Search claims · PlantClaims

Breeding, agronomy & horticulture 439

Associationobservedoriginal resultmeasured

Claim · model-written summary

Spatial and temporal corn yield variability were uncorrelated.

Taxon: Corn Silaget0
Outcome: Spatial and temporal variabilitye0

Not reportedSettingDirection

Verbatim

e0
“Spatial and temporal variability were uncorrelated”
✓ located verbatim in the source · abstract, characters 1120–1170

t0
“Corn Silage”
✓ located verbatim in the source · title, characters 31–42

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

Other claims from this paper (3)

Observation Corn silage yield varied among farms, fields, within fields, and across years.
Observation Variable management zones covered 30 to 44% of each farm.
Method / resource Yield stability maps can be used to develop management zones.

Observationobservedoriginal resultsignificant (as stated)measured

Claim · model-written summary

Corn had significantly less temporal yield variability than soybean or sorghum.

Taxon: CornZea mays subsp. mayse0
Outcome: temporal yield variabilitye0
Direction: lesse0
Comparator: soybean or sorghume0

Verbatim

e0
“Corn had significantly less temporal yield variability than soybean or sorghum.”
✓ located verbatim in the source · abstract, characters 1216–1295

Title

Fractal Analysis of Temporal Yield Variability of Crop Sequences: Implications for Site‐Specific Management

Bahman Eghball; Gary E. Varvel Agronomy Journal 1997. doi:10.2134/agronj1997.00021962008900060001x

Other claims from this paper (6)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Temporal soybean yield variability was 20–22%.

Taxon: soybeanGlycine maxe7
Outcome: soybean yield variabilitye7
Value: 20–22%e7
Qualifier: temporale7

Verbatim

e7
“Spatial and temporal soybean yield variability was 16–26% and 20–22%, respectively”
✓ located verbatim in the source · abstract, characters 2005–2087

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

Other claims from this paper (14)

Intervention effect No-tillage gave a higher economic return than conventional tillage in soybean.
Intervention effect Input costs excluding land rental and crop insurance were lower with no-tillage than with conventional tillage.
Intervention effect The soybean yield response to tillage systems was not significant at different locations.
Intervention effect The C‐C‐S rotation gave greater soybean yields (9%) than the C‐S rotation in five of seven locations.
Intervention effect The C‐C‐S rotation gave higher economic returns (11%) than the C‐S rotation in five of seven locations.
Association Site-specific effects of rotation on soybean yield were greater than tillage system effects.
Association The effect of C‐S, C‐C‐S and C‐C rotations on soybean was consistently higher in southern than in northern locations.
Association Stable economic returns over time for corn and soybean were more associated with the C‐S rotation than with C‐C‐S and C‐C.
Intervention effect C‐S cropping system yields and economic returns were superior to C‐C.
Observation Soybean yield varied from 1.5 to 5.0 Mg ha −1 across Iowa.
Observation Soybean yield variability across Iowa was 21%.
Observation Spatial soybean yield variability was 16–26%.
Intervention effect No-tillage input cost was $49 ha −1 lower than that for CP, DR and MP tillage.
Intervention effect Tillage systems did not produce soybean yield differences with either rotation.

Physiology, metabolism & abiotic stress 138

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Observationobservedoriginal resultmeasured

Claim · model-written summary

Spatial variability was below temporal variability and frequently below 1 mm.

Taxon: cornZea mays subsp. mayse1
Outcome: Spatial variabilitye0
Direction: less thane0
Value: 1 mme0

Verbatim

e0
“Spatial variability was less than temporal variability, and frequently less than 1 mm”
✓ located verbatim in the source · abstract, characters 1234–1319

t0
“crop yield”
✓ located verbatim in the source · title, characters 79–89

e1
“by corn and soybean phenological phase”
✓ located verbatim in the source · abstract, characters 1035–1073

Title

Spatial and temporal precipitation variability as a component of site-specific crop yield variability

M. R. O'Neal Purdue e-Pubs (Purdue University System) 2000

Other claims from this paper (10)

