journal of hydrology · received 2 april 2016 received in revised form 7 june 2016 accepted 27 june...

Journal of Hydrology 540 (2016) 757–773

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Journal of Hydrology

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Research papers

Economic performance of irrigation capacity development to adapt toclimate in the American Southwest

http://dx.doi.org/10.1016/j.jhydrol.2016.06.0570022-1694/� 2016 Elsevier B.V. All rights reserved.

⇑ Corresponding author.E-mail addresses: [email protected] (F.A. Ward), [email protected]

(T.L. Crawford).

Frank A. Ward ⇑, Terry L. CrawfordNew Mexico State University, Las Cruces, NM 88003, USA

a r t i c l e i n f o

Article history:Received 2 April 2016Received in revised form 7 June 2016Accepted 27 June 2016Available online 28 June 2016This manuscript was handled by G. Syme,Editor-in-Chief

Keywords:Food securityClimateIrrigationInstitutionsPolicy

a b s t r a c t

Growing demands for food security to feed increasing populations worldwide have intensified the searchfor improved performance of irrigation, the world’s largest water user. These challenges are raised in theface of climate variability and from growing environmental demands. Adaptation measures in irrigatedagriculture include fallowing land, shifting cropping patterns, increased groundwater pumping, reservoirstorage capacity expansion, and increased production of risk-averse crops. Water users in the Gila Basinheadwaters of the U.S. Lower Colorado Basin have faced a long history of high water supply fluctuationsproducing low-valued defensive cropping patterns. To date, little research grade analysis has investigatedeconomically viable measures for irrigation development to adjust to variable climate. This gap has madeit hard to inform water resource policy decisions on workable measures to adapt to climate in the world’sdry rural areas. This paper’s contribution is to illustrate, formulate, develop, and apply a new methodol-ogy to examine the economic performance from irrigation capacity improvements in the Gila Basin of theAmerican Southwest. An integrated empirical optimization model using mathematical programming isdeveloped to forecast cropping patterns and farm income under two scenarios (1) status quo withoutadded storage capacity and (2) with added storage capacity in which existing barriers to developmentof higher valued crops are dissolved. We find that storage capacity development can lead to a highervalued portfolio of irrigation production systems as well as more sustained and higher valued farm liveli-hoods. Results show that compared to scenario (1), scenario (2) increases regional farm income by 30%, inwhich some sub regions secure income gains exceeding 900% compared to base levels. Additional storageis most economically productive when institutional and technical constraints facing irrigated agricultureare dissolved. Along with additional storage, removal of constraints on weak transportation capacity, lim-ited production scale, poor information access, weak risk-bearing capacity, limited management skills,scarce labor supply, low food processing capacity, and absolute scale constraints, all can raise the eco-nomic value of additional irrigation capacity development. Our results light a path forward to policy mak-ers, water administrators, and farm managers, who bear the burden of protecting farm income, food andwater security, and rural economic development in the world’s dry regions faced by the need to adapt toclimate variability.

� 2016 Elsevier B.V. All rights reserved.

1. Introduction

1.1. Issue and context

Growing demands for food security to feed increasing popula-tions worldwide have intensified the search for improved perfor-mance of irrigation, the world’s largest water user. Thesechallenges are raised in the face of climate and from growing envi-ronmental demands. Adaptation measures in irrigated agriculture

include fallowing land, shifting cropping patterns, increasedgroundwater pumping, reservoir storage capacity expansion, andincreased production of risk-averse crops. Water users in the GilaBasin headwaters of the U.S. Lower Colorado Basin have faced along history of high water supply fluctuations (Bestgen andPropst, 1989), resulting in a history of producing low-valued defen-sive cropping patterns. The 2004 Arizona Water Settlements Act(AWSA) provides an additional annual average of 14,000 acre feetof water annually to New Mexico (2004). This study evaluatesthe economic value of using some of that water for agriculturalpurposes.

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758 F.A. Ward, T.L. Crawford / Journal of Hydrology 540 (2016) 757–773

1.2. Previous work

Much research work and water development planning has beenconducted assessing and taking advantage of the economic value ofwater in irrigation produced by the development of additional irri-gation infrastructure for handling uncertain and random watersupplies, to protect food security and rural livelihoods. The desireto establish flexible institutions to increase the beneficial use ofwater in the face of growing demands for scarce, variable, unpre-dictable, and climate altered supplies is the most compelling issuefor economic development for people who live in dry places, suchas our study region in the Gila Basin headwaters (Figs. 1 and 2).Growing human population and increasing demands for protectingendangered species and other environmental values continue totake place in many parts of the arid and semi-arid regions of theworld (Ward and Michelsen, 2002).

Fig. 1. Gila Basin, irrigation r

While policymakers and water managers continue to expressgrowing interest in the economic consequences of expandedgroundwater or surface reservoir storage as a climate adaptationmeasure, much less published scholarly peer reviewed researchhas addressed these choices. However, some notable academicstudies have been conducted. For example, an innovative 2005study by a team of California investigators examined spatially dis-aggregated estimates of more than 100 data series on streamflow,ground water, and reservoir evaporation. The authors found thatmost scenarios with increased precipitation resulted in less avail-able water because of the current storage systems’ weak capacityto catch increased winter streamflow to offset reduced summerrunoff. While additional reservoir storage could increase the over-all economic capacity to handle a series of drought years, noattempt was made to quantify the scale of these economic benefitsor costs (Zhu et al., 2005).

egion, New Mexico, USA.

Fig. 2. Lower Colorado River Basin with Gila Basin detail, Southwest USA.

F.A. Ward, T.L. Crawford / Journal of Hydrology 540 (2016) 757–773 759

A 2008 investigation from California examined economicallyoptimal operational changes and adaptations for California’s com-plex water supply system for a dry scenario climate warming withyear 2050 water demands and land use (Medellin-Azuara et al.,2008). Results from their work uncovered numerous promisingalternatives for water managers, in which economically optimizedoperations of ground and surface water storage could be alteredsignificantly to adapt to climate change. A dry-warm climatechange will increase the seasonal storage range, with gains thatcould be produced by the development of additional reservoir stor-age capacity for surface reservoirs and aquifers for storing wet yearsupplies for later use in a series of dry years (Medellin-Azuaraet al., 2008).

A 2010 work addressed climate impacts on reservoir perfor-mance for potential future scenarios in India (Raje andMujumdar, 2010). The authors examined measures to increase reli-abilities with respect to multiple use purposes of hydropower, irri-gation and flood control. Their findings suggest that reservoirdevelopment and operation plans for flood control may need tobe adjusted in basins where climate change points to an increasingprobability of droughts and floods, similar to our study region ofthe Gila Basin headwaters.

Another 2010 work investigated the Yakima River Reservoirsystem in the US State of Washington. Effects of water supplychanges there on irrigated agriculture were investigated using areservoir system model connected to a hydrological model usingscenarios from 20 climate models included in the 2007 IPCCAssessment. The authors concluded that improved reservoir man-agement would be needed to adapt to growing patterns of moreextreme floods and droughts to protect economic losses that wouldotherwise be suffered by junior water right holders (Vano et al.,2010).

An innovative work from 2014 published in this Journal exam-ined the need to assess a series of comprehensive adaptations tocurrent and future climate scenarios. That assessment provided

insight into policy measures to adapt efficiently to varying climaticconditions. That work was motivated by the need to analyze vari-ous policy measures that could include selected mitigation oradaptation strategies to accommodate a varying or changing cli-mate. Application of their approach was illustrated using an exist-ing model of Tampa Bay Florida. Several mitigation scenariosincluding reservoir capacity expansion, as well as operationalchanges were investigated to evaluate Tampa Bay’s system perfor-mance under varying climatic conditions. The approach illustratesthe economic and water resource benefits of comprehensive sys-tem planning that are easily understandable by a range of decisionmakers and stakeholders (Asefa et al., 2014).

Some very recent investigations on the role of reservoir devel-opment and management in climate adaptation were publishedin this Journal in 2015 alone. These include works from China(Zhang et al., 2015), an international assessment of climate impactson hydrologic drought (Wanders and Wada, 2015), in the monsoondominated Himalaya Mountains (Neupane et al., 2015), on glacialmelt in the Upper Indus (Mukhopadhyay and Khan, 2015), the Lon-don water supply system (Matrosov et al., 2015), typhoon adapta-tion in Asia (Hsu et al., 2015), Pareto Improvements for the Nile(Habteyes et al., 2015), alpine watersheds generally (Grussonet al., 2015), food security debates in Afghanistan (Gohar et al.,2015), water management in Switzerland (Fatichi et al., 2015),and the Lesser Zab in Iran and Iraq (Al-Faraj and Al-Dabbagh, 2015)

1.3. Gaps

Despite the many accomplishments described above as well asothers, little research grade analysis has investigated economicallyviable measures for irrigation development measures to adjust tovariable or possibly changing climate (Berbel and Gómez-Limón,2000; Meza et al., 2008). The presence of this gap has made it hardto for policy analysts to inform water resource policy debates anddecisions on workable irrigation improvement measures to adapt


to climate in the world’s dry rural areas, such as the southwesternUnited States.

