global warming, impact on agriculture and adaptation strategy

NATURAL RESOURCE M ODELINGVolum e 25, Number 3, August 2012

GLOBAL WARMING, IMPACT ON AGRICULTUREAND ADAPTATION STRATEGY

KRISHNA P. PAUDELDepartment of Agricultural Economics and Agribusiness

Louisiana State University and LSU AgCenterBaton Rouge, LA 70803

E-mail: [email protected]

L. UPTON HATCHDepartment of Agricultural and Resource Economics

North Carolina State UniversityRaleigh, NC 27695

E-mail: [email protected]

Abstract. Sensitivity of the Southeastern US agricul-ture sector to temperature increases will be based largely onaccompanying changes in precipitation, extent of the warming,and relative impact on competing crops grown in the area.The impact of climate change in 10 Southeastern US coun-ties was investigated under three different climate scenariosfor two different reference years (2030 and 2090). Seven ma-jor crops grown in the area were selected to study the impacton crop yield, irrigation acreage, and optimal choice of cropsin a representative farm in each of the southeastern states. Ifwarming is moderate and also brings a considerable increasein precipitation—as indicated by the Hadley model—then, theeffect on yields, water use, and income will be mostly benign.If warming is moderate without increased precipitation andthe water for irrigation is available, then the effects on theagriculture sector are still mostly negligible. If warming is notmoderate and no increased precipitation materializes, farm-ers could realize quite dramatic negative consequences for rowcrop agriculture in the Southeastern United States.

Key Words: Climate change, water use, crop mix,returns.

∗Corresponding author. Krishna P. Paudel; Department of AgriculturalEconomics and Agribusiness, 225 Agricultural Administration Building,Louisiana State University, Baton Rouge, LA 70803, USA, E-mail: [email protected]

Received by the editors on 1s t August, 2011. Accepted 17th February, 2012.

Copyright c© 2012 W iley Period ica ls, Inc.

456

GLOBAL WARMING AND AGRICULTURE 457

1. Introduction. Global warming has the potential to greatlyinfluence agriculture worldwide because of the sector’s obvious depen-dence on weather and climate. The Southeastern United States, resid-ing in a temperate zone, may have beneficial plant growth with increas-ing temperatures up to a certain point after which the effects of heatstress will dominate, resulting in declining yields. The point at whichthese detrimental effects will occur varies greatly depending upon thecrop, soil moisture, rainfall, carbon dioxide (CO2) levels, and otherlocation-specific variables. Thus, to make the discussion of heat effectson agriculture relevant, local parameters must be given detailed consid-eration. That is, generalities about climate effects must be translatedinto specific local effects before they can become useful to producers inthe agricultural community.

Management response is an important element in understanding theeffects of climate change on any economic sector, and agriculture isno exception. Farmers should be able to mitigate the negative im-pact or utilize the positive impact of climate change by an appro-priate adaptation strategy (Mount and Li [1994], Rosenzweig et al.[1994], Kaiser et al. [1995]). In addition to these studies focusing onfarm-level adaptations, there are other studies dealing with national-level effect of climate change on US agriculture (Adams et al. [1995]).Darwin et al. [1995] extended their analysis to international agricul-ture and concluded that, when considered on a worldwide scale, al-most all of the impacts simulated by various climate scenarios wouldbe mitigated by adaptations relevant to management of production,price effects, and shifting land use. That is, the net worldwide effectwould be negligible if producers and consumers were allowed to adjustto the new price and production relationships created by new climateconditions.

Lewandrowski and Schimmelpfennig [1999] concluded that theeconomic impacts of climate change on US agriculture and the USeconomy may not be that severe although some regions may well beimpacted more than the others. They also add that competition for re-source use will change land use and crop and livestock production sys-tems. The positive impact of climate change on agriculture has beenreported by several other authors (Weber and Hauer [2003], McCarl[2008]). On the contrary, Schlenker and Roberts [2009], John et al.[2005] and Seo et al. [2005] reported that climate change would have a

458 K.P. PAUDEL AND L.U. HATCH

negative impact on crop yield. Interestingly, Deschenes and Greenstone[2007] claimed no effect of climate change on the US agriculture sector.

