting discrete choice models to data that are aggregated to proportions. sThis hmiting assumption is required because our data set does not distinguish between new and old technologies. However, since new technologies - such as low energy precision and surge irrigation - are a relatively small amount of total acreage for our sample period, this should not be a major limitation. ‘This assumption was required because distrtbutions of crops by technologies were not available. Making the direct assumption of the overall distribution extending to each technology appears to be a reasonable approximation and seems more appropriate than an ad hoc weighting that varies the crop distributtons differently for different technologies.

312

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

Suppressing as

the time subscript,

profits

(xii) can be written,

using

revenues

rij

Cijl+

nij=rij-(

Cijc

+

Cijf

+

Cyw).

(7)

The probability that this choice will be chosen is dependent on the expected profitability given in eq. (7); or prob ({i, j}) =f(nij). We use the USDA State Level Costs of Production Publication for 1986 to determine the weights, W, for each input variable in a total cost function: cijq =

P4*Kjq;

9=

19C, CW,

(8)

where pq is the price of input q and the time subscripts are suppressed. Previous research using similar cost functions rely on the estimated weights from the regression in order to model the influence of each factor on the dependent variable.’ In our opinion, this approach is too ambitious given the available data and leads to inefficient estimates. In our specification, the weights W are assumed constant over time but the component costs C are allowed to change over time as the input prices p vary. Input price series were obtained (by state) from the USDA Agricultural Statistics.’ Therefore, an accurate total cost measure, c,,, can be calculated using eq. (8): Cij=

Cijl + CiJc+ Cyf + Cij,.

(9)

The econometric model estimates the relative weights, CL~and ~1~, of (dollar) revenue and (dollar) costs respectively, on the probability of changing to a new crop/technology combination {i, j}: Prob {i, j} =f(~~,)=f(~l~r~~+~(~~i~) =f(‘%lrij+CIz[Cijl+

Cijc+

(10) Cijf+

cij,])

=f(a,rij+tL2CPIWijl+Pc~jc+Pf~jf+Pw~jwl) =S(ollr,,+(C(,~jl)P,+(a2~J,)P,+(cc,~jf)P,+(cr,~j,)P,),

or =f(z,rij+z,P,+z,P,+z,P,+z,P,).

If only revenue

and price variables

are included,

five (rl,z,,zc,zf

and rw)

‘We also included the four cost variables in an earlier model specification. However, collinearity of the cost variables caused the ML estimation procedure to break down. ‘The price of capital used was the average Interest rate on outstanding agricultural loans.

the

T.N. Cason and R.T. Uhlaner,

Agriculture and water and energy demand

instead of two (LX~and CQ) parameters need to be estimated. estimated parameters satisfy the following restrictions:

However,

313

the

and a2Wjq=

Zqr

for

q=l,c,f,w.

(11)

Taking account of this information (wj,) from the available data increases the efficiency of the model estimation.’ This provides an important advantage to our specification.” Finally, we discuss the data for the dependent variable. Little variation exists in total acreage proportions because a large amount of acreage is planted with the same crop over the sample period. Therefore, we apply eq. (6) to model marginal farmer decisions rather than total acreage utilization. These proportions reflect how decisions were made on the margin, that is, for new acreage and acreage that shifts in its utilization. First, base acreage is computed for each crop alternative as the minimum acreage planted with that crop over the sample period. The amount of marginal acreage in year t relative to the base for each crop is calculated by subtracting the base acreage from the year t total acreage for that crop. Hence, the proportion of new acres with choice {i, j} in year t (PROPNEWij,) is calculated as the new acreage with choice {i, j} divided by total new acreage across all {i, j) alternatives.’ r The primary data source for the acreage proportions is the USDA National Agricultural Service County Estimates. These data cover the sample period 1972-86 and included planted and harvested acreage and yield by crop for various dry and irrigation technologies. The irrigated acreage is divided into sprinkler and gravity using assumption A.4, according to the yearly proportions provided by the Irrigation Journal Annual Survey. Finally, we added the acreage diverted by government programs (for each crop and each state) reported by the USDA Set-Aside Program Annual Reports to the dry acreage. This makes the assumption that farmers ‘Furthermore, we obtained technology-specific yield information (y) for each year of the sample by county from the USDA Agricultural Statisttcs Service, so revenue yield could be explicitly included as r,, = y,, * p,, where p, is the price of crop i. Price data were obtained from the USDA’s Agricultural Statistics and Crop Value Summary, by state and year. For reasons similar to the cost variable Improvement, this has significant advantages over simply including crop price (p,) as a regressor. “Cost and revenues were deflated to constant 1977 dollars by the USDA’s index of prices patd by farmers and index of prtces received by farmers, respectively. “The total planted acreage in each state followed an upward trend over the sample period. However, because of shafts between crops and technologies, acreage was not smallest for every crop/technology combination in the first year in the sample. Consequently, the base year was not the first year of the sample for all crops and technologies.

