Search claims · PlantClaims

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Sesame seed yield began to decrease at a plant water stress index of 0.31.

Taxon: sesameSesamum indicume0
Outcome: threshold plant water stress index valuee0
Direction: decreasee0
Value: 0.31e0

Verbatim

e0
“the threshold plant water stress index value, where sesame seed yield starts to decrease, was calculated as 0.31”
✓ located verbatim in the source · abstract, characters 1568–1680

Title

Determination of Irrigation Time by Utilizing Plant Water Stress Index (CWSI) Values of II. Crop Sesame Genotype in Siirt Conditions

UÇAK, Ali Beyhan; ARSLAN, Hüseyin; ÖZÇINAR, Aynur Bilmez; et al. Zenodo (CERN European Organization for Nuclear Research) 2022. doi:10.5281/zenodo.5921767

Other claims from this paper (11)

Observation Full irrigation supplied 575.00 mm in the first year.
Observation Full irrigation supplied 576.66 mm in the following year.
Observation Sesame water consumption under full irrigation was 606.00 mm in the first year.
Observation Sesame water consumption under full irrigation was 646.33 mm in the second year.
Observation Sesame yield under full irrigation was 1110.34 kg per hectare in the first year.
Observation Sesame yield under full irrigation was 1319.00 kg per hectare in the second year.
Method / resource The sesame unstressed lower limit was Tc-Ta=0.373-2.42 VPD in the first year.
Method / resource The sesame unstressed lower limit was Tc-Ta=0.74-2.52 VPD in the second year.
Method / resource The fully stressed sesame upper limit was 2.04 °C in the first year.
Method / resource The fully stressed sesame upper limit was 1.77 °C in the second year.
Association Sesame seed yield was negatively related to plant water stress index.

Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

Using SNP subsets based on functional annotation did not significantly decrease chickpea grain-yield prediction accuracy.

Taxon: chickpeaCicer arietinume1
Outcome: prediction accuracy for grain yielde0
Direction: did not significantly decreasee0
Comparator: whole SNP datasete0

Not reportedValue

Verbatim

e0
“Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.”
✓ located verbatim in the source · abstract, characters 1672–1840

e1
“chickpea breeding lines”
✓ located verbatim in the source · abstract, characters 333–356

Title

Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment

Yongle Li; Pradeep Ruperao; Jacqueline Batley; et al. The Plant Genome 2021. doi:10.1002/tpg2.20166

Other claims from this paper (7)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The model accurately predicted reduced cucumber fruit yield under shading.

Taxon: cucumberCucumis sativuse0
Treatment: shading treatmentse0
Outcome: cucumber fruit yielde0
Direction: decreasee0
Value: decreasee0

Not reportedComparator

Verbatim

e0
“The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting.”
✓ located verbatim in the source · abstract, characters 1249–1428

Title

Estimation of Cucumber Fruit Yield Cultivated Under Different Light Conditions in Greenhouses

Inseo Hong; Jin Yu; Seung Jae Hwang; et al. Horticulturae 2024. doi:10.3390/horticulturae10101117

Other claims from this paper (3)

Method / resource The improved model predicted cucumber yield under control and shading conditions with R2 greater than 0.98.
Method / resource With supplemental lighting, the model predicted cucumber yield with R2 greater than 0.99.
Method / resource The model accurately predicted increased cucumber fruit yield with supplemental lighting.

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Estimates from images before harvest showed only a slight decrease in performance compared with the statistic obtained just before harvest.

Taxon: WheatTriticum aestivumt0
Outcome: performancee1
Direction: slight decreasee1
Comparator: the statistic obtained just before the harveste1

Verbatim

t0
“Wheat Yield Estimates”
✓ located verbatim in the source · title, characters 67–88

e1
“only a slight decrease in performance is observed compared to the statistic obtained just before the harvest”
✓ located verbatim in the source · abstract, characters 1342–1450

Title

Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Remy Fieuzal; Vincent Bustillo; David Collado; et al. Agronomy 2020. doi:10.3390/agronomy10030327

Other claims from this paper (7)

Observation The best performances were obtained when NDVI was combined with the yield maps collected during the crop rotation.
Method / resource In 2014 the coefficient of determination (R2) of the yield estimate was 0.44, the lowest season.
Method / resource In 2014 the root mean square error (RMSE) of the yield estimate was 8.13 quintals per hectare.
Method / resource In the three other years the R2 of the yield estimate was close to or above 0.60.
Method / resource In the three other years the RMSE of the yield estimate was lower than 7 q.h−1.
Method / resource The relative error of the yield estimate in the three other years was inferior to 11.3%.
Method / resource Early and accurate yield estimates were obtained three months before the end of the crop cycle.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The crop water stress index threshold at which sunflower seed yield started to decrease was 0.33 from infrared thermometer measurements at irrigation time.

