Using Canopy Measurements to Predict Soybean Seed Yield
Abstract
:1. Introduction
2. Materials and Methods
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Location | Soil Series | Soil Taxonomy | Tillage | PC 1 | GPS |
---|---|---|---|---|---|
Casselton | Kindred | Fine-silty, mixed, superactive, frigid Typic Endoaquolls | CT | SB | 46.882, −97.251 |
Bearden | Fine-silty, mixed, superactive, frigid Aeric Calciaquolls | ||||
Fargo | Fargo | Fine, smectitic, frigid Typic Epiaquerts | NT | W | 46.932, −96.859 |
Ryan | Fine, smectitic, frigid Typic Natraquerts | ||||
Prosper | Bearden | Fine-silty, mixed, superactive, frigid Aeric Calciaquolls | CT | W | 47.001, −97.112. |
Lindaas | Fine, smectitic, frigid Typic Argiaquolls |
Location | Planting Date | |||||||
---|---|---|---|---|---|---|---|---|
1 | 2 | Depth | NO3-N | P | K | pH | OM | |
DOY 1 | cm | kg ha−1 | mg kg−1 | g kg−1 | ||||
─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ 2019 ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ | ||||||||
Casselton | 137 | 154 | 0–15 | 16 | 8 | 368 | 7.4 | 5.2 |
15–61 | 37 | 7 | 303 | 7.5 | 3.9 | |||
Fargo | 137 | 154 | 0–15 | 8 | 15 | 495 | 7.8 | 5.8 |
15–61 | 14 | 5 | 300 | 7.8 | 4.0 | |||
Prosper | 136 | 149 | 0–15 | 35 | 20 | 232 | 7.9 | 3.4 |
15–61 | 57 | 6 | 176 | 8.2 | 2.5 | |||
─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ 2020 ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ ─ | ||||||||
Casselton | 142 | 153 | 0–15 | 19 | 18 | 360 | 7.5 | 4.8 |
15–61 | 18 | 7 | 279 | 7.8 | 4.5 | |||
Fargo | 133 | 149 | 0–15 | 22 | 18 | 489 | 7.7 | 5.4 |
15–61 | 26 | 6 | 353 | 8.0 | 4.0 | |||
Prosper | 143 | 153 | 0–15 | 21 | 30 | 269 | 7.2 | 4.5 |
15–61 | 24 | 17 | 216 | 7.4 | 3.2 |
FGCC 1 | PAR | NDVI | ||||
---|---|---|---|---|---|---|
Stage 2 | R2 3 | RMSE | R2 | RMSE | R2 | RMSE |
V2 | 0.05 | 710 | 0.01 | 728 | 0.01 | 725 |
V4 | 0.21 | 646 | 0.21 | 647 | 0.19 | 653 |
R1 | 0.43 | 551 | 0.24 | 635 | 0.41 | 560 |
R3 | 0.49 | 519 | 0.30 | 608 | 0.05 | 708 |
R5 | 0.52 | 507 | 0.01 | 724 | 0.65 | 434 |
R7 | 0.16 | 668 | 0.23 | 637 | 0.01 | 728 |
Parameter 1 | Stepwise Regression | Lasso Regression |
---|---|---|
Adj. R2 | 0.68 | 0.66 |
Validated Adj. R2 | 0.69 | 0.67 |
RMSE | 411 | 425 |
AIC | 3346 | 3362 |
Variables Used 2 | NDVI.R1 PAR.R1 NDVI.R3 FGCC.R3 NDVI.R5 PAR.R5 | NDVI.R1 NDVI.R3 FGCC.R3 NDVI.R5 FGCC.R5 |
Method | Equation 1 |
---|---|
Stepwise | Ŷ = 874 × NDVI.R1 − 8 × PAR.R1 + 1913 × NDVI.R3 + 9 × FGCC.R3 + 9357 × NDVI.R5 – 13 × PAR.R5 − 5604 |
Lasso | Ŷ = 40 × NDVI.R1 + 562 × NDVI.R3 + 7 × FGCC.R3 + 8185 × NDVI.R5 + 5 × FGCC.R5 − 4921 |
Established Plant Density | ||||||
---|---|---|---|---|---|---|
FGCC 1 | PAR | NDVI | ||||
Stage 2 | Adj. R2 3 | RMSE | Adj. R2 | RMSE | Adj. R2 | RMSE |
V2 | 0.05 | 711 | 0.01 | 729 | 0.01 | 726 |
V4 | 0.22 | 643 | 0.21 | 647 | 0.20 | 654 |
R1 | 0.44 | 548 | 0.24 | 636 | 0.42 | 557 |
R3 | 0.49 | 519 | 0.30 | 609 | 0.05 | 710 |
R5 | 0.52 | 508 | 0.01 | 725 | 0.65 | 435 |
R7 | 0.16 | 669 | 0.23 | 638 | 0.01 | 729 |
FGCC 1 | Adj. R2 | RMSE | Equation |
---|---|---|---|
Growth Stage 2 | |||
R3 | 0.49 | 510 | Ŷ = 33.4 × FGCC.R3 + 662.3 |
R5 | 0.52 | 510 | Ŷ = 50.3 × FGCC.R5 − 868.2 |
R3 R5 | 0.54 | 479 | Ŷ = 18.8 × FGCC.R3 + 29.4 × FGCC.R5 − 603.2 |
V2 R1 R3 R5 | 0.56 | 470 | Ŷ = −7 × FGCC.V2 + 7 × FGCC.R1 + 13 × FGCC.R3 + 25 × FGCC.R5 − 132 3 |
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Schmitz, P.K.; Kandel, H.J. Using Canopy Measurements to Predict Soybean Seed Yield. Remote Sens. 2021, 13, 3260. https://doi.org/10.3390/rs13163260
Schmitz PK, Kandel HJ. Using Canopy Measurements to Predict Soybean Seed Yield. Remote Sensing. 2021; 13(16):3260. https://doi.org/10.3390/rs13163260
Chicago/Turabian StyleSchmitz, Peder K., and Hans J. Kandel. 2021. "Using Canopy Measurements to Predict Soybean Seed Yield" Remote Sensing 13, no. 16: 3260. https://doi.org/10.3390/rs13163260