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

314

optimally allocate acreage, and then modify this decision by diverting acreage from their low quality dry fields in response to the government programs. It is important to include this acreage because government programs (especially the Payment in Kind - ‘PIK’ - program) are very important determinants of crop acreage shifts.

4. Estimation

results

Estimation is carried which is a restatement

out with the following multinomial logit model, of eq. (6) using the substitution uii,=(B1 * liir) +

tB2* Cijt): PROPNEw.j,+

1=

exp C(Bl * rijt) + (B* * cij*) + 6ijzl 1 1 expCtBI* rij*) +(B2 * Cijt)+ 6,jll’

alli

(12)

dJ

where rijt = yijt * pit (the revenue yield per acre), cijt is the production cost per acre delined in eq. (9), and BIJf is an i.i.d. error term following the logistic distribution. The estimated B, and B, relate the impact of per-acre revenue and cost, respectively, into acreage proportion changes. The time subscripts reflect the assumption that the previous year’s revenue and cost variables are used in Since both revenues and costs are making current planting decisions. the magnitude of these estimated expressed in constant 1977 dollars, coefficients indicate the relative importance of each in explaining acreage changes. Estimation is carried out separately for each region. Because of data constraints, three composite irrigation technologies are used: dry, sprinkler and gravity. The major crops are included - three crops in Colorado and Washington and four crops in California and Oregon, and between two and four crops for the individual counties in California. The included crops account for between 70 and 90 percent of the total acreage of the area.” The model is estimated for the 1Cyear sample period from 1973 to 1986. Estimation is constrained to this period because of data availability. Table 1 presents the maximum likelihood estimation results. All but one of the coefficient signs are correct; that is, for nearly all states and counties an increase in revenue per acre increases the proportion planted with that crop/ technology combination (note the positive sign on the B, estimated coeflicients), and an increase in cost per acre decreases the forecasted proportion (note the negative estimated values of B,). Furthermore, many coefficients are statistically different from zero. “Furthermore, the rune counties m California for which we provide contain just under 60 percent of the total planted acreage in the state.

individual

estimates

T.N. Cason and R.T. Vhlaner, Agriculture and water and energy demand Table Estimated

1

logit models.” Loglikelihood

Restricted log-likelihood

State

County

(%enue)

Colorado

All

0.0221** (0.0095)

- 0.0544** (0.0208)

-26.1

-30.8

Oregon

All

0.0153** (0.0078)

- 0.0669** (0.0226)

-28.8

-34.8

Washington

All

0.003 1 (0.0084)

-0.0508** (0.0261)

-25.7

-30.8

Cahforma

All

0.0025 (0.002 1)

-0.0174* (0.0099)

- 30.6

-32.2

California

Colusa

0.0047** (0.0023)

-0.0346** (0.0108)

-21.6

- 27.2

California

Fresno

0.0020 (0.002 1)

- 0.0070 (0.0100)

-287

-29.1

California

Kern

0.0027 (0.0019)

-0.0087 (0.0096)

- 27.6

-29.1

California

Kings

0.0012 (0.0093)

