“…Additionally, previous models that utilize steady‐state data potentially overestimate CO 2 assimilation and do not account for the loss of productivity due to the lags in efficiency during induction and relaxation (Pearcy, 1990 ; Taylor & Long, 2017 ; Wang, Burgess, et al, 2020 ) and will underestimate water loss (McAusland et al, 2016 ; Qu et al, 2016 ; McAusland et al, 2020 ). However, increased focus has recently been dedicated to characterizing, understanding, and modeling photosynthesis in non‐steady‐state conditions, to more accurately reflect conditions in the field (Kaiser et al, 2015 ; Kaiser et al, 2017 ; McAusland et al, 2016 ; Qu et al, 2016 ; Soleh et al, 2016 , 2017 ; Kaiser et al, 2018 ; Deans et al, 2019 ; Deans et al, 2019 ; De Souza et al, 2020 ; Wang, Burgess, et al, 2020 ; Acevedo‐Siaca, Coe, Quick, et al, 2020 ; Acevedo‐Siaca, Coe, Wang, et al, 2020 ; McAusland et al, 2020 ). As a result, new targets for improving photosynthetic efficiency have been identified that could improve performance in non‐steady‐state conditions, such as increasing the speed of induction, reducing forgone assimilation during induction, reducing water loss, improving stomatal kinetics, or relaxing NPQ more quickly (Woodrow & Mott, 1989 ; Lawson & Blatt, 2014 ; McAusland et al, 2016 ; Kromdijk et al, 2016 ; Glowacka et al, 2018 ; Qu et al, 2016 ; De Souza et al, 2020 ; Acevedo‐Siaca, Coe, Quick, et al, 2020 ; Acevedo‐Siaca, Coe, Wang, et al, 2020 ; McAusland et al, 2020 ; Qu et al, 2020 ).…”