McCarl [2008] projected the impact of climate change on US cropyield in 2030 and 2090 under adaptation and no adaptation scenariosbased on all climate change models (Hadley, Canadian, CommonwealthScientific and Industrial Research Organization model (CSIRO), andRegional Climate Model (RCM)). McCarl points out that crop yieldsare likely to increase by 3–88% in 2030 and by 5–35% in 2090 un-der no adaptation scenarios. Yield increases are even greater underadaptation scenarios. As far as the agricultural economic surplus, hefound that globally climate change would generate economic surplusesin the range of $3–12 billion if proper adaptation measures are in place.Tropical countries suffer greatly from climate change (Seo et al. [2005])whereas temperate climate countries like Canada may benefit from cli-mate change (Weber and Hauer [2003]). The impact of climate changeon agriculture has been the focus of two issues (69,1 and 70,1) in theJournal, Climatic Change, and the third and fourth protocols on cli-mate change published by the Intergovernmental Protocol on ClimateChange (IPCC, 2011). The unanimous view of articles published inthese collections is that, although agriculture may get impacted nega-tively in many circumstances, proper adaptation and mitigation strate-gies should help to overcome these negative impacts. Salinger et al.[2005] see the role of agroclimatologists as very important in copingwith the adverse effects of climate change on agriculture.The compet-itive position of agricultural production in various regions, and specif-ically within important growing areas within a region, may be alteredby climate changes in ways that have important implications for landand water resource uses (Hatch et al. [1999]). Climate change has thepotential to be both a problem and an opportunity to agricultural pro-ducers and policy makers, based on the location and use-specific impli-cations of expected impacts. Changing climatic conditions will providea premium to those with the ability to respond to the potential re-alignment of resources and institutions influencing competitive advan-tage in many locations and product and input uses. The effects of cli-mate change may be negligible in national and international scales, butthe effects in terms of crops grown, water utilizations, and resulting in-come levels at local levels may be substantial. Creating the capacity torespond to the challenges posed by climate change should be a priority


FIGURE 1. Southeastern US counties, each in different states, selected tostudy the impact of climate change on crop mix, irrigation water use, andfarm income.

of agricultural and natural resource policy makers (Butt et al. [2006],Kurukulasuriya [2006]).

This paper addresses the need for location-specific effects of increasedtemperature on agriculture by presenting an analysis of crop responses,crop mix changes, water uses, and income effects in 10 representativecounties of the Southeastern United States (Figure 1). Heterogeneityof the southeastern agricultural sector is addressed by focusing on 10different counties so as to provide a more detailed picture as of howsmall areas adapts to climate change.

2. Methods and models. There are primarily two ways to mea-sure the impact of climate change in agriculture. One way to assessthe impact is to use an integrated assessment model. A drawback ofthis approach is that it is data and human capital intensive so its useis primarily limited to developed countries. An alternative to the inte-grated assessment model is a hedonic model proposed by Mendelsohn


et al. [1994]. In the hedonic model of assessing the impact of climatechange, one regresses crop yield to precipitation and temperature totease out the impact of these factors on crop yield. This approachmakes it possible to assess the impact of climate change on crop yield,even in developing countries. We use an integrated assessment modelto find the impact of climate change in agriculture.

The optimization model used a representative 200-acre farm for eachlocation. Both irrigated and nonirrigated options were included in thedecision set and crop mix was selected based on net income, total waterneed, and total cost per acre. The percentage of the irrigated land in therepresentative farm was considered to be the same as that of the county.Farm labor and capital availabilities were based on the average laborand capital available in an average size farm in the region. Farmerswere allowed to borrow capital and hire labor if the marginal valueproduct was higher than the marginal cost.

The objective function can be specified as either net returns to land oras income above variable cost, depending on the optimization strategy(Paudel et al. [1998]). Net returns to land would be used if a new cropenterprise were considered in the optimization, since the fixed costsof switching to a crop uncommon in the local area can be substantial.Income above variable cost (IAVC) was used in this study because cropresponse simulations were only completed for crops currently grown inthe study area.

Initially, a farm plan was optimized to the current or baseline condi-tion. Based on the climate change scenarios assumed in Hadley, Hot,and Very Hot, optimal crop mix was obtained in each case and theresult was recorded for net income, water use, and crop mix changes.The approach followed here closely resembles the approach followed inthe paper by Hatch et al. [2000].