-0.0013 (0.0019)

- 28.7

-29.1

California

San Joaquin

0.0070 (0.0054)

-0.0164 (0.0159)

-21.6

-22.6

California

San Luis Obispo

-0.0252 (0.0183)

-0.0385** (0.0176)

- 14.3

-25.1

California

Sutter

0.0044** (0.0022)

-0.0283** (0.0110)

-23.7

-27.2

California

Tulare

0.0051* (0.0031)

-0.0146 (0.0109)

- 30.6

-32.2

California

Yolo

0 0026 (0.0026)

-0.0237** (0.0108)

- 24.7

- 27.2

‘Standard error m parentheses. *Denotes significance at the 10 percent **Denotes significance at the 5 percent

315

level. level.

Significant coefficients and explanatory power of the model indicate that yield gain/output price increase and cost savings induce farmers to switch into a different crop/technology combination. More importantly, the costinduced switching effect is typically several times as large as the revenueinduced effect. This is entirely consistent with previous studies13 and may stem from several factors. One possible explanation is that revenues are much more variable and uncertain than costs at planting time because they depend on both weather and output price, while most costs are incurred at planting time. Therefore, much of the variability in revenues may be ‘%ee, for example, Caswell and Zilberman errors associated with these estimates generally different.

(1985, 1986). However, note that the standard indicate that the magnitudes are not statistically

316

T.N. Cason and R.T. Vhlaner, Agriculture and water and energy demand

considered by the farmer to be short-term fluctuations which do not induce switching. Finally, the coefficients are generally consistent across states, although the county-level estimates are usually lower. This provides evidence that we have estimated important stable relationships between costs, revenues and acreage proportions and not simply characterized the covariance in these variables over the sample period. These results suggest that our functional form is robust.

5. Example: Assessing the impact of a new technology Crop irrigation requires a vast amount of water, obtained primarily from groundwater pumping or surface deliveries. The former source affects water quality and the depth of the water table, while the latter source is subject to weather variability. Furthermore, irrigation pumping requires a significant amount of energy. In many areas, much of this energy comes from electricity. Given the short time series and use of only the major crops and composite irrigation technologies, the reader should regard the estimated parameters merely as suggestive of what more relined research will reveal. Nonetheless, this section uses the estimates to assess the impact of the introduction of a new irrigation technology. The example uses the model to consider the availability of a hypothetical high-efficiency irrigation technology and resulting changes in electricity and water requirements. Although we do not do so here, the model can also be used to forecast the impact of changing input and output prices through their effect on crop and technology choices. Suppose that the distribution of crops in a hypothetical California county in 1990 is given by panel A of table 2. Also, suppose that all the relative input and output prices, as well as crop yields, remain constant at their 1988 levels. Using some simplified crop and technology definitions and historical weather for California’s Central Valley, this 1990 acreage distribution results in seasonal usage of roughly 1,276 GWH for irrigation. This is shown in table 2. The equation for forecasting the composition of the marginal acreage uses the estimated Bi parameters in the model eq. (12):

PROPNEV$,+

exp C(O.003* rij,) ~(0.024

1= 1

C

aIll

allj

* cijt)]

exp c(O.003 * rij,) - (0.024 * cij,)] '

(13)

For this example, we use the median of the 13 estimated parameters for each Bi to represent the typical sensitivity of the sample. First, we compute a baseline case, where the relevant variables are not changing over time; that is, for all i, j and t. With these constant values, the cijt=Cij1988 and rlJf=rLJf1988v

T.N. Cason and R.T. Uhlaner, Agriculture

and water and energy demand

317

Table 2 Baseline forecasts Technologies

with no new technology. Barley

Cotton

Corn

Wheat

Total

0 7.934 9,698 2,204

440 17,632 22,040 5,730

2,203 171,471 212,907 53.771

19,836

45,842

440.800

0 10,901 12,457 6,118

1,627 18,827 23,107 7,397

3,945 169,448 209,027 58,265

29,476

50.956

Panel A: I990 base Dry Furrow Gated pipe Hand move Total

0 133,122 165,300 41.435

1,763 12,783 15,869 4,408

339.857

34,823

Total acres in area: Total GWH sales: Total water use:

440,800 1,276 2,614,860 acre-feet

Panel B: 1991 forecast Dry Furrow Gated pipe Hand move Total

0 126,496 157,216 39,646

2,319 13,224 16,248 5,104

323,358

36,895

Total GWH sales: Total water use:

1,242 2,547,lOO acre-feet

Panel C: I995 forecast

Dry Furrow Gated pipe Hand move Total

0 103,760 128,722 33.175

3.49 1 14,304 16,812 7,877

0 21,828 22,331 19,144

265,657

42,484

63,303

Total GWH sales: Total water use:

5,655 22,728 26,558 13,885

9,147 162,620 194,424 74,08 1

68.827

1,125 2,308,300 acre-feet

Panel D. 2000 forecast Dry Furrow Gated pipe Hand move Total

0 81,178 100,423 26,752

4,659 15,380 17,372 10,628

208,353

48,038

Total GWH sales: Total water use:

0 32.681 32,143 32,08 1

9,658 26,607 29,992 20,330

96.905

86.586

14,317 155,845 179,930 89,791

1,008 2.071.200 acre-feet

model extrapolates into the future the historical acreage utilization time trend that generated the estimated parameters. This results in new forecasted acreage distributions that determine water and electricity usage for this baseline case. These forecasts of acreage utilization and electricity usage are provided in table 2, panels B through D for one year (1991), five years (1953 and ten years (2000), respectively. Consistent with the estimation

318

T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

approach based on decisions regarding marginal acreage, we assume that no more than 5 percent of the total acreage can change in a particular year. The baseline case given above can be compared to another scenario in which a new ultra-high-efficiency drip irrigation technology becomes available beginning in 1991. Assume that this new technology has capital, labor and fertilizer costs equal to that of the existing drip technology, but it has a water application efficiency of 95 percent and a pump efficiency of 75 percent.14 Since this technology has a higher application efficiency, more of the pumped water actually reaches the crop root zone in this irrigation process; furthermore, the higher pump efficiency significantly increases the amount of water that can be pumped with each unit of energy. These two effects combine to greatly reduce the operating cost of this technology, making it more attractive than the standard drip technology that currently has a small amount of penetration. The structural form of our model allows us to forecast the adoption of this hypothetical new drip technology, as well as the resulting impact on water and energy usage. This example maintains the relative input and output prices and crop yields at their 1988 levels. The availability of this new low-cost highly efficient irrigation technology stimulates transition to more efficient irrigated farming. This is because this high-efficiency drip technology requires, on average, two-thirds of the energy required by conventional irrigation alternatives to pump each acre-foot of water to the crop. Table 3 traces its adoption over one-year, five-year, and ten-year horizons, panels A, B and C, respectively. After ten years, this new technology captures over a third of the total acreage, nearly all of it to irrigate a much greater amount of corn. Consequently, the total GWH consumed (relative to the baseline case) decreases from 1,008 to 929GWH after ten years, a net saving of nearly eight percent. This is illustrated in the left-most row of table 4. Table 4 also demonstrates that, since the transition to this new drip technology is associated with a dramatic increase in corn cultivation from wheat and barley cultivation (compare panel D of table 2 with panel C of table 3), the total amount of water used does not decrease relative to the baseline case. This is because corn in California’s Central Valley requires about three times the total irrigation water as wheat or barley. Therefore, while irrigation on average is much more efficient in the year 2000 under this scenario, the increase in corn acreage counteracts the more efficient use of the available water so that the total water use is unchanged. This result demonstrates the importance of considering crop and technology decisions jointly. Again, it is important to emphasize that this example is meant to be illustrative. For example, the energy and water savings calculated above are IdDrip technology and typical irrigation

that is currently available has an application efficiency pumps operate at an effkiency of 50 to 60 percent.

of about

90 percent,

T.N. Cason and R.T. Uhlaner,

and water and energy demand

Agriculture

Table 3 Forecasts Technologies

with new high efficiency

drip technology.

