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Evaluation of dry matter accumulation in triticale by different sigmoidal growth models in west anatolia of turkey

机译:用不同的S形增长模型评价火鸡西安那托利亚黑小麦中干物质的积累

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Monitoring biological growth of field crops is important for planning and timing agricultural practices. In order to assess biological growth pattern of dry matter accumulation in triticale Egeyildizi triticale variety were grown in ?anakkale conditions in 2012-2013 and 2013-2014 growing seasons with continuous plant samplings from seedling emergence until seed maturation. Gompertz, Logistic, Logistic Power and Richards growth models are fitted to actual growth data and their predictions were compared. Results suggested that all sigmoidal growth models successfully explained triticale dry matter accumulation over 98 % R2 values and low mean square errors, Richards model fitted best for both years with an R2 value over 99 %. Dry matter accumulation were also investigated as a result of average temperature, precipitation, growth degree days and cumulative growth degree days with stepwise regression. Rresults indicated that average weather temperature had a similar pattern across both growing seasons and had a major influence on dry matter accumulation. Since Richards sigmoidal growth model may be adequately described growth pattern of triticale by generally high R2 with lower Mean Square Error (MSE) values.
机译:监测大田作物的生物生长对于计划和安排农业实践至关重要。为了评估小黑麦中干物质积累的生物生长模式,2012-2013年和2013-2014年的生长季节中,小黑麦Eticyildizi小黑麦品种在anakkale条件下进行了种植,从幼苗出苗到种子成熟一直进行连续取样。将Gompertz,Logistic,Logistic Power和Richards的增长模型拟合到实际增长数据,并比较了它们的预测。结果表明,所有的S型增长模型都成功地解释了小黑麦干物质在R2值超过98%且均方误差较低的情况下的积累,Richards模型在R2值超过99%的情况下最适合两年。还通过逐步回归研究了平均温度,降水量,生长天数和累积生长天数的结果,得出干物质的积累。结果表明,平均温度在两个生长季节都有相似的模式,并且对干物质积累有重要影响。由于Richards的S型增长模型可以通过通常较高的R2和较低的均方误差(MSE)值来充分描述小黑麦的生长方式。

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