Search claims · PlantClaims

Method / resourceobservedoriginal resultnegativesimulated

Claim · model-written summary

The SDI models indicated a linear decline in relative yield with increasing wetness.

Taxon: CORNZea mays subsp. mayst0
Outcome: relative yielde0
Direction: linear decreasee0
Comparator: increasing wetness (SDI values)e0

Verbatim

e0
“The SDI models indicated a linear decrease in the relative yield with increasing wetness (SDI values)”
✓ located verbatim in the source · abstract, characters 823–924

t0
“CORN”
✓ located verbatim in the source · title, characters 0–4

Title

CORN GROWTH AS AFFECTED BY EXCESS SOIL WATER

S. Mukhtar; J. L. Baker; R. S. Kanwar Transactions of the ASAE 1990. doi:10.13031/2013.31348

Other claims from this paper (9)

Observation Corn was most susceptible to flooding at the early vegetative stage.
Intervention effect Flooding caused maximum plant canopy height reduction at the early vegetative stage.
Intervention effect Flooding caused maximum dry matter reduction at the early vegetative stage.
Intervention effect Flooding caused maximum grain yield reduction at the early vegetative stage.
Observation Early vegetative crop susceptibility factor was 0.64.
Observation Late vegetative crop susceptibility factor was 0.44.
Observation Flowering crop susceptibility factor was 0.15.
Observation Yield formation crop susceptibility factor was 0.19.
Observation Yield SDI regression lines differed considerably between years in the undrained area.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Contributions of the flag leaf and fourth leaf estimated from the grain-yield decrease under shading agreed closely with 14CO2 estimates.

Taxon: Sorghum vulgareSorghum bicolort0
Outcome: percentage contributions to the graine0
Direction: agreed closelye0
Comparator: estimates obtained by using 14CO2e0

Verbatim

e0
“The percentage contributions to the grain by the flag leaf and fourth leaf, estimated from the decrease in grain yield when they were shaded, agreed closely with the estimates obtained by using 14CO2”
✓ located verbatim in the source · abstract, characters 801–1000

t0
“Sorghum vulgare”
✓ located verbatim in the source · title, characters 31–46

Title

Studies of grain production in Sorghum vulgare . II. Sites responsible for grain dry matter production during the post-anthesis period

KS Fischer; GL Wilson Australian Journal of Agricultural Research 1971. doi:10.1071/ar9710039

Other claims from this paper (5)

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 / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

The optical sensor algorithm can decrease environmental contamination due to excess nitrogen fertilization.

Taxon: Cropt0
Outcome: environmental contaminatione0
Direction: decreasee0
Value: 0.4 m2e0

Verbatim

t0
“Crop”
✓ located verbatim in the source · title, characters 35–39

e0
“Using our optical sensor‐based algorithm that employs yield prediction and N responsiveness by location (0.4 m2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization”
✓ located verbatim in the source · abstract, characters 1791–2008

Title

Optical Sensor‐Based Algorithm for Crop Nitrogen Fertilization

W. R. Raun; J. B. Solie; M. L. Stone; et al. Communications in Soil Science and Plant Analysis 2005. doi:10.1080/00103620500303988

Other claims from this paper (3)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

A seedling infection index permitted estimates of subsequent yield decrease.

Taxon: wheat, oats and barleyAvena sativa Hordeum vulgareTriticum aestivumt0
Outcome: estimatese0
Value: 1.4 × (SH+LA+LL)+18e0

Verbatim

e0
“Symptoms of seedling infection incorporated into an index of infection permit estimates to be made o eventual decreases in yield by applying the formula DY = 1.4 × (SH+LA+LL)+18.”
✓ located verbatim in the source · abstract, characters 681–859

t0
“varieties of wheat, oats and barley”
✓ located verbatim in the source · abstract, characters 43–78

Title

Some effects of barley yellow dwarf virus on spring and winter cereals in glasshouse trials

J. K. DOODSON; P. J. W. SAUNDERS Annals of Applied Biology 1970. doi:10.1111/j.1744-7348.1970.tb04593.x

Other claims from this paper (6)

Observation Severely affected cereal plants were stunted.
Intervention effect BYDV decreased cereal grain yields through fewer ears and grains and smaller grains.
Observation Many winter barley varieties were killed by BYDV infection.
Observation Winter wheat varieties were more tolerant than oat and barley varieties.
Association Individual seedling symptoms correlated with reductions in yield.
Method / resource Individual seedling symptoms did not accurately classify all varieties by infection susceptibility.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Y/M selection substantially decreased grain moisture.

