THE ECONOMIC IMPACTS OF THE COMPACT WATER BANK

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Burkett Huey

April 2016

THE ECONOMIC IMPACTS OF THE COMPACT WATER BANK

Burkett Huey

April 2016

Economics

Abstract

The Colorado River Compact Water Bank would transfer pre-Colorado River Compact

water used in forage irrigation to post-Compact water users in the event of a Compact

curtailment by fallowing irrigated crops. Several studies indicate that fallowing irrigated

acreage in rural communities reduces total economic activity. This study develops three

scenarios to estimate the indirect effects, or changes in total economic activity, from

fallowing irrigated acreage using IMPLAN, an input-output analysis program. Then I

measure the indirect impacts of a potential lease payment. Finally I add these effects

together to find that the Compact Water Bank would likely increase economic activity in

Montrose County, Colorado.

KEYWORDS: (Rotational Fallowing, Water Transfer, Input-Output Analysis, Compact

Water Bank)

JEL CODE: Q25, Q13

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED

UNAUTHORIZED AID ON THIS THESIS

Acknowledgements:

I would like to thank my thesis advisor, Mark Smith, for helping me each step of

the way of this long project, and for guiding me when I was lost. I would not have been

able to complete this without his help. I would like to thank Roy Jo Sartin for helping

with my writing style and spending so much time brainstorming and editing with me. I’d

like to thank Ale Chavez for helping with my final edits and formatting. I’d like to thank

Brendan Boepple, Brook Larson, and Eric Perramond at the State of the Rockies for

providing me with an excellent background in Colorado water and funding this project.

I’d like to thank Van Skilling for providing me with funding for my data. Finally I’d like

to thank my family and friends for supporting me throughout this entire project.

Table of Contents

Introduction ......................................................................................................................... 1

Problem ........................................................................................................................... 1

Method ............................................................................................................................ 3

Data ................................................................................................................................. 4

Expected Results ............................................................................................................. 5

Literature Review ............................................................................................................... 7

Water Law Overview ...................................................................................................... 7

Secondary Impacts of Water Markets Literature ............................................................ 9

Empirical Analysis of Water Transfers ......................................................................... 14

Programs to Mitigate Indirect Effects ........................................................................... 16

Contributions of This Study .......................................................................................... 17

Background ....................................................................................................................... 19

Colorado River Interstate Compacts ............................................................................. 19

Compact Water Bank Transfer Mechanism .................................................................. 24

Montrose County .......................................................................................................... 28

Methodology ..................................................................................................................... 33

Input-Output Analysis and IMPLAN............................................................................ 33

Overview of How IMPLAN Processes Rotational Fallowing Programs...................... 36

Model Development for Montrose County ................................................................... 37

Best Case ................................................................................................................... 40

Average Case ............................................................................................................ 41

Worst Case ................................................................................................................ 42

Results ............................................................................................................................... 47

Fallowing Irrigated Acreage ......................................................................................... 49

Best Case: Proportional Fallowing ........................................................................... 50

Best Case: Least Cost Fallowing .............................................................................. 51

Average Case: Proportional Fallowing ..................................................................... 52

Average Case: Least Cost Fallowing ........................................................................ 52

Worst Case: Proportional Fallowing ......................................................................... 53

Worst Case: Least Cost Fallowing ............................................................................ 54

Lease Payments ............................................................................................................. 54

Lease Payment: Household Income .......................................................................... 55

Lease Payment: Agricultural Capital ........................................................................ 56

Net Impacts ................................................................................................................... 56

Sector Analysis ............................................................................................................. 59

Conclusion ........................................................................................................................ 65

Limitations of this Study ............................................................................................... 66

Closing Arguments ....................................................................................................... 68

References: ........................................................................................................................ 69

Appendix A: List of Assumptions .................................................................................... 74

Appendix B: Crop Enterprise Budgets …………...…....Colorado College Online Archive

Appendix C: Impacts of Compact Water Bank…...…... Colorado College Online Archive

List of Figures

Figure 1.1: Map of the western slope of Colorado with pre-Compact water Rights marked

in light green. Montrose County is appropriate to study because of its pre-1922

appropriated and adjudicated water rights. Montrose County is outlined in bold ............. 2

Figure 3.1: A map of the Colorado River basin in the United States. Lee’s Ferry, circled,

splits the Lower Basin and the Upper Basin………………………………………….….21

Figure 3.2: Map of transmountain diversions in Colorado. Many of these diversions move

water from west to east………………………….……………………………………….27

Figure 3.3: Map of the Uncompahgre Valley irrigation project in Montrose

County………………………………………………………………………...………….29

Figure 4.1: Returns to an acre of irrigation in the best case scenario. As farmers reduce

their evapotranspiration, they reduce their output and their income………………..…...40

Figure 4.2 Average case returns to an acre of irrigation. Reducing

evapotranspiration increases grain farmer’s income………………………………..……41

Figure 4.3 Worst case returns to an acre of irrigation. Reducing evapotranspiration

increases all irrigator’s income…………………………...……………………………...42

List of Tables

Table 3.1: Supply limited consumptive use by water rights category…………...………24

Table 3.2: Potential categories of Water Bank use. This shows that Front Range

municipalities are dependent on Colorado River water…………………...……………..25

Table 3.3: Fallowing costs incurred by irrigators operating in the Grand and

Uncompahgre Valley for average Minimum and maximum five year commodity

prices………………………………………………………………………...…………...30

Table 4.1: A simplified input-output table. Industries’ output (rows) is used in the

economy as inputs for another industry (columns)……………………….……………...34

Table 4.2 Value of alfalfa and corn in Montrose County under various climatic scenarios

that change price and quantity of output produced……………………………………....38

Table 4.3: Conversion of each crop’s proportion of irrigated acreage to fallowed irrigated

acreage………………………….....……………………………………………………..44

Table 4.4: Least Cost Acres Fallowed, with the percentages of reduced irrigated acreage

in parenthesis…………………………...………………………………………………..44

Table 5.1: Indirect and induced effects from proportional fallowing in the best case

scenario…………………………………………………………………………………..50

Table 5.2: Indirect and induced effects from least cost fallowing in the best case

scenario………………………………………………………………………………..…51

Table 5.3: Indirect and induced effects from proportional fallowing in the average case

scenario...………………………………………………………………………………...52

Table 5.4: Average case indirect and induced effects from fallowing using the least cost

method...………………………………………………………………………………….52

Table 5.5: Worst case proportional fallowing indirect and induced effects from

fallowing…………………………………………………………………………………53

Table 5.6: Worst case least cost indirect and induced effects from fallowing…………..54

Table 5.7: Effects of increasing household spending through a lease payment…………55

Table 5.8: Effects of increasing agricultural capital spending through a lease payment.

…………………………………………………………………………………………...56

Table 5.9: Best case scenario net impacts of fallowing and lease payment…………..…57

Table 5.10: Average case scenario net impacts of fallowing and lease payment………..58

Table 5.11: Worst case scenario net impacts of fallowing and lease payment…………..58

Table 5.12: Top sectors indirectly negatively affected by fallowing in the best case (blue

shading denotes presence on crop enterprise budgets)……………………………….….60

Table 5.13: Top sectors indirectly negatively affected by fallowing in the average case

(blue shading denotes presence on crop enterprise budgets)…………………………….61

Table 5.14: Top sectors indirectly negatively affected by fallowing in the worst case (blue

shading denotes presence on crop enterprise budgets)…………………………………..62

Table 5.15: Top sectors positively impacted by lease payment used in household

spending and BEA’s agricultural production spending pattern…………………….........63

1

Chapter 1:

Introduction

Problem

The Colorado River provides drinking water to nearly 40 million people and

irrigates 5.5 million acres of land both inside and outside its river basin, including the

urban Front Range of Colorado (USBR, 2012). The Colorado River Compact of 1922

governs the river. The Compact divides the Colorado River Basin into the Upper Basin

and Lower Basin, and requires the Upper Basin to deliver 75 million acre-feet of water

every 10 years to the Lower Basin (USBR, 1922). If the Upper Basin does not deliver 75

million acre-feet to the Lower Basin, the Upper Basin must cease using water developed

after the Compact (USBR, 1948). Climate change in the highly variable Colorado River

increases the probability the Upper Basin may be unable to meet Compact’s requirements

and enter a Compact curtailment sometime in the future (USBR, 2012).

The Water Bank Group is working to mitigate risk of not meeting Compact

obligations by introducing a Compact Water Bank. The Compact Water Bank would

compensate pre-Compact water rights holders, mainly irrigators on Colorado’s western

slope, to enter a deficit irrigation rotational fallowing agreement and transfer the saved

irrigation water to municipalities if a Compact curtailment occurs (CRWCD, 2012). The

Compact Water Bank would move water to cities in Front Range river basins to maintain

residential and industrial water use.

2

Figure 1.1: Map of the western slope of Colorado with pre-Compact water rights marked

in light green. Montrose County is appropriate to study because of its pre-1922

appropriated and adjudicated water rights. Montrose County is outlined in bold

Adapted from: http://swwcd.org/wp-content/uploads/2014/01/Dan-Birch-Water-Bank-

The-Next-Step.pdf

3

Inter-basin water transfers can generate negative indirect economic impacts to

rural communities (Howe & Goemans, 2003; McMahon & Smith 2012; Thorvaldson &

Pritchett, 2006). I examine Montrose County, an area with pre-Compact water rights that

produces low value forage crops, see Figure 1.1. I compare the indirect losses from a

rotational fallowing agreement to the indirect benefits of lease payments in Montrose

County’s economy so individuals involved in the Compact Water Bank’s development

have better information on the Bank’s effects on those not compensated by lease

payments.

Method

I use input-output analysis to estimate the secondary impacts of water transfers.

IMPLAN (IMpacts for PLANning), an input-output analysis software, can model a

rotational fallowing program through reducing irrigation water available to the economy

and model lease payments as an increase in household spending in Montrose County.

IMPLAN shows the direct effects to the individuals who transfer water, the indirect

effects from “changes in inter-industry purchases as they respond to new demands” in

water transferring industries, and induced effects from “changes in spending from

households as income increases or decreases due to the changes in production” (Schmit et

al., 2011). This study focuses on the indirect and induced effects from fallowing and

lease payments.

I model an irrigation water reduction in three candidate crops examined by the

Colorado River District: alfalfa, silage corn and grain corn. Alfalfa is drought tolerant,

able to grow in deficit irrigation circumstances, and has a large presence on the western

4

slope (Hanson et al., 2008). Corn is replanted each year, which reduces fallowing costs.

These three crops are examined because they are used for grain and forage, which have

relatively low value compared to other crops in Colorado (United States Department of

Agriculture, 2012). Alfalfa, grain corn, and silage corn farmers have the most to gain by

reducing irrigation and accepting a lease payment. To model the reduction in irrigation

for these specific crops, this research uses the analysis-by-parts method to develop

sectors for alfalfa, silage corn and grain corn and exogenously reduce irrigated acreage in

Montrose County.

IMPLAN models the secondary impacts of reducing irrigation water by

proportionally reducing the amount of other intermediate agricultural inputs purchased.

The intermediate purchase reduction will consequently reduce economic output in the

county because other the inputs produced are no longer purchased for agricultural

production. A water reduction will eventually correspond to a reduction in dollars of

inputs purchased and will reduce employment and total output in several sectors,

including agricultural output.

Data

IMPLAN’s data for Montrose County is a set of multipliers that describe the

response of Montrose’s economy to a stimulus. IMPLAN’s multipliers are generated

from national and regional datasets and show the economic relationships between

industries (MIG, 2015a). The data also shows the relative proportions of commodities

used in each industry. This data is available through Minnesota IMPLAN Group (MIG)

and is used as the framework for this analysis.

