Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Normalized RMSE values were at most 20%.

Taxon: hybrid riceOryza sativat0
Outcome: normalized RMSE valuese0
Value: ≤20%e0

Verbatim

e0
“the normalized RMSE values were ≤20%”
✓ located verbatim in the source · abstract, characters 1211–1247

t0
“hybrid rice”
✓ located verbatim in the source · title, characters 33–44

Title

Modelling nitrogen management in hybrid rice for coastal ecosystem of West Bengal, India

Sukamal Sarkar; Krishnendu Ray; Sourav Garai; et al. PeerJ 2023. doi:10.7717/peerj.14903

Other claims from this paper (7)

Method / resource APSIM captured physiological changes under varied nitrogen rates.
Method / resource Simulated and observed yield had R2 at least 0.812.
Method / resource APSIM overestimated calibration yield data but this was corrected in validation.
Method / resource APSIM underestimated calibrated storage-organ N, later corrected on validation.
Method / resource Observed and simulated storage-organ N were strongly correlated in calibration.
Method / resource Observed and simulated storage-organ N were strongly correlated in validation.
Method / resource The projected optimal N rate rises from 160 to 200 kg N ha −1 under elevated CO2.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Validation found small RMSE between simulated and observed phosphorus dynamics values.

Outcome: RMSE between simulated and observed valuese0
Direction: smalle0
Comparator: observed valuese0

Verbatim

e0
“the RMSE between simulated and observed values was small”
✓ located verbatim in the source · abstract, characters 1676–1732

Title

Simulation on Phosphorus Accumulation and Phosphorus Content in FACE Rice

Yulong Wang Nongye huanjing kexue xuebao 2008

Other claims from this paper (4)

Intervention effect FACE increased phosphorus accumulation in rice at different growth stages.
Intervention effect FACE increased phosphorus content in rice at different growth stages.
Method / resource The models fitted phosphorus dynamics under FACE and normal conditions well.
Method / resource Validation found significant correlation coefficients for the phosphorus dynamics models at the 5% level.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The model RMSE values were below 10 percent of observed means.

Outcome: RMSE valuese0
Direction: less thane0
Value: 10 percente0

Verbatim

e0
“These RMSE values were less than 10 percent of observed data means.”
✓ located verbatim in the source · abstract, characters 478–545

Title

Evaluation of the CERES-wheat model in Ahvaz condition

bahram andarziyan; abdolmahdi bakhshande; mohamad banayan aval; et al. DOAJ (DOAJ: Directory of Open Access Journals) 2009. doi:10.22067/gsc.v6i1.1171

Other claims from this paper (5)

Method / resource CERES-wheat predicted anthesis date with RMSE of 2.5d.
Method / resource CERES-wheat predicted maturity date with RMSE of 5d.
Method / resource CERES-wheat predicted grain yield with RMSE of 640 kg/ha.
Method / resource CERES-wheat predicted biomass production with RMSE of 460 kg/ha.
Recommendation The calibrated and validated model is recommended for research in Ahvaz conditions.

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

RMSE values of 10 or less represented observations well.

Taxon: tropic ageratumt0
Outcome: predictions represented observations welle0
Value: RMSE values ≤ 10e0

Verbatim

e0
“RMSE values ≤ 10 indicated that predictions represented observations well.”
✓ located verbatim in the source · abstract, characters 1102–1176

t0
“tropic ageratum (Ageratum conyzoides)”
✓ located verbatim in the source · title, characters 29–66

Title

Seedling emergence model for tropic ageratum (Ageratum conyzoides)

Friday Ekeleme; Frank Forcella; Dave W. Archer; et al. Weed Science 2005. doi:10.1614/ws-03-147r1

Other claims from this paper (3)

Method / resource Hydrothermal time predicted observed field emergence of tropic ageratum with r2 = 0.83.
Method / resource Model prediction error was at most 10.1 in southeastern Nigeria.
Method / resource Model prediction error was 8.9 in Los Banos, Philippines.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The coarsest-scale LAI retrieval by the ensemble multiscale filter has better accuracy (RMSE = 0.45) than MODIS LAI values (RMSE = 1.30).

Outcome: accuracye1
Direction: bettere1
Value: (RMSE) = 0.45e1
Comparator: MODIS LAI valuese1

Not reportedTaxon

Verbatim

e1
“The retrieved LAI values at the coarsest scale provided better accuracy with the aggregated LAI reference map values (root mean square error (RMSE) = 0.45) compared with that obtained from the MODIS LAI values (RMSE = 1.30)”
✓ located verbatim in the source · abstract, characters 1708–1931

Title

Multiscale Estimation of Leaf Area Index from Satellite Observations Based on an Ensemble Multiscale Filter

Jingyi Jiang; Zhiqiang Xiao; Jindi Wang; et al. Remote Sensing 2016. doi:10.3390/rs8030229

Other claims from this paper (1)

Method / resource LAI retrieved at each spatial resolution is in good agreement with the aggregated LAI reference map values.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

All RMSE values of the transplanting models were less than 7.5%.

