Search claims · PlantClaims

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.

Associationobservedoriginal resultsignificant (as stated)simulated

Claim · model-written summary

Temporal variability across sowing times significantly affected soybean yield.

Taxon: soybeanGlycine maxe0
Treatment: different sowing timese0
Outcome: soybean yielde0

Not reportedDirection

Verbatim

e0
“temporal variability associated with different sowing times significantly impacts soybean yield.”
✓ located verbatim in the source · abstract, characters 1077–1173

Title

Why should space-time variability of rainfall be considered in precision agriculture for soybean in Piracicaba, São Paulo state?

Thais Letícia dos Santos; Rafael Battisti; Klaus Reichardt; et al. Scientia Agricola 2025. doi:10.1590/1678-992x-2024-0202

Other claims from this paper (2)

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.

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)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Fluctuations in temperature amplitudes account for 32% of the temporal variability of grass yield.

Taxon: cloverothymothy grasse2
Treatment: fluctuations in temperature amplitudese1
Outcome: temporal variability of the yielde1
Value: 32%e1

Not reportedDirection

Verbatim

e1
“It was revealed that in total these parameters determine about 43% of the variability in grass productivity, but fluctuations in temperature amplitudes are responsible for 32% of the temporal variability of the yield”
✓ located verbatim in the source · abstract, characters 581–797

e2
“The yield monitoring (1998–2023) of cloverothymothy grass stands of 1 year of use within the moraine hill was carried out in order to find patterns in the influence of weather conditions on it”
✓ located verbatim in the source · abstract, characters 0–192

Title

Correlation between perennial grass productivity and landscape and climatic conditions

D. A. Ivanov; N. A. Kharhardinov; K. S. Kurpas Vestnik of the Russian agricultural science 2024. doi:10.31857/s2500208224040056

Other claims from this paper (11)

Associationobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Yield variability was positively correlated with the probability of gleying from seasonal waterlogging.

Taxon: maize, soybean, wheat and cottonGossypium hirsutum Glycine maxTriticum aestivumZea mayse0 e1
Outcome: temporal yield variabilitye0 e1
Direction: positively correlatede0 e1
Qualifier: probability of observing gleying processes associated with waterlogging for part of the yeare0 e1
Setting: US Midweste0 e1

Verbatim

e0
“temporal yield variability is positively correlated (p < 0.05) with the probability of observing gleying processes associated with waterlogging for part of the year”
✓ located verbatim in the source · abstract, characters 2190–2354

e1
“maize, soybean, wheat and cotton in the US Midwest”
✓ located verbatim in the source · abstract, characters 696–746

Title

Drivers of within-field spatial and temporal variability of crop yield across the US Midwest

Bernardo Maestrini; Bruno Basso Scientific Reports 2018. doi:10.1038/s41598-018-32779-3

Other claims from this paper (2)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The method estimated spatial and temporal PPFD variability with 4.4% relative root mean square error.

Taxon: strawberryFragaria ×ananassae1
Outcome: PPFDe0
Value: 4.4%e0
Setting: greenhousee1

Not reportedDirection

Verbatim

e1
“strawberry fruit yield in a greenhouse”
✓ located verbatim in the source · abstract, characters 411–449

e0
“This method enabled successful estimation of spatiotemporal variability in PPFD and A with relative root mean square errors of 4.4% and 11.0%, respectively”
✓ located verbatim in the source · abstract, characters 451–606

Title

Spatiotemporally variable incident light, leaf photosynthesis, and yield across a greenhouse: fine-scale hemispherical photography and a photosynthesis model

Kensuke Kimura; Daisuke Yasutake; Kota Koikawa; et al. Precision Agriculture 2022. doi:10.1007/s11119-022-09933-z

Other claims from this paper (9)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The method estimated spatial and temporal leaf photosynthetic rate variability with 11.0% relative root mean square error.

Taxon: strawberryFragaria ×ananassae1
Outcome: Ae0
Value: 11.0%e0
Setting: greenhousee1

Not reportedDirection

Verbatim

e1
“strawberry fruit yield in a greenhouse”
✓ located verbatim in the source · abstract, characters 411–449

e0
“This method enabled successful estimation of spatiotemporal variability in PPFD and A with relative root mean square errors of 4.4% and 11.0%, respectively”
✓ located verbatim in the source · abstract, characters 451–606

Title

Spatiotemporally variable incident light, leaf photosynthesis, and yield across a greenhouse: fine-scale hemispherical photography and a photosynthesis model

Kensuke Kimura; Daisuke Yasutake; Kota Koikawa; et al. Precision Agriculture 2022. doi:10.1007/s11119-022-09933-z

Other claims from this paper (9)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Regional and temporal variation in rainfall was a leading contributor to crop yield fluctuations.

Taxon: crope0
Outcome: crop yield fluctuationse0
Qualifier: rainfalle0
Setting: regional and temporale0

Not reportedCompoundDirection

Verbatim

e0
“specific regional and temporal variations in rainfall and temperature as the two leading contributors to changes in crop yield fluctuations”
✓ located verbatim in the source · abstract, characters 1300–1439

Title

A Data-Driven Assessment of Crop Yield Variability and Global Food Security under Climate Change

Akwabeng, Prince Michael; Awele, Okolie; Jegede, Oluwafemi Afolabi; et al. Zenodo (CERN European Organization for Nuclear Research) 2026. doi:10.5281/zenodo.18428909

Other claims from this paper (2)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Regional and temporal variation in temperature was a leading contributor to crop yield fluctuations.

Taxon: crope0
Outcome: crop yield fluctuationse0
Qualifier: temperaturee0
Setting: regional and temporale0

Not reportedDirection

Verbatim

e0
“specific regional and temporal variations in rainfall and temperature as the two leading contributors to changes in crop yield fluctuations”
✓ located verbatim in the source · abstract, characters 1300–1439

Title

A Data-Driven Assessment of Crop Yield Variability and Global Food Security under Climate Change

Akwabeng, Prince Michael; Awele, Okolie; Jegede, Oluwafemi Afolabi; et al. Zenodo (CERN European Organization for Nuclear Research) 2026. doi:10.5281/zenodo.18428909

Other claims from this paper (2)

“temporal yield variability”

Claim types

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Over time

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