5

This research creates unique sectors for forage crops through farm enterprise

budgets and agricultural statistics. Farm enterprise budgets show the intermediate inputs

farmers buy, and how much they spend on each input. Specific farm enterprise budgets

for western slope irrigated alfalfa, grain corn, and silage corn are available through

Colorado State University (Colorado State University Agricultural Extension, 2014).

This study uses agricultural statistics coupled with estimates from the Colorado State’s

Agricultural Extension to estimate the total value of each crop’s production, proprietor

income, employment, and employee compensation. Impact analysis of these custom

sectors shows the indirect effects of reduced irrigation water in Montrose County,

Colorado.

Expected Results

I estimate the dollar value of indirect and induced impacts in Montrose County

from instituting a rotational fallowing program and inserting a lease payment. The

analysis estimates the effects of a reduction in irrigation water to producers of

intermediate inputs for forage agriculture and the effects of lease payments. I model the

lease payment as an increase in consumer spending and capital expenditures on

agriculture. I compare the magnitude of the leasing agreement’s positive impacts to the

negative impacts from reducing irrigation water. This research identifies which sectors

will benefit from the lease payments, and which will not. Following McMahon and

Smith (2012), this study shows that a lease payment can indirectly compensate an

agricultural community for fallowing irrigated acreage in certain situations (McMahon &

Smith, 2012). This project’s results could be useful for local stakeholders seeking

6

information on how an irrigation water transfer and lease payment could indirectly

impact Montrose County’s economy and for regional planners seeking to estimate the

indirect value of water used for forage irrigation in Montrose County or similar areas in

the Upper Basin.

This thesis frames the problem of indirect effects resulting from water transfers

through a potential transfer, the Compact Water Bank, and uses input-output analysis to

show if a proposed solution is enough to mitigate the indirect effects. I use previous

research to state a case for measuring indirect effects in water transfers and show lease

payments can compensate for indirect impacts.

7

Chapter 2:

Literature Review

Although most of Colorado’s water is used for irrigation, municipalities are

willing to pay much more for water than farmers or ranchers can generate on a per-unit

basis (Ivanenko & Flynn, 2010; Brewer et al. 2007). Economists have proposed market

solutions to this resource misallocation, but there are serious political barriers to enact

reforms due to indirect economic effects (Considerations for Agricultural to Urban Water

Transfers, 2008).

This literature review uses Colorado’s relevant water law to show limitations in

Colorado’s water market. Then, this review uses water market theory to argue that cities

should compensate for indirect economic effects from inter-basin water transfers.

Finally, the analysis assesses previous studies that use input-output analysis to estimate

the indirect negative effects of water transfers and the indirect positive effects from lease

payments. My goal is to show that negative indirect impacts from water transfers are

significant and that lease payments can generate sufficient indirect positive effects to

compensate for the indirect negative effects.

Water Law Overview

Colorado water law is based on prior appropriation, which originated in mining

communities and evolved to govern all of Colorado’s water (Hobbs, 2004). Prior

8

appropriation manages water through state water ownership, and allows water courts to

distribute ‘rights’ for individuals to put water to ‘beneficial use’1 without owning it

(Hobbs, 2004). The oldest water rights on a particular river are fulfilled first, and

subsequent water rights are fulfilled in order of their appropriation date. Most of

Colorado’s rivers are over appropriated; water courts decreed more water rights than

there is water in the river (Hobbs, 2004; Thorvaldson & Pritchett, 2006). Junior rights

may not be fulfilled if a river does not produce much water in a given year, so senior

rights provide more reliable water. Since most rivers are over-appropriated, new water

development will not solve supply shortages. Cities looking for reliable water purchase

existing water rights.

Colorado water law allows for regulated water right transfers among water users

since an early Colorado Supreme Court case, Strickler v. City of Colorado Springs

(1891). Water rights can be legally transferred after three processes are completed. The

water court must approve the transfer (Hobbs, 2004). Water court measures the historic

consumptive use, water that is consumed and does not return to a stream, to ensure that

the right amount is transferred (Hobbs, 2004). Finally, water courts ensure that

downstream water rights are able to continue diverting their appropriated water (Hobbs,

2004). Water transfers are a lengthy and expensive process and can take several years in

court. Colorado’s water law relates to the Compact Water Bank because the law

regulates and prevents Colorado’s water markets from fully developing.

1 Beneficial use is a legal term to define acceptable purposes for a water right. Examples of beneficial use include irrigation, municipal, and instream flow (Hobbs, 2004).

9

Secondary Impacts of Water Markets Literature

This section begins with a theoretical explanation of indirect effects from water

transfers and then divides the literature on indirect effects of water trading in Colorado

into researchers who agree or disagree with the notion that municipalities should

compensate for indirect effects. My goal is to show that if water transfers generate

significant indirect regional impacts, then the basin-of-origin should be compensated for

the indirect losses.

A general equilibrium model shows why indirect effects exist in water transfers:

job search costs (Bourgeon et al., 2008). The model presents two scenarios after a water

transfer: large and small job search costs for former agricultural workers. The first

scenario predicts that high job search costs decrease regional welfare because agricultural

laborers cannot find work in the non-agricultural sector. Those who believe

municipalities should compensate for negative indirect effects show that after inter-basin

water transfers, laborers and capital are not repurposed productively in the non-

agricultural sector. These researchers use input-output analysis to empirically estimate

indirect effects of water transfers (Howe et al. 1990; Howe & Goemans, 2003;

Thorvaldson & Pritchett, 2006). The second scenario shows that small job search costs

produce an increase in per capita regional welfare after a water transfer. These

researchers believe that the free market will increase total welfare, so municipalities do

not need to compensate for indirect effects because agricultural laborers will easily find

more productive jobs (Young, 1986; Haddad 2000).

Strict neoclassical economic theory states that factors of production are mobile, so

agricultural capital and labor should reallocate to more productive uses easily after a

10

water transfer (Howe, 1998). By this logic, indirect costs are irrelevant because the

indirectly displaced workers have new opportunities for work in more productive sectors.

Neoclassical costs and benefits should go “to whomsoever they may accrue,” in water

transactions because the seller’s losses are offset by larger benefits for the purchasers

(Young, 1986). In the long run, resources will flow towards more productive water uses,

which is a better outcome for society (Young, 1986). The calculation these economists

propose for judging water transfers is that the total direct and indirect benefits should

exceed the total direct and indirect costs, conveyance costs, and transaction costs (Young,

2003). This calculation does not include the timing or location of benefits from the water

transfer. This is efficient when the selling community moves their factors of production

towards buyers to enjoy the direct and indirect benefits from a water transfer.

Neoclassical economists assume indirect impacts are irrelevant because factors of

production move to the highest payers: the water purchasers.

Opposing agricultural economists disagree with classical analysis because the

nature of water transfers and agricultural capital prevents the selling communities from

indirectly enjoying the benefits of water transfers. The benefits from water sales accrue

to far away locations in the future because cities purchase water from several watersheds

and well in advance of need (Howe, 1998). The communities around water-selling

individuals should, by economic logic, move towards the water purchasing location,

because the water purchasing location would be willing to pay more for capital. One

study shows that farmers, as factors of production, choose to stay in the selling location,

which prevents capital from moving to higher value uses (Weber, 1989). This critiques

the neoclassical assumption that factors of production are mobile because “job search and

11

moving costs are real,” and are not accounted in neoclassical economics (Howe, 1998;

Livingston 1995). The costs of inter-basin water transfers accrue to the basin of origin

immediately, which generates unemployment and idle stranded capital, that constitutes “a

real economic loss for the basin of origin” (Howe, 1998).

These economists suggest judging welfare on regional perspective because of

factor immobility (MacDonnell & Rice, 2008; MacDonnell & Howe, 1986). They

believe that regulating transfers can help ensure positive outcomes. Examples of

regulated transfers include lease payments to impacted communities or community funds

that “compensate unprotected parties and for whatever purpose the citizenry prefers”

(MacDonnell & Howe, 1986). These solutions have successfully mitigated indirect

effects when they are applied correctly (McMahon & Smith, 2012).

Strict neoclassical water economists advocate for a water market because market

mechanisms increase efficiency. Markets provide information on scarcity value through

prices and are efficient because individuals with the highest water value to purchase

water (Haddad, 2000). Some economists use Water Strategist, a newsletter containing an

incomplete water transaction dataset, to show how the limited market is developing and

to see if market mechanisms efficiently reallocate water (Brewer et al., 2006; Brown,

2006). Although the available market data is limited, between 1987-2005 western states

recorded a combined total of over 3,200 transactions, market based mechanisms are

effective in reallocating water from low value agriculture to high value urban areas.

Urban areas are willing to pay premium prices for water (Brewer et al., 2006). These

economists argue that market mechanisms generate economically efficient outcomes and

12

should be expanded because these transactions are between willing buyers and willing

sellers (Brewer et al., 2006; Haddad, 2000).

Opposing economists agree reallocating water to higher value uses benefits

society, but contend that large inter-basin water transfers can have large indirect effects to

the basin of origin, which is a market failure (MacDonnell & Rice, 2008; Livingston

1995). These scholars argue large indirect effects do not always occur from water

transfers, but basins-of-origin should be compensated when they experience large indirect

effects (Howe & Goemans, 2003). These compensation programs are examples of

Kaldor-Hicks efficiency because the beneficiaries compensate the water sellers (Haddad,

2000). To estimate the indirect effects from water transfers and thus find the necessary

payment to fully compensate for indirect effects, studies use input-output analysis to

model an economy and “reduce” agricultural output. Specific studies are addressed in the

next section.

Strict neoclassical economists criticize input-output analysis because it overstates

indirect effects. Input-output analysis assumes that consumers and firms can only

consume and produce goods in fixed ratios, and thus cannot substitute relatively less

scarce goods or inputs, which would be capital in the Compact Water Bank, for more

scarce goods, water in the Compact Water Bank (Young 2003). Input-output analysis

assumes that changes in production and consumption decisions only occur because of the

income effect, which inherently overestimates the indirect effects from a reduction in

irrigated acreage (Young, 2003). Other researchers have shown that IMPLAN’s data

does not accurately reflect local conditions because data is based on national averages

(McKean et al. 1998). These scholars present six recommendations to more accurately

13

model an economy: using survey production data, properly allocating proprietor and

property income, correcting agricultural output, fixing regional purchasing coefficients,

adjusting employment impacts based on full-time equivalents, and revising error caused

by price fluctuation (McKean et al. 1998). These researchers argue that input-output

analysis is not a valid estimator of indirect effects because it overstates the effects.

Although this study cannot fix the inherent overstatement of indirect impacts from

using input-output analysis, it corrects for several parts of the input-output program’s

data. This study’s results only apply to the short-run, it shows the indirect effects of

instantaneously making water scarcer in an economy, and assumes that choices that

substitute capital for water, such as improving irrigation systems, take time to enact. This

project applies some of the recommendations: I use farm enterprise budgets for survey

production data, I adjust agricultural output to reflect conditions in Montrose County, and

I use prices from several years to correct for price fluctuation errors. Although input-

output analysis shows water’s maximum possible indirect value, I improve the data to

reduce unnecessary bias. Three recommendations that I could not use due to data

limitations are correcting regional purchasing coefficients, reallocating proprietor and

property income, and basing employment impacts on full-time equivalents. Although

input-output analysis is an overstatement, I contend that the maximum possible value of

water is a useful point of reference. At the maximum possible value of water, it is likely

the community will be at least as well off and likely better off after a water transfer,

which would mitigate indirect impacts in a community.

14

Empirical Analysis of Water Transfers

Previous scholars have used input-output analysis to estimate the indirect value of

water. Since input-output analysis inherently overestimates the value of water, input-

output analysis is suited to show that there are high indirect impacts from water transfers.

I use these studies as a guide on how to perform input-output analysis to show the

indirect impacts of reduced irrigated acreage. These studies also provide context for my

research, and show if the indirect effects are relatively higher in Montrose County than in

other regions. This review investigates previous input-output studies that show the

indirect value of water in Colorado and studies on rotational fallowing programs.

