Search claims · PlantClaims

Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

The controller caused no significant decrease in simulated maize yield relative to constant irrigation.

Taxon: maizeZea mayse1
Outcome: yielde0
Direction: without any significant decreasee0
Comparator: constant irrigation method of the DSSAT modele0

Verbatim

t0
“Precision Irrigation”
✓ located verbatim in the source · title, characters 44–64

e0
“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”
✓ located verbatim in the source · abstract, characters 2249–2427

e1
“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”
✓ located verbatim in the source · abstract, characters 1911–1999

Title

Reinforcement Learning Based Controller For Precision Irrigation

Shivaram Irukula OakTrust (Texas A&M University Libraries) 2015

Other claims from this paper (2)

Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

Using SNP subsets based on functional annotation did not significantly decrease chickpea grain-yield prediction accuracy.

Taxon: chickpeaCicer arietinume1
Outcome: prediction accuracy for grain yielde0
Direction: did not significantly decreasee0
Comparator: whole SNP datasete0

Not reportedValue

Verbatim

e0
“Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.”
✓ located verbatim in the source · abstract, characters 1672–1840

e1
“chickpea breeding lines”
✓ located verbatim in the source · abstract, characters 333–356

Title

Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment

Yongle Li; Pradeep Ruperao; Jacqueline Batley; et al. The Plant Genome 2021. doi:10.1002/tpg2.20166

Other claims from this paper (7)

Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

The controller caused no significant decrease in simulated maize leaf area index relative to constant irrigation.

Taxon: maizeZea mayse1
Outcome: leaf area indexe0
Direction: without any significant decreasee0
Comparator: constant irrigation method of the DSSAT modele0

Verbatim

e0
“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”
✓ located verbatim in the source · abstract, characters 2249–2427

e1
“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”
✓ located verbatim in the source · abstract, characters 1911–1999

Title

Reinforcement Learning Based Controller For Precision Irrigation

Shivaram Irukula OakTrust (Texas A&M University Libraries) 2015

Other claims from this paper (2)

Method / resourceobservedoriginal resultno effectsimulated

Claim · model-written summary

At the warmest Brazilian location, some models simulated no yield effect.

Taxon: soybeanGlycine maxt0
Outcome: yielde0
Direction: no effecte0
Comparator: modelse0
Setting: Brazile0

Verbatim

e0
“In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield”
✓ located verbatim in the source · abstract, characters 1109–1262

t0
“soybean”
✓ located verbatim in the source · title, characters 4–11

Title

Are soybean models ready for climate change food impact assessments?

Kritika Kothari; Rafael Battisti; Kenneth J. Boote; et al. European Journal of Agronomy 2022. doi:10.1016/j.eja.2022.126482

Other claims from this paper (10)

Method / resource Models varied considerably in simulated soybean yield responses to temperature and CO2.
Method / resource At the coolest Argentine location, models simulated a 24% yield reduction under 3 °C warming.
Method / resource At the coolest Argentine location, other models simulated yield increases up to 29%.
Method / resource At the warmest Brazilian location, simulated yield losses reached 38% under 3 °C warming.
Method / resource Raising CO2 from 360 to 540 ppm produced simulated yield increases of 6% to 31%.
Method / resource Calibration did not reduce variation across models.
Method / resource Calibration altered temperature yield responses in some models.
Method / resource Ensemble means reduced uncertainty in soybean yield simulations.
Method / resource Ensemble mean temperature and CO2 responses resembled literature results.
Recommendation Further model development using elevated CO2 and temperature experiments was recommended.

Method / resourceobservedoriginal resultno effectprojected

Claim · model-written summary

EPIC projected no change in corn yield under the fine scale scenario.

Taxon: cornZea mays subsp. mayst0
Direction: no changee0
Comparator: fine scale scenarioe0

Not reportedOutcomeValue

Verbatim

e0
“EPIC simulated a decrease for the coarse scenario but no change for the fine scale scenario”
✓ located verbatim in the source · abstract, characters 568–659

t0
“for wheat and corn”
✓ located verbatim in the source · abstract, characters 61–79

Title

Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios

L. O. Mearns; T. Mavromatis; E. Tsvetsinskaya; et al. Journal of Geophysical Research Atmospheres 1999. doi:10.1029/1998jd200061

Other claims from this paper (5)

Method / resource CERES projected moderate corn yield decreases under both scenarios.
Method / resource EPIC projected a corn yield decrease under the coarse scenario.
Method / resource The models also differed in simulated wheat yields.
Mechanism Model differences in yield arose from contrasting formation of final yield despite similar potential biomass simulation.
Method / resource Crop model type was identified as an uncertainty in impact assessment alongside climate scenario spatial resolution.

“yield decrease”

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