1.4. Objectives

This paper’s contribution is to illustrate, formulate, develop, andapply an innovative methodology to examine the economic perfor-mance from irrigation capacity improvements in the Gila Basin ofthe American Southwest.

2. Methods of analysis

2.1. Study area and issues

Increasing current and future needs that assure demands forfood security are secured to feed increasing populations worldwidecontinue to motivate the search for technical, institutional, andeconomic measures to improve performance of irrigation, theworld’s largest water user in arid and semi-arid regions. Thesechallenges are made even greater in the face of climate variabilityand change as well as from emerging and new environmentaldemands and requirements for improved water quality, quantity,timing, and location. A few climate adaptation measures that canbe taken by irrigation farmers and water policymakers include fal-lowing land (French and Schultz, 1984), shifting cropping patterns(Döll, 2002), increased groundwater pumping (Shah, 2009), reser-voir storage capacity expansion (Adger et al., 2003), and increasedproduction of risk-averse crops (Smit and Skinner, 2002). Oneexample of such an adaptation enactment in our Gila Basin head-waters study region comes from the AWSA. The AWSA providesto New Mexico up to an additional annual average of14,000 acre-feet per year from the Gila River Basin (2004).

The current investigation focuses on the economic value of put-ting to beneficial use 9500 acre feet of the AWSA water for irri-gated agriculture in the Gila River Headwater region ofsouthwest New Mexico. The study areas consist of three irrigatedareas in this region: Virden Valley, Cliff-Gila Valley, and Demingarea (Figs. 1 and 2). For the purpose of this investigation, the AWSAwater, if made available, is assigned to these three crop-irrigatingcounties in the following way (Fig. 1):

� Virden area, Hidalgo County, New Mexico: 2500 Acre Feet peryear.

� Cliff-Gila Valley, Grant County, New Mexico: 2000 Acre Feetper year.

� Deming area, Luna County, New Mexico: 5000 Acre Feet peryear.

These three counties in NewMexico were selected because theyeither have a long history of crop irrigation sourced from the GilaRiver (Hidalgo and Grant Counties) or are economically and spa-tially close to the headwaters of the Gila Basin (Luna County).

Our analysis is conducted to secure a more detailed understand-ing of the economics of crop irrigation for two scenarios:

1. Scenario 1, the baseline, assigns neither additional water norstorage capacity for use in crop irrigation, and reflects condi-tions in the base year, 2012.

2. Scenario 2 assigns additional water and storage capacity to theregion for crop irrigation, without current existing agriculturaldevelopment constraints, an optimistic scenario.

2.2. Overview of methods

Our analysis is motivated by the need to answer the question ofwhether or not storage capacity development can lead to a higher

valued portfolio of irrigation production systems as well as moresustained and higher valued farm livelihoods.

2.3. Data sources

Numerous data sources were used to inform our analysis. Dataon land in production by crop for the year 2012 were secured fromthe most recent USDA Census of Agriculture (USDA Census ofAgriculture, 2014). The census publishes detailed data every fiveyears on agricultural activity, with some detail provided for eachof the major crops by US county.

Information on prices, yields, and costs of production wassecured by consulting the NewMexico State University Crop Enter-prise budgets. These budgets are posted online by the College ofAgriculture, Consumer, and Environmental Sciences, ACES(Hawkes, 2013). The budgets are updated periodically for themajor crops by New Mexico county.

The budgets were also refined from detailed informationsecured at several producer panel meetings on agricultural landuse and water applied by crop and county, as well as the cost ofpumping groundwater. These meetings were conducted specifi-cally for the purpose of this study, and took place periodically overthe period of April – August 2014 (Allen, 2014; Blandford, 2014;Lowry, 2014).

Another important data source is published by the NationalAgricultural Statistics Service in the Annual Statistics Bulletin (USDepartment of Agriculture and New Mexico Department ofAgriculture, 2014). Data categories used in that report were Pricesand Income, Field Crops, and Vegetables and Nuts. County Profilesfrom that data source for New Mexico were also consulted. Fore-cast data on prices, costs, and yields were secured from publishedUSDA projections (US Department of Agriculture EconomicResearch Service, 2014).

Other USDA publications were consulted where detailed priceor yield data were unavailable for New Mexico (US Departmentof Agriculture Economic Research Service, 2013a, 2013b, 2014,2012, 2013, 2014). For economic returns that could be earned oncrops not currently grown in the study region, budgets from theUniversity of California Extension Service were used with a yieldadjustment to adapt to our Gila Basin headwater region climate.

2.4. Enterprise budgets

For production agriculture, enterprise budgets provide a deci-sion framework for short- and long-run economic analyses of pro-posed decisions, including both our policy scenarios. These budgetsguide understanding of costs and returns of an agricultural produc-tion activity and are used to evaluate farm economic impacts ofalternative grower choices or policy proposals that would affectgrower costs or returns. Information from budgeting and theiruse can guide producers to make economically sound farm busi-ness decisions. For this study, we used enterprise budgets as aframework to evaluate on farm effects of each of our two policyscenarios. The budgets were used to evaluate economic impactsto producers in the study area associated with scenario 2 comparedto scenario 1, in which new surface water would be supplied alongwith complementary storage. As part of this scenario, resource,management, information and other constraints to future agricul-tural development in the region would be dissolved, an optimisticfuture for regional irrigators.

Enterprise budgets project costs and returns for an activity suchas cultivating chile and other vegetables, grain, grapes, or pecans fora production period. Each budget specifies a system of production,required inputs, and an annual sequence of operations. It also sum-marizes the costs and returns associated with the process. Enter-prise budgets are typically based on a one-year cycle. For


enterpriseswhere production spansmore thanone year (e.g., grapesor pecan orchards), the budget includes income and expenses for arepresentative one-year period, in which the early years reflect theexpenses of crop establishment which secure little to no revenue.Developing budgets for a specific geographic area can highlight cer-tain cost items andprice relationships thatmight otherwise be over-looked. Enterprise budgets were used also to assess the cost ofcultivating income-losing crops, such as sorghum and wheat thatare required to protect long run soil fertility. Such rotations in ourstudy region are practiced to protect long term yields on higher val-ued crops such as chile (Bosland and Walker, 2004).

Our budgets are for a representative farm (Hawkes, 2013). Rep-resentative farm budgets are based on costs and returns for pro-ducing a crop or crop mix in a specified region. Representativefarm budgets have been used in the USA since the 1920s. Thesebudgets are used to conduct analysis of policy proposals affectinga large number of farms at the regional level. The approach usedat New Mexico State University is to build typical managementskills into a synthetic farm. Data are assembled using consensusproducer panels who are asked to agree on costs and returns fora typical farm growing a specified crop with a specified set ofinputs, typical for the region. Data were assembled using consen-sus producer panels asked to agree on costs and returns for a typ-ical farm growing a specified crop with a specified set of inputs,typical for the region (Feuz and Skold, 1990). Historically, severalapplications of enterprise budgets have been seen:

� Adjust for prices, yields, practices, and or costs to fit the plan-ning situation being considered, for which a classic water studywas done for the Ogallala Aquifer (Torell et al., 1990).

� Adjust for other soil or production conditions (Koch et al., 2004).� Adjust for climate or climate zone (Williams, 1990).� Adjust for access to market (Qaim and Traxler, 2005).� Adjust for access to management expertise (Schurle and Erven,1979).

� Adjust for infrastructure (Qaim, 2010).� Adjust for food processing facilities (Linton et al., 2011)

2.5. Optimization model

An optimization model was developed with the intent of pro-ducing a unified framework for irrigation policy analysis for our

Fig. 3. Model fl

Gila Basin headwater study region. Optimization models for irriga-tion have a long history (Georgiou and Papamichail, 2008). Its mis-sion was to identify an irrigation water use pattern that maximizesdiscounted net present value of farm income over the period 2013–2050. It predicts cropping patterns, water use, land use, and farmincome over each of those future years. The model detail consistsof 17 crops, 3 counties, and 2 policy scenarios. The model uses dataon observed cropping patterns, production costs and returns,yields, and water use per acre. The model predicts total wateruse for irrigated agriculture by crop, county, and year for each ofour three counties for both policy scenarios. The model results pre-sent a long-run analysis of the farm net income associated with useof water for irrigation for both policy scenarios. Production costsinclude establishment, capital, operating, and replacement costsof all cropping enterprises.