Indirect effects are frequently studied in the Arkansas River basin because the

basin has a history of water transfers that have generated salient indirect effects. Input-

output analysis was first used to estimate indirect effects to the state of Colorado from the

water transfers in Arkansas River Basin on a per acre-foot basis to appraise the indirect

value of water (Howe et al. 1990). Later scholars improved the input-output method by

focusing on regional effects and using enterprise budgets for production functions

(Taylor et al. 1993). Modern studies have compared the indirect impacts within the

Arkansas Valley to other regions and found that indirect effects are larger in the Arkansas

Valley because the transfers are generally out-of-basin and move large blocks of waters

(Howe & Goemans 2003; Thorvaldson & Pritchett, 2006). To show indirect effects on

different population sizes, these studies estimate negative indirect output impacts on a

per-capita basis (Howe & Goemans 2003; Thorvaldson & Pritchett, 2006). The Arkansas

Valley is a “specialized, marginal agricultural community,” so severe negative indirect

15

effects persist because factors of agricultural production have trouble reallocating to more

productive uses after a water transfer (Howe & Goemans, 2003).

The second most frequently studied region is the South Platte because water

transfers have not negatively impacted the economy. Water transfers in the South Platte

are unique because the water-sellers are relatively close to the water purchasers, which

could make it easier for water sellers to enjoy the benefits of water transfers. Some

scholars argue that water transfers in the South Platte have benefited the region because

the water-sellers have better “mobility of resources and employment opportunities”

(Howe & Goemans, 2003). Another study finds that the South Platte have higher indirect

effects from a dollar reduction in irrigated agriculture, but have the lowest negative

impact per capita because its river basin is more populated than other basins

(Thorvaldson & Pritchett, 2006). The South Platte shows the positive potential of

reallocating water to higher value uses.

Researchers use input-output analysis to examine the effects of rotational

fallowing programs, a proposed transfer mechanism for the Compact Water Bank. This

research models the negative indirect effects from water transfers and then modeling the

positive indirect effects of lease payments. Although the Palo Verde Valley is in

California, it is a good case study on the indirect effects of a rotational fallowing. The

study found that the proposed lease payment would not be enough to fully compensate

for negative indirect impacts of a reduction in irrigated acreage, but an appropriate

payment could negate the indirect impacts (M. Cubed, 2002). Another example, a

rotational fallowing program in the Arkansas Valley, shows that the indirect effects are

fully compensated through a lease payment, and has facilitated dryland farming. Dryland

16

farming further increased the benefits to the region (McMahon & Smith, 2012).

Importantly, both studies show farmers spend lease payments on consumer goods, not the

negatively indirectly impacted industries (McMahon & Smith, 2012; M. Cubed, 2002).

Although a community could be fully compensated for indirect effects, it does not mean a

lease payment will allow an economy to remain the same. These studies are closest

models for my current project in Montrose County because they show the indirect

negative and positive effects of a rotational fallowing program.

Programs to Mitigate Indirect Effects

Two methods have been used to mitigate indirect effects from water transfers:

directly paying farmers and setting up a community fund. The first is directly

compensating farmers for indirectly lost output, hoping that farmers will spend extra

money and stimulate the economy. Although IMPLAN assumes farmers act like other

consumers and will spend money on consumer goods, this is not necessarily the case.

Farming is a capital-intensive industry; previous farmers who sold irrigation water in the

Arkansas Valley used the revenue to reduce their farm debt, which “creates no new jobs

in absence of local investment opportunities” (Weber 1989; Howe & Goemans 2003).

Conversely, the revenue could potentially be used to improve the farm equipment, which

would stimulate the indirectly impacted industries. At least two rotational fallowing

programs directly compensate farmers, the Palo Verde Land Management Crop Rotation

and Water Supply Program in Southeastern California and the Arkansas Valley Super

Ditch in Southeastern Colorado. The Compact Water Bank will directly compensate

17

farmers, so although the model shows indirect positive effects to consumer spending

from a lease payment, this may not necessarily be the case (CRWCD, 2012).

The other program to mitigate indirect impacts stems from MacDonnell and

Howe’s (1986) suggestion to create a community fund that would work to mitigate

indirect impacts. The Metropolitan Water District in Los Angeles developed Palo

Verde’s Community Improvement Fund (CIF) to “[provide] funding that will create

economic opportunities” (CIF-Blythe, 2015). The CIF is used for community

development grants, such as hospital projects or funding the Colorado River Fair, and

loans for business owners (CIF-Blithe, 2015). The CIF’s exclusive goal is to create or

maintain employment (CIF-Blythe; Personal Communication with Charles Hull,

February 7th, 2015). The program can only benefit institutions that apply, which limits

the potential recipients (Personal Communication with Charles Hull, February 7th, 2015).

Although the fund is not perfect, it has stimulated economic activity in the Palo Verde

Valley.

Input-output analysis is an imperfect estimator of indirect positive impacts from

lease payments it because cannot exactly model the methods that compensate the basin-

of-origin. Input-output analysis is still a useful tool to estimate the impacts of lease

payments because it shows how changes in income indirectly effect an economy.

Contributions of This Study

This study builds on previous research to expand the current literature to a new

location, shows the impacts of fallowing in various price and output scenarios and uses

18

new methods to examine the effects of a lease payment. This analysis expands the

literature’s geographic scope to a new area: Colorado’s western slope.

Second, this research includes several scenarios to show the impacts of fallowing

in several different climatic conditions. This could be useful because the price and output

of crops produced in Montrose County is not constant. The different climatic scenarios

show the range of possible indirect impacts to Montrose County. Third, this inquiry

models deliberately fallowing specific crops to minimize the forgone proprietor income.

Although it is impossible to know exactly which crops will produce the highest proprietor

income in a given year, this could be a useful tool for estimating the effects of targeting

the lowest value crops.

Fourth, this examination models lease payments to include farm capital

investments. This would useful because it shows the impacts of different lease payment

consumption patterns. If certain consumption patterns generate more output or benefit

the sectors negatively impacted from fallowing, then incentives could be used to

encourage farmers to spend their lease payments in these consumption patterns.

19

Chapter 3:

Background

The Colorado River Compact Water Bank would work within interstate law to

allow high-value users to continue using Colorado River water during an extreme

drought. The Colorado River Compact requires Colorado’s Upper Basin to deliver 75

million acre-feet of water to the Lower Basin every 10 years (USBR, 1922). If a drought

prevents the Upper Basin from delivering enough water, then the law states that the

Upper Basin must entirely cease consumptively using the Colorado River (USBR, 1948).

This curtailment would include Colorado River water used for the Front Range’s

municipal water supply (USBR, 1948; Steger, 2012). The Compact Water Bank would

transfer exempt pre-Compact agricultural water through existing transmountain

diversions to maintain high-value uses, such as municipal water supply. This paper

examines the indirect effects of the Compact Water Bank on Montrose County, an early-

settled forage irrigation community. I focus on Montrose County because it is a likely

source of the Compact Water Bank’s pre-Compact water. To resolve problems stemming

from the Colorado River’s inflexible interstate compacts, the Compact Water Bank would

transfer water from lower value uses, such as Montrose County’s forage irrigation, to

higher value uses during an exceptional drought.

Colorado River Interstate Compacts

The Compact Water Bank would ensure that Colorado could continue to provide

water to residential, commercial and industrial uses during an extreme drought that

20

violates the Colorado River Compact. This section explains the history and terms of

Colorado River’s interstate compacts to show how these inflexible agreements would

negatively impact Colorado’s water supply in an extreme drought.

The rapidly developing West during the early 20th century forced the Colorado

River Compact’s development. Since the United States Supreme Court Case Wyoming v.

Colorado (1922), prior appropriation applies across state boundaries, meaning that water

rights on interstate waterways are fulfilled in order of their adjudication. Following the

Supreme Court’s decision, fast-growing California threatened to quickly establish and

adjudicate water rights on a large portion of the Colorado River. These water rights

would be senior to water rights adjudicated in slower developing states, and could

potentially prevent other states from developing Colorado River water (Gelt, 1997).

California’s fast water development would have spurred further growth in California, but

could have seriously hindered the rest of the Colorado River Basin’s growth. In

response, Delph Carpenter, Colorado’s State Engineer, organized representatives from all

of the Colorado Basin states to create an interstate compact that would guarantee each

state a portion of the Colorado River (Gelt, 1997).

21

Figure 3.1: A map of the Colorado River basin in the United States. Lee’s Ferry, circled,

splits the Lower Basin and the Upper Basin.

Adapted from: http://www.usbr.gov/lc/images/maps/CRBSmap.jpg

22

The negotiations resulted in the Colorado River Compact, but it did not fully

divide the Colorado River between western states. The Compact’s developers divided

the Colorado River at Lee’s Ferry, see Figure 3.1 (USBR, 1922). Then, the Compact’s

developers appropriated about half the river, 7.5 million acre-feet per year, to the Upper

Basin, the states upstream from Lee’s Ferry, and to the Lower Basin, the downstream

states (USBR, 1922). Arizona’s representative aptly complained that the Compact

“doesn’t arrive at any conclusion, … it leaves two divisions to work out their own

salvation” because the Compact does not guarantee each state a portion of the Colorado

River (Gelt, 1997). The Compact’s developers assumed a total river flow of 16.4 million

acre-feet per year, which was an overestimate (Gelt, 1997). The Colorado River’s actual

long-term average is about 13.5 million acre-feet per year (Gelt, 1997). Long-term tree

ring data shows that the Colorado River Basin has long term droughts that drop river

flows below the long term average for extended periods, so natural variation in low flow

periods could challenge flow requirements (Woodhouse et al., 2006). Climate change is

projected to further reduce the Colorado River’s water supply (USBR, 2012). Although

the Upper Basin has a limited ability to create water, it is obligated to deliver 75 million

acre-feet to Lee’s Ferry every 10 years (USBR, 1922). The Colorado River Compact is

inflexible to new situations, but it is the law of the river and unlikely to change.

The Upper Basin Compact of 1948 develops how the Upper Basin States will

respond if they are unable to meet their Colorado River Compact obligations. If the

Upper Basin is unable to produce 75 million acre-feet at Lee’s Ferry, the Basin agreed to

completely stop consumptively using Colorado River water to meet the Colorado River

Compact’s obligations (USBR, 1948). No one in the Upper Basin with post-Compact

23

Colorado River water rights would be able to use water (USBR, 1948). This would be a

Compact curtailment. The only exemptions are Colorado River water rights “perfected

prior to November 24, 1922”, as they are excluded from the requirement to curtail water

use. Since a Compact curtailment has never occurred, there is disagreement over which

rights would be excluded from a Compact curtailment (USBR, 1948). The Water Bank

Group suggests water rights before two dates would not be subject to a Compact

curtailment: water rights appropriated or adjudicated before November 24th, 1922, the

date of the Colorado River Compact, or water rights appropriated or adjudicated before

June 25th 1929, the date of the Boulder Canyon Project Act (CWRCD, 2012). Following

the Colorado Compact Water Bank’s study, this research will assume that water rights

appropriated or adjudicated before June 25th, 1929 would be exempt from a Compact

curtailment (CWRCD, 2012).

To maintain stable water flows, the Bureau of Reclamation built Glen Canyon

Dam above Lee’s Ferry in 1963 (USBR, 2016). The reservoir, Lake Powell, regulates the

river by saving water during high-flow years. Lake Powell then uses the saved water to

discharge at least enough to meet Compact and international requirements, 8.23 million

acre-feet per year (7.5 million acre-feet for the Lower Basin and .73 million acre-feet for

Mexico) (USBR, 2016). If drought continues to lower Lake Powell’s water levels,

eventually Glen Canyon Dam will not be able to release enough water to meet Compact

obligations. Although a Compact curtailment is unlikely in the immediate future, climate

change and long-term variability could at some point reduce Lake Powell’s water level to

a point that threatens the Colorado River Compact.

