Abstract
Accurate mapping of crop yields is essential for informed agricultural decision-making and optimal allocation of resources. Current crop yield datasets are deficient in large-scale, high-resolution information regarding the long-term spatial and temporal distribution of crop yields. To address this challenge, we developed a method of vegetation photosynthesis model combined with transition coefficient, producing a detailed dataset with 10 m resolution, covering major regions of maize, rice, and soybean in Northeast China from 2016 to 2021. The method introduces a dynamic observation index ($${{\rm{APAR}}}_{{\varepsilon }_{g}}$$) and a composite yield-conversion coefficient (a), which presents an innovative method for estimating crop yields without field measurements. Validation results show that, for maize, rice, and soybean, the model achieves r values of 0.39, 0.51, and 0.52; MREs of 12.14%, 11.96%, and 14.06%; and rRMSEs of 16.97%, 16.12%, and 17.26%, respectively. The dataset offers valuable insights into crop yield distribution, supporting better agricultural decision-making and resource optimization.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by-nc-nd. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 10
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.
- Outcome
- dataset with 10 m resolutione0
- Value
- 10 me0
- Qualifier
- without field measurementse1
- Setting
- Northeast Chinae0
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
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”
e1“an innovative method for estimating crop yields without field measurements”
t0“maize”
Claim · model-written summary
Validation of the yield model for maize gave r values of 0.39.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“r values of 0.39, 0.51, and 0.52”
t0“maize”
Claim · model-written summary
Validation of the yield model for maize gave MREs of 12.14%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“MREs of 12.14%, 11.96%, and 14.06%”
t0“maize”
Claim · model-written summary
Validation of the yield model for maize gave rRMSEs of 16.97%.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“rRMSEs of 16.97%, 16.12%, and 17.26%”
t0“maize”
Claim · model-written summary
Validation of the yield model for rice gave r values of 0.51.
- Taxon
- rice
Oryza sativat0 - Outcome
- r valuese0
- Value
- 0.51e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“r values of 0.39, 0.51, and 0.52”
t0“rice”
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
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“MREs of 12.14%, 11.96%, and 14.06%”
t0“rice”
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
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“rRMSEs of 16.97%, 16.12%, and 17.26%”
t0“rice”
Claim · model-written summary
Validation of the yield model for soybean gave r values of 0.52.
- Taxon
- soybean
Glycine maxt0 - Outcome
- r valuese0
- Value
- 0.52e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“r values of 0.39, 0.51, and 0.52”
t0“soybean”
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
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“MREs of 12.14%, 11.96%, and 14.06%”
t0“soybean”
Claim · model-written summary
Validation of the yield model for soybean gave rRMSEs of 17.26%.
- Taxon
- soybeanGlycine maxt0
- Outcome
- rRMSEse0
- Value
- 17.26%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“rRMSEs of 16.97%, 16.12%, and 17.26%”
t0“soybean”