1Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Collaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and Ministry, Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
2Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China
3State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology, Ministry of Agriculture, Nanjing Agricultural University, Nanjing, 210095 Jiangsu, China
6Department of Forestry, Mississippi State University, Mississippi State 39759, USA
7Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Institut National de la Recherche Agronomique, Unité Mixte de Recherche 1114 Domaine Saint-Paul, Avignon Cedex 84914, France
8Department of Ecology, College of Environmental Sciences, and Key Laboratory of Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
| Received 18 Apr 2021 |
Accepted 27 Jul 2021 |
Published 06 Sep 2021 |
Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing environment. Terrestrial laser scanning (TLS) is a well-suited tool to study structural rhythm under field conditions. Recent studies have used TLS to describe the structural rhythm of trees, but no consistent patterns have been drawn. Meanwhile, whether TLS can capture structural rhythm in crops is unclear. Here, we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series TLS. The seasonal rhythm was studied using TLS data collected at four key growth periods, including jointing, bell-mouthed, heading, and maturity periods. Circadian rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress conditions. Results showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels. (1) Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed stage. Leaf azimuth was stable after the jointing stage. (2) Some individual-level structural rhythms (e.g., azimuth and projected leaf area/PLA) were consistent with leaf-level structural rhythms. (3) The circadian rhythms of some traits (e.g., PLA) were not consistent under standard and cold stress conditions. (4) Environmental factors showed better correlations with leaf traits under cold stress than standard conditions. Temperature was the most important factor that significantly correlated with all leaf traits except leaf azimuth. This study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.
