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“temporal yield variability”

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Observationobservedoriginal resultmeasured

Claim · model-written summary

In other nitrogen treatments, soil alkali-hydrolyzable nitrogen fell, rose after topdressing, fell again, then rose after boll opening.

Taxon: cottonGossypium hirsutumt0
Treatment: other treatmentse0
Outcome: soil alkali-hydrolyzable nitrogene1
Direction: decreased firstly,increased after topdressing,decreased again,and increasede0

Verbatim

e0
“that of other treatments decreased firstly,increased after topdressing,decreased again,and increased after the beginning of boll opening”
✓ located verbatim in the source · abstract, characters 837–973

t0
“Cotton Field”
✓ located verbatim in the source · title, characters 58–70

e1
“content of soil alkali-hydrolyzable nitrogen”
✓ located verbatim in the source · abstract, characters 684–728

Title

Spatial and Temporal Variability of Available Nutrient in Cotton Field at Flower and Boll Stage and Its Effect on Lint Yield and Fiber Quality

Yang Zhi ACTA AGRONOMICA SINICA 2008

Other claims from this paper (15)

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

The best model estimated missing year yield at 5.51 Mg/ha, which fell short of the 4.9 Mg/ha acceptable error threshold.

Taxon: Zea mays L.Zea mayse0
Outcome: best performance for estimating data for a missing field (6.46 Mg/ha) and yeare4
Direction: fell short ofe4
Value: 5.51 Mg/hae4
Comparator: acceptable error threshold of 4.9 Mg/hae4

Verbatim

e0
“Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability”
✓ located verbatim in the source · abstract, characters 0–111

e4
“the best performance for estimating data for a missing field (6.46 Mg/ha) and year (5.51 Mg/ha) fell short of the acceptable error threshold of 4.9 Mg/ha”
✓ located verbatim in the source · abstract, characters 1251–1404

Title

Exploring the Use of High-Resolution Satellite Images to Estimate Corn Silage Yield Within Field

Srinivasagan N. Subhashree; Manuel Marcaida; Shajahan Sunoj; et al. Remote Sensing 2024. doi:10.3390/rs16214081

Other claims from this paper (4)

Observationobservedoriginal resultmeasured

Claim · model-written summary

Conventional and no-till yields were similar across crop phases.

Taxon: corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and winter wheat ( Triticum aestivum L.)Glycine maxTriticum aestivumZea mayse1
Outcome: yieldse0
Direction: similare0
Comparator: CT and NT systemse0

Verbatim

e0
“yields in the CT and NT systems were similar across all crop phases of the rotation”
✓ located verbatim in the source · abstract, characters 1261–1344

e1
“The four systems follow a corn ( Zea mays L.), soybean [ Glycine max (L.) Merr.], and winter wheat ( Triticum aestivum L.) 3‐yr rotation under conventional (CT), no‐till (NT), low‐input (LI), or organic (ORG) management”
✓ located verbatim in the source · abstract, characters 780–999

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

Other claims from this paper (10)

Observation Conventional and no-till wheat yields exceeded low-input wheat yields.
Observation Organic corn yields were lower than other systems.
Observation Organic winter wheat yields were lower than other systems.
Observation Soybean yields were similar across management systems.
Observation Organic soybean yield variability was highest, at 48%.
Observation Organic winter wheat yield variability was highest, at 33%.
Observation Low-input corn yield variability was 40% lower than conventional.
Observation No-till soybean yield variability was 33% lower than conventional.
Observation No-till corn yield variability was similar to conventional.
Observation No-till wheat yield variability was similar to conventional.

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“temporal yield variability”

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