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ETD RECORD
Remote sensing of wheat nitrogen status for improved protein management in dryland systems
Citation
Eitel, Jan Ulrich Hermann.. (2008). Remote sensing of wheat nitrogen status for improved protein management in dryland systems. Theses and Dissertations Collection, University of Idaho Library Digital Collections. https://www.lib.uidaho.edu/digital/etd/items/etd_86.html
- Title:
- Remote sensing of wheat nitrogen status for improved protein management in dryland systems
- Author:
- Eitel, Jan Ulrich Hermann.
- Date:
- 2008
- Keywords:
- Wheat--Nitrogen content--Remote sensing Soils--Nitrogen content--Remote sensing Dry farming
- Program:
- Natural Resources
- Abstract:
- Growers require pre-harvest information about grain protein to optimize nitrogen (N) fertilizer inputs and grain harvest. The aim of this dissertation was to predict final grain protein of dryland wheat based on mid-seasonal remote sensing data. Grain protein predictions have relied on weather, cultivar and crop N status information. The latter has been remotely sensed by means of spectral indices. These indices generally employ narrow wavebands (<40 nm) that are sensitive to chlorophyll a and b content and leaf area index (LAI) both of which usually co-vary with variations in crop N status. However, remote sensing crop N status is complicated by N-independent variations in LAI and soil background reflectance. Chapter 1 shows that N-independent variations in LAI confound remote predictions of crop N status that are based on single indices, but have only a minor effect if combined indices are used. The new combined index derived from the ratio of Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) has the lowest sensitivity to variations in LAI (r{esc}p2{esc}s = 0.01) and the highest sensitivity to crop N status (r{esc}p2{esc}s = 0.54). Chapter 2 evaluates the sensitivity of spectral indices to variation in soil reflectance. The results indicate that spectral indices are affected by soil background reflectance when LAI <1.5 although the effect on overall index variability is small (<6%) when compared to LAI (<97%). Chapter 3 tests if broad-band satellite data (waveband>40 nm) are a viable alternative to higher-cost and less-available narrow-band imagery (waveband <40 nm). The results suggest that broad-band satellite data are suitable when the new index MCARI/MTVI2 is employed. Chapter 4 describes the methodology and assumptions of a new protein prediction approach that combines remote predictions of crop N status with weather and cultivar performance data. The models show to predict final grain protein concentration (GPC) well at the model development site (0.87 [less than or equal to] Residual Mean Square Error [less than or equal to] 50.93; 0.74 [less than or equal to] r{esc}p2{esc}s [less than or equal to] 0.77) but less well when validated with independent data (0.87 [less than or equal to] Residual Mean Square Error [less than or equal to] 1.90; 0.06 [less than or equal to] r{esc}p2{esc}s [less than or equal to] 0.68.
- Description:
- Thesis (Ph. D., Natural Resources)--University of Idaho, December 2008.
- Major Professor:
- Paul E. Gessler.
- Defense Date:
- December 2008.
- Type:
- Text
- Format Original:
- xvii, 108 leaves :ill. ;29 cm.
- Format:
- record
Rights
- Rights:
- In Copyright - Educational Use Permitted. For more information, please contact University of Idaho Library Special Collections and Archives Department at libspec@uidaho.edu.
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