Search claims · PlantClaims

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Multifractal methods characterized spatial and temporal leaf area variation and its multiscale yield relation.

Taxon: maizeZea mayse0
Outcome: spatial and temporal variability of maize leaf area and multiscale relationship between leaf area and yielde0

Not reportedComparatorValue

Verbatim

e0
“Multifractal theory can effectively characterize spatial and temporal variability of maize leaf area and multiscale relationship between leaf area and yield”
✓ located verbatim in the source · abstract, characters 734–890

Title

Spatial variability of maize leaf area and relationship with yield

Lingling Zhang; Peng Yu; Jilong Liu; et al. Agronomy Journal 2021. doi:10.1002/agj2.20963

Other claims from this paper (5)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Temporal precipitation variability was 0.17 to 3.40 mm·day−1.

Taxon: cornZea mays subsp. mayse1
Outcome: Median absolute deviation from a reference yeare0
Value: 0.17 to 3.40 mm·day−1e0

Verbatim

e0
“Median absolute deviation from a reference year was 0.17 to 3.40 mm·day−1 (temporal/year-to-year variability)”
✓ located verbatim in the source · abstract, characters 1123–1232

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

Year-to-year yield variability was high in Fuji due to alternate bearing.

Taxon: APPLESt0
Material: Fujie0
Outcome: Temporal variability (differences from year to year) in yielde0
Direction: highe0
Qualifier: due to alternate bearinge0

Verbatim

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

e0
“Temporal variability (differences from year to year) in yield was low in 'Red Chief and high in 'Fuji' trees due to alternate bearing”
✓ located verbatim in the source · abstract, characters 1060–1193

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 Yield and quality had significant spatial variability.
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 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.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Organic soybean yield variability was highest, at 48%.

Taxon: soybeanGlycine maxe0
Outcome: Temporal yield variabilitye1
Direction: higheste0
Value: 48%e0

Verbatim

e0
“was highest in the ORG system during the soybean (CV = 48%)”
✓ located verbatim in the source · abstract, characters 1735–1794

e1
“Temporal yield variability differed among management systems and rotation phases and was highest in the ORG system during the soybean (CV = 48%) and winter wheat (CV = 33%) phases of the rotation”
✓ located verbatim in the source · abstract, characters 1650–1845

Title

Temporal Yield Variability under Conventional and Alternative Management Systems

Richard G. Smith; Fabian D. Menalled; G. P. Robertson Agronomy Journal 2007. doi:10.2134/agronj2007.0096

Other claims from this paper (10)

Observation Conventional and no-till yields were similar across crop phases.
Observation Conventional and no-till wheat yields exceeded low-input wheat yields.
Observation Organic corn yields were lower than other systems.
Observation Organic winter wheat yields were lower than other systems.
Observation Soybean yields were similar across management systems.
Observation Organic winter wheat yield variability was highest, at 33%.
Observation Low-input corn yield variability was 40% lower than conventional.
Observation No-till soybean yield variability was 33% lower than conventional.
Observation No-till corn yield variability was similar to conventional.
Observation No-till wheat yield variability was similar to conventional.

Observationobservedoriginal resultmeasured

Claim · model-written summary

At a nonmanured site, temporal yield stability across dry years was associated with spatial yield difference variability in response to N (r = 0.96).

Taxon: cornZea mays subsp. mayse0
Outcome: temporal yield stability across dry yearse4
Value: r = 0.96e4
Qualifier: At a nonmanured sitee4

Verbatim

e0
“We evaluated two corn ( Zea mays L.) hybrids at two N rates”
✓ located verbatim in the source · abstract, characters 116–175

e4
“At a nonmanured site, spatial yield difference variability in response to N existed with temporal yield stability across dry years ( r = 0.96)”
✓ located verbatim in the source · abstract, characters 776–918

Title

Spatial Yield Response of Two Corn Hybrids at Two Nitrogen Levels

Tawainga W. Katsvairo; William J. Cox; Harold M. Van Es; et al. Agronomy Journal 2003. doi:10.2134/agronj2003.1012

Other claims from this paper (9)

Intervention effectobservedoriginal resultpositivesimulated

Claim · model-written summary

Irrigation reduced spatial and temporal yield variability.

Taxon: CornZea mays subsp. mayst0
Treatment: Irrigatione0
Outcome: variability both spatially and temporallye0
Direction: lesse0

Not reportedComparator

Verbatim

e0
“Irrigation improved yield by at least 1000 kg ha-1 in half of the years simulated, and alsoshowed to have less variability both spatially and temporally”
✓ located verbatim in the source · abstract, characters 1067–1219

t0
“Corn”
✓ located verbatim in the source · title, characters 73–77

Title

Simulation of Spatially Variable Precision Irrigation and Its Effects On Corn Growth Using CERES-Maize

Kendall DeJonge; Amy Kaleita 2006 Portland, Oregon, July 9-12, 2006 2006. doi:10.13031/2013.20710

Other claims from this paper (6)

Intervention effect Irrigation improved simulated yield by at least 1000 kg ha-1 in half the years.
Intervention effect Precision irrigation yielded slightly more than scheduled uniform irrigation.
Association Topography most influenced spatial yield variability.
Observation Largest yield improvements occurred on steep sideslopes and hilltops.
Observation Irrigation returned economically in only three of 28 years.
Association High capital costs were the leading constraint on economic feasibility.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Post-heading heat stress and average temperature explained about 29% of the observed spatial and temporal yield variability.

