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Study on Antibiotic Prophylactic Use Behavior and Influencing Factors of Livestock and Poultry Breeding Farms and Households from Risk Cognitive Perspective 被引量:5
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作者 Chengxing MAO 《Asian Agricultural Research》 2018年第4期52-59,64,共9页
Based on the theory of planned behavior( TPB),Taking 399 livestock and poultry farms and households in Shandong Province as samples,risk cognitive variables were introduced. Besides,with the aid of the structural equa... Based on the theory of planned behavior( TPB),Taking 399 livestock and poultry farms and households in Shandong Province as samples,risk cognitive variables were introduced. Besides,with the aid of the structural equation model( SEM) and partial least squares method( PLS),through an empirical analysis on antibiotic prophylactic behavior of livestock and poultry breeding farms and households in the context of " antibiotic free" production,the response mechanism of " antibiotic free" production was explored. Results indicated that the use of antibiotic prophylaxis by livestock and poultry farms and households is still very common. In the observation samples,61. 4% clearly expressed they would use antibiotic prophylaxis; the understanding of hazard of improper antibiotic prophylaxis was inadequate,only 32. 3% breeding households believed that the overuse of antibiotic prophylaxis is the main reason leading to excessive drug residue in animal products. This study was in line with the process of deduction of TPB. The breeding households' reduction of antibiotic prophylaxis is influenced by their intentions,while the intention is influenced by the attitude,subjective norms and perceptual behavior control; different risks have different influences,but most risks have greater influence on perceptual behavior control,because the perceptual behavior control determines whether breeding households have the ability of implementing the corresponding behavior. 展开更多
关键词 Antibiotic-free Theory of planned behavior(TPB) Risk perception Structural equation model(SEM) partial least squares method(PLS)
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Method for UAV spraying pattern measurement with PLS model based spectrum analysis 被引量:2
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作者 Ruirui Zhang Yao Wen +5 位作者 Longlong Li Liping Chen Gang Xu Yanbo Huang Qing Tang Tongchuan Yi 《International Journal of Agricultural and Biological Engineering》 SCIE EI CAS 2020年第3期22-28,共7页
Unmanned aerial vehicle(UAV)chemical application is widely used for crop protection,and spraying pattern is one of the most important factors that influence the chemical control efficacy.A method for UAV spraying patt... Unmanned aerial vehicle(UAV)chemical application is widely used for crop protection,and spraying pattern is one of the most important factors that influence the chemical control efficacy.A method for UAV spraying pattern measurement with partial least squares(PLS)model based spectrum analysis was proposed in this study to measure the UAV spraying pattern more accurately.The method involved the steps of fluorescent tracer solution spray and its droplets collection,the spectrum on paper strip acquiring,spectrum processing and analysis,PLS modeling.In order to verify the applicability of the method and obtain the parameters of the PLS model,UAV spraying experiments were performed in the field.Then Fluorescent tracer solution was sprayed and its droplets are collected by paper strip,and the original spectrum on paper strip obtained by a spectrometer was processed by the Savitzky-Golay and standard normalized variable(SNV)method.The prediction model of coverage rate selected as the droplet deposition parameter to measure the UAV spraying pattern,was established by using PLS method.To verify the superiority of the PLS model,a traditional linear regression(LR)model of coverage rate was established simultaneously.The results demonstrate that the method with PLS model based spectrum analysis can measure the UAV spraying pattern effectively,and PLS model has a better performance of RV2=0.94 and RMSEP=0.9446 than that of the LR model. 展开更多
关键词 pattern measurement UAV spraying spectrum model coverage rate partial least squares method
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Measurements and analysis of water content in winter wheat leaf based on terahertz spectroscopy 被引量:2
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作者 Bin Li Yuan Long Hao Yang 《International Journal of Agricultural and Biological Engineering》 SCIE EI CAS 2018年第3期178-182,共5页
Wheat is a major grain crop in China.Water is one of the most important factors which influence the lifecycle and yield of wheat.It is of great significance to study the water content at the key stages of wheat growth... Wheat is a major grain crop in China.Water is one of the most important factors which influence the lifecycle and yield of wheat.It is of great significance to study the water content at the key stages of wheat growth in order to make irrigation decision to raise its yield.As Terahertz(THz)spectroscopy is a brand new sensing technology and sensitive to water absorption,the relationship between terahertz spectra and water content in winter wheat leaf was investigated and a preliminary result was presented in this paper.Forty winter wheat leaves samples with diverse range of water content(42.8%-72.5%)were collected.The Terahertz time domain spectra(THz-TDS)were first obtained and then transformed into Frequency-domain amplitude with the Fast Fourier Transformation(FFT)method.The absorption and refractive index spectra were then calculated.The spectra were linearly fitted to obtain the slope and intercept used for building a calibration model.The partial least squares(PLS)method and linear regression were employed to establish models to determine leaf water content in the winter wheat.The predicted correlation coefficient and the root mean square error of the optimal model established with the Frequency-domain amplitude parameter at 0.3 THz by linear regression were 0.812%and 4.4%,respectively.The results showed that terahertz spectroscopy performed well in water content prediction and could be an effective and potential method for leaf water content measurement in winter wheat. 展开更多
关键词 terahertz spectroscopy winter wheat gravimetric water content(GWC) partial least squares method
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