The optimization model describes and forecasts irrigation wateruse demands through use of a farm management model based oncosts and returns for the study region’s major irrigated crops.The optimization model assumes that growers seek the goal ofselecting a farm net income (profit) maximizing crop mix, as wellas an associated quantity of land and quantity of water used. Thedrivers of the model are a specified future pattern of crop prices,crop yields, and farm production costs (Fig. 3). Its most importantmathematical equations are described in the appendix.

Our framework for analyzing water use in irrigated agricultureimplements a similar methodology originally developed by Howitt(1995), who coined the term ‘‘positive mathematical program-ming” (PMP). In our innovative implementation of PMP, behavioralrequirements for farm income maximization, known as the firstorder conditions for income maximization, from standard interme-diate level microeconomic theory taught at most colleges and uni-versities, are used to specify and estimate two parameters of a cropyield function. Those parameters in the crop yield function areused to explain the income maximization behavior observed. Thatfunction shows declining yields in the face of an expanded scale ofland and water use. Additional details are in an earlier work(Dagnino and Ward, 2012).

Our implementation of PMP is based on the principle that opti-mization models of farm production and water resource use shouldbe and were calibrated to match conditions in the base year. Policyanalyses based on optimization models lack credibility when they

owchart.

Fig. 4. USDA plant hardiness zone map.


show a deviation between base period model outcomes and actualobserved results on the ground.

Worldwide where irrigation is practiced, an important source ofnonlinearity in crop water production function is heterogeneousland quality, resulting in declining yields as the total amount ofincreased land in production and water applied for a given cropplanted. While declining land quality at the regional level withan expanded scale of production for any given crop simplifies themany sources of declining yields, it captures much of the observedfarm behavioral response. Our PMP optimization framework is anapproach that approximately matches water use, crop mix, prof-itability, and land in production by county and crop for the baseyear (2012).

2.5.1. SoftwareThe model code was written using the GAMS (General Algebraic

Modelling System) software, version 24.2. GAMS is a registeredtrademark (GAMS Development Corportation, 2014). The modelwas solved with the continuous nonlinear programming CONOPTpackage. Details of the software are available on the corporation’swebsite. Models for water resources management have been underdevelopment at the New Mexico State University ACES since themid-1990s. One of the early optimization models in waterresources there was developed for work on drought adaptation inthe Rio Grande Basin for Colorado, New Mexico, and Texas(Booker et al., 2005). Several research articles have been publishedin a number of scientific and scholarly journals since that time.

2.5.2. Model data used2.5.2.1. Irrigated land. Data on irrigated land in production for thebase year (2012) were secured by crop, county, and policy scenariofor the most important crops grown. For the base scenario, infor-mation was secured from the USDA Census of Agriculture (USDACensus of Agriculture, 2014) with adjustments based on local

knowledge by each County Extension Agent. Where national cen-sus data were limited by disclosure problems, we consulted theCounty Extension Agent for estimates based on historical and cur-rent knowledge assembled over many years from continued con-sultation, planning, and working with regional growers. In somecases, the New Mexico Agricultural Statistics were also consultedto check for consistency (New Mexico Department of Agriculture,2013).

For scenario 2, producer panels were assembled. For eachcounty, a group of county extension agent-assembled growerswere asked how their current cropping patterns would have chan-ged for the year 2012 if project water and storage had been avail-able for their use. The general pattern emerging from Luna Countywas that growers would expand their acreage in approximate pro-portion to the additional water, with little change in cropping pat-terns, since regional growers had already adapted over many yearsto the most profitable cropping patterns. The other two countieswould also expand their land in production in proportion to theadditional water and storage, but would also see a shift in croppingpatterns, favoring production of higher income but risker crops.This would occur because of greater control over surface waterapplication timing would enable growers to better bear that risk.

To reflect the potential land cultivated to high valued crops notcurrently grown in the region, existing constraints to high valuedcrops would be dissolved. For that part of the scenario, we didnot rely on producer panels, since it was difficult for growers torespond to this scenario. For that scenario, alternative sources wereused: Irrigated land in Luna County could shift into higher valuedcrops for the new acreage, but would be unlikely to shift croppingpatterns on existing acreage. For future years, in which adjustmentcould occur over a period of years, the USDA plant hardiness zonedescription (US Department of Agriculture, 2012) was used (Fig. 4).That description identified out-of-study region cropping patternsin equal or colder climates that could potentially support higher

Table1a

Land

inprod

uction

bycrop

,reg

ion,

andpo

licy,

Upp

erGila

Basin,

USA

,201

2(acres).

Crop

GrantCou

nty,N

M,U

SA(Cliff-G

ilaarea

)Hidalgo

Cou

nty,N

M,U

SA(V

irde

narea

)Lu

naCou

nty,N

M,U

SA(D

emingarea

)To

tal,allco

unties

Historical

(201

2)W

ithAW

SAwater

&storag

eHistorical

(201

2)W

ithAW

SAW

ater

&Storag

eHistorical

(201

2)W

ithAW

SAwater

&storag

eHistorical

(201

2)W

ithAW

SAwater

&storag

e

Withou

tcu

rren

tAg

deve

lopm

entco

nstraints

Withou

tcu

rren

tAg

deve

lopm

entco

nstraints

Withou

tcu

rren

tAg

deve

lopm

entco

nstraints

Withou

tcu

rren

tAg

deve

lopm

entco

nstraints

1-alfalfa

260

318

350

452

9131

9757

9741

10,527

2-irriga

ted_

pasture

1720

1990

300

270

00

2020

2260

3-su

dan_g

rass

2024

100

129

00

120

154

4-dry_

bean

s0

00

024

726

424

726

45-sn

ap_b

eans

00

00

121

129

121

129

6-co

rn0

040

051

626

8028

6430

8033

807-so

rghum

00

100

129

1368

1462

1468

1591

8-whea

t0

00

079

885

379

885

39-co

tton

00

700

903

1998

2135

2698

3039

10-grape

s20

4840

104

309

330

369

482

11-green

_chile

00

150

194

4119

4402

4269

4595

12-fall_on

ions

00

00

601

642

601

642

13-m

id_sea

son_o

nions

00

00

601

642

601

642

14-late_

season

_onions

00

00

601

642

601

642

15-w

atermelon

s0

00

034

837

234

837

216

-pecan

s0

8460

146

1878

2007

1938

2237

Total

2020

2464

2200

2843

24,800

26,501

29,020

31,809

DataSo

urces:

USCen

susof

Agriculture

(201

4),N

ewMex

icoDep

artm

entof

Agriculture

(201

2),A

llen

(201

4),B

landford(201

4)an

dLo

wry

(201

4).


valued cropping activity under the optimistic high valued cropscenario.

To account for the inertia holding back the highest valued cropsfrom entering production not currently grown in the region, weassumed delays of six years after 2013 would be required todevelop the expertise, infrastructure, experience, and markets thatcurrently constrain Gila regional growers from producing theirmaximum economic potential in irrigated agriculture. An addi-tional source was the US Agriculture Census. Its data were usedto identify acreages of high valued crops that could optimisticallybe profitably brought to each of the three counties. These high val-ued crops include selected melons, raspberries, blackberries, andstrawberries. We consulted regions outside our study region withsimilar or harsher climate zone than each county. Those regionsincluded the US state of Washington, the higher elevations of Cal-ifornia, southwest Colorado, San Juan County of New Mexico, andparts of the Ogallala Aquifer south of Lubbock, Texas. Data on landin production by county for both scenarios are summarized inTable 1a.

2.5.2.2. Water use. Table 2 presents crop water application data.These data reflect water applications by crop, county, and waterpolicy scenario. Total water applications for Luna County were cal-culated as water application rates per acre multiplied by acres inproduction, summed over crops. For water use per acre, we con-sulted the NMSU published enterprise budgets (Hawkes, 2013) aswell as the producer panel meetings assembled in the spring andsummer, 2014 (Allen, 2014; Blandford, 2014; Lowry, 2014).

2.5.2.3. Price, yield, and cost. For the first (base) scenario, informa-tion on prices, yields, and costs of production for Luna Countywas secured by consulting the New Mexico State University CropEnterprise budgets. They are posted online and are available forpublic use (Hawkes, 2013). Additional detail was secured on thecost of pumping groundwater (Allen, 2014; Blandford, 2014;Lowry, 2014).

