Scientia Agricultura Sinica ›› 2026, Vol. 59 ›› Issue (4): 781-792.doi: 10.3864/j.issn.0578-1752.2026.04.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Improved Leaf Phosphorus Content Estimation of Winter Wheat Using Ensemble Hyperspectral Dimensionality Reduction Method

QIAN Jin1(), LI YingXue2, WU Fang3, ZOU XiaoChen1()   

  1. 1 School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044
    2 School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044
    3 Xinghua Meteorological Bureau, Xinghua 225700, Jiangsu
  • Received:2025-08-17 Online:2026-02-10 Published:2026-02-10
  • Contact: ZOU XiaoChen

RichHTML

3

PDF

11

Abstract:

【Objective】Phosphorus is a critical nutrient element for crop growth and development, directly influencing photosynthesis and physiological functions. Accurate monitoring of leaf phosphorus content is essential for efficient crop management and yield prediction. In this study, an ensemble hyperspectral dimensionality reduction model was proposed for estimating leaf phosphorus content based on different spectral preprocessing methods combined with various spectral dimensionality reduction techniques, in order to provide a theoretical basis for hyperspectral diagnosis of crop phosphorus nutrition.【Method】Through two years of field experiments, canopy spectral reflectance and leaf phosphorus content were collected for winter wheat during four key growth stages (jointing, heading, flowering, and grain filling) under three nitrogen application levels. Derived spectral features were generated using De-trending transformation, standard normal variate transformation, and first-order derivative reflectance transformation. Spectral dimensionality reduction was performed using successive projections algorithm (SPA), least absolute shrinkage and selection operator (LASSO), and competitive adaptive reweighted sampling (CARS). Single estimation models and integrated estimation models were constructed by combining random forest regression (RF), support vector regression (SVR), and partial least squares regression (PLSR).【Result】The leaf phosphorus content-sensitive spectral features were primarily concentrated in the visible, near-infrared, and short-wave infrared bands. Compared to original spectral reflectance and other derived spectral features, first-order derivative-derived spectral features demonstrated significant advantages in detecting leaf phosphorus content. Among the single models, the highest estimation accuracy was achieved when CARS was used for dimensionality reduction to select sensitive first-order derivative-derived features, driving the RF algorithm, with R2=0.843 and RMSE=0.038% in the training dataset, and R2=0.756 and RMSE=0.057% in the testing dataset. The integrated hyperspectral dimensionality reduction estimation further improved model accuracy. The integrated model driven by first-order derivative-derived spectral features and constructed using RF regression achieved the best estimation performance, with R2=0.932 and RMSE=0.025% in the training dataset, and R2=0.817 and RMSE=0.049% in the testing dataset.【Conclusion】Ensemble hyperspectral dimensionality reduction method effectively enhanced the estimation accuracy of leaf phosphorus content in winter wheat, providing theoretical support for large-scale monitoring of crop nutrition and growth under different hyperspectral remote sensing platforms, crop types, and growth conditions in the future.

Key words: hyperspectral remote sensing, winter wheat, estimation of phosphorus content, hyperspectral dimensionality reduction, ensemble machine learning

Fig. 1

Location of the research area and schematic of the experimental field layout"

Fig. 2

Identification of sensitive spectral features based on different dimensionality reduction methods (a): Dimensionality reduction of OR using SPA; (b): Dimensionality reduction of OR using LASSO; (c): Dimensionality reduction of OR using CARS; (d): Dimensionality reduction of DT spectra using SPA; (e): Dimensionality reduction of DT spectra using LASSO; (f): Dimensionality reduction of DT spectra using CARS; (g): Dimensionality reduction of FDR spectra using SPA; (h): Dimensionality reduction of FDR spectra using LASSO; (i): Dimensionality reduction of FDR spectra using CARS; (j): Dimensionality reduction of SNV spectra using SPA; (k): Dimensionality reduction of SNV spectra using LASSO; (l): Dimensionality reduction of SNV spectra using CARS. Red markers indicate selected bands and the black line shows the mean spectrum"

Table 1

Leaf phosphorus content estimation based on single hyperspectral dimensionality reduction method"

