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“overall model performance”

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2010 ’20

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Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

Plant hydraulic modules alone do not significantly improve overall model performance under observed climate variability.

Outcome: overall model performancee0
Direction: cannot significantly improvee0

Not reportedComparator

Verbatim

e0
“when considering ecosystem responses to the observed climate variability, plant hydraulic modules alone cannot significantly improve the overall model performance”
✓ located verbatim in the source · abstract, characters 1567–1729

Title

When do plant hydraulics matter in terrestrial biosphere modelling?

Athanasios Paschalis; Martin G. De Kauwe; Manon Sabot; et al. Global Change Biology 2023. doi:10.1111/gcb.17022

Other claims from this paper (5)

Method / resource Models with plant hydraulics predict a slower onset of plant water stress as drought develops.
Method / resource Models with plant hydraulics produce diurnal water and carbon flux variability closer to observations.
Method / resource Including plant water capacitance improves modeled diurnal water and carbon flux dynamics.
Method / resource Representing permanent xylem damage produces a significant drought legacy effect that limits plant productivity during recovery.
Method / resource Plant hydraulic modules reproduce more realistic water and carbon dynamics.

Method / resourceobservedoriginal resultno effectmeasured

Claim · model-written summary

Adding lateral angular components hardly affected overall rice LAI model performance.

Taxon: RiceOryza sativat0
Outcome: overall performance of the modelse0
Direction: hardly affectede0
Comparator: horizontal viewing angular parameterse0

Verbatim

e0
“Adding variables to incorporate lateral angular components to the horizontal viewing angular parameters hardly affected the overall performance of the models”
✓ located verbatim in the source · abstract, characters 869–1026

t0
“Rice”
✓ located verbatim in the source · title, characters 35–39

Title

Regression-Based Models to Predict Rice Leaf Area Index Using Biennial Fixed Point Continuous Observations of Near Infrared Digital Images

Michio Shibayama; Toshihiro Sakamoto; Eiji Takada; et al. Plant Production Science 2011. doi:10.1626/pps.14.365

Other claims from this paper (4)

Associationobservedoriginal resultno effectmeasured

Claim · model-written summary

Including rainfall as an exogenous variable did not improve model performance.

Taxon: vegetationt0
Treatment: inclusion of rainfall as an exogenous explanatory variablee2
Outcome: model performancee2

Not reportedDirection

Verbatim

t0
“NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots”
✓ located verbatim in the source · title, characters 0–104

e2
“Overall, model performance did not improve with the inclusion of rainfall as an exogenous explanatory variable”
✓ located verbatim in the source · abstract, characters 1158–1268

Title

NDVI time series stochastic models for the forecast of vegetation dynamics over desertification hotspots

Pedro R. Mutti; Paulo S. Lúcio; Vincent Dubreuil; et al. International Journal of Remote Sensing 2019. doi:10.1080/01431161.2019.1697008

Other claims from this paper (8)

Method / resource Box-Jenkins and BJT models performed slightly better than Holt-Winters models in all hotspots.
Method / resource Mean NDVI series were modelled with a correlation of up to 0.94.
Method / resource The minimum mean absolute percentage error of the mean NDVI models was 5.1%.
Method / resource NDVI variance models performed slightly worse than mean NDVI models, with a correlation of up to 0.82.
Method / resource The minimum mean absolute percentage error of the NDVI variance models was 22.0%.
Method / resource The combined models performed better at representing dry and degraded vegetation states than robust and heterogeneous vegetation in wet periods.
Observation The forecasts for one seasonal period ahead were satisfactory.
Recommendation Such models could be used as tools for monitoring short-term vegetation states.

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