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Dong T, Wu F, Tsujii Y, Townsend PA, Yang N, Xu W, Liu S, Swenson NG, Lamour J, Han W, Smith NG, Shi Y, Yan L, Tian D, Jiang M, Wang Z, Liu X, Dai G, Dong J, Sardans J, Reich PB, Lambers H, Serbin SP, Peñuelas J, Wu J, Yan Z. Deciphering the variability of leaf phosphorus-allocation strategies using leaf economic traits and reflectance spectroscopy across diverse forest types. THE NEW PHYTOLOGIST 2025. [PMID: 40384503 DOI: 10.1111/nph.70219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2024] [Accepted: 04/27/2025] [Indexed: 05/20/2025]
Abstract
Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P-use strategies across diverse plant communities. We explored relationships between five leaf P fractions (orthophosphate P, Pi; lipid P, PL; nucleic acid P, PN; metabolite P, PM; and residual P, PR) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait-based models and spectral models for leaf P fractions and compared their predictive abilities. We found that plants exhibiting conservative strategies increased the proportions of PN and PM, but decreased the proportions of Pi and PL, thus enhancing photosynthetic P-use efficiency, especially under P limitation. Spectral models outperformed trait-based models in predicting cross-site leaf P fractions, regardless of concentrations (R2 = 0.50-0.88 vs 0.34-0.74) or proportions (R2 = 0.43-0.70 vs 0.06-0.45). These findings enhance our understanding of leaf P-allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large-scale leaf P fractions and plant P-use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models.
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Affiliation(s)
- Tingting Dong
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Fengqi Wu
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yuki Tsujii
- Forestry and Forest Products Research Institute, Tsukuba, 305-8687, Japan
| | - Philip A Townsend
- Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Nan Yang
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Weiying Xu
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Shuwen Liu
- School of Biological Sciences, The University of Hong Kong, Hong Kong, 999077, China
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI, 53706, USA
| | - Nathan G Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46565, USA
| | - Julien Lamour
- Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, 31062, France
| | - Wenxuan Han
- Key Laboratory of Plant-Soil Interactions, Ministry of Education, College of Resources and Environmental Sciences, China Agricultural University, Beijing, 100193, China
| | - Nicholas G Smith
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, 79409, USA
| | - Yue Shi
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Li Yan
- School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia
| | - Di Tian
- State Key Laboratory of Efficient Production of Forest Resources, Beijing Forestry University, Beijing, 100083, China
| | - Mingkai Jiang
- College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhihui Wang
- Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China
| | - Xiaojuan Liu
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Guanhua Dai
- Research Station of Changbai Mountain Forest Ecosystems, Chinese Academy of Sciences, Antu, 133613, China
| | - Jinlong Dong
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Menglun, 666303, China
- National Forest Ecosystem Research Station at Xishuangbanna, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Menglun, 666303, Yunnan, China
| | - Jordi Sardans
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193, Spain
- CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193, Spain
| | - Peter B Reich
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
- Department of Forest Resources, University of Minnesota, St Paul, 55108, MN, USA
- Institute for Global Change Biology, and School for the Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hans Lambers
- School of Biological Sciences, University of Western Australia, Perth, WA, 6009, Australia
- School of Grassland Science, Beijing Forestry University, Beijing, 100083, China
| | - Shawn P Serbin
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
| | - Josep Peñuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Barcelona, Catalonia, 08193, Spain
- CREAF, Cerdanyola del Vallès, Barcelona, Catalonia, 08193, Spain
| | - Jin Wu
- School of Biological Sciences, The University of Hong Kong, Hong Kong, 999077, China
- Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong, 999077, China
- State Key Laboratory of Agrobiotechnology, Chinese University of Hong Kong, Hong Kong, 999077, China
| | - Zhengbing Yan
- State Key Laboratory of Forage Breeding-by-Design and Utilization, Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
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2
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Wu F, Liu S, Lamour J, Atkin OK, Yang N, Dong T, Xu W, Smith NG, Wang Z, Wang H, Su Y, Liu X, Shi Y, Xing A, Dai G, Dong J, Swenson NG, Kattge J, Reich PB, Serbin SP, Rogers A, Wu J, Yan Z. Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types. THE NEW PHYTOLOGIST 2025; 246:481-497. [PMID: 39558787 DOI: 10.1111/nph.20267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 10/24/2024] [Indexed: 11/20/2024]
Abstract
Leaf dark respiration (Rdark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (Vcmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed Rdark variability and its associations with Vcmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site Rdark than commonly used traits like LMA, N and P concentrations, but univariate trait-Rdark relationships were always weak (r2 ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-Rdark relationships, accurately predicted cross-site Rdark (r2 = 0.65) and pinpointed the factors contributing to Rdark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding Rdark, challenging the use of empirical trait-Rdark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating Rdark, which could ultimately improve process modeling of terrestrial plant respiration.
