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Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Author

Listed:
  • Sahila Beegum

    (Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
    Nebraska Water Center, Robert B. Daugherty Water for Food Global Institute, 2021 Transformation Drive, University of Nebraska, Lincoln, NE 68588, USA)

  • Muhammad Adeel Hassan

    (Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA
    Oak Ridge Institute for Science and Education, Oak Ridge, TN 37830, USA)

  • Purushothaman Ramamoorthy

    (Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA)

  • Raju Bheemanahalli

    (Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA)

  • Krishna N. Reddy

    (USDA-ARS, Crop Production Systems Research Unit, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA)

  • Vangimalla Reddy

    (Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD 20705, USA)

  • Kambham Raja Reddy

    (Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS 39762, USA)

Abstract

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R 2 = 0.66), enabling better detection of phenotypic variations ( p < 0.05) among the varieties compared to physiological-related traits (from R 2 = 0.21 to R 2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Suggested Citation

  • Sahila Beegum & Muhammad Adeel Hassan & Purushothaman Ramamoorthy & Raju Bheemanahalli & Krishna N. Reddy & Vangimalla Reddy & Kambham Raja Reddy, 2024. "Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton," Agriculture, MDPI, vol. 14(7), pages 1-14, June.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1054-:d:1425968
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    References listed on IDEAS

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    1. Hafiz Shahzad Ahmad & Muhammad Imran & Fiaz Ahmad & Shah Rukh & Rao Muhammad Ikram & Hafiz Muhammad Rafique & Zafar Iqbal & Abdulaziz Abdullah Alsahli & Mohammed Nasser Alyemeni & Shafaqat Ali & Tanve, 2021. "Improving Water Use Efficiency through Reduced Irrigation for Sustainable Cotton Production," Sustainability, MDPI, vol. 13(7), pages 1-12, April.
    2. William Ridley & Stephen Devadoss, 2023. "Competition and trade policy in the world cotton market: Implications for US cotton exports," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(5), pages 1365-1387, October.
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