Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (14): 2862-2873.doi: 10.3864/j.issn.0578-1752.2024.14.013

• FOOD SCIENCE AND ENGINEERING • Previous Articles     Next Articles

Establishment of Prediction Models for Sodium, Potassium and Magnesium Content in Milk of Chinese Holstein Cows Based on Mid-Infrared Spectroscopy

HAO LeiXiao1(), CHU Chu1(), WEN PeiPei1, PENG SongYue1, YANG Zhuo1, ZOU HuiYing1, FAN YiKai1, WANG HaiTong1, LIU WenJu1, WANG DongWei1, LIU WeiHua2, YANG JunHua2, ZHAO Juan2, LI WeiQi3, WEN Wan3, ZHOU JiaMin3, ZHANG ShuJun1()   

  1. 1 College of Animal Science and Technology, School of Animal Medicine, Huazhong Agricultural University, Wuhan 430070
    2 Ningxia Veterinary Drug and Feed Supervision Institute, Yinchuan 750000
    3 Ningxia Hui Autonomous Region Animal Husbandry Workstation, Yinchuan 750000
  • Received:2024-01-04 Accepted:2024-04-01 Online:2024-07-24 Published:2024-07-24
  • Contact: ZHANG ShuJun

Abstract:

【Background】 Accurate detection of sodium (Na), potassium (K) and magnesium (Mg) content in milk contributes to healthy dairy farming and is a prerequisite for stabilizing the quality of dairy products. However, the current conventional methods for detecting mineral content in milk are expensive and time-consuming, so there is a need for a low-cost and rapid method to measure the Na, K and Mg content in milk. 【Objective】 The purpose of this study was to investigate the potential of using milk-infrared spectroscopy (MIRS) to predict the Na, K and Mg content in milk from Chinese Holstein cows, to provide a rapid detection technique for the determination of Na, K and Mg content in milk, and to provide a large amount of phenotypic data for the herd management and genetic breeding of dairy cows. In addition, the ability of different feature selection algorithms to improve the MIRS quantitative prediction model for predicting Na, K and Mg content in milk were compared. 【Method】 A total of 255 milk samples from healthy Holstein cows from North China were used for this study. Firstly, MIRS data of milk samples were collected using MilkoScanTMFT+, and the true values of Na, K and Mg content in milk samples were determined using inductively coupled plasma atomic emission spectrometry. Subsequently, using the MIRS data as the predictor variables and the true values of Na, K and Mg content as the dependent variables, four spectral preprocessing algorithms (first-order derivative, second-order derivative, SG smoothing, and standard normal transform), four feature selection algorithms [uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), genetic algorithm, and least angle regression (LAR)] and nine modelling algorithms (partial least squares regression, support vector machines, Random Forest and Elasticity Network, etc.) were used to establish MIRS quantitative prediction models for predicting Na, K and Mg content in milk, respectively, and the optimal model combination (Feature Selection Algorithm + Spectral Preprocessing Algorithm + Modelling Algorithm) was selected. 【Result】 Overall, the CARS algorithm improved the Na, K and Mg content prediction models better than the UVE, GA and LAR algorithm. The Na content prediction model developed based on CARS feature selection algorithm, first-order derivative preprocessing and elastic network modelling algorithm was the most effective, and the model had a coefficient of determination of prediction set (RP2)=0.72, root mean squared error of prediction set (RMSEp)=63.28 mg∙kg-1, mean absolute error of prediction set (MAEp)=49.03 mg∙kg-1, and performance deviation ratio (ratio)=1.90. The best K content prediction model was developed based on the CARS feature selection algorithm, raw spectra and support vector machine modelling algorithm, which had RP2=0.57, RMSEp=141.49 mg∙kg-1, MAEp=116.24 mg∙kg-1, RPD=1.57. Mg content prediction model developed based on CARS feature selection algorithm, raw spectra and partial least squares regression modelling algorithm was the most effective, the model RP2=0.51, RMSEp=12.08 mg∙kg-1, MAEp=9.84 mg∙kg-1, and RPD=1.30. 【Conclusion】 It was feasible to use MIRS to predict Na and K content in milk from Chinese Holstein cows, which could predict Na content with a high degree of accuracy and approximate quantitative prediction of K content (for distinguishing between low and high K concentration samples). The use of the CARS algorithm to extract the characteristic bands before modelling improved the accuracy of the MIRS prediction model, and greatly reduced the computing time to improve the efficiency of the MIRS model in predicting phenotypic data.

