Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water sh...Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water shortages and the overuse of fertilizers.The field experiment had twelve treatments and a control(CK)trial including two irrigation amounts(I1,100%ETm;I2,60%ETm;where ETm is the maximum evapotranspiration),two nitrogen applications(N1,360 kg ha^(−1);N2,120 kg ha^(−1))and three biochar application levels(B1,60 t ha^(−1);B_(2),30 t ha^(−1)and B3,0 t ha^(−1)).A multi-objective synergistic irrigation-nitrogen-biochar application system for improving tomato yield,quality,water and nitrogen use efficiency,and greenhouse emissions was developed by integrating the techniques of experimentation and optimization.First,a coupled irrigation-nitrogen-biochar plot experiment was arranged.Then,tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation-nitrogen-biochar dosage and yield,comprehensive quality of tomatoes(TCQ),irrigation water use efficiency(IWUE),partial factor productivity of nitrogen(PFPN),and net greenhouse gas emissions(NGE).Finally,a multi-objective dynamic optimization regulation model of irrigation-nitrogen-biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method.The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield,IWUE and PFPN,while it had an inhibitory effect on NGE.In addition,the optimal allocation amounts of water and fertilizer were different under different scenarios.The yield of the S1 scenario increased by 8.31%compared to the B_(1)I_(1)N_(2) treatment;TCQ of the S2 scenario increased by 5.14%compared to the B_(2)I_(2)N_(1) treatment;IWUE of the S3 scenario increased by 10.01%compared to the B1I2N2 treatment;PFPN of the S4 scenario increased by 9.35%compared to the B_(1)I_(1)N_(2) treatment;and NGE of the S5 scenario decreased by 11.23%compared to the B_(2)I1N1 treatment.The optimization model showed that the coordination of multiple objectives considering yield,TCQ,IWUE,PFPN,and NGE increased on average from 4.44 to 69.02%compared to each treatment when the irrigation-nitrogen-biochar dosage was 205.18 mm,186 kg ha^(−1)and 43.31 t ha^(−1),respectively.This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.展开更多
[Objectives] This study was conducted to solve the problems of complex near-infrared spectrum information of soybean lysine, serious collinearity and insufficient predictive ability of full-spectrum modeling. [Methods...[Objectives] This study was conducted to solve the problems of complex near-infrared spectrum information of soybean lysine, serious collinearity and insufficient predictive ability of full-spectrum modeling. [Methods] A new variable selection method, i.e., variable combination model population analysis method, was used to select characteristic wavelengths of soybean lysine near infrared spectra. The binary matrix sampling strategy and exponential decay function were used at first to delete the variables providing no information and select the near-infrared characteristic wavelengths of soybean lysine, which were then combined the partial least square method to establish a prediction model. Compared with other variable selection methods, the Monte Carlo variable combination model population analysis method selected the least wavelength points and the model had the strongest predictive ability. The variable combination model population analysis method adopting the binary matrix sampling strategy made up for the shortcomings of the single Monte Carlo sampling method. [Results] The experimental results showed that the Monte Carlo variable combination model population analysis algorithm could better select the characteristic wavelengths of soybean lysine NIR spectra and improve the reliability of the prediction model. However, in general, the accuracy of the lysine prediction model is not satisfactory, and it needs to be further reconstructed and optimized in future research work. The reason might be that the determination accuracy of the chemical value of lysine content was insufficient, or it might be caused by the poor absorption of the hydrogen-containing group of lysine in the near-infrared spectrum region and the poor correlation with proteins. [Conclusions] This study provides a reference for soybean high-lysine breeding.展开更多
基金supported by the National Natural Science Foundation of China(52222902 and 52079029)。
