Vol. 51—1997 - NorthEastern Weed Science Society

89
Weed See
Germination Pattern and its Implication in Weed Prediction Under
Changing Temperatures
GUANGYONGZOU and RICHARDA. ASHLEYI
ABSTRACT
Experimen was conducted to determine the suitability of using a nonlinear poikilotherm rate
equation t describe the relationship between germination rate and temperature, and a
Weibull fu dion to fit the cumulative seed germination for three annual weed species.
Temperatu es ranges from 10 to 340C were evaluated at 30C intervals. The parameters
estimated m this stUdy are applicable to models to predict weed emergence patterns.
Coupling th models to a simulation model for weed prediction could improve the accuracy
because it avoids the drawbacks of the degree-day approach from both biophysical and
statistical spects. Nomenclature: redroot pigweed (Amaranthus retroflexus L #2AMARE),
lambsqua rs (Chenopodium album L. #CHEAL), and large crabgrass (Digitaria sanguinalis
(L) Scop. DIGSA).
Additional ndex words. Seed germination, temperature, poikilotherm, simulation, Weibull
function.
INTRODUCTION
Timin is the core of any integrated pest management (IPM) program. Since the term
"IPM" was fi t coined in the 1960's, there has been a plethora of studies on pest forecast
and predictio (2) with the intention of scheduling timely pest control measures.
As op osed to the epidemic nature of other pests, weeds are relatively constant and
sessile. This robably delayed the recognition of the importance of weed prediction. The
body of kno edge in weed prediction is not as mature as in other pest predictions.
Increasing en ironmental and regulatory concerns have become stimuli to increase weed
control effici ncy and reduce herbicide usage. Reliable weed prediction is becoming
paramount in many integrated weed management programs (7). Specifically, weed
emergence p rn prediction can aid producers in selecting the appropriate crop and control
practices, an can suggest optimum planting dates to maximize crop competitiveness over
weeds. Studi s have suggested that weed prediction could decrease the risk of weed control
failure in her icide reduced-rate application programs (12, 13).
Nayl r (11) is among the first researchers to predict weed infestation by using a
predictive in ex derived from the number of weed seeds present in the soil. More recent
works based n the similar idea include: Wilson et al, (17), ForceUa (6) and Mallet (10). All
oftheir work did contribute information for weed management, but they exclude time vector,
which limits he applicability, especially given that weed emergence in the field is sporadic.
The damage caused by the same absolute weed density with different emergence patterns
are quite di rent (3).
1 Graduate assi tant and Professor, Departmentof Plant Science,Universityof Connecticut, Storrs, CT 06269.
2 Letters folio ing this symbol are a WSSA approved computercode from CompositeList of Weeds. Revised
1989.Availabl from WSSA, 1508 W. Univ. Ave., Champaign,IL 61821-3133.