首页> 外文期刊>日本作物學會紀事 >Crop scientifical studies on the yield-forecast of lowland rice. : Preliminary report : I. Crop scientifical forecast on the yield of lowland rice and the annual variations relating to the constitutional factors of its yield.
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Crop scientifical studies on the yield-forecast of lowland rice. : Preliminary report : I. Crop scientifical forecast on the yield of lowland rice and the annual variations relating to the constitutional factors of its yield.

机译:稻米产量预测的作物科学研究。 :初步报告:I。作物科研预测对低地米的产量及其与产量宪法因素有关的年度变化。

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(1) The authors intended to forecast the yield by judging the growing process of rice plants. For this purpose they tried to analyze the factors constituting the yield of rice crop and to trace up their growing processes. (2) The following four factors were considered to constitute the yield of rice crop. (i) Number of panicles per unit area. (ii) Number of spikelets per panicle. (iii) Fruiting percentage. (iv) Weight of 1, 000 grains (brown rice). (3) First of all, the annual variations of these four factors were studied as fundamental data for forecasting the values of them. Using the data of the studies on climate-adaptability of the crop performed at Konosu Agricultural Experiment Farm, in which nine varieties - including early, intermediate and late maturing ones - were used as material, the annual variations of the factors were investigated by calculating coefficients of variation through nineteen years. (4) The results obtained were as follows : (i) Variation coefficients of the number of panicles ranged from 10.2 % t 15.6 %. The less was the number of panicles in varieties, the greater was the coefficient of variation ; the correlation coefficient between them had been so significant that it was r = 0.88. (ii) Coefficients of variation of the number of spikelets per panicle ranged from 6.5 % to 16.6 %, and those of intermediate maturing varieties were smaller than that of any other varieties in general. (iii) Coefficients of variation of the fruiting percentage and the weight of 1, 000 grains were 5.0∼9.1 %, 2.2∼4.4 % respectively, and only the slight differences among varieties were seen. (5) It was found in every variety above mentioned that both the number of panicles and the number of spikelets per panicle showed the largest variation, the fruiting percentage followed next, and the weight of 1, 000 grains was in final order. (6) These results obtained are very useful for the yield-forecast of rice crop because of the following reason : it is supposed that the order of these four factors determined in the growing period of rice plant may be such as the first, the number of panicles, the second, the number of spikelets, the third, the fruiting percentage and the fourth, the weight of 1, 000 grains ; this order agrees with the descending order of values in their annual variations. That is, the larger is the annual variation in the factors, the earlier it is determined in the stage of growth. (7) The annual variations of these four factors are available as the accuracy-index of the yield-forecast. For example, the number of panicles, the number of spikelets per panicle and the fruiting percentage, exactly being forecasted and only the weight of 1, 000 grains remaining unknown, we can decide that the yield forecast is, under about 70 % probability, within 3.3 % probable error which is the mean value of variation of 1, 000 grains weight.
机译:(1)作者旨在通过判断水稻植物的生长过程来预测产量。为此目的,他们试图分析构成稻田产量的因素,并追踪其越来越多的过程。 (2)以下四个因素被认为是稻米作物产量。 (i)每单位面积的圆锥数量。 (ii)每穗的尖峰数。 (iii)结果百分比。 (iv)重量为1,000粒(糙米)。 (3)首先,研究了这四种因素的年度变化作为预测它们价值的基本数据。利用在科松农业实验农场进行的作物的气候适应性研究数据,其中九个品种 - 包括早期,中间和晚期成熟的,用作材料,通过计算系数来研究因素的年度变化差异到十九年。 (4)所获得的结果如下:(i)圆锥数量的变化系数为10.2%t15.6%。少是品种的圆锥片段,变异系数越大;它们之间的相关系数如此重要的是r = 0.88。 (ii)每根穗分的穗分的变异系数范围为6.5%至16.6%,中间成熟品种的数量小于任何其他品种的那些。 (iii)结果百分比变化的系数分别为1,000粒的重量分别为5.0〜9.1%,分别为2.2〜4.4%,并且仅看到品种之间的细微差异。 (5)在上述各种曲线中发现,每穗的穗状花序数量和每穗的尖峰数显示出最大的变异,结果百分比随后,重量为1,000粒。 (6)所获得的结果对于稻米作物的产量预测非常有用,因为以下原因是:这应该是,在水稻植物的生长期间确定这四种因素的顺序可能是第一个,而不是第一个圆锥花序,二,穗穗数,第三,结果百分比和第四,重量为1,000颗粒;该订单同意其年度变化中的值下降令。也就是说,较大的是因素的年度变化,在增长的阶段确定的较早者。 (7)这四种因素的年度变化可作为产量预测的准确性指数提供。例如,圆锥花序数,每穗的尖峰数和结果百分比,完全被预测,并且只有1,000粒仍然未知的重量,我们可以决定在大约70%的概率下3.3%可能的误差是1,000颗粒重量的平均值。

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