Cotton

Corn

Barley

Wheat

Total

0 7,427 9,186 2,323 21,890 -

467 16,843 21,031 5,285 40 __

2,354 162,845 202,543 51,124 21,934

40,827

43,666

Panel A: 1990 forecast

Dry Furrow Gated pipe Hand move New drip Total

0 126,298 157,035 339,408 0 __

1,887 12,276 15,291 4,108 4 -

322,741

33,567

Total GWH sales: Total water use:

1,233 2,547,300 acre-feet

Panel B: 1995 forecast Dry Furrow Gated pipe Hand move New drip

0 102,874 127,911 32,099 4

1,543 10,015 12,470 3,363 18 -

-

Total

262,889

27,409

114,643

Total GWH sales: Total water use:

0 6,114 7,542 1,957 99,030

401 13,753 17,169 4,342 185

1,944 132,756 165,093 41,761 99,237

35,850

1,080 2,309,OOO acre-feet

Panel C 2000~foreca.x Dry Furrow Gated pipe Hand move New drip

0 79,608 98,977 24,843 9 __

1,203 7.771 9,671 2,627 31

-

Total

203,438

21,304

Total GWH sales: Total water use:

0 4,805 5,907 1.600 175,659 __

335 10,685 13,330 3,407 331 -

187,970

28,088

1,538 102,869 127,885 32,478 176,029

929 2,072,400 acre-feet

Table 4 Comparison

of baseline

Baseline GWH usage New drip GWH usage Ratio of new drip GWH over baseline Baseline water use New drip water use Ratio of new drip water over baseline aWater

use in thousands

and new drip technology

scenarios.”

1991 forecast

1995 forecast

2000 forecast

1,242 1,233

1,125 1,080

1,008 929

0.993 2,547 2,547

0.960 2,308 2,309

0 922 2,07 1 2,072

1.000

1.000

1.000

of acre-feet.

319

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T.N. Cason and R.T. Uhlaner, Agriculture and water and energy demand

quite sensitive to the assumption that exactly 5 percent of the acreage is allowed to change each year. The advantage of our approach is that alternative assumptions and their resulting forecasts can easily be compared, so that relationships and sensitivities can be clearly understood. 6. Summary This paper has demonstrated that a structural model of discrete farmer production decisions can be operationalized with the use of aggregate acreage, cost and revenue data. Relationships between acreage proportions and production costs and revenues have been estimated for different states and individual counties. The definitions of our explanatory variables take into account known production cost relationships, which increases the efficiency of our estimates over models that allow the data to generate the importance of each component. Several interesting conclusions can be drawn from this model. First, farmer decisions (and thus acreage proportions) are responsive to changes in costs and expected revenues of production alternatives. Second, these responses are often statistically significant and are consistent with a positive model of profit-maximization because increased revenues and decreased production costs increase the acreage of a particular crop/technology combination. Furthermore, the estimated relationships between changes in acreage proportions and cost and revenues of the alternatives are consistent across states, indicating that stable and meaningful relationships have been characterized. Finally, costs are several times more important than revenues in inducing switching of one crop/technology combination into another, which is consistent with previous evidence. An example is included to illustrate the forecasting applications that are supported by this framework. The results can be directly applied to irrigation energy forecasting and water conservation planning. A number of useful extensions of this research exist. First, the model as specified could be estimated for any number of additional regions. Second, county-level data could be used to generate a cross-sectional time series, increasing the power of our estimation; based on the similarity across states, pooling across larger regions is also suggested. Finally, extending the estimation acreage to cover less important crops may also be enlightening. In sum, the use of this MNL/proportions methodology appears quite useful and can be used as an easily understood planning and policy tool.

Benson, Verel W., Curtis A. Everson and Rodney L. Sharp, 1981, Irrigation an energy-short economy, ERS 670 (U.S. Department of Agriculture, Economics Division, Economic Research Service, Washington, DC).

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