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Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

The controller caused no significant decrease in simulated maize yield relative to constant irrigation.

Taxon
maizeZea mayse1
Outcome
yielde0
Direction
without any significant decreasee0
Comparator
constant irrigation method of the DSSAT modele0

Verbatim

t0

“Precision Irrigation”

✓ located verbatim in the source · title, characters 44–64
e0

“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”

✓ located verbatim in the source · abstract, characters 2249–2427
e1

“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”

✓ located verbatim in the source · abstract, characters 1911–1999

Title

Reinforcement Learning Based Controller For Precision Irrigation

Shivaram Irukula OakTrust (Texas A&M University Libraries) 2015

Other claims from this paper (2)
Method / resourceobservedoriginal resultno effectnot significant (as stated)measured

Claim · model-written summary

Using SNP subsets based on functional annotation did not significantly decrease chickpea grain-yield prediction accuracy.

Taxon
chickpeaCicer arietinume1
Outcome
prediction accuracy for grain yielde0
Direction
did not significantly decreasee0
Comparator
whole SNP datasete0

Not reportedValue

Verbatim

e0

“Compared with the whole SNP dataset, a subset of SNPs did not significantly decrease prediction accuracy for grain yield despite consisting of a smaller number of SNPs.”

✓ located verbatim in the source · abstract, characters 1672–1840
e1

“chickpea breeding lines”

✓ located verbatim in the source · abstract, characters 333–356

Title

Genomic prediction of preliminary yield trials in chickpea: Effect of functional annotation of SNPs and environment

Yongle Li; Pradeep Ruperao; Jacqueline Batley; et al. The Plant Genome 2021. doi:10.1002/tpg2.20166

Other claims from this paper (7)
Method / resourceobservedoriginal resultno effectnot significant (as stated)simulated

Claim · model-written summary

The controller caused no significant decrease in simulated maize leaf area index relative to constant irrigation.

Taxon
maizeZea mayse1
Outcome
leaf area indexe0
Direction
without any significant decreasee0
Comparator
constant irrigation method of the DSSAT modele0

Verbatim

e0

“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”

✓ located verbatim in the source · abstract, characters 2249–2427
e1

“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”

✓ located verbatim in the source · abstract, characters 1911–1999

Title

Reinforcement Learning Based Controller For Precision Irrigation

Shivaram Irukula OakTrust (Texas A&M University Libraries) 2015

Other claims from this paper (2)
Method / resourceobservedoriginal resultnot significant (as stated)measured

Claim · model-written summary

Automatic irrigation did not significantly increase rice yield of about 8 ton hectare−1.

Outcome
rice yielde0
Direction
increasee0
Value
8 ton hectare−1e0
Qualifier
has not provene1

Verbatim

e0

“a significant increase of rice yield (of about 8 ton hectare−1)”

✓ located verbatim in the source · abstract, characters 1120–1183
e1

“the automatic system has not proven a decrease of water consumptions (ranging from 2000 to 3700 mm) or a significant increase of rice yield (of about 8 ton hectare−1)”

✓ located verbatim in the source · abstract, characters 1017–1183

Title

Evaluating performances of the first automatic system for paddy irrigation in Europe

Daniele Masseroni; Peter Moller; Reece Tyrell; et al. Agricultural Water Management 2018. doi:10.1016/j.agwat.2017.12.019

Other claims from this paper (4)
Method / resourceobservedoriginal resultnot significant (as stated)measured

Claim · model-written summary

Reducing modeled yield factors from three to two insignificantly reduced yield calculation accuracy.

Taxon
barley and winter triticaleHordeum vulgare×Triticosecalee1
Treatment
two (food and moisture)e0
Outcome
accuracy of calculating the grain crop yielde0
Direction
reducese0
Comparator
three (food, moisture and heat)e0

Verbatim

e0

“decrease in the number of yield-forming factors taken into account in the mathematical model from three (food, moisture and heat) to two (food and moisture) reduces the accuracy of calculating the grain crop yield insignificantly.”

✓ located verbatim in the source · abstract, characters 1639–1869
e1

“the data of independent field experiments of Professor N.N. Semenenko with barley and winter triticale has been used.”

✓ located verbatim in the source · abstract, characters 1191–1308

Title

Mathematical model of agricultural crop yield

A. P. Likhatsevich Proceedings of the National Academy of Sciences of Belarus Agrarian Series 2021. doi:10.29235/1817-7204-2021-59-3-304-318

Other claims from this paper (2)

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