24

Compact Water Bank Transfer Mechanism

The Compact Water Bank plans to work as an insurance policy against Compact

curtailment. Users with post-Compact Colorado River water rights such as Front Range

cities would “subscribe” to the Compact Water Bank to “avoid or minimize curtailments

of diversions from post-Compact Water Rights” (CRWCD, 2012). The Compact Water

Bank’s developers modeled potential operations in several different ways, but this

background focuses on the objective of using pre-Compact agricultural water to supply a

portion of post-Compact water use. This section describes the Compact Water Bank’s

potential supply and demand, and explains how the bank intends to operate.

Table 3.1: Supply limited consumptive use by water rights category

Retrieved from: http://www.coloradoriverdistrict.org/wp-content/uploads/2015/10/Water-

Bank-Phase-1-Report_Final-DRAFT_June-2012.pdf

25

Table 3.2: Potential categories of Water Bank use. This shows that Front Range

municipalities are dependent on Colorado River water.

Retrieved from http://www.coloradoriverdistrict.org/wp-content/uploads/2015/10/Water-

Bank-Phase-1-Report_Final-DRAFT_June-2012.pdf

If a Compact curtailment occurs, the Colorado River Compact Water Bank will

supply post-Compact water users with pre-Compact agricultural water rights from the

western slope. The Compact Water Bank would pay willing irrigators who grow low-

value, fallowable forage crops to stop irrigating their land. Then, the bank would transfer

the saved water to various subscribers. Several crops on Colorado’s western slope can

sustain fallowing: hay, grass pasture, corn, dry beans, and small grains. A comparison

between Tables 3.1 and 3.2 shows that alfalfa and irrigated corn, the crops examined in

this study, use just under 144,000 acre feet per year, which is less than the total post

compact municipal and industrial depletions, 350,000. The Compact Water Bank only

intends to supply a maximum of 90,000 acre-feet to various post-Compact water users if

a Compact curtailment occurs, which could be provided by fallowing irrigated corn and

alfalfa (CWRCD, 2014). The Water Bank Group’s analysis of potential Compact

26

shortages suggests that most shortages would be over 500,000 acre-feet, so this analysis

exclusively models the maximum Compact Water Bank supply scenario (CRWCD,

2012).

Some irrigators are hesitant to participate in the Water Bank because they believe

transferring water would change their agricultural way of life. The Compact Water Bank

attempts to mitigate these concerns. To ensure that the way of life is relatively

unchanged, the Water Bank Group intends to only activate the water bank in a rare

Compact curtailment event. The bank would use alternative transfer mechanisms so

irrigators maintain water right ownership (CRWCD, 2014).

The Colorado Compact Water Bank would be an essential safeguard to

Colorado’s municipal water supply. Table 3.2 shows a list of potential subscribers to the

Compact Water Bank. Front Range municipalities demand the most water because they

are large and dependent on western slope water. Some municipal estimates show that

Denver and Colorado Springs supply 50% and 70% of their municipal water supplies

from the Colorado River Basin, so a sudden Colorado River curtailment would

dramatically reduce the cities’ water supply (Vanderschure, n.d; Steger, 2012). Water

planners suggest the Front Range’s 300,000 acre-foot deficit from a Compact curtailment

in Table 3.2 would supply about 600,000 households’ annual water use (Waskom, 2011).

Municipalities are willing to pay premium prices for water, so these users would most

likely be the top-bidding subscribers to the Water Bank (Brewer et al., 2006). Front

Range municipalities are likely subscribers to the Compact Water Bank because they

would need to replenish their water supply, and are willing to pay the most.

27

Municipal water planners’ concerns about subscribing to the program are risk

based. Municipal water supplies generally look for permanent supplies to ensure a

reliable yield (CRWCB, 2014). Although the Compact Water Bank would not provide

continuous water supply, it would provide water during a sudden supply shock.

Figure 3.2: Map of transmountain diversions in Colorado. Many of these diversions move

water from west to east.

Retrieved from:

http://water.state.co.us/SurfaceWater/SWRights/PublishingImages/TRANSMTN700.jpg

The Compact Water Bank intends to physically move water to its subscribers

through existing transmountain diversions. Transmountain diversions use tunnels to

28

move water across river basin boundaries. These diversions are generally used to move

water from the wetter western slope to the urban Front Range, as Figure 3.2 shows.

Some transmountain diversions are pre-Compact, such as the Grand River Ditch, and

would not be affected by a Compact curtailment but several major transmountain

diversions use post-Compact water rights, particularly the Adams Tunnel, the Roberts

Tunnel, and the Boustead Tunnel, which collectively transfer over 300,000 acre-feet per

year (Colorado Foundation for Water Education, 2014).

Montrose County

J.W Gunnison first described southwestern Colorado in the 19th Century as “a

desert unfit for cultivation and inhabitation only by savages” (Dudley, 2004). Since then,

southwestern Colorado’s Montrose County has become an agricultural region. Montrose

raises cattle and grows forage feed. The Water Bank Group tested feasibility case studies

in Montrose County and found that farmers use pre-Compact water rights to irrigate low

value crops, which makes the area an ideal water source for the Compact Water Bank.

29

Figure 3.3: Map of the Uncompahgre Valley irrigation project in Montrose County.

Retrieved from:

http://www.waterhistory.org/histories/reclamation/uncompahgre/uncompahgre.pdf

Montrose County is an early-settled farming community with pre-Compact water

rights. Farmers first settled in Montrose County’s Uncompahgre Valley in the late 1800s,

intending to sell food to nearby miners (Dudley 2004). The local Uncompahgre River

naturally has relatively low water flow; water was scarce as the region grew (Dudley

2004). In 1909, the United States Bureau of Reclamation built the Uncompahgre Project,

which uses a large tunnel and canal to supplement the Uncompahgre River with water

from the Gunnison River (Dudley, 2004). This project allows the Valley to irrigate

around 37,000 acres of land with pre-Compact water, shown in Figure 3.3 (Dudley,

2004). Another pre-Compact water right in Montrose County is the Colorado

30

Cooperative Ditch, near Nucla, Colorado that irrigates over 5,000 acres of land

(CRWCD, 2012). Montrose County has substantial pre-Compact water rights; these

ditches use a significant amount of water on each irrigated acre.

Table 3.3: Fallowing costs incurred by irrigators operating in the Grand and

Uncompahgre Valley for average Minimum and maximum five year commodity prices2.

Retrieved from: http://www.coloradoriverdistrict.org/wp-content/uploads/2015/10/Water-

Bank-Phase-1-Report_Final-DRAFT_June-2012.pdf

Montrose County farms low-value fallowable crops. Montrose County produces

forage crops such as alfalfa, grain corn and silage corn for cattle or sheep feed as shown

in Table 3.1. There are some higher-value crops in Montrose, such as Christmas trees

and berries and nuts, but these crops are less widespread, and are less economically

viable for fallowing programs than forage crops (United States Department of

2 Harvest savings are the costs of agricultural production. Farmers would not need to pay for costs of production if they fallow, so costs of production are saved. Fallowing costs are forgone revenue that irrigators face from fallowing. Fallowing costs depend on the price and quantity of agricultural production.

31

Agriculture, 2012). Forage crops use a significant amount of water. An acre of alfalfa at

the town of Montrose’s elevation requires 30.7 inches of evapotranspiration throughout

the growing season, which adds up over Montrose County’s 18,000 acres of irrigated

alfalfa (CRWCD, 2013; United States Department of Agriculture, 2012). Many irrigators

in the Uncompahgre Project and the Colorado Cooperative ditch sell hay to local ranches,

so the price of the crop is an appropriate measure of the crop’s value, unlike ranches that

grow forage to feed their herd (CRWCD, 2013). A “comparative, order of magnitude”

report on the forgone value from fallowing land in the Uncompahgre Valley and the

nearby Grand Valley shows losses from fallowing alfalfa range from $0 to $195 per acre

and from fallowing corn range from $77 per acre to $743 as shown in Table 3.2

(CRWCD, 2013). Montrose County is predicted to contribute about a third of the

Compact Water Bank’s pre-Compact water supply partially because the county uses its

significant pre-Compact water to irrigate low value crops (Personal Communication with

Chris Treese, November 16th, 2015)

Montrose’s economy is based on agriculture, so examining indirect effects from a

water transfer is appropriate. Colorado’s department of local affairs shows that

agriculture accounts for about 13% of jobs in Montrose County (Colorado Department of

Local Affairs, 2015). The Montrose Chamber of Commerce shows several local indirect

agricultural industries, such as Rocky Mountain Bio-Ag, which produces local organic

herbicides and fertilizer, and Mountain Quality Marketing, a group of agricultural

consultants. These companies would likely be negatively impacted by a decrease in

agricultural activity (Montrose Chamber of Commerce, 2015). Although this is not an

exhaustive list of indirect agricultural industries in Montrose County, it shows that

32

indirect industries are present. Agriculture is one of few industries in the region reporting

a growing labor force (Garner, 2014). Montrose County has indirect agricultural

industries so a water transfer could threaten future economic development.

The Compact Water Bank is an untested program that would benefit the state of

Colorado by providing water for high-value uses during a water crisis. Following Howe

& MacDonnell (1986), this project analysis focuses on the costs and benefits to the

region of origin, Montrose County. The advantage to Montrose County is that willing

participants would receive reliable lease income during an extreme drought. The

disadvantages to Montrose County are that it would reduce agricultural output, which

would reduce the need for farmers to purchase agricultural inputs, and would indirectly

negatively affect Montrose’s economy. Directly, if farmers are willing participants, then

it is implied that they are better off from the program. The following section shows the

methodology for testing if lease payments generate enough indirect consumption to

compensate for the indirect negative effects of the Compact Water Bank.

33

Chapter 4:

Methodology

Input-output analysis is a predictive model that allows users to show the impacts

of potential increases or decreases in an industry’s spending or production3. This analysis

is especially useful for showing how changes in one industry affect other industries and

the broader economy. I, like previous researchers, use IMPLAN, an input-output

program, to show the impacts of reducing irrigated acreage and compensating irrigators

with a lease payment (McMahon & Smith, 2012). To improve accuracy, I used

agricultural statistics and crop enterprise budgets from several years to customize my

IMPLAN model with sectors for irrigated forage. Since IMPLAN does not explicitly

show its processes with the results, I first briefly describe input-output analysis and

IMPLAN’s model, how the model processes rotational fallowing programs, and finally

explain how I developed my custom model for Montrose County to provide more clarity

how I achieved my results.

Input-Output Analysis and IMPLAN

Input-output analysis combines several large matrixes to show industries

consuming inputs to produce their outputs in an economy. Input-output models assume

3 An industry in input-output analysis is a “group of establishments engaged in the same or similar types of economic activity,” and is often use interchangeably with “sector” (BEA, 2006).

34

that economic activity can be divided into sectors and then display all sectors on tables to

represent an economy (Miller and Blair, 2009). The tables show transactions between

industries such as a car manufacturer purchasing steel as an input. The model shows

‘final demand’, which would be consumers implicitly demanding steel through

purchasing a cars4 (Miller and Blair, 2009). These demands are compiled among

intermediate and final consumers of all commodities produced in an economy to show all

economic activity.

Table 4.1: A simplified input-output table. Industries’ output (rows) is used in the

economy as inputs for another industry (columns).

Into

Sector 1:

Agriculture

Sector 2:

Manufacturing

Sector 3:

Households

Total

Output

From

Sector 1: Agriculture $50 $40 $110 $200

Sector 2 Manufacturing $70 $30 $150 $250

Sector 3: Households $80 $180 $40 $300

Total Input

$200 $250 $300

Retrieved from: Input-Output Economics (Leontief, 1986).