Key words: milk, mid-infrared spectroscopy, minerals, sodium, potassium, magnesium, machine learning

Table 1

Coefficients of determination and root mean square errors in prediction data sets of MIRS models developed by different references for predicting Na, K and Mg content in milk"

数据量
Number of data
特征选择方法
Feature selection method
光谱预处理
Spectral preprocessing
建模算法
Modelling algorithm
钠Nab 钾Kb 镁Mgb 参考文献
Reference
1281 212个波点a
212 wavepoints
一阶导数
First-order derivative
PLSR 0.49 (62.24) 0.52 (106.69) 0.69 (7.78) [1]
923 UVE 一阶导数
First-order derivative
PLSR 0.40 (70) 0.69 (120) 0.65 (12.5) [5]
92 - - PLSR 0.65 (64) 0.36 (136) 0.65 (11) [22]
153 去除水后的区域
Areas after water removal
- PLSR 0.25 (51.6) 0.34 (78.5) 0.26 (4.1) [25]
91 BiPLS - BiPLS 0.75 (40.2) 0.55 (71.4) 0.68 (6.4) [26]
526 去除水后的区域
Areas after water removal
无预处理
No pre-processing
PLSR 0.63 (70) 0.33 (110) 0.46 (10) [27]

Table 2

Descriptive statistics of sodium, potassium and magnesium content (mg∙kg-1) in milk derived from the ICP-AES analysis"

矿物质
Mineral
数据量
Number of samples
平均值
Mean (mg∙kg-1)
标准差
SD
变异系数
CV (%)
最小值
Min (mg∙kg-1)
最大值
Max (mg∙kg-1)
钠 Na 246 488.17 120.11 24.60 319.59 1090.150
钾 K 255 1470.76 222.50 15.13 926.75 2085.168
镁 Mg 255 113.95 15.66 13.74 66.61 155.960

Fig. 1

Mean spectra of milk samples SWIR: Short-wavelength infrared or near-infrared; MWIR: Mid-wavelength infrared (3.0-8.0 µm); LWIR: Long-wavelength infrared (8.0-15.0 µm). The gray area represents the mean ± standard deviation range of absorbance"

Table 3

Effects of different feature selection methods on the performance of Na, K and Mg prediction model"

性状
Trait
特征提取
Feature
selection
特征数量
Feature
number
最优预处理
Best
pretreatment
指标 Metrics
校准集 Calibration set 预测集 Prediction set
RMSEc MAEc Rc2 RMSEp MAEp RP2 RPD

Na
全波段 Full band 1060 SNV 92.21 67.69 0.42 98.30 76.92 0.28 1.22
GA 423 2D 84.31 59.86 0.52 100.66 77.43 0.26 1.19
CARS 82 1D 64.58 50.03 0.72 66.18 51.23 0.69 1.81
LAR 100 SG 101.87 70.37 0.32 90.31 68.74 0.28 1.33
UVE 290 None 88.75 63.20 0.49 78.61 61.90 0.42 1.53

K
全波段 Full band 1060 SNV 247.90 156.33 0.29 186.39 150.65 0.29 1.19
GA 437 1D 225.38 137.11 0.43 178.13 130.68 0.32 1.25
CARS 81 None 181.23 126.76 0.62 162.80 128.80 0.48 1.37
LAR 100 2D 231.57 142.46 0.36 213.00 169.86 0.28 1.04
UVE 105 2D 233.16 133.09 0.38 168.15 134.39 0.37 1.32

