Search claims · PlantClaims

Associationobservedoriginal resultmeasured

Claim · model-written summary

Spatial and temporal corn yield variability were uncorrelated.

Taxon: Corn Silaget0
Outcome: Spatial and temporal variabilitye0

Not reportedSettingDirection

Verbatim

e0
“Spatial and temporal variability were uncorrelated”
✓ located verbatim in the source · abstract, characters 1120–1170

t0
“Corn Silage”
✓ located verbatim in the source · title, characters 31–42

Title

Combining Spatial and Temporal Corn Silage Yield Variability for Management Zone Development

Tulsi P. Kharel; Angel Maresma; Karl J. Czymmek; et al. Agronomy Journal 2019. doi:10.2134/agronj2019.02.0079

Other claims from this paper (3)

Observation Corn silage yield varied among farms, fields, within fields, and across years.
Observation Variable management zones covered 30 to 44% of each farm.
Method / resource Yield stability maps can be used to develop management zones.

Observationobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Yield and quality had significant spatial variability.

Taxon: APPLESt0
Outcome: spatial variability in yield and qualitye0

Verbatim

t0
“APPLES”
✓ located verbatim in the source · title, characters 63–69

e0
“Significant spatial variability in yield and quality were found”
✓ located verbatim in the source · abstract, characters 797–860

Title

SPATIAL AND TEMPORAL VARIABILITY OF YIELD AND FRUIT QUALITY IN APPLES

K.D. Aggelopoulou; G.D. Nanos; T.A. Gemtos Acta Horticulturae 2010. doi:10.17660/actahortic.2010.877.96

Other claims from this paper (9)

Observation Quality varied less spatially than yield.
Observation Soluble solids content and flesh firmness had low spatial variability.
Observation Fruit mass, acidity and skin colour had high spatial variability.
Observation Year-to-year yield variability was low in Red Chief.
Observation Year-to-year yield variability was high in Fuji due to alternate bearing.
Observation Quality characteristics varied significantly across years.
Intervention effect Cold storage reduced flesh firmness and acidity.
Intervention effect Cold storage raised soluble solids content in Red Chief.
Intervention effect Cold storage did not change soluble solids content in Fuji.

Associationobservedoriginal resultsimulated

Claim · model-written summary

Spatial soil variability affected simulated maize yield.

Taxon: maizeZea mayse0
Treatment: spatial soil variabilitye0
Outcome: maize yielde0

Not reportedSettingDirection

Verbatim

e0
“maize yield was impacted by spatial soil variability”
✓ located verbatim in the source · abstract, characters 756–808

Title

Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate

Rintu Sen; Zachary T. Zambreski; Vaishali Sharda Agronomy 2023. doi:10.3390/agronomy13030906

Other claims from this paper (4)

Method / resource Spatially distributed soils simulated maize yield better than using the dominant county soil.
Association Higher temperature and lower growing-season precipitation projected greater maize yield loss.
Observation At century end, RCP4.5 projected 28% higher maize yield loss.
Observation At century end, RCP8.5 projected 45% higher maize yield loss.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Temporal variability mattered more than spatial variability for germinable seed storage.

Taxon: seedse0
Outcome: storage of germinable seedse0
Direction: more importante0
Comparator: spatial variabilitye0

Verbatim

e0
“temporal variability, which was more important to the storage of germinable seeds than spatial variability”
✓ located verbatim in the source · abstract, characters 449–555

Title

Spatial and Temporal Variation in the Seed Bank of a Semiarid Grassland

Debra P. Coffin; William K. Lauenroth American Journal of Botany 1989. doi:10.2307/2444773

Other claims from this paper (6)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Temporal variability dominated spatial variability in the study.

Taxon: Cropt0
Outcome: Temporal variabilitye0
Direction: much more dominante0
Comparator: spatial variabilitye0

Verbatim

e0
“Temporal variability was much more dominant than spatial variability in this study.”
✓ located verbatim in the source · abstract, characters 1441–1524

t0
“Crop Sequences”
✓ located verbatim in the source · title, characters 50–64

Title

Fractal Analysis of Temporal Yield Variability of Crop Sequences: Implications for Site‐Specific Management

Bahman Eghball; Gary E. Varvel Agronomy Journal 1997. doi:10.2134/agronj1997.00021962008900060001x

Other claims from this paper (6)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Only Brix showed spatial variability.