Table 4.1 shows the relationships between inputs and outputs. The $200 dollars of

agricultural output are used as inputs unequally among agriculture, manufacturing and

households. In this case, households purchase and consume most of agricultural output,

$110. Manufacturers generally consume household output, $150 worth of labor. The

output of one sector is the input to another sector, so this model shows how different areas

of the economy are interconnected. Thus, this model is suited to show how impacts in one

sector are not exclusively borne by the sector: the sector’s producers and consumers are

also impacted.

4 Final Demand is used to denote the last individuals to demand a good in a economy. This term also refers to consumers or government purchasing a good and never reselling it (BEA, 2006)

35

IMPLAN uses a more advanced input-output model to show the interdependence

between industries. The 2014 IMPLAN model divides economies into 536 sectors.

IMPLAN uses several tables such as the “make table” to show the value of commodities

and the industries that produce them and the “use” table to show the industries that

consume commodities and labor through production (BEA, 2006). IMPLAN separately

models final demand through using ‘value added,’ or dollars that go to individuals such as

employees, proprietors or government (MIG, 2015a). IMPLAN then creates consumption

functions for individuals and local government (MIG, 2015a).

IMPLAN has several inherent simplifying assumptions that are essential to

understanding the model’s framework. The first assumption, constant returns to scale is,

“the same quantity of inputs is needed per unit of output, regardless of the level of

production”; large producers and small producers use the same production function (MIG,

2015b). The second, no supply constraints, means that there are “no restrictions to raw

materials,” so a water reduction has to be modeled as a reduction in irrigated acreage

(MIG, 2015b). Water as a natural resource cannot be limited. The third, fixed input

structure, assumes industries must use the same inputs in the same proportions to produce

output. If a producer reduces an input, they must also proportionally reduce other inputs

in their production function because industries cannot substitute any input for another

input (MIG, 2015b). The fourth and fifth, the industry-technology commodity-technology

assumptions, assume that the entire industry uses the same technology to produce outputs

and commodities (MIG, 2015b). Finally, the model is static. Impacts to the model only

show short run effects, before price changes occur (MIG 2015b).

36

Overview of How IMPLAN Processes Rotational Fallowing Programs

This model simulates the Compact Water Bank through modeling the impacts of

fallowing irrigated acreage and increasing spending through a lease payment, which is

similar to previous research (McMahon and Smith, 2012). Reducing irrigated acreage

reduces agricultural output. Previous research shows reducing irrigated acreage also

reduces the amount of indirect inputs irrigators purchase to produce these goods, due to

constant returns to scale and the fixed input structure assumptions (Thorvaldson and

Pritchett, 2006). Demand for intermediate agricultural inputs is smaller and the

producers of intermediate agricultural inputs decrease production to compensate. Now,

the intermediate producers’ lower demand for their inputs reduces production their

indirect industries. In this way, indirect effects continue throughout the economy.

The model shows that indirectly affected industries reduce the amount of labor

they hire based on reduced agricultural demand. This induces a reduction in general

consumption because laborers have less money to purchase consumer goods. The input-

output model will show the indirect and induced effects from fallowing alfalfa, grain

corn, and silage corn.

Since the Compact Water Bank does not currently have a proposed lease

payment, I model the positive effects of a historical lease payment, the Rocky Ford lease

of $528 per acre fallowed (McMahon and Smith, 2012). Previous research shows when

farmers spend lease payments on consumer goods, the industries that benefit from a lease

payment are different from the indirectly negatively effected industries (McMahon and

Smith, 2012).

37

Model Development for Montrose County

I performed an IMPLAN analysis-by-parts study in Montrose County, which

creates custom sectors and changes their productions to show the indirect impacts of a

change in part of a sector. I generated sectors to represent alfalfa, grain corn, and silage

corn in Montrose County and then reduced them to model a fallowing program. I needed

to make several additional assumptions for this study, which are listed in Appendix A.

To provide more clarity on my model’s specifications, this section shows why I edited

the IMPLAN’s model, how I developed a custom model, and the specific actions I used

to show the impacts of reduced irrigated acreage and a lease payment.

MIG creates the IMPLAN software by combining national statistics with local

data, but it does not exactly represent local conditions, particularly on farms. I used 2014

IMPLAN data for Montrose County as the baseline for my analysis. IMPLAN starts by

collecting data from the Bureau of Labor Statistics’ Quarterly Census of Employment to

find local employment, wage and salary data (MIG, 2015a). IMPLAN adjusts the BEA’s

benchmark input-output tables to sum to local data (MIG, 2015a). IMPLAN uses a

variety of data sources including the Consumer Expenditure Survey and Annual Survey

of Manufacturers to develop industry spending patterns and the total industry output.

These are national data-sources that are modified to fit local conditions. MIG admits

some of the data sources, particularly the Bureau of Labor Statistics, have “sparse

coverage of farms,” and recommends combining IMPLAN data with local data if

available more accurately represent local conditions.

I created unique sectors for alfalfa, grain corn, and silage corn to improve

IMPLAN’s representation of Montrose County’s agriculture. I found the output and

38

price for alfalfa, grain corn, and silage corn in Montrose County from 1989-2007 and

2012, the latest complete data available 5 (NASS, 2016). Since Colorado’s silage corn

prices are not available, I used a “quick and dirty method” to find the price of a ton of

silage by multiplying the price of a bushel of grain corn by 8 (Nennich & Hendrix 2012).

Then, I used the CPI to adjust for price inflation and brought all dollar values to the

model year, 2014. I multiplied the price and quantity together to find the total dollar

value of output in Montrose County and divided by the 2012 Census of Agriculture’s

total acreage to find the average dollar value of yield per acre. Due to data limitations on

the quantity of irrigated alfalfa produced, I assumed yield per acre in each particular crop

would be constant in irrigated and non-irrigated land. This simplifies reality, but I do not

believe it will seriously impact the results because non irrigated acreage only accounts for

about 5% of alfalfa, 9% of grain corn, and 5% of silage corn in Montrose County (United

States Department of Agriculture, 2012). I multiplied the average value of yield per acre

by the number irrigated acres in Montrose County to find the value of irrigated output in

Montrose County.

Table 4.2 Value of alfalfa and corn in Montrose County under various climatic scenarios

that change price and quantity of output produced. In millions of dollars.

Climatic Scenario Value of Irrigated

Alfalfa

Value of Irrigated

Grain Corn

Value of Irrigated

Silage Corn

Best $31.9 $12.2 $7.7

Average $11.4 $4.8 $3

Worst $4.3 $1.9 $.9

Source: Author

Since the value of agricultural output varies significantly from year to year, I

constructed three scenarios to show different potential situations. I assumed farmers in

5 Some years have two data points for agricultural production, because the Census of Agriculture and National Agricultural Statistics reported different values. I kept both values in this analysis.

39

Montrose County cannot influence the price of their crops, so I separated price from the

quantity. I created the “best case” scenario with the highest recorded prices and yields,

the “worst case” scenario with the lowest recorded yields and the “average case” scenario

with average yields and price, as Table 4.2 shows.

I used Colorado State’s Crop Enterprise Budgets to appropriate farmers’ spending

between intermediate agricultural inputs (Colorado State Agricultural Extension, 2015).

These budgets show southwestern farmers’ production expenses in dollars for specific

crops on a per-acre basis. Then I followed previous methodology to translate the specific

expenses on farm enterprise budgets into input output sectors, and supplemented the

missing information with a second methodology—please see Appendix B for more

information on sectoring the expenses (Pritchett & Thorvaldson, 2006; Willis and

Holland 1997). I divided the specific expenses by the total per-acre expenses to find the

proportion of each dollar spent on each expense, or each expense’s absorption coefficient,

which will be used for my unique sector’s production function. I calculated employee

compensation from the production function, and calculated proprietor income from

subtracting the total costs from the total value of irrigated output.

Input-output models assume a constant returns to scale and a fixed input cost

structures. If the quantity of one input is reduced by 10%, then the producer must reduce

all other inputs by 10% because irrigators cannot substitute goods. If all input purchases

are reduced by 10% then output is reduced by 10% because of constant returns to scale.

Following these principles, I developed graphs to show linear fallowing costs at the

beginning of the season in each scenario. These graphs’ data are available through

Colorado River Water Conservation District’s data on evapotranspiration at the town of

40

Montrose’s elevation, and agricultural statistics for the value of crops, and the crop

enterprise budgets data on costs (CRWCD, 2012; NASS, 2016; Colorado State, 2015) 6.

Best Case

Figure 4.1: Returns to an acre of irrigation in the best case scenario. As farmers reduce

their evapotranspiration, they reduce their output and their income.

Source: Author

The best case, Figure 4.1, shows the costs of reducing evapotranspiration, or the

water that is consumed by plants, in terms of forgone sales. Grain corn has the lowest

forgone returns from fallowing, likely because my model generally shows data from the

1990s and 2000s, which had low corn prices. Indirect effects will likely be higher in this

scenario because this shows the best possible agricultural scenario, high prices and high

yields. This shows alfalfa requires a higher amount of water evapotranspiration at full

irrigation than corn, because it is a thirstier crop.

6 Due to data limitations, I assumed all of Montrose County was at the town of Montrose’s elevation.

$-

$500.00

$1,000.00

$1,500.00

1 4 7 10 13 16 19 22 25 28 31

Ne

t R

etu

rns

to a

n A

cre

of

La

nd

Evapotranspiration Reduction in Inches

Net Returns to an Acre of Irrigation: Best Case

Alfalfa

Grain Corn

Silage Corn

41

Average Case

Figure 4.2: Average case returns to an acre of irrigation. Reducing

evapotranspiration increases grain farmer’s income.

Source: Author

In the average case, Figure 4.2, proprietor income is much lower for each of the

crops. Grain corn is outstanding because the net returns at full irrigation are negative,

meaning farmers producing this crop are generating a loss on each acre they produce.

Therefore, fallowing at the beginning of the season increases grain corn farmers’ income

in this model because farmers are not spending money on intermediate inputs.

-$250.00

-$200.00

-$150.00

-$100.00

-$50.00

$-

$50.00

$100.00

$150.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Ne

t R

etu

rns

to a

n a

cre

of

lan

d

Evapotranspiration Reduction in Inches

Net Returns to an Acre of Irrigation: Average Case

Alfalfa

Grain Corn

Silage Corn

42

Worst Case

Figure 4.3 Worst case returns to an acre of irrigation. Reducing evapotranspiration

increases irrigator’s income by saving harvest costs.

Source: Author

The worst case scenario, Figure 4.3, has low yields and low prices. All alfalfa,

grain corn, and silage corn farmers, generate losses from irrigating. This scenario is the

best case for fallowing at the beginning of the season through the Compact Water Bank,

because reducing irrigated acreage would save irrigators money.

IMPLAN’s data comes pre-loaded with values for sectors that aggregate corn and

alfalfa among other crops. Corn and other crops are aggregated in ‘grain crops’ and

alfalfa is aggregated with other crops in ‘all other crops’. To exclusively show the effects

of fallowing corn and alfalfa, I disaggregated the value of the crops from IMPLAN’s

sectors. I added custom sectors to IMPLAN’s model and used the industry change

‘activity’ to set the value of output in my scenarios7. I estimated employment by adding

the number of irrigated farms to Dr. Dalstead’s non-proprietor employment estimates8

7 A group of events that change the production of an industry (MIG, 2015c) 8 I assumed all farms were sole proprietorships, so the number of farms would be the number of proprietors. IMPLAN counts proprietors as employees (MIG, 2016).