Mg
全波段 Full band 1060 2D 14.11 9.30 0.43 13.59 10.75 0.32 1.15
GA 396 None 13.71 9.94 0.47 13.13 10.66 0.31 1.19
CARS 81 None 12.34 8.66 0.55 12.08 9.84 0.51 1.30
LAR 100 SNV 14.28 9.48 0.42 13.45 10.59 0.30 1.16
UVE 466 SG 13.52 9.17 0.47 12.96 11.36 0.43 1.21

Table 4

Effect of different modelling algorithms on the model performance of Na, K and Mg prediction models"

性状
Trait
建模算法
Modeling algorithm
最优预处理
Best pretreatment
指标Metrics
校准集 Calibration set 预测集 Prediction set
RMSEc MAE Rc2 RMSEp MAE RP2 RPD

Na
PLSR 2D 64.58 50.03 0.72 66.18 51.23 0.69 1.81
RR 1D 71.43 55.61 0.66 67.90 52.79 0.65 1.77
LASSO 1D 62.93 48.76 0.73 63.54 49.36 0.71 1.89
EN 1D 62.97 48.78 0.73 63.28 49.03 0.72 1.90
SVM SG 59.85 35.54 0.80 96.33 71.01 0.28 1.25
SSR 1D 71.17 55.28 0.66 67.54 52.60 0.65 1.78
PPR 2D 46.07 35.26 0.86 76.30 58.81 0.69 1.57
RF None 41.03 27.97 0.95 104.51 73.13 0.14 1.15
GBM None 58.04 34.07 0.81 101.02 74.19 0.22 1.19

K
PLSR None 181.23 126.76 0.62 162.80 128.80 0.48 1.37
RR None 185.17 124.12 0.61 154.38 125.82 0.50 1.44
LASSO None 181.48 125.26 0.62 161.40 130.77 0.48 1.38
EN None 177.75 122.73 0.63 160.71 126.61 0.49 1.38
SVM None 144.79 50.26 0.79 141.49 116.24 0.57 1.57
SSR None 187.47 125.51 0.60 157.15 128.85 0.49 1.42
PPR SG 105.24 81.34 0.87 152.53 129.32 0.54 1.46
RF None 130.01 70.37 0.92 156.31 121.46 0.54 1.42
GBM None 186.74 92.54 0.64 168.64 134.17 0.40 1.32

Mg
PLSR None 12.34 8.66 0.55 12.08 9.84 0.51 1.30
RR None 12.56 8.63 0.54 12.34 9.99 0.49 1.27
LASSO SNV 12.97 8.89 0.51 12.30 9.62 0.48 1.27
EN SNV 13.01 8.89 0.51 12.27 9.51 0.49 1.28
SVM 1D 9.12 3.12 0.79 13.12 10.99 0.42 1.19
SSR 1D 13.97 9.39 0.44 13.32 11.22 0.39 1.18
PPR 2D 8.16 6.11 0.80 15.01 11.25 0.26 1.04
RF 1D 8.32 5.18 0.94 15.06 12.43 0.37 1.04
GBM 1D 11.51 6.97 0.70 14.73 12.01 0.28 1.06

Fig. 2

Scatter plots between predicted and true values on the calibration set (red triangles) and the prediction set (blue circles) for the best model for predicting Na, K and Mg content in milk Blue dashed line: y=x"

