Marín-Benito, J.M.1*; Pot, V.1; Alletto, L.2; Mamy, L.1,3; Bedos, C.1; Barriuso, E.1; Benoit, P.1

1 INRA, AgroParisTech, UMR 1091 EGC, 78850 Thiverval-Grignon, France 2 Université de Toulouse – INPT-École d’ingénieurs de Purpan, UMR 1248 AGIR, 31076 Toulouse, France

3 INRA, UR 251 PESSAC, 78026 Versailles, France * Corresponding author: E-mail address: jmarin@grignon.inra.fr

MATERIALS AND METHODS

RESULTS

ACKNOWLEDGEMENTS: JM Marin-Benito thanks the ANR Systerra for the financial support of his first post-doctoral contract, and the financial support of ONEMA through the public tender 2011 of the MEDDE programme « Evaluation et réduction des risques liés à l’utilisation des pesticides » as a support of the application of the third axis of the French Plan Ecophyto 2018 for his second post-doctoral contract. The authors thank Simon Giuliano for his collaboration with the field data.

REFERENCES: Carsel RF, Imhoff JC, Hummel PR, Cheplick JM, Donigian Jr AS, 1998. PRZM-3: a model for predicting pesticide and nitrogen fate in the crop root and unsaturated soil zones: users manual for release 3.12. National Exposure Research Laboratory, Office of Research and Development, USEPA, Athens, GA

Larsbo M, Jarvis NJ (2003). MACRO 5.0. A model of water flow and solute transport in macroporous soil. Technical description. Rep Emergo 2003:6, Swedish University of Agricultural Sciences, Uppsala, Sweden, 49 pp.

Leistra M, van der Linden AMA, Boesten JJTI, Tiktak A, van den Berg F (2001). PEARL model for pesticide behaviour and emissions in soil-plant systems: description of the processes. Alterra Rep 13, Wageningen University and Research Centre, Wageningen, The Netherlands, 115 pp.

FOOTPRINT (2013). The FOOTPRINT Pesticide Properties Database. University of Hertfordshire. Available at http://sitem.herts.ac.uk/aeru/footprint/es/index.htm

Nash JE, Sutcliffe JV (1970). River flow forecasting through conceptual models. Part I: a discussion of principles. J. Hydrol. 10:282-290.

Simulations were based on field experimentations set up in Toulouse area (France).

MONITORED HERBICIDE

S-metolachlor (water solubility=480 mg/L (20ºC); log Kow=3.05)

Application: annual at 1.25 (2011) and 1.52 kg a.i./ha (2012). Before this experiment, it was never applied over the experimental plots.

FIELD SITE

FIELD INSTRUMENTATION

Temperature, water content and soil pressure head were monitored at 20, 50 and 100 cm depth.

Water flow measurements and quantification of pesticide leaching were carried out with tension plate lysimeters installed at 100 cm depth.

• Temperature sensors (T) • TDR probes (water content) • Tensiometers (soil pressure head)

20 cm

50 cm

100 cm Tension plate lysimeters (water and herbicide leaching)

EVALUATION OF MODEL PERFORMANCE

The goodness of fit between observed and simulated values was evaluated with the efficiency coefficient (EF) proposed by Nash and Sutcliffe (1970).

MACRO and PEARL simulations showed similar water flow dynamics for the whole period and simulated quite well the soil water content in the soil profile whilst PRZM did not do it properly. The soil water content at 20 cm depth was slightly underestimated by MACRO and PEARL during the maize flowering period and slightly overestimated at 50 cm depth before this period.

The three models overestimated the total water volume leachate at 1m depth by factors of 1.2 (PEARL), 2.1 (MACRO) and 3.7 (PRZM). MACRO simulated very few macropore flow.

SMOC was observed and quantified in the water leachate at 1m depth 404 days after the first application date. However, the models predicted that after 621 days SMOC still had not reached that depth. Further work will be done with the macropore module of MACRO to improve the simulated SMOC concentration at the lysimeter depth.

The soil temperature was properly simulated by the three models.

These results showed the complexity in parameterizing the water transfer models to describe given experimental conditions and the need to calibrate the models to improve the fit of observed data. Further simulations will take into account the cropping system including a cover crop of oat, vetch and Phacelia (see also Alletto et al., in this conference, and the project website http://www6.inra.fr/micmac-design).

Depth (cm)

Clay (%)

Silt (%)

Sand (%)

OM (%)

BD (kg/m3)

0-10 32.2 45.2 22.6 2.38 1500

10-30 34.6 42.8 22.6 1.85 1500

30-60 35.5 44.0 20.5 1.63 1560

60-100 43.8 39.4 16.8 1.23 1630

100-200 33.9 22.1 44.0 1.23 1630

Ɵr (m3/m3)

Ɵs (m3/m3)

α (cm-1)

n (-)

Ksat (m/d)

Kb (m/d)

Ɵb (m3/m3)

ASCALE (m)

ƟFC (pF=2)

(m3/m3) ƟWP

(m3/m3)

0.080 0.414 0.010 1.464 0.528 0.014 0.411 0.020 0.352 0.114

0.082 0.419 0.011 1.434 1.560 0.024 0.415 0.020 0.352 0.120

0.081 0.406 0.011 1.409 0.240 0.034 0.402 0.015 0.343 0.122

0.084 0.399 0.014 1.307 0.024 0.007 0.394 0.015 0.338 0.146

0.071 0.381 0.020 1.258 0.240 0.026 0.373 0.030 0.312 0.142

SOIL PHYSICOCHEMICAL and HYDRAULIC CHARACTERISTICS

MODELS

PRZM 3.12 (Carsel et al., 1998)