-$600.00

-$500.00

-$400.00

-$300.00

-$200.00

-$100.00

$-

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Ne

t R

etu

rns

to a

n a

cre

of

lan

d

Evapotranspiration Reduction in Inches

Net Returns to an Acre of Irrigation: Worst Case

Alfalfa

Grain Corn

Silage Corn

43

(United States Department of Agriculture, 2012; Personal Communication with Norm

Dalstead, January 22nd 2016). Then, I set the values of employee compensation and

proprietor income to the values I calculated from my scenarios and the crop enterprise

budgets. If farmers operate at loss from irrigating, I assumed fallowing at the beginning

of the season would save money and I counted these saved harvest costs as proprietor

income. Since the output, employment, proprietor income, and employee compensation

values for corn and alfalfa are already aggregated into the ‘grain crops’ and ‘all other

crops’ sectors I subtracted the values of my new, crop specific sector from the value of

the ‘grain crops’ and ‘all other crops’ sector to avoid double-counting agricultural output.

The industry change activity generates indirect and induced effects because it changes

farmer’s spending patterns.

I model a water reduction by modeling a reduction in irrigated acreage. Due to

the assumption of fixed supply constraints, reducing the amount of water available will

proportionally reduce the yield from irrigated acreage, which generates indirect effects to

their intermediate suppliers. These indirectly effected industries are generally not

directly compensated for these indirect losses. I model a fallowing program two ways.

Following McMahon and Smith (2012), I proportionally reduce irrigated acreage in

Montrose County. Second, I use least cost method that minimizes the forgone proprietor

income from a fallowing program. I compare the results between the two scenarios in the

results section. To ensure that I only show the indirect effects of this change, I subtract

the indirect and induced effects from the industry change from the indirect and induced

effects of reducing irrigated acreage.

44

Table 4.3: Conversion of each crop’s proportion of irrigated acreage to fallowed irrigated

acreage.

Crop Proportion of

Irrigated Forage (1)

Acre-feet transferred

(Proportion x 30000 AF)

(2)

Acres fallowed

(Acre-feet lost / irrigation

requirement) (3)

Alfalfa 56% 16868 Acre-feet 6593 Acres

Grain Corn 31% 9324 Acre-feet 9785 Acres

Silage Corn 13% 3808 Acre-feet 2218 Acres

Total 100% 30000 acre-feet 14243 acres

Source: Author

To proportionally reduce irrigated agriculture, I found the proportion of irrigated

acreage used to produce each crop from agricultural statistics (United States Department

of Agriculture, 2012,). Then, I multiplied that proportion by 30,000, the amount of acre-

feet needed for the Compact Water Bank from Montrose County. This number, shown in

Table 4.3 column 2, shows how much water would be fallowed from each crop. Finally,

I divided the amount of water the Compact Water Bank would transfer by each crop’s

irrigation requirement. Finally, Table 4.3 column 3, shows the number of acres would be

fallowed in each crop.

Table 4.4: Least Cost Acres Fallowed, with the percentages of reduced irrigated acreage

in parenthesis.

Alfalfa acres

fallowed

Grain corn

acres fallowed

Silage corn

fallowed

Total

Best case 4961 (29%) 10083 (100%) 0 (0%) 15043

Average case 4961 (29%) 10083 (100%) 0 (0%) 15043

Worst case 2197 (13%) 10083 (100%) 4118 (100%) 16398

Source: Author

45

To generate the least cost method of fallowing, I minimized forgone proprietor

income from fallowing. Figures 4.1-4.3 show that grain corn has the lowest forgone

proprietor income from fallowing in the best case and the highest savings in the average

or worst case. Grain corn has the lowest value to proprietors in all cases, so I first

fallowed all grain corn acreage in each least cost scenario. Fallowing grain corn saved

about 17,300 of the 30,000 acre-feet needed for the Compact Water Bank. As Table 4.4

shows, alfalfa has the second lowest fallowing costs in the best and average cases, thus I

fallowed 29% of alfalfa to provide the rest the water required. In the worst case scenario,

silage is more economical to fallow than alfalfa, so silage corn is completely fallowed

before 13% of alfalfa. The least cost method fallows more acres from production than

the proportional method because the least cost system fallows more corn than alfalfa.

Alfalfa has a higher irrigation requirement than corn, thus reducing an acre of alfalfa

saves more water. The least cost method is designed to minimize forgone proprietor

income, and target the farmers that would be most economically suited to fallowing.

Then, I measure the positive indirect impacts to the community from a lease

payment. Following McMahon and Smith (2012), I model a lease payment through

increasing IMPLAN’s household spending, which models consumer purchases, for the

average Montrose farmer. The other method I use to model a lease payment is increased

capital spending on farm equipment through using the BEA’s spending pattern for

agricultural production. This is modeled as an intuitional spending pattern in IMPLAN.

Again, I subtract the indirect effects of the industry change from the indirect effects of a

lease payment to only show the effects of a lease payment.

46

I used a $528 lease payment per acre fallowed to show the effects of increased

household income and farm capital expenditures. The proportional method of fallowing

generates a total lease payment of $7,520,0389. The least cost method generates a lease

payment of $7,943,017 in the best and average case, and a higher lease payment of

$8,658,341 in the worst case10. To exclusively show the impacts of a lease payment, I

subtracted the effects of the industry change from the effects of a change in household

spending.

This model is far from perfect, but it improves IMPLAN’s representation of

alfalfa, grain corn, and silage corn farmers in Montrose County. I needed to make several

simplifying assumptions in addition to the general input-output assumptions involving

price, ownership, and yield per acre11. This model improves on IMPLAN’s original

information on Montrose County’s farm related activities by using local data and

production functions. This is especially necessary for agricultural production as some of

their data sources admit to having “sparse coverage of farms.” With this improved

model, I show the indirect impacts of fallowing shock proposed by the Colorado River

Conservation district and examine if the positive impacts from a historical lease payment

would be enough to compensate for these indirect effects.

9 14232 acres fallowed proportionally x 528 = 7,520,038 10 15043 acres fallowed least cost x 528 = 7,943,017 for best and average case 16398 acres fallowed least cost x 528 = 8,658,341 for worst case 11 The assumptions I refer to here are: the price of silage corn is equal to the price of grain corn multiplied by eight, all farms are sole proprietorships, and yield per acre is equal in irrigated and non-irrigated land. A full list of assumptions can be found in Appendix A

47

Chapter 4:

Results

The Compact Water Bank intends to fallow forage crops in Montrose County in

the event of a Compact curtailment, and transfer the saved water to other users around the

state (CWRCB, 2012). This will involve fallowing irrigated acreage and a lease payment

to compensate the farmers. My method of measuring indirect effects, IMPLAN, provides

direct, indirect and induced effects, but I focus on the indirect and induced effects from

the fallowing program. I combine the direct, indirect and induced effects from the

leasing program into one number to simply show the impacts.

This results section first shows the impacts of fallowing irrigated acreage in

Montrose County. I examine three climatic conditions, and model fallowing each crop in

two ways in each condition. This creates six fallowing scenarios:

1. Best Case: Proportional Fallowing:

2. Best Case: Least Cost Fallowing

3. Average Case: Proportional Fallowing

4. Average Case: Least Cost Fallowing

5. Worst Case: Proportional Fallowing

6. Worst Case: Least Cost Fallowing:

The best case shows the best possible scenario for agriculture: high output and

crop prices. The average case has average output and crop prices. The worst case has the

worst output and prices. I fallow each crop proportionally, by percentage of irrigated

forage acreage, and through a least cost method, that minimizes forgone proprietor

income. I analyze each climatic scenario individually to show that the least cost method

reduces the forgone economic activity from fallowing, particularly in the consumer

48

sector, and that if agricultural conditions are bad, fallowing can indirectly benefit an

economy.

Second, I assess the impacts of a $528 lease payment. I model two lease spending

patterns: increased household spending and increased farm capital spending in each of the

six fallowing cases, resulting in twelve test cases. Since several climatic cases fallow the

same amount of land, receive the same lease payment and produce the same indirect

effects, there are only six unique results:12.

1. All Climatic Cases, Proportional Fallowing, lease increases household

spending

2. All Climatic Cases: Proportional Fallowing, lease increases farm capital

spending

3. Best Case/Average Case: Least Cost fallowing, lease increases household

spending

4. Best Case/Average Case: Least Cost fallowing, lease increases farm

capital spending

5. Worst Case: Least Cost fallowing, lease increases household spending

6. Worst Case: Least Cost fallowing, lease increases farm capital spending

All proportional fallowing cases reduces irrigated acreage by the same amount,

so there are only two proportional situations: the extra income could be spent on

household goods or on farm capital. The least cost method of fallowing reduces a

variable amount of acreage, and has four results: lease payments spent on household

goods or farm capital in the best and average case, and lease payments spent on

household goods or farm capital in the worst case. This section analyzes the impacts of

each spending pattern.

12 14232 acres fallowed proportionally x $528 = $7,520,038 for all cases

15043 acres fallowed least cost x $528 = $7,943,017 for best and average case 16398 acres fallowed least cost x $528 = $8,658,341 for worst case

49

Finally, I show net effects of the Compact Water Bank in each case. I use the

output impacts from fallowing and from the lease payments to show that the total impacts

to Montrose County to show in most cases, the Compact Water Bank benefits Montrose

County. Then, I compare the sectors that benefit from the lease payment to the sectors

that are negatively impacted from fallowing to show that lease payments do not

compensate the indirectly negatively impacted sectors.

Fallowing Irrigated Acreage

The Compact Water Bank intends to have forage irrigators fallow their crops in

the event of a Compact curtailment, so that the bank can transfer the saved water

(CWRCB, 2012). Previous research finds that indirect negative effects from fallowing

irrigated acreage can be large in specialized agricultural economies (Howe & Goemans,

2003). Thus, an estimate of the indirect effects from fallowing to uncompensated

industries could be beneficial knowledge for the Compact Water Bank’s developers.

Indirect effects in the following tables are rounded to the nearest thousand. All impacts

to jobs and indirect effects per capita are rounded to the nearest integer. Detailed reports

of the effects of fallowing can be found in Appendix C. In this section, I compare the

short-run indirect impacts of proportional fallowing to least cost fallowing in each

climate condition.

50

Best Case: Proportional Fallowing

Table 5.1: Indirect and induced effects from proportional fallowing in the best case

scenario

Best Case Least

Cost Fallowing

Proportional

Indirect

Proportional

Induced

Proportional Total

Employment -20 -86 -107

Value added -$624,000 -$4,548,000 -$5,173,000

Output -$2,309,000 -$9,725,000 -$12,034,000

Output Per

Capita

-$56 -$238 -$294

Output Per Acre

Fallowed

-$162 -$682 -$844

Source: Author

Proportional fallowing in the best case scenario for agriculture has the largest

negative impacts from fallowing, shown in Table 5.1. Farmers would have generated a

large income in the best case scenario, so induced effects have the highest negative

impacts. Most of the negative effects are to consumer goods, rather than indirect

agricultural inputs, because farmers are no longer able use their high income for

consumptive spending. The specific sectors impacted are addressed in the sector analysis

section. This scenario has high agricultural output and high prices, so fallowing produces

the highest negative indirect effects in Table 5.113. Value added represents forgone

employee, proprietor and government income from fallowing. This scenario shows the

highest opportunity cost for agriculture.

13 In these tables, indirect effects refer to “changes in inter-industry purchases as they respond to new demands of the directly affected industries” (Schmit et al., 2013).

51

Best Case: Least Cost Fallowing

Table 5.2: Indirect and induced effects from least cost fallowing in the best case scenario

Best Case Least

Cost Fallowing

Least Cost Indirect

Least Cost Induced Least Cost Total

Employment -19 -63 -83

Value added -$989,000 -$3,720,000 -$4,709,000

Output -$2,203,000 -$7,137,000 -$9,340,000

Output Per Capita -$54 -$175 -$229

Output Per Acre

Fallowed

-$146 -$474 -$620

Source: Author

In the best case scenario, the least cost method of fallowing, Table 5.2, minimizes

the forgone proprietor income from fallowing. Proprietors have more remaining income

to spend on household consumer goods in the least cost fallowing scenario, Table 5.2,

than in the proportional fallowing scenario, Table 5.1. This effect is represented through

the smaller induced effects14. The proportional method of fallowing reduces the forgone

output by $12 million, Table 5.1, and the least cost method reduces forgone income by

the relatively smaller $9.3 million, Table 5.2. Previous research notes that indirect

effects are more prevalent in sparsely populated regions (Howe and Goemans, 2003).

