ابن تیمیہ متفرد کیوں؟ اسباب و علل کاتجزیاتی مطالعہ A Research Review of the Reasons of Ibn Taymiyya’s Distinctions

Ibn Taymiyya is known as a controversial figure due to his differences. Most of the scholars have differed with him on most of the jurisprudential and principled issues. There are many reasons for Ibn Tamiya’s differences. The difference between intellect and its use is the most important, that is, the way of thinking. The jurisprudential ability and competence that elevated him to the status of ijtihad was a result of uniqueness in ijtihad and jurisprudence. Disagreements can be caused by the circumstances of that era and the behavior of the people of that era. All the principles and their preferred methods based on which he solved jurisprudential and doctrinal issues and all the reasons why he disagreed with a section of the ummah and the ummah know these differences in the form of the differences of Ibn Taymiyya. It is very important that his jurisprudential insight and ijtihad efforts be revealed, although in many issues the majority of the scholars of the ummah have disagreed with him and their opinion differs from the great taste. Ibn Tamiya’s method of inference in jurisprudential and principled issues and the principles by which he formed an opinion on an issue and on what basis he preferred principles in solving problems are the key issues that will be discussed in the following article.

Stability Analysis for Production Traits in Bread Wheat

Cross over interaction is of primary interest when developing relatively stable and high yielding crop cultivars as plant breeders are not only interested in the ranking of genotypes but also how the ranks fluctuate within test sites. Multi-environment trials were conducted to determine genotype by environment interaction (GEI) for production traits in wheat. Eighty-one wheat genotypes were field-planted in alpha lattice design with two replicates across nine environments (E-1 to E-9) in Khyber Pakhtunkhwa, Pakistan during 2013/14, 2014/15 and 2015/16. The E-1, E-2 and E-3 refer to Peshawar during 2013/14, 2014/15 and 2015/16; E-4 and E-5 to Nowshera; E- 6 and E-7 to Swabi while E-8 and E-9 to Charsadda during 2014/15 and 2015/16, respectively. Combined analysis of variance revealed significant GEI for all traits, suggesting that genotypic performance was inconsistent across environments. The GEI explained maximum proportion of total variation and thus had dominating effect in phenotypic expression. Based on mean performance across nine test environments, genotype G-79 had higher number of tillers (181 tillers m-2), grains spike-1 (71), grain yield (4862 kg ha-1) and harvest index (33.8 %) and was thus identified as a leading genotype. Similarly, G-79 produced maximum grain yield in all test environments i.e. E-1 (4537 kg ha-1), E-2 (4840 kg ha-1), E-4 (5035 kg ha-1), E-5 (4976 kg ha-1), E-6 (4797 kg ha-1), E-7 (5024 kg ha-1), E-8 (4767 kg ha-1), E-9 (4886 kg ha-1), except E-3. Similarly, E-1, E-2, E-3, E-6 and E-7 were declared as highly productive environments for grain yield. Correlation coefficients among production traits revealed significant associations of grain yield with tillers m-2 (rg = 0.72**), grains spike-1 (rg = 0.41**), 1000-grain weight (rg = 0.30**) and harvest index (rg = 0.90**). Significant GEI justified further analysis using various stability models. The AMMI analysis revealed a major role of GEI in the expression of tillers m-2 (78.6%), grains spike-1 (71.7%), 1000-grain weight (77.9%) and grain yield (72.4%), realizing inconsistent performance across environments. The GEI sum of squares for grain yield was 5 times larger than that of genotypes, suggesting the possible existence of mega environments. The AMMI analysis partitioned GEI sum of squares into eight principal components. First two principal components (PC1 and PC2) explained half of the variation due to GEI, indicating that the first two principal components were sufficient to explain the complex patterns of GE interaction. Based on AMMI1 and AMMI2 biplots, G-79 was unanimously declared as stable and high yielding genotype. Similarly, first two principal components of GGE biplot analysis cumulatively explained 36.5%, 62.6%, 56.8% and 54.8% variations due to GEI for tillers m-2, grains spike-1, 1000-grain weight and grain yield, respectively. The GGE biplot analysis confirmed the differential response of genotypes across environments, suggesting environment-based expression of genes. The GGE biplot also declared G- 79 as an ideal genotype for tillers m-2, grains spike-1 and grain yield, whereas G-36 for 1000-grain weight. The E-09 appeared to be the most discriminating and representative environment for grains spike-1 and grain yield while E-02 for tillers m-2 and E-01 for 1000-grain weight. The GGE biplot grouped Peshawar (E-01, E-02 and E-03), Nowshera (E-04 only) and Charsadda (E-08 and E-09) as one megaenvironment for yield and yield components. Stability parameters such as AMMI stability value (ASV), regression coefficient (bi), Wricke’s ecovalence (Wi), cultivar superiority measure (Pi), coefficient of variation (CV), Shukla stability measure (σi 2) and non-parametric stability measures Si (1) and Si (2) were strongly correlated with mean performance of genotype, inferring that these models can interchangeably be used. Critical comparison of various statistical models as applied to the data in this study transpired that the recommendations of AMMI and GGE analyses got refined when reviewed in conjunction with other stability models. Application of various stability models in this study identified G-79 as stable and widely adapted genotype for grain yield and its components, therefore, G-79 is recommended for commercialization after necessary procedural requirements.