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Taxon: winter wheat ✕

Taxon groups are the verbatim names found in the abstracts (case-insensitive), not normalised: maize, corn and Zea mays are separate groups here. The Organism view merges them through NCBI Taxonomy.

Method / resourceobservedoriginal resultnegativesimulated

Claim · model-written summary

For early sown wheat the ensemble missed the winter decrease in aboveground biomass from senescence.

Taxon
winter wheatTriticum aestivumt0
Outcome
decrease of crop above ground biomasse0
Direction
decreasee0
Comparator
winter monthse0

Verbatim

e0

“the MME does not capture the decrease of crop above ground biomass during winter months due to senescence”

✓ located verbatim in the source · abstract, characters 1009–1114
t0

“winter wheat”

✓ located verbatim in the source · title, characters 14–26

Title

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Sibylle Dueri; Hamish Brown; Senthold Asseng; et al. Journal of Experimental Botany 2022. doi:10.1093/jxb/erac221

Other claims from this paper (4)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

NDVI sensitivity decreased as nitrogen rate increased.

Taxon
WINTER WHEATTriticum aestivumt0
Treatment
N ratee0
Outcome
NDVI sensitivitye0
Direction
decreasee0

Not reportedComparator

Verbatim

e0

“NDVI sensitivity tended to decrease as N rate increased”

✓ located verbatim in the source · abstract, characters 477–532
t0

“WINTER WHEAT”

✓ located verbatim in the source · title, characters 84–96

Title

RED EDGE AS A POTENTIAL INDEX FOR DETECTING DIFFERENCES IN PLANT NITROGEN STATUS IN WINTER WHEAT

Yumiko Kanke; William Raun; John Solie; et al. Journal of Plant Nutrition 2012. doi:10.1080/01904167.2012.689912

Other claims from this paper (5)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

With the RF algorithm, fused images lowered RMSE by 19.7%.

Taxon
Winter WheatTriticum aestivumt0
Outcome
RMSEe1
Direction
decreasee1
Value
19.7%e1
Qualifier
RF algorithme1

Verbatim

t0

“Winter Wheat”

✓ located verbatim in the source · title, characters 108–120
e1

“particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE”

✓ located verbatim in the source · abstract, characters 2399–2528

Title

Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source

Xinwei Li; Xiangxiang Su; Jun Li; et al. Agriculture 2024. doi:10.3390/agriculture14101797

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

With sufficient basal fertilizer, the THP method decreased topdressing N (NFT) by 4.20% relative to the TLF method in winter wheat.

Taxon
Winter WheatTriticum aestivumt0
Outcome
NFTe0
Direction
decreasee0
Value
4.20%e0
Comparator
TLF methode0

Verbatim

e0

“the THP method followed the TRL and TLZ methods to reduce NFT but maintained as much yield as the TLF method with a decrease of NFT by 4.20%”

✓ located verbatim in the source · abstract, characters 1137–1277
t0

“Topdressing for Winter Wheat”

✓ located verbatim in the source · title, characters 16–44

Title

Optimization of Topdressing for Winter Wheat by Accurate Growth Monitoring and Improved Production Estimation

Jingchun Ji; Jianli Liu; Jingjing Chen; et al. Remote Sensing 2021. doi:10.3390/rs13122349

Other claims from this paper (6)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Predicting grain maturity under continuous shade also required lower maximum air temperature.

Taxon
winter wheatTriticum aestivume1
Outcome
correct prediction of this growth stagee0
Comparator
CS treatmente0

Verbatim

e0

“A correct prediction of this growth stage in the CS treatment required a decrease of the daily maximum air temperature in addition to the reduction of global radiation.”

✓ located verbatim in the source · abstract, characters 1034–1202
e1

“winter wheat (Triticum aestivum L.)”

✓ located verbatim in the source · abstract, characters 377–412

Title

Does STICS crop model correctly simulate crop growth and productivity under shade

Sidonie Artru; Benjamin Dumont; Françoise Ruget; et al. Field Crops Research 2017

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

With the RF algorithm, fused images raised R2 by 18.8%.

Taxon
Winter WheatTriticum aestivumt0
Outcome
R2e1
Direction
increasee1
Value
18.8%e1
Qualifier
RF algorithme1

Verbatim

t0

“Winter Wheat”

✓ located verbatim in the source · title, characters 108–120
e1

“particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE”

✓ located verbatim in the source · abstract, characters 2399–2528

Title

Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source

Xinwei Li; Xiangxiang Su; Jun Li; et al. Agriculture 2024. doi:10.3390/agriculture14101797

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

With the RF algorithm, fused images raised RPD by 26.5%.

Taxon
Winter WheatTriticum aestivumt0
Outcome
RPDe1
Direction
increasee1
Value
26.5%e1
Qualifier
RF algorithme1

Verbatim

t0

“Winter Wheat”

✓ located verbatim in the source · title, characters 108–120
e1

“particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE”

✓ located verbatim in the source · abstract, characters 2399–2528

Title

Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source

Xinwei Li; Xiangxiang Su; Jun Li; et al. Agriculture 2024. doi:10.3390/agriculture14101797

Other claims from this paper (6)

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