Evaluation of Relative Efficiency of Several Selection Indices for Prediction of Expected Genetic Advance in Bread Wheat Grain Yield (Triticum aestivum L)

Al-Najjar, Safwan; Al-Zubaidy, Kh

doi:10.25130/tjas.19.4.7

Cited by 1 publication

(1 citation statement)

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“…Most studies have illustrated that 1000-kernel weight is an indicator of wheat production. The 1000kernel weight was 31.60 [13],32.29 [14], 33.5 [15] and [16], 34.42 [17], 36.8 [18] ,38.7 [19],42.9 [14], 44.22 [20] for the IPA 99 varieties. Furthermore, the 1000-kernel weight was 32.43 [14] and 35.4 [15] for the Buhooth22 variety.…”

Section: Grain Weightmentioning

confidence: 99%

Determination the Wheat Weight and Volume: Mathematical Approach

Al-Amery

Albadri

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Grain size and shape are important yield indicators. A hint for reexamining the visual markers of grain weight can be found in the wheat grain width. A digital vernier caliper is used to measure length, width, and thickness. The data consisted of 1296 wheat grains, with measurements for each grain. In this data set, the average weight (We) of the twenty-four grains was measured and recorded. To determine measure of the length (L), width (W), thickness (T), weight (We), and volume(V). These features were manipulated to develop two mathematical models that were passed on to the multiple regression models. The results of the weight model demonstrated that the length and width of the grains were significantly different p < 0.0001. The coefficient of grain width was higher than that of grain length, indicating that grain width was more important than grain length. Furthermore, the overall model for volume was significantly different, (F (2,1295); =446832, p < 0.0001), regression(R2)-Square= 0.9986, Adj R-Sq=0.9986; and the RMSE of the experimental was RMSE=0.0002. The results of the weight model showed that the length, width, and thickness of the grains were significantly different p < 0.0001. The coefficient of grain the thickness was greater than the coefficient of grain length, indicating that the grain thickness was more significant than the grain length, and the coefficient of the grain width was more significant than the grain length. Moreover, the overall model for volume was significantly different (F (3,1295); =71809.6, p < 0.0001), regression (R2) and Adj-R-Sq = 0.9940 were equal, and RMES=0.3399. The introduced model may allow farmers to predict the weight and volume of wheat production during the wheat grain season, depending on the grain length, width, and thickness.

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