Abstract
Abstract Crop yield prediction is a critical measurement, especially in the time when parts of the world are suffering from farming issues. Yield forecasting gives an alert regarding economic trading, food production monitoring, and global food security. This research was conducted to investigate whether active optical sensors could be utilized for potato ( Solanum tuberosum L.) yield prediction at the mid.le of the growing season. Three potato cultivars (Russet Burbank, Superior, and Shepody) were planted and six rates of N (0, 56, 112, 168, 224, and 280 kg ha −1 ), ammonium sulfate, which was replaced by ammonium nitrate in the 2nd year, were applied on 11 sites in a randomized complete block design, with four replications. Normalized difference vegetation index (NDVI) and chlorophyll index (CI) measurements were obtained weekly from the active optical sensors, GreenSeeker (GS) and Crop Circle (CC). The 168 kg N ha −1 produced the maximum potato yield. Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield. Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient. The exponential and linear models showed a better fit of the data. Soil organic matter content increased the yield significantly but did not affect the prediction models. The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.
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Claims 7
Claim · model-written summary
168 kg N ha −1 produced the maximum potato yield.
- Taxon
- potato
Solanum tuberosume0 - Treatment
- 168 kg N ha −1e0
- Outcome
- potato yielde0
- Direction
- maximume0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The 168 kg N ha −1 produced the maximum potato yield.”
Claim · model-written summary
Indices at the 16th and 20th leaf stages correlated significantly with tuber yield.
- Taxon
- potatoSolanum tuberosumt0
- Organ / stage
- 16th and 20th leaf growth stagese0
- Outcome
- tuber yielde0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Indices measurements obtained at the 16th and 20th leaf growth stages were significantly correlated with tuber yield.”
t0“potato”
Claim · model-written summary
Multiple regression using NDVI and CI improved prediction accuracy and determination coefficient.
- Taxon
- potatoSolanum tuberosume0
- Outcome
- accuracy of the prediction model and increase the determination coefficiente0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient.”
Claim · model-written summary
Exponential and linear models fit the data better.
- Taxon
- potatoSolanum tuberosumt0
- Outcome
- better fit of the datae0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The exponential and linear models showed a better fit of the data.”
t0“potato”
Claim · model-written summary
Soil organic matter content significantly increased potato yield.
- Taxon
- potatoSolanum tuberosumt0
- Outcome
- yielde0
- Direction
- increasede0
- Setting
- Soil organic matter contente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Soil organic matter content increased the yield significantly but did not affect the prediction models.”
t0“potato”
Claim · model-written summary
Soil organic matter content did not affect prediction models.
- Taxon
- potatoSolanum tuberosumt0
- Outcome
- prediction modelse0
- Direction
- did not affecte0
- Setting
- Soil organic matter contente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Soil organic matter content increased the yield significantly but did not affect the prediction models.”
t0“potato”
Claim · model-written summary
The authors identify the 18th and 20th leaf stages as best for sensor prediction.
- Taxon
- potatoSolanum tuberosumt0
- Organ / stage
- 18th and 20th leaf growth stagese0
- Outcome
- best time to use the sensors for yield predictione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The 18th and 20th leaf growth stages are the best time to use the sensors for yield prediction.”
t0“potato”
The same questions in other papers
nitrogen · yield · Solanum tuberosum (potato)Results differ
108 papers in the same direction as this paper · 20 in the opposite direction · 3 reporting no difference from an alternative
- no effect
- negative
- negative
128 papers more in this group
See all 132 papers →Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.