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Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping

Authors
  • Dwivedi, Priyanka1
  • Ramawat, Naleeni2
  • Raju, Dhandapani3, 4
  • Dhawan, Gaurav1
  • Gopala Krishnan, S.1
  • Chinnusamy, Viswanathan3, 4
  • Bhowmick, Prolay Kumar1
  • Vinod, K. K.1
  • Pal, Madan4
  • Nagarajan, Mariappan5
  • Ellur, Ranjith Kumar1
  • Bollinedi, Haritha1
  • Singh, Ashok K.1
  • 1 Division of Genetics, ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi , (India)
  • 2 Amity Institute of Organic Agriculture, Amity University, Noida , (India)
  • 3 Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi , (India)
  • 4 Division of Plant Physiology, ICAR-IARI, New Delhi , (India)
  • 5 Rice Breeding and Genetics Research Centre, ICAR-IARI, Aduthurai , (India)
Type
Published Article
Journal
Frontiers in Plant Science
Publisher
Frontiers Media SA
Publication Date
Jan 05, 2022
Volume
12
Identifiers
DOI: 10.3389/fpls.2021.752730
Source
Frontiers
Keywords
Disciplines
  • Plant Science
  • Original Research
License
Green

Abstract

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant’s response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

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