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.

Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

CaNPK and NPK without manure gave high yield and low variability.

Taxon: Winter ryeSecale cerealee0
Treatment: CaNPK and NPK, without additional organic manuree0
Outcome: high yield and low temporal yield variabilitye0

Verbatim

e0
“Winter rye given CaNPK and NPK, without additional organic manure demonstrated high yield and low temporal yield variability.”
✓ located verbatim in the source · abstract, characters 1407–1532

Title

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Marcin Studnicki; Janna Macholdt; Andy Macdonald; et al. Agronomy 2021. doi:10.3390/agronomy11030519

Other claims from this paper (4)

Intervention effect Manure reduced rye yield variability under Ca and CaPK.
Intervention effect Organic input increased rye yield variability under CaNPK and NPK.
Observation Ca and CaPK without manure gave low, unstable yields.
Association Increasing soil organic carbon was associated with larger and more stable yields.

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.

Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Manure reduced rye yield variability under Ca and CaPK.

Taxon: ryeSecale cerealee0
Treatment: additional manuree0
Outcome: temporal yield variability of ryee0
Direction: reducede0
Qualifier: Ca and CaPK (without mineral N)e0

Not reportedComparator

Verbatim

e0
“additional manure in ‘sub-optimal’ mineral fertilizer treatments, such as Ca and CaPK (without mineral N), reduced the temporal yield variability of rye”
✓ located verbatim in the source · abstract, characters 1069–1221

Title

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Marcin Studnicki; Janna Macholdt; Andy Macdonald; et al. Agronomy 2021. doi:10.3390/agronomy11030519

Other claims from this paper (4)

Intervention effect Organic input increased rye yield variability under CaNPK and NPK.
Observation CaNPK and NPK without manure gave high yield and low variability.
Observation Ca and CaPK without manure gave low, unstable yields.
Association Increasing soil organic carbon was associated with larger and more stable yields.

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)

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.

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.

“temporal yield variability”

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