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“decrease in A”

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Taxon: crop ✕

Taxon groups are the verbatim names found in the abstracts (case-insensitive), not normalised: maize, corn and Zea mays are separate groups here. The Organism view merges them through NCBI Taxonomy.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Water and fertilizer usage decreased by 25%.

Taxon: cropt0
Outcome: water and fertilizer usagee0
Direction: decreasee0
Value: 25%e0

Not reportedComparator

Verbatim

e0
“25% decrease in water and fertilizer usage”
✓ located verbatim in the source · abstract, characters 749–791

t0
“Crop”
✓ located verbatim in the source · title, characters 90–94

Title

Transforming Precision Agriculture with Quantum Computing: A Novel Algorithm for Boosting Crop Yields and Optimizing Resources

Anshit Mukherjee; Biswadip Basu Mallik EPJ Web of Conferences 2025. doi:10.1051/epjconf/202532501004

Other claims from this paper (3)

Method / resource The approach improved crop yield predictive accuracy by about 30%.
Method / resource Illness control capacity improved by 40%.
Mechanism The enhanced illness control was said to decrease crop losses.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Time-Series Driven RL slightly decreased RMSE relative to SUBPLEX.

Taxon: cropt0
Outcome: RMSEe0
Direction: decreasee0
Comparator: SUBPLEXe1

Not reportedValue

Verbatim

e0
“a slight decrease in RMSE, while significantly reducing the computational burden by 365 times”
✓ located verbatim in the source · abstract, characters 1156–1249

e1
“compared to 0.76 for SUBPLEX”
✓ located verbatim in the source · abstract, characters 1122–1150

t0
“crop”
✓ located verbatim in the source · title, characters 56–60

Title

Reinforcement learning-based assimilation of the WOFOST crop model

Haochong Chen; Xiangning Yuan; Jian Kang; et al. Smart Agricultural Technology 2024. doi:10.1016/j.atech.2024.100604

Other claims from this paper (3)

Method / resource Time-Series Driven RL achieved MAE 0.65 versus 0.76 for SUBPLEX.
Method / resource Time-Series Driven RL reduced computational burden by 365 times.
Method / resource Time-Series Driven RL and SUBPLEX had similar performance in a multi-year stability test.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AI methods decreased fertilizer application by 20% without sacrificing output.

Taxon: Cropt0
Outcome: fertilizer applicatione0
Direction: decreasee0
Value: 20%e0
Qualifier: without sacrificing outpute0

Verbatim

e0
“a 20% decrease in fertilizer application, all without sacrificing output”
✓ located verbatim in the source · abstract, characters 633–705

t0
“Crop Recommendation and Yield Maximization”
✓ located verbatim in the source · title, characters 36–78

Title

Agri- wiz An AI Driven Platform for Crop Recommendation and Yield Maximization

Jathar Raj Girish INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 2025. doi:10.55041/ijsrem50964

Other claims from this paper (2)

Method / resource AI methods increased yield prediction accuracy by 15%.
Method / resource AI methods reduced water usage by up to 30%.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

The stacked ensemble model significantly decreases RMSE compared with traditional machine learning models.

Taxon: crope0
Outcome: RMSEe1
Direction: decreasee1
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 / resource The stacked ensemble model achieves superior crop yield prediction performance compared with traditional machine learning models.
Method / resource The stacked ensemble model notably enhances accuracy compared with traditional machine learning models.
Method / resource Mean Absolute Error of the stacked ensemble model is 7.20 tons/hectare.
Method / resource Mean Square Error of the stacked ensemble model is 15570.32 tons 2 /hectare 2.
Method / resource Root Mean Square Error of the stacked ensemble model is 124.78 tons/hectare.
Method / resource Coefficient of determination (R2) of the stacked ensemble model is 0.98.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Both neural network models showed rapid initial loss declines followed by gradual declines.

