Search claims · PlantClaims

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The point when the percentage increase peaked and then slowed marked when fruit-bearing percentage was destined.

Taxon: RiceOryza sativat0
Outcome: period in which fruit bearing percentage is destinede0

Not reportedComparatorValue

Verbatim

e0
“the period when the rate of increase in the percentage above mentioned reaches a maximum and thereafter the rate begins to decrease remarkably has been proved to be the period in which fruit bearing percentage is destined”
✓ located verbatim in the source · abstract, characters 2340–2561

t0
“Lowland Rice (Preliminary report)”
✓ located verbatim in the source · title, characters 45–78

Title

Crop-Scientific Studies on Yield-Forecast of Lowland Rice (Preliminary report) : XI. On Forecast of Fruit Bearing Percentage in Rice (1)

Seizo MATSUSHIMA; Takio MANAKA; Nobuyuki KOMATSU Japanese Journal of Crop Science 1954. doi:10.1626/jcs.23.41b

Other claims from this paper (9)

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Machine learning and optimization identified in-season phenomic traits for reducing reliance on end-season soybean phenotyping.

Taxon: SoybeanGlycine maxt0
Outcome: identify a suite of in-season phenomic traitse0
Comparator: resource-intensive end-season phenotypinge0

Verbatim

e0
“The results illuminated the capability of fusing ML and optimization techniques to identify a suite of in-season phenomic traits that will allow breeding programs to decrease the dependence on resource-intensive end-season phenotyping (e.g., seed yield harvest).”
✓ located verbatim in the source · abstract, characters 1049–1311

t0
“Soybean”
✓ located verbatim in the source · title, characters 121–128

Title

Development of Optimized Phenomic Predictors for Efficient Plant Breeding Decisions Using Phenomic-Assisted Selection in Soybean

Kyle Parmley; Koushik Nagasubramanian; Soumik Sarkar; et al. Plant Phenomics 2019. doi:10.34133/2019/5809404

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

OCS predicted mean iron content gain of 6.9%.

Taxon: common beansPhaseolus vulgarist0
Outcome: mean Fe and Zn contente0
Direction: increasee0
Value: 6.9e0

Verbatim

e0
“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”
✓ located verbatim in the source · abstract, characters 1230–1431

t0
“common beans”
✓ located verbatim in the source · title, characters 142–154

Title

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

Renu Saradadevi; Clare Mukankusi; Li Li; et al. The Plant Genome 2021. doi:10.1002/tpg2.20156

Other claims from this paper (13)

Observation Genomic heritability for grain yield was 0.45 ± 0.10.
Observation Genomic heritability for cooking time was 0.50 ± 0.15.
Observation Genomic heritability for iron was 0.57 ± 0.12.
Observation Genomic heritability for zinc was 0.61 ± 0.13.
Association Iron and zinc had a significant favorable genetic correlation of 0.91 ± 0.06.
Association Yield and iron had a significant favorable genetic correlation of 0.66 ± 0.17.
Association Yield and zinc had a significant favorable genetic correlation of 0.44 ± 0.19.
Association Cooking time and iron had a significant favorable genetic correlation of -0.57 ± 0.21.
Association Cooking time and zinc had a significant favorable genetic correlation of -0.67 ± 0.20.
Method / resource OCS predicted mean grain yield gain of 12.4%.
Method / resource OCS predicted mean cooking time reduction of 9.3%.
Method / resource OCS predicted mean zinc content gain of 4.6%.
Method / resource OCS predicted low achieved coancestry of 0.032.

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

OCS predicted mean zinc content gain of 4.6%.

Taxon: common beansPhaseolus vulgarist0
Outcome: mean Fe and Zn contente0
Direction: increasee0
Value: 4.6%e0

Verbatim

e0
“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”
✓ located verbatim in the source · abstract, characters 1230–1431

t0
“common beans”
✓ located verbatim in the source · title, characters 142–154

Title

Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa

Renu Saradadevi; Clare Mukankusi; Li Li; et al. The Plant Genome 2021. doi:10.1002/tpg2.20156

Other claims from this paper (13)

Observation Genomic heritability for grain yield was 0.45 ± 0.10.
Observation Genomic heritability for cooking time was 0.50 ± 0.15.
Observation Genomic heritability for iron was 0.57 ± 0.12.
Observation Genomic heritability for zinc was 0.61 ± 0.13.
Association Iron and zinc had a significant favorable genetic correlation of 0.91 ± 0.06.
Association Yield and iron had a significant favorable genetic correlation of 0.66 ± 0.17.
Association Yield and zinc had a significant favorable genetic correlation of 0.44 ± 0.19.
Association Cooking time and iron had a significant favorable genetic correlation of -0.57 ± 0.21.
Association Cooking time and zinc had a significant favorable genetic correlation of -0.67 ± 0.20.
Method / resource OCS predicted mean grain yield gain of 12.4%.
Method / resource OCS predicted mean cooking time reduction of 9.3%.
Method / resource OCS predicted mean iron content gain of 6.9%.
Method / resource OCS predicted low achieved coancestry of 0.032.

