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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The stacked ensemble model achieves superior crop yield prediction performance compared with traditional machine learning models.

Taxon
crope0
Outcome
crop yield prediction performancee1
Direction
superiore1
Comparator
traditional machine learning modelse1

Verbatim

e0

“Predicting crop yields remains a crucial challenge in agriculture”

✓ located verbatim in the source · abstract, characters 9–74
e1

“The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models”

✓ located verbatim in the source · abstract, characters 1125–1374

Title

Stacked ensemble model for accurate crop yield prediction using machine learning techniques

Ramesh V; Kumaresan P Environmental Research Communications 2025. doi:10.1088/2515-7620/adb9c0

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

Claim · model-written summary

Prediction performance decreased strongly as fermentation time increased.

Taxon
Zea maysZea mayse0
Outcome
performance to predict MFYe2
Direction
strong decreasee2
Qualifier
with ongoing fermentation timee2

Not reportedComparator

Verbatim

e0

“Maize ( Zea mays L.) is the most commonly used substrate for methane production through anaerobic fermentation and is gaining further importance in Germany”

✓ located verbatim in the source · abstract, characters 0–155
e2

“Chemical composition models were always inferior to NIR spectroscopy models and showed a strong decrease in performance to predict MFY with ongoing fermentation time”

✓ located verbatim in the source · abstract, characters 1396–1561

Title

Determination of Methane Fermentation Yield and its Kinetics by near Infrared Spectroscopy and Chemical Composition in Maize

Christoph Grieder; Greta Mittweg; Baldev S. Dhillon; et al. Journal of Near Infrared Spectroscopy 2011. doi:10.1255/jnirs.959

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

Claim · model-written summary

OCS predicted mean cooking time reduction of 9.3%.

Taxon
common beansPhaseolus vulgarist0
Outcome
mean CKTe0
Direction
decreasee0
Value
9.3%e0

Verbatim

e0

“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”

✓ located verbatim in the source · abstract, characters 1230–1431
t0

“common beans”

✓ located verbatim in the source · title, characters 142–154

Title

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

Renu Saradadevi; Clare Mukankusi; Li Li; et al. The Plant Genome 2021. doi:10.1002/tpg2.20156

Other claims from this paper (13)
Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

The reinforcement-learning controller reduced simulated water consumption by almost 40% compared with constant irrigation.

Taxon
maizeZea mayse1
Outcome
water consumptione0
Direction
decreasee0
Value
almost 40%e0
Comparator
constant irrigation method of the DSSAT modele0

Verbatim

e0

“Using simple modular approach of DSSAT to calculate daily yield and leaf area index along with the proposed reinforcement learning controller, almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model”

✓ located verbatim in the source · abstract, characters 2106–2364
e1

“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”

✓ located verbatim in the source · abstract, characters 1911–1999

Title

Reinforcement Learning Based Controller For Precision Irrigation

Shivaram Irukula OakTrust (Texas A&M University Libraries) 2015

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed irrigation system decreased water use by 59.61%.

Taxon
crope1
Outcome
water usee1
Direction
decreasee1
Value
59.61%e1
Comparator
existing conventional irrigation systemse0

Verbatim

e0

“In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance.”

✓ located verbatim in the source · abstract, characters 874–1034
e1

“Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%.”

✓ located verbatim in the source · abstract, characters 1035–1194

Title

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

Chaowanan Jamroen; Preecha Komkum; Chanon Fongkerd; et al. IEEE Access 2020. doi:10.1109/access.2020.3025590

Other claims from this paper (2)
Method / resourceobservedoriginal resultnot significant (as stated)measured

Claim · model-written summary

Reducing modeled yield factors from three to two insignificantly reduced yield calculation accuracy.

Taxon
barley and winter triticaleHordeum vulgare×Triticosecalee1
Treatment
two (food and moisture)e0
Outcome
accuracy of calculating the grain crop yielde0
Direction
reducese0
Comparator
three (food, moisture and heat)e0

Verbatim

e0

“decrease in the number of yield-forming factors taken into account in the mathematical model from three (food, moisture and heat) to two (food and moisture) reduces the accuracy of calculating the grain crop yield insignificantly.”