For the second scenario, the producer panels were used toassess grower response to AWSA water and storage if it had beenavailable in 2012. Generally, Luna County growers indicated thatcrop prices, yields, and costs would not likely to experience muchchange from actual levels observed in 2012 because of the highlevel of skill, technology, access to markets, and infrastructurealready assembled. For the other counties, the cropping patternswould change under scenario 2 to reflect better control over sur-face water. This is discussed in more detail below under results.

For scenario 2 for high valued crops, data on prices, costs, andyields were secured from the land-grant universities where avail-able in those out-of-region locations. Agriculture Census data wereconsulted for prices and yields where enterprise budgets were notavailable (US Department of Agriculture Economic ResearchService, 2012, 2013, 2014). Price, yield, and cost data were com-bined to calculate net income per unit land, for which table 3shows results.

2.6. Producer panels

Assisted by the land-grant university county extension agents,producer panels were assembled for each county. The intent ofeach panel was to identify up-to-date information on the eco-nomics of crop irrigation as well as producer response to condi-tions defined by scenario 2. Special attention was focused ondetails of crop prices, yields, and costs of production. For eachpanel meeting, the stated agenda was to assess economic impactsin their growing area, of the additional amount of surface water if itcould be made available through additional storage. Details foreach panel meeting are available from the authors by request.

Table 2Water Use for Irrigation by Crop, Region, and Policy, Gila Basin Headwater Region, USA, 2012 (acre feet/year).

Crop Grant County (Cliff-Gila area) Hidalgo County (Virden area) Luna County (Deming area)

Historical (2012) With AWSA Water & Storage Historical(2012)

With AWSA water & storage Historical(2012)

With AWSA water & storage

Without current Agdevelopment constraints



1-alfalfa 1170 1429 1575 2033 27,393 29,2722-irrigated_pasture 7740 8955 1350 1215 0 03-sudan_grass 53 65 450 581 0 04-dry_beans 0 0 0 0 576 6155-snap_beans 0 0 0 0 282 3016-corn 0 0 1668 2153 7906 84487-sorghum 0 0 333 430 3420 36558-wheat 0 0 0 0 1333 14249-cotton 0 0 2100 2710 4336 463310-grapes 67 161 140 364 773 82611-green_chile 0 0 687 887 14,417 15,40512-fall_onions 0 0 0 0 1803 192713-mid_season_onions 0 0 0 0 1803 192714-late_season_onions 0 0 0 0 1803 192715-watermelons 0 0 0 0 1044 111616-pecans 0 421 300 731 6,010 6422

Total surface water applications (ac ft/yr) 9030 11,031 8603 11,104 72,896 77,897Added surface water (ac ft/yr) 0 2000 0 2500 0 5000

Data: New Mexico State University Cooperative Extension, Grant, Hidalgo, and Luna Counties (2013), Allen (2014), Blandford (2014) and Lowry (2014).

Table 1bForecast land in production by crop, region, and policy, Upper Gila Basin, USA, 2050 (acres).

Crop Grant County, NM, USA (Cliff-Gila area) Hidalgo County, NM, USA (Virden area) Luna County, NM, USA (Deming area) Total, all counties

Historical (2012 waterconditions)









1-alfalfa 272 284 292 1116 7177 7483 7741 88832-irrigated_pasture 1707 0 0 0 0 0 1707 03-sudan_grass 20 0 66 0 0 0 86 04-dry_beans 0 11 0 33 233 244 233 2885-snap_beans 0 0 0 20 0 0 0 206-corn 0 36 343 353 2194 2249 2537 26397-sorghum 0 134 55 176 1481 1577 1536 18878-wheat 0 78 0 617 864 920 864 16169-cotton 0 0 1401 348 2759 2855 4160 320210-grapes 21 22 39 38 293 310 352 37011-green_chile 0 403 166 529 4460 4749 4626 568212-fall_onions 0 78 0 98 827 879 827 105513-mid_season_onions 0 82 0 100 850 904 850 108614-late_season_onions 0 105 0 113 973 1034 973 125215-watermelons 0 2187 0 97 1142 1202 1142 348616-pecans 0 132 56 221 1718 1821 1774 217417-high_valued_crops 0 112 0 162 0 430 0 704

Total 2020 3665 2418 4021 24,972 26,657 29,410 34,342

764F.A

.Ward,T.L.Craw

ford/Journal

ofHydrology

540(2016)

757–773

Table 3Net revenue per unit land by region, crop, and policy, Gila Basin Headwater Region, USA 2012 (dollars/acre/year).

Crop Grant County (Cliff-Gila area) Hidalgo County (Virden area) Luna County (Deming area)

Historical(2012)


With AWSA water & Storage Historical(2012)





1-alfalfa 1366 1366 1366 1366 1099 10992-irrigated_pasture 60 60 42 42 0 03-sudan_grass 860 860 842 842 0 04-dry_beans 0 0 0 0 650 6505-snap_beans 0 0 0 0 15 156-corn 0 0 694 694 459 4597-sorghum 0 0 �81 �81 �278 �2788-wheat 0 0 0 0 �166 �1669-cotton 0 0 73 73 264 26410-grapes 3558 3558 3558 3558 2722 272211-green_chile 0 0 4595 4595 6118 611812-fall_onions 0 0 0 0 2453 245313-mid_season_onions 0 0 0 0 2304 230414-late_season_onions 0 0 0 0 1747 174715-watermelons 0 0 0 0 302 30216-pecans 0 3112 3112 3112 2777 2777

Data Sources: New Mexico State University Cooperative Extension, Grant, Hidalgo, and Luna Counties (2013), Allen (2014), Blandford (2014) and Lowry (2014).


Two panel meetings were held for Luna County, while one eachwas held for Hidalgo and Grant Counties in the spring and summer,2014.

2.7. Scenarios

2.7.1. Without irrigation capacity improvementThe without water/storage scenario performs an accounting of

irrigated land in production in the three county study area, totalwater applied, gross revenue, net revenue, total income, and totalincome per acre foot of water applied without AWSA water andwithout additional storage. It reflects the status quo conditionsfor crop irrigation for the study region for the most recent yearfor which New Mexico agricultural statistics and national agricul-ture census data are available, 2012. Total crop water use in year2012 is estimated at 9030, 8603, and 72,896 acre feet per year,respectively for Grant County, Hidalgo County, and Luna County.These estimates are based on observed acreage and estimated cropwater use per acre that varies by crop.

2.7.2. With irrigation capacity improvement2.7.2.1. Historical cropping patterns. Given future developments inmarkets, technology, and demand for products, it is likely thatthe crop mix will change for the Grant, Hidalgo, and Luna Countyregion. Historically one can look backwards and see considerablechange over many years until the current time. In the mid-1960s,the number one crop in New Mexico and also in the AWSA regionin cash receipts was cotton; wheat was number two. The leadingcrops currently are alfalfa, pecans, and chile peppers measured incash receipts. Cotton has dropped to the fourth highest crop whenmeasured by cash receipts. Wheat is now number eight in cashreceipts. The next 35–40 years can be expected to show as muchchange as the past 50 years in crop mix. As described above,increases in water use for scenario 2 were set at 2000, 2500, and5000 acre feet for Grant County, Hidalgo County, and Luna Countyrespectively compared to scenario 1.

2.7.2.2. Overcoming current income earning constraints. Overcomingconstraints for future high valued crops presented to us an impor-tant analytical challenge. Currently our results showing land inproduction identifies a single high value crop in the base year2012 that produced more net revenue per acre than any other crop

grown in similar climate zones for each of the three counties in ourGila Basin headwaters region. However, taking the longer viewtowards 2050, past experience has shown that today’s high valuecrop may be displaced by other crops in future years. Annualweather variation may delay production, or excess production ina thin market reduces prices, making prices highly variable and dif-ficult to predict. A common practice of many producers or areas ofproduction with high value crops is to spread their risk across sev-eral historically high value crops in a varying mix of acres plantedto each crop. Total acres planted to this group of crops may bestable while the acres planted to an individual crop will vary fromyear to year.

To account for the variability problem of measuring returns tohigh value crops, we used a set of target crops to develop a groupor set of crops which will maintain consistent high net returns overthe long term accounting for producer ability to make neededshifts in planting to maintain more consistent high levels ofreturns. To estimate the long term net value added by the addi-tional water under scenario 2, this group is modeled as a singleblended crop. The high value crop set was made up of crops withnet returns of greater than $6200 per acre and included a mix ofraspberries, strawberries, blackberries, and assorted melons. Allhave been grown in New Mexico historically. This blended cropwould not enter the crop mix under scenario 2 until year 2019,to allow time to adjust to the new water and storage.