Method / resourceobservedoriginal resultnegativeprojected

Claim · model-written summary

Including CO2 by temperature interactions raises projected white chalky grain percentage by up to 16% versus earlier assessments.

Taxon: riceOryza sativat0
Outcome: percentage of white chalky grainse0
Direction: increasee0
Value: 16%e0
Qualifier: especially in RCP8.5e0
Comparator: previous assessment resultse0

Verbatim

e0
“Nationwide production was estimated to decrease by up to 28% and the percentage of white chalky grains to increase up to 16% relative to the previous assessment results, especially in RCP8.5”
✓ located verbatim in the source · abstract, characters 1259–1449

t0
“rice”
✓ located verbatim in the source · title, characters 51–55

Title

Revision of estimates of climate change impacts on rice yield and quality in Japan by considering the combined effects of temperature and CO₂ concentration

Yasushi ISHIGOOKA; Toshihiro HASEGAWA; Tsuneo KUWAGATA; et al. Journal of Agricultural Meteorology 2021. doi:10.2480/agrmet.d-20-00038

Other claims from this paper (3)

Method / resourceobservedoriginal resultsimulated

Claim · model-written summary

Simulated spatial patterns of emerged Striga hermonthica plants resembled the distribution observed in farmers’ fields

Taxon: millete1
Interactor: S. hermonthicaStriga hermonthicae2parasite
Outcome: The spatial patterns of emerged S. hermonthica plantse2
Comparator: distribution typically observed in farmers’ fieldse3

Verbatim

e1
“Spatial expansion of S. hermonthica and decrease in millet yield in a field was slower, on average, when stochasticity of attachment of seedlings to the host was included and compared to the deterministic model”
✓ located verbatim in the source · abstract, characters 476–686

e2
“The spatial patterns of emerged S. hermonthica plants”
✓ located verbatim in the source · abstract, characters 688–741

e3
“resembled the distribution typically observed in farmers’ fields”
✓ located verbatim in the source · abstract, characters 837–901

Title

Long‐term management of Striga hermonthica: strategy evaluation with a spatio‐temporal population model

T A VAN MOURIK; F J J A BIANCHI; W VAN DER WERF; et al. Weed Research 2008. doi:10.1111/j.1365-3180.2008.00638.x

Other claims from this paper (7)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The listed drought indices showed high power of discrimination among genotypes.

Taxon: bread wheatTriticum aestivumt0
Outcome: high power of discrimination among genotypese0
Qualifier: at all stress intensitiese0
Comparator: stress and non-stressed conditionse0

Verbatim

e0
“The mean productivity (MP), modified stress tolerance index (MSTIk), superiority index (Pi), mean relative performance (MRP), relative efficiency index (REI), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HARM) and relative decrease in yield (RDY) showed a high power of discrimination among genotypes, and expressed significant correlations with yields under both stress and non-stress…” Show full quote
✓ located verbatim in the source · abstract, characters 526–985

t0
“bread wheat”
✓ located verbatim in the source · title, characters 78–89

Title

Efficiency of drought tolerance indices under different stress severities for bread wheat selection

Sahar Bennani; Nasserlehaq Nsarellah; Mohammed Jlibene; et al. Australian Journal of Crop Science 2017. doi:10.21475/ajcs.17.11.04.pne272

Other claims from this paper (10)

Method / resource The listed drought indices showed significant correlations with yields under both stress and non-stressed conditions.
Method / resource These indices selected high mean yield with low mean variance at 20% selection pressure.
Method / resource Index efficiency decreased as drought stress exceeded 35%.
Method / resource At 57% stress, only Pi and MP targeted the highest performances.
Method / resource MRP, REI, GMP, RDY and STI were interchangeable.
Observation AUS30355 was among the best performers for yield and stability.
Observation Gladius was among the best performers for yield and stability.
Observation Amir-2 was among the best performers for yield and stability.
Observation AUS30354 was among the best performers for yield and stability.
Recommendation The selected genotypes were recommended for release or breeding parents.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

SymbioMamba reduces the parameter count to 6.2 M relative to monolithic state-space baselines.