Method / resource The best neural net had an overall yield prediction error of 11.9%.
Method / resource Farm scale yield error was 10.9%.
Method / resource County scale yield error was 10.5%.
Method / resource State scale yield error was 7.9%.
Observation Spatial precipitation variability among farm gauges was 0.25 to 1.73 mm·day−1.
Observation Temporal precipitation variability was 0.17 to 3.40 mm·day−1.
Method / resource Simulated corn yield differed by 15.8% across precipitation sources.
Method / resource Simulated yield differed from the long term mean by 21.5%.
Observation The most profitable nitrogen strategy did not depend on precipitation data source.
Observation Using the nearest NWS station was more profitable than measuring precipitation on site.

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)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Temporal precipitation variability was 0.17 to 3.40 mm·day−1.

Taxon: cornZea mays subsp. mayse1
Outcome: Median absolute deviation from a reference yeare0
Value: 0.17 to 3.40 mm·day−1e0

Verbatim

e0
“Median absolute deviation from a reference year was 0.17 to 3.40 mm·day−1 (temporal/year-to-year variability)”
✓ located verbatim in the source · abstract, characters 1123–1232

t0
“crop yield”
✓ located verbatim in the source · title, characters 79–89

e1
“by corn and soybean phenological phase”
✓ located verbatim in the source · abstract, characters 1035–1073

Title

Spatial and temporal precipitation variability as a component of site-specific crop yield variability

M. R. O'Neal Purdue e-Pubs (Purdue University System) 2000

Other claims from this paper (10)

Method / resource The best neural net had an overall yield prediction error of 11.9%.
Method / resource Farm scale yield error was 10.9%.
Method / resource County scale yield error was 10.5%.
Method / resource State scale yield error was 7.9%.
Observation Spatial precipitation variability among farm gauges was 0.25 to 1.73 mm·day−1.
Observation Spatial variability was below temporal variability and frequently below 1 mm.
Method / resource Simulated corn yield differed by 15.8% across precipitation sources.
Method / resource Simulated yield differed from the long term mean by 21.5%.
Observation The most profitable nitrogen strategy did not depend on precipitation data source.
Observation Using the nearest NWS station was more profitable than measuring precipitation on site.

Methods & resources 85

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Method / resourceauthor interpretationoriginal resultsimulated

Claim · model-written summary

A theoretical model suggests plausible conditions in which precision farming reduces temporal yield variability.

Treatment: precision farminge0
Outcome: temporal yield variabilitye0
Direction: reducee0

Not reportedTaxonComparatorValue

Verbatim

e0
“A theoretical model is presented that suggests that there are plausible circumstances under which precision farming can reduce temporal yield variability”
✓ located verbatim in the source · abstract, characters 336–489

Title

RISK MANAGEMENT POTENTIAL OF PRECISION FARMING TECHNOLOGIES

Lowenberg-DeBoer, James RePEc: Research Papers in Economics

Other claims from this paper (1)

Intervention effect An on farm trial supports risk reducing benefits from site specific phosphorus and potassium management.

Method / resourceobservedoriginal resultpositivesimulated

Claim · model-written summary

A theoretical model suggests precision farming can reduce temporal yield variability under plausible circumstances.

Treatment: precision farminge0
Outcome: temporal yield variabilitye0
Direction: reducee0
Qualifier: plausible circumstancese0

Not reportedTaxonComparator

Verbatim

e0
“A theoretical model is presented that suggests that there are plausible circumstances under which precision farming can reduce temporal yield variability.”
✓ located verbatim in the source · abstract, characters 345–499

Title

Risk Management Potential of Precision Farming Technologies

J. Lowenberg-DeBoer Journal of Agricultural and Applied Economics 1999. doi:10.1017/s1074070800008555

Other claims from this paper (1)

Intervention effect An on-farm trial supported risk reduction from site-specific phosphorus and potassium management.

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

Fractal analysis was judged useful for quantifying temporal yield variation.