文摘Integrated water and fertilizer management is important for promoting sustainable development of facility agriculture,and biochar plays an important role in guaranteeing food production,as well as alleviating water shortages and the overuse of fertilizers.The field experiment had twelve treatments and a control(CK)trial including two irrigation amounts(I1,100%ETm;I2,60%ETm;where ETm is the maximum evapotranspiration),two nitrogen applications(N1,360 kg ha^(−1);N2,120 kg ha^(−1))and three biochar application levels(B1,60 t ha^(−1);B_(2),30 t ha^(−1)and B3,0 t ha^(−1)).A multi-objective synergistic irrigation-nitrogen-biochar application system for improving tomato yield,quality,water and nitrogen use efficiency,and greenhouse emissions was developed by integrating the techniques of experimentation and optimization.First,a coupled irrigation-nitrogen-biochar plot experiment was arranged.Then,tomato yield and fruit quality parameters were determined experimentally to establish the response relationships between irrigation-nitrogen-biochar dosage and yield,comprehensive quality of tomatoes(TCQ),irrigation water use efficiency(IWUE),partial factor productivity of nitrogen(PFPN),and net greenhouse gas emissions(NGE).Finally,a multi-objective dynamic optimization regulation model of irrigation-nitrogen-biochar resource allocation at different growth stages of tomato was constructed which was solved by the fuzzy programming method.The results showed that the application of irrigation and nitrogen to biochar promoted increase in yield,IWUE and PFPN,while it had an inhibitory effect on NGE.In addition,the optimal allocation amounts of water and fertilizer were different under different scenarios.The yield of the S1 scenario increased by 8.31%compared to the B_(1)I_(1)N_(2) treatment;TCQ of the S2 scenario increased by 5.14%compared to the B_(2)I_(2)N_(1) treatment;IWUE of the S3 scenario increased by 10.01%compared to the B1I2N2 treatment;PFPN of the S4 scenario increased by 9.35%compared to the B_(1)I_(1)N_(2) treatment;and NGE of the S5 scenario decreased by 11.23%compared to the B_(2)I1N1 treatment.The optimization model showed that the coordination of multiple objectives considering yield,TCQ,IWUE,PFPN,and NGE increased on average from 4.44 to 69.02%compared to each treatment when the irrigation-nitrogen-biochar dosage was 205.18 mm,186 kg ha^(−1)and 43.31 t ha^(−1),respectively.This study provides a guiding basis for the sustainable management of water and fertilizer in greenhouse tomato production under drip irrigation fertilization conditions.
基金Supported by Agricultural Development Fund Plan of Chongqing Academy of Agricultural Sciences(NKY-2020AC008)Project of Chongqing Science and Technology Bureau(Ycstc,2019cc0101,CQYC201903216,Ycstc,2020ac1102,cstc2019jscx-gksbX0138)+1 种基金National Agricultural Science Germplasm Resources Jiangjin Observation and Experimental StationChongqing Grain and Oil Crop Field Scientific Observation and Research Station。
文摘[Objectives] This study was conducted to solve the problems of complex near-infrared spectrum information of soybean lysine, serious collinearity and insufficient predictive ability of full-spectrum modeling. [Methods] A new variable selection method, i.e., variable combination model population analysis method, was used to select characteristic wavelengths of soybean lysine near infrared spectra. The binary matrix sampling strategy and exponential decay function were used at first to delete the variables providing no information and select the near-infrared characteristic wavelengths of soybean lysine, which were then combined the partial least square method to establish a prediction model. Compared with other variable selection methods, the Monte Carlo variable combination model population analysis method selected the least wavelength points and the model had the strongest predictive ability. The variable combination model population analysis method adopting the binary matrix sampling strategy made up for the shortcomings of the single Monte Carlo sampling method. [Results] The experimental results showed that the Monte Carlo variable combination model population analysis algorithm could better select the characteristic wavelengths of soybean lysine NIR spectra and improve the reliability of the prediction model. However, in general, the accuracy of the lysine prediction model is not satisfactory, and it needs to be further reconstructed and optimized in future research work. The reason might be that the determination accuracy of the chemical value of lysine content was insufficient, or it might be caused by the poor absorption of the hydrogen-containing group of lysine in the near-infrared spectrum region and the poor correlation with proteins. [Conclusions] This study provides a reference for soybean high-lysine breeding.