The mathematical program model used to run simulation can beformulated for each base, as

Max π{cj} =

n∑

j=1

pj cjAj −m∑

i

rixi −n∑

j=1

hjw −n∑

j=1

kjR

subject to


(i) Land Constraints:n∑

j

Aj ≤ L,

n∑

j

Asj ≤ L,

n∑

j

Awj ≤ L,

n∑

j

AIsj ≤ αL,

n∑

j

AIwj ≤ αL,

Aj ≤ βL.

(ii) Capital Constraint:

n∑

j

kjAj −n∑

j

BjAj ≤ 0.

(iii) Labor Constraint:

n∑

j

HjAj −n∑

j

hj ≤ 1500.

Definition of variables and parameters used in the objective functionand constraint equations are provided in Table 1. Available land (L)is divided into summer (As) and winter (Aw ) land because of likelydouble cropping scenario with wheat and soybeans. Additionally, aportion of total land is irrigated land (AI ). We assumed that a singlecrop cannot be planted in more than 50% of total available cropland.This is done to ensure that no single crop is chosen thereby provid-ing the diversity in the final crop mix. An operator is unconstrained


TABLE 1. Parameter and variable definitions used in optimization equations.

Definitions

pj Per unit price of crop output j

cj Output of crop j produced in an acreAj Area under crop j

ri Per unit price of input i

xi Total amount of ith input purchased by farmhj Total hours of hired labor used in j th crop productionw Per hour wage rate of hired laborkj Amount of capital borrowed to produce j th crop outputR Short-term interest rate charged on borrowed capitalL Total land area (200 acres)Asj Area of land devoted to crop j in summerAwj Area of land devoted to crop j in winterAIsj Area of irrigated land devoted to crop j in summerAIwj Area of irrigated land devoted to crop j in winterα Fraction of land area that is irrigated (0 ≤ α ≤ 1)β Fraction of land area dedicated to crop j (0 ≤ β < 1)Bj Borrowed capital repayment for crop j productionHj Operators labor used in crop j productionhj Hired labor used in crop j production

in borrowing capital at the current market interest rate. We used aseasonal short-term 10% per annum interest rate consistent with thecurrent farm credit market. Combined labor used in area A should notexceed 1500 hours (farm couple’s labor supply) plus hired labor hours.This 1500 labor hour constraint is equivalent to two operators workingin the farm full time during crop growing seasons. However, a farmis allowed to hire labor freely from a competitive market at a goingwage rate of $6.50 per hour. We also consider hired labor hours to bea complete substitute of operator labor hours. In the peanut growingareas of the southeast, peanuts are marketed through a quota systemwhich garners higher prices compared to unrestricted peanuts knownas additional peanut production. Quota peanut amount is restricted


to the quantity calculated based on the historical production record,and in our case, a representative farm was restricted to 120,000 poundsin a given year. Prices (pj ) for the crop output used are the averageprice of commodity during the last 10 years (1990–2000). Other thanthe stated constraints, information in the model regarding inputs andprices were obtained from the enterprise budgets developed by Coop-erative Extension Services of each respective state.

3. Data.

3.1. Climate data. Climate data were obtained from one of themost widely used general circulation models/global climate model(GCM’s)—Hadley model (Hadley Center HADCM2, Johns et al.[1997]). The 2030 Hadley model projections for the Southeast indi-cate an increase in maximum summer temperatures of 1.3◦C (2.3◦F)and a maximum winter temperatures increase of 0.6◦C (1.1◦F). Theincreased mean annual temperature of 1◦C (1.8◦F) by 2030 and 2.3◦C(4.1◦F) by 2100 represents a smaller degree of projected warming thanfor any other region of the United States. According to the Hadleyclimate scenario, the Southeast will remain the wettest region of theUnited States for the next 100 years. Mean annual precipitation is pre-dicted to increase slightly (3%) by 2030 and by 20% by 2095; most ofthe increase during the next century is predicted to occur during thesummer months. The precipitation changes predicted for 2090 by theHADCM2 model are consistent with Eastern and Midwestern parts ofthe United States.

