The potential of dynamic physiological traits in young tomato plants to predict field-yield performance

Sanbon Chaka Gosa, Amit Koch, Itamar Shenhar, Joseph Hirschberg, Dani Zamir, Menachem Moshelion

Research output: Contribution to journalArticlepeer-review

Abstract

To address the challenge of predicting tomato yields in the field, we used whole-plant functional phenotyping to evaluate water relations under well-irrigated and drought conditions. The genotypes tested are known to exhibit variability in their yields in wet and dry fields. The examined lines included two lines with recessive mutations that affect carotenoid biosynthesis, zeta z2083 and tangerine t3406, both isogenic to the processing tomato variety M82. The two mutant lines were reciprocally grafted onto M82, and multiple physiological characteristics were measured continuously, before, during and after drought treatment in the greenhouse. A comparative analysis of greenhouse and field yields showed that the whole-canopy stomatal conductance (gsc) in the morning and cumulative transpiration (CT) were strongly correlated with field measurements of total yield (TY: r2 = 0.9 and 0.77, respectively) and plant vegetative weight (PW: r2 = 0.6 and 0.94, respectively). Furthermore, the minimum CT during drought and the rate of recovery when irrigation was resumed were both found to predict resilience.

Original languageEnglish
Article number111122
JournalPlant Science
Volume315
DOIs
StatePublished - Feb 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • Adaptation, Physiological/genetics
  • Crops, Agricultural/genetics
  • Dehydration/physiopathology
  • Drought tolerance
  • Droughts
  • Forecasting
  • Functional phenotyping
  • Gene Expression Regulation, Plant
  • Genes, Plant
  • Genetic Variation
  • Genotype
  • Lycopersicon esculentum/genetics
  • Mutation
  • Phenotype
  • Physiological trait
  • Plant Physiological Phenomena/genetics
  • Time-series measurements
  • Tomato
  • Yield prediction

All Science Journal Classification (ASJC) codes

  • Genetics
  • Agronomy and Crop Science
  • Plant Science

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