Taxon: sunflowerHelianthus annuuse0
Outcome: Crop water stress index value thresholde0
Value: 0.33e0

Verbatim

e0
“Crop water stress index value threshold at which sunflower seed yield started to decrease was calculated as 0.33 using the infrared thermometer measurements at the time of irrigation”
✓ located verbatim in the source · abstract, characters 970–1152

Title

Determination of Irrigation Time Using Plant Water Stress Index Values of Second Crop Sunflower in Semi-Arid Climate Conditions

Ali Beyhan Uçak; Halis Seçme Turkish Journal of Agriculture - Food Science and Technology 2021. doi:10.24925/turjaf.v9i12.2289-2295.4641

Other claims from this paper (5)

Method / resource The lower limit (non-stressed baseline) for sunflower plant water stress index was Tc-Ta=-2.528xVPD+0.749 with R2=0.814.
Method / resource The upper limit, at which the plant is completely under water stress, was determined as +3.27 degrees C.
Association Sunflower seed yield was negatively correlated with CWSI values.
Observation Water consumption under full irrigation was 721 mm.
Observation Sunflower seed yield under full irrigation was 3516.00 kg/ha.

Method / resourceobservedoriginal resultnegativesimulated

Claim · model-written summary

The model simulated lower cereal yield due to nitrogen competition.

Taxon: cereale0
Outcome: cereal yielde0
Direction: decreasee0
Qualifier: competition for nitrogene0

Not reportedComparator

Verbatim

e0
“a decrease in cereal yield due to competition for nitrogen”
✓ located verbatim in the source · abstract, characters 2413–2471

Title

Ability of the STICS soil-crop model for simulating performances and inter-specific interactions in tropical cereal-legume intercropping

de Freitas, Mathilde; Couedel, Antoine; Falconnier, Gatien N.; et al. HAL (Le Centre pour la Communication Scientifique Directe) 2024

Other claims from this paper (9)

Method / resource STICS simulated cereal grain yield with similar accuracy for sole crops and intercrops.
Method / resource STICS accuracy for legumes was lower under intercropping than sole cropping.
Method / resource STICS correctly reproduced cereal legume competition and complementarity in Mali and Burkina Faso.
Method / resource STICS came close to reproducing cereal legume competition and complementarity in Brazil.
Method / resource In Senegal STICS overestimated legume partial land equivalent ratio.
Method / resource In Senegal STICS underestimated cereal partial land equivalent ratio.
Method / resource The model simulated lower legume yield due to light competition.
Method / resource Water stress had little simulated effect on yield and crop interactions.
Method / resource The authors judge STICS accuracy acceptable for tropical intercropping where legume yields are not too low.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The artificial neural network reportedly decreased error in predicting crop yield data.

Taxon: crope0
Outcome: error rate from crop yield datae0
Direction: decreasee0

Not reportedComparatorValue

Verbatim

e0
“artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data”
✓ located verbatim in the source · abstract, characters 1311–1456

Title

Predictive Classification Model of Crop Yield Data Using Artificial Neural Network

R. Kanagaraj; E. Elakiya; A. Kumaresan; et al. 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) 2023. doi:10.1109/icirca57980.2023.10220791

Method / resourceobservedoriginal resultnegativesimulated

Claim · model-written summary

At the warmest Brazilian location, simulated yield losses reached 38% under 3 °C warming.

Taxon: soybeanGlycine maxt0
Treatment: + 3 °C temperature risee0
Outcome: yielde0
Direction: decreasee0
Value: 38%e0
Setting: Brazile0

Verbatim

e0
“In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield”
✓ located verbatim in the source · abstract, characters 1109–1262

t0
“soybean”
✓ located verbatim in the source · title, characters 4–11

Title

Are soybean models ready for climate change food impact assessments?

Kritika Kothari; Rafael Battisti; Kenneth J. Boote; et al. European Journal of Agronomy 2022. doi:10.1016/j.eja.2022.126482

Other claims from this paper (10)

Method / resource Models varied considerably in simulated soybean yield responses to temperature and CO2.
Method / resource At the coolest Argentine location, models simulated a 24% yield reduction under 3 °C warming.
Method / resource At the coolest Argentine location, other models simulated yield increases up to 29%.
Method / resource At the warmest Brazilian location, some models simulated no yield effect.
Method / resource Raising CO2 from 360 to 540 ppm produced simulated yield increases of 6% to 31%.
Method / resource Calibration did not reduce variation across models.
Method / resource Calibration altered temperature yield responses in some models.
Method / resource Ensemble means reduced uncertainty in soybean yield simulations.
Method / resource Ensemble mean temperature and CO2 responses resembled literature results.
Recommendation Further model development using elevated CO2 and temperature experiments was recommended.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Feedback lighting reduced CO2 required for fresh mass yield by 17%.

Taxon: basilOcimum basilicumt0
Outcome: CO 2 required to generate fresh mass yielde0
Direction: decreasee0
Value: 17%e0

Verbatim

e0
“delivering a 17% decrease in CO 2 required to generate fresh mass yield”
✓ located verbatim in the source · abstract, characters 1008–1079

t0
“basil at the end of the photoperiod”
✓ located verbatim in the source · abstract, characters 785–820

Title

Green instructions: Intelligent lighting via real-time chlorophyll fluorescence feedback: Enhancing yield and energy efficiency in controlled environment agriculture

Jim Stevens; Phillip Davey; Piotr Kasznicki; et al. Smart Agricultural Technology 2025. doi:10.1016/j.atech.2025.101593

Other claims from this paper (4)

Observation Basil demanded less light at the end of the photoperiod.
Method / resource Feedback lighting improved yield per unit power.
Method / resource Feedback lighting increased yield by 13.5%.
Method / resource Feedback lighting reduced energy consumption per fresh mass by 6.2%.

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%.

“yield decrease”

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