Random Forest models of A) crop yield and B) weed density at harvest with % permutation importance of each of the predictor variables in their prediction accuracy.

Random Forest models of A) crop yield and B) weed density at harvest with % permutation importance of each of the predictor variables in their prediction accuracy.

<p>Random Forest models of A) crop yield and B) weed density at harvest with % permutation importance of each of the predictor variables in their prediction accuracy.</p>

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Bibliographic Details
Main Author: Pavle Pavlovic (22298220) (author)
Other Authors: Jed B. Colquhoun (22298223) (author), Nicholas E. Korres (22298226) (author), Christopher A. Landau (22298229) (author), Rui Liu (54031) (author), Carolyn J. Lowry (22298232) (author), Nicolas F. Martin (12420895) (author), Ed Peachey (17484645) (author), Barbara Scott (12667299) (author), Lynn M. Sosnoskie (22298235) (author), Mark J. VanGessel (22298238) (author), Martin M. Williams II (7434539) (author)
Published: 2025
Subjects:
Biotechnology
Ecology
Sociology
Inorganic Chemistry
Science Policy
Plant Biology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
div >< p
2019 – 2023
driven weather variability
changing weather patterns
snap bean processors
snap bean (<
early season growth
>< sup ><
22 predictor variables
phaseolus vulgaris </
suggesting row cultivation
snap bean yield
higher average temperatures
determine model accuracy
escape control practices
weed management practices
residual weed density
snap bean
row cultivation
management practices
escape control
weed density
weed management
environmental variables
></ sup
r </
2 </
crop yield
crop growth
weed species
weather also
time periods
test set
residual weeds
remainder used
pseudo -<
production regions
level information
ideal selectivity
environmental factors
emerging challenges
coming decades
climate change
>) production
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