Due to large uncertainties associated with the GCM, a sensitivityanalysis with varying temperature, precipitation, and CO2 levels, wasused to consider a range of climate model results for the Southeast. Infact, the discrepancies across climate change, specifically uncertaintyin distribution of future temperature change, has been a topic of muchdiscussion in current literature due to its large impact on economicanalysis related to climate change (Weitzman [2009], Costello et al.[2010]). The discrepancies between the Hadley and Canadian models(Boer and Denis [1997]) are particularly relevant in addressing agricul-tural effects in the Southeast because they differ on whether rainfallwill increase or decrease. CGCM1 indicates less precipitation (20% less


by 2030) whereas HADCM2 simulates more (3% by 2030 and by 20%by 2095). In this regard, the negative effects on unirrigated agricultureare much more dramatic using the Canadian than the Hadley GCM.More detail on the climate data is contained in National Climate As-sessment Reports (National Assessment Synthesis Team, US GlobalChange Research Program [2000], Ritschard et al. [2002]).

Crop responses were obtained through simulations using tempera-ture, precipitation, and solar radiation data for the current climate(1976–1995) and two sensitivity scenarios for three time periods (baseperiods, 2030, and 2090). Daily weather data information is obtainedfrom the VEMAP project developed by Kittel et al. [1997]. Soil charac-teristics utilized included soil texture, bulk density, and water holdingcapacity at different depths for all locations. We considered carbonfertilization effect because buildup of CO2 in atmosphere can increasephotosynthesis, crop growth, and crop output. The CO2 fertilization ef-fect has been well documented in agronomic and economics literature(Adams et al. [1998]). Precipitation, temperature, and CO2 changesunder each of the assumption are shown below.

Scenarios 2030 2090

Hadley Temperature TemperaturePrecipitation PrecipitationCO2 Change CO2 Change

Hot Temperature +1◦C Temperature +1◦CCO2 , 445 PPM CO2 , 680 PPM

Very Hot Temperature +2◦C Temperature +5◦CCO2 , 445 PPM CO2 , 680 PPM

Precipitation, temperature, and solar radiation data from current cli-mate (1976–1995) and Hot and Very Hot were used in crop responsemodels to simulate changes in yield and water use for 10 selected coun-ties in the Southeast. These counties were chosen on the bases of totalacreage devoted to agricultural production and diversity of the cropsproduced. The counties chosen for comparing the impact of climatechange are Houston in Alabama (AL), Poinsett in Arkansas (AR),Suwannee in Florida (FL), Irwin in Georgia (GA), Davies in Kentucky


(KY), Acadia in Louisiana (LA), Bolivar in Mississippi (MS), Duplinin North Carolina (NC), Sumter in South Carolina (SC), and Gibsonin Tennessee (TN) (see Figure 1).

3.2. Production and economics data. Crop price data werecollected from the US Department of Agriculture (USDA)/NationalAgricultural Statistics Service (NASS) website. Seven major rowcrops historically grown in the Southeast—corn, cotton, peanuts,rice, sorghum, soybeans, and wheat—were considered in this analy-sis (USDA/NASS [1997, 1998]) and were assumed to continue theirdominance in the future. Long-term forecasting of crop output pricesbased on present price information would be inaccurate because 100years of recorded price information will not give a reasonably low meansquare error (MSE) for a 90-year forecast. Additionally, given the factthat time-series data for commodity prices were annual, very few datapoints were available to make a reliable prediction for 2090. Therefore,we did not use forecasted prices to calculate the net income in the fu-ture. We used 10-year spot market average prices to optimize croppingdecision for the selected 10 locations and three time periods. Farmmanagement decision making was considered to be representative offarms in that state.

The irrigated crop acreage in each farm was obtained from the per-centage of average irrigated land in that state. The capital availablefor the farm is the average capital amount available to a farm in theregion. Peanut quota was obtained from the peanut quota allocated toan average size farm in each of the peanut growing states. The enter-prise budget provided the variable cost needed for each crop. Laborrequirements for each crop were obtained from the enterprise budget.Ten years of data (1989–1998) on county average yields and farm-levelprices received were extracted from the NASS data. The nominal pricedata were adjusted to constant 1998 dollars using the GDP implicitprice index.