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Affiliation(s)
- Fengqi Wu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Shuwen Liu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Hong Kong, 999077, China
| | - Julien Lamour
- Centre de Recherche sur la Biodiversité et l'Environnement (CRBE), Université de Toulouse, CNRS, IRD, Toulouse INP, Université Toulouse 3 - Paul Sabatier (UT3), Toulouse, 31062, France
| | - Owen K Atkin
- Division of Plant Sciences, Research School of Biology, Australian National University, Canberra, ACT, 2601, Australia
| | - Nan Yang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
| | - Tingting Dong
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Weiying Xu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Nicholas G Smith
- Department of Biological Sciences, Texas Tech University, Lubbock, TX, 79409, USA
| | - Zhihui Wang
- Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, 510070, China
| | - Han Wang
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing, 100084, China
| | - Yanjun Su
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Xiaojuan Liu
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
| | - Yue Shi
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
| | - Aijun Xing
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
| | - Guanhua Dai
- Research Station of Changbai Mountain Forest Ecosystems, Chinese Academy of Sciences, Antu, 133613, China
| | - Jinlong Dong
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Menglun, 666303, China
- National Forest Ecosystem Research Station at Xishuangbanna, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, Menglun, 666303, Yunnan, China
| | - Nathan G Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Jens Kattge
- Max Planck Institute for Biogeochemistry, Hans Knöll Str. 10, 07745, Jena, Germany
- iDiv - German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Puschstraße 4, 04103, Leipzig, Germany
| | - Peter B Reich
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
- Department of Forest Resources, University of Minnesota, St Paul, MN, 55108, USA
- Institute for Global Change Biology, and School for the Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Shawn P Serbin
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, 20771, USA
| | - Alistair Rogers
- Climate & Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jin Wu
- Division for Ecology and Biodiversity, School of Biological Sciences, The University of Hong Kong, Hong Kong, 999077, China
| | - Zhengbing Yan
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Xiangshan, Beijing, 100093, China
- China National Botanical Garden, Beijing, 100093, China
- University of Chinese Academy of Sciences, Yuquanlu, Beijing, 100049, China
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Yang Y, Liu X, Zhao Y, Tang G, Nan R, Zhang Y, Sun F, Xi Y, Zhang C. Evaluation of wheat drought resistance using hyperspectral and chlorophyll fluorescence imaging. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2025; 219:109415. [PMID: 39729967 DOI: 10.1016/j.plaphy.2024.109415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 12/05/2024] [Accepted: 12/16/2024] [Indexed: 12/29/2024]
Abstract
Photosynthesis drives crop growth and production, and strongly affects grain yields; therefore, it is an ideal trait for wheat drought resistance breeding. However, studies of the negative effects of drought stress on wheat photosynthesis rates have lacked accurate evaluation methods, as well as high-throughput techniques. We investigated photosynthetic capacity under drought stress in wheat varieties with varying degrees of drought stress resistance using hyperspectral and chlorophyll fluorescence (ChlF) imaging data. We analyzed various morpho-physiological traits involved in wheat drought tolerance, including tiller number, leaf relative water content, and malondialdehyde content, to determine the relationships between drought resistance and hyperspectral and ChlF data. The results showed that the spectral first derivative ratio (FDR) between drought stress and control conditions in the 680-760 nm region was closely related to photosynthetic capacity and drought tolerance and that hyperspectral imaging can be used to monitor ChlF parameters, with bands sensitive to ChlF identified in two spectral regions (539-764 nm and 832-989 nm). The spectral first derivative at 989 nm had the strongest linear relationship with the minimal fluorescence (R2 = 0.49). An uninformative variable elimination algorithm indicated that FDRs in the green (504-609 nm), red (724-751 nm), and near-infrared (944-946 nm) light regions had great potential as indices of drought resistance. A support vector machine model based on the FDRs of these characteristic bands identified wheat drought resistance with 97.33% accuracy. These findings provide insight into the application of high-throughput technologies in studying drought resistance and photosynthesis in wheat.
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Affiliation(s)
- Yucun Yang
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Xinran Liu
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Yuqing Zhao
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Gaijuan Tang
- Hybrid Rapeseed Research Center of Shaanxi Province, Yangling, 712100, China
| | - Rui Nan
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Yuzhen Zhang
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Fengli Sun
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Yajun Xi
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China
| | - Chao Zhang
- State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, College of Agronomy, Northwest A&F University, Yangling, 712100, China; Key Laboratory of Wheat Biology and Genetic Improvement on Northwestern China, Ministry of Agriculture and Rural Affairs, Xianyang, 712100, China.