PEARL 4.4.4 (Leistra et al., 2001)

MACRO 5.2 (Larsbo & Jarvis, 2003)

MODEL PARAMETERIZATION

Laboratory measurements

Field measurements

Rosetta´s pedotransfer functions

FOOTPRINT DataBase (2013)

INTRODUCTION OBJECTIVE The current challenge in sustainable agriculture is to introduce new agricultural cropping systems that ensure a safe food supply and avoid negative environmental impacts due, in particular, to pesticide inputs. Designing innovative cropping systems with low-pesticide inputs also implies to assess their environmental performance by quantifying the reduction of pesticide fluxes out of agricultural fields. Such assessment can be achieved by long-term field experiments and/or by model simulations.

The objective of this work was to compare the ability of three pesticide fate models to describe the behavior of water and S-metolachlor (SMOC) (one of the most used herbicide for maize) as observed under field conditions in two cropping systems: (1) a conventional maize monoculture system, and (2) an innovating maize cropping system including a cover crop (oat-vetch-Phacelia) during the fallow period and mulch residues after cover crop destruction.

SIMULATED SMOC CONCENTRATION IN THE LIQUID PHASE AT 20, 50 AND 100 cm DEPTH

0

5

10

15

20

25

30

SM

OC

co

nc

en

tra

tio

n (

µg

/L) 20 cm PRZM PEARL MACRO

1st SMOC

application

2nd SMOC

application

Date

0.00

0.05

0.10

0.15

0.20

0.25

0.30

SM

OC

co

nc

en

tra

tio

n (

µg

/L) 50 cm

0.0E+00

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

2/5/10 10/8/10 18/11/10 26/2/11 6/6/11 14/9/11 23/12/11 1/4/12 10/7/12 18/10/12 26/1/13

SM

OC

co

nc

en

tra

tio

n (

µg

/L) 100 cm

621 days

after 1st

applic.

DISCUSSION AND CONCLUSIONS

FIRST TESTED SCENARIO Focused on the conventional maize monocropping

system including two irrigated cropping periods with a fallow period managed with bare soil.

Tillage: 30 cm depth (1 month before the maize sowing) and 8 cm depth to sow the maize.

0

10

20

30

40

50

60

70

80

18

/4/1

1

13

/6/1

1

21

/6/1

1

25

/7/1

1

19

/9/1

1

17

/10

/11

14

/11

/11

16

/1/1

2

1/2

/12

7/3

/12

6/4

/12

26

/4/1

2

22

/5/1

2

12

/6/1

2

16

/7/1

2

7/8

/12

29

/10

/12

5/1

2/1

2

17

/12

/12

Wa

ter

dra

ina

ge

(m

m)

Date

Observed PRZM PEARL MACRO

OBSERVED AND SIMULATED WATER DRAINAGE AT 100 cm DEPTH

OBSERVED AND SIMULATED SMOC CONCENTRATION (µg/L) IN THE

LYSIMETER AT 100 cm DEPTH

Date Observed PRZM PEARL MACRO

12/06/12 0.23 8.3E-09 0 0

16/07/12 0.15 2.5E-08 0 0

05/12/12 0.17 1.7E-06 8.8E-10 1.1E-08

Irrigation

MAIZE

MAIZE MAIZE

bare soil

bare soil

bare soil

Irrigation

Irrigation

Ministère de l’Agriculture, de l’Agroalimentaire et de la forêt

Ministère de l’Ecologie, du Développement durable et de

l’Energie

-10

-5

0

5

10

15

20

25

30

T (

ºC)

20 cm Observed PRZM PEARL MACRO

-5

0

5

10

15

20

25

30

T (

ºC)

50 cm

0

5

10

15

20

25

30

6/6/11 20/8/11 3/11/11 17/1/12 1/4/12 15/6/12 29/8/12 12/11/12

T (

ºC)

Date

100 cm

OBSERVED AND SIMULATED SOIL TEMPERATURE AT 20, 50 AND 100 cm DEPTH

EFPRZM =0.92

EFPEARL =0.92

EFMACRO =0.92

EFPRZM =0.90

EFPEARL =0.78

EFMACRO =0.91

EFPRZM =0.90

EFPEARL =0.57

EFMACRO =0.95

OBSERVED AND SIMULATED SOIL WATER CONTENT AT 20, 50 AND 100 cm DEPTH

0.0

0.1

0.2

0.3

0.4

0.5

Wa

ter

co

nte

nt

(cm

3/c

m3)

20 cm Observed PRZM PEARL MACRO

0.0

0.1

0.2

0.3

0.4

0.5

Wa

ter

co

nte

nt

(cm

3/c

m3)

50 cm

0.0

0.1

0.2

0.3

0.4

0.5

2/5/10 10/8/10 18/11/10 26/2/11 6/6/11 14/9/11 23/12/11 1/4/12 10/7/12 18/10/12 26/1/13

Wa

ter

co

nte

nt

(cm

3/c

m3)

Date

100 cm

EFPRZM =-5.24; EFPEARL=-0.13; EFMACRO =-0.17

EFPRZM =-1.89; EFPEARL=0.17; EFMACRO =0.36

EFPRZM =-0.09; EFPEARL=-0.08; EFMACRO =0.12