原始及衍生光谱
Original and derived spectra		 降维算法
Dimension
reduction
algorithm		 RF SVR PLSR
训练集Train 测试集Test 训练集Train 测试集Test 训练集Train 测试集Test
R2 RMSE (%) R2 RMSE (%) R2 RMSE (%) R2 RMSE (%) R2 RMSE (%) R2 RMSE (%)
OR SPA 0.708 0.052 0.644 0.068 0.609 0.060 0.502 0.081 0.561 0.064 0.485 0.082
LASSO 0.748 0.048 0.699 0.063 0.624 0.059 0.563 0.076 0.572 0.063 0.577 0.075
CARS 0.680 0.054 0.667 0.066 0.594 0.062 0.456 0.085 0.520 0.067 0.550 0.077
DT SPA 0.636 0.058 0.592 0.073 0.556 0.064 0.521 0.079 0.537 0.066 0.545 0.077
LASSO 0.721 0.051 0.700 0.063 0.560 0.064 0.555 0.077 0.574 0.063 0.600 0.073
CARS 0.654 0.057 0.607 0.072 0.555 0.064 0.530 0.079 0.741 0.049 0.709 0.062
FDR SPA 0.739 0.049 0.665 0.066 0.512 0.060 0.537 0.078 0.577 0.063 0.480 0.083
LASSO 0.725 0.051 0.671 0.066 0.602 0.061 0.634 0.069 0.538 0.065 0.581 0.074
CARS 0.843 0.038 0.756 0.057 0.596 0.061 0.673 0.066 0.790 0.044 0.763 0.057
SNV SPA 0.726 0.051 0.707 0.062 0.546 0.064 0.489 0.082 0.492 0.069 0.371 0.091
LASSO 0.781 0.045 0.693 0.064 0.642 0.058 0.609 0.072 0.603 0.061 0.571 0.075
CARS 0.750 0.048 0.679 0.065 0.614 0.060 0.537 0.078 0.782 0.045 0.694 0.063

Fig. 3

Optimal estimation model of three hyperspectral dimension reduction methods (a): RF regression with SPA on SNV spectra; (b): RF regression with LASSO on OR; (c): RF regression with CARS on FDR spectra"

Fig. 4

Leaf phosphorus content estimation based on different hyperspectral dimensionality reduction methods and ensemble machine learning"

Fig. 5

Optimal leaf phosphorus content estimation based on ensemble machine learning algorithms (a): Ensemble machine learning with SPA dimensionality reduction and SNV spectra; (b): Ensemble machine learning with LASSO dimensionality reduction and DT spectra; (c): Ensemble machine learning with CARS dimensionality reduction and FDR spectra"

Fig. 6

Leaf phosphorus content estimation based on different machine learning algorithms and ensemble hyperspectral dimensionality reduction"

Fig. 7

Optimal model for estimating leaf phosphorus content with ensemble dimension reduction methods (a): Ensemble dimensionality reduction and FDR driven RF; (b): Ensemble dimensionality reduction and FDR driven SVR; (c): Ensemble dimensionality reduction and FDR driven PLSR"

[1]
WANG C, XU L W, RAN Q X, PANG J Y, LAMBERS H, HE J. Crop domestication increased photosynthetic phosphorus-use efficiency associated with changes in leaf phosphorus fractions under low soil phosphorus conditions. Plant and Soil, 2025, 509(1): 915-928.

doi: 10.1007/s11104-024-06898-y
[2]
CHEN G, RAN Q X, WANG C, PANG J Y, REN M J, WANG Z Y, HE J, LAMBERS H. Enhancing photosynthetic phosphorus use efficiency through coordination of leaf phosphorus fractions, allocation, and anatomy during soybean domestication. Journal of Experimental Botany, 2025, 76(5): 1446-1457.

doi: 10.1093/jxb/erae427
[3]
VIVEIROS J, MORETTI L G, PACOLA M, JACOMASSI L M, DE SOUZA F M, RODRIGUES V A, BOSSOLANI J W, PORTUGAL J R, CARBONARI C A, CRUSCIOL C A C. Foliar application of phosphoric acid mitigates oxidative stress induced by herbicides in soybean, maize, and cotton crops. Plant Stress, 2024, 13: 100543.