Taxon: maizeZea mayst0
Outcome: grain moisturee0
Direction: decreasee0
Comparator: Y/Me1

Not reportedValue

Verbatim

e0
“had a large decrease in grain moisture”
✓ located verbatim in the source · abstract, characters 1416–1454

e1
“The Y/M selection differential for yield was only 64% of that of yield”
✓ located verbatim in the source · abstract, characters 1340–1410

t0
“Selection Criteria for Grain Yield and Moisture in Maize Yield Trials”
✓ located verbatim in the source · title, characters 0–69

Title

Selection Criteria for Grain Yield and Moisture in Maize Yield Trials

J. T. Eta‐Ndu; S. J. Openshaw Crop Science 1992. doi:10.2135/cropsci1992.0011183x003200020010x

Other claims from this paper (12)

Association Grain yield and moisture were positively correlated.
Observation Yield means departed from normality in 18% of experiments.
Observation Moisture departed from normality in 32% of experiments.
Observation Regression of yield on moisture varied substantially, averaging 12.5 kg ha −1 yield per g kg −1 moisture.
Method / resource REG yield selection differential was 92% of direct selection.
Method / resource BSE yield selection differential was 91% of direct selection.
Method / resource DSG yield selection differential was 89% of direct selection.
Method / resource REG, BSE and DSG changed moisture less than selection on yield.
Method / resource Y/M yield selection differential was only 64% of direct selection.
Association Index success was not associated with nonnormality.
Recommendation The authors recommend BSE or REG to identify high-yielding hybrids within maturity groups.
Recommendation The authors advise evaluating selection indices against breeder circumstances and objectives.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The variability response index quantitatively expressed yield decline from soil potassium variability.

Outcome: Decrease in yield due to spatial variability of soil Ke0
Direction: Decreasee0
Comparator: relatively uniform distribution of soil Ke0

Not reportedTaxon

Verbatim

e0
“Decrease in yield due to spatial variability of soil K was quantitatively expressed by the variability response index (VRI), i.e., the product of the variance of soil K and the second derivative of yield with respect to exchangeable K obtained under a relatively uniform distribution of soil K”
✓ located verbatim in the source · abstract, characters 509–802

Title

CORN RESPONSE TO SPATIAL VARIABILITY OF RESIDUAL POTASSIUM

J. P. NDIAYE; R. S. YOST Soil Science 1989. doi:10.1097/00010694-198907000-00001

Other claims from this paper (4)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Cultivar yield changes under competition were generally similar in blends and paired rows.

Outcome: percentage yield increase or decreasee0
Direction: similare0
Comparator: blend and the paired‐rowse0

Verbatim

e0
“The percentage yield increase or decrease of each cultivar in competition generally was similar in the blend and the paired‐rows”
✓ located verbatim in the source · abstract, characters 1113–1241

Title

Evaluation of Intergenotypic Competition with a Paired‐Row Technique¹

W. R. Fehr Crop Science 1973. doi:10.2135/cropsci1973.0011183x001300050024x

Other claims from this paper (2)

Method / resource Paired rows identified good and poor competitors for every soybean cultivar pair tested.
Method / resource Rows 8 cm apart did not perfectly simulate competition in a 1:1 blend.

Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

The controller caused no significant decrease in simulated maize leaf area index relative to constant irrigation.

Taxon: maizeZea mayse1
Outcome: leaf area indexe0
Direction: without any significant decreasee0
Comparator: constant irrigation method of the DSSAT modele0

Verbatim

e0
“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”
✓ located verbatim in the source · abstract, characters 2249–2427

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 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.

“yield decrease”

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