Taxon: cropse5
Outcome: quantifying temporal variability in yielde5
Qualifier: usefule5

Not reportedComparatorValue

Verbatim

e5
“It appears that fractal analysis is useful in quantifying temporal variability in yield of various crops”
✓ located verbatim in the source · abstract, characters 1973–2077

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

Woody plants 52

Show all 52 →

Associationobservedoriginal resultmeasured

Claim · model-written summary

Climatic conditions strongly affected temporal variability of oil palm production.

Taxon: oil palme0
Outcome: temporal variability of oil palm productione0
Setting: climatic conditionse0

Not reportedDirection

Verbatim

e0
“climatic conditions had strong effects on the temporal variability of oil palm production.”
✓ located verbatim in the source · abstract, characters 1654–1744

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

Other claims from this paper (7)

Intervention effect Low empty fruit bunch application increased cumulative oil palm yield over 15 years by 2.4% versus chemical fertilizer.
Intervention effect Medium empty fruit bunch application increased cumulative yield by 5.9% versus the reference.
Intervention effect High empty fruit bunch application increased cumulative yield by 4.8% versus the reference.
Intervention effect Annual yield and temporal production stability did not differ significantly among treatments.
Intervention effect Medium empty fruit bunch application produced higher soil organic carbon than chemical fertilizer.
Association Soil organic carbon was positively associated with annual crop yield after a two-year lag.
Association Relative humidity was positively associated with annual crop yield after a two-year lag.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Year-to-year yield variability was low in Red Chief.

Taxon: APPLESt0
Material: Red Chiefe0
Outcome: Temporal variability (differences from year to year) in yielde0
Direction: lowe0

Verbatim

t0
“APPLES”
✓ located verbatim in the source · title, characters 63–69

e0
“Temporal variability (differences from year to year) in yield was low in 'Red Chief and high in 'Fuji' trees due to alternate bearing”
✓ located verbatim in the source · abstract, characters 1060–1193

Title

SPATIAL AND TEMPORAL VARIABILITY OF YIELD AND FRUIT QUALITY IN APPLES

K.D. Aggelopoulou; G.D. Nanos; T.A. Gemtos Acta Horticulturae 2010. doi:10.17660/actahortic.2010.877.96

Other claims from this paper (9)

Observation Yield and quality had significant spatial variability.
Observation Quality varied less spatially than yield.
Observation Soluble solids content and flesh firmness had low spatial variability.
Observation Fruit mass, acidity and skin colour had high spatial variability.
Observation Year-to-year yield variability was high in Fuji due to alternate bearing.
Observation Quality characteristics varied significantly across years.
Intervention effect Cold storage reduced flesh firmness and acidity.
Intervention effect Cold storage raised soluble solids content in Red Chief.
Intervention effect Cold storage did not change soluble solids content in Fuji.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Year-to-year yield variability was high in Fuji due to alternate bearing.

Taxon: APPLESt0
Material: Fujie0
Outcome: Temporal variability (differences from year to year) in yielde0
Direction: highe0
Qualifier: due to alternate bearinge0

Verbatim

t0
“APPLES”
✓ located verbatim in the source · title, characters 63–69

e0
“Temporal variability (differences from year to year) in yield was low in 'Red Chief and high in 'Fuji' trees due to alternate bearing”
✓ located verbatim in the source · abstract, characters 1060–1193

Title

SPATIAL AND TEMPORAL VARIABILITY OF YIELD AND FRUIT QUALITY IN APPLES

K.D. Aggelopoulou; G.D. Nanos; T.A. Gemtos Acta Horticulturae 2010. doi:10.17660/actahortic.2010.877.96

Other claims from this paper (9)

Observation Yield and quality had significant spatial variability.
Observation Quality varied less spatially than yield.
Observation Soluble solids content and flesh firmness had low spatial variability.
Observation Fruit mass, acidity and skin colour had high spatial variability.
Observation Year-to-year yield variability was low in Red Chief.
Observation Quality characteristics varied significantly across years.
Intervention effect Cold storage reduced flesh firmness and acidity.
Intervention effect Cold storage raised soluble solids content in Red Chief.
Intervention effect Cold storage did not change soluble solids content in Fuji.

