Try:droughtFusariumnitrogen fertilizerArabidopsisseed germination
Claim · model-written summary
Spatial and temporal corn yield variability were uncorrelated.
Not reportedSettingDirection
Verbatim
e0“Spatial and temporal variability were uncorrelated”
t0“Corn Silage”
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
Claim · model-written summary
Temporal variability of grain yield, nitrogen response and net revenue increased with nitrogen rate.
Verbatim
e0“Temporal variability of grain yield, N response, and net revenue increased with N rate.”
t0“Maize”
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
Claim · model-written summary
Temporal variability across sowing times significantly affected soybean yield.
Glycine maxe0Not reportedDirection
Verbatim
e0“temporal variability associated with different sowing times significantly impacts soybean yield.”
Title
Thais Letícia dos Santos; Rafael Battisti; Klaus Reichardt; et al. Scientia Agricola 2025. doi:10.1590/1678-992x-2024-0202
Claim · model-written summary
High facies patchiness reduced yield or increased its temporal variability and reduced predictability.
Verbatim
e0“High facies patchiness either reduces yield or increases its temporal variability and reduces predictability.”
e1“grain crops”
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
Claim · model-written summary
Fields with high ECa variability showed a stronger association between spatial and temporal yield patterns and ECa.
Gossypium hirsutumt0Verbatim
t0“Irrigated Cotton Yield in the Southern High Plains”
e1“Fields with high variability in ECa exhibited a stronger association between spatial and temporal yield patterns and ECa”
Title
Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains
Wenxuan Guo Agronomy 2018. doi:10.3390/agronomy8120298
Claim · model-written summary
Climatic conditions strongly affected temporal variability of oil palm production.
Not reportedDirection
Verbatim
e0“climatic conditions had strong effects on the temporal variability of oil palm production.”
Title
Long-term crop residue application maintains oil palm yield and temporal stability of production
Hsiao-Hang Tao; Jake L. Snaddon; Eleanor M. Slade; et al. Agronomy for Sustainable Development 2017. doi:10.1007/s13593-017-0439-5
Claim · model-written summary
Post-heading heat stress and average temperature explained about 29% of the observed spatial and temporal yield variability.
Triticum aestivume0Not 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”
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
Claim · model-written summary
Fluctuations in temperature amplitudes account for 32% of the temporal variability of grass yield.
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”
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”
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
Claim · model-written summary
Temporal yield variability increased from about 40% on powerful binomial soils to about 65% on low-powerful ones.
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 %)”
t0“grass yield”
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
Claim · model-written summary
Yield variability was positively correlated with the probability of gleying from seasonal waterlogging.
Gossypium hirsutumGlycine maxTriticum aestivum
Zea mayse0e1Verbatim
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”
e1“maize, soybean, wheat and cotton in the US Midwest”
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
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