ARC Centre of Excellence for Translational Photosynthesis, Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, Queensland, Australia.
ARC Centre of Excellence for Translational Photosynthesis, Division of Plant Science, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory, Australia.
Plant Cell Environ. 2023 Jan;46(1):23-44. doi: 10.1111/pce.14453. Epub 2022 Oct 20.
Photosynthetic manipulation provides new opportunities for enhancing crop yield. However, understanding and quantifying the importance of individual and multiple manipulations on the seasonal biomass growth and yield performance of target crops across variable production environments is limited. Using a state-of-the-art cross-scale model in the APSIM platform we predicted the impact of altering photosynthesis on the enzyme-limited (A ) and electron transport-limited (A ) rates, seasonal dynamics in canopy photosynthesis, biomass growth, and yield formation via large multiyear-by-location crop growth simulations. A broad list of promising strategies to improve photosynthesis for C wheat and C sorghum were simulated. In the top decile of seasonal outcomes, yield gains were predicted to be modest, ranging between 0% and 8%, depending on the manipulation and crop type. We report how photosynthetic enhancement can affect the timing and severity of water and nitrogen stress on the growing crop, resulting in nonintuitive seasonal crop dynamics and yield outcomes. We predicted that strategies enhancing A alone generate more consistent but smaller yield gains across all water and nitrogen environments, A enhancement alone generates larger gains but is undesirable in more marginal environments. Large increases in both A and A generate the highest gains across all environments. Yield outcomes of the tested manipulation strategies were predicted and compared for realistic Australian wheat and sorghum production. This study uniquely unpacks complex cross-scale interactions between photosynthesis and seasonal crop dynamics and improves understanding and quantification of the potential impact of photosynthesis traits (or lack of it) for crop improvement research.
光合作用的调控为提高作物产量提供了新的机遇。然而,在多变的生产环境中,理解和量化个别和多种调控对目标作物季节性生物量增长和产量性能的重要性仍然有限。本研究利用 APSIM 平台中的最先进的跨尺度模型,通过多年多点的作物生长模拟,预测了改变光合作用对酶限制(A)和电子传递限制(A)速率、冠层光合作用、生物量生长和产量形成的季节性动态的影响。模拟了一系列提高 C小麦和 C高粱光合作用的有前景的策略。在季节性结果的前十分位数中,预测产量增益适度,范围在 0%到 8%之间,具体取决于调控和作物类型。我们报告了光合作用的增强如何影响生长作物的水和氮胁迫的时间和严重程度,从而导致非直观的季节性作物动态和产量结果。我们预测,单独增强 A 可以在所有水氮环境中产生更一致但较小的产量增益,单独增强 A 会产生更大的增益,但在更边缘的环境中是不可取的。同时增加 A 和 A 可以在所有环境中产生最高的增益。对测试的调控策略的产量结果进行了预测,并与澳大利亚小麦和高粱的实际生产进行了比较。本研究独特地揭示了光合作用与季节性作物动态之间的复杂跨尺度相互作用,提高了对光合作用特性(或缺乏)对作物改良研究的潜在影响的理解和量化。