Molecular genetics & genomics 10

Show all 10 →

Observationobservedoriginal resultmeasured

Claim · model-written summary

Repeatability was 0.89.

Taxon: Coffea canephoraCoffea canephorat0
Outcome: repeatabilitye0
Value: 0.89e0

Verbatim

e0
“repeatability was 0.89”
✓ located verbatim in the source · abstract, characters 690–712

t0
“Coffea canephora”
✓ located verbatim in the source · title, characters 110–126

Title

Modeling temporal genetic variability using mixed models improves yield stability and selection efficiency in Coffea canephora

Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043

Other claims from this paper (10)

Method / resource M7 had the lowest AIC and BIC of evaluated models.
Observation Plot-level heritability ranged from 0.64 to 0.68.
Observation Genotype-mean heritability ranged from 0.84 to 0.86.
Observation Selective accuracy ranged from 0.92 to 0.94.
Association Genetic correlations indicated atypical 2023 behavior attributed to heat stress during grain filling.
Observation Relative selection efficiency rose cumulatively by 4.4% after four years.
Observation Bicudo combined a high predicted genotypic value and elevated persistence index.
Observation A1 combined a high predicted genotypic value and elevated persistence index.
Observation AD1 combined a high predicted genotypic value and elevated persistence index.
Method / resource Flexible mixed models were interpreted as improving reliability of genetic evaluation.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Selective accuracy ranged from 0.92 to 0.94.

Taxon: Coffea canephoraCoffea canephorat0
Outcome: selective accuracye0
Value: 0.92 to 0.94e0

Verbatim

e0
“selective accuracy ranged from 0.92 to 0.94.”
✓ located verbatim in the source · abstract, characters 718–762

t0
“Coffea canephora”
✓ located verbatim in the source · title, characters 110–126

Title

Modeling temporal genetic variability using mixed models improves yield stability and selection efficiency in Coffea canephora

Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043

Other claims from this paper (10)

Method / resource M7 had the lowest AIC and BIC of evaluated models.
Observation Plot-level heritability ranged from 0.64 to 0.68.
Observation Genotype-mean heritability ranged from 0.84 to 0.86.
Observation Repeatability was 0.89.
Association Genetic correlations indicated atypical 2023 behavior attributed to heat stress during grain filling.
Observation Relative selection efficiency rose cumulatively by 4.4% after four years.
Observation Bicudo combined a high predicted genotypic value and elevated persistence index.
Observation A1 combined a high predicted genotypic value and elevated persistence index.
Observation AD1 combined a high predicted genotypic value and elevated persistence index.
Method / resource Flexible mixed models were interpreted as improving reliability of genetic evaluation.

Observationobservedoriginal resultmeasured

Claim · model-written summary

Genotype-mean heritability ranged from 0.84 to 0.86.

Taxon: Coffea canephoraCoffea canephorat0
Outcome: genotype-mean heritabilitye0
Value: 0.84 to 0.86e0

Verbatim

e0
“genotype-mean heritability from 0.84 to 0.86”
✓ located verbatim in the source · abstract, characters 644–688

t0
“Coffea canephora”
✓ located verbatim in the source · title, characters 110–126

Title

Modeling temporal genetic variability using mixed models improves yield stability and selection efficiency in Coffea canephora

Alex Campanharo; Deurimar Herênio Gonçalves Júnior; Máskio Daros; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1840043

Other claims from this paper (10)

Method / resource M7 had the lowest AIC and BIC of evaluated models.
Observation Plot-level heritability ranged from 0.64 to 0.68.
Observation Repeatability was 0.89.
Observation Selective accuracy ranged from 0.92 to 0.94.
Association Genetic correlations indicated atypical 2023 behavior attributed to heat stress during grain filling.
Observation Relative selection efficiency rose cumulatively by 4.4% after four years.
Observation Bicudo combined a high predicted genotypic value and elevated persistence index.
Observation A1 combined a high predicted genotypic value and elevated persistence index.
Observation AD1 combined a high predicted genotypic value and elevated persistence index.
Method / resource Flexible mixed models were interpreted as improving reliability of genetic evaluation.