Taxon: wheatTriticum aestivume0
Treatment: post-heading heat stress and average temperaturee0
Outcome: yield variabilitye0
Value: 29%e0

Not reportedDirection

Verbatim

e0
“The correlation between measured grain yields and post-heading heat stress and average temperature were statistically significant in the entire wheat-producing region, and explained about 29% of the observed spatial and temporal yield variability”
✓ located verbatim in the source · abstract, characters 1148–1394

Title

Post‐heading heat stress and yield impact in winter wheat of China

Bing Liu; Leilei Liu; Liying Tian; et al. Global Change Biology 2013. doi:10.1111/gcb.12442

Other claims from this paper (5)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

NDVI K-means delineated sunflower subfield yield variability early.

Taxon: sunflower cropHelianthus annuust0
Outcome: subfield crop spatial and temporal yield variabilitye0

Not reportedComparator

Verbatim

t0
“sunflower crop”
✓ located verbatim in the source · title, characters 51–65

e0
“the k-means algorithm has early delineated the subfield crop spatial and temporal yield variability.”
✓ located verbatim in the source · abstract, characters 982–1082

Title

Understanding the spatio-temporal behaviour of the sunflower crop for subfield areas delineation using Sentinel‐2 NDVI time-series images in an organic farming system

Stefano Marino Heliyon 2023. doi:10.1016/j.heliyon.2023.e19507

Other claims from this paper (9)

Observation Sunflower achene yield ranged from 1.3 to 3.77 t ha −1 with significant within-field variability.
Observation Hand-sampled cluster 1 had mean achene yield of 3.54 t ha −1.
Observation Hand-sampled cluster 2 had mean achene yield of 2.98 t ha −1.
Observation Hand-sampled cluster 3 had mean achene yield of 2.07 t ha −1.
Method / resource The best subfield identification period ran from inflorescence development to fruit development.
Observation Cluster 1 covered 42.4% of area and 50% of achene yield.
Observation Cluster 2 covered 35% of both surface and yield.
Observation Cluster 3 covered 22.2% of area and 15% of achene yield.
Method / resource Sentinel-2 NDVI K-means delineated sunflower subfield areas.

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

Models reproduced temporal yield variability for maize with correlation up to 0.888.

Taxon: maizeZea mayse0
Outcome: time series correlation coefficients (r)e0
Value: 0.888e0
Comparator: observed temporal variabilitye0
Setting: global scalee0

Verbatim

e0
“maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale)”
✓ located verbatim in the source · abstract, characters 839–1089

Title

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Christoph Müller; Joshua Elliott; James Chryssanthacopoulos; et al. Geoscientific model development 2017. doi:10.5194/gmd-10-1403-2017

Other claims from this paper (10)

Method / resource Models reproduced temporal yield variability for wheat with correlation up to 0.673.
Method / resource Models reproduced temporal yield variability for soybean with correlation up to 0.643.
Method / resource Most models reproduced rice yield variability poorly.
Method / resource Many models reproduced yield variability for most major producing countries, and some did for all countries.
Method / resource Crop-model ensemble explained more yield variability than regression models for maize.
Method / resource Crop-model ensemble explained more yield variability than regression models for soybean.
Method / resource Crop-model ensemble did not explain more yield variability than regression models for wheat.
Method / resource Crop-model ensemble did not explain more yield variability than regression models for rice.
Recommendation Authors propose the best performing model by crop and region as a benchmark.
Method / resource The model evaluation system was made accessible for comparison with reference data and the GGCMI benchmark.

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

Models reproduced temporal yield variability for wheat with correlation up to 0.673.

Taxon: wheatTriticum aestivume0
Outcome: time series correlation coefficients (r)e0
Value: 0.673e0
Comparator: observed temporal variabilitye0
Setting: global scalee0

Verbatim

e0
“maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale)”
✓ located verbatim in the source · abstract, characters 839–1089

Title

Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

Christoph Müller; Joshua Elliott; James Chryssanthacopoulos; et al. Geoscientific model development 2017. doi:10.5194/gmd-10-1403-2017

Other claims from this paper (10)

Method / resource Models reproduced temporal yield variability for maize with correlation up to 0.888.
Method / resource Models reproduced temporal yield variability for soybean with correlation up to 0.643.
Method / resource Most models reproduced rice yield variability poorly.
Method / resource Many models reproduced yield variability for most major producing countries, and some did for all countries.
Method / resource Crop-model ensemble explained more yield variability than regression models for maize.
Method / resource Crop-model ensemble explained more yield variability than regression models for soybean.
Method / resource Crop-model ensemble did not explain more yield variability than regression models for wheat.
Method / resource Crop-model ensemble did not explain more yield variability than regression models for rice.
Recommendation Authors propose the best performing model by crop and region as a benchmark.
Method / resource The model evaluation system was made accessible for comparison with reference data and the GGCMI benchmark.

“temporal yield variability”

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