Taxon: Cropt0
Outcome: losse0
Direction: decreasee0
Qualifier: effective learning and convergencee0
Comparator: early epochse0

Verbatim

e0
“Both the causal inference neural network (CINN) and deep neural network (DNN) models show a rapid initial decrease in loss during the early epochs, followed by a more gradual decline, indicating effective learning and convergence.”
✓ located verbatim in the source · abstract, characters 660–890

t0
“Crop Yields”
✓ located verbatim in the source · title, characters 70–81

Title

Impact of Advanced Agriculture Technologies and Energy Consumption on Crop Yields in Modern Agriculture Using Deep Learning Techniques

Khan Baz; Zhu Zhen; Hashmat Ali Food and Energy Security 2025. doi:10.1002/fes3.70076

Other claims from this paper (4)

Method / resource CINN consistently began with lower training loss than DNN.
Method / resource The machine learning models improved cereal yield forecasting.
Association The agricultural Product Complexity Index and crop yields showed feedback causality at different lags.
Association Fertilizer use and agricultural yields showed feedback causality at different lags.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

The genome-wide approach significantly reduced experiment design time, including PCR validation, with one-time computation overhead.

Taxon: cropt0
Outcome: time required to design a CRISPR experimente0
Direction: decreasee0
Comparator: overhead compute time required once per genomee0

Verbatim

e0
“The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run.”
✓ located verbatim in the source · abstract, characters 724–953

t0
“crop”
✓ located after whitespace/quote normalisation · title, characters 0–4

t1
“crop”
✓ located after whitespace/quote normalisation · title, characters 0–4

Title

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation

Hans Müller Paul; Dave D. Istanto; Jacob Heldenbrand; et al. BMC Bioinformatics 2022. doi:10.1186/s12859-022-04593-2

Other claims from this paper (3)

Method / resource CROPSR supplies genome-wide gRNA design and evaluation.
Method / resource The scoring model can give scores in repetitive, A/T-rich regions where existing tools sometimes failed.
Method / resource The gRNA scoring model significantly improved prediction accuracy over existing tools, even for non-crop genomes.

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 / resource The proposed irrigation system decreased electrical energy consumption by 67.35%.
Method / resource The proposed irrigation system increased crop yield by 22.58%.

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 / resource The proposed irrigation system decreased water use by 59.61%.
Method / resource The proposed irrigation system increased crop yield by 22.58%.

Method / resourceobservedoriginal resultsimulated

Claim · model-written summary

The model showed a large range of overwatering before crop production declined.

Taxon: cropt0
Outcome: decline affect in crop productione0
Comparator: over wateringe0

Not reportedValue

Verbatim

t0
“crop”
✓ located verbatim in the source · title, characters 72–76

e0
“the model shows that there is a large range of over watering that can occur before a decline affect in crop production can be seen”
✓ located verbatim in the source · abstract, characters 716–846

Title

Helpful Farming Model: How to decrease your water foot while maximizing crop yield.

Paige Morgan Digital Commons - USU (Utah State University) 2017

Other claims from this paper (2)

Method / resource AquaCrop predictions came within 10% of historical crop yields.
Recommendation The authors conclude that AquaCrop can provide environmental and financial savings to farmers.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The stacked ensemble model notably enhances accuracy compared with traditional machine learning models.

Taxon: crope0
Outcome: accuracye1
Direction: enhancemente1
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 / resource The stacked ensemble model achieves superior crop yield prediction performance compared with traditional machine learning models.
Method / resource The stacked ensemble model significantly decreases RMSE compared with traditional machine learning models.
Method / resource Mean Absolute Error of the stacked ensemble model is 7.20 tons/hectare.
Method / resource Mean Square Error of the stacked ensemble model is 15570.32 tons 2 /hectare 2.
Method / resource Root Mean Square Error of the stacked ensemble model is 124.78 tons/hectare.
Method / resource Coefficient of determination (R2) of the stacked ensemble model is 0.98.

Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.

“decrease in A”

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