Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

AquaCrop simulated well the decrease of silage maize biomass yield under drought, as happened in the field.

Taxon: Silage Maizet0
Outcome: biomass yielde1
Comparator: as happened in the fielde1

Verbatim

e1
“AquaCrop simulated well the decrease of the biomass yield (B-yield) of silage maize in response to drought as happened in the field”
✓ located verbatim in the source · abstract, characters 595–726

t0
“Silage Maize”
✓ located verbatim in the source · title, characters 10–22

Title

Irrigated Silage Maize Yield and Water Productivity Response to Deficit Irrigation in an Arid Region

H. R. Salemi; Mohd Amin Mohd Soom; Sayed‐Farhad Mousavi; et al. Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) 2011

Other claims from this paper (10)

Intervention effect Silage maize biomass yield was decreased by 9.9% under deficit irrigation compared with fully irrigated conditions.
Method / resource The coefficient of determination for simulated silage maize B-yield was 0.95.
Method / resource The coefficient of determination for simulated water productivity of silage maize was 0.99.
Method / resource The coefficient of determination of 0.77 for actual evapotranspiration was not satisfactory.
Method / resource Across three years, RMSE of the AquaCrop simulations ranged from 0.90% to 3.85%.
Method / resource Across three years, the d index of the AquaCrop simulations ranged from 0.98 to 1.
Method / resource Across three years, ME of the AquaCrop simulations ranged from 1.25% to 6.4%.
Method / resource Across three years, CRM of the AquaCrop simulations ranged from -0.027 to 0.03.
Method / resource Across three years, E of the AquaCrop simulations ranged from 0.817 to 100%.
Method / resource A local second-degree polynomial crop water production function for silage maize was presented.

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 / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

The FAO-based methodology used to assess the risk of cotton yield decrease was considered efficient after validation.

Taxon: cottonGossypium hirsutume0
Outcome: risk of yield decreasee3

Not reportedComparatorValue

Verbatim

e0
“The objective of this work was to develop the zoning of the risk estimation of cotton yield reduction in the state of Mato Grosso, using the FAO method”
✓ located verbatim in the source · abstract, characters 215–366

e3
“Trough the validation of the obtained results, it can be considered that the methodology adopted in this work to verify the risk of yield decrease proved to be efficient”
✓ located verbatim in the source · abstract, characters 864–1033

Title

Zoning of Water Deficiency Risk for Conventional Cotton in Mato Grosso

Elizangela Selma da Silva; José Holanda Campelo Júnior; Francisco De Almeida Lobo; et al. Journal of Experimental Agriculture International 2019. doi:10.9734/jeai/2019/v35i430213

Other claims from this paper (3)

Association The southernmost regions of Mato Grosso had the highest cotton yield reduction risk, attributed to lower precipitation.
Intervention effect Sowing period 1 had the lowest cotton yield reduction risk.
Association The late-cycle cultivar in season 4 had the highest cotton yield reduction risk.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

NaN03-soluble soil nickel better predicted lettuce yield decrease than total or CaCl2-soluble soil nickel.

Taxon: lettuceLactuca sativae0
Treatment: NaN03‐soluble soil Nie0
Outcome: decrease in yielde0
Direction: better suitede0
Qualifier: irrespective of soil typee0
Comparator: total soil Ni (Added+ Native) and CaCl2 soluble soil Nie0

Verbatim

e0
“NaN03‐soluble soil Ni, irrespective of soil type, is better suited to predict the decrease in yield and increase in Ni‐concentration of lettuce leaves as compared to total soil Ni (Added+ Native) and CaCl2 soluble soil Ni.”
✓ located verbatim in the source · abstract, characters 489–711

Title

The effect of graded doses of nickel on the yield, the nickel content of lettuce and the soil respiration

S.K. Gupta; H. Hani; E. Santschi; et al. Toxicological & Environmental Chemistry Reviews 1987. doi:10.1080/02772248709357189

Other claims from this paper (4)

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.

“yield decrease”

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