Taxon: maizeZea mayse0
Outcome: parameter counte1
Direction: reducinge1
Value: 6.2 Me1
Comparator: monolithic state-space baselinese1

Verbatim

e0
“Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an R2 of 0.915”
✓ located verbatim in the source · abstract, characters 1151–1309

e1
“Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines”
✓ located verbatim in the source · abstract, characters 1311–1501

Title

SymbioMamba: An Efficient Dual-Stream State-Space Framework for Real-Time Maize Disease and Yield Analysis on UAV Platforms

Zihuan Wang; Yuru Wang; Bocheng Zhou; et al. Agriculture 2026. doi:10.3390/agriculture16070801

Other claims from this paper (7)

Method / resource SymbioMamba achieves 89.4% mAP@0.5 on the maize UAV dataset.
Method / resource SymbioMamba achieves an R2 of 0.915 on the maize UAV dataset.
Method / resource SymbioMamba improves mAP@0.5:0.95 by 2.4% over the industry-standard YOLOv11.
Method / resource SymbioMamba has a 50% decrease in parameter count relative to monolithic state-space baselines.
Method / resource Yield prediction error is significantly reduced to an RMSE of 485.6 kg/ha.
Method / resource SymbioMamba runs at 38.2 FPS on the NVIDIA Jetson AGX Orin platform.
Method / resource SymbioMamba has a compact model size of 2.4 G FLOPs.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

SymbioMamba improves mAP@0.5:0.95 by 2.4% over the industry-standard YOLOv11.

Taxon: maizeZea mayse0
Outcome: mAP@0.5:0.95e1
Direction: improvese1
Value: 2.4%e1
Comparator: industry-standard YOLOv11e1

Verbatim

e0
“Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an R2 of 0.915”
✓ located verbatim in the source · abstract, characters 1151–1309

e1
“Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines”
✓ located verbatim in the source · abstract, characters 1311–1501

Title

SymbioMamba: An Efficient Dual-Stream State-Space Framework for Real-Time Maize Disease and Yield Analysis on UAV Platforms

Zihuan Wang; Yuru Wang; Bocheng Zhou; et al. Agriculture 2026. doi:10.3390/agriculture16070801

Other claims from this paper (7)

Method / resource SymbioMamba achieves 89.4% mAP@0.5 on the maize UAV dataset.
Method / resource SymbioMamba achieves an R2 of 0.915 on the maize UAV dataset.
Method / resource SymbioMamba reduces the parameter count to 6.2 M relative to monolithic state-space baselines.
Method / resource SymbioMamba has a 50% decrease in parameter count relative to monolithic state-space baselines.
Method / resource Yield prediction error is significantly reduced to an RMSE of 485.6 kg/ha.
Method / resource SymbioMamba runs at 38.2 FPS on the NVIDIA Jetson AGX Orin platform.
Method / resource SymbioMamba has a compact model size of 2.4 G FLOPs.

Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

The listed drought indices showed significant correlations with yields under both stress and non-stressed conditions.

Taxon: bread wheatTriticum aestivumt0
Outcome: significant correlations with yields under both stress and non-stressed conditionse0
Qualifier: at all stress intensitiese0
Comparator: stress and non-stressed conditionse0

Verbatim

e0
“The mean productivity (MP), modified stress tolerance index (MSTIk), superiority index (Pi), mean relative performance (MRP), relative efficiency index (REI), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HARM) and relative decrease in yield (RDY) showed a high power of discrimination among genotypes, and expressed significant correlations with yields under both stress and non-stress…” Show full quote
✓ located verbatim in the source · abstract, characters 526–985

t0
“bread wheat”
✓ located verbatim in the source · title, characters 78–89

Title

Efficiency of drought tolerance indices under different stress severities for bread wheat selection

Sahar Bennani; Nasserlehaq Nsarellah; Mohammed Jlibene; et al. Australian Journal of Crop Science 2017. doi:10.21475/ajcs.17.11.04.pne272

Other claims from this paper (10)

Method / resource The listed drought indices showed high power of discrimination among genotypes.
Method / resource These indices selected high mean yield with low mean variance at 20% selection pressure.
Method / resource Index efficiency decreased as drought stress exceeded 35%.
Method / resource At 57% stress, only Pi and MP targeted the highest performances.
Method / resource MRP, REI, GMP, RDY and STI were interchangeable.
Observation AUS30355 was among the best performers for yield and stability.
Observation Gladius was among the best performers for yield and stability.
Observation Amir-2 was among the best performers for yield and stability.
Observation AUS30354 was among the best performers for yield and stability.
Recommendation The selected genotypes were recommended for release or breeding parents.

“yield decrease”

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