Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Corn yield variability was modeled better than soybean yield variability.

Taxon: CornZea mays subsp. mayse0
Outcome: yield variabilitye0
Direction: bettere0
Comparator: soybean yield variabilitye0

Verbatim

e0
“Corn yield variability was better modeled than was soybean yield variability.”
✓ located verbatim in the source · abstract, characters 1361–1438

Title

Relating Hyperspectral Image Bands and Vegetation Indices to Corn and Soybean Yield

Gab-Sue Jang; Kenneth A. Sudduth; Suk Young Hong; et al. National Remote Sensing Bulletin 2006

Other claims from this paper (5)

Method / resource Late-season NG best estimated corn yield in 2003, with r2 of 0.632.
Method / resource Late-season NG best estimated soybean yield in 2003, with r2 of 0.467.
Method / resource Multiple-band stepwise regression explained similar yield variation.
Method / resource Remote sensing estimated 2003 water-limited yields better.
Method / resource Remote-sensing yield estimates were poorer in 2004 under adequate moisture, with r2 below 0.3.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Yield variability correlated across seasons.

Taxon: cottonGossypium hirsutumt0
Outcome: Yield variabilitye0
Setting: growing seasonse0

Not reportedDirection

Verbatim

e0
“Yield variability was correlated across growing seasons.”
✓ located verbatim in the source · abstract, characters 1667–1723

t0
“COTTON”
✓ located verbatim in the source · title, characters 15–21

Title

VARIABILITY OF COTTON YIELD AND QUALITY

Michael K. Elms; Cary J. Green; Phillip N. Johnson Communications in Soil Science and Plant Analysis 2001. doi:10.1081/css-100103012

Other claims from this paper (10)

Observation Lint yield and fruiting sites showed the greatest within-field variability.
Observation Lint quality parameters showed the lowest variability.
Association Yield correlated with production of fruiting sites each season.
Observation Soil nitrate concentrations varied highly.
Association Cotton yield was negatively correlated with nitrogen in 1997.
Association Cotton yield was positively correlated with calcium in 1997.
Association Cotton yield was positively correlated with soil pH in 1997.
Association Cotton yield was positively correlated with CEC in 1997.
Observation Gross revenue varied considerably, mainly because yield varied.
Recommendation The authors suggest variable nitrogen application as a possible management option.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Climate variability explained one quarter of crop yield variability.

Outcome: variabilitye0
Value: a quartere0
Qualifier: climate variabilitye0

Not reportedTaxonSettingDirection

Verbatim

e0
“with a quarter of this variability explained by climate variability”
✓ located verbatim in the source · abstract, characters 624–691

Title

Climate-Driven Crop Yield and Yield Variability and Climate Change Impacts on the U.S. Great Plains Agricultural Production

Meetpal S. Kukal; Suat Irmak Scientific Reports 2018. doi:10.1038/s41598-018-21848-2

Other claims from this paper (7)

Observation Crop yield variability was one quarter of mean regional yields.
Association Temperature and precipitation explained crop yield variability individually or together in different locations.
Association Observed warming benefited maize yields.
Association Observed warming harmed sorghum yields.
Association Observed warming harmed soybean yields.
Association Observed precipitation trends benefited maize, sorghum and soybean yields.
Intervention effect Irrigated yields were more robust to climate impacts than non-irrigated yields.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Yield variability explained around 1.1% of cassava production variability.

Taxon: CassavaManihot esculentat0
Treatment: yield variabilitye0
Outcome: variability in productione1
Value: 1.1%e0

Not reportedDirection

Verbatim

e0
“around 1.1% by yield variability”
✓ located verbatim in the source · abstract, characters 1206–1238

e1
“variability in production”
✓ located verbatim in the source · abstract, characters 1129–1154

t0
“Cassava”
✓ located verbatim in the source · title, characters 0–7

Title

Cassava production in africa: A panel analysis of the drivers and trends

Waidi Gbenro Adebayo Heliyon 2023. doi:10.1016/j.heliyon.2023.e19939

Other claims from this paper (7)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Climate variability explains up to about 78% of wheat yield variability across Indian states.

Taxon: wheatTriticum aestivumt0
Treatment: Climate variabilitye6
Outcome: wheat yield variabilitye6
Value: up to ∼78%e6

Not reportedDirection

Verbatim

t0
“affecting wheat yield in India”
✓ located verbatim in the source · title, characters 44–74

e6
“Climate variability explains up to ∼78% wheat yield variability across Indian states”
✓ located verbatim in the source · abstract, characters 1150–1234

Title

Temperature and precipitation are adversely affecting wheat yield in India

Anand Madhukar; Vivek Kumar; Kavya Dashora Journal of Water and Climate Change 2022. doi:10.2166/wcc.2022.443

Other claims from this paper (6)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Rainfed yield variability exceeded irrigated yield variability.