Data on prices, yields, and costs were used from areas similar togrowing condition as our Gila Basin headwater region, with a sim-ilar plant hardiness index. Areas with similar climate and growingseasons are primarily in the inland higher elevation parts of Cali-fornia, southwest Colorado, inland higher elevation parts of Oregonand Washington and the region near Lubbock, Texas, where theplant hardiness climate falls in the range of our three Gila Basinstudy counties (Fig. 4).

Climate and growing season are critical factors for the profitablecultivation of many high value crops because of sensitivity toweather. One factor is the minimum temperature that a fruit spe-cies can endure. The amount of injury from freezing in dormant tis-sue is influenced by three factors: (a) the rate at which thetemperature falls, (b) the duration of the low temperature, and(c) the rate of thawing (Burke et al., 1976; Childers, 1961). Late fallvigorous growth can delay maturing and hardening of tree crops towithstand freezing temperature. Tree crops such as apples,


peaches, pears, cherries, apricots, or grapes also require a dor-mancy and rest period. Apples require a certain number of hoursof chilling below a threshold level. Depending on variety, chillingrequirements can range from 800 to 1000 h. For this reason, noneof these tree crops were used because temperatures in the GilaBasin headwater region experience too much variability to growany of those crops to earn a predictable income. Soil pH in thestudy region will only allow neutral to alkaline adapted crops tobe grown (pH > 7). Most soils are sandy or sandy loam and welldrained (Yao and Heerema, 2014).

Presence of soil fungus, nematodes, viruses or other diseases inthe soil can prevent satisfactory growth or crop yield. If the culti-vation of a crop continuously encourages the buildup of these pop-ulations, then steps must be taken to reduce the population by croprotation and choice of crop to let the pest or fungus to die outbefore replanting. An important example in Luna County with sig-nificant implications for our study is chile, which eventually couldrequire a three to five-year rotation cycle.

For labor-intensive crops, current policy restrictions on guestworker program will need to be relaxed to allow economicallyaffordable harvest of high value crops. Mechanical harvesting, cul-tivation, and thinning development may be required if adequatelabor supply or cost is not available.

We assume that existing constraints to experience, skill andmanagement are temporary, and can be overcome given adequatetime and resources. Extension or other teaching resources may berequired to assist with training or classes, with short courses toovercome deficiencies. Many high value crops require intensivemanagement, technical knowledge and practiced technical skillto be economically successful. A critical mass of such individualswill need to be attracted from other commercially high scale areasto supplement the managerial skill necessary for growing highervalued crops in our study area.

Anticipated climate warming will have little negative effect onthe capacity of growers in our study area to cultivate high valuecrops in terms of growing season. Currently many high valuedcrops are grown in California under ever warmer conditions thancurrently exist in our Gila Basin headwaters area. It may howeveradd more variability to early season start to production as weatheroscillates between warm and freezing temperatures. Warmerweather may require additional but unknown water per acre inde-pendent of additional AWSA water and storage. With limited watersupplies, that warmer weather may require reduced acreage toprotect hydrologic balance. Added water demands per acre thatcould result from climate warming were not calculated for ourstudy. Water scarcity could also be overcome with more efficientirrigation and production practices as long as crop ET (evaporationand transpiration) does not increase. However, if assumed moreefficient practices were not sufficient to offset climate changeeffects, then crop mix changes and/or acreage reduction wouldbe necessary.

We assume that existing constraints to experience, skill andmanagement are temporary, and can be overcome given adequatetime and resources. Extension or other teaching resources may berequired to assist with training or classes, with short courses toovercome deficiencies. Many high value crops require intensivemanagement, technical knowledge and practiced technical skillto be economically successful. A critical mass of such individualswill need to be attracted from other commercially high scale areasto supplement the managerial skill necessary for growing highervalued crops in our study area (Ward and Crawford, 2014).

An important requirement for growing a high value crop prof-itably is efficient transportation and marketing infrastructure topermit an adequate scale of transport from farm to market(Omamo, 1998). Our analysis is based on the assumption that thisinfrastructure constraint could be overcome with time for adjust-

ment and with possible state or federal subsidies. Benefits of suchdevelopments have been analyzed by previous research (Gloverand Simon, 1975).

Complete specialization in any single high value crop is unlikelyto occur with the new water and storage. For this reason, weassume that cropping patterns in similar plant hardiness regionswill be limited by the degree of specialization in regions wheresimilar crops are grown. Because of susceptibility to disease orweather for a given crop, producers at the panel meetingsexpressed a desire to grow several different crops to spread risksof price, weather, or disease. We assume that no more than one-quarter of the new land brought into production under scenario2 will be allocated to new high-valued crops not previously grownin the region.

Risk comes in several forms, and includes production risk due toweather and potential disease related risks, although irrigationwith benefit of additional storage reduces this risk considerablyespecially when faced with the emerging risk of climate variabilityand change (Jones, 2000). Price and cost related risk are also addi-tional factors that limit expansion of high valued crops in regionssuch as ours that have not historically had access to adequate stor-age. For financial risk, risk pooling is a classic solution. Co-ops canperform that pooling function for risk, either though access toinsurance pools, storage, and marketing pools.

Management needs could be met via recruiting of talent withbonuses funded by groups or the state. Additional training optionsfor employers are offered via New Mexico State University or com-munity colleges basic business courses. Courses could be distanceeducation, either asynchronous or synchronous. Constraintscaused by low absolute scale could be overcome, in part, with col-lective action in the procurement of machinery, supplies, sales, andprofessional services. There are size minimums for the support offood processing. Financial help through State programs like bondarrangement could be considered.

3. Results

3.1. Land

Table 1a shows irrigated land in production by crop, region, andscenario for the three county region for the base year 2012 (NewMexico Department of Agriculture, 2013; USDA Census ofAgriculture, 2014). A larger amount of land for the Luna CountyRegion, equal to 26,501 acres would occur under scenario 2 com-pared to the lower amount of land equal to 24,800 acres under sce-nario 1. This gain of about 1700 acres is projected to occur if newacreage enters under an arrangement of bidding for the additional5000 acre feet of water and storage. The table shows that the landin that region expands proportionally by about 6.8% to all cropscurrently under irrigation. For both scenarios, the largest acreageis currently cultivated for alfalfa, about 37% of the total, followedby green chile (17%), corn (11%), cotton (8%), and pecans (8%).

The table also shows that for the other two county areas, Grantand Hidalgo Counties, a wide range of specialty crops can be grownwith water and storage, reducing the current need by growers togrow defensive crops (Meza et al., 2008) like irrigated pasture.Lacking adequate storage and associated unreliable supplies,defensive crops are planted to produce modest yields in the faceof uncontrolled river flow in the Gila River that currently occursin both counties. Potentially, higher valued crops risk producinglittle to no yield in the face of unreliable or poorly-timed surfacesupplies.

The introduction of 5000 acre feet of surface water to LunaCounty will support additional acreage, but as shown in the table,will have little effect on the cropping patters for the base year. The


additional water would unlikely be used to a large extent forrecharge, since growers in our producer panel meetings expresseda preference for a water bidding arrangement for distributing thenewwater, in which the new water would go to the highest bidder.

The table also presents important results for the other twocounties, Grant and Hidalgo. It clearly shows the influence of weakcontrol over the timing and quantity of water supplies, althoughthere is currently some permitted groundwater backup to reducewater timing risks in the Virden area. In periods of the growingseason when surface water is absent or unreliable, growers pumptheir wells, where they have a pumping permit, to make up forthe water deficit. However, pumping incurs production costs thatcan be avoided with reliable surface water.

Table 1b shows cropping patterns in the last year of our time hori-zon, 2050. As expected its contents show a large reduction in irrigatedpasture with a considerable growth in acreage of high valued existingcrops as well as a considerable growth in the new high valued cropsas discussed above. The gain in high valued crop acreage is larger thanthe reduction in forages because the higher valued crops use onlyabout two-thirds as much water per unit land. But the higher valuedcrops require a much more precisely timed level of water.

Three notable patterns emerge from Tables 1a and 1b.

1. An expanded scale of production of all crops occurs under sce-nario 2.

2. A transition to higher valued specialty crops such as grapes andpecans occurs before 2019, with an attendant smaller growth inacreage of most lower valued crops such as alfalfa.

3. An even larger transition to higher valued crops occurs from theyears 2019–2050, as producers shift in large scale into grapes,chile, onions, melons, pecans, and into other high valued crops.

