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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)
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)
Associationobservedoriginal resultmeasured

Claim · model-written summary

High facies patchiness reduced yield or increased its temporal variability and reduced predictability.

Taxon
grain cropse1
Outcome
yielde0
Direction
reducese0
Setting
High facies patchinesse0

Verbatim

e0

“High facies patchiness either reduces yield or increases its temporal variability and reduces predictability.”

✓ located verbatim in the source · abstract, characters 1535–1644
e1

“grain crops”

✓ located verbatim in the source · abstract, characters 13–24

Title

Factors of intra-field phytomass variability in steppe agricultural landscapes of Kazakhstan

F.K. Kiryanov-Gref; A.V. Khoroshev; K.A. Anatskaya Bulletin of the Karaganda University “Biology medicine geography Series” 2024. doi:10.31489/2024bmg1/150-158

Other claims from this paper (6)
Observationobservedoriginal resultmeasured

Claim · model-written summary

Wheat yield gaps showed temporal variability of 9 to 18%.

Outcome
temporal variabilitye0
Value
9 to 18%e0

Verbatim

e0

“High regional differences in wheat yield gaps were observed, with values ranging from 1.64 t/ha in Casablanca Settat to 4.12 t/ha in Marrakech Safi, and temporal variability ranging from 9 to 18%.”

✓ located verbatim in the source · abstract, characters 1163–1359

Title

The impact of precipitation, temperature, and soil moisture on wheat yield gap quantification: evidence from Morocco

Lahcen Ousayd; Terence Epule Epule; Salwa Belaqziz; et al. Agriculture & Food Security 2025. doi:10.1186/s40066-024-00509-w

Other claims from this paper (7)
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 effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Manure reduced rye yield variability under Ca and CaPK.

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)
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)
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)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

NDVI and EVI were more effective at detecting spatial and temporal vine vigor variability during mid-to-late seasons than during early seasons.

Taxon
grapevineVitist0
Outcome
detecting spatial and temporal vine vigor variabilitye1
Direction
more effectivee1
Comparator
early seasonse1

Verbatim

t0

“grapevine”

✓ located verbatim in the source · title, characters 43–52
e1

“more effective at detecting spatial and temporal vine vigor variability during mid-to-late seasons than during early seasons”

✓ located verbatim in the source · abstract, characters 1770–1894

Title

Assessing spatial and temporal patterns of grapevine vigor and yield using satellite remote sensing

Daniel Kingsley Cudjoe; Yazhou Sun; Justin Scheiner; et al. Frontiers in Horticulture 2026. doi:10.3389/fhort.2026.1816162

Other claims from this paper (12)

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