Developing a temperature-light based spatial growth model

for purple nutsedge

The 2nd International Conference on: Novel and Sustainable Weed Management in Arid and Semi-

Arid Agro-Ecosystems

Ran lati1,2, Hanan Eizenberg2

, and Sagi Filin1

1 Mapping and Geo-Information, Technion - Israel Institute of

Technology, 2Newe Ya’ar Research Center, ARO

Purple nutsedge (Cyperus rotundus)

•Among world's most troublesome weeds

•High photo-synthetically efficiency (C4 plant)

•Rapid growth during the summer in irrigated

crops

Rapid spatial growth

Biology- vegetative growth

45 DAP14 DAP

Purple nutsedge “patches”

High infestation level

Vegetative spatial-growth model

(Webster, Weed Science, 2005)

Purple nutsedge spatial-growthgaps of knowledge

• Modeling and prediction purple nutsedge spatial

growth

• Quantification the impact of growth factors

• Interaction between growth factors

Objectives

Developing a spatial-growth predictive model

for purple nutsedge

Temperature-radiation based model

Understanding the relative contribution

of temperature and radiation on its growth

Field studies 2008

• Weeds grown under diverse environmental condition

• Wide range of temperature and radiation

• Temperature- weeds were planted at 4 planting

dates: Jun. 08, Jul. 08, Aug. 08, Oct. 08

• Radiation- weeds grown under 4 shading levels:

0%, 20%, 45% and 60%

Actual environmental measurementsTemperature and radiation were continuously logged

Leaf cover area was measured 5 times

Using image data methods

Weed-growth modelsBased on temperature and radiation

Individual plants were grown for 60 daysOne tuber was buried

Field study 2008

)( basemean TTDD

Environmental measurements

TemperaturesData logger [C°]

Photosynthetic active radiation

PARPyranometer

[µmol m-2 s-1]

Tbase- minimal growth temperature (10°C)

Tmean- mean daily temperature

CPAR- daily cumulative PAR

11

1 2

ni i

i

PAR PARCPAR t

Leaf cover area measurements- using image data

Weed-growth models- assumptions

• Annual model is composed of seasonal sub-

models

• Plant's growth is exponentially related to time

under optimal and constant conditions

• Under varying conditions- plant growth is

better described by physiological-time

7:00 12:00 19:00

Thermal model(degree-days)

L - leaf cover area

L0 - initial leaf cover area

a - growth rate

DD0 exp

aLL

EDD0 exp

aLL

Photo-thermal model(Effective-degree-day)

L - leaf cover area

L0 - initial lead cover area

a - growth rate

EDD- effective-degree-days

1 1 1*EDD DD f CPAR f- PAR coefficient

The conductance concept:

Effective-degree-day(EDD)

(Aikman and Scaife, Annals of Botany 1993)

Environmental growth factors

Optimal temperatures for

purple nutsedge growth are

25-35°C

Naamat et al., current conference

18-21°C28-33°C

June 08 July 08 August 08 October 080

0.5

1

1.5

2

0% shading 20% shading 45% shading 60% shading

Planting date

Fin

al le

af

cover

are

a (

m2)

Final leaf cover area (SED=0.0874)

10 15 20 25 30 35 40 45 50 55 600

0.5

1

1.5

2R² = 0.932344240490629

Mean CPAR (Mmol m-2d-1)

Fin

al le

af

cover

are

a (

m2

)

Summary

• Under optimal temperature, purple nutsedge growth

is linearly related to PAR

• Below optimal temperature range, PAR level does not

affect purple nutsedge growth

Predictive growth models

0 400 800 12000

0.5

1

1.5

2

R² = 0.615556974464567

Physiological time (DD)

Leaf

cover

are

a (

m2

)

0 400 800 12000

0.5

1

1.5

2

R² = 0.91834065094133

Physiological time (EDD)

Leaf

cover

are

a (

m2

)

Thermal

Seasonal growth-models

Photo-thermal

(Growth season: August-September)

0 400 800 12000

0.5

1

1.5

2R² = 0.900717928168844

Physiological time (EDD)

Leaf

cover

are

a (

m2

)

Annual growth-model

Photo-thermal

Growth season: June-December

Final conclusions

• Temperature

• Major growth factor required for purple nutsedge

• Insufficient for purple nutsedge spatial growth

prediction

• PAR

• Determines purple nutsedge growth under optimal

temperatures conditions

• Does not affect purple nutsedge growth below

optimal temperature range

• The photo-thermal model

• Successfully integrates temperature and PAR

measurements

• Integration of temperature and PAR improves

the prediction ability of the model

• Enables annual prediction of purple nutsedge

spatial growth

• Accurate under varying temperature and PAR

conditions

Final conclusions

Thanks

• EWRS - for the generous scholarship

• Advisors- Hanan and Sagi

• Tal L., Gay and Evgeny

• Members in the Dept. of Weed Research in Newe

Yaar

• Fellow students- Tal N., Daliya, Shalev , Rim, Fadi

and Amit