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“adjusted R2”

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Observationobservedoriginal resultmeasured

Claim · model-written summary

The path model had high adjusted R2.

Taxon: sorghumSorghumt0
Outcome: adjusted R2 valuee0
Direction: highe0
Value: 0.89e0

Verbatim

e0
“high adjusted R2 value (0.89)”
✓ located verbatim in the source · abstract, characters 1472–1501

t0
“sorghum”
✓ located verbatim in the source · title, characters 50–57

Title

Correlations and path analysis of yield traits in sorghum grown in southwestern highlands of Uganda

R. Akatwijuka; P.R. Rubaihayo; T.L. Odong African Crop Science Journal 2019. doi:10.4314/acsj.v27i3.8

Other claims from this paper (11)

Association Grain yield correlated strongly with panicle weight.
Association Grain yield correlated with panicle width.
Association Grain yield correlated with stem girth.
Association Grain yield correlated with leaf width.
Association Grain yield had a nonsignificant negative correlation with days to 50% flowering.
Association Panicle weight had the highest direct effect on grain yield.
Association Plant height had a high direct effect on grain yield.
Association Stem girth had a high indirect contribution to grain yield.
Association Leaf width had a high indirect contribution to grain yield.
Association Panicle width had a high indirect contribution to grain yield.
Observation The path model had a low residual effect.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Gradient boosting achieved an 89.95% adjusted R2 score in crop yield prediction.

Outcome: adjusted R2 scoree0
Value: 89.95%e0
Qualifier: gradient boostinge0

Not reportedComparator

Verbatim

e0
“89.95% of adjusted R2 score for gradient boosting”
✓ located verbatim in the source · abstract, characters 1263–1312

Title

An Investigation on Machine Learning Algorithms for Crop Yield Prediction

Raj Singh; R.M. Bhavadharini 2023 International Conference on System, Computation, Automation and Networking (ICSCAN) 2023. doi:10.1109/icscan58655.2023.10395193

Other claims from this paper (5)

Method / resource Gradient boosting achieved 93.95% accuracy in crop yield prediction.
Method / resource Gradient boosting achieved a 92.06% R2 score in crop yield prediction.
Method / resource Extreme gradient boosting achieved 93.95% accuracy in crop yield prediction.
Method / resource Extreme gradient boosting achieved an R2 score of 93.45% in crop yield prediction.
Method / resource Extreme gradient boosting achieved an adjusted R2 score of 91.25 in crop yield prediction.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The double log production function had adjusted R2 of 61%.

Taxon: strawberryFragaria ×ananassat0
Outcome: adjusted R2e0
Value: 61%e0

Not reportedDirection

Verbatim

t0
“strawberry”
✓ located verbatim in the source · title, characters 36–46

e0
“The double log function showed the best fit with adjusted R2 of 61%.”
✓ located verbatim in the source · abstract, characters 740–808

Title

Resource use efficiency analysis in strawberry production in selected areas of Bangladesh

M Khatun; MA Rashid; S Khandoker; et al. SAARC Journal of Agriculture 2019. doi:10.3329/sja.v17i1.42771

Other claims from this paper (10)

Observation Strawberry farming was profitable but costly.
Association Sapling input positively affected strawberry output.
Association Fertilizer input positively affected strawberry output.
Association Labour input negatively affected strawberry output.
Association Chemical input negatively affected strawberry output.
Observation Sapling, land and water inputs were underutilized.
Observation Labour, cowdung and fertilizer inputs were overused.
Observation 37% of farmers reported strawberry disease attacks.
Recommendation Farmers needed to adjust resource use to improve farm profit.
Recommendation The authors recommended farmer training for efficient resource use.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The fit of the browse-crown area relationship was better for photograph-based crown area (PCA, r2 adjusted = 0.75) than for field crown area (CA, r2 adjusted = 0.73) in Balanites.