doi: 10.1016/j.stress.2024.100543
[4]
YANG Q R, ZHANG H Y, ZHANG X, GENG S N, ZHANG Y J, MIAO Y H, LI L T, WANG Y L. Optimized phosphorus application enhances canopy photothermal responses, phosphorus accumulation, and yield in summer maize. Agronomy, 2025, 15(3): 514.

doi: 10.3390/agronomy15030514
[5]
班松涛, 田明璐, 常庆瑞, 王琦, 李粉玲. 基于无人机高光谱影像的水稻叶片磷素含量估算. 农业机械学报, 2021(8): 163-171.
BAN S T, TIAN M L, CHANG Q R, WANG Q, LI F L. Estimation of rice leaf phosphorus content using UAV-based hyperspectral images. Transactions of the Chinese Society for Agricultural Machinery, 2021(8): 163-171. (in Chinese)
[6]
SIEDLISKA A, BARANOWSKI P, PASTUSZKA-WOŹNIAK J, ZUBIK M, KRZYSZCZAK J. Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance. BMC Plant Biology, 2021, 21(1): 28.

doi: 10.1186/s12870-020-02807-4 pmid: 33413120
[7]
DE OLIVEIRA K M, FURLANETTO R H, RODRIGUES M, DOS SANTOS G L A A, REIS A S, TEIXEIRA CRUSIOL L G, RAFAEL NANNI M, CEZAR E, DE OLIVEIRA R B. Assessing phosphorus nutritional status in maize plants using leaf-based hyperspectral measurements and multivariate analysis. International Journal of Remote Sensing, 2022, 43(7): 2560-2580.

doi: 10.1080/01431161.2022.2064198
[8]
WANG J J, SHI T Z, LIU H Z, WU G F. Successive projections algorithm-based three-band vegetation index for foliar phosphorus estimation. Ecological Indicators, 2016, 67: 12-20.

doi: 10.1016/j.ecolind.2016.02.033
[9]
SAWUT M, HU X, ABULAITI Y, YIMAER R, MAIMAITIAILI B, LIU S S, PANG R. Estimation of leaf phosphorus content in cotton using fractional order differentially optimized spectral indices. Plants, 2025, 14(10): 1457.

doi: 10.3390/plants14101457
[10]
XIAO Q L, TANG W T, ZHANG C, ZHOU L, FENG L, SHEN J X, YAN T Y, GAO P, HE Y, WU N. Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves. Plant Phenomics, 2022, 2022: 9813841.

doi: 10.34133/2022/9813841
[11]
杨伟博, 李映雪, 吴芳, 邹晓晨. 联合多种衍生光谱特征的冬小麦叶绿素含量估算. 农业工程学报, 2025, 41(15): 165-173.
YANG W B, LI Y X, WU F, ZOU X C. Estimating chlorophyll content of winter wheat using multiple derivative canopy spectra. Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(15): 165-173. (in Chinese)
[12]
李岚涛, 汪善勤, 任涛, 马驿, 魏全全, 高雯晗, 鲁剑巍. 基于高光谱的冬油菜叶片磷含量诊断模型. 农业工程学报, 2016, 32(14): 209-218.
LI L T, WANG S Q, REN T, MA Y, WEI Q Q, GAO W H, LU J W. Evaluating models of leaf phosphorus content of winter oilseed rape based on hyperspectral data. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(14): 209-218. (in Chinese)
[13]
CHEN S M, HU T T, LUO L H, HE Q, ZHANG S W, LU J S. Prediction of nitrogen, phosphorus, and potassium contents in apple tree leaves based on in-situ canopy hyperspectral reflectance using stacked ensemble extreme learning machine model. Journal of Soil Science and Plant Nutrition, 2022, 22(1): 10-24.

doi: 10.1007/s42729-021-00629-3
[14]
CHEN X K, LI F L, CHANG Q R. Combination of continuous wavelet transform and successive projection algorithm for the estimation of winter wheat plant nitrogen concentration. Remote Sensing, 2023, 15(4): 997.

doi: 10.3390/rs15040997
[15]
LI Z P, CHEN Z, CHENG Q, DUAN F Y, SUI R X, HUANG X Q, XU H G. UAV-based hyperspectral and ensemble machine learning for predicting yield in winter wheat. Agronomy, 2022, 12(1): 202.

