Refine ·

Claim type

Research area

Polarity

Statistics

More filters

Clear filters
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

Field testing found a significant decrease in bird numbers in targeted areas.

Interactor
birdsAvese0pest
Outcome
number of birds in the targeted arease0
Direction
decreasinge0
Value
significante0

Verbatim

e0

“the result of the prototype field-testing showed a significant effect in decreasing the number of birds in the targeted areas”

✓ located verbatim in the source · abstract, characters 1282–1407
e1

“Filipino rice farmers, resulting in losses in annual harvest yield. Farmers suffer the challenge of crop damage by birds”

✓ located verbatim in the source · abstract, characters 45–165

Title

Bird-Repelling Device for Agricultural Application using Motion Detection

Maribelle Dequilla-Pabiania; Chris Julian C. Alcedo; Henry G. Morales; et al. 2024 16th International Conference on Computer and Automation Engineering (ICCAE) 2024. doi:10.1109/iccae59995.2024.10569619

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Smart irrigation increased crop yield by 15%.

Taxon
Sugarcanet0
Outcome
crop yielde0
Direction
increasinge0
Value
$15 \%$e0

Not reportedComparator

Verbatim

t0

“Sugarcane Cultivation”

✓ located verbatim in the source · title, characters 50–71
e0

“the system can decrease water consumption by up to $25 \%$ while increasing crop yield by $15 \%$”

✓ located verbatim in the source · abstract, characters 698–795

Title

IoT-Based Smart Irrigation System for Sustainable Sugarcane Cultivation: Enhancing Resource Management and Optimizing Crop Yield

Raksha Vishwanath; Sinchana N Makam; H S Gururaja 2024 First International Conference on Software, Systems and Information Technology (SSITCON) 2024. doi:10.1109/ssitcon62437.2024.10796232

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

Claim · model-written summary

A literature lettuce yield of 700 kg m −2 yr −1 would require a 51% shorter crop cycle of 6.8 days.

Taxon
lettuceLactuca sativae0
Outcome
crop cycle timee0
Direction
decreasee0
Value
51%e0
Qualifier
6.8 dayse0
Comparator
700 kg m −2 yr −1e0

Verbatim

e0

“Further improvements in VF lettuce yields from the literature (700 kg m −2 yr −1 ) would require an unprecedented 51% decrease in crop cycle time (6.8 days).”

✓ located verbatim in the source · abstract, characters 813–970

Title

Think sink, not source: how vertical farming’s potential is limited by crop breeding

Gertjan Meeuws; Daan Heeling; José M. Mogollón; et al. Frontiers in Plant Science 2025. doi:10.3389/fpls.2025.1621684

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

Claim · model-written summary

Sentinel-2 correction slowed LAI decline from the milking stage.

Taxon
winter wheatTriticum aestivume1
Outcome
LAI decreasee0
Direction
slowed downe0
Comparator
milking stagee0

Verbatim

e0

“the LAI decrease from the milking stage was slowed down”

✓ located verbatim in the source · abstract, characters 1071–1126
e1

“growth stage of winter wheat across three counties”

✓ located verbatim in the source · abstract, characters 374–424

Title

Winter wheat yield estimation based on assimilated Sentinel-2 images with the CERES-Wheat model

Zheng-chun LIU; Chao WANG; Ru-tian BI; et al. Journal of Integrative Agriculture 2021. doi:10.1016/s2095-3119(20)63483-9

Other claims from this paper (7)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

At harvest, inflorescence and bunch detection error fell slightly to 46%.

Taxon
GrapevineVitist0
Organ / stage
Inflorescences and bunchese1
Outcome
MA%Ee0
Direction
slight decreasee0
Value
46%e0

Verbatim

e0

“with a slight decrease at harvest (MA%E=46%)”

✓ located verbatim in the source · abstract, characters 1502–1546
e1

“Inflorescences and bunches showed the highest MA%E”

✓ located verbatim in the source · abstract, characters 1396–1446
t0

“Grapevine”

✓ located verbatim in the source · title, characters 0–9

Title

Grapevine yield components detection using image analysis: a case study with the white cultivar ‘Encruzado’

G. Victorino; C.M. Lopes Acta Horticulturae 2021. doi:10.17660/actahortic.2021.1314.22

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

Claim · model-written summary

SAR signals saturated and were weakly sensitive to declining crop water content during senescence.

Outcome
SAR signals saturate and are weakly sensitive to the decrease of the crop water contente0
Qualifier
during the senescencee0

Not reportedComparatorValue

Verbatim

e0

“during the senescence, the SAR signals saturate and are weakly sensitive to the decrease of the crop water content”

✓ located verbatim in the source · abstract, characters 1185–1299
t0

“yield of corn”

✓ located verbatim in the source · title, characters 86–99

Title

Assimilation of SAR and optical data into an agro-meteorological model for monitoring yield of corn

Maël Ameline; Rémy Fieuzal; Julie Betbeber; et al. ScholarsArchive (Brigham Young University) 2016

Other claims from this paper (3)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

A new model represented both increasing and decreasing lettuce fresh yield.

Taxon
iceberg lettucet0
Outcome
fresh yielde0
Comparator
a new modele0

Verbatim

e0

“a new model was created to reflect both the increase and decrease in fresh yield”

✓ located verbatim in the source · abstract, characters 960–1040
t0

“iceberg lettuce”

✓ located verbatim in the source · title, characters 74–89

Title

Effects of water and salinity stresses on growth, yield, and water use of iceberg lettuce

Ahmet Kurunc Journal of the Science of Food and Agriculture 2021. doi:10.1002/jsfa.11223

Other claims from this paper (11)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The model intercept decreased as target grain yield decreased.

Taxon
winter wheatTriticum aestivumt0
Outcome
model’s intercepte0
Direction
reducede0
Comparator
grain yield goale0

Verbatim

e0

“The model’s intercept was reduced according to the decrease in the grain yield goal.”

✓ located verbatim in the source · abstract, characters 1922–2006
t0

“winter wheat”

✓ located verbatim in the source · title, characters 30–42

Title

Optimizing nitrogen rates for winter wheat using in-season crop N status indicators

Raffaele Meloni; Eleonora Cordero; Luca Capo; et al. Field Crops Research 2024. doi:10.1016/j.fcr.2024.109545

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

Claim · model-written summary

Regression analyses described the ozone and yield relationship.

Outcome
models describing this relationshipe0

Not reportedComparator

Verbatim

e0

“regression analyses provided models describing this relationship”

✓ located verbatim in the source · abstract, characters 1007–1071
e1

“Stoneville 213 was sensitive to O 3 ‐induced yield decrease”

✓ located verbatim in the source · abstract, characters 943–1002
e2

“cotton ( Gossypium hirsutum L.)”

✓ located verbatim in the source · abstract, characters 368–399

Title

Injury and Yield Response of Cotton to Chronic Doses of Ozone and Sulfur Dioxide

Allen S. Heagle; W. W. Heck; V. M. Lesser; et al. Journal of Environmental Quality 1986. doi:10.2134/jeq1986.00472425001500040011x

Other claims from this paper (4)
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)

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.