In the Virden Valley, just over one third of the irrigated land inproduction is planted in crops that adapt to water applications thatare difficult to time, such as alfalfa, irrigated pasture, and Sudangrass. Impacts of weak current control over surface water timingunder scenario 1 is shown even more clearly in the Cliff-Gila Valleyin Grant County, where nearly all of the region’s irrigated land of2020 acres is planted to forage crops (2000 out of 2020). Foragecrops tolerate unreliable surface water supplies as long as somemonsoon rains occur in late summer. Currently the Gila River typ-ically goes dry in some reaches in the Cliff-Gila Valley during atleast part of the growing season. Scenario 2 results shows a mixof higher valued crops entering with greater control over watersupply. A good example is the introduction of pecans in GrantCounty that would occur with new storage and water.

3.2. Water

Table 2 shows total water applied for crop irrigation by crop andcounty for the study area in the base year 2012. Water applicationsare measured in total acre feet applied by crop, county, and sce-nario. Water applications in acre-feet per acre equal total waterapplied (Table 2) divided by total land in production (Table 1).Table 2 shows an amount equal to 2000 additional acre feet ofwater applied for both policy scenarios for the Cliff-Gila area and2500 additional acre feet applied for the Virden area.

Table 2 presents a consistent message as shown by Table 1a.Under current water supply, timing, and use patterns, Luna Countyhas by far the greatest amount of water applied to irrigated land, atjust under 73,000 acre-feet per year. It also produces a high abso-lute level and a high proportion of high valued specialty crops suchas cotton, fruits, vegetables, and nuts. Cotton prices were weak in2012, so total cotton water use in Luna County was a compara-tively small 6% of the county’s total agricultural water use. Tree,fruit, vegetable crops consumed a remarkably high 38% of total

water in Luna County, an important indicator of the economicvalue associated with a high level of control of crop water applica-tion timing.

Under scenario 2, estimated water use in Grant County willincrease from 9030 to 11,030 acre feet per year an annual increaseof 2000 acre feet. For Hidalgo County, water use will increase from8613 to 11,013, an annual increase of 2500 acre feet. For Luna County,water use will increase from 72,896 to 77,896 acre feet per year, anannual increase of 5000 acre feet. For the optimization model runsapplied to future years, these new quantities of water cannot beexceeded for the with water and storage scenario 2 for any future year.

The table shows that growers in Hidalgo and Grant Countiesindicated an increased scale of production along with additionalacreage and water use for higher income crops currently grownin smaller amounts in the region. For the case of scenario 2, grow-ers were unable to respond with great confidence, because condi-tions defined by that scenario lie considerably outside theirpersonal experience. So, for that scenario an adjustment periodof six years is assumed to be required, to take advantage of poten-tial reductions in resource constraints currently holding growersback from growing the highest income crops. Additional gains inacreage of highest valued crops under scenario 2 would occur inthe years after 2019, not shown in Table 2, since Table 2 onlyincludes acreage for the base year (2012).

3.3. Net income per unit land

Table 4 shows net revenue per acre by crop, region, and policyscenario for the base year 2012. Since this table presents results ofsubtracting average cost per acre from gross revenue per acre, ithas much more influence on total land allocated by growers amongthe various crops. Where not constrained by other factors, produc-ers in our panels explained that they attempt to allocate more landto the crops earning higher net revenue per unit land.

The table illustrates the importance of fruits, vegetables, andnuts as producers of high net revenue per acre, for which valuesare typically from two to ten times the level of forage and fieldcrops. The economic value of forage produced or rented is deriveddirectly from the supply of beef cattle that it produces. Higher beefprices or lower beef production costs raise the economic value offorage grazed by livestock. The demand for forage is a deriveddemand, derived from the demand for beef. That is, the economicvalue of forage is derived from beef prices earned from cattle graz-ing as well as the costs to ranchers of beef production. Higher for-age prices for leased forage occur when beef prices increase orwhen ranching production costs decline (Byerley et al., 1999).

In addition, agronomic constraints are respected by growerswhen making economically-driven cropping choices. For example,chile is a very high valued crop, but continued cropping of chile onthe same land leads to a build-up of soil borne disease, reducingyields over time (Goldberg, 2009). The solution adopted by grow-ers in Luna County is crop rotation to reduce prevalence and costof soil borne disease. Currently Luna County growers use a 3- to4-year crop rotation cycle for chile. Small grains following a yearof chile are cultivated to reduce soil pathogens. These rotationcrops typically produce negative net income, as shown in Table 4for wheat and sorghum. These rotation crops would not be grownin the current quantities if not required to make chile economicallyviable. So income losses from small grains are effectively a cost ofproduction paid to secure the high returns from chile. Some othercrop not currently grown such as tomatoes and lavender are clo-sely related to chile and suffer from similar diseases, according toour growers in the Deming panels.

Grant County is shown to produce mostly irrigated pasture,which produces a low net income per acre, again a defensive cropcultivated to handle unreliable surface supplies from the Gila River.

Table 5Discounted net present value of farm income by county and policy, Upper Gila Basin, NM, USA, 2013–2050 (US dollars).

Region Area Without AWSA water & storage With AWSA water & storage

With current Ag development constraints Without current Ag development constraints

(Scenario 1 base) (Scenario 2 alternative)

Grant County, NM, USA Cliff-Gila Area 19,491,945 196,169,792Hidalgo County, NM, USA Virden area 76,757,064 257,407,495Luna County, NM, USA Deming area 1,834,716,160 2,059,230,453

Total, region 1,930,965,169 2,512,807,740Economic gain compared to base 0 581,842,571

Table 4Total net farm income by year and water development policy, Upper Gila Basin, USA, 2013–2050 ($1000 US per year).

Year Grant County, NM, USA (Cliff-Gila area) Hidalgo County, NM, USA (Virden area) Luna County, NM, USA (Deming area)

Historical(2012)







2013 547 1020 1916 4162 46,713 49,9172014 593 1080 2080 4406 50,232 53,6782015 641 1142 2256 4662 53,916 57,6142016 692 1208 2444 4930 57,753 61,7122017 740 1271 2630 5197 61,592 65,8062018 784 1329 2771 5423 65,131 69,5652019 829 7817 2920 11,427 68,809 80,1502020 876 8256 3072 11,869 72,557 84,1292021 925 8686 3219 12,291 76,138 87,9292022 975 9133 3373 12,730 79,855 91,8732023 993 9502 3509 13,061 82,661 94,8502024 1000 9844 3611 13,336 85,005 97,3412025 1006 10,198 3697 13,613 87,361 99,8472026 1013 10,567 3786 13,898 89,786 102,4262027 1019 10,950 3877 14,190 92,281 105,0792028 1025 11,347 3969 14,491 94,847 107,8092029 1032 11,760 4064 14,800 97,487 110,6182030 1038 12,129 4141 15,034 99,482 112,7392031 1045 12,483 4208 15,233 101,164 114,5242032 1052 12,849 4276 15,434 102,873 116,3392033 1058 13,229 4346 15,638 104,610 118,1852034 1065 13,531 4416 15,841 106,324 120,0062035 1072 13,797 4487 16,045 108,039 121,8282036 1078 14,067 4559 16,251 109,777 123,6762037 1085 14,342 4632 16,460 111,539 125,5482038 1092 14,622 4706 16,671 113,326 127,4472039 1098 14,907 4781 16,885 115,137 129,3722040 1105 15,197 4857 17,102 116,972 131,3232041 1112 15,492 4934 17,322 118,834 133,3012042 1119 15,792 5012 17,545 120,720 135,3072043 1126 16,097 5090 17,771 122,633 137,3402044 1133 16,407 5170 18,000 124,571 139,4012045 1140 16,722 5251 18,228 126,531 141,4842046 1147 17,043 5334 18,459 128,518 143,5962047 1154 17,369 5417 18,693 130,531 145,7372048 1161 17,700 5501 18,930 132,573 147,9082049 1168 18,038 5587 19,170 134,642 150,1082050 1175 18,381 5673 19,413 136,740 152,339

Ave value water($US/ac-ft)

83.27


Still, evidence from our producer panels indicated that if watersupplies were more reliable the farmland could produce more than$3000 per acre in net revenues. This provides a signal of potentiallyvery profitable irrigated agriculture, a high valued crop thatcould expand considerably in future years with acceptableupstream storage and a more secure water right for farming andranching than currently exists in the region. Even in the base year2012, without time for adaptation, our results in Table 1 showmore than a doubling of current acreage in Grant and HidalgoCounties in both grapes and pecans for scenario 2 compared to sce-nario 1.

Hidalgo County produces a higher level of high valued crops,showing some corn at $694 net income per acre along with amodest acreage of grapes and green chile, both producing morethan $3000 per acre along with some pecans, which we estimateproduced over $3000 per acre in the base year of 2012, due to lar-ger levels of permitted pumping capacity.

