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.

Mechanismobservedoriginal resultmeasured

Claim · model-written summary

Yield, rather than temporal yield variability, drove stability.

Taxon: bioenergy cropst0
Outcome: Stabilitye0
Qualifier: driven by yield as opposed to temporal variability of yielde0

Verbatim

e0
“Stability was driven by yield as opposed to temporal variability of yield.”
✓ located verbatim in the source · abstract, characters 590–664

t0
“bioenergy crops”
✓ located verbatim in the source · title, characters 13–28

Title

Diversifying bioenergy crops increases yield and yield stability by reducing weed abundance

Jacob M. Jungers; Yi Yang; Christopher W. Fernandez; et al. Science Advances 2021. doi:10.1126/sciadv.abg8531

Other claims from this paper (5)

Association Species richness increased biomass yield by reducing weed abundance.
Association Species richness increased interannual yield stability by reducing weed abundance.
Intervention effect Nitrogen fertilization increased yield.
Intervention effect Nitrogen fertilization reduced yield stability.
Recommendation The authors conclude that increasing bioenergy crop diversity can improve farmer economics through yield gains, lower yield variability and reduced weed control inputs.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Maximum temporal yield variability was 14.7% between 2014 and 2017.

Taxon: paddy cropt0
Outcome: Maximum temporal variabilitye0
Direction: Maximume0
Value: 14.7 %e0
Qualifier: between the year 2014 and 2017e0

Verbatim

e0
“Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017.”
✓ located verbatim in the source · abstract, characters 750–832

t0
“Paddy Crop”
✓ located verbatim in the source · title, characters 32–42

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

Observationobservedoriginal resultmeasured

Claim · model-written summary

Spatial and temporal cotton yield variability differed among fields.

Taxon: CottonGossypium hirsutumt0
Outcome: spatial and temporal yield variabilitye1
Qualifier: varied with the fielde1
Setting: Southern High Plainst0

Verbatim

t0
“Irrigated Cotton Yield in the Southern High Plains”
✓ located verbatim in the source · title, characters 31–81

e1
“The amount and pattern of spatial and temporal yield variability varied with the field”
✓ located verbatim in the source · abstract, characters 705–791

Title

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

Wenxuan Guo Agronomy 2018. doi:10.3390/agronomy8120298

Other claims from this paper (8)

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.

Associationobservedoriginal resultsimulated

Claim · model-written summary

Temporal variability of grain yield, nitrogen response and net revenue increased with nitrogen rate.

Taxon: MaizeZea mayst0
Treatment: N ratee0
Outcome: Temporal variability of grain yield, N response, and net revenuee0
Direction: increasede0

Verbatim

e0
“Temporal variability of grain yield, N response, and net revenue increased with N rate.”
✓ located verbatim in the source · abstract, characters 1751–1838

t0
“Maize”
✓ located verbatim in the source · title, characters 48–53

Title

Spatio‐Temporal Nitrogen Fertilizer Response in Maize: Field Study and Modeling Approach

Susana M. Albarenque; Bruno Basso; Octavio P. Caviglia; et al. Agronomy Journal 2016. doi:10.2134/agronj2016.02.0081

Other claims from this paper (8)

Intervention effect Nitrogen rate significantly affected maize yield in both fields.
Association Homogeneous zone significantly affected maize yield in Field 2.
Intervention effect Nitrogen response was affected only by nitrogen rate.
Intervention effect Simulated maize yield was significantly affected by nitrogen.
Method / resource The model accounted for spatial variability, with an HZ effect and HZ-by-nitrogen interaction.
Association The economic optimum nitrogen rate differed among homogeneous zones in both fields.
Recommendation The authors propose zone-specific nitrogen rates to improve nitrogen use efficiency and grower profits and reduce environmental impacts.
Recommendation The authors suggest selecting nitrogen rate by homogeneous zone to reduce environmental and economic risk.

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

Maximum temporal yield variability in paddy was 14.7%, observed between 2014 and 2017.

Taxon: paddy crope0
Outcome: temporal variabilitye3
Value: 14.7 %e3
Qualifier: between the year 2014 and 2017e3

Verbatim

e0
“assess yield variability of paddy crop using automatic yield monitoring (AYM) system”
✓ located verbatim in the source · abstract, characters 25–109

e3
“Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017”
✓ located verbatim in the source · abstract, characters 750–831

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/jae2018551.1687

Other claims from this paper (4)

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 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.

“temporal yield variability”

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