The impact of climate change on crop yield was obtained using theCROPGRO and the CERES models in DSSAT V 3.5 (Tsuji et al.[1998]). The management practices adopted for each of the selectedcrops were obtained from the respective state Cooperative ExtensionService. Estimated irrigated yield for each crop in each county was de-veloped by taking the percentage differences between dry and irrigated


yield from county-specific simulation data and applying the percentageto the base dry land yield obtained from the NASS. Once the dry andirrigated yields for each crop in each county were established, these be-came the base year yields for the sensitivity scenarios: Hadley, Hot, andVery Hot scenarios. The base yields were extended into 2030 and 2090yields for the Hadley scenario by calculating the percentage changesfrom the county-level agronomic simulation of the Hadley climate sce-narios and applying these percentages to the base data. This approachwas deemed necessary for economic estimation because the agronomicsimulations need to be validated in a real-world scenario.

4. Results. Optimization models were ran for three climate sce-narios and three different time periods (base period, 2030, and 2090).The overall results are presented in terms of crop mix, net income,and water use. Table 2 shows how crop acreage and crop mix changedby 2030 and 2090 in comparison with the base level. Table 3 showsthe result in terms of net income. In Table 4, we show how water-usepattern changes under different climate change scenarios. Comparisonswere made against the base scenario on what happens under each ofthe assumed climate change scenarios.

The optimization model result indicated the highest base-level netincome for a representative farm was in AR, whereas the lowest base-level net income was for the farm in NC. Our results show that threestates, namely AR, FL, and NC had higher returns than the base levelin all scenarios. States suffering significantly under the Very Hot sce-nario were KY, AL, and LA. In other states, 2090 turned out to bedetrimental, specifically if the Very Hot scenario prevailed.

A representative farm in KY showed the most serious impact fromclimate change. In the Very Hot 2090 scenario, the net income wasonly 64% of the base-level return. NC had the most positive impact ofclimate change as indicated by as much as 129% greater net incomethan the base level if the temperature/rainfall of 2090 Hot scenario wasto prevail.

Water use was predicted to be the highest in AR during the baseyear because of the substantial land area under rice production. Thehighest amount of water use occurred under the Very Hot scenario inAR during 2090. The lowest amount of irrigation water use was in NC.

GLOBAL WARMING AND AGRICULTURE 467T

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me

(US$

)of

the

repr

esen

tati

vefa

rm.

AL

AR

FL

GA

KY

LA

MS

NC

SCT

N

Bas

e20

,810

36,2

0430

,364

35,3

9420

,595

18,5

0339

,461

9121

27,6

9521

,793

Had

ley,

2030

27,2

2035

,391

33,9

7845

,848

20,4

0917

,426

47,0

5312

,077

46,8

3926

,445

Had

ley,

2090

32,1

3645

,932

37,9

6555

,734

31,5

8816

,637

55,9

0517

,948

63,0

7129

,243

Hot

,20

3027

,057

38,4

8138

,566

44,6

6522

,853

21,6

1548

,365

13,5

0535

,172

25,2

78H

ot,20

9033

,778

46,4

7148

,706

54,6

6327

,686

24,3

6448

,853

20,9

0843

,924

31,2

42V

ery

Hot

,20

3018

,240

37,7

0334

,213

37,2

7019

,007

17,5

0444

,838

11,5

8629

,523

22,7

09V

ery

Hot

,20

9014

,191

39,8

6133

,267

32,8

1813

,288

17,7

4648

,113

10,5

1021

,553

18,8

57


TABLE 4. Total water use in the representative farm (inches-acre).

AL AR FL GA KY LA MS NC SC TN

Base 99 1271 314 229 55 386 852 26 75 45Hadley, 2030 90 1176 373 217 54 445 777 40 52 43Hadley, 2090 66 812 355 177 28 663 697 15 193 29Hot, 2030 142 1328 189 112 61 339 857 54 68 48Hot, 2090 147 1306 212 259 62 356 913 72 74 48Very Hot, 2030 150 1390 232 138 68 365 947 85 77 54Very Hot, 2090 223 1751 236 445 92 525 1277 69 113 71Maximum 223 1751 373 445 92 663 1277 85 193 71Minimum 66 812 189 112 28 339 697 15 52 29Maximum to

base ratio2.25 1.38 1.19 1.94 1.67 1.72 1.5 3.27 2.57 1.58

Minimum tobase ratio

0.67 0.64 0.6 0.49 0.51 0.88 0.82 0.58 0.69 0.64

The maximum ratio to base-level water use was 2.57 in SC under the2090 Hadley scenario. The lowest amount of water use to level was inGA for the Hot 2030 scenario.