✓ located verbatim in the source · abstract, characters 1639–1869
e1

“the data of independent field experiments of Professor N.N. Semenenko with barley and winter triticale has been used.”

✓ located verbatim in the source · abstract, characters 1191–1308

Title

Mathematical model of agricultural crop yield

A. P. Likhatsevich Proceedings of the National Academy of Sciences of Belarus Agrarian Series 2021. doi:10.29235/1817-7204-2021-59-3-304-318

Other claims from this paper (2)
Method / resourceobservedoriginal resultnegativesignificant (as stated)measured

Claim · model-written summary

Marker-based selection significantly decreased the response to selection compared with the other approaches

Treatment
MSe4
Outcome
response to selectione4
Direction
decreasee4
Qualifier
P < 0.05e4
Comparator
the other approachese4

Verbatim

e0

“Ten populations of Washington State University (WSU) winter wheat breeding lines including a diversity panel”

✓ located verbatim in the source · abstract, characters 421–529
e4

“MS was not successful in terms of R relative to the other selection approaches; using this strategy resulted in a significant (P < 0.05) decrease in response to selection compared with the other approaches”

✓ located verbatim in the source · abstract, characters 1537–1742

Title

Gains through selection for grain yield in a winter wheat breeding program

Dennis N. Lozada; Brian P. Ward; Arron H. Carter PLoS ONE 2020. doi:10.1371/journal.pone.0221603

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

Claim · model-written summary

The hybrid model reduced RMSE by 12% relative to linear regression, CNN and Transformer-only baselines.

Outcome
Root Mean Square Error (RMSE)e1
Direction
decreasee1
Value
12%e1
Comparator
baseline models of the linear regression, CNN, and Transformer only modelse1

Not reportedTaxon

Verbatim

e1

“The results of the tests yielded a 12% decrease in Root Mean Square Error (RMSE) along with 0.08 increases of the R2 value in comparison to the baseline models of the linear regression, CNN, and Transformer only models”

✓ located verbatim in the source · abstract, characters 784–1002

Title

Region-based Crop Yield Prediction using a Transformer-CNN Hybrid Model for Agricultural Forecasting

R. Nalan; M. Rupak Ram; H.Mohammed Nihal; et al. 2025 International Conference on Intelligent Computing and Control Systems (ICICCS) 2025. doi:10.1109/iciccs65191.2025.10985479

Other claims from this paper (1)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed irrigation system decreased electrical energy consumption by 67.35%.

Taxon
crope1
Outcome
electrical energy consumptione1
Direction
decreasee1
Value
67.35%e1
Comparator
existing conventional irrigation systemse0

Verbatim

e0

“In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance.”

✓ located verbatim in the source · abstract, characters 874–1034
e1

“Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%.”

✓ located verbatim in the source · abstract, characters 1035–1194

Title

An Intelligent Irrigation Scheduling System Using Low-Cost Wireless Sensor Network Toward Sustainable and Precision Agriculture

Chaowanan Jamroen; Preecha Komkum; Chanon Fongkerd; et al. IEEE Access 2020. doi:10.1109/access.2020.3025590

Other claims from this paper (2)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Simulated relative yield loss was fairly accurate, with an average deviation of 2.5 percentage points excluding outliers.

Taxon
Indian wheate1
Outcome
simulations of relative yield losse7
Value
2.5 percentage pointse7

Verbatim

e1

“the improved model can now capture the decrease in protein concentration that occurs in Indian wheat exposed to elevated O3”

✓ located verbatim in the source · abstract, characters 526–649
e7

“simulations of relative yield loss being fairly accurate (average deviation of 2.5 percentage points excluding outliers)”

✓ located verbatim in the source · abstract, characters 2011–2131

Title

Modelling ozone-induced changes in wheat amino acids and protein quality using a process-based crop model

Jo Cook; Durgesh Singh Yadav; Felicity Hayes; et al. Biogeosciences 2025. doi:10.5194/bg-22-1035-2025

Other claims from this paper (8)

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