3.4. Total net income

Table 5 shows net farm income by crop, region, year and policyscenario for the years 2013–2050. For any given crop, region, year,


and policy scenarios, total net farm income is calculated as (price/ton � yield/acre � cost/acre) � (acres in production), as mentionedearlier. Despite the very large number of data entries in the table,four important patterns emerge.

First, the table shows a greater proportion of high valued cropsin Grant and Hidalgo Counties under scenario 2 than under sce-nario 1. As described for the previous table this pattern of crop pat-tern shifting occurs because of a greater control over surface watersupplies than occurs currently.

The second pattern is the entrance into the production mixassociated with the highest valued crops as discussed earlier inthis paper, beginning in the year 2019 for Grant and Luna Coun-ties after a period of adjustment is allowed to shift into these spe-cialized crops and after overcoming constraints that precludethese crops from being grown currently. Detailed cropping pat-terns are not shown in Table 4 by year to economize on limitedspace. These specialized high valued crops include berries, mel-ons, and related crops with a hardiness index reflecting the highdesert climate of the Gila Basin headwater region of New Mexico.These are crops that can be grown in our study region’s climateand location and which have been historically grown in thestate.

The third pattern shown in the table reflects an increased scaleof production in Luna County brought on by access to lower costsurface water that adds to the existing economically accessiblegroundwater. This expanded scale would occur with no signifi-cant change in cropping patterns as pumping drawdown onthe local aquifer is reduced in the face of additional availablewater.

A fourth pattern revealed in the table is one of generally risingincomes in future years brought on by a widening future gapbetween net revenue (price � yield) and cost of production (costper acre). Forecasts on price, yield, and costs come from USDAsources as described earlier (US Department of AgricultureEconomic Research Service, 2014, 2012, 2013, 2014).

Fig. 5. Discounted ne

3.5. Discounted net present value

Table 5 and Fig. 5 show the discounted net present value of netfarm income by county and policy scenario. The overarching pat-tern can be summarized by a few observations:

For reasons described earlier in this paper, the lower discountednet present value occurs for scenario 1. Summed over the threecounties, the total is about $1.931 billion. While it is an economicvalue of considerable magnitude, it is the lower of the two scenar-ios because scenario 1 provides no additional water or storage andcomes from eliminating no development constraints currently fac-ing growers. Therefore, the year-by-year future cropping mixunder that scenario, acreages, and net income per acre forecastby the optimization model see only modest changes from thoseof the base year 2012.

As described earlier, a considerably higher net present valueoccurs for scenario 2. Summed over our three counties, that scenar-io’s total is $2.513 billion, a gain of about 30% compared to out-comes from scenario 1. These gains occur because of the uniquegrower behavior responding to conditions defined by scenario 2.

The first reason for the large gain is that given time for adjust-ment, growers in Grant and Hidalgo Counties gradually shift intocropping patterns currently seen in Luna County because of bettercontrol of water supply than currently possible. Although yieldswill be lower in Grant than Luna County because of its higher ele-vation, most of the crops grown in Luna County (except cotton) canbe grown in Grant County under this optimistic future scenario. Asecond reason for the large gain under this scenario is that addi-tional water and storage allow growers to shift into high valuedcrops on up to a third of their new acreage brought into productionwith that additional water and storage. Additional details on eachof the scenarios are provided below.

The table shows that scenario 1, the base case, produces onlymodest levels of value farm income in net present value termsearned in Grant County, equal to $19.5 million, about 1% of the

t present value.


total of $1.930 billion earned over the three county region. HidalgoCounty is forecast to earn $76.8 million in NPV (about 4% of thetotal). Luna County earns by far the largest amount, equal to$1.835 billion, or 95% of the total for the three county region,because of growers’ very large control over application of waterto crop needs, relying most heavily on groundwater pumping.

Grant County experienced the largest growth from scenario 2,increasing from $19.5 million to $196.2 million, a 906% gain. HidalgoCounty achieves a discounted net present value of benefits equal to$257.4 million under scenario 2, a growth of 334% from the base. LunaCounty achieves a total of $2.059 billion under scenario 3, a growth ofjust under 12% from its scenario 1, since Luna County growers alreadyhave a high level of control over their crop water applications.

Gains from scenario 2 compared to the base come from severalsources. First are the additional gains permitted by the shifting ofcropping patterns in Grant and Hidalgo Counties to reflect 2012conditions in Luna County. While not experienced immediately,these conditions are assumed to be possible after a six-year delayas growers gain knowledge, expertise, and access to informationand labor motivated by the greater access to controlled water stor-age permitted by additional supplies. The second additional gaincomes from the cultivation of high valued crops not currently har-vested in the three county region on up to a quarter of the newlands brought into production thanks to expanded storage. Basedon evidence from the University of California enterprise budgets(University of California Cooperative Extension, 2014) adjustedfor southwest New Mexico’s climate conditions, those crops areassumed to earn higher incomes after a delay of six years. Theseincomes are calculated at $7713 per acre for Grant County,$10,795 per acre for Hidalgo County, and $16,163 per acre for LunaCounty. Obviously, these are optimistic incomes per acre, muchhigher than farming incomes typically earned in that region inour base year of 2012. Considerable technical, experience, educa-tional, resource and institutional constraints that currently facegrowers in this region would need to be overcome for thoseincome gains to turn into actual economic gains on the ground.

Gains in farm income from the storage infrastructure set anupper bound on the economic value of additional storage capacitywith water if constructed if the only use of water were for agricul-ture. With agriculture as the only use, the total cost of infrastruc-ture needs to be subtracted to show a discounted net presentvalue of the benefits minus the costs of the infrastructure pluswater. However, in fact, additional infrastructure if built wouldhave multiple uses. Use of that storage capacity would apply notonly to agricultural use of 9500 acre feet per year assumed in thisstudy, but also to up to 4500 acre feet per year for municipal,industrial, environmental and recreational uses. After the total costof the storage and distribution infrastructure is determined atsome future time, some kind of cost sharing method for pro ratingthat fixed cost would need be determined.

4. Discussion

This paper’s contribution to the literature has been to illustrate,formulate, develop, and apply a methodology to examine the eco-nomic performance from irrigation capacity improvements in theGila Basin of the American Southwest. An integrated empiricaloptimization model using mathematical programming was devel-oped to forecast cropping patterns and farm income under two sce-narios (1) status quo without added storage capacity and (2) withadded storage capacity in which existing barriers to developmentand successful cultivation of higher valued crops are dissolved.

Our findings indicate that storage capacity development canlead to a higher valued portfolio of irrigation production systemsas well as more sustained and higher valued farm livelihoods.Results show that compared to scenario (1), scenario (2) increases

regional farm income by 30%, in which some sub regions secureincome gains exceeding 900% compared to base levels. Additionalstorage is most economically productive when institutional andtechnical constraints facing irrigated agriculture are dissolved.Along with additional storage, removal of constraints on weaktransportation capacity, limited production scale, poor informationaccess, weak risk-bearing capacity, limited management skills,scarce labor supply, low food processing capacity, and absolutescale constraints, all can raise the economic value of additionalirrigation capacity development, as described in more detail below.

Potential gains to farm income in the headwater region of theGila Basin associated with additional water and storage are by nomeans unprecedented. Reclamation projects built in New Mexicoand elsewhere in the west from the inception of Reclamation’s irri-gation development mandate in the early 20th century haveincreased farm income by several hundred percent. An importantsource of income growth from improved technology occurred fromthe introduction and widespread use of groundwater pumpingfrom aquifers that occurred after World War II (Pisani, 1992,2003; US Bureau of Reclamation, 2014).

One important documented example of an increased scale andvalue of irrigated agriculture comes from the Rio Grande Projectarea of southern New Mexico and West Texas. Irrigated acreagein the Rio Grande Project area expanded from 26,230 acres in1910 to 45,986 in 1917, with gradual increases to 88,714 acresby 1945 (Woznaik, 1998). These increases in scale are likely tocome from several factors. These include greater reliability ofwater supplies, a growing commercialization of agriculture, pricingwater affordably, such as at a rate defined by farmers’ ability topay. Other historical factors include establishment of food process-ing facilities (Woznaik, 1998) and the signing of the Rio GrandeCompact of 1938, establishing a sliding scale for sharing wateramong Colorado, New Mexico, and Texas (Ward, 2013). Otherimportant reasons include improved drainage in the Rio Grande Pro-ject area installed in the 1920’s. This was complemented by agreater willingness to bear risk by irrigators to plant high incomecrops as a consequence of more reliable control over water suppliesin addition to greater access to reliable farm labor and food process-ing facilities (Autobee, 1994; Woznaik, 1998). Our results tablesdescribed above also show a greater willingness to bear risk in theface of additional reliable water supply associated with scenario 2.