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Haynes RS, Lucieer A, Brodribb TJ, Tonet V, Cimoli E. Predicting key water stress indicators of Eucalyptus viminalis and Callitris rhomboidea using high-resolution visible to short-wave infrared spectroscopy. PLANT, CELL & ENVIRONMENT 2024; 47:4992-5006. [PMID: 39119823 DOI: 10.1111/pce.15083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 07/18/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024]
Abstract
Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought-induced mortality. Here, we explore the use of high-resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf-level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψleaf), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψleaf for both species (R2 > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R2 > 0.89; C. rhomboidea, R2 > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R2 > 0.90; C. rhomboidea, R2 > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.
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Affiliation(s)
- Ryan S Haynes
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Arko Lucieer
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Timothy J Brodribb
- School of Biological Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
| | - Vanessa Tonet
- School of Forestry & Environmental Studies, Yale University, New Haven, Connecticut, USA
| | - Emiliano Cimoli
- School of Geography, Planning and Spatial Sciences, University of Tasmania, Sandy Bay, Tasmania, Australia
- Insitute of Marine and Antarctic Studies (IMAS), University of Tasmania, Battery Point, Tasmania, Australia
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Gill AK, Gaur S, Sneller C, Drewry DT. Utilizing VSWIR spectroscopy for macronutrient and micronutrient profiling in winter wheat. FRONTIERS IN PLANT SCIENCE 2024; 15:1426077. [PMID: 39544538 PMCID: PMC11560459 DOI: 10.3389/fpls.2024.1426077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 09/27/2024] [Indexed: 11/17/2024]
Abstract
This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance observations and partial least squares regression (PLSR) to predict foliar concentrations of macronutrients (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur), micronutrients (boron, copper, iron, manganese, zinc, molybdenum, aluminum, and sodium), and moisture content in winter wheat. A total of 360 fresh wheat leaf samples were collected from a wheat breeding population over two growing seasons. These leaf samples were used to collect VSWIR reflectance observations across a spectral range spanning 350 to 2,500 nm. These samples were then processed for nutrient composition to allow for the examination of the ability of reflectance to accurately model diverse chemical components in wheat foliage. Models for each nutrient were developed using a rigorous cross-validation methodology in conjunction with three distinct component selection methods to explore the trade-offs between model complexity and performance in the final models. We examined absolute minimum predicted residual error sum of squares (PRESS), backward iteration over PRESS, and Van der Voet's randomized t-test as component selection methods. In addition to contrasting component selection methods for each leaf trait, the importance of spectral regions through variable importance in projection scores was also examined. In general, the backward iteration method provided strong model performance while reducing model complexity relative to the other selection methods, yielding R 2 [relative percent difference (RPD), root mean squared error (RMSE)] values in the validation dataset of 0.84 (2.45, 6.91), 0.75 (1.97, 18.67), 0.78 (2.13, 16.49), 0.66 (1.71, 17.13), 0.68 (1.75, 14.51), 0.66 (1.72, 12.29), and 0.84 (2.46, 2.20) for nitrogen, calcium, magnesium, sulfur, iron, zinc, and moisture content on a wet basis, respectively. These model results demonstrate that VSWIR reflectance in combination with modern statistical modeling techniques provides a powerful high throughput method for the quantification of a wide range of foliar nutrient contents in wheat crops. This work has the potential to advance rapid, precise, and nondestructive field assessments of nutrient contents and deficiencies for precision agricultural management and to advance breeding program assessments.
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Affiliation(s)
- Anmol Kaur Gill
- Department of Food, Agricultural, and Biological Engineering, Ohio State University, Columbus, OH, United States
| | - Srishti Gaur
- Department of Food, Agricultural, and Biological Engineering, Ohio State University, Columbus, OH, United States
| | - Clay Sneller
- Department of Horticulture and Crop Science, Ohio State University, Wooster, OH, United States
| | - Darren T. Drewry
- Department of Food, Agricultural, and Biological Engineering, Ohio State University, Columbus, OH, United States
- Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, United States
- Translational Data Analytics Institute, Ohio State University, Columbus, OH, United States
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Ji F, Li F, Hao D, Shiklomanov AN, Yang X, Townsend PA, Dashti H, Nakaji T, Kovach KR, Liu H, Luo M, Chen M. Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset. THE NEW PHYTOLOGIST 2024; 243:111-131. [PMID: 38708434 DOI: 10.1111/nph.19807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/07/2024] [Indexed: 05/07/2024]
Abstract
Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.