Plant interactions & biotic stress 9

Show all 9 →

Observationobservedoriginal resultmeasured

Claim · model-written summary

Common smut preferred dry conditions.

Taxon: CornZea mays subsp. mayst0
Interactor: Ustilago zeae (Beckm.) Ungere0pathogen
Outcome: preferred dry conditionse0

Verbatim

e0
“common smut [ Ustilago zeae (Beckm.) Unger] that preferred dry conditions”
✓ located verbatim in the source · abstract, characters 1130–1203

t0
“Corn”
✓ located verbatim in the source · title, characters 36–40

Title

Spatial and Temporal Variability of Corn Growth and Grain Yield

S. Machado; E. D. Bynum; T. L. Archer; et al. Crop Science 2002. doi:10.2135/cropsci2002.1564

Other claims from this paper (10)

Observation Grain yields formed four distinct spatial clusters.
Association In dry 1998, yield clusters were strongly influenced by elevation and soil texture.
Association Yields were higher at high than low elevations where water use was high and soil texture heavy.
Association Common smut reduced grain yield at low elevations.
Observation Wet 1999 yield clusters combined differing elevations and soil textures.
Method / resource Measured parameters forecast grain yield better in 1998 than 1999.
Observation Net assimilation rate differed before the 12-leaf stage.
Recommendation Net assimilation rate may support early in-season management decisions.
Observation Biomass differences appeared after the 12-leaf stage.
Recommendation Biomass measurements may guide decisions during this season and the next.

Associationobservedoriginal resultnegativemeasured

Claim · model-written summary

Common smut reduced grain yield at low elevations.

Taxon: CornZea mays subsp. mayst0
Interactor: Ustilago zeae (Beckm.) Ungere0pathogen
Outcome: Grain yieldse0
Direction: reducede0
Setting: low elevationse0

Verbatim

e0
“Grain yields at low elevations also were reduced by common smut [ Ustilago zeae (Beckm.) Unger] that preferred dry conditions”
✓ located verbatim in the source · abstract, characters 1078–1203

t0
“Corn”
✓ located verbatim in the source · title, characters 36–40

Title

Spatial and Temporal Variability of Corn Growth and Grain Yield

S. Machado; E. D. Bynum; T. L. Archer; et al. Crop Science 2002. doi:10.2135/cropsci2002.1564

Other claims from this paper (10)

Observation Grain yields formed four distinct spatial clusters.
Association In dry 1998, yield clusters were strongly influenced by elevation and soil texture.
Association Yields were higher at high than low elevations where water use was high and soil texture heavy.
Observation Common smut preferred dry conditions.
Observation Wet 1999 yield clusters combined differing elevations and soil textures.
Method / resource Measured parameters forecast grain yield better in 1998 than 1999.
Observation Net assimilation rate differed before the 12-leaf stage.
Recommendation Net assimilation rate may support early in-season management decisions.
Observation Biomass differences appeared after the 12-leaf stage.
Recommendation Biomass measurements may guide decisions during this season and the next.

Associationobservedoriginal resultmeasured

Claim · model-written summary

Bacterial community structure had a weak association with melon yield and quality.

Taxon: melonCucumis meloe0
Interactor: bacteriale0symbiont
Outcome: melon yield and qualitye0
Qualifier: weak influencee0

Not reportedDirection

Verbatim

e0
“linear regression analysis revealed a weak influence of bacterial community structure on melon yield and quality”
✓ located verbatim in the source · abstract, characters 1166–1278

Title

Temporal Variability in the Rhizosphere Bacterial and Fungal Community Structure in the Melon Crop Grown in a Closed Hydroponic System

Yu-Pin Lin; Chiao-Ming Lin; Hussnain Mukhtar; et al. Agronomy 2021. doi:10.3390/agronomy11040719

Other claims from this paper (11)

not stated 6

Observationobservedoriginal resultmeasured

Claim · model-written summary

Zinc, boron and molybdenum showed narrower ranges.