4.1. Major findings by state. Only major items are highlightedin the state-specific results described below. More details about cropmix changes are presented in Table 2.

4.1.1. AL.

Crop mix: The optimal crop mix during the base level comprisedcorn, peanuts, soybeans, and irrigated cotton. Hadley 2030 and 2090outcomes in terms of crop mixes were similar to Hot 2030 and 2090optimal solutions. In comparison to the base-level scenario, corn disap-peared and cotton appeared in the crop mix in these years. In the VeryHot scenario, peanuts, rather than cotton, were planted as an irrigatedcrop.

Net income: Income of the representative farm increased aboutthe same amount in both 2030 and 2090 under the Hadley and Hot


scenarios. Net income decreased compared to the base level in both2030 and 2090 in the Very Hot scenario. The net income decrease wasas much as 30% in the Very Hot scenario in 2090.

Water use: Water use declined in the Hadley scenario compared tothe base level. Water use under the Hot Scenario increased by as muchas 47% compared to the base-level water use. The water use in the VeryHot scenario is almost double that of the base level and is the highestamong all other scenarios.

4.1.2. AR.

Crop mix: AR is a primarily rice growing state in the Southeast.Corn, wheat, irrigated soybean, and irrigated rice were the optimalcrop mix chosen in the base level. Crop mix did not change comparedto the base level under both the Hadley and Hot scenarios in 2030 and2090. Crop mix changed in the Very Hot scenario in 2030 and 2090,with the most obvious change being the selection of sorghum and theomission of corn in the optimal crop mix.

Net income: Almost all scenarios showed income increases com-pared to the base-level net income. Net income was the highest inthe Hot scenario in both 2030 and 2090.

Water use: Water use declined in the Hadley scenario in 2030 and2090. Water use increased substantially in the Very Hot scenario (38%)and slightly in Hot scenario (3%).

4.1.3. FL.

Crop mix: The optimal crop mix during the base level comprisedcorn, peanuts, and irrigated cotton. During 2090, the crop mix changesslightly from the base level under the Hadley and Hot scenarios. Cropmix changed substantially in 2090 under the Very Hot scenario, withpeanuts dominating the optimal crop mix.

Net income: Income increased from the base level under the Hadleyscenario and it showed an increasing trend in both reference years (2030and 2090). Although income in the Very Hot scenario was higher thanthe base level, the overall lowest income among all three scenarios wasduring 2090.


Water use: Under the Hadley scenario, water use increased slightly,but in Hot and Very Hot scenarios water use was substantially lowerthan the base level and Hadley scenarios. The lower water requirementwas due to a reduction in the cotton planted area that was replaced bypeanuts.

4.1.4. GA.

Crop mix: The optimal crop mix during the base level comprisedcotton (both dryland and irrigated), peanuts, and soybeans. Crop mixstayed the same during 2030 and 2090 as the base level except in theVery Hot scenario in which corn was included among the optimal cropmix.

Net income: Net income was higher than base level in all scenariosexcept for the Very Hot scenario in 2090. Net income decreased by 7%under the Very Hot scenario compared to the base scenario.

Water use: Water use declined under the Hadley scenario. Water useincreased in year 2090 in both Hot and Very Hot scenarios. Water usewent up substantially under the Very Hot scenario in 2090 comparedto the base level (94% increase).

4.1.5. KY.

Crop mix: Crop mix remained the same across all years and allscenarios. The base period optimal crop mix included corn, sorghum,soybeans, wheat, and irrigated soybeans.

Net income: Net income slightly decreased in 2030 but went upagain in 2090 compared with the base level in both Hadley and Hotscenarios. Net income declined in the Very Hot scenario by as much as45% than the base level.

Water use: Water use declined under the Hadley scenario, but in-creased in Hot and Very Hot scenarios. Water use was found to bethe highest for the Very Hot scenario in 2090 which showed 67% morewater use than during the base level.


