4.1 Article

Non-Destructive Leaf Area Estimation Model for Overall Growth Performances in Relation to Yield Attributes of Cassava (Manihot esculenta Cranz) under Water Deficit Conditions

Journal

Publisher

UNIV AGR SCI & VETERINARY MED CLUJ-NAPOCA
DOI: 10.15835/nbha47311487

Keywords

cassava; growth performances; leaf area index; non-destructive model; vegetation indices

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Funding

  1. National Research Council of Thailand (NRCT)
  2. Cluster and Program Management Office (CPMO) of the National Science and Technology Development Agency (NSTDA) [P-13-00634]

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Cassava is a tropical storage root crop, a source of carbohydrate and alternative energy. It has been classified as drought tolerant plant for the whole life cycle, except during the root initiation stage (120-150 DAP). Leaf area index (LAI) is one of the most parameters representing the overall growth and yield prediction in cassava. The aim of this investigation was to validate the physiological and growth performance of cassava in responses to water deficit stress in the field trial as well as to investigate the leaf area index as an important factor to cassava growth and storage root bulking. Leaf relative water content in cassava declined significantly upon a long period of water withholding, and regulated non-photochemical quenching (NPQ), leading to chlorophyll degradation, reduced number of leaves and limited leaf area index (LAI) and loss of storage root yield when compared with well-irrigated plants. Non-destructive leaf area estimation model under water deficit stress condition using spectral reflectance to determine the LAI and VIs was validated. The Ratio Vegetation Index (RVI) was suitable model with high coefficient of determination (R-2 = 0.89). However, the RVI as LAI at 150 DAP (120 d water withholding) could be considered as the critical point to indicate cassava growth and yield performance. Based on the results, cassava growth, biomass and yield in the different environments may further be investigated, taking into consideration the genotypic variation and using remote sensing technology for rapid monitoring and accurate and cost-effective data assessment.

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