Growth Analysis of Atrazine-Resistant and Atrazine-Sensitive Biotypes of Chenopodium album

Seed from atrazine-sensitive and atrazine-resistant biotypesof lambsquarters (Chenopodium album L.) was sown in pots containingquartz sand. Plants were grown under a controlled environmentin a growth room. On the 15th day after planting, four randomly-selectedplants of each biotype were harvested, and both fresh and dryweights of leaves, stems, roots and whole plants, together withleaf area, were determined for each plant. This was repeatedat 5-day intervals until the 60th day after sowing. Data from the resulting replicated 2 x 10 factorial configurationwere analysed using BMDP multiple regression programmes andorthogonal polynomials to produce best fit polynomial expressionsof time-to-harvest and biotype for the natural logarithm ofeach response. From these empirical models of plant growth,predictor functions for relative growth rates, leaf area andweight ratios, specific leaf area and unit leaf rate were generated. Examination of computer-generated plots of the various growthindices suggested that time-of-harvest was the most importantfactor in determining the magnitude of the responses. However,the two biotypes exhibited different growth patterns as indicatedby the presence of a significant biotype effect or biotype xtime interaction in all cases. The faster-maturing atrazine-sensitiveplants tended to have fairly dense leaves with relatively smallarea whereas the atrazine-resistant plants initially producedsmall root systems and relatively low density leaves. Moreover,the resistant biotype, despite being lighter at 2 weeks, weighedthe same as the sensitive plants by the 60th day due to a consistentlyhigher relative growth rate. The advantages of using balancedor orthogonal configurations together with multiple regressionprocedures to derive data-based empirical models of plant growthare discussed. Limitations to the routine use of empirical modelsare also considered. Chenopodium album L., lambsquarters, atrazine resistance, growth analysis, orthogonal polynomials, multiple regression analysis