4.1.6. LA.

Crop mix: The optimal crop mix during the base level comprisedcorn, sorghum, wheat, rice, and irrigated soybeans. The 2090 resultsshowed changed in crop mix under both Hadley and Hot scenarios.Under the Very Hot scenario, rice, an important crop in LA, was com-pletely out of the optimal crop mix.

Net income: Net income declined in the Hadley and Very Hot sce-narios. However, the Hadley scenario turned out to be more detrimen-tal than the Very Hot scenario. Net income increased under the Hotscenario in all reference years when compared to the base level.

Water use: Water use decreased under the Hadley scenario, wentup in the Hot scenario over the base level and substantially increasedin the Very Hot scenario (more than 125% from the base level).

4.1.7. MS.

Crop mix: The optimal crop mix during the base level comprisedcorn, sorghum, wheat, rice, irrigated cotton, and irrigated soybeans.The Hadley scenario had a different crop mix than the base level. Otherscenarios had similar crop mix as the base level.

Net income: Net income showed an increasing trend in all scenarioscompared to the base scenario. Highest net income was found in theHadley 2090 scenario.

Water use: Water use declined in the Hadley scenario but increasedin the Hot and Very Hot scenarios. Water use increased by 50% fromthe base level in the Very Hot 2090 scenario.

4.1.8. NC.

Crop mix: The optimal crop mix during the base level comprisedcotton, sorghum, soybeans, wheat, and irrigated sorghum. Sorghum,which was present in the base period, is absent from all crop mix exceptin the Very Hot 2030 scenario. Irrigated sorghum, which was present


in the base period, is absent from all crop mix except in the Hot 2030scenario. Hot and Hadley scenarios have different crop mix than thebase year. The Very Hot scenario has a similar crop mix as the baselevel, although sorghum was missing from the optimal crop mix.

Net income: Net income went up in all scenarios compared to thebase with a positive trend under the Hadley and Hot scenarios. Highestincome was observed under the Hot 2090 scenario.

Water use: Water use increased in the Hot and Very Hot scenarioscompared to the base level. Even in the Hadley scenario, water useincreased in 2030 but declined in 2090 by 43% from the base level.

4.1.9. SC.

Crop mix: The optimal crop mix during the base level comprisedcorn, cotton, peanuts (both dryland and irrigated), and soybeans.Hadley 2090 and Hot 2030 were projected as having different cropmix (no corn) than the base level. The Very Hot scenario has the samecrop mix as the base year although crops such as soybeans disappearedfrom the crop mix in 2090.

Net income: Net income increased in both the Hadley and Hot sce-narios compared to the base level. Hadley scenarios generated betterincome compared to other scenarios. Net income was lowest under the2090 Very Hot scenario.

Water use: Water use declined in the Hot scenario. The highestamount of water use was found under the Hadley scenario in 2090.Water use was 157% more than the base-level use.

4.1.10. TN.

Crop mix: The optimal crop mix during the base level comprisedcorn, cotton, sorghum, soybeans (both dryland and irrigated) andwheat. Crop area changed from the base level but the crop mix wasalmost the same in all years. Sorghum was not in the optimal crop mixunder the Hot and Very Hot scenarios, although it was in the optimalsolution during the base level.


Net income: The highest net income was found under the 2090 Hotscenario and the lowest net income was found under the 2090 VeryHot scenario. Net income went up in the Hadley and Hot scenarios butdecreased by 13% in the 2090 Very Hot scenario from the base level.

Water use: Water use declined under the Hadley scenario, increasedmoderately under the Hot scenario and increased substantially underthe Very Hot scenario. Water use was the highest under the 2090 VeryHot scenario (58% more than the base level).

5. Conclusions. Our results indicated that global warming’s im-pact on Southeastern US agriculture is very sensitive to the alternativescenarios generated by GCM models. Most likely climate simulationsindicate only minor negative impacts and if higher rainfall is combinedwith higher temperatures, could even have positive effects. If, however,warming is in the higher range of the GCM results and rainfall doesnot increase, negative effects could be substantial. In particular, un-der these negative scenarios, frequent periods of deficit moisture wouldplace a premium on drought management skills and drought tolerantvarieties. Due to substantial uncertainties associated with the GCM,sensitivity analysis on temperature, precipitation, and CO2 levels wascrucial to this analysis. In general, if precipitation increases, SouthernUS farmers will benefit, unless temperatures rise into the upper rangesof the GCM simulations.

