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Effects of soil, climate and cultivation techniques on cotton yield inCentral Greece, using different statistical methods

机译:使用不同的统计方法,土壤,气候和耕作技术对希腊中部棉花产量的影响

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摘要

This study aims to identify and quantify the relationship between the environmental and crop management variables and the cotton yield in the Thessaly plain in Central Greece. A total of 349 fields spread along the area were selected where cotton yield, soil and management data were measured for three consecutive growing seasons. A combination of statistical tools such as one-way and n-way analysis of variance (ANOVA), linear regression analysis and factor analysis was used for the identification and confirmation of the role of soil and management variables under different climatic conditions. ANOVA showed that soil order, topsoil and subsoil texture, carbonates, cultivar, previous uses of the sampling sites, defoliation and the spatial and temporal variation of the climate were significant for the yield (P < 0.001). Regression analysis confirmed the results of ANOVA and suggested that 50% of the yield variance is accounted for by soil variables, about the same percentage (47%) is accounted for by management variables, while soil and management variables together explain 65% of the yield variance. Factor analysis was applied on the data in two ways: (i) by including yield variable between the variables and (ii) by not including yield. Both analyses resulted in ten factors which were identified by the same groups of variables. Results from the first factor analysis suggested that 61% of the total yield variance is accounted for by the ten factors. Factors F1 and F2 explain about half of this variance while the factor F5 explains one third of it. Regression analysis on the factor scores calculated from the second factor analysis showed that factors F1, F2, F5 and F7 explain 41% of the total yield variance. In both analyses factor F1 is defined mainly from soil variables, while F2, F5 and F7 mainly from management variables.
机译:本研究旨在确定和量化希腊中部色萨利平原的环境和作物管理变量与棉花产量之间的关系。选择沿该地区分布的总共349个田地,在连续三个生长季节中测量了棉花产量,土壤和管理数据。统计方法的结合使用,如单向和n向方差分析(ANOVA),线性回归分析和因子分析,用于识别和确认土壤和管理变量在不同气候条件下的作用。方差分析表明,土壤产量,表层土壤和下层土壤质地,碳酸盐,品种,取样地点的先前用途,落叶和气候的时空变化对产量都有重要影响(P <0.001)。回归分析证实了方差分析的结果,并建议50%的产量方差由土壤变量引起,大约相同的百分比(47%)由管理变量引起,而土壤和管理变量共同解释了65%的产量方差。通过两种方式对数据进行因子分析:(i)通过在变量之间包括收益变量,以及(ii)不包括收益。两项分析均得出了由同一组变量确定的十个因素。第一因素分析的结果表明,十个因素占总产量方差的61%。因子F1和F2解释了这一差异的一半,而因子F5解释了这一差异的三分之一。根据第二次因子分析计算出的因子得分的回归分析表明,因子F1,F2,F5和F7解释了总产量方差的41%。在这两种分析中,因子F1主要由土壤变量定义,而F2,F5和F7主要由管理变量定义。

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