doi: 10.3390/agronomy12010202
[16]
呼斯乐, 包玉龙, 图布新巴雅尔, 陶际峰, 郭恩亮. 基于无人机高光谱和集成学习的春小麦叶绿素含量反演. 中国农业科技导报, 2025, 27(06): 93-103.
HU S L, BAO Y L, TUBUXINBAYAER , TAO J F, GUO E L. Chlorophyll content inversion of spring wheat based on unmanned aerial vehicle hyperspectral and integrated learning. Journal of Agricultural Science and Technology, 2025, 27(6): 93-103. (in Chinese)
[17]
BRANDT P, BEYER F, BORRMANN P, MÖLLER M, GERIGHAUSEN H. Ensemble learning-based crop yield estimation: A scalable approach for supporting agricultural statistics. GIScience & Remote Sensing, 2024, 61(1): 2367808.
[18]
ZHOU X J, YANG J H, SU Y, HE K, FANG Y L, SUN X Y, JU Y L, LIU W Z. Aggregation and assessment of grape quality parameters with visible-near-infrared spectroscopy: Introducing a novel quantitative index. Postharvest Biology and Technology, 2024, 218: 113131.

doi: 10.1016/j.postharvbio.2024.113131
[19]
REN G X, SUN Y M, LI M H, NING J M, ZHANG Z Z. Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): Near-infrared spectroscopy and evolutionary algorithms. Journal of the Science of Food and Agriculture, 2020, 100(10): 3950-3959.

doi: 10.1002/jsfa.10439 pmid: 32329077
[20]
SUN X D, SUBEDI P, WALKER R, WALSH K B. NIRS prediction of dry matter content of single olive fruit with consideration of variable sorting for normalisation pre-treatment. Postharvest Biology and Technology, 2020, 163: 111140.

doi: 10.1016/j.postharvbio.2020.111140
[21]
SHI S J, ZHANG W H, MA Y Y, CAO C G, ZHANG G Y, JIANG Y. Near-infrared spectroscopy combined with effective variable selection algorithm for rapid detection of rice taste quality. Biosystems Engineering, 2024, 237: 214-219.

doi: 10.1016/j.biosystemseng.2023.12.004
[22]
ANWAR M R, EMEBIRI L, IP R H L, LUCKETT D J, CHAUHAN Y S, ZELEKE K T. Least absolute shrinkage and selection operator regression used to select important features when predicting wheat yield from various genotype groups. The Journal of Agricultural Science, 2024, 162(3): 245-259.

doi: 10.1017/S0021859624000479
[23]
ZHANG H Y, HE L, CHEN Q W, ABDULRAHEEM M I, MA G, ZHANG Y F, GU J J, HU J D, WANG C Y, FENG W. Multi-angular spectroscopic detection of winter wheat nitrogen fertilizer utilization status using integrated feature selection and machine learning. Computers and Electronics in Agriculture, 2025, 231: 109916.

doi: 10.1016/j.compag.2025.109916
[24]
王震, 李映雪, 吴芳, 邹晓晨. 冠层光谱红边参数结合随机森林机器学习估算冬小麦叶绿素含量. 农业工程学报, 2024, 40(4): 166-176.
WANG Z, LI Y X, WU F, ZOU X C. Estimation of winter wheat chlorophyll content by combing canopy spectrum red edge parameters with random forest machine learning. Transactions of the Chinese Society of Agricultural Engineering, 2024, 40(4): 166-176. (in Chinese)
[25]
SOLTANIKAZEMI M, MINAEI S, SHAFIZADEH-MOGHADAM H, MAHDAVIAN A. Field-scale estimation of sugarcane leaf nitrogen content using vegetation indices and spectral bands of Sentinel-2: Application of random forest and support vector regression. Computers and Electronics in Agriculture, 2022, 200: 107130.

doi: 10.1016/j.compag.2022.107130
[26]
SHEN L Z, GAO M F, YAN J W, WANG Q Z, SHEN H. Winter wheat SPAD value inversion based on multiple pretreatment methods. Remote Sensing, 2022, 14(18): 4660.

doi: 10.3390/rs14184660
[27]
王清华, 朱格格, 方雯, 刘诗诗, 鲁剑巍. 基于高光谱遥感的油菜叶片氮磷养分含量诊断. 作物学报, 2025, 51(5): 1326-1337.

