Changes in the fate of pesticides used in cotton production systems

under potential climate change

Robin A. J. Taylor, Xiuying (Susan) Wang & Thomas J. Gerik

Texas AgriLife Blackland Research & Extension Center, Temple TX

International SWAT ConferencePula, Sardinia, Italy24-25 June 2015

Cotton is One of the Highest Pesticide Use Systems

The following is a sample of acaricides, insecticides & herbicides used on cotton:

2, 4-D (H) Endothall (H) Monocarbamide (H)

Abamectin (I) Ethephon (H) MSMA (H)

Acephate (I) Etoxazole (A) Novaluron (I)

Acetamiprid (I) Fipronil (I) Oxamyl (I)

Aldicarb (I) Flonicamid (I) Paraquat (H)

Bacillus aureus (I) Flumioxazin (H) Pendimethalin (H)

Bifenthrin (I) Fomesafen (H) Profenofos (I)

Carfentrazone-ethyl (H) Glyphosate (H) Pyraflufen-ethyl (H)

Chlorpyrifos (I) Glufosinate (H) Pyrithiobac-sodium (H)

Cyclanilide (PGR) Imidacloprid (I) S-Metolachlor (H)

Cyfluthrin (I) Indoxacarb (I) Sodium chlorate (H)

Cypermethrin (I) Lambda-cyhalothrin (I) Thiamethoxam (I)

Dicamba (H) Malathion (I) Thidiazuron (PGR)

Dicofol (A) Mepiquat chloride (PGR) Tribufos (I)

Dicrotophos (I) Methyl parathion (I) Trifluralin (H)

Diuron (H) Metolachlor (H) Zeta-cypermethrin (I)

Evaluate the potential environmental impact of cotton production systems

under climate change:

Develop EPIC simulations for four cotton production regions using data

collected by and in collaboration with Cotton Incorporated staff.

A representative area was chosen from each region

Three representative soils were chosen for each area

Objectives

Data type Source

Soils Soils_5 database, National Soil Survey Laboratory

Crop Management USDA-Economic Research Service, Natural Resource Survey Regional Summary, USDA Ag Census, Farm and Ranch Survey, and numerous regional cotton specialists and producers.

Weather (precipitation and temperature)

Geophysical Fluid Dynamics Laboratory’s CM2 climate model under the pessimistic SRES-A2 emissions scenario

Pesticide properties USDA-NRCS/UMass Extension pesticide properties database (Plotkin et al., 2012)

Plotkin, S., J. K. Bagdon, and E. S. Hesketh. 2012. USDA-NRCS/UMass Extension pesticide

properties database. Amherst, Mass.: USDA Natural Resources Conservation Service. Available at:

www.nrcs.usda.gov/wps/portal/nrcs/detailfull/national/landuse/crops/npm/?cid=stelprdb1044769

Input Data Source

West(California)

Southwest(Texas)

Mid-South(Arkansas)

Southeast(Georgia)

Alamoclay

(BD=1.54)

Randallclay

(BD=1.45)

Alligatorclay

(BD=1.40)

Alvisoclay loam (BD=1.42)

Mercedsilty loam (BD=1.57)

Acuffloam

(BD=1.50)

Commercesilty loam (BD=1.48)

Izagorasilty sand (BD=1.55)

Atwatersand

(BD=1.45)

Amarillofine sandy loam

(BD=1.34)

Crevassesandy loam (BD=1.42)

Orangeburgsandy loam (BD=1.53)

Soil albedo

Hydrologic soil group

Soil attributes by layer: depth

field capacity, wilting point, bulk density

% sand, % silt, % clay, % organic carbon

soil pH

saturated conductivity

water holding capacity

Soil Characteristics

Cotton Management Practices

Location Tillage Irrigation Fertilizer

Keiser, AR

35.7°N, 90.1°W, 70mDisk, Bedder Furrow Irrigated

March: 11.2 kg N/ha

14.7 kg P/haJune: 106.4 kg

N/ha

Merced, CA

37.3°N, 120.5°W, 52m

Heavy disk, Five-bottom plow, Land plane, Lister,

Disk, Bedder

Furrow IrrigatedDec: 11.20 kg N/ha

13.20 kg P/ha

Albany, GA

31.6°N, 84.2°W, 60mNo-till Dryland

March: 11.2 kg N/ha21.5 kg P/ha

June: 82.9 kg N/ha

Lubbock, TX

33.7°N, 101.8W, 992m

Disk, Bedder,Row cultivator

Pivot Irrigated

Dryland

March: 11.8 kg N/ha11.8 kg P/ha

June: 112.6 kg N/ha

March: 11.8 kg N/ha11.8 kg P/ha

June: 44.2 kg N/ha

Comparison of Observed and Projected Climate at Albany, GA from 2001 to 2010

Projected and observed maximum & minimum temperatures are highly correlated but with maximum temperature slightly overestimated; month by month estimates differing by only 10%.

The temporal sequence of projected and observed precipitation are moderately well correlated although the projected and observed total annual precipitation differ by only 10% (projected 1131 mm vs 1258 mm observed).

