IMPLEMENTASI R-SHINY UNTUK ANALISIS BIPLOT KOMPONEN UTAMA (Studi Kasus: Penggunaan Alat Kontrasepsi pada Peserta Aktif KB di Provinsi Jawa Tengah Tahun 2019)

The population problem is a fairly complex and complicated problem. Therefore, Indonesia seeks to control the birth rate with the Family Planning program. The implementation of this program can be evaluated through statistical data. The statistical analysis used is biplot principal component analysis to see the relationship between districts/cities in choosing the contraceptive device/method used, the variance of each contraceptive device/method, the correlation between contraceptive devices/methods, and the superiority value of the contraceptive device/method in the population. each district/city. The problem with performing the analysis is the limitations of easy-to-use open source software. As with R, users must understand writing code to perform data analysis. Therefore, to perform a biplot analysis of the principal components, an RShiny application has been created using RStudio. The R-Shiny that has been made has many advantages, including complete results which include data display, data transformation, SVD matrix, to graphs along with plot graph interpretation. The results of the principal component biplot analysis using R-Shiny with α =1 have the advantage of a good principal component biplot, which is 95.63%. This shows that the biplot interpretation of the main components produced can be explained well the relationship between the district/city and the contraceptive methods/devices used.

Subjective and Objective Insecurity in Spanish Cities

This article related crime rates to social risk factors and to the feeling of insecurity in Spain. The first finding of this study, financed by National I + D Plan CSO2016-77549-P, AEI-FEDER, was the direct relation between crime rates and some sociodemographic factors such as population, unemployment, urban land area, and hotel occupancy, based on the question of which social risk factors correlate to crime rates. The second finding was that social factors drive citizens’ feelings of insecurity, according to whether feelings of insecurity are linked to crime rates or perceived risk factors. The research was based on a quantitative methodology, using two data sources: reworked official statistics treated by HJ-Biplot analysis; a 2019 CATI survey with N = 3904, sample error between 5.2% and 3.7% according to territory, 95% confidence level. The main conceptual conclusion of the study was the link between well-being and security. The main methodological contribution was the application of HJ-Biplot analysis to the social sciences.

Multi-environment trials data analysis: An efficient biplot analysis approach

The analysis of multi-environment trials (MET) data has a long history in plant breeding and agricultural research, with the earliest approaches being based on ANOVA methods. ANOVA-based biplot analysis has been used for a long time in analyzing MET data, and advances have been made employing different modeling approaches. This paper presents MET data analysis using mixed model approaches, and compares three methods of biplot analysis, namely genotype main effects plus genotype by environment interaction (GGE) analysis, factor analytic multiplicative mixed (FAMM) model analysis, and combined model analysis. Ten grain yield datasets from the national variety trial series conducted by the Ethiopian institute of agricultural research were used for this study. Our results revealed that spatial and FA model provide a significant improvement in analyzing MET data. This was demonstrated with evidence of heritability measure. We demonstrated that biplot analysis based on the approached of combined model analysis provides a substantial increase in the total percentage of genotype by environment (G×E) variance explained by the first two multiplicative components for both types of balanced and unbalanced datasets. Thus, by estimating the G×E mean values with the best linear unbiased predictions using spatial+FA (FAMM model analysis), and thereby conducting biplot analysis based on the combined model analysis, plant breeding and trial evaluation programs can have a more robust platform for evaluation of crop cultivars with greater confidence in discriminating superior cultivars across a range of environments.

Grain yield stability analysis using AMMI and GGE biplot models in different breeding zones of Bangladesh

During the 2018-2019 Boro season (dry season), 70 rice genotypes were examined with alpha lattice experimental design with the goal of measuring grain yield stability analysis. Results indicated that AMMI analysis explained 100% of the G×E variance, while captured 81.74% variance. Based on the GGE and AMMI analysis, the most stable and high yielding genotype was identified G41 followed by G22, G26, G58, G24 and G61. The AMMI 1 biplot analysis revealed that the first primary component of interaction (IPC1) factor was responsible for 64.2 % variation due to G × E interaction. On other hand, the second primary component (PC2) factor accounted for 35.8% variation of the G × E interaction. These two-primary component (PC1 and PC2), all together accounted for 100% variation of the G × E interaction. The contribution of G68 was highest to the interaction followed by G70, G58, G42, G61, G45, G38, G14, G33, G60, G53, and G9. Best environment analysis indicated that the ranking was Rajshahi < Gazipur < Cumilla. GGE biplot analysis accounted for 81.74% variation comprising two principal components PC1 and PC2 with 45.62% and 36.12% variations respectively. Rajshahi was more stable than Gazipur. Based on environment analysis genotypes, G22, G26, G58, and G44 can be recommended as best stable genotypes that breeding zone. However, the genotype G61 was identified adapted to Cumilla breeding zone.

BIPLOT ANALYSIS FOR IDENTIFICATION OF SUPERIOR GENOTYPES IN A RECOMBINANT INBRED POPULATION OF WHEAT UNDER RAINFED CONDITIONS

Physiological traits of wheat genotypes and their trait relation to drought conditions are important to identify the genotype in target environments. Thus, genotype selection should be based on multiple physiological traits in variable environments within the target region. This study was conducted at Punjab Agricultural University during rabi crop seasons 2012-13 and 2013-14 to study the recombinant inbred lