By research area › Breeding, agronomy & horticulture › Method / resource

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The polymorphic markers successfully detected wild rice introgressions in progenies of wide hybridization.

Taxon: wild riceOryzeaee0
Outcome: successfully detected wild rice introgressionse0
Setting: wide hybridization progeniese0

Not reportedComparator

Verbatim

e0
“the polymorphic markers successfully detected wild rice introgressions from the wide hybridization progenies”
✓ located verbatim in the source · abstract, characters 1521–1629

Title

Development and validation of genome-wide polymorphic InDel marker set for harnessing the CC-genome wild rice species in the genus Oryza

Patricia Izabelle M. Lopez; Sherry Lou Hechanova; Charng-Pei Li; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1733586

Other claims from this paper (5)

Method / resourceobservedoriginal resultpositivebasis n/a

Claim · model-written summary

A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.

Taxon: maizeZea mayst0
Outcome: dataset with 10 m resolutione0
Value: 10 me0
Qualifier: without field measurementse1
Setting: Northeast Chinae0

Verbatim

e0
“producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021”
✓ located verbatim in the source · abstract, characters 406–544

e1
“an innovative method for estimating crop yields without field measurements”
✓ located verbatim in the source · abstract, characters 702–776

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave r values of 0.39.

Taxon: maizeZea mayst0
Outcome: r valuese0
Value: 0.39e0

Verbatim

e0
“r values of 0.39, 0.51, and 0.52”
✓ located verbatim in the source · abstract, characters 857–889

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave MREs of 12.14%.

Taxon: maizeZea mayst0
Outcome: MREse0
Value: 12.14%e0

Verbatim

e0
“MREs of 12.14%, 11.96%, and 14.06%”
✓ located verbatim in the source · abstract, characters 891–925

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for maize gave rRMSEs of 16.97%.

Taxon: maizeZea mayst0
Outcome: rRMSEse0
Value: 16.97%e0

Verbatim

e0
“rRMSEs of 16.97%, 16.12%, and 17.26%”
✓ located verbatim in the source · abstract, characters 931–967

t0
“maize”
✓ located verbatim in the source · title, characters 7–12

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for rice gave r values of 0.51.

Taxon: riceOryza sativat0
Outcome: r valuese0
Value: 0.51e0

Verbatim

e0
“r values of 0.39, 0.51, and 0.52”
✓ located verbatim in the source · abstract, characters 857–889

t0
“rice”
✓ located verbatim in the source · title, characters 14–18

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for rice gave MREs of 11.96%.

Taxon: riceOryza sativat0
Outcome: MREse0
Value: 11.96%e0

Verbatim

e0
“MREs of 12.14%, 11.96%, and 14.06%”
✓ located verbatim in the source · abstract, characters 891–925

t0
“rice”
✓ located verbatim in the source · title, characters 14–18

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for rice gave rRMSEs of 16.12%.

Taxon: riceOryza sativat0
Outcome: rRMSEse0
Value: 16.12%e0

Verbatim

e0
“rRMSEs of 16.97%, 16.12%, and 17.26%”
✓ located verbatim in the source · abstract, characters 931–967

t0
“rice”
✓ located verbatim in the source · title, characters 14–18

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for soybean gave r values of 0.52.

Taxon: soybeanGlycine maxt0
Outcome: r valuese0
Value: 0.52e0

Verbatim

e0
“r values of 0.39, 0.51, and 0.52”
✓ located verbatim in the source · abstract, characters 857–889

t0
“soybean”
✓ located verbatim in the source · title, characters 23–30

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave MREs of 14.06%.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Validation of the yield model for soybean gave MREs of 14.06%.

Taxon: soybeanGlycine maxt0
Outcome: MREse0
Value: 14.06%e0

Verbatim

e0
“MREs of 12.14%, 11.96%, and 14.06%”
✓ located verbatim in the source · abstract, characters 891–925

t0
“soybean”
✓ located verbatim in the source · title, characters 23–30

Title

A 10 m maize, rice and soybean yield dataset from 2016 to 2021 in Northeast China

Fei Teng; Minglei Wang; Wenjiao Shi; et al. Scientific Data 2026. doi:10.1038/s41597-026-06719-0

Other claims from this paper (9)

Method / resource A yield dataset at 10 m resolution was produced for major maize, rice and soybean regions of Northeast China for 2016 to 2021, with a method that estimates yields without field measurements.
Method / resource Validation of the yield model for maize gave r values of 0.39.
Method / resource Validation of the yield model for maize gave MREs of 12.14%.
Method / resource Validation of the yield model for maize gave rRMSEs of 16.97%.
Method / resource Validation of the yield model for rice gave r values of 0.51.
Method / resource Validation of the yield model for rice gave MREs of 11.96%.
Method / resource Validation of the yield model for rice gave rRMSEs of 16.12%.
Method / resource Validation of the yield model for soybean gave r values of 0.52.
Method / resource Validation of the yield model for soybean gave rRMSEs of 17.26%.

Method / resource 289,819 claims

289,819 claims