Search claims · PlantClaims

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.

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Yield maps may be unhelpful for future management when temporal variability is great.

Taxon: Cropt0
Outcome: may not be useful in making future management decisionse0
Qualifier: when temporal variability is greate0

Verbatim

e0
“Yield maps, which are used as an indication of past management in site‐specific cases, may not be useful in making future management decisions when temporal variability is great.”
✓ located verbatim in the source · abstract, characters 1623–1801

t0
“Crop Sequences”
✓ located verbatim in the source · title, characters 50–64

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)

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)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Spatial soybean yield variability was 16–26%.

Taxon: soybeanGlycine maxe7
Outcome: soybean yield variabilitye7
Value: 16–26%e7
Qualifier: Spatiale7

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 Temporal soybean yield variability was 20–22%.
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.

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

ECa-based site-specific management is limited to cotton fields with high spatial variability and a strong association between yield and ECa patterns.

Taxon: CottonGossypium hirsutumt0
Outcome: application of ECa for site-specific managemente1
Qualifier: limited to fields with high spatial variabilitye1

Verbatim

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

e1
“The application of ECa for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and ECa variation patterns”
✓ located verbatim in the source · abstract, characters 1012–1215

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)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

The authors conclude that site-specific yield-map fertilizer recommendations may not be possible without identifying causes of yield variability.

Taxon: cornZea mays subsp. mayst0
Outcome: fertilizer recommendations on a site specific basise7
Qualifier: without identifying agents causing the grain yield variabilitye7

Verbatim

t0
“Corn Grain Yields”
✓ located verbatim in the source · title, characters 34–51

e7
“Using grain yield maps for fertilizer recommendations on a site specific basis may not be possible without identifying agents causing the grain yield variability”
✓ located verbatim in the source · abstract, characters 6419–6580

Title

Spatial and Temporal Stability of Corn Grain Yields

J. A. Lamb; R. H. Dowdy; J. L. Anderson; et al. jpa 1997. doi:10.2134/jpa1997.0410

Other claims from this paper (7)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Such variability should be considered for effective application of nutrients in agroforestry.

Taxon: maizeZea mayse0
Outcome: effective application of nutrientse5

Verbatim

e5
“Such variability should be considered for effective application of nutrients in agroforestry”
✓ located verbatim in the source · abstract, characters 2404–2496

e0
“maize height, grain yield, and leaf nitrogen (N) concentrations were significantly higher at the upslope than downslope side of tree-grass rows”
✓ located verbatim in the source · abstract, characters 1023–1166

Title

Spatial and temporal variation in crop productivity and relation with soil fertility within upland agroforestry

Van Hung Do; Nguyen La; Göran Bergkvist; et al. Field Crops Research 2024. doi:10.1016/j.fcr.2024.109675

Other claims from this paper (6)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Yield data can be used to delineate zones for variable management.

Taxon: cropt0
Outcome: delineate zones for variable managemente4

Verbatim

t0
“crop yield zones on hummocky terrain”
✓ located verbatim in the source · title, characters 66–102

e4
“Yield data can be used to delineate zones for variable management”
✓ located verbatim in the source · abstract, characters 1686–1751

Title

Spatial and temporal variability of soil fertility in relation to crop yield zones on hummocky terrain

A. Moulin; D. A. Derksen; Debra L. McLaren; et al. 2002

Other claims from this paper (3)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Variable rate application of water, plant growth regulators, nitrogen and harvest aids may be implemented based on spatial variability of crop growth conditions.

Taxon: CottonGossypium hirsutumt0
Outcome: Variable rate application of water, plant growth regulators, nitrogen, harvest aidse1
Qualifier: based on the spatial variability of crop growth conditions at specific timese1

Verbatim

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

e1
“Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times”
✓ located verbatim in the source · abstract, characters 1728–1907

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)

Recommendationauthor interpretationoriginal resultbasis n/a

Claim · model-written summary

Further work is needed on the relationship of tissue nutrient concentration and yield before tissue sampling is used for nutrient management.

Taxon: canolaBrassica napus var. napuse0
Outcome: investigate the relationships of tissue concentration and yielde5

Verbatim

e0
“to evaluate the variability of canola ( Brassica napus L.) nutrient concentrations across various growth stages”
✓ located verbatim in the source · abstract, characters 281–392

e5
“Further work must be done to investigate the relationships of tissue concentration and yield, and the viability of tissue sampling for nutrient management”
✓ located verbatim in the source · abstract, characters 1476–1630

Title

Evaluation of temporal variability on tissue nutrient concentrations of canola

Vaughn Reed; Apurba K. Sutradhar; D. Brian Arnall; et al. Agrosystems Geosciences & Environment 2022. doi:10.1002/agg2.20235

Other claims from this paper (4)

“temporal yield variability”

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