The indirect effects would be relatively high on sparsely populated Montrose County in

the best case scenario15. The total indirect effects are high compared to previous

estimates in both fallowing methods because this scenario maximizes opportunity cost of

irrigated agriculture by using the highest output and prices recorded (McMahon and

Smith, 2012).

14 In these tables, induced effects refer to “changes in spending from households as income increases or decreases due to the changes in production” (Schmit et al., 2013). 15 The recorded population in Montrose county is 40,873 (Census Quick Facts, 2014)

52

Average Case: Proportional Fallowing

Table 5.3: Indirect and induced effects from proportional fallowing in the average case

scenario

Average Case Proportional

Fallowing

Proportional

Indirect

Proportional

Induced

Proportional

Total

Employment -10 -3 -13

Value added -$503,000 -$173,000 -$676,000

Output -$1,120,000 -$334,000 -$1,453,000

Output Per Capita -$27 -$8 -$35

Output Per Acre Fallowed -$74 -$22 -$96

Source: Author

Proportional fallowing in the average case, Table 5.3, displays similar results to

proportional fallowing best case scenario, Table 5.2. Fallowing irrigated acreage causes

indirect and induced negative effects. Importantly, most of the output is indirectly lost,

Table 5.3, which contrasts the best case scenario in which most of the output is lost

through induced effects, Table 5.2. This shows that most of the negative effects are to

indirect agricultural inputs, rather than consumer goods. Farmers have less forgone

income in the average case than in the best case.

Average Case: Least Cost Fallowing

Table 5.4: Average case indirect and induced effects from fallowing using the least cost

method

Average Case

Least Cost

Fallowing

Least Cost

Indirect

Least Cost

Induced

Least Cost

Total

Employment -11 11 -1

Value added -$588,000 $620,000 $32,000

Output -$1,311,000 $1,188,000 -$123,000

Output Per Capita -$32 $29 -$3

Output Per Acre

Fallowed

-$87 $78 -$8

Source: Author

The least cost method of fallowing generates comparatively small negative

effects. Indirect effects of fallowing are similar in the least cost method of fallowing,

53

Table 5.4, to the proportional method of fallowing, Table 5.3, in the average case.

Induced effects from fallowing in Table 5.4 are positive because the least cost method

fallows grain corn. Since grain corn farming caused a loss on operations, completely

fallowing it in the least cost scenario saves proprietor income through saving harvest

costs. I modeled saving harvest costs on loss-producing crops as proprietor income,

which creates positive induced effects without a lease payment.

Worst Case: Proportional Fallowing

Table 5.5: Worst case proportional fallowing indirect and induced effects from fallowing

Worst Case

Proportional Fallowing

Proportional

Indirect

Proportional

Induced

Proportional

Total

Employment -4 +31 +27

Value added -$189,000 $1,804,000 $1,615,000

Output -$420,000 $3,459,000 $3,039,000

Output Per Capita -$10 $85 $74

Output Per Acre

Fallowed

-$26 $211 $185

Source: Author

The proportional worst case scenario, Table 5.5, has higher positive induced

effects than the least cost average case, Table 5.4, from fallowing because all crops

generate a loss in the scenario. The indirect costs of fallowing, representing forgone

agricultural input purchases, are smaller in the worst case than in other cases. This

scenario presumes that farming would not produce much indirect activity without

fallowing. There is a low opportunity cost to fallowing. The positive effects from saved

proprietor income outweigh the negative effects of reduced agricultural input purchases,

so fallowing benefits the economy without a lease payment.

54

Worst Case: Least Cost Fallowing

Table 5.6: Worst case least cost indirect and induced effects from fallowing

Worst Case Least Cost

Fallowing

Least Cost

Indirect

Least Cost

Induced

Least Cost

Total

Employment -5 +34 +29

Value added -$265,000 $1,991,000 $1,726,000

Output -$591,000 $3,818,000 $3,228,000

Output Per Capita -$14 $93 $79

Output Per Acre

Fallowed

-$36 $233 $197

Source: Author

In the least cost worst scenario, Table 5.6, fallowing produces even higher

positive induced effects. This is because I fallowed the crops with the highest operations

loss before fallowing crops with lower operations loss. Again, the induced positive

induced effects from fallowing can outweigh the negative indirect effects of fallowing

without any lease payment.

Lease Payments

An essential component of the Compact Water Bank is compensating irrigators

for fallowing (CWRCB, 2012). The following tables show the direct, indirect, and

induced effects the lease payment as a single value for employment, total value added,

and output. For the six unique lease payment results, I show the effects of increasing

household consumption and agricultural capital spending functions to estimate the effects

of a lease payment. Detailed tables of lease impacts are available in Appendix C. All

proportional cases have the same lease payment and the same impacts, so they are

represented as a single row. Similarly, the best case and average case least cost scenarios

have the same lease payment and are shown as one row for the same reason. This

55

subsection compares the impacts of farmers using lease payments on consumer spending

to using lease payments for agricultural improvements.

Lease Payment: Household Income

Table 5.7: Effects of increasing household spending through a lease payment

Employment Total Value

Added

Output Output per

capita

Output

per acre

Multiplier

Proportional

All Cases

+56 $3,192,000 $6,317,000 $155 $420 0.84

Least Cost

Best

Case/Average

Case

+59 $3,372,000 $6,672,000 $163 $444 0.84

Worst Case

Least Cost

+66 $3,771,000 $7,273,000 $178 $444 0.84

Source: Author

The household spending institutional spending pattern replicates consumer

spending for an average Montrose farmer’s income. Table 5.7 shows that there is not

much difference between the output stimulated by the lease payment from the least cost

method and proportional method of fallowing. The least cost method has slightly higher

positive effects because it fallows more acres and generates a higher lease payment to

Montrose County16. The constant multiplier in Table 5.7 demonstrates the lease payment

for least cost fallowing is not more efficient in generating output. Importantly, the output

multiplier is less than one in Table 5.7, so it would take a lease payment larger than the

output forgone to indirectly compensate a community for fallowing irrigated agriculture.

16 14232 acres fallowed proportionally x $528 = $7,520,038 for all cases

15043 acres fallowed least cost x $528 = $7,943,017 for best and average case 16398 acres fallowed least cost x $528 = $8,658,341 for worst case

56

Lease Payment: Agricultural Capital

Table 5.8: Effects of increasing agricultural capital spending through a lease payment Employment Total Value

added (USD $)

Output

(USD $)

Output per

capita (USD

$)

Output per

acre

Multiplier

Proportional All

Cases

+39

$2,901,000 8,673,000

$212.19 $577 1.15

Least cost Best

Case/Average

Case

+41

$3,064,000 9,161,000

$224.13 $609 1.15

Worst case least

cost

+43 $3,340,000 9,986,000

$244.32 $609 1.15

Source: Author

I used the BEA’s farm production spending pattern to model the effects of

increasing agricultural capital spending, detailed in Table 5.8. The BEA’s farm

production spending pattern represents spending on improving farm equipment and

fixtures. Again, the effects of lease payments spent on agricultural capital spending

stimulate about the same output in proportional fallowing and the least cost fallowing.

For the same lease payment, the agricultural spending pattern stimulates more output, but

less employment than household spending stimulates. The output multiplier in Table 5.8

is greater than one, indicating a dollar lease payment creates more than one dollar of

output in the short run.

Net Impacts

This section provides the net impacts from the Compact Water Bank. It is

important to note that both the climatic conditions and the least cost method of fallowing

are inherently retrospective. In this model, irrigators fallow irrigated acreage before they

know what the climatic conditions will be. This assumes that irrigators know how much

their crop would be worth and how much they would produce at the beginning of the

57

growing season, which is impossible. These results compare the indirect output

stimulated by farming activity to the indirect output stimulated by a lease payment, but

these situations are mutually exclusive. It is impossible to know what climatic condition a

particular year would have been until after the growing season.

Table 5.9: Best case scenario net impacts of fallowing and lease payment

Best Case:

Proportional

Fallowing

Household

Income (USD $)

Best Case:

Proportional

Fallowing

Agricultural

Capital (USD $)

Best Case:

Least cost

Fallowing

Household

Income (USD $)

Best Case: Least

cost Fallowing

Agricultural

Capital (USD $)

Net Effects -5,717,000 -3,361,000 -2,668,000 -180,000

Per Capita -140 -82 -$65 -4

Source: Author

The best case scenario, Table 5.9, shows that the lease payment, regardless of the

fallowing method and the lease payment consumption structure, does not compensate for

the indirect negative effects of fallowing. If farmers would have received record prices

and output during the season they fallowed, then the Compact Water Bank would not be

able to compensate for the indirect effects loss of irrigation. I believe this is not a likely

scenario, because the Compact Water Bank would only be invoked in an exceptional and

persistent drought.

58

Table 5.10: Average case scenario net impacts of fallowing and lease payment

Average Case:

Proportional

Fallowing

Household

Income

Average Case:

Proportional

Fallowing

Agricultural

Capital

Average Case:

Least cost

Fallowing

Household

Income

Average Case:

Least cost

fallowing

Agricultural

Capital

Net Effects $4,863,000 $7,219,000 $6,481,000 $8,969,000

Per Capita $119 $177 $159 $219

Source: Author

In the average case, Table 5.10, the Compact Water Bank fully compensates for

indirect negative effects, regardless of the fallowing mechanism or lease payment

spending pattern. This would mean that the output stimulated by a lease payment is more

than the forgone output from fallowing. The Compact Water Bank would generate more

economic activity in Montrose County than irrigating would have produced in the

average case, so it would be good for Montrose County’s economy for forage irrigators to

participate.

Table 5.11: Worst case scenario net impacts of fallowing and lease payment

Worst Case:

Proportional

Fallowing

Household

Income

Worst Case:

Proportional

Fallowing

Agricultural

Capital

Worst Case:

Least cost

Fallowing

Household

Income

Worst Case:

Least cost

fallowing

Agricultural

Capital

Net Effects

$6,270,000 $8,626,000 $7,186,000 $9,898,000

Per Capita $153 $211 $176 $242

Source: Author

In the worst case scenario, Table 5.11, a historical lease payment again fully

compensates for forgone output. The output stimulated by the Compact Water Bank in

excess of irrigating land is greater than in the average case, Table 5.11. This is because

the opportunity cost of irrigating land is smaller; all crops produce a loss from irrigating.

59

This is the best possible outcome for Montrose County from the Compact Water Bank,

because Montrose County would have had a terrible irrigating year, but instead fallowed

and received a lease payment.

Sector Analysis

Previous research found that the indirect industries that fallowing negatively

impacts are not the industries that benefit from a household lease payment (McMahon

and Smith, 2012). This challenges the notion that a leasing program can fully

compensate for forgone output, because some stakeholders are worse off from the

program. This section provides a descriptive sample of the industries that are negatively

affected by fallowing and positively impacted. Although the following tables do not

contain all of the negative impacts and positive impacts, most of the indirect impacts are

accounted for within the tables.

