Dataset Information

Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice.

ABSTRACT: Phosphorus (P) is an essential mineral nutrient and one of the key factors determining crop productivity. P-deficient plants exhibit visual leaf symptoms, including chlorosis, and alter spectral reflectance properties. In this study, we evaluated leaf inorganic phosphate (Pi) contents, plant growth and reflectance spectra (420-790 nm) of 172 Thai rice landrace varieties grown hydroponically under three different P supplies (overly sufficient, mildly deficient and severely deficient conditions). We reported correlations between Pi contents and reflectance ratios computed from two wavebands in the range of near infrared (720-790 nm) and visible energy (green-yellow and red edge) (r > 0.69) in Pi-deficient leaves. Artificial neural network models were also developed which could classify P deficiency levels with 85.60% accuracy and predict Pi content with R2 of 0.53, as well as highlight important waveband sections. Using 217 reflectance ratio indices to perform genome-wide association study (GWAS) with 113,114 SNPs, we identified 11 loci associated with the spectral reflectance traits, some of which were also associated with the leaf Pi content trait. Hyperspectral measurement offers a promising non-destructive approach to predict plant P status and screen large germplasm for varieties with high P use efficiency.

SUBMITTER: Pinit S

PROVIDER: S-EPMC9020724 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice.

Pinit Sompop S Ruengchaijatuporn Natthanan N Sriswasdi Sira S Buaboocha Teerapong T Chadchawan Supachitra S Chaiwanon Juthamas J

PloS one 20220420 4

Phosphorus (P) is an essential mineral nutrient and one of the key factors determining crop productivity. P-deficient plants exhibit visual leaf symptoms, including chlorosis, and alter spectral reflectance properties. In this study, we evaluated leaf inorganic phosphate (Pi) contents, plant growth and reflectance spectra (420-790 nm) of 172 Thai rice landrace varieties grown hydroponically under three different P supplies (overly sufficient, mildly deficient and severely deficient conditions). ...[more]

PMID: 35443012

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Project description:Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNCLi, i = 1, 2, 3, 4), and the relationship between the LNCLi and the LNC at the canopy level (LNCCanopy). A vertical distribution model of the LNC was constructed based on the relative canopy height. Furthermore, the relationship between different vegetation indices (VIs) and the LNCCanopy, the LNCLi, and the LNC vertical distribution model parameters were studied. We also compared the following three methods for estimating the LNC in different leaf layers in rice canopy: (1) estimating the LNCCanopy by VIs and then estimating the LNCLi based on the relationship between the LNCLi and LNCCanopy; (2) estimating the LNC in any leaf layer of the rice canopy by VIs, inputting the result into the LNC vertical distribution model to obtain the parameters of the model, and then estimating the LNCLi using the LNC vertical distribution model; (3) estimating the model parameters by using VIs directly and then estimating the LNCLi by the LNC vertical distribution model. The results showed that the LNC in the bottom of rice canopy was more susceptible to different N rates, and changes in the LNC with the relative canopy height could be simulated by an exponential model. Vegetation indices could estimate the LNC at the top of rice canopy. R705/(R717+R491) (R2 = 0.763) and the renormalized difference vegetation index (RDVI) (1340, 730) (R2 = 0.747) were able to estimate the parameter "a" of the LNC vertical distribution model in indica rice and japonica rice, respectively. In addition, method (2) was the best choice for estimating the LNCLi (R2 = 0.768, 0.700, 0.623, and 0.549 for LNCL1, LNCL2, LNCL3, and LNCL4, respectively). These results provide technical support for the rapid, accurate, and non-destructive identification of the vertical distribution of nitrogen in rice canopies.

Dataset Information

Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice.

Publications

Hyperspectral and genome-wide association analyses of leaf phosphorus status in local Thai indica rice.

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