Search claims · PlantClaims

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.

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)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Average crop yield varied less over time than soil parameters varied spatially.

Taxon: crope4
Outcome: Temporal variability in average crop yielde4
Direction: much smallere4
Comparator: typical spatial variability values reported by others for soil parameterse4
Setting: USAe4

Verbatim

e4
“Temporal variability in average crop yield in the USA was much smaller than typical spatial variability values reported by others for soil parameters.”
✓ located verbatim in the source · abstract, characters 1822–1972

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.
Method / resource Fractal analysis was judged useful for quantifying temporal yield variation.

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)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs.

Taxon: CottonGossypium hirsutumt0
Outcome: long-term site-specific management of crop inputse1
Qualifier: high spatial variability and a clear temporal stability patterne1

Verbatim

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

e1
“Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs”
✓ located verbatim in the source · abstract, characters 1378–1528

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)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Temporal yield variability increased from about 40% on powerful binomial soils to about 65% on low-powerful ones.

Taxon: grasst0
Outcome: Time variability of yielde0
Direction: increasese0
Value: ≈ 65 %e0
Qualifier: ≈ 40 %e0
Setting: geological structure of the soilse0

Verbatim

e0
“Time variability of yield depends on the geological structure of the soils ‒ increases with the transition from powerful binomials (≈ 40 %) to low-powerful ones (≈ 65 %)”
✓ located verbatim in the source · abstract, characters 770–939

t0
“grass yield”
✓ located verbatim in the source · title, characters 25–36

Title

Study of the dynamics of grass yield within the agricultural landscape based on long-term monitoring

D. A. Ivanov; O. V. Karaseva; M. V. Rublyuk; et al. Agricultural science Euro-North-East 2022. doi:10.30766/2072-9081.2022.23.2.221-229

Other claims from this paper (14)

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Temporal variability in biomass and LAI was mapped for the study area and individual farms.

Taxon: cropse1
Outcome: Temporal variability in biomass and LAIe0
Value: individual farmse0

Verbatim

e0
“Temporal variability in biomass and LAI were mapped for the study area and highlighted for individual farms.”
✓ located verbatim in the source · abstract, characters 1511–1619

e1
“six crops (wheat, barley, chickpea, lentil, vetch and cumin)”
✓ located verbatim in the source · abstract, characters 290–350

Title

Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images

P. S. Thenkabail International Journal of Remote Sensing 2003. doi:10.1080/01431160710155974

Other claims from this paper (5)

Method / resource The model mapped crop LAI and biomass with at least 81% overall accuracy.
Method / resource Within-field variability was mapped with 74–100% accuracy.
Method / resource Between-field variability was mapped with 76–100% accuracy.
Method / resource Historical images enabled extrapolation and quantification of crop characteristics.
Association Field-measured grain yields had significant relationships with Landsat-derived indices.

Observationobservedoriginal resultprojected

Claim · model-written summary

Predicted seasonal aquatic weed biomass yield ranged from 0.70 to 1.18 kg per square meter, with spatial and temporal variability.

Taxon: Aquatic Weedt0
Outcome: Seasonal biomass yield predictionse0
Value: 0.70 kg to 1.18 kg per square metere0

Verbatim

e0
“Seasonal biomass yield predictions ranged from 0.70 kg to 1.18 kg per square meter, highlighting significant spatial and temporal variability.”
✓ located verbatim in the source · abstract, characters 1287–1429

t0
“Aquatic Weed”
✓ located verbatim in the source · title, characters 34–46

Title

Utilizing Machine Learning to Map Aquatic Weed Yields for Biochar Production: A Case Study in the Bangweulu Wetlands, Zambia

Fabian Banda; Leonard Simukoko; Mulenga Kalumba; et al. Advances in Environmental and Engineering Research 2025. doi:10.21926/aeer.2502020

Other claims from this paper (4)

Method / resource Gradient Boosted Regression Trees performed best for aquatic weed yield prediction, with R2 0.63.
Method / resource The best model had mean absolute error of 0.08.
Method / resource The best model had root mean square error of 0.29.
Observation Vegetation indices, climate and topography were key predictive variables for aquatic weed yield.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

NDVI yield maps captured temporal and spatial variation in the study area.

Taxon: wheatTriticum aestivumt0
Outcome: temporal and spatial variabilitye0
Comparator: yield mapse0

Verbatim

e0
“Through yield maps, the NDVI perceived the temporal and spatial variability in the study area.”
✓ located verbatim in the source · abstract, characters 1515–1609

t0
“Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina”
✓ located verbatim in the source · title, characters 0–111

Title

Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina

Mariano F. Lopresti; Carlos M. Di Bella; Américo J. Degioanni Information Processing in Agriculture 2015. doi:10.1016/j.inpa.2015.06.001

Other claims from this paper (5)

Method / resource The day 289 image gave the best NDVI fit for early wheat yield estimation.
Association Better weather conditions corresponded to higher wheat yields in yield maps.
Association Higher soil productivity corresponded to more area with higher yields.
Method / resource The calibrated model estimated official department yields with R squared 0.75.
Method / resource The method predicted wheat yield 30 days before harvest.

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)

“temporal yield variability”

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Over time

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