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Advanced Decision-Making Irrigation Regulated by VPD Changed the Circadian Transpiration Pattern of Tomatoes

  • Jiaxing He,
  • Lele Ma,
  • Wenxin Li,
  • Chenxi Zhu,
  • Minggao Liu,
  • Jianming Li

摘要

Saturated vapor pressure deficit (VPD) is plant transpiration’s main driving force and regulates stomatal behavior. In theory, VPD can predict plant transpiration and determine irrigation. Still, the circadian transpiration of plants needs to be clarified for the rapid, short-term response of VPD. Here we set up two VPD environments (low VPD, high VPD) to irrigate three different varieties of tomatoes using our team’s advanced decision irrigation system. The study monitored the diurnal transpiration changes, morphological growth, leaf characteristics, water status, gas exchange, and photosynthesis of the tomatoes. The result showed that when that decision system was used for irrigation, the low VPD environment increased the water potential of roots, stems, and leaves during the daytime, alleviated the hydraulic restriction, and increased the proportion of nighttime transpiration of various tomato varieties. It was likely that the plants changed their circadian transpiration rhythm, and higher stomatal conductance, water use efficiency, and photosynthetic production performance during the daytime were obtained through higher nighttime transpiration. In addition, transpiration showed a response-ability to predict and adjust VPD in advance. There was a very high correlation between environmental factor VPD and plant transpiration during the daytime. Among them, when adding the time lag of −1 h and −0.5 h, the overall decision coefficient R2 between the transpiration rate and VPD of each tomato variety was higher than without time delay. We use the daytime transpiration data of 30 min as fitting examples. The decision coefficients between transpiration and VPD accumulation within 30 min were 0.90, 0.81, and 0.89, respectively. But this correlation was insignificant at night. This study provided a new idea for the real-time and accurate prediction of irrigation for protected tomatoes using transpiration decisions.