Taxon: broccoliBrassica oleracea var. italicae0
Outcome: RMSE valuese1
Value: less than 7.5%e1

Verbatim

e0
“large-scale broccoli production in Ningxia”
✓ located verbatim in the source · abstract, characters 55–97

e1
“with all RMSE values being less than 7.5%”
✓ located verbatim in the source · abstract, characters 1284–1325

Title

Plant Type Suitable for Mechanized Transplanting of Broccoli in Ningxia

Xulu Wang; Wei Tian; Xiaojun Qin; et al. Agronomy 2025. doi:10.3390/agronomy15122791

Other claims from this paper (9)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

RMSE values were between 0.7 and 2.2.

Taxon: riceOryza sativat0
Outcome: RMSE valuese0
Value: between 0.7 and 2.2e0

Verbatim

e0
“RMSE values are between 0.7 and 2.2”
✓ located verbatim in the source · abstract, characters 1339–1374

t0
“rice growth model”
✓ located verbatim in the source · title, characters 57–74

Title

Assimilation technique of remote sensing information and rice growth model based on particle swarm optimization

Tian YongChao National Remote Sensing Bulletin 2010

Other claims from this paper (6)

Method / resource PSO outperformed SA in running efficiency and assimilation result.
Method / resource LAI allowed good inversion of sowing date and seeding rate.
Method / resource LNA better predicted nitrogen rate.
Method / resource LAI yielded a better overall inversion result.
Method / resource Relative errors for initial growth model parameters were less than 2.5%.
Method / resource Relative errors for yield were less than 5%.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

During validation, TVI had R2 of 0.90, RMSE of 3.11%, and RMSE% of 16.96.

Taxon: WheatTriticum aestivumt0
Outcome: R2 valuese0
Value: 0.90e0
Qualifier: RMSE values were 3.11% and 3.31%, and the RMSE% values were 16.96 and 18.05e0

Verbatim

e0
“During model validation, the R2 values were 0.90 and 0.90, the RMSE values were 3.11% and 3.31%, and the RMSE% values were 16.96 and 18.05”
✓ located verbatim in the source · abstract, characters 2102–2240

t0
“Wheat Coverage”
✓ located verbatim in the source · title, characters 11–25

Title

Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor

Jinran Liu; Pengfei Chen; Xingang Xu 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) 2018. doi:10.1109/agro-geoinformatics.2018.8476059

Other claims from this paper (7)

Method / resource RedEdge-M multispectral images showed potential for estimating wheat coverage.
Association Selected spectral indices were closely related to wheat coverage.
Method / resource TVI displayed among the best performances for wheat coverage estimation.
Method / resource NDRE displayed among the best performances for wheat coverage estimation.
Method / resource During calibration, TVI had R2 of 0.96, RMSE of 1.56%, and RMSE% of 8.91.
Method / resource During calibration, NDRE had R2 of 0.97, RMSE of 1.50, and RMSE% of 8.55.
Method / resource During validation, NDRE had R2 of 0.90, RMSE of 3.31%, and RMSE% of 18.05.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

During validation, NDRE had R2 of 0.90, RMSE of 3.31%, and RMSE% of 18.05.

Taxon: WheatTriticum aestivumt0
Outcome: R2 valuese0
Value: 0.90e0
Qualifier: RMSE values were 3.11% and 3.31%, and the RMSE% values were 16.96 and 18.05e0

Verbatim

e0
“During model validation, the R2 values were 0.90 and 0.90, the RMSE values were 3.11% and 3.31%, and the RMSE% values were 16.96 and 18.05”
✓ located verbatim in the source · abstract, characters 2102–2240

t0
“Wheat Coverage”
✓ located verbatim in the source · title, characters 11–25

Title

Estimating Wheat Coverage Using Multispectral Images Collected by Unmanned Aerial Vehicles and a New Sensor

Jinran Liu; Pengfei Chen; Xingang Xu 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) 2018. doi:10.1109/agro-geoinformatics.2018.8476059

Other claims from this paper (7)

Method / resource RedEdge-M multispectral images showed potential for estimating wheat coverage.
Association Selected spectral indices were closely related to wheat coverage.
Method / resource TVI displayed among the best performances for wheat coverage estimation.
Method / resource NDRE displayed among the best performances for wheat coverage estimation.
Method / resource During calibration, TVI had R2 of 0.96, RMSE of 1.56%, and RMSE% of 8.91.
Method / resource During calibration, NDRE had R2 of 0.97, RMSE of 1.50, and RMSE% of 8.55.
Method / resource During validation, TVI had R2 of 0.90, RMSE of 3.11%, and RMSE% of 16.96.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Model RMSE values ranged from 0.25 to 0.46.

Taxon: CROPt0
Outcome: RMSEe0
Value: 0.25 to 0.46e0

Verbatim

e0
“The RMSE values ranged from 0.25 to 0.46”
✓ located verbatim in the source · abstract, characters 2122–2162

t0
“AGRICULTURAL CROP YIELDS”
✓ located verbatim in the source · title, characters 58–82

Title

APPLYING MACHINE LEARNING FOR ANALYSIS AND FORECASTING OF AGRICULTURAL CROP YIELDS

Aigul Mimenbayeva; Gulnur Issakova; Balausa Tanykpayeva; et al. Scientific Journal of Astana IT University 2024. doi:10.37943/17lkyf9288

Other claims from this paper (3)

Method / resource Random Forest outperformed Decision Tree and SVM for potato yield forecasting.
Method / resource Random Forest Regressor had the best fit with R2 of 0.97865.
Method / resource Model R2 values were generally positive.

“RMSE values”

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