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Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Manure reduced rye yield variability under Ca and CaPK.

Treatment
additional manuree0
Outcome
temporal yield variability of ryee0
Direction
reducede0
Qualifier
Ca and CaPK (without mineral N)e0

Not reportedComparator

Verbatim

e0

“additional manure in ‘sub-optimal’ mineral fertilizer treatments, such as Ca and CaPK (without mineral N), reduced the temporal yield variability of rye”

✓ located verbatim in the source · abstract, characters 1069–1221

Title

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Marcin Studnicki; Janna Macholdt; Andy Macdonald; et al. Agronomy 2021. doi:10.3390/agronomy11030519

Other claims from this paper (4)
Intervention effectobservedoriginal resultnegativemeasured

Claim · model-written summary

Organic input increased rye yield variability under CaNPK and NPK.

Treatment
additional organic inpute0
Outcome
variable rye yieldse0
Direction
more variablee0
Qualifier
CaNPK and NPKe0
Comparator
those with no additional manuree0

Verbatim

e0

“additional organic input led to more variable rye yields in already ‘optimal’ treatments including mineral N (CaNPK and NPK), compared to those with no additional manure”

✓ located verbatim in the source · abstract, characters 1236–1405

Title

Effects of Fertilizers and Manures on Temporal Yield Variability of Winter Rye

Marcin Studnicki; Janna Macholdt; Andy Macdonald; et al. Agronomy 2021. doi:10.3390/agronomy11030519

Other claims from this paper (4)
Intervention effectobservedoriginal resultnegativemeasured

Claim · model-written summary

Yield was 62% lower in low vigor plots than in high vigor plots.

Taxon
Barberat0
Treatment
LVe1
Outcome
yielde1
Direction
severely curtailede1
Value
62%e1
Comparator
HVe1

Verbatim

t0

“steep Barbera vineyard”

✓ located verbatim in the source · title, characters 68–90
e1

“Final vine size and yield were severely curtailed in LV (−49% and −62% as compared to HV, respectively)”

✓ located verbatim in the source · abstract, characters 1222–1325

Title

Seasonal interactions between spatial and temporal variability in a steep Barbera vineyard

Matteo Gatti; Karen Rahal; Roberto D’Ambrosio; et al. Precision Agriculture 2026. doi:10.1007/s11119-026-10317-w

Other claims from this paper (11)
Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Topographic attribute inputs reduced RRMSE of the 1D-CNN by about 13%.

Taxon
soybeanGlycine maxe0
Treatment
TA-Inputse4
Outcome
RRMSEe4
Direction
reducede4
Value
∼ 13 %e4

Not reportedComparator

Verbatim

e0

“predicting within-field soybean yields”

✓ located verbatim in the source · abstract, characters 208–246
e4

“The TA-Inputs enhanced accuracy in 1D-CNN (reduced RRMSE by ∼ 13 %)”

✓ located verbatim in the source · abstract, characters 1188–1255

Title

Predicting spatial and temporal variability in soybean yield using deep learning and open source data

Deborah V. Gaso; Laura Elena Cue La Rosa; Laila A. Puntel; et al. European Journal of Agronomy 2025. doi:10.1016/j.eja.2024.127498

Other claims from this paper (5)
Intervention effectobservedoriginal resultpositivesimulated

Claim · model-written summary

A rate of 120 kg N ha−1 was optimal for increasing barley grain yield while meeting malting grain nitrogen requirements.

Treatment
120 kg N ha−1e0
Outcome
grain yielde0
Direction
increasee0

Not reportedComparator

Verbatim

e0

“120 kg N ha−1 is the optimal rate to increase grain yield while still keeping within the grain N% requirements for malting quality”

✓ located verbatim in the source · abstract, characters 935–1065
t0

“Spring Barley”

✓ located verbatim in the source · title, characters 36–49

Title

Spatial and Temporal Variability of Spring Barley Yield and Quality Quantified by Crop Simulation Model

Davide Cammarano; Jonathan Holland; Domenico Ronga Agronomy 2020. doi:10.3390/agronomy10030393

Other claims from this paper (2)
Intervention effectobservedoriginal resultpositivesimulated

Claim · model-written summary

Earlier dry bean planting increased predicted yield by over 80 percent relative to standard timing.

