Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

A theoretical model suggests precision farming can reduce temporal yield variability under plausible circumstances.

Treatment: precision farminge0
Outcome: temporal yield variabilitye0
Direction: reducee0
Qualifier: plausible circumstancese0

Not reportedTaxonComparator

Verbatim

e0
“A theoretical model is presented that suggests that there are plausible circumstances under which precision farming can reduce temporal yield variability.”
✓ located verbatim in the source · abstract, characters 345–499

Title

Risk Management Potential of Precision Farming Technologies

J. Lowenberg-DeBoer Journal of Agricultural and Applied Economics 1999. doi:10.1017/s1074070800008555

Other claims from this paper (1)

Intervention effect An on-farm trial supported risk reduction from site-specific phosphorus and potassium management.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The monitoring system showed real-time temporal and spatial yield variation in the paddy field.

Taxon: paddye0
Outcome: real-time temporal and spatial variability classification and quantificatione0
Value: real-timee0

Verbatim

e0
“The AYM system could show the real-time temporal and spatial variability classification and quantification in paddy field.”
✓ located verbatim in the source · abstract, characters 833–955

Title

Yield Variability Assessment in Paddy Crop using Automatic Yield Monitoring System

N. S. Chandel; K. N. Agrawal Journal of Agricultural Engineering (India) 2019. doi:10.52151/jae2019563.1687

Other claims from this paper (4)

Method / resource The yield monitor calibration factor for paddy crop was 6.79.
Method / resource A one-hectare plot was divided into five yield zones spanning below 1900 to above 5000 kg.ha-1.
Method / resource Automatic monitor yield differed from actual yield by (+) 2.8 to (+)5.1 per cent.
Observation Maximum temporal yield variability was 14.7% between 2014 and 2017.

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)

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.

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

Models reproduced temporal yield variability for soybean with correlation up to 0.643.

Taxon: soybeanGlycine maxe0
Outcome: time series correlation coefficients (r)e0
Value: 0.643e0
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 wheat with correlation up to 0.673.
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 resultpositivemeasured

Claim · model-written summary

NDVI yield maps captured temporal and spatial variation in the study area.

Taxon: wheatTriticum aestivumt0
Outcome: temporal and spatial variabilitye0
Comparator: yield mapse0

Verbatim

e0
“Through yield maps, the NDVI perceived the temporal and spatial variability in the study area.”
✓ located verbatim in the source · abstract, characters 1515–1609

t0
“Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina”
✓ located verbatim in the source · title, characters 0–111

Title

Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina

Mariano F. Lopresti; Carlos M. Di Bella; Américo J. Degioanni Information Processing in Agriculture 2015. doi:10.1016/j.inpa.2015.06.001

Other claims from this paper (5)

Method / resource The day 289 image gave the best NDVI fit for early wheat yield estimation.
Association Better weather conditions corresponded to higher wheat yields in yield maps.
Association Higher soil productivity corresponded to more area with higher yields.
Method / resource The calibrated model estimated official department yields with R squared 0.75.
Method / resource The method predicted wheat yield 30 days before harvest.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Clustering identified stable spatial and temporal yield clusters.

Taxon: riceOryza sativae1
Outcome: stable, physically meaningful clusters with recognizable spatial and temporal structuree0

Not reportedComparator

Verbatim

e0
“The clustering procedure successfully identified stable, physically meaningful clusters with recognizable spatial and temporal structure”
✓ located verbatim in the source · abstract, characters 954–1090

e1
“rice fields”
✓ located verbatim in the source · abstract, characters 547–558

Title

Factors Underlying Yield Variability in Two California Rice Fields

Alvaro Roel; Richard E. Plant Agronomy Journal 2004. doi:10.2134/agronj2004.1481

Other claims from this paper (2)

Method / resource Randomization may offer an alternative to fuzzy clustering.
Method / resource CART identified some but not all factors behind cluster patterns.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Clustering Sentinel-2 NDVI detected early spatial and temporal crop variation.

Taxon: cropt0
Outcome: crop spatial and temporal variabilitye0
Value: eight different stagese0

Verbatim

t0
“crop”
✓ located verbatim in the source · title, characters 46–50

e0
“The cluster analysis of NDVI data from Sentinel-2 collected at eight different stages detected the crop spatial and temporal variability at an early stage”
✓ located verbatim in the source · abstract, characters 1425–1579

Title

Understanding the spatio-temporal behavior of crop yield, yield components and weed pressure using time series Sentinel-2-data in an organic farming system

Stefano Marino European Journal of Agronomy 2023. doi:10.1016/j.eja.2023.126785

Other claims from this paper (6)

“temporal yield variability”

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Over time

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