Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Met...Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.展开更多
Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soy...Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soybean （Glycine max L. Merill） growing area located in Northeast China, was evaluated using soil quality index （SQI） methods. Each SQI was computed using a minimum data set （MDS） selected using principal components analysis （PCA） as a data reduction technique. Eight MDS indicators were selected from 20 physical and chemical soil measurements. The MDS accounted for 74.9% of the total variance in the total data set （TDS）. The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods. The results showed that SQI values correlated well with soybean yield （r = 0.658＊＊） when indicators in MDS were weighted by the regression coefficient computed for each yield and index. Stepwise regression between yield and principal components （PCs） indicated that available boron （AvB）, available phosphorus （AvP）, available potassium （AvK）, available iron （AvFe） and texture were the main factors limiting soybean yield. The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.展开更多
文摘Objective] The alm was to survey 10 characters of 8 fresh edibIe soy-bean varieties, analyze maln Ioading factors using principal component analysis, and estabIish muItipIe regression equation on fresh pod yield. [Methods] Through princi-pal component analysis on 10 characters of 8 fresh edibIe soybean varieties, char-acters reIated to fresh pod yield of fresh edibIe soybean were cIarified. [Results] Af-ter the principal components analysis, pod weight per pIant, 100-seed weight and pod number per pIant of fresh edibIe soybean were chosen to study their reIation with the yield of fresh edibIe soybean, moreover, it was demonstrated that the reIa-tion was Iinear reIation, thus it was suitabIe for muItivariate regression analysis. Fi-nal y, the mathematical expression formuIa about fresh pod yield was estabIished. [Conclusions] There were three characters affecting fresh pod yield, nameIy, pod weight per pIant, 100-seed weight and pod number per pIant, the mathematical equation was y=816.732＋4.145X6-0.718X8-0.985X9 （X6： pod weight per pIant; X8： 100-seed weight; X9： pod number per pIant）.
基金Supported by the Knowledge Innovation Program of Chinese Academy of Sciences(No.KSCX1-YW-09-02)the National Basic Research Program of China(No.2013CB127401)+1 种基金the National Natural Science Foundation of China(No.41271309)the International Plant Nutrition Institute (IPNI) China Program
文摘Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems affect soil resources. Soil quality of Hailun County, a typical soybean （Glycine max L. Merill） growing area located in Northeast China, was evaluated using soil quality index （SQI） methods. Each SQI was computed using a minimum data set （MDS） selected using principal components analysis （PCA） as a data reduction technique. Eight MDS indicators were selected from 20 physical and chemical soil measurements. The MDS accounted for 74.9% of the total variance in the total data set （TDS）. The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods. The results showed that SQI values correlated well with soybean yield （r = 0.658＊＊） when indicators in MDS were weighted by the regression coefficient computed for each yield and index. Stepwise regression between yield and principal components （PCs） indicated that available boron （AvB）, available phosphorus （AvP）, available potassium （AvK）, available iron （AvFe） and texture were the main factors limiting soybean yield. The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.