Taxon: Balanites aegyptiacaBalanites aegyptiacat0
Outcome: the fit of the relationshipe2
Value: r2 adjusted = 0.75e2
Qualifier: r2 adjusted = 0.73e2
Comparator: CAe2

Verbatim

t0
“Acacia dudgeoni and Balanites aegyptiaca”
✓ located verbatim in the source · title, characters 79–119

e2
“the fit of the relationship was better for PCA (r2 adjusted = 0.75) compared to CA (r2 adjusted = 0.73) for Balanites”
✓ located verbatim in the source · abstract, characters 895–1012

Title

Prediction models for estimating available fodder of two savanna tree species (Acacia dudgeoni and Balanites aegyptiaca) based on field and image analysis measures

P Savadogo; B Elfving African Journal of Range and Forage Science 2007. doi:10.2989/ajrfs.2007.24.2.2.156

Other claims from this paper (7)

Method / resource Regression models relating browse biomass to tree variables were significant for both species.
Method / resource Predictive power of the browse model for Acacia dudgeoni was low (r2 < 0.50), lower than for Balanites.
Method / resource Predictive power of the browse model for Balanites aegyptiaca was high (r2 > 0.75), higher than for Acacia.
Method / resource For Acacia, the regression coefficient for CA was 45%.
Method / resource For Acacia, the regression coefficient for PCA was 33%.
Association Browse biomass from destructive sampling was correlated with crown area and measured tree variables.
Recommendation Image analysis could offer an objective method for estimating browse biomass changes when coupled with dendrometric measures.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

MARS adjusted R² was 0.989.

Taxon: Sorghum-Sudangrass hybride1
Outcome: adjusted R²e0
Direction: higheste0
Value: 0.989e0

Verbatim

e0
“highest R² value (0.993) and adjusted R² value (0.989)”
✓ located verbatim in the source · abstract, characters 1252–1306

e1
“the Sorghum-Sudangrass hybrid were used”
✓ located verbatim in the source · abstract, characters 247–286

Title

Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters

Şenol Çelik; Halit Tutar; Erdal Gönülal; et al. Scientific Reports 2024. doi:10.1038/s41598-024-72746-9

Other claims from this paper (16)

Observation Average plant height was 306.27 cm.
Observation Average stem diameter was 9.47 mm.
Observation Average fresh herbage yield was 10852.51 kg/da.
Observation Average crude protein ratio was 9.66%.
Observation Average acid detergent fiber was 33.39%.
Observation Average neutral detergent fiber was 51.85%.
Observation Average acid detergent lignin was 9.76%.
Observation Average dry matter digestibility was 62.88%.
Observation Average dry matter intake was 2.34%.
Observation Average relative feed value was 114.68.
Method / resource MARS had the best fit for herbage yield.
Method / resource MARS RMSE was 246.
Method / resource MARS MAPE was 1.926.
Method / resource MARS SD ratio was 0.085.
Method / resource MARS AIC was 845.
Method / resource MARS R² was 0.993.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The Gompertz model had adjusted R2 of 0.96.

Taxon: Soybean plantsGlycine maxt0
Outcome: adjusted R2e0
Value: 0.96e0

Verbatim

e0
“The adjusted R2 value of 0.96 is close to 1.”
✓ located verbatim in the source · abstract, characters 781–825

t0
“Soybean plants have limited growth”
✓ located verbatim in the source · abstract, characters 0–34

Title

Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration

Fandi Rezian Pratama Gultom; Solimun Solimun; Nurjannah Nurjannah JTAM (Jurnal Teori dan Aplikasi Matematika) 2022. doi:10.31764/jtam.v6i4.8617

Other claims from this paper (6)

Method / resource Bootstrap resampling made no significant difference in Gompertz model value.
Method / resource The model explained 96 percent of plant-height variation.
Method / resource Predicted soybean growth before and after resampling coincided.
Method / resource Initial growth values before and after resampling were 14, 05 and 14.18.
Method / resource Maximum growth values before and after resampling were 55.13 and 55.60.
Method / resource Bootstrap resampling overcame residual normality issues without changing original information.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The developed forecast model had high adjusted R2.

Taxon: Bengal gramt0
Outcome: adjusted R2e0
Value: high valuee0

Verbatim

e0
“high value of adjusted R2 in the developed forecast model”
✓ located verbatim in the source · abstract, characters 957–1014

t0
“Bengal Gram”
✓ located verbatim in the source · title, characters 23–34

Title

Forecasting Models for Bengal Gram Using Weather Parameters for Bidar District of Karnataka State, India

Amaresh; A S Police Patil; Jagrati B Deshmanya International Journal of Environment and Climate Change 2025. doi:10.9734/ijecc/2025/v15i34756

Other claims from this paper (4)

Association Weekly correlations between Bengal gram yield and weather parameters ranged from -0.62 to +0.65.
Association Bengal gram yield had an almost linear trend with year.
Method / resource For 2019, forecasts were 0.5 to 6 % below actual yields.
Method / resource The best fitted model enabled pre-harvest forecasting in the 10th SMW.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Agromet models had adjusted R2 values from 0.38 to 0.78.

