Claim · model-written summary
Volume based giant foxtail damage coefficients differed between years.
- Taxon
- soybean
Glycine maxe1 - Interactor
- giant foxtaile0weed
- Outcome
- giant foxtail or common lambsquarters damage coefficientse0
- Comparator
- between yearse0
Not reportedMaterialGeneCompoundOrgan / stageSecond interactorSecond interactor roleTreatmentSettingDirectionValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Based on weed relative volume, estimates of giant foxtail or common lambsquarters damage coefficients differed between years.”
e1“predictors of soybean yield loss”
Title
Shawn P. Conley; David E. Stoltenberg; Chris M. Boerboom; et al. Weed Science 2003. doi:10.1614/0043-1745(2003)051[0402:psylig]2.0.co;2
Other claims from this paper (11)
- Method / resource Density based soybean yield loss coefficients near zero weed density differed between years.
- Method / resource Density based estimates of maximum soybean yield loss were similar between years.
- Method / resource Leaf area based giant foxtail damage coefficients differed between years.
- Method / resource Leaf area based common lambsquarters damage coefficients differed between years.
- Method / resource Common lambsquarters leaf area based maximum soybean yield loss estimates differed between years.
- Method / resource Giant foxtail leaf area based maximum soybean yield loss estimates did not change within or between seasons.
- Method / resource Volume based common lambsquarters damage coefficients differed between years.
- Method / resource Common lambsquarters volume based maximum soybean yield loss estimates differed between years.
- Method / resource Giant foxtail volume based maximum soybean yield loss estimates did not change within or between seasons.
- Association Giant foxtail was more competitive than common lambsquarters for soybean yield loss under all three predictor bases.
- Recommendation The authors conclude additional parameters may be required to predict mixed weed competition accurately.