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Maize leaf area estimation in different growth stages based on allometric descriptors
- Source :
- African Journal of Agricultural Research. 13:202-209
- Publication Year :
- 2018
- Publisher :
- Academic Journals, 2018.
-
Abstract
- The literature points out the need for leaf area (LA) calibration models that are suitable for specific varieties (variety-specific). These models should be capable of coping with different crop conditions, growth stages, and agronomic practices. The objective of the current study was to develop a model for estimating the LA of ​​maize (Zea mays L.), considering the entire growth cycle, based on non-destructive allometric measurements. The proposed model was derived from a multiple regression analysis of LA data obtained from digital image processing, including the number of leaves per plant (NL) and the product of major leaf length per major leaf width of the greater leaf (MLL × MLW). A high percent of data variability in the LA of maize plants was explained by the model, both in the calibration and validation phases (R2 = 0.90; n = 30). Overall, the selected model presented good performance in the estimation of LA of maize, variety PAN 53, cultivated under the conditions of the present study area. Additionally, the model enabled the estimation of LA at different stages of the crop cycle. The results indicated a positive potential for using the developed model to support several maize cultural practices. Key words: Allometry, non-destructive measurement, modelling, Zea mays.
- Subjects :
- 0106 biological sciences
Estimation
Growth cycle
Data variability
Calibration (statistics)
Regression analysis
04 agricultural and veterinary sciences
01 natural sciences
Zea mays
Crop
Agronomy
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Allometry
General Agricultural and Biological Sciences
010606 plant biology & botany
Mathematics
Subjects
Details
- ISSN :
- 1991637X
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- African Journal of Agricultural Research
- Accession number :
- edsair.doi...........f46c7c9e0c7e08bb683c5713ca2130bd
- Full Text :
- https://doi.org/10.5897/ajar2017.12916