George Paul, P. V. Vara Prasad, Scott A. Staggenborg, and Prasanna H.Gowda

Kansas State University; and CPRL – USDA – ARS, Bushland, Texas

Assessing Impact of Climate Change and Variability and on Crop Production in Ogallala Region Using

Regional Climate Data

Introduction

The Ogallala Aquifer Region consisting

of 232 counties spread over 8 states of

Conterminous United States is facing

declining water levels and deteriorating

water quality. Coupled with, is the

climate change, largely affecting the

productivity of crop and water

availability.

Objectives

1. To analyze the climate variability and change across the

Ogallala region

2. To quantify the impact of climate change on the agricultural

production in the Ogallala region

3. Analyze influence of various Crop Management Decision and

Genetic Traits as adaptation / mitigation options for coping with

climate variability and change.

• The three regional climate models (RCM) used in this study

were Canadian RCM, Italian RCM and the Hadley RCM (UK)

• The A2 climate scenario data for historic period (1971-2000) and

future (2041-2070) were acquired from North American Regional

Climate Change Assessment Program (NARCCAP).

• Observed baseline climate data (1971-2000) were used for

validation.

Work flow and Significant features of the study

• Spatial crop modeling was performed in AEGIS/WIN, a GIS

interface available in DSSAT (Decision Support System for

Agrotechnology Transfer) software suite.

• CERES-Sorghum and CERES-Wheat were used to simulate

phenology, growth and grain yield.

• Management, genetic advancements (changes) and effects of

elevated CO2 were incorporated as part of future model runs

and crop simulations were conducted under irrigated and

rainfed conditions.

Work flow and Significant features of the study

Climate Change: Increased temperatures over the

past 30 years

y = 0.0001x + 19.133

0

5

10

15

20

25

30

35

40

Jan

-68

Jul-

69

Jan

-71

Jul-

72

Jan

-74

Jul-

75

Jan

-77

Jul-

78

Jan

-80

Jul-

81

Jan

-83

Jul-

84

Jan

-86

Jul-

87

Jan

-89

Jul-

90

Jan

-92

Jul-

93

Jan

-95

Jul-

96

Jan

-98

Max

imu

m T

em

pe

ratu

re (

C)

Month

y = 0.0002x + 10.582

-5

0

5

10

15

20

25

30

35

Jan

-68

Au

g-6

9

Mar

-71

Oct

-72

May

-74

No

v-7

5

Jun

-77

Jan

-79

Au

g-8

0

Mar

-82

Oct

-83

May

-85

No

v-8

6

Jun

-88

Jan

-90

Au

g-9

1

Mar

-93

Oct

-94

May

-96

No

v-9

7

Max

imu

m T

em

pe

ratu

re (

C)

Month

Goshen , WyomingLubbock, Texas

The A2 climate scenario

0

5

10

15

20

25

30

35

40

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Max

imu

m T

em

pe

ratu

re (

C)

Month

HRMRCMCRMObserved

Climate Change: Model uncertainties and temporal

variability: Lubbock, Texas

0

5

10

15

20

25

30

35

40

45

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Max

imu

m T

em

pe

ratu

re (

C)

Month

HRM

RCM

CRM

Future Predicted Climate Past Predicted and Observed Climate

-10

-5

0

5

10

15

20

25

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Min

imu

m T

em

pe

ratu

re ( C

)

Month

HRM

RCM

CRM

Climate Change: Model uncertainties and temporal

variability : Lubbock, Texas

-10

-5

0

5

10

15

20

1 2 3 4 5 6 7 8 9 10 11 12

Min

imu

m T

em

pe

ratu

re ( C

)Month

HRMRCMCRMObserved

Future Predicted Climate Past Predicted and Observed Climate

0

20

40

60

80

100

120

140

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecP

reci

pit

atio

n (

mm

)

Month

HRMRCMCRMObserved

0

20

40

60

80

100

120

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Pre

cip

itat

ion

(m

m)

Month

HRM

RCM

CRM

Future Predicted Climate Past Predicted and Observed Climate

Climate Change: Model uncertainties and temporal

variability : Lubbock, Texas

Climate Change: Spatial Variability: Max Temp

Change in Maximum Temperature (°C) from Baseline Climate for the month of

July as predicted by HRM

Climate Change: Spatial Variability: Min Temp

Change in Minimum Temperature from Baseline Climate for the month of July

as predicted by HRM

Climate Change: Spatial Variability: Precipitation

Change in Precipitation from Baseline Climate for the month of July as

predicted by HRM

DSSAT – CERES – Wheat Model

Winter Wheat

Soil – Ulysses silt loam

Cultivar – Newton

Plant Population – 1,779,535 plants/ha

Planting Date – October 1

Crop model Inputs

Wheat Irrigated

Wheat Dryland

DSSAT – CERES - Sorghum

Grain Sorghum

Soil – Ulysses silt loam

Cultivar – Pioneer 8333

Plant Population – 160,000 plants/ha

Planting Date – June 1

Crop Model Inputs

Sorghum Irrigated

Sorghum Dryland

Sorghum Dryland, Cultivar change

Short duration to Long duration (30 d increase)

Sorghum Dryland, Planting Date Change

Early Planting (30 d)

Genetic Traits Ranges

Initial

Value

Final

Value

Yield

change

range (%)

Avg. yield

change

(%)

Thermal time grain filling

to maturity (P5) 450-600 540 600 7-10 +8

Partitioning to panicle

(G2) 4.5-6.5 6 6.5 4-7 +6

Phylochron interval

(PHINT) 40-49 49 40 3-7 +5.5

Relative leaf size (G1) 3-22 11 13 1-2 +1

Management

Cultivar duration 90-150 100 130 0-100 +10

Planting date May - July June May 0-100 +10

Carbon dioxide 380 - 660 330 475 - 600 10 - 20 +14

+54

Climate Change (Temp) 4-6 base line 4-6 10-60 -30 to -40

Comprehensive Change – Genetics, Management

and Climate (Grain Sorghum) – 30 years simulations

Conclusions1. Climate models (A2 scenario) suggest increases in

temperature and highly variable rainfall distribution

2. Distinct variations in the three climate models suggestinherent uncertainty in predictions

3. Changes in climate will influence crop productivity

4. Genetics and crop management decisions can help

manage/mitigate yield losses

5. Agricultural production in the Ogallala region would behighly susceptible to the future climates and therefore

requires appropriate mitigation/adaptation strategies

Acknowledgements

Colleagues at Crop Physiology Lab, Kansas State University

North American Regional Climate Change Assessment Program

(NARCCAP) for providing the data

This research was supported in part by the Ogallala Aquifer Program,

a consortium between USDA-Agricultural Research Service, Kansas

State University, Texas AgriLife Research, Texas AgriLife Extension

Service,TexasTech University andWestTexas A&M University.

QUESTIONS ?????????