Not only did historical irrigated acreage increase in the RioGrande Project Area, but economic values of land per acre for irri-gation farming increased by even more. Data are spotty, but themessage from history is clear: As construction for Rio Grande Pro-ject storage commenced in 1906, land values averaged $17.50 anacre. Seven years later in 1913, anticipating the value of the addi-tional water, the price of the same unimproved land increasedfrom $50 to $75 an acre. Soon after that, developed land for orch-ards and gardens within 10 miles distance of El Paso, Texas, sold for$650–1200 an acre (Autobee, 1994). Clearly, the lessons of historyreveal numerous opportunities for considerable increases in farm-ing incomes near the study region, as described in detail below. Asdiscussed above, the high value crops assumed in our analysis forscenario 2 are apples, grapes and pecans before 2019, and melons,raspberries, blackberries, and strawberries after 2019.

Overall, several important messages emerge from our findings.All focus on the need for water resource planners in many of therural regions of the American southwest to investigate measuresto overcome the very considerable existing barriers that limit theprofitable cultivation of high valued crops not currently cultivatedat scale. This is a special challenge facing growers in Hidalgo andGrant Counties.

Measures to remove labor constraints bear serious examination.For labor-intensive crops, current policy restrictions on guestworker program could be relaxed to allow economically affordable


harvest of high value crops not currently cultivated in our threecounty area. Development of advanced mechanical harvesting,cultivation, and thinning development would be an alternative ifaffordable labor is unavailable.

Grower experience in cultivating new high valued crops is pre-sently poorly-developed. These constraints could be overcomegiven adequate time and resources. Land grant university exten-sion or other instruction may be required to assist with workshopsto overcome limited experience with these high valued cropsrequiring specialized skills and knowledge. Numerous high valuecrops require intensive management, technical knowledge andpracticed technical skill to be successful. A critical mass of suchpeople will need to be attracted from other commercially prof-itable areas to supplement the managerial skill necessary for grow-ing higher valued crops in our study region.

Our scenario 2 assumed that mechanization could be developedto substitute for labor shortages with the accompanying necessaryadaptation for plants to be adaptable to machine cultivated or har-vested. This will require the development of determinate varietiesof high value plants (Silvertooth et al., 1993), which allow for onepass harvesting. Typically, many potentially high value plants arenon-determinate requiring multiple pass picking as the crop ripensor matures, which raises production costs and reduces profitability.

5. Conclusion

A forecastworldpopulationof9 billionby2040continues tomoti-vate the search for improved performance of irrigation, the largestwater consumer in most arid regions. Growing evidence of climatewarming raises the stakes. Reservoir storage capacity developmentis one of many climate adaptationmeasures that could raise the eco-nomic performance of irrigation in contributing to food security.

This paper contributes to the literature by formulating, develop-ing, and applying an innovative methodology to examine the eco-nomic returns from irrigation capacity improvements in theheadwaters of the Lower Colorado Basin in the American South-west. An empirical mathematical programming model is devel-oped to forecast cropping patterns and farm income under twoscenarios. The two scenarios are (1) status quo with no new wateror storage capacity and (2) with added storage capacity with addi-tional water, in which existing barriers to development of highervalued crops are dissolved.

Our results showed that storage capacity development can leadto a higher valued package of irrigation production in addition tohigher valued farm livelihoods. Results show that compared to sce-nario (1), scenario (2) increases regional farm income by just undera third, in which some sub regions secure income growth of a fac-tor of 9 compared to base levels. New storage capacity is most eco-nomically efficient when institutional and technical constraintsfacing irrigated agriculture are eliminated. Results of this workpresent a path to policy makers, water administrators, and farmmanagers, who have the greatest responsibility for sustaining farmincome, food and water security, and rural economic developmentin the world’s arid regions that confront the need for resilientadjustment to climate variability and change. Despite the limitedscope of our work, the innovative methods developed here openup a path for further economic analysis of measures to protect foodand water security in the world’s dry regions.

Acknowledgements

The authors are grateful for financial support by the New Mex-ico Interstate Stream Commission and the New Mexico Agricul-tural Experiment Station.

Appendix A. Appendix (mathematical documentation)

We present here the essential elements of the Gila Basin modelstructure. The same model structure is used for each crop, county,and policy scenario. That structure has the following, indices, data,variables, equations, objective, and bounds.

A.1. Indices

Indices, termed SETS in software such as GAMS, reflect thedimensions over which the model is defined. They are the founda-tion on which the remainder of the model rests. Those indices are:

j = crop
(17 crops listed in the results tables) c = county (Grant, Hidalgo, Luna counties) p = policy scenario (1–2, described elsewhere in detail) t = year (2013–2050)
A.2. Parameters

Parameters are data read by the model. They are:

Price_p
(j, t) Crop prices ($ US per ton) Cost_p (j, c, t) production cost per acre ($ US per acre) W_p (c, p, t) Water available for
irrigation
(acre feet peryear)
B0_p
(j, c, p) Maximum crop yield (yield/acre) B1_p (j, c, p) Loss in yield from one
addedacre in production

(yield/acre)

i_p
Discountrate
(3.75%)
(unitless)
The suffix _p is added at the end of each parameter to distinguish
parameters from variables when seeing both in the equations. Thediscount rate used is 3.75%, the rate guiding water resource invest-ments by the U.S. Bureau of Reclamation since March 2013 (FederalRegister, 2013).
A.3. Unknown variables:

Several unknown variables are used for the model:

W_v
(j, p, c, t) Crop water applied (acre feet/acre) Land_v (j, p, c, t) Land irrigated (acres) Yield_v (j, p, c, t) Crop yield (tons/acre) TNI_v (j, p, c, t) Total net farm income ($ US per year) NPV_v (p, c) Net present value of total
net farm income
($US)
The suffix _v is added at the end of each variable name to distinguishvariables from parameters when they appear in the equations.

A.4. Equations:

Equations are used to link the parameters and variables. Themost important equations used are:
TNIAvðj;p;c; tÞ ¼ Price pðj; tÞ �Yield vðj;p;c; tÞ�Cost pðj;c; tÞ; ð1ÞEq. (1) states that net income per irrigated acre, gross margin,equals price multiplied by yield minus variable costs includingthe cost of water.
Yield vðj;p;c;tÞ¼B0 pðj;p;cÞþB1 pðj;p;cÞ�Land vðj;p;c;tÞ ð2ÞEq. (2) states that each crop’s yield decreases with an expandednumber of acres, in which B0_p and B1_p are calculated to repro-duce base period (2012) observed acreage.

TNI vðj;p; c; tÞ ¼ TNIA vðj;p; c; tÞ � Land vðj;p; c; tÞ ð3Þ


Eq. (3), the objective function, states that total net income for eachcrop is net income per acre multiplied by the number of acres inproduction. The number of acres in production is not known inadvance for future years, and is optimized by the model, reflectingthe outcome of net income-maximizing actions by growers whilerespecting water supply constraints.

NPV vðc;pÞ ¼ sumððj; tÞ;TNI vðj;p; c; tÞÞ=½ð1þ i pÞt� ð4ÞEq. (4), the objective function, states that net present value of totalland under irrigation is the total net income summed over time peri-ods, in which nominal income from each period is reduced by a dis-count factor in the denominator to reflect the time value of money.

A.5. Constraint

Water supply is limited in the Gila Basin headwater region forall scenarios considered. This water scarcity was captured by thefollowing equation:

sumððjÞ;W vðj;p; c; tÞÞ < W pðc;p; tÞ ð5ÞEq. (5) states that total water applied summed over crops cannotexceed total available water for each county, policy scenario, andyear. For the first scenario, total available water was calculatedbased on observed (2012) land in production multiplied by applica-tion per acre summed over crops for each county. For the secondscenario, total available water was augmented by the amountsdescribed earlier in this paper. These amounts are 2500 acre feetper year more for Hidalgo County, 2000 acre feet per year morefor Grant County, and 5000 acre feet per year more for Luna County.

A.6. Objective function

The objective function was specified to:

Maximize NPV vðc;pÞ ð6ÞEq. (6) finds cropping andwater use decisions thatmaximize the dis-counted net present value of farm income by county and policy sce-nario. It reflects growers’ attempts to cultivate the amount of land inproduction summed over crops and allocated over years that maxi-mizes net present value in Eq. (4) while respecting the water supplyconstraint in (5). This constrained maximization takes place for eachcounty and each policy scenario, for a total of 6 model runs (Fig. 3).

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