Hyperspectral reflectance-based partial least squares regression models for predicting cotton leaf physiological traits

Alterations in the mechanistic drivers of photosynthesis have the
potential to improve crop productivity, but their measurement is
inherently time-consuming using traditional methods. High-throughput
approaches to estimate photosynthesis using hyperspectral reflectance
could be developed by leveraging variation in cotton (Gossypium
hirsutum L.) leaf traits generated through nitrogen
management, synthetic growth regulation strategies, and leaf position
within the canopy. Currently, no such models exist for cotton, and
interactions among the aforementioned factors are relatively unexplored
for cotton leaf traits.  This study aimed to (1) evaluate the
effects of N application rate, mepiquat chloride (MC) management, and leaf
position within the canopy on photosynthesis and its components, and (2)
develop and validate hyperspectral reflectance-based partial least squares
regression (PLSR) models for predicting cotton leaf physiological traits.
N rate and leaf position interacted to affect net photosynthetic rate
(AN), electron transport rate, chlorophyll a, and chlorophyll b, while
mepiquat chloride influenced only leaf pigments and specific leaf weight
(SLW). AN reductions under N deficiency were driven by declines in the
maximum rate of Rubisco carboxylation (Vc,max) and
ribulose-1,5-bisphosphate regeneration (Jmax​), whereas high N had no
effect on AN. PLSR models exhibited good to high predictive accuracy, with
R² values ranging from 0.62 to 0.87 for most traits, except for SLW. These
findings enhance our understanding of the physiological responses to N
rate, MC strategy, and leaf position and highlight the potential of
hyperspectral reflectance-based PLSR as a high-throughput tool for
predicting leaf physiological traits to improve photosynthetic efficiency
in cotton.

光合作用机制驱动因子的改造具备提升作物生产力的潜力，但传统检测方法往往耗时耗力。基于高光谱反射率的光合速率高通量估算方法，可依托氮肥调控、人工生长调控策略以及冠层内叶位差异所产生的陆地棉（Gossypium hirsutum L.）叶片性状变异来构建。目前尚未有针对陆地棉的此类模型，且上述因素间的交互作用对陆地棉叶片性状的影响尚有待系统探索。本研究旨在：（1）评估施氮量、矮壮素（MC）调控以及冠层叶位对光合作用及其组分的影响；（2）构建并验证基于高光谱反射率的偏最小二乘回归（PLSR）模型，以预测陆地棉叶片生理性状。研究结果显示，施氮量与叶位的交互作用会显著影响净光合速率（AN）、电子传递速率、叶绿素a以及叶绿素b的水平，而矮壮素仅对叶片色素含量与比叶重（SLW）产生调控作用。氮素缺乏条件下净光合速率的下降，源于核酮糖二磷酸羧化酶最大羧化速率（Vc,max）与核酮糖-1,5-二磷酸再生速率（Jmax）的降低，而高氮供应对净光合速率无显著影响。偏最小二乘回归模型展现出良好至优异的预测精度，除比叶重外，多数性状的决定系数（R²）介于0.62至0.87之间。本研究结果加深了学界对施氮量、矮壮素调控策略以及冠层叶位所引发的生理响应的理解，同时凸显了基于高光谱反射率的偏最小二乘回归模型作为高通量检测工具，在预测陆地棉叶片生理性状、进而提升棉花光合效率方面的应用潜力。