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A claim can belong to several research areas, so group counts add up to more than the total.
Organism groups follow NCBI Taxonomy: the spellings of one organism are merged, a claim naming several organisms sits in each group, and claims whose value names no organism are under “not stated”.
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
Yield, rather than temporal yield variability, drove stability.
Verbatim
e0“Stability was driven by yield as opposed to temporal variability of yield.”
t0“bioenergy crops”
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
Claim · model-written summary
Maximum temporal yield variability was 14.7% between 2014 and 2017.
Verbatim
e0“Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017.”
t0“Paddy Crop”
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
Claim · model-written summary
Corn had significantly less temporal yield variability than soybean or sorghum.
Zea mays subsp. mayse0Verbatim
e0“Corn had significantly less temporal yield variability than soybean or sorghum.”
Title
Bahman Eghball; Gary E. Varvel Agronomy Journal 1997. doi:10.2134/agronj1997.00021962008900060001x
Claim · model-written summary
Spatial variability was below temporal variability and frequently below 1 mm.
Zea mays subsp. mayse1Verbatim
e0“Spatial variability was less than temporal variability, and frequently less than 1 mm”
t0“crop yield”
e1“by corn and soybean phenological phase”
Title
M. R. O'Neal Purdue e-Pubs (Purdue University System) 2000
Claim · model-written summary
Temporal precipitation variability was 0.17 to 3.40 mm·day−1.
Zea mays subsp. mayse1Verbatim
e0“Median absolute deviation from a reference year was 0.17 to 3.40 mm·day−1 (temporal/year-to-year variability)”
t0“crop yield”
e1“by corn and soybean phenological phase”
Title
M. R. O'Neal Purdue e-Pubs (Purdue University System) 2000
Claim · model-written summary
Spatial and temporal cotton yield variability differed among fields.
Gossypium hirsutumt0Verbatim
t0“Irrigated Cotton Yield in the Southern High Plains”
e1“The amount and pattern of spatial and temporal yield variability varied with the field”
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
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
Spatial and temporal yield patterns in low-variability cotton fields might be more influenced by weather or other factors across seasons.
Gossypium hirsutumt0Verbatim
t0“Irrigated Cotton Yield in the Southern High Plains”
e1“For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons”
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
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
Multifractal methods characterized spatial and temporal leaf area variation and its multiscale yield relation.
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”
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
Claim · model-written summary
Models reproduced temporal yield variability for maize with correlation up to 0.888.
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)”
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
Claim · model-written summary
Wheat yield gaps showed temporal variability of 9 to 18%.
Triticum aestivume0Verbatim
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%.”
Title
Lahcen Ousayd; Terence Epule Epule; Salwa Belaqziz; et al. Agriculture & Food Security 2025. doi:10.1186/s40066-024-00509-w
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
Models reproduced temporal yield variability for wheat with correlation up to 0.673.
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)”
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
Claim · model-written summary
Temporal soybean yield variability was 20–22%.
Glycine maxe7Verbatim
e7“Spatial and temporal soybean yield variability was 16–26% and 20–22%, respectively”
Title
Soybean Spatiotemporal Yield and Economic Variability as Affected by Tillage and Crop Rotation
Mahdi M. Al‐Kaisi; Sotirios Archontoulis; David Kwaw‐Mensah Agronomy Journal 2016. doi:10.2134/agronj2015.0363
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
Organic soybean yield variability was highest, at 48%.
Glycine maxe0Verbatim
e0“was highest in the ORG system during the soybean (CV = 48%)”
e1“Temporal yield variability differed among management systems and rotation phases and was highest in the ORG system during the soybean (CV = 48%) and winter wheat (CV = 33%) phases of the rotation”
Title
Temporal Yield Variability under Conventional and Alternative Management Systems
Richard G. Smith; Fabian D. Menalled; G. P. Robertson Agronomy Journal 2007. doi:10.2134/agronj2007.0096
Claim · model-written summary
Clustering identified stable spatial and temporal yield clusters.
