Translator Disclaimer
1 January 2017 Predicting Yield Losses in Rice Mixed-Weed Species Infestations in California
Author Affiliations +

Although many pests constrain rice production, weeds are considered to be the major barrier to achieving optimal yields. A predictive model based on naturally occurring mixed-species infestations in the field would enable growers to target the specific weed group that is the greatest contributor to yield loss, but as of now no such models are available. In 2013 and 2014, two empirical hyperbolic models were tested using the relative cover at canopy closure of groups of weed species as independent variables: grasses, sedges, broadleaves, grasses and sedges combined, grasses and broadleaves combined, and all weed species combined. Models were calibrated using data from experiments conducted at the California Rice Experiment Station, in Biggs, CA, and validated across four sites over 2 years, for a total of 7 site-year combinations. Of the three major weed groups, grasses, sedges, and broadleaves, the only groups positively related to yield loss in the multispecies infestation were grasses. At the model calibration site, grasses and sedges combined best predicted yield loss (corrected Akaike information criterion [AICc] = -21.5) in 2013, and grasses alone best predicted yield loss (AICc = -19.0) in 2014. Across the validation sites, the model using grasses and sedges combined was the best predictor in 5 out of 7 site-years. Accuracy of the predicted values at the model validation sites ranged from 6% mean average error to 17% mean average error. No single model and set of parameters accurately predicted losses across all years and locations, but relative cover of grasses and sedges combined at canopy closure was the best estimate over the most sites and years.

Nomenclature: Rice, Oryza sativa L.

© Weed Science Society of America, 2016
Whitney B. Brim-DeForest, Kassim Al-Khatib, and Albert J Fischer "Predicting Yield Losses in Rice Mixed-Weed Species Infestations in California," Weed Science 65(1), 61-72, (1 January 2017).
Received: 16 May 2016; Accepted: 1 August 2016; Published: 1 January 2017

Get copyright permission
Back to Top