Outcome: adjusted R2e0
Value: 0.38 to 0.78e0

Not reportedTaxon

Verbatim

e0
“Agromet-models (adjusted R2 = 0.38 to 0.78)”
✓ located verbatim in the source · abstract, characters 1444–1487

Title

Integrating weather model & Remote sensing indices for wheat yield prediction in Haryana, India

Man Jeet; Anurag; Ram Niwas; et al. Environment Conservation Journal 2022. doi:10.36953/ecj.021811-2118

Other claims from this paper (3)

Method / resource Integrated weather and NDVI models predicted wheat yield better than weather-only models.
Method / resource MODIS-NDVI models had adjusted R2 values from 0.61 to 0.86 and performed better than weather models.
Method / resource Pixel-based MODIS-NDVI values were more effective for wheat yield prediction when integrated with weather parameters.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The nonirrigated model had R2 of 0.4983 and adjusted R2 of 0.4682.

Taxon: lentilsLens culinarise1
Outcome: R2e0
Value: 0.4983e0
Qualifier: adjusted R2 = 0.4682e0

Verbatim

e0
“without irrigation R = 0.7059; R2 = 0.4983; adjusted R2 = 0.4682”
✓ located verbatim in the source · abstract, characters 1029–1093

e1
“growing lentils for grain”
✓ located verbatim in the source · abstract, characters 290–315

Title

Modern aspects of informatization of agricultural production based on modeling and forecasting the production process of lentils under different conditions of moisture supply

S. O. Lavrenko; N. M. Lavrenko; M. V. Maksymov Міжвідомчий тематичний науковий збірник Меліорація і водне господарство 2022. doi:10.31073/mivg202201-323

Other claims from this paper (14)

Method / resource Regression models for lentil grain yield had high reported reliability and practical value.
Method / resource The nonirrigated regression model had R of 0.7059.
Method / resource The nonirrigated model had standard estimation error 0,1232.
Method / resource The irrigated regression model had R of 0,6131.
Method / resource The irrigated model had R2 of 0.3759 and adjusted R2 of 0.3385.
Method / resource The irrigated model had standard estimation error 0,2591.
Method / resource A GRNN yield model had architecture 4-12-7-1.
Method / resource The GRNN had learning, control and test productivity of 0.215, 0.290 and 0.362 respectively.
Method / resource The GRNN had learning, control and test errors of 0.136, 0.049 and 0.066.
Method / resource Multiple correlation for nonlinear yield factor patterns was 0.96.
Association Moisture conditions ranked first for lentil yield with impact factor 4.21.
Association Plant density ranked second with impact factor 1.62.
Association Mineral fertilizer rate ranked third with impact factor 1.61.
Association Tillage depth ranked fourth with impact factor 1.01.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The irrigated model had R2 of 0.3759 and adjusted R2 of 0.3385.

Taxon: lentilsLens culinarise1
Outcome: R2e0
Value: 0.3759e0
Qualifier: adjusted R2 = 0.3385e0

Verbatim

e0
“with irrigation R = 0,6131; R2 = 0.3759; adjusted R2 = 0.3385”
✓ located verbatim in the source · abstract, characters 1177–1238

e1
“growing lentils for grain”
✓ located verbatim in the source · abstract, characters 290–315

Title

Modern aspects of informatization of agricultural production based on modeling and forecasting the production process of lentils under different conditions of moisture supply

Other claims from this paper (14)

Method / resource Regression models for lentil grain yield had high reported reliability and practical value.
Method / resource The nonirrigated regression model had R of 0.7059.
Method / resource The nonirrigated model had R2 of 0.4983 and adjusted R2 of 0.4682.
Method / resource The nonirrigated model had standard estimation error 0,1232.
Method / resource The irrigated regression model had R of 0,6131.
Method / resource The irrigated model had standard estimation error 0,2591.
Method / resource A GRNN yield model had architecture 4-12-7-1.
Method / resource The GRNN had learning, control and test productivity of 0.215, 0.290 and 0.362 respectively.
Method / resource The GRNN had learning, control and test errors of 0.136, 0.049 and 0.066.
Method / resource Multiple correlation for nonlinear yield factor patterns was 0.96.
Association Moisture conditions ranked first for lentil yield with impact factor 4.21.
Association Plant density ranked second with impact factor 1.62.
Association Mineral fertilizer rate ranked third with impact factor 1.61.
Association Tillage depth ranked fourth with impact factor 1.01.

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