Oryza sativae1Not reportedComparator
Verbatim
e0“The clustering procedure successfully identified stable, physically meaningful clusters with recognizable spatial and temporal structure”
e1“rice fields”
Title
Factors Underlying Yield Variability in Two California Rice Fields
Alvaro Roel; Richard E. Plant Agronomy Journal 2004. doi:10.2134/agronj2004.1481
Claim · model-written summary
The approach generated spatial rice yield maps capturing within-field variability.
Oryza sativat0Verbatim
e0“the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability”
t0“Rice Yield Prediction”
Title
Rubén Simeón; Alba Agenjos-Moreno; Constanza Rubio; et al. Agriculture 2026. doi:10.3390/agriculture16020201
Claim · model-written summary
Heat episode variability was negatively associated with observed rice grain yield across all subregions.
Oryza sativat0Verbatim
e0“Significant negative ( p < 0.05) impacts of the variability of high‐temperature episodes on the observed grain yield were found in all subregions.”
t0“Rice Yield in China”
Title
High‐Temperature Episodes with Spatial‐Temporal Variation Impacted Middle‐Season Rice Yield in China
Xinsu Sun; Zhiwei Long; Guangpeng Song; et al. Agronomy Journal 2018. doi:10.2134/agronj2017.09.0510
Claim · model-written summary
Repeatability was 0.89.
Coffea canephorat0Verbatim
e0“repeatability was 0.89”
t0“Coffea canephora”
Title
Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043
Claim · model-written summary
Selective accuracy ranged from 0.92 to 0.94.
Coffea canephorat0Verbatim
e0“selective accuracy ranged from 0.92 to 0.94.”
t0“Coffea canephora”
Title
Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043
Claim · model-written summary
Genotype-mean heritability ranged from 0.84 to 0.86.
Coffea canephorat0Verbatim
e0“genotype-mean heritability from 0.84 to 0.86”
t0“Coffea canephora”
Title
Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043
Claim · model-written summary
Leaf area index was the most variable canopy property.
Manihot esculentat0Verbatim
e0“Leaf area index had higher variability among different canopy properties”
t0“cassava (Manihot esculenta)”
Title
C.S. Suchitra; G. Byju Current Horticulture 2015
Claim · model-written summary
Root yield showed weak spatial dependence in the unfertilized field.
Verbatim
e0“that of unfertilized field was greater than 75% (81.82), indicating weak dependency”
t0“cassava (Manihot esculenta)”
Title
C.S. Suchitra; G. Byju Current Horticulture 2015
Claim · model-written summary
Root yield showed strong spatial dependence in the fertilized field.
Verbatim
e0“nugget to sill ratios of root yield were lesser than 25% (24.84), exhibiting strong spatial dependency”
t0“cassava (Manihot esculenta)”
Title
C.S. Suchitra; G. Byju Current Horticulture 2015
Claim · model-written summary
Bacterial community structure had a weak association with melon yield and quality.
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Cucumis meloe0Not reportedDirection
Verbatim
e0“linear regression analysis revealed a weak influence of bacterial community structure on melon yield and quality”
Title
Yu-Pin Lin; Chiao-Ming Lin; Hussnain Mukhtar; et al. Agronomy 2021. doi:10.3390/agronomy11040719
Claim · model-written summary
Fruit weight and quality moderately responded to magnesium deficiency.
Cucumis meloe1Not reportedDirection
Verbatim
e0“fruit weight and quality moderately responded to Mg and K deficiency”
e1“melon yield and quality”
Title
Yu-Pin Lin; Chiao-Ming Lin; Hussnain Mukhtar; et al. Agronomy 2021. doi:10.3390/agronomy11040719
Claim · model-written summary
Fruit weight and quality moderately responded to potassium deficiency.
Cucumis meloe1Not reportedDirection
Verbatim
e0“fruit weight and quality moderately responded to Mg and K deficiency”
e1“melon yield and quality”
Title
Yu-Pin Lin; Chiao-Ming Lin; Hussnain Mukhtar; et al. Agronomy 2021. doi:10.3390/agronomy11040719
Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.
Zea mays