Abstract
Applying a portion of total nitrogen (N) during the growing season has the potential to improve nitrogen use efficiency (NUE) by achieving greater synchrony between N supply and crop N demand, allowing for responsive adjustments to actual field conditions. Three studies from 2017-2019 evaluated using reactive sensor and proactive Maize-N model for determining in-season N requirements via fertigation in corn. The first study evaluated the integration of reactive sensor and proactive Maize-N model for determining the timing and rate of in-season N via fertigation. Overall, reactive and proactive fertigation treatments reduced total N applied by 35 to 65 kg N ha-1 thus increasing NUE and profit compared to the University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithm treatments with no significant difference in yields. The second study evaluated Maize-N model for predicting economic optimum N rate (EONR), N uptake, and soil nitrate-N. Overall, Maize-N underestimated N rate recommendations by 47 kg N ha-1 compared to the calculated actual EONR with no significant differences in yield. However, the Maize-N EONR reduced profit by 33.5 $ ha-1. Maize-N underestimated N uptake by 24.7 kg N ha-1. Additionally, Maize-N overestimated soil nitrate-N, but a calibrated model improved agreement between predicted and observed soil nitrate-N by 67.5%. The third study evaluated the performance of active and passive crop canopy sensors compared to the SPAD meter in terms of assessing in-season corn N status. Reasonable correlation at any growth stage did not always lead to the same fertigation decision, as the same decision for two sensors can be achieved if both sensor's SI values are greater than 0.95 thresholds or both less than or equal to 0.95. The overall fertigation decisions that matched between SPAD SI and active sensor SI across all growth stages and site years was 72%, and 37 to 48% between SPAD SI and passive sensor SI. Crop canopy sensor and Maize-N model integration will likely result in more accurate N rate and timing decisions. Advisor: Richard B. Ferguson
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Claims 12
Claim · model-written summary
Reactive and proactive fertigation reduced total N applied by 35 to 65 kg N ha-1 compared with the UNL and H-S algorithms.
- Treatment
- reactive and proactive fertigation treatmentse1
- Outcome
- total N appliede1
- Direction
- reducede1
- Value
- 35 to 65 kg N ha-1e1
- Comparator
- University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithme2
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“reactive and proactive fertigation treatments reduced total N applied by 35 to 65 kg N ha-1”
e2“compared to the University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithm treatments”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Reactive and proactive fertigation increased NUE and profit compared with the UNL and H-S algorithms.
- Treatment
- reactive and proactive fertigation treatmentse1
- Outcome
- NUE and profite3
- Direction
- increasinge3
- Comparator
- University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithme2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“reactive and proactive fertigation treatments reduced total N applied by 35 to 65 kg N ha-1”
e2“compared to the University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithm treatments”
e3“thus increasing NUE and profit”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Reactive and proactive fertigation gave no significant difference in yields compared with the UNL and H-S algorithms.
- Treatment
- reactive and proactive fertigation treatmentse1
- Outcome
- yieldse4
- Direction
- no significant differencee4
- Comparator
- University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithme2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“reactive and proactive fertigation treatments reduced total N applied by 35 to 65 kg N ha-1”
e2“compared to the University of Nebraska-Lincoln (UNL) algorithm and Holland and Schepers (H-S) algorithm treatments”
e4“with no significant difference in yields”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Maize-N underestimated the N rate recommendation by 47 kg N ha-1 relative to the calculated actual EONR.
- Outcome
- N rate recommendationse5
- Direction
- underestimatede5
- Value
- 47 kg N ha-1e5
- Comparator
- calculated actual EONRe5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e5“Maize-N underestimated N rate recommendations by 47 kg N ha-1 compared to the calculated actual EONR”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Maize-N gave no significant difference in yield relative to the calculated actual EONR.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e5“Maize-N underestimated N rate recommendations by 47 kg N ha-1 compared to the calculated actual EONR”
e6“with no significant differences in yield”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
The Maize-N EONR reduced profit by 33.5 $ ha-1.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e7“the Maize-N EONR reduced profit by 33.5 $ ha-1”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Maize-N underestimated N uptake by 24.7 kg N ha-1.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Maize-N underestimated N uptake by 24.7 kg N ha-1”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Maize-N overestimated soil nitrate-N relative to observed values.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Maize-N overestimated soil nitrate-N”
e1“a calibrated model improved agreement between predicted and observed soil nitrate-N by 67.5%”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
A calibrated model improved agreement between predicted and observed soil nitrate-N by 67.5%.
- Treatment
- calibrated modele1
- Outcome
- agreement between predicted and observed soil nitrate-Ne1
- Direction
- improvede1
- Value
- 67.5%e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“a calibrated model improved agreement between predicted and observed soil nitrate-N by 67.5%”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Fertigation decisions matched between SPAD SI and active sensor SI in 72% of cases across all growth stages and site years.
- Treatment
- active sensor SIe0
- Outcome
- fertigation decisions that matchede0
- Value
- 72%e0
- Qualifier
- across all growth stages and site yearse0
- Comparator
- SPAD SIe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The overall fertigation decisions that matched between SPAD SI and active sensor SI across all growth stages and site years was 72%, and 37 to 48% between SPAD SI and passive sensor SI”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Fertigation decisions matched between SPAD SI and passive sensor SI in 37 to 48% of cases.
- Treatment
- passive sensor SIe0
- Outcome
- fertigation decisions that matchede0
- Value
- 37 to 48%e0
- Comparator
- SPAD SIe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The overall fertigation decisions that matched between SPAD SI and active sensor SI across all growth stages and site years was 72%, and 37 to 48% between SPAD SI and passive sensor SI”
t0“MAIZE NITROGEN MANAGEMENT”
Claim · model-written summary
Reasonable correlation at any growth stage did not always lead to the same fertigation decision.
- Treatment
- Reasonable correlation at any growth stagee0
- Outcome
- same fertigation decisione0
- Direction
- did not always lead toe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Reasonable correlation at any growth stage did not always lead to the same fertigation decision”
t0“MAIZE NITROGEN MANAGEMENT”
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