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 areaL0 - initial leaf cover areaa - growth rate

DD0 exp

aLL

EDD0 exp

aLL

Photo-thermal model(Effective-degree-day)

L - leaf cover areaL0 - initial lead cover areaa - growth rateEDD- 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

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

Planting date

Fina

l lea

f cov

er a

rea

(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)

Fina

l lea

f cov

er a

rea

(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

cov

er a

rea

(m2)

0 400 800 12000

0.5

1

1.5

2

R² = 0.91834065094133

Physiological time (EDD)

Leaf

cov

er a

rea

(m2)

Thermal

Seasonal growth-models

Photo-thermal

(Growth season: August-September)

0 400 800 12000

0.5

1

1.5

2 R² = 0.900717928168844

Physiological time (EDD)

Leaf

cov

er a

rea

(m2)

Annual growth-modelPhoto-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