Outcome: Narrower ranges were observed for zinc, boron and molybdenume0

Not reportedTaxon

Verbatim

e0
“Narrower ranges were observed for zinc, boron and molybdenum”
✓ located verbatim in the source · abstract, characters 670–730

Title

Spatial and temporal variability of soil micronutrients and their relationships with wheat (Triticum aestivum L.) yield and some major soil variables

Menna Assefa African Journal of Agricultural Research 2022. doi:10.5897/ajar2022.15971

Other claims from this paper (7)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Iron concentrations ranged from 1.80 to 8.20 mg/kg.

Outcome: concentrations of irone0
Value: 1.80 – 8.20 mg/kge0

Not reportedTaxon

Verbatim

e0
“The concentrations of iron ranged from 1.80 – 8.20 mg/kg”
✓ located verbatim in the source · abstract, characters 612–668

Title

Spatial and temporal variability of soil micronutrients and their relationships with wheat (Triticum aestivum L.) yield and some major soil variables

Menna Assefa African Journal of Agricultural Research 2022. doi:10.5897/ajar2022.15971

Other claims from this paper (7)

Associationobservedoriginal resultmeasured

Claim · model-written summary

Soil pH was the major factor negatively influencing availability of the evaluated micronutrients, except molybdenum.

Treatment: soil pHe0
Outcome: availability of the evaluated micronutrientse0
Direction: negativelye0

Not reportedTaxon

Verbatim

e0
“soil pH was the major factor negatively influenced the availability of the evaluated micronutrients, except molybdenum”
✓ located verbatim in the source · abstract, characters 771–889

Title

Spatial and temporal variability of soil micronutrients and their relationships with wheat (Triticum aestivum L.) yield and some major soil variables

Menna Assefa African Journal of Agricultural Research 2022. doi:10.5897/ajar2022.15971

Other claims from this paper (7)

Development, reproduction & morphology 1

Show all 1 →

Associationobservedoriginal resultmeasured

Claim · model-written summary

Vegetative and reproductive stages of cotton changed with the rain distribution during development.

Taxon: herbaceous cottone4
Treatment: rain distributione1
Outcome: vegetative and reproductive stagese1
Direction: changede1

Verbatim

e4
“development of herbaceous cotton”
✓ located verbatim in the source · abstract, characters 102–134

e1
“The vegetative and reproductive stages of the crop changed according to the rain distribution during the cottondevelopment phases”
✓ located verbatim in the source · abstract, characters 858–987

Title

TEMPORAL VARIABILITY OF PLUVIAL PRECIPITATION AND YIELD OF HERBACEOUS COTTON IN DIFFERENT SYSTEMS OF SOIL MANAGEMENT AND NITROGEN FERTILIZATION VARIABILIDADE TEMPORAL DA PRECIPITAÇÃO PLUVIOMÉTRICA E RENDIMENTO DO ALGODOEIRO HERBÁCEO EM DIFERENTES SISTEMAS DE MANEJO DE SOLO E DE ADUBAÇÃO NITROGENADA

Gilvaneide Alves de Azeredo; Ivandro de França da Silva; Albericio Pereira de Andrade; et al. DOAJ (DOAJ: Directory of Open Access Journals) 2007. doi:10.5216/pat.v36i2.2148

Other claims from this paper (1)

Association Cotton responses varied widely among soil management systems and nitrogen doses over time.

Ecology, evolution & diversity 1

Show all 1 →

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.

“temporal yield variability”

Claim types

Direction of the findings

Over time

Claim type

Research area

Polarity

Statistics

Breeding, agronomy & horticulture 439

Physiology, metabolism & abiotic stress 138

Methods & resources 85

Woody plants 52

Molecular genetics & genomics 10

Plant interactions & biotic stress 9

not stated 6

Development, reproduction & morphology 1

Ecology, evolution & diversity 1