Observed & Projected Temperatures (C)

y = 1.1997x - 9.1358

R2 = 0.9712

y = 0.9557x + 0.3138

R2 = 0.9852

0

10

20

30

40

0 10 20 30 40

Observed (2001-2010)

Pro

ject

ed

TMAX

TMIN

Observed & Projected Precipitation (mm)

y = 0.8608x + 18.32

R2 = 0.6461

0

50

100

150

200

0 50 100 150 200

Observed (2001-2010)

Pro

ject

ed

PRCP

Monthly Temperatures

-30

-20

-10

0

10

20

30

40

50

60

11

32

53

74

96

17

38

59

71

09

12

11

33

14

51

57

16

91

81

19

32

05

21

72

29

24

12

53

26

52

77

28

93

01

31

33

25

33

73

49

Arkansas Max & Min Temps (C)

2001-10 2021-30 2081-90

-30

-20

-10

0

10

20

30

40

50

11

32

53

74

96

17

38

59

71

09

12

11

33

14

51

57

16

91

81

19

32

05

21

72

29

24

12

53

26

52

77

28

93

01

31

33

25

33

73

49

California Max & Min Temps (C)

2001-10 2021-30 2081-90

-30

-20

-10

0

10

20

30

40

50

60

11

32

53

74

96

17

38

59

71

09

12

11

33

14

51

57

16

91

81

19

32

05

21

72

29

24

12

53

26

52

77

28

93

01

31

33

25

33

73

49

Georgia Max & Min Temps (C)

2001-10 2021-30 2081-90

-30

-20

-10

0

10

20

30

40

50

60

11

32

53

74

96

17

38

59

71

09

12

11

33

14

51

57

16

91

81

19

32

05

21

72

29

24

12

53

26

52

77

28

93

01

31

33

25

33

73

49

Texas Max & Min Temps (C)

2001-10 2021-30 2081-90

Monthly Precipitation

0

50

100

150

200

250

300

1

15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

21

1

22

5

23

9

25

3

26

7

28

1

29

5

30

9

32

3

33

7

35

1

Arkansas Precipitation (mm/month)

2001-10 2021-30 2081-90

0

50

100

150

200

250

300

350

1

15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

21

1

22

5

23

9

25

3

26

7

28

1

29

5

30

9

32

3

33

7

35

1

California Precipitation (mm/month)

2001-10 2021-30 2081-90

0

50

100

150

200

250

300

350

1

15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

21

1

22

5

23

9

25

3

26

7

28

1

29

5

30

9

32

3

33

7

35

1

Georgia Precipitation (mm/month)

2001-10 2021-30 2081-90

0

20

40

60

80

100

120

140

160

180

1

15

29

43

57

71

85

99

11

3

12

7

14

1

15

5

16

9

18

3

19

7

21

1

22

5

23

9

25

3

26

7

28

1

29

5

30

9

32

3

33

7

35

1

Texas Precipitation (mm/month)

2001-10 2021-30 2081-90

Analysis of Pesticide Runoff

• Changes in pesticide surface runoff losses were scored for analysis.

1. Change from the 1st decade (2001-10)to the 3rd decade (2021-30)

2. Change from the 1st decade (2001-10) to the 9th decade (2081-90)

> -50% (halving) = -2 (Major Decrease) -5% to -50% = -1 (Decrease) -5% to +10% = 0 (No Change) +10 to +100% = 1 (Increase) > +100% (doubling) = 2 (Major Increase)

Pesticide Runoff by Soil - Change from Decade 1 to Decade 3

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mid-South (AR) West (CA) Southeast (GA) Southwest (TX)Irrigated Irrigated Dryland Dryland Irrigated

MD

D

NC

I

MI

Pesticide Runoff by Soil - Change from Decade 1 to Decade 9

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mid-South (AR) West (CA) Southeast (GA) Southwest (TX)Irrigated Irrigated Dryland Dryland Irrigated

MD

D

NC

I

MI

Pesticide Runoff by Site - Change from Decade 1 to Decade 3

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mid-South (AR) West (CA) Southeast (GA) Southwest (TX)Irrigated Irrigated Dryland Dryland Irrigated

MD

D

NC

I

MI

Pesticide Runoff by Site - Change from Decade 1 to Decade 9

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mid-South (AR) West (CA) Southeast (GA) Southwest (TX)Irrigated Irrigated Dryland Dryland Irrigated

MD

D

NC

I

MI

Change Scores are unrelated to Chemical Properties• Pesticide fate (surface water and sediment) depends on pesticide

chemical properties, but climate change will not change the relationship.

Variable Correlation

Log[Solubility (ppm)] -0.2243 ns

Log[KOC (Tm3)] 0.1976 ns

Half-life on Foliage (d) 0.0501 ns

Half-life in Soil (d) -0.0551 ns

Wash-off Fraction (%) -0.1972 ns

Factors Affecting Changes in Pesticide Fate - 1

Variable Dryland Irrigated

Wilting Point (m/m) -0.5850 * -0.0877 ns

Field Capacity (m/m) -0.7020 ** -0.1444 ns

Saturated Conductivity (mm/h) 0.5559 * 0.0454 ns

Bulk Density (T/m3) 0.5355 * 0.6628 **

Factors Affecting Changes in Pesticide Fate - 2

Change Scores are related to Soil Properties• Change scores increase with conductivity and decrease with soil water holding capacity in rainfed but not irrigated cotton

• Change scores increase with to bulk density in both rainfed and irrigated cotton

Change Score vs. Bulk Density

• Bulk density is the best predictor of pesticide fate under climate change.

• There’s a small difference between dryland and irrigated.

• May be important as precipitation declines, increasing the need for irrigation to maintain productivity.

Summary

• Pesticide runoff from cotton will change as climate warms – both increases and decreases.

• Runoff changes will not depend on pesticide physico-chemical properties - unless there are temperature-dependencies we are unaware of.

• Soil characteristics will have the biggest influence on changes in pesticide runoff as climate warms - in particular Bulk Density.