Quinoa genotypes under deficit irrigation: integrating phenotyping and remote sensing for water use efficiency in arid Peru

Betancur-Choquehuayta R.S.; Molina-Gonzales E.E.; Flores-Subeleta D.J.; Gutiérrez-Rosales R.O.; Anculle-Arenas A.; Llasaca-Calizaya N.W.; Bustamante-Muñoz J.L.; Mayta-Anco M.E. · Frontiers in Plant Science (2026) · DOI: 10.3389/fpls.2026.1810225

Resumen

Within the context of climate change, quinoa (Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg-1). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems. Copyright

Instituciones: Universidad Nacional de San Agustín de Arequipa; Universidad de Huánuco