Record Details

Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?

CGSpace

View Archive Info
 
 
Field Value
 
Title Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy?
 
Creator Quiróz, R.
Loayza, H.
Barreda, Carolina
Gavilán, C.
Posadas, A.
Ramírez, D.
 
Subject CANOPY
REMOTE SENSING
CROPPING SYSTEMS
 
Description Data acquisition for parameterization is one of the most important limitations for the use of potato crop growth models. Non destructive techniques such as remote sensing for gathering required data could circumvent this limitation. Our goal was to analyze the effects of incorporating ground-based spectral canopy reflectance data into two light interception models with different complexity. A dynamic- hourly scale- canopy photosynthesis model (DCPM), based on a non-rectangular hyperbola applied to sunlit and shaded leaf layers and considering carbon losses by respiration, was implemented (complex model). Parameters included the light extinction coefficient, the proportion of light transmitted by leaves, the fraction of incident diffuse photosynthetically active radiation and leaf area index. On the other hand, a simple crop growth model (CGM) based on daily scale of light interception, light use efficiency (LUE) and harvest index was parameterized using either canopy cover (CGMCC) or the weighted difference vegetation index (CGMWDVI). A spectroradiometer, a chlorophyll meter and a multispectral camera were used to derive the required parameters. CGMWDVI improved yield prediction compared to CGMCC. Both CGMWDVI and DCPM showed high degree of accuracy in the yield prediction. Since large LUE variations were detected depending on the diffuse component of radiation, the improvement of simple CGM using remotely sensed data is contingent on an appropriate LUE estimation. Our study suggests that the incorporation of remotely sensed data in models with different temporal resolution and level of complexity improves yield prediction in potato.
Peer Review
 
Date 2016-12-13T12:24:34Z
2016-12-13T12:24:34Z
2017
 
Type Journal Article
 
Identifier Quiroz, R.; Loayza, H.; Barreda, C.; Gavilán, C.; Posadas, A.; Ramírez, D. A. 2017. Linking process-based potato models with light reflectance data: Does model complexity enhance yield prediction accuracy? European Journal of Agronomy. (Netherlands). ISSN 1161-0301. 82 (Part A):104-112.
1161-0301
https://hdl.handle.net/10568/78288
 
Language en
 
Format 2 p.
 
Source European Journal of Agronomy