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Affiliation(s)
- Fujiang Ji
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Fa Li
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Dalei Hao
- Atmospheric, Climate, & Earth Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA
| | - Alexey N Shiklomanov
- NASA Goddard Space Flight Center, 8800 Greenbelt Road, Mail code: 610.1, Greenbelt, MD, 20771, USA
| | - Xi Yang
- Department of Environmental Sciences, University of Virginia, 291 McCormick Road, Charlottesville, VA, 22904, USA
| | - Philip A Townsend
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Hamid Dashti
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Tatsuro Nakaji
- Uryu Experimental Forest, Hokkaido University, Moshiri, Horokanai, Hokkaido, 074-0741, Japan
| | - Kyle R Kovach
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Haoran Liu
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Meng Luo
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
| | - Min Chen
- Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Dr., Madison, WI, 53706, USA
- Data Science Institute, University of Wisconsin-Madison, 447 Lorch Ct, Madison, 53706, WI, USA
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7
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Corrigendum to Predicting leaf traits across functional groups using reflectance spectroscopy. THE NEW PHYTOLOGIST 2024; 241:2300-2303. [PMID: 38240349 DOI: 10.1111/nph.19535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
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Zhang H, Plett JM, Catunda KLM, Churchill AC, Moore BD, Powell JR, Power SA, Yang J, Anderson IC. Rapid quantification of biological nitrogen fixation using optical spectroscopy. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:760-771. [PMID: 37891011 DOI: 10.1093/jxb/erad426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/27/2023] [Indexed: 10/29/2023]
Abstract
Biological nitrogen fixation (BNF) provides a globally important input of nitrogen (N); its quantification is critical but technically challenging. Leaf reflectance spectroscopy offers a more rapid approach than traditional techniques to measure plant N concentration ([N]) and isotopes (δ15N). Here we present a novel method for rapidly and inexpensively quantifying BNF using optical spectroscopy. We measured plant [N], δ15N, and the amount of N derived from atmospheric fixation (Ndfa) following the standard traditional methodology using isotope ratio mass spectrometry (IRMS) from tissues grown under controlled conditions and taken from field experiments. Using the same tissues, we predicted the same three parameters using optical spectroscopy. By comparing the optical spectroscopy-derived results with traditional measurements (i.e. IRMS), the amount of Ndfa predicted by optical spectroscopy was highly comparable to IRMS-based quantification, with R2 being 0.90 (slope=0.90) and 0.94 (slope=1.02) (root mean square error for predicting legume δ15N was 0.38 and 0.43) for legumes grown in glasshouse and field, respectively. This novel application of optical spectroscopy facilitates BNF studies because it is rapid, scalable, low cost, and complementary to existing technologies. Moreover, the proposed method successfully captures the dynamic response of BNF to climate changes such as warming and drought.
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Affiliation(s)
- Haiyang Zhang
- College of Life Sciences, Hebei University, Baoding, China
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jonathan M Plett
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Karen L M Catunda
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Amber C Churchill
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
- Department of Ecology, Evolution and Behavior, University of Minnesota, 140 Gortner Laboratory, 1479 Gortner Ave., St Paul, MN 55108, USA
| | - Ben D Moore
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jeff R Powell
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Sally A Power
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Jinyan Yang
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
| | - Ian C Anderson
- Hawkesbury Institute for the Environment, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia
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Cushman KC, Albert LP, Norby RJ, Saatchi S. Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue. THE NEW PHYTOLOGIST 2023; 240:1707-1711. [PMID: 37915249 DOI: 10.1111/nph.19237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 11/03/2023]
Abstract
This article is an Editorial to the Virtual issue on ‘Remote sensing’ that includes the following papers Chavana‐Bryant et al. (2017), Coupel‐Ledru et al. (2022), Cushman & Machado (2020), Disney (2019), D'Odorico et al. (2020), Dong et al. (2022), Fischer et al. (2019), Gamon et al. (2023), Gu et al. (2019), Guillemot et al. (2020), Jucker (2021), Koh et al. (2022), Konings et al. (2019), Kothari et al. (2023), Martini et al. (2022), Richardson (2019), Santini et al. (2021), Schimel et al. (2019), Serbin et al. (2019), Smith et al. (2019, 2020), Still et al. (2021), Stovall et al. (2021), Wang et al. (2020), Wong et al. (2020), Wu et al. (2021), Wu et al. (2017), Wu et al. (2018), Wu et al. (2019), Xu et al. (2021), Yan et al. (2021). Access the Virtual Issue at www.newphytologist.com/virtualissues.
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Affiliation(s)
- K C Cushman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
| | - Loren P Albert
- College of Forestry, Oregon State University, Corvallis, OR, 97331, USA
| | - Richard J Norby
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, 37996, USA
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA
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