Model results showed that crop mix would change substantially iftemperature change is at the higher ranges suggested by GCM models.The overall prognosis of the results is that climate change would impactthe crop mix over time. Crop mix change would generally have posi-tive impact on net income except when the prevailing climate changescenario is Very Hot. If the Very Hot scenario prevails, the demand forwater would go up substantially in many of the states considered inthe study.

In the Southeast, the change in crop mix is beneficial mainly due toa CO2 fertilization effect and greater than usual amount of precipita-tion. The results can be easily extrapolated beyond the farm level asfarmers are likely to adjust similarly. Availability of water for irrigationis a major uncertainty in assessing climate change impacts on agricul-ture. Crop production areas that share watersheds with rapidly growing


urban areas will most likely not be able to mitigate climate effects withincreasing irrigation.

Our results reinforce the conclusion that the sensitivity of theSoutheastern US agriculture sector to temperature increases dependlargely on accompanying changes in precipitation, the extent of warm-ing, availability of water for irrigation, and relative impact on compet-ing crop growing areas. If warming is moderate and also brings a con-siderable increase in precipitation, as indicated by the Hadley model,effects on yields, water use, and income will be mostly benign. How-ever, the relative impact on competing crop growing areas may mitigatethese apparently beneficial local effects, to the extent that SoutheasternUS production represents a substantial share, resulting in reductions inprices as supply of these crops increases in world markets. If warmingis moderate and without increased precipitation, effects on the agri-cultural sector are still mostly beneficial, provided that the water forirrigation is available. In this case, the relative effects among competingcrop growing areas in terms of yield and access to water will determinethe winners and losers. The remaining possibility, that warming is notmoderate and no increased precipitation materializes, could have dra-matic negative consequences for row crop agriculture in the Southeast.These negative consequences would take the form of declining yields,declining income, and increased dependence on a water supply thatmay be restricted based on growing urban demand.

The likely adaptation to increasing temperature and decreasing pre-cipitation is a move toward more drought-tolerant crops. In terms ofthe crops generally available and well-known in the Southeast, sorghumis the most likely crop to fill this need. Another adaptation mechanismmay be to facilitate incorporating weather forecasts in crop planting de-cisions. Farmer-friendly information from the US Drought Monitor andits dissemination to farmers by state Cooperative Extension Servicesmay help in choosing optimal crops and making crop area decisions onan annual basis. Public research investments in drought tolerant cropvarieties may help ameliorate the impact of climate change if, in fact,the Very Hot scenario becomes a reality.

Antle and Capalbo [2010] provide some suggestions to overcomingthe adaptation hurdle related to potential negative impact of climatechange in a long run in US agriculture. They suggest: (i) eliminating


farm subsidy programs so that farmers can freely select crop and cropvarieties suitable for their region, (ii) eliminating some forms of dis-aster payment, (iii) implementing flexibility in environmental subsidyprograms such as the conservation reserve program, (iv) intensifyingfood production systems, (v) improving land, water, and forest man-agement and enabling regional cooperation, and (vi) strengthening re-search in critical areas. Patt et al. [2010] add that to be effective inte-grated assessment models should improve the treatment of adaptationby considering uncertainty, nonmarket values, role of extreme events,and information use.

Our view is that the assessment of the potential climate-induced im-pact on the agriculture sector could be improved by addressing two fun-damental limitations of this analysis: more detailed analysis of physicaland economic impacts in other important agricultural regions world-wide (such as by using a computable general equilibrium model andmodels with consideration to temperature change uncertainty) and con-sideration of the possibility that an entirely different farming systemmight develop. Until all major crop-growing areas worldwide have re-ceived comparable scrutiny, overall economic effects cannot be properlyassessed. In addition, new crops and new crop growing areas should beintegrated into the assessment.

Acknowledgments. We thank Shrikanta S. Jagtap, and JimJones for providing simulated crop yield data and Arlen Smith forresearch assistance. We gratefully acknowledge thorough comments byan anonymous reviewer which has substantially improved the qualityof the final paper. Funding for this research was provided by NASAand Cooperative State Research, Education, and Extension Service,US Department of Agriculture, under Agreement No. LAB94068.

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