doi: 10.3724/SP.J.1006.2025.44157
WANG Q H, ZHU G G, FANG W, LIU S S, LU J W. Diagnosis of nitrogen and phosphorus nutrient content in rapeseed leaves based on hyperspectral remote sensing. Acta Agronomica Sinica, 2025, 51(5): 1326-1337. (in Chinese)

doi: 10.3724/SP.J.1006.2025.44157
[28]
MAHAJAN G R, SAHOO R N, PANDEY R N, GUPTA V K, KUMAR D. Using hyperspectral remote sensing techniques to monitor nitrogen, phosphorus, sulphur and potassium in wheat (Triticum aestivum L.). Precision Agriculture, 2014, 15(5): 499-522.

doi: 10.1007/s11119-014-9348-7
[29]
LI D, WANG C Y, JIANG H, PENG Z P, YANG J, SU Y X, SONG J, CHEN S S. Monitoring Litchi canopy foliar phosphorus content using hyperspectral data. Computers and Electronics in Agriculture, 2018, 154: 176-186.

doi: 10.1016/j.compag.2018.09.007
[30]
ACUÑA-ACOSTA D M, CASTELLANOS A E, LLANO-SOTELO J M, SARDANS J, PEÑUELAS J, ROMO-LEON J R, KOCH G W. Higher phosphorus and water use efficiencies and leaf stoichiometry contribute to legume success in drylands. Functional Ecology, 2024, 38(10): 2271-2285.

doi: 10.1111/fec.v38.10
[31]
TSUJII Y, ATWELL B J, LAMBERS H, WRIGHT I J. Leaf phosphorus fractions vary with leaf economic traits among 35 Australian woody species. New Phytologist, 2024, 241(5): 1985-1997.

doi: 10.1111/nph.19513 pmid: 38189091
[32]
ZHU G G, WANG Q H, ZHANG S M, GUO T Y, LIU S S, LU J W. A meta-analysis of crop leaf nitrogen, phosphorus and potassium content estimation based on hyperspectral and multispectral remote sensing techniques. Field Crops Research, 2025, 329: 109961.

doi: 10.1016/j.fcr.2025.109961
[33]
MENG X T, BAO Y L, LIU J G, LIU H J, ZHANG X L, ZHANG Y, WANG P, TANG H T, KONG F C. Regional soil organic carbon prediction model based on a discrete wavelet analysis of hyperspectral satellite data. International Journal of Applied Earth Observation and Geoinformation, 2020, 89: 102111.