Outcome: variability in yielde0
Direction: largee0
Comparator: irrigated conditionse0
Setting: rainfede0

Not reportedTaxon

Verbatim

e0
“The large variability in yield was noted under rainfed as compared to irrigated conditions”
✓ located verbatim in the source · abstract, characters 865–955

Title

Simulating the impact of climate change on chickpea yield under rainfed and irrigated conditions in Madhya Pradesh

A.K.SRIVASTAVA; SANDIP SILAWAT; K.K.AGRAWAL Journal of Agrometeorology 2016. doi:10.54386/jam.v18i1.910

Other claims from this paper (6)

Method / resource The model closely simulated phenology and biomass.
Method / resource The model underestimated seed yield in both cultivars.
Method / resource JG 11 had more simulation error.
Method / resource Observed and simulated values differed substantially under rainfed conditions.
Intervention effect Climate scenarios projected a 102.8 percent irrigated seed yield increase.
Intervention effect Climate scenarios projected a 187.7 percent irrigated seed yield increase.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Maize had the highest yield variability.

Taxon: maizeZea mayse0
Outcome: yield variabilitye0
Direction: higheste0

Verbatim

e0
“maize had the highest yield variability”
✓ located verbatim in the source · abstract, characters 1272–1311

Title

Yield prediction at field level

Oleksandr Kryvoshein; Oleksii Kryvobok; Dmytro Zhylchenko Ukrainian journal of remote sensing 2024. doi:10.36023/ujrs.2024.11.4.275

Other claims from this paper (14)

Method / resource Precipitation alone had limited field-level yield predictive power despite regional yield correlation.
Observation Sunflower and winter wheat had more stable yields.
Method / resource Maize yield model achieved R-squared 0.8.
Method / resource Maize yield model RMSE was 1.5 t/ha.
Method / resource Maize model normalized RMSE was 20%.
Method / resource August vegetation indices and sowing date were the most significant maize predictors.
Method / resource Sunflower yield model R-squared was 0.4.
Method / resource Sunflower yield model RMSE was 0.9 t/ha.
Method / resource Sunflower model normalized RMSE was 23%.
Method / resource May and July average LAI were key sunflower predictors.
Method / resource Winter wheat yield model R-squared was 0.35.
Method / resource Winter wheat yield model RMSE was 0.9 t/ha.
Method / resource Winter wheat model normalized RMSE was 15%.
Recommendation Authors recommend additional soil, microclimate and management factors to improve field-level prediction.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Yield variability exceeded nutritional and compositional variability.

Taxon: cornZea mays subsp. mayst0
Outcome: yield variabilitye0
Direction: greatere0
Comparator: nutritional and biomass compositional variabilitye0

Verbatim

e0
“yield variability was greater than nutritional and biomass compositional variability.”
✓ located verbatim in the source · abstract, characters 995–1080

t0
“Corn”
✓ located verbatim in the source · title, characters 43–47

Title

The Effects of Hybrid Relative Maturity on Corn Stover for Ethanol Production and Biomass Composition

Steve Wilkens; Paul J. Weimer; Joseph G. Lauer Agronomy Journal 2015. doi:10.2134/agronj15.0123

Other claims from this paper (5)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Yearly yield variability exceeded variability among trees for both avocado cultivars.

Taxon: AvocadoPersea americanat0
Outcome: Yearly variabilitye0
Direction: greatere0
Comparator: variability among treese0

Verbatim

e0
“Yearly variability was greater than variability among trees for both cultivars”
✓ located verbatim in the source · abstract, characters 354–432

t0
“Sample Size Estimates for Avocado Yield Experiments”
✓ located verbatim in the source · title, characters 0–51

Title

Sample Size Estimates for Avocado Yield Experiments

Bruce Schaffer; R.M. Baranowski Journal of the American Society for Horticultural Science 1986. doi:10.21273/jashs.111.6.985

Other claims from this paper (7)

Observation Booth 8 had greater yearly total fruit weight variability than Peterson.
Method / resource Fifteen trees per treatment were needed to detect a 10–20% total fruit weight difference at 5% significance.
Method / resource Each added tree only slightly lowered the detectable percentage difference.
Method / resource Increasing experiment duration reduced the detectable percentage difference.
Method / resource After three years, additional years only slightly lowered detectable differences for both bearing patterns.
Recommendation The authors recommend 10 to 15 trees per treatment in avocado yield experiments.
Recommendation The authors recommend conducting avocado yield experiments for at least three years.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Furocoumarin yield showed wide variability.

Outcome: furocoumarin yielde0
Direction: wide range of variabilitye0

Not reportedTaxon

Verbatim

e0
“The species showed wide range of variability for furocoumarin yield”
✓ located verbatim in the source · abstract, characters 101–168

Title

Genetic variability in Heracleum candicans wall

G. M. Devagiri; J. M. Singh; Romesh Chand; et al. Indian Journal of Genetics and Plant Breeding (The) 1997

Other claims from this paper (5)

“yield variability”

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