Spring wheat yield prediction with empirical regression models using different biomass parameters
Transition to smart agriculture demands tools for non-invasive monitoring of cultivated plants biomass. One of the most widespread and informative biomass indicators is leaf area index (LAI). LICOR 2200C has become de facto standard in modern ecological research for non-invasive LAI estimation. In t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/04/bioconf_i-craft2024_01052.pdf |
| Summary: | Transition to smart agriculture demands tools for non-invasive monitoring of cultivated plants biomass. One of the most widespread and informative biomass indicators is leaf area index (LAI). LICOR 2200C has become de facto standard in modern ecological research for non-invasive LAI estimation. In this paper, on the example of spring wheat crops of the RSAU-MTAA experimental field, the efficiency of yield and biomass parameters prediction using data from AccuPAR LP-80 and LI-COR LAI 2200C was compared. LAI data from both devices obtained at different phenological phases of spring wheat were used as predictor for spring wheat yield models. Comparing the generated models show superiority of AccuPAR LP-80 in yield prediction while LI-COR LAI 2200C shown better result in overall biomass prediction. |
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| ISSN: | 2117-4458 |