Taxon: arabica coffeeCoffea arabicat0
Outcome: values of ° Brixe0
Direction: spatial variabilitye0

Verbatim

e0
“Only the values of ° Brix showed spatial variability”
✓ located verbatim in the source · abstract, characters 1361–1413

t0
“arabica coffee”
✓ located verbatim in the source · title, characters 31–45

Title

Quality spatial variability of arabica coffee and its relationship with Brix degree and fruit reflectance.

S. A. Silva; Daniel Marçal de Queiroz; Francisco de Assis de Carvalho Pinto; et al. 2012

Other claims from this paper (2)

Association Red-band reflectance was highly correlated with coffee cherry quality.
Association Brix did not significantly correlate with coffee cherry quality.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Spatial variability was below temporal variability and frequently below 1 mm.

Taxon: cornZea mays subsp. mayse1
Outcome: Spatial variabilitye0
Direction: less thane0
Value: 1 mme0

Verbatim

e0
“Spatial variability was less than temporal variability, and frequently less than 1 mm”
✓ located verbatim in the source · abstract, characters 1234–1319

t0
“crop yield”
✓ located verbatim in the source · title, characters 79–89

e1
“by corn and soybean phenological phase”
✓ located verbatim in the source · abstract, characters 1035–1073

Title

Spatial and temporal precipitation variability as a component of site-specific crop yield variability

M. R. O'Neal Purdue e-Pubs (Purdue University System) 2000

Other claims from this paper (10)

Method / resource The best neural net had an overall yield prediction error of 11.9%.
Method / resource Farm scale yield error was 10.9%.
Method / resource County scale yield error was 10.5%.
Method / resource State scale yield error was 7.9%.
Observation Spatial precipitation variability among farm gauges was 0.25 to 1.73 mm·day−1.
Observation Temporal precipitation variability was 0.17 to 3.40 mm·day−1.
Method / resource Simulated corn yield differed by 15.8% across precipitation sources.
Method / resource Simulated yield differed from the long term mean by 21.5%.
Observation The most profitable nitrogen strategy did not depend on precipitation data source.
Observation Using the nearest NWS station was more profitable than measuring precipitation on site.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Spatial variability increased during the next eight years.

Outcome: spatial variabilitye0
Direction: increasede0

Not reportedTaxon

Verbatim

e0
“spatial variability increased”
✓ located verbatim in the source · abstract, characters 1041–1070

Title

Vegetation response to restoration management of a blanket bog damaged by drainage and afforestation

Mark H. Hancock; Daniela Klein; Roxane Andersen; et al. Applied Vegetation Science 2018. doi:10.1111/avsc.12367

Other claims from this paper (7)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Cocoa monthly yield showed high spatial variability, except in January.

Taxon: CocoaTheobroma cacaoe1
Outcome: spatial variabilitye1
Qualifier: except in Januarye1

Verbatim

e1
“Cocoa monthly yield, except in January, presented high spatial variability”
✓ located verbatim in the source · abstract, characters 924–998

Title

Spatial-temporal variability of leaf chlorophyll and its relationship with cocoa yield

Caique C. Medauar; Ícaro M. Galvão; Luis C. C. Carvalho; et al. Revista Brasileira de Engenharia Agrícola e Ambiental 2018. doi:10.1590/1807-1929/agriambi.v22n3p164-169

Other claims from this paper (3)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Spatial variability of potassium affected the spatial variability of soybean grain yield.

Taxon: SoybeanGlycine maxt0
Compound: potassium (K)e7
Outcome: spatial variability of grain yielde7

Not reportedDirection

Verbatim

t0
“A New Proposal for Soybean Plant Stand: Variation Based on the Law of the Minimum”
✓ located verbatim in the source · title, characters 0–81

e7
“Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield”
✓ located verbatim in the source · abstract, characters 1381–1502

Title

A New Proposal for Soybean Plant Stand: Variation Based on the Law of the Minimum

Fábio Henrique Rojo Baio; Ricardo Gava; Larissa Pereira Ribeiro Teodoro; et al. Plants 2024. doi:10.3390/plants13223193

Other claims from this paper (5)

“spatial variability”

Claim types

Direction of the findings

Over time

Claim type

Research area

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