Wheat Yield and Important Traits Influenced by Interaction of Potassium and Irrigation Levels Evaluated at Number of Locations in the Country by AMMI Analysis and Non-Parametric Measures

Q4 Environmental Science

Environment and Ecology Research Pub Date : 2023-09-01 DOI:10.60151/envec/pwpy1898

R.P. Meena, Ajay Verma, S. C. Tripathi

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引用次数: 0

Abstract

AMMI analysis of treatments consisted of levels of potassium with irrigations observed highly significant effects of locations, treatments, and T×L interactions for wheat yield. About 53.4% of the total variations in yield values was due to locations followed by 26.3% and 10.8% by treatments and interactions effects. Further analysis found 59.7% contributed by AMMI1 while 17.2% and 9.4% by AMMI2 and AMMI3 components for thousands grain weight as total of first two components cumulative to 76.9% of the total variation. The sums of squares for G×E signal and noise were 56.7% and 43.3% of interaction effects for grains per spike as the sum of squares of T×L signal was 2.58 times of treatments effects and IPC1 alone was 3.54 times the treatments effects. Measures ASV and ASV1 recommended T6, T5, T4 for wheat yield while measures utilized 81.6% of interaction sum of squares whereas MASV and MASV1 measures based on 98.4% identified T3, T5, T8, T4 treatments. Maximum average for thousands grains weight; GAI selected T8, T9, T6 whereas as per HM values treatments T5, T2, T8 would be more desirable. Grains per spike found the measures RPGV and HMRPGV settled for T8, T9, T5 treatments. Non parametric measures for yield observed Si1 selected T3, T2, T5 as opposed to T6, T4, T1 by Si2 values. T6, T4, T1 genotypes considered by Si3 Si4 measure considered T6, T4, T1 next Si5 for T6, T3, T4 and Si6 pointed towards T6, T4, T8 genotypes while Si7 favored T6, T1, T4 genotypes. Composite measures for thousands grains weight found NPi(1) for T3,T4,T7 while as per NPi(2) for T4,T3,T7, NPi(3) T4,T3,T2, NPi (4) found T4, T5, T7 as suitable treatment combinations. Multivariate hierarchical clustering as per Ward’s method for wheat yield observed first irrigation level with three potassium levels formed a cluster and other irrigation levels with potassium application remained in other one. At the first node of demarcation for thousands grains weight IPC5 exhibited MASV with MASV1, ASV1, IPC4, ASV, Si1 Si2 Si3 Si4 Si5 Si6 Si7 NPi(1), CV in one side and mean, GAI, PRVG, IPC1, HM, IPC1, NPi(2) NPi(3) NPi (4) on other side. The performance of treatments based on AMMI and non-parametric measures would be more meaningful for identification of suitable irrigation and potassium levels for wheat sustainable production.

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利用AMMI分析和非参数测量评价了钾肥与灌溉水平互作对小麦产量和重要性状的影响

AMMI分析包括灌溉钾水平的处理，观察到位置、处理和T×L相互作用对小麦产量的高度显著影响。在产量变化总量中，位置因素占53.4%，处理和互作效应分别占26.3%和10.8%。进一步分析发现，AMMI1对千粒重的贡献率为59.7%，AMMI2和AMMI3对千粒重的贡献率分别为17.2%和9.4%，前两个分量累计占总变异的76.9%。G×E信号和噪声的平方和分别为每穗粒交互作用效应的56.7%和43.3%，其中T×L信号的平方和是处理效应的2.58倍，单独IPC1的平方和是处理效应的3.54倍。措施ASV和ASV1推荐采用T6、T5、T4处理，措施的互作平方和利用率为81.6%，而基于MASV和MASV1措施的互作平方和利用率为98.4%。千粒重的最大平均值;GAI选择T8, T9, T6，而根据HM值，T5, T2, T8处理更可取。每穗粒数的RPGV和HMRPGV在T8、T9、T5处理下趋于稳定。对于产量的非参数测量，Si1根据Si2的值选择了T3、T2、T5，而不是T6、T4、T1。Si3考虑的是T6、T4、T1基因型，Si4考虑的是T6、T4、T1基因型，Si5考虑的是T6、T3、T4基因型，Si6考虑的是T6、T4、T8基因型，Si7考虑的是T6、T1、T4基因型。千粒重复合测量结果显示，T3、T4、T7的NPi值为(1)，T4、T3、T7的NPi值为(2)，T4、T3、T2的NPi值为(3)，NPi值为(4)，T4、T3、T2的NPi值为(5)。采用Ward的多变量分层聚类方法对小麦产量进行聚类分析，发现第一个灌溉水平和三个施钾水平形成一个聚类，其他施钾灌溉水平在另一个聚类中。在千粒重分界的第一个节点，IPC5表现出MASV特征，一边是MASV1、ASV1、IPC4、ASV、Si1、Si2、Si4、Si5、Si6、Si7 NPi(1)、CV，另一边是mean、GAI、PRVG、IPC1、HM、IPC1、NPi(2)、NPi(3)、NPi(4)。基于AMMI和非参数措施的处理效果对小麦可持续生产的适宜灌溉和钾水平的确定更有意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environment and Ecology Research Environmental Science-Pollution

CiteScore

1.30

自引率

0.00%

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