doi: 10.1016/j.jag.2020.102111
[1] KONG Yuan, CUI ShaSha, LI Mei, LI Jian, YANG SiYu, FANG Feng, LIU ShuaiShuai, LIU MingPing, ZENG Yan, GAO XingXiang, BAI LianYang. Spatiotemporal Distribution Dynamics of Five Grass Weed Species Including Lolium multiflorum in Winter Wheat Fields of the Huang- Huai-Hai Region [J]. Scientia Agricultura Sinica, 2026, 59(4): 807-823.
[2] XIAN QingLin, XIAO JianKe, GAO AQing, GAO LiChuang, LIU Yang. Effects of Planting Patterns Combined with Soil Moisture Measurement and Supplementary Irrigation on the Yield and Water Use Efficiency of Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 589-601.
[3] LÜ XuDong, SUN ShiYuan, LI YaNan, LIU YuLong, WANG YanQun, FU Xin, ZHANG JiaYing, NING Peng, PENG ZhengPing. Effects of Intelligent Mechanized Layered Fertilization on Root-Soil Nutrient Distribution and Yield in Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(1): 129-146.
[4] PU LiXia, ZHANG JiaRui, YE JianPing, HUANG XiuLan, FAN GaoQiong, YANG HongKun. The Combined Effects of 16, 17-Dihydro Gibberellin A5 and Straw Mulching on Tillering and Grain Yield of Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(9): 1735-1748.
[5] SHI Fan, LI WenGuang, YI ShuSheng, YANG Na, CHEN YuMeng, ZHENG Wei, ZHANG XueChen, LI ZiYan, ZHAI BingNian. The Variation Characteristics of Soil Organic Carbon Fractions Under the Combined Application of Organic and Inorganic Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(4): 719-732.
[6] FANG KangRui, DING ShiJie, CHEN YuShan, YANG BingGeng, GUO TengFei, XU XinPeng, ZHAO ShiCheng, WANG XiuBin, HUANG ShaoMin, QIU ShaoJun, HE Ping, ZHOU Wei. In-Season Release Rate of Nitrogen and Phosphorus in Manure Fertilizers During the Wheat Season in Typical Fluvo-Aquic Soil Under the Combined Application of Chemical and Manure Fertilizers [J]. Scientia Agricultura Sinica, 2025, 58(24): 5234-5246.
[7] SHE YingJun, ZHOU ZiZhe, WU Ming, GUO Wei, SHI ChangJian, HU Chao, LI Ping. Effects of Groundwater Depth and Nitrogen Application on the Distribution of Soil Water and Salt and the Nutrient Absorption and Utilization of Winter Wheat [J]. Scientia Agricultura Sinica, 2025, 58(20): 4285-4304.
[8] WANG RongRong, XU NingLu, HUANG XiuLi, ZHAO KaiNan, HUANG Ming, WANG HeZheng, FU GuoZhan, WU JinZhi, LI YouJun. Effects of One-Off Irrigation and Nitrogen Fertilizer Management on Grain Yield and Quality in Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(1): 43-57.
[9] GAO XingXiang, KONG Yuan, ZHANG YaoZhong, LI Mei, LI Jian, JIN Yan, ZHANG GuoFu, LIU ShuaiShuai, LIU MingPing, ZENG Yan, BAI LianYang. Analysis on Distribution and Change of Weed Community in Winter Wheat Field in Henan Province [J]. Scientia Agricultura Sinica, 2025, 58(1): 91-100.
[10] ZANG ShaoLong, LIU LinRu, GAO YueZhi, WU Ke, HE Li, DUAN JianZhao, SONG Xiao, FENG Wei. Classification and Identification of Nitrogen Efficiency of Wheat Varieties Based on UAV Multi-Temporal Images [J]. Scientia Agricultura Sinica, 2024, 57(9): 1687-1708.
[11] GAO ChenKai, LIU ShuiMiao, LI YuMing, ZHAO ZhiHeng, SHAO Jing, YU HaoLin, WU PengNian, WANG YanLi, GUAN XiaoKang, WANG TongChao, WEN PengFei. The Related Driving Factors of Water Use Efficiency and Its Prediction Model Construction in Winter Wheat [J]. Scientia Agricultura Sinica, 2024, 57(7): 1281-1294.
[12] GAO ShangJie, LIU XingRen, LI YingChun, LIU XiaoWan. Effects of Biochar and Straw Return on Greenhouse Gas Emissions and Global Warming Potential in the Farmland [J]. Scientia Agricultura Sinica, 2024, 57(5): 935-949.
[13] ZHU RuiMing, ZHAO RongQin, JIAO ShiXing, LI XiaoJian, XIAO LianGang, XIE ZhiXiang, YANG QingLin, WANG Shuai, ZHANG HuiFang. Spatial Distribution and Driving Factors of Winter Wheat Irrigation Carbon Emission Intensity at Township Level in Henan Province [J]. Scientia Agricultura Sinica, 2024, 57(5): 950-964.
[14] ZHANG Rong, LIU LinRu, FU KaiXia, WU ZiJun, SONG YiFan, WANG LuYuan, HOU GeGe, HE Li, FENG Wei, DUAN JianZhao, WANG YongHua, GUO TianCai. Regulatory of Exogenous Melatonin on Floret Development and Carbon Nutrient Metabolism in Winter Wheat Under Drought Stress [J]. Scientia Agricultura Sinica, 2024, 57(23): 4644-4657.
[15] DONG KuiJun, ZHANG YiTao, LIU HanWen, ZHANG JiZong, WANG WeiJun, WEN YanChen, LEI QiuLiang, WEN HongDa. Effects of Nitrogen Reduction Application of Summer Maize- Soybean Intercropping on Agronomic Traits and Economic Benefits as well as Its Yield of Subsequent Wheat [J]. Scientia Agricultura Sinica, 2024, 57(22): 4495-4506.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd