Search claims · PlantClaims

Method / resourceauthor interpretationoriginal resultsimulated

Claim · model-written summary

A theoretical model suggests plausible conditions in which precision farming reduces temporal yield variability.

Treatment: precision farminge0
Outcome: temporal yield variabilitye0
Direction: reducee0

Not reportedTaxonComparatorValue

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 336–489

Title

RISK MANAGEMENT POTENTIAL OF PRECISION FARMING TECHNOLOGIES

Lowenberg-DeBoer, James RePEc: Research Papers in Economics

Other claims from this paper (1)

Intervention effect An on farm trial supports risk reducing benefits from site specific phosphorus and potassium management.

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 / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

Fractal analysis was judged useful for quantifying temporal yield variation.

Taxon: cropse5
Outcome: quantifying temporal variability in yielde5
Qualifier: usefule5

Not reportedComparatorValue

Verbatim

e5
“It appears that fractal analysis is useful in quantifying temporal variability in yield of various crops”
✓ located verbatim in the source · abstract, characters 1973–2077

Title

Fractal Description of Temporal Yield Variability of 10 Crops in the United States

Bahman Eghball; James F. Power Agronomy Journal 1995. doi:10.2134/agronj1995.00021962008700020003x

Other claims from this paper (9)

Observation Soybean yield rose about twofold from 1930 to 1990.
Observation Oat yield rose about twofold.
Observation Barley yield rose about twofold.
Observation Maize yield rose about sixfold.
Observation Most crops had large yield differences after 1960.
Observation Yield fractal dimensions ranged from 1.20 to 1.47.
Observation Rice had the least short-term yield variation.
Observation Oat and soybean had the greatest short-term yield variation.
Observation Average crop yield varied less over time than soil parameters varied spatially.

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 / resourceobservedoriginal resultsimulated

Claim · model-written summary

Agreement between simulated and observed temporal variability was lower.

Outcome: agreement of simulated and observed temporal variabilitye0
Direction: lowere0
Comparator: simulated and observede0

Not reportedTaxon

Verbatim

e0
“The agreement of simulated and observed temporal variability is lower due to non-climatic factors”
✓ located verbatim in the source · abstract, characters 1390–1487

Title

How model and input uncertainty impact maize yield simulations in West Africa

Katharina Waha; Neil Huth; Peter Carberry; et al. Environmental Research Letters 2015. doi:10.1088/1748-9326/10/2/024017

Other claims from this paper (7)

Method / resource Soil, climate and management input resolution influenced simulated maize yields in both models.
Method / resource Differences between models exceeded differences between input data.
Method / resource Climate and management information produced larger differences than soil information.
Method / resource Both models represented spatial yield variability well despite little soil and management information.
Method / resource APSIM simulated greater variation between locations than LPJmL.
Observation Maize yields doubled in Burkina Faso between 1987 and 1991 following research and development investment.
Method / resource The most detailed input data did not necessarily improve model performance.

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.

“temporal yield variability”

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