60

Table 5.12: Top sectors indirectly negatively affected by fallowing in the best case (blue

shading denotes presence on crop enterprise budgets)

Best Case Proportional Sectors

Negatively Impacted

Best Case Least Cost Sectors Negatively

Impacted

Owner-occupied dwellings Owner-occupied dwellings

Real estate Real estate

Offices of physicians Insurance agencies, brokerages, and related

activities

Limited-service restaurants Non-depository credit intermediation and

related activities

Non-depository credit intermediation

and related activities Offices of physicians

Wireless telecommunications carriers

(except satellite)

Accounting, tax preparation, bookkeeping,

and payroll services

Other financial investment activities Limited-service restaurants

Insurance agencies, brokerages, and

related activities Other financial investment activities

Positively Impacted Industries: Best

Case Proportional

Positively Impacted Industries: Best

Case Least cost

N/A N/A

Source: Author

Table 5.12 has the top 7 industries are indirectly negatively impacted from a

fallowing program in the best case scenario. To see the full indirect effects of fallowing

and the lease payment see Appendix C. In the best case scenario, no industries are

positively affected because fallowing does not generate any positive induced effects. In

the best case, consumer goods are negatively impacted because most of the forgone

output is proprietor income, which generates induced negative effects and reduces

household consumption. Consumer sectors such as “wireless telecommunications” and

“limited service restaurants” are negatively impacted. Although indirectly several sectors

are present on the production function, these are also broad sectors that are not unique to

agricultural production.

61

Table 5.13: Top sectors indirectly negatively affected by fallowing in the average case

(blue shading denotes presence on crop enterprise budgets)

Average Case Proportional Sectors

Negatively Impacted

Average Case Least Cost Sectors

Negatively Impacted

Insurance agencies, brokerages, and

related activities

Insurance agencies, brokerages, and related

activities

Nondepository credit intermediation

and related activities Scientific research and development services

Scientific research and development

services

Nondepository credit intermediation and

related activities

Accounting, tax preparation,

bookkeeping, and payroll services

Accounting, tax preparation, bookkeeping,

and payroll services

Water, sewage and other systems Water, sewage and other systems

Support activities for agriculture and

forestry Support activities for agriculture and forestry

Owner-occupied dwellings Legal services

Real estate Other local government enterprises

Positively Impacted Industries:

Average Case Proportional

Positively Impacted Industries: Average

Case Least cost

N/A Owner-occupied dwellings

Offices of physicians

Limited-service restaurants

Real estate

Wireless telecommunications carriers (except

satellite)

Source: Author

In the average case, Table 5.13, the indirectly negatively impacted industries are

intermediate inputs to forage agriculture, as shown in Appendix C. Tables 5.3-5.4

estimate the negative indirect impacts are larger than the negative induced impacts in the

average case, so more intermediate agricultural inputs have the large negative impacts

than in the best case. Several of these intermediate agricultural inputs are likely unique to

agriculture, such as “support services for agriculture and forestry”. In the least cost

scenario, as Table 5.4 shows, fallowing produces some indirect positive effects because

fallowing grain corn reduces farm losses in Montrose County. This generates positive

impacts to consumer good sectors, as the bottom section of Table 5.13 shows.

62

Table 5.14: Top sectors indirectly negatively affected by fallowing in the worst case (blue

shading denotes presence on crop enterprise budgets)

Worst Case Proportional Sectors

Negatively Impacted

Worst Case Least Cost Sectors

Negatively Impacted

Scientific research and development

services

Scientific research and development

services

Insurance agencies, brokerages, and related

activities

Insurance agencies, brokerages, and

related activities

Nondepository credit intermediation and

related activities

Accounting, tax preparation, bookkeeping,

and payroll services

Support activities for agriculture and

forestry

Nondepository credit intermediation and

related activities

Water, sewage and other systems Support activities for agriculture and

forestry

Accounting, tax preparation, bookkeeping,

and payroll services

Water, sewage and other systems

Construction machinery manufacturing Construction machinery manufacturing

Beef cattle ranching and farming, including

feedlots and dual-purpose ranching and

farming

Beef cattle ranching and farming,

including feedlots and dual-purpose

ranching and farming

Dairy cattle and milk production Dairy cattle and milk production

Positively Impacted Industries: Worst

Case Proportional

Positively Impacted Industries:

Average Case Least cost

Owner-occupied dwellings Owner-occupied dwellings

Real estate Real estate

Offices of physicians Offices of physicians

Limited-service restaurants Limited-service restaurants

Wireless telecommunications carriers

(except satellite)

Wireless telecommunications carriers

(except satellite)

Source: Author

The worst case scenario, Table 5.14 shows similar results to the average case

scenario. Again, Tables 5.5-5.6 show most negative effects occur to indirect industries,

rather than induced industries. As in the average case, the indirectly impacted sectors are

intermediate agricultural inputs that are unique to agriculture. Additionally, some of the

sectors for which forage production is an input, such as dairy and beef cattle production,

are negatively impacted. Since fallowing increases proprietor income in the worst case,

consumer sectors benefit from fallowing.

63

Table 5.15: Top sectors positively impacted by lease payment used in household

spending and BEA’s agricultural production spending pattern

Household Spending (1) Agricultural Production (2)

Owner-occupied dwellings Construction machinery manufacturing

Real estate Wholesale trade

Offices of physicians Architectural, engineering, and related services

Wireless telecommunications

carriers (except satellite) Truck transportation

Limited-service restaurants

Ornamental and architectural metal work

manufacturing

Electric power transmission and

distribution Owner-occupied dwellings

Wholesale trade Real estate

Other financial investment activities Retail - Motor vehicle and parts dealers

Source: Author

The two lease payment spending patterns do not impact the same sectors, as

shown Table 5.15. Household spending benefits the consumer sectors. These sectors are

indirectly negatively impacted in the best case scenario, but are generally not present on

crop enterprise budgets. The agricultural production spending pattern also does not

generally does not benefit the sectors negatively impacted by fallowing. The exceptions

are real estate and construction machinery manufacturing. Neither lease payment

mechanism is able to compensate the sectors that are negatively impacted by a fallowing

program.

This study modeled the Compact Water Bank as a fallowing and leasing program.

Irrigators could fallow proportionally, or could fallow based on their economic situation.

The results illustrate that when irrigators fallow their least valuable crops, the induced

negative effects from fallowing are smaller than if they fallow their crops proportionally.

This project simulates irrigators spending their lease payments on household

consumption and agricultural capital, which depending on the agricultural situation, could

compensate for indirect negative effects. Both spending patterns do not benefit the

64

indirectly negatively impacted sectors in the worst and average case. The next section

concludes with an assessment of the results and implications of the study.

65

Chapter 5:

Conclusion

The Compact Water Bank is a unique program that would provide water to post-

Compact water users during a persistent drought that prevents these users, such as Front

Range cities, from diverting water. I set out to compare the indirect effects of fallowing

to the indirect benefits of lease payments in Montrose County to examine if a lease

payment could compensate for the indirect losses of fallowed acreage. Depending on the

climatic scenario and fallowing method, Montrose County can be compensated by a

historic lease payment, but the industries that are negatively impacted by fallowing do not

benefit from a lease payment.

In the best case scenario, there is the highest opportunity cost to fallowing the

most foregone output. Most of this forgone output is proprietor income, so consumer

sectors, like restaurants, are the most negatively impacted sectors. The lease payment

does not fully compensate for indirectly lost output because farming generates large

profits and high proprietor incomes. It seems unlikely, however, that during an extreme

and persistent drought that invokes the Compact Water Bank irrigators would receive

record high output and high prices.

In the average and the worst case scenario, Montrose County would be indirectly

better off from a rotational fallowing program than from irrigation. A lease payment

stimulates more consumption than the forgone consumption from fallowing. This effect

occurs regardless of the lease payment consumption pattern. In the worst case scenario,

fallowing saves money from harvest costs, which benefits the economy in the short run

66

without any lease payment. Unfortunately, it is impossible to know the price and output

of a crop in advance due to global climate and market fluctuations.

By fallowing the crops with the lowest dollar value of output before fallowing

other crops, the least cost method of fallowing minimizes the forgone proprietor income.

Although this method assumes that a farmer knows a crop’s value at planting, the results

from least cost fallowing could be a useful tool for planning a short-term water transfer.

If a water planner could predict which farmers would lose the least from fallowing, it

would reduce the negative indirect effects to the broader economy, particularly to

consumer goods.

The lease payment indirectly benefits the economy because individuals spend

their extra income on goods and services. Depending on how farmers spend lease

payments, they benefit the different sectors of the economy. I modeled two consumption

patterns for each scenario: farmers spending lease payments on household consumption

or on farm capital spending. IMPLAN predicts that if farmers spend their lease payments

on consumption goods, sectors that produce consumer goods, such as restaurants, benefit.

If farmers spend lease payments on improving their farm, capital industries such as

machinery manufacturing benefit. In most cases, the lease payments do not benefit the

indirectly negatively impacted sectors, intermediate agricultural inputs.

Limitations of this Study

This study is an estimation of the positive and negative impacts of the Compact

Water Bank. The main limitations of this research are data availability, the assumption of

certainty, and several unaccounted variables in the model.

67

A major limitation of this research, particularly the least cost method of fallowing

and the climatic scenarios is that there is an assumption of certainty in price and

production. Farmers cannot accurately know if prices and output will be high or low in a

given year, if they did, they could choose to fallow during the years they would produce a

loss without the Compact Water Bank.

Some of my assumptions simplify reality. My research used price and output data

for years of 1989-2007, and 2012, which generally have lower corn prices than today.

This year selection biases my model by showing that corn generates lower revenue than it

may actually produce. In addition, I did not have data on silage corn prices or on farm

employment. So I estimated these values. Although silage corn has the smallest irrigated

acreage of the three crops in Montrose County, this still influenced my choices on

fallowing in the least cost method. Data on yield from irrigated and non-irrigated land

was unavailable; thus, I assumed that irrigated acreage and non-irrigated acreage had the

same yield per acre. This assumption implies that there is no benefit to irrigating land in

the high desert, which is inaccurate. These estimations pose an accuracy problem to the

model. As a consequence, future research would benefit from better data that includes all

relevant information. A complete list of assumptions can be found in Appendix A.

There are several real aspects of farming that I was not able to incorporate in this

model, such as crop insurance and debt. If farmers’ losses in the worst case are

subsidized by crop insurance, then the benefits of fallowing are overstated. I was also

unable to show farmers paying off debt with lease payments in this model, which is a real

possibility from lease payments (Weber, 1989).

68

Closing Arguments

The Compact Water Bank can make Colorado and Montrose County indirectly

better off. If a Compact curtailment ever occurred, it would be essential to provide water

to Colorado’s cities because they would suddenly be unable to access over half of their

water resources (Steger, 2012). The Compact Water Bank’s rotational fallowing program

would be an excellent way to provide water in such an immediate shortage situation. The

Bank would allow water to rapidly change uses from agricultural to non-agricultural use,

which is not easy in Colorado’s slow moving water law.

The Compact Water Bank would likely benefit Montrose County. Irrigators

would be compensated for fallowing and the community would likely recover the lost

output, but the benefits would accrue to different sectors. If the Compact Water Bank

could predict which crops would have the lowest dollar value of output, then the forgone

output could be reduced. Although this study does not show how the Compact Water

Bank would exactly compensate the areas with lost output, the program would stimulate

consumption, which reduces indirect effects. This research does not definitively show

that the Compact Water Bank will benefit Montrose County in all scenarios, yet it

suggests that the Compact Water Bank would leave forage irrigation communities

indirectly better off.

69

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Appendix A: List of Assumptions

Constant returns to scale.

No supply constraints.

Fixed input structure

Industry technology assumption

Commodity technology assumption

Static model

Homogenous yield per acre, output for each acre of a particular crop has

the same output as all other acres.

All farms are sole proprietorships that are owned and operated by a single

farmer. Farmers hire additional labor according to Dr. Dalstead’s fixed

proportions.

Farmers are price takers.

The price of silage corn is equal to the price of grain corn multiplied by

eight. (Nennich & Hendrix, 2012)

Fallowing in a situation that would generate a loss from irrigation is

assumed to be proprietor income.

All of Montrose County is at the town of Montrose’s elevation