[1]
CHRISTOPHE O S, GRELET C, BERTOZZI C, VESELKO D, LECOMTE C, HÖECKELS P, WERNER A, AUER F J, GENGLER N, DEHARENG F, SOYEURT H. Multiple breeds and countries’ predictions of mineral contents in milk from milk mid-infrared spectrometry. Foods, 2021, 10(9): 2235.
[2]
BERNAL A, ZAFRA M A, SIMÓN M J, MAHÍA J. Sodium homeostasis, a balance necessary for life. Nutrients, 2023, 15(2): 395.
[3]
FIORENTINI D, CAPPADONE C, FARRUGGIA G, PRATA C. Magnesium: Biochemistry, nutrition, detection, and social impact of diseases linked to its deficiency. Nutrients, 2021, 13(4): 1136.
[4]
MCLEAN R M, WANG N X. Potassium. Advances in Food and Nutrition Research. Amsterdam: Elsevier, 2021: 89-121.
[5]
VISENTIN G, PENASA M, GOTTARDO P, CASSANDRO M, DE MARCHI M. Predictive ability of mid-infrared spectroscopy for major mineral composition and coagulation traits of bovine milk by using the uninformative variable selection algorithm. Journal of Dairy Science, 2016, 99(10): 8137-8145.

doi: S0022-0302(16)30524-0 pmid: 27522421
[6]
QAYYUM A, KHAN J, HUSSAIN R, AVAIS M, AHMAD N, KHAN M S. Investigation of milk and blood serum biochemical profile as an indicator of sub-clinical mastitis in cholistani cattle. Pakistan Veterinary Journal, 2016, 36: 275-279.
[7]
NORBERG E. Electrical conductivity of milk as a phenotypic and genetic indicator of bovine mastitis: A review. Livestock Production Science, 2005, 96: 129-139.
[8]
COPPA M, REVELLO-CHION A, GIACCONE D, FERLAY A, TABACCO E, BORREANI G. Comparison of near and medium infrared spectroscopy to predict fatty acid composition on fresh andthawed milk. Food Chemistry, 2014, 150: 49-57.
[9]
AKHGAR C K, NÜRNBERGER V, NADVORNIK M, VELIK M, SCHWAIGHOFER A, ROSENBERG E, LENDL B. Fatty acid prediction in bovine milk by attenuated total reflection infrared spectroscopy after solvent-free lipid separation. Foods, 2021, 10(5): 1054.
[10]
SOYEURT H, GRELET C, MCPARLAND S, CALMELS M, COFFEY M, TEDDE A, DELHEZ P, DEHARENG F, GENGLER N. A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra. Journal of Dairy Science, 2020, 103(12): 11585-11596.

doi: 10.3168/jds.2020-18870 pmid: 33222859
[11]
BONFATTI V, CECCHINATO A, CARNIER P. Short communication: predictive ability of Fourier-transform mid-infrared spectroscopy to assess CSN genotypes and detailed protein composition of buffalo milk. Journal of Dairy Science, 2015, 98(9): 6583-6587.

doi: 10.3168/jds.2015-9730 pmid: 26188571
[12]
FRIZZARIN M, GORMLEY I C, BERRY D P, MURPHY T B, CASA A, LYNCH A, MCPARLAND S. Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods. Journal of Dairy Science, 2021, 104(7): 7438-7447.

doi: 10.3168/jds.2020-19576 pmid: 33865578
[13]
COPPA M, VANLIERDE A, BOUCHON M, JURQUET J, MUSATI M, DEHARENG F, MARTIN C. Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system. Journal of Dairy Science, 2022, 105(11): 9271-9285.
[14]
SHADPOUR S, CHUD T C S, HAILEMARIAM D, PLASTOW G, OLIVEIRA H R, STOTHARD P, LASSEN J, MIGLIOR F, BAES C F, TULPAN D, SCHENKEL F S. Predicting methane emission in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of Dairy Science, 2022, 105(10): 8272-8285.

doi: 10.3168/jds.2021-21176 pmid: 36055858
[15]
HO P N, LUKE T D W, PRYCE J E. Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia. Journal of Dairy Science, 2021, 104(4): 4467-4477.

doi: 10.3168/jds.2020-19603 pmid: 33551158
[16]
LUKE T D W, ROCHFORT S, WALES W J, BONFATTI V, MARETT L, PRYCE J E. Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra. Journal of Dairy Science, 2019, 102(2): 1747-1760.

doi: S0022-0302(18)31122-6 pmid: 30594377
[17]
TIPLADY K M, TRINH M H, DAVIS S R, SHERLOCK R G, SPELMAN R J, GARRICK D J, HARRIS B L. Pregnancy status predicted using milk mid-infrared spectra from dairy cattle. Journal of Dairy Science, 2022, 105(4): 3615-3632.

doi: 10.3168/jds.2021-21516 pmid: 35181140
[18]
BRAND W, WELLS A T, SMITH S L, DENHOLM S J, WALL E, COFFEY M P. Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning. Journal of Dairy Science, 2021, 104(4): 4980-4990.

doi: 10.3168/jds.2020-18367 pmid: 33485687
[19]
MENSCHING A, ZSCHIESCHE M, HUMMEL J, GRELET C, GENGLER N, DÄNICKE S, SHARIFI A R. Development of a subacute ruminal acidosis risk score and its prediction using milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 2021, 104(4): 4615-4634.

doi: 10.3168/jds.2020-19516 pmid: 33589252
[20]
DENHOLM S J, BRAND W, MITCHELL A P, WELLS A T, KRZYZELEWSKI T, SMITH S L, WALL E, COFFEY M P. Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning. Journal of Dairy Science, 2020, 103(10): 9355-9367.

doi: S0022-0302(20)30619-6 pmid: 32828515
[21]
BONFATTI V, HO P N, PRYCE J E. Usefulness of milk mid-infrared spectroscopy for predicting lameness score in dairy cows. Journal of Dairy Science, 2020, 103(3): 2534-2544.

doi: S0022-0302(19)31132-4 pmid: 31882209
[22]
SOYEURT H, BRUWIER D, ROMNEE J M, GENGLER N, BERTOZZI C, VESELKO D, DARDENNE P. Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry. Journal of Dairy Science, 2009, 92(6): 2444-2454.

doi: 10.3168/jds.2008-1734 pmid: 19447976
[23]
GRELET C, DARDENNE P, SOYEURT H, FERNANDEZ J A, VANLIERDE A, STEVENS F, GENGLER N, DEHARENG F. Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods, 2021, 186: 97-111.

doi: 10.1016/j.ymeth.2020.07.012 pmid: 32763376
[24]
BONFATTI V, DEGANO L, MENEGOZ A, CARNIER P. Short communication: Mid-infrared spectroscopy prediction of fine milk composition and technological properties in Italian Simmental. Journal of Dairy Science, 2016, 99(10): 8216-8221.

doi: S0022-0302(16)30489-1 pmid: 27497897
[25]
MALACARNE M, VISENTIN G, SUMMER A, CASSANDRO M, PENASA M, BOLZONI G, ZANARDI G, DE MARCHI M. Investigation on the effectiveness of mid-infrared spectroscopy to predict detailed mineral composition of bulk milk. The Journal of Dairy Research, 2018, 85(1): 83-86.
[26]
FRANZOI M, NIERO G, PENASA M, DE MARCHI M. Development of infrared prediction models for diffusible and micellar minerals in bovine milk. Animals, 2019, 9(7): 430.
[27]
ZAALBERG R M, POULSEN N A, BOVENHUIS H, SEHESTED J, LARSEN L B, BUITENHUIS A J. Genetic analysis on infrared- predicted milk minerals for Danish dairy cattle. Journal of Dairy Science, 2021, 104(8): 8947-8958.
[28]
GOTTARDO P, DE MARCHI M, CASSANDRO M, PENASA M. Technical note: Improving the accuracy of mid-infrared prediction models by selecting the most informative wavelengths. Journal of Dairy Science, 2015, 98(6): 4168-4173.

doi: 10.3168/jds.2014-8752 pmid: 25828654
[29]
NIERO G, PENASA M, GOTTARDO P, CASSANDRO M, DE MARCHI M. Short communication: selecting the most informative mid-infrared spectra wavenumbers to improve the accuracy of prediction models for detailed milk protein content. Journal of Dairy Science, 2016, 99(3): 1853-1858.

doi: S0022-0302(16)00040-0 pmid: 26774721
[30]
XIAO S J, WANG Q H, LI C F, LIU W J, ZHANG J J, FAN Y K, SU J D, WANG H T, LUO X L, ZHANG S J. Rapid identification of A1 and A2 milk based on the combination of mid-infrared spectroscopy and chemometrics. Food Control, 2022, 134: 108659.
[31]
褚楚, 张静静, 丁磊, 樊懿楷, 包向男, 向世馨, 刘锐, 罗雪路, 任小丽, 李春芳, 刘文举, 王亮, 刘莉, 李永青, 江汉, 李委奇, 孙伟, 李喜和, 温万, 周佳敏, 张淑君. 基于中红外光谱的牛奶中三种氨基酸含量预测模型的建立及应用. 畜牧兽医学报, 2023, 54(8): 3299-3312.

doi: 10.11843/j.issn.0366-6964.2023.08.016
CHU C, ZHANG J J, DING L, FAN Y K, BAO X N, XIANG S X, LIU R, LUO X L, REN X L, LI C F, LIU W J, WANG L, LIU L, LI Y Q, JIANG H, LI W Q, SUN W, LI X H, WEN W, ZHOU J M, ZHANG S J. Establishment and application of prediction model of three amino acids in milk based on mid-infrared spectroscopy. Acta Veterinaria et Zootechnica Sinica, 2023, 54(8): 3299-3312. (in Chinese)

doi: 10.11843/j.issn.0366-6964.2023.08.016
[32]
LI H D, LIANG Y Z, XU Q S, CAO D S. Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. Analytica Chimica Acta, 2009, 648(1): 77-84.

doi: 10.1016/j.aca.2009.06.046 pmid: 19616692
[33]
ZHAO X X, SONG Y T, ZHANG Y P, CAI G Z, XUE G H, LIU Y, CHEN K W, ZHANG F, WANG K, ZHANG M, GAO Y D, SUN D X, WANG X, LI J B. Predictions of milk fatty acid contents by mid-infrared spectroscopy in Chinese Holstein cows. Molecules, 2023, 28(2): 666.
[34]
GOI A, DE MARCHI M, COSTA A. Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy. Journal of Dairy Science, 2023, 106(12): 8341-8356.
[35]
CENTNER V, MASSART D L, DE NOORD O E, DE JONG S, VANDEGINSTE B M, STERNA C. Elimination of uninformative variables for multivariate calibration. Analytical Chemistry, 1996, 68(21): 3851-3858.

doi: 10.1021/ac960321m pmid: 21619260
[1] YANG QiRui, LI LanTao, ZHANG Xiao, ZHANG Qian, ZHANG YinJie, ZHANG Duo, WANG YiLun. Effects of Potassium Application Dosage on Yield, Quality and Light Temperature Physiological Characteristics of Summer Peanut [J]. Scientia Agricultura Sinica, 2024, 57(7): 1335-1349.
[2] PEI ShuYao, CAO HongXia, ZHANG ZeYu, ZHAO FangYang, LI ZhiJun. Physiological Response of Potted Tomatoes to NaCl and Na2SO4 Brackish Water Irrigation [J]. Scientia Agricultura Sinica, 2024, 57(3): 570-583.
[3] ZHANG Bing, YANG YanYan, FENG QianHui, SHI Wen, FANG YiZhen, HUANG JiaBao, SHI DeShun. Effects of Sodium Selenite on the in vitro Maturation of Porcine Oocytes and Their Embryonic Development Potentials [J]. Scientia Agricultura Sinica, 2024, 57(17): 3482-3493.
[4] YANG WenHui, LUO HaoCheng, DONG ErWei, WANG JinSong, WANG Yuan, LIU QiuXia, HUANG XiaoLei, JIAO XiaoYan. Effects of Long-Term Fertilization and Deep Plough on Crop Potassium Utilization and Soil Potassium Forms in Maize-Sorghum Rotation System [J]. Scientia Agricultura Sinica, 2024, 57(12): 2390-2403.
[5] SHI HaoLei, CAO HongXia, ZHANG WeiJie, ZHU Shan, HE ZiJian, ZHANG Ze. Leaf Area Index Inversion of Cotton Based on Drone Multi-Spectral and Multiple Growth Stages [J]. Scientia Agricultura Sinica, 2024, 57(1): 80-95.
[6] GUO Yan, JING YuHang, WANG LaiGang, HUANG JingYi, HE Jia, FENG Wei, ZHENG GuoQing. UAV Multispectral Image-Based Nitrogen Content Prediction and the Transferability Analysis of the Models in Winter Wheat Plant [J]. Scientia Agricultura Sinica, 2023, 56(5): 850-865.
[7] LIU Na, XIE Chang, HUANG HaiYun, YAO Rui, XU Shuang, SONG HaiLing, YU HaiQiu, ZHAO XinHua, WANG Jing, JIANG ChunJi, WANG XiaoGuang. Effects of Potassium Application on Root and Nodule Characteristics, Nutrient Uptake and Yield of Peanut [J]. Scientia Agricultura Sinica, 2023, 56(4): 635-648.
[8] HUO RunXia, ZHANG Zhe, LI WenPing, ZHANG YangYang, LIAO ShiPeng, REN Tao, LI XiaoKun, LU ZhiFeng, CONG RiHuan, LU JianWei. 40 Years’ Change Characteristics of Soil Basic Properties in the Main Planting Area of Winter Oilseed Rape [J]. Scientia Agricultura Sinica, 2023, 56(23): 4696-4705.
[9] GUO YongMei, LIU Yang, WU Rui, YAN SuMei, ZHAO YanLi, GUO XiaoYu. Effect of Interaction Between Vitamin A and Acetic Acid on the Expression of Genes Related to Milk Composition Synthesis in Bovine Mammary Epithelial Cells [J]. Scientia Agricultura Sinica, 2023, 56(21): 4344-4358.
[10] YANG GaiQing, WANG LinFeng, LI WenQing, ZHU HeShui, FU Tong, LIAN HongXia, ZHANG LiYang, TENG ZhanWei, ZHANG LiJie, REN Hong, XU XinYing, LIU XinHe, WEI YuXuan, GAO TengYun. Study on Milk Quality Based on Circadian Rhythm [J]. Scientia Agricultura Sinica, 2023, 56(2): 379-390.
[11] LI MianYan, WANG LiXian, ZHAO FuPing. Research Progress on Machine Learning for Genomic Selection in Animals [J]. Scientia Agricultura Sinica, 2023, 56(18): 3682-3692.
[12] XIE Xue, LU YanHong, LIAO YuLin, NIE Jun, ZHANG JiangLin, SUN YuTao, CAO WeiDong, GAO YaJie. Effects of Returning Chinese Milk Vetch and Rice Straw to Replace Partial Fertilizers on Double Season Rice Yield and Soil Labile Organic Carbon [J]. Scientia Agricultura Sinica, 2023, 56(18): 3585-3598.
[13] LI JiRong, LIU Xin, WANG Jun, CAO XiaoGang, CI Dun. Fractionation Effect of Stable Carbon and Nitrogen Isotope Ratios in Yak Dairy Products Processing [J]. Scientia Agricultura Sinica, 2023, 56(10): 1982-1993.
[14] LIU Shuo,ZHANG Hui,GAO ZhiYuan,XU JiLi,TIAN Hui. Genetic Variations of Potassium Harvest Index in 437 Wheat Varieties [J]. Scientia Agricultura Sinica, 2022, 55(7): 1284-1300.
[15] FENG ZiHeng,SONG Li,ZHANG ShaoHua,JING YuHang,DUAN JianZhao,HE Li,YIN Fei,FENG Wei. Wheat Powdery Mildew Monitoring Based on Information Fusion of Multi-Spectral and Thermal Infrared Images Acquired with an Unmanned Aerial Vehicle [J]. Scientia Agricultura Sinica, 2022, 55(5): 890-906.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!