Determining the critical period for grass control in high-yielding cotton using Japanese millet as a mimic weed

Abstract

Field studies were conducted over five seasons from 2004 to 2015 to determine the critical period for weed control (CPWC) in high-yielding, irrigated cotton using a competitive mimic grass weed, Japanese millet. Japanese millet was planted with or after cotton emergence at densities of 10, 20, 50, 100, and 200 plants m−2. Japanese millet was added and removed at approximately 0, 150, 300, 450, 600, 750, and 900 degree days of crop growth (GDD). Data were combined over years. Japanese millet competed strongly with cotton, with season-long interference resulting in an 84% reduction in cotton yield with 200 Japanese millet plants m−2. The data were fit to extended Gompertz and logistic curves including weed density as a covariate, allowing a dynamic CPWC to be estimated for densities of 10 to 200 Japanese millet plants m−2. Using a 1% yield-loss threshold, the CPWC commenced at 65 GDD, corresponding to 0 to 7 d after crop emergence (DAE), and ended at 803 GDD, 76 to 98 DAE with 10 Japanese millet plants m−2, and 975 GDD, 90 to 115 DAE with 200 Japanese millet plants m−2. These results highlight the high level of weed control required throughout the cropping season in high-yielding cotton to ensure crop losses do not exceed the cost of weed control.

Developing a multispecies weed competition model for high-yielding cotton

“…The data sets used by Charles et al (2019bCharles et al ( , 2020aCharles et al ( , 2020b were analyzed using R statistical software, version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) with a significance level of P < 0.05. Regression analysis was used to test the relationships between the relative cotton-lint yield (i.e., lint yield relative to the weed-free control in each season); and weed density, biomass, and height, using the coefficient of determination (r 2 ) to assess the fit of each model.…”

“…Regression analysis was used to test the relationships between the relative cotton-lint yield (i.e., lint yield relative to the weed-free control in each season); and weed density, biomass, and height, using the coefficient of determination (r 2 ) to assess the fit of each model. Data were fit to Gompertz, logistic and exponential functions as described by Charles et al (2019bCharles et al ( , 2020b, with the exponential function substituted for the logistic function where the shape of the curve did not allow the logistic function to be fit, or where the exponential function improved the fit of the data, as indicated by the Akaike information criterion (AIC). Weed density, height, and biomass were then fit to the CPWC relationships described by Charles et al (2019bCharles et al ( , 2020b using the AIC to determine the model with the best fit.…”

“…We tested the fit of weed density, biomass, and height on the CPWC relationships for Japanese millet published by Charles et al (2020b) and found an improvement in the fit of the CTWR when weed biomass was substituted for weed density, but no improvement in the CWFP relationship with any combination of weed density, biomass, or height (Figure 4). Charles et al (2020a) similarly reported that the CTWR relationship for mungbean was improved when weed biomass was substituted for weed density in the relationship, but that the CWFP model was not improved by substituting either weed biomass or weed height into the relationship.…”

“…Charles et al (2020a) similarly reported that the CTWR relationship for mungbean was improved when weed biomass was substituted for weed density in the relationship, but that the CWFP model was not improved by substituting either weed biomass or weed height into the relationship. The CPWC defined for Japanese millet using the new CTWR curve begins earlier in the season than the original CPWC (Charles et al 2020b), beginning at 26 GDD with 10 g weed m −2 and 8 GDD with 2 kg weed m −2 . There was no substantial change in the points of minimum yield loss with the new model.…”

“…Knezevic and Datta (2015) suggest using growing degree days (GDD) as the measure of time to reduce seasonal differences, and Charles et al (2019bCharles et al ( , 2020aCharles et al ( , 2020b reported that using GDD as the time descriptor overcame the effects of season on the CPWC relationship in fully irrigated cotton in Australian conditions. In an additional step, Charles et al (2019bCharles et al ( , 2020aCharles et al ( , 2020b included weed density as an extra term in the equations used to define the CPWC, and so were able to account for this factor in their CPWC models. In addition, Charles et al (2020a) tested the possibility of using weed height or weed biomass (kilograms of aboveground dry matter per square meter) in place of weed density in their CPWC models and found that the critical time for weed removal (CTWR) relationship was improved for the mimic weed mungbean in fully irrigated cotton when weed biomass was used in the model.…”