The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production
Abstract
:1. Introduction
2. Materials and Methods
2.1. Field Trial Conditions
2.2. Plant N Status
2.3. Canopy Reflectance and Calculation of Vegetation Indices
2.4. Data Analysis
3. Results
3.1. Growing Conditions
3.2. N Application Rates and Strategies’ Effects on Yield
3.3. N Application Rates and Strategies’ Effects on Nup, DM, Na, NNI
3.4. Models’ Performance on Na, DM, Nup, NNI and Seed Yield Predictions
3.5. Prediction of Seed Yield Based on Reflectance Measurements
4. Discussion
4.1. Response Curves
4.2. Yield Models Performance
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Index | Formula | Reference |
---|---|---|
Normalized Difference Vegetation Index (NDVI) | [30] | |
Normalized Difference Red Edge (NDRE) | [31] | |
Ratio Vegetation Index I (RVI I) | [32] | |
Ratio Vegetation Index II (RVI II) | [33] | |
Modified Chlorophyll Absorption Ratio Index (MCARI) | [34] | |
Modified Triangular Vegetation Index 2 (MTVI 2) | [35] | |
MCARI / MTVI 2 | [36] | |
Transformed Chlorophyll Absorption Reflectance Index (TCARI 1) | [37] | |
Optimized Soil Adjusted Vegetation Index (OSAVI) | [38] | |
TCARI/ OSAVI | [39] | |
Modified Soil-Adjusted Vegetation Index (MSAVI) | [40] | |
Green Normalized Difference Vegetation Index (GNDSI) | [41] | |
Red Edge Soil-Adjusted Vegetation Index (RESAVI) | [42] | |
Difference Vegetation Index (DVI) | [43] | |
Red Edge Ratio Vegetation Index (RERVI) | [44] | |
Red Edge Difference Vegetation Index (REDVI) | [30] | |
Optimized Vegetation Index 1 (VIopt 1) | [44] |
N Treatment | Lodging at Flowering | Lodging at Harvest | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
2001 | 2002 | 2003 | 2004 | 2001– 2004 | 2001 | 2002 | 2003 | 2004 | 2001– 2004 | |
N40 | 1c | 20e | 1d | 1e | 6 | 65c | 85c | 68b | 26d | 61 |
N80 | 3c | 28d | 33bc | 22d | 22 | 80b | 89bc | 97a | 59c | 81 |
N120 | 16b | 36bc | 38ab | 35bc | 31 | 82ab | 93ab | 98a | 71abc | 86 |
N160 | 21a | 39ab | 40a | 43ab | 36 | 85a | 93ab | 98a | 83a | 90 |
N200 | 25a | 44a | 35abc | 46a | 38 | 86a | 98a | 96a | 81a | 72 |
N40+80 | 14b | 38abc | 40a | 31c | 31 | 80b | 94ab | 98a | 75ab | 87 |
N80+40 | 13b | 38ab | 39a | 35bc | 31 | 81b | 92ab | 98a | 68bc | 85 |
N40+40+40 | 5c | 31cd | 31c | 19d | 22 | 79b | 94ab | 99a | 60c | 83 |
Average | 12 | 34 | 32 | 29 | - | 80 | 92 | 94 | 65 | - |
N Treatment | Seed Yield (kg ha−1) | ||||
---|---|---|---|---|---|
2001 | 2002 | 2003 | 2004 | 2001–2004 | |
N40 | 856e | 933e | 1162e | 937f | 949d |
N80 | 1001d | 1253d | 1379d | 1335de | 1194c |
N120 | 1154bc | 1500bc | 1574bc | 1485bc | 1373b |
N160 | 1250ab | 1593ab | 1795a | 1566ab | 1498a |
N200 | 1332a | 1688a | 1752a | 1409cd | 1508a |
N40+80 | 1182b | 1536bc | 1607b | 1580a | 1417ab |
N80+40 | 1204bc | 1452c | 1533bc | 1470c | 1373b |
N40+40+40 | 1135c | 1319d | 1471cd | 1303e | 1275c |
Year | Biological Optimum | Economical Optimum | NUE | |||
---|---|---|---|---|---|---|
N Rate | Seed Yield | N Rate | Seed Yield | BO | EON | |
2001 | - | - | - | - | - | - |
2002 | 206 | 1678 | 187 | 1669 | 0.12 | 0.13 |
2003 | 211 | 1783 | 188 | 1773 | 0.02 | 0.05 |
2004 | 125 | 1487 | 138 | 1550 | 0.26 | 0.25 |
2001–2004 | 196 | 1514 | 175 | 1503 | 0.16 | 0.18 |
Calibration | Test Set Validation | |||||||
---|---|---|---|---|---|---|---|---|
PLSR | ||||||||
Response Variable | n | #PLSR comp | R2 | RMSECV | n | R2 | RMSEP | Bias |
Na, % | 582 | 7 | 0.71 | 0.77 | 236 | 0.67 | 0.65 | 0.26 |
DM, t ha−1 | 594 | 7 | 0.55 | 2.8 | 164 | 0.43 | 3.9 | 3.5 |
Nup, kg ha−1 | 572 | 5 | 0.28 | 48 | 284 | 0.38 | 39 | −2.2 |
NNI | 582 | 6 | 0.33 | 0.34 | 236 | 0.39 | 0.28 | 0.09 |
SVM | ||||||||
Response Variable | n | #SVs | R2 | RMSECV | n | R2 | RMSEP | Bias |
Na, % | 583 | 405 | 0.84 | 0.56 | 241 | 0.77 | 0.47 | 0.12 |
DM, t ha−1 | 600 | 454 | 0.69 | 2.4 | 288 | 0.52 | 6.7 | 5.4 |
Nup, kg ha−1 | 589 | 520 | 0.31 | 48 | 288 | 0.46 | 36.7 | −3.03 |
NNI | 586 | 582 | 0.36 | 0.34 | 243 | 0.40 | 0.26 | 0.07 |
Year | Calibration | Cross Validation | ||||
---|---|---|---|---|---|---|
n | #PLSR comp. | R2 | RMSEC | R2 | RMSECV | |
2001 | 77 | 2 | 0.63 | 1354 | 0.51 | 156 |
2002 | 40 | 3 | 0.93 | 81 | 0.87 | 112 |
2003 | 40 | 1 | 0.81 | 112 | 0.79 | 117 |
2004 | 40 | 1 | 0.63 | 151 | 0.58 | 161 |
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Gislum, R.; Thomopoulos, S.; Gyldengren, J.G.; Mortensen, A.K.; Boelt, B. The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production. Nitrogen 2021, 2, 229-243. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen2020015
Gislum R, Thomopoulos S, Gyldengren JG, Mortensen AK, Boelt B. The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production. Nitrogen. 2021; 2(2):229-243. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen2020015
Chicago/Turabian StyleGislum, René, Stamatios Thomopoulos, Jacob Glerup Gyldengren, Anders Krogh Mortensen, and Birte Boelt. 2021. "The Use of Remote Sensing to Determine Nitrogen Status in Perennial Ryegrass (Lolium perenne L.) for Seed Production" Nitrogen 2, no. 2: 229-243. https://0-doi-org.brum.beds.ac.uk/10.3390/nitrogen2020015