Taxon
dry beane0
Treatment
growing dry bean earliere0
Outcome
predicted dry bean yielde0
Direction
increasede0
Value
over 80 %e0
Comparator
standard management scenarioe0

Verbatim

e0

“growing dry bean earlier than the standard management scenario (December to March) increased predicted dry bean yield by over 80 % with the available water resources and associated temporal variability”

✓ located verbatim in the source · abstract, characters 1459–1660

Title

Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology

R. Mompremier; Y. Her; G. Hoogenboom; et al. Agricultural Water Management 2020. doi:10.1016/j.agwat.2020.106429

Other claims from this paper (2)
Intervention effectobservedoriginal resultnegativemeasured

Claim · model-written summary

Final vine size was 49% lower in low vigor plots than in high vigor plots.

Taxon
Barberat0
Treatment
LVe1
Outcome
Final vine sizee1
Direction
severely curtailede1
Value
49%e1
Comparator
HVe1

Verbatim

t0

“steep Barbera vineyard”

✓ located verbatim in the source · title, characters 68–90
e1

“Final vine size and yield were severely curtailed in LV (−49% and −62% as compared to HV, respectively)”

✓ located verbatim in the source · abstract, characters 1222–1325

Title

Seasonal interactions between spatial and temporal variability in a steep Barbera vineyard

Matteo Gatti; Karen Rahal; Roberto D’Ambrosio; et al. Precision Agriculture 2026. doi:10.1007/s11119-026-10317-w

Other claims from this paper (11)
Intervention effectobservedoriginal resultpositivesimulated

Claim · model-written summary

A rate of 120 kg N ha−1 kept barley grain nitrogen within malting quality requirements.

Treatment
120 kg N ha−1e0
Outcome
grain N%e0
Direction
keeping withine0

Not reportedComparator

Verbatim

e0

“120 kg N ha−1 is the optimal rate to increase grain yield while still keeping within the grain N% requirements for malting quality”

✓ located verbatim in the source · abstract, characters 935–1065
t0

“Spring Barley”

✓ located verbatim in the source · title, characters 36–49

Title

Spatial and Temporal Variability of Spring Barley Yield and Quality Quantified by Crop Simulation Model

Davide Cammarano; Jonathan Holland; Domenico Ronga Agronomy 2020. doi:10.3390/agronomy10030393

Other claims from this paper (2)
Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Weather inputs reduced RRMSE of the 1D-CNN and LSTM by about 2% when tested across years.

Taxon
soybeanGlycine maxe0
Treatment
W-Inputse5
Outcome
RRMSEe5
Direction
reducede5
Value
∼ 2 %e5
Qualifier
when tested across-yearse5

Not reportedComparator

Verbatim

e0

“predicting within-field soybean yields”

✓ located verbatim in the source · abstract, characters 208–246
e5

“W-Inputs led to a small improvement in 1D-CNN and LSTM (reduced RRMSE by ∼ 2 %) when tested across-years”

✓ located verbatim in the source · abstract, characters 1263–1367

Title

Predicting spatial and temporal variability in soybean yield using deep learning and open source data

Deborah V. Gaso; Laura Elena Cue La Rosa; Laila A. Puntel; et al. European Journal of Agronomy 2025. doi:10.1016/j.eja.2024.127498

Other claims from this paper (5)
Intervention effectobservedoriginal resultpositivemeasured

Claim · model-written summary

Low vigor plots achieved much better and more complete grape maturity than high vigor plots.

Taxon
Barberat0
Treatment
LVe1
Outcome
grape maturitye2
Direction
much bettere2
Comparator
HVe1

Verbatim

t0

“steep Barbera vineyard”

✓ located verbatim in the source · title, characters 68–90
e1

“Final vine size and yield were severely curtailed in LV (−49% and −62% as compared to HV, respectively)”

✓ located verbatim in the source · abstract, characters 1222–1325
e2

“which, on the other hand, achieved much better and complete grape maturity”

✓ located verbatim in the source · abstract, characters 1326–1400

Title

Seasonal interactions between spatial and temporal variability in a steep Barbera vineyard

Matteo Gatti; Karen Rahal; Roberto D’Ambrosio; et al. Precision Agriculture 2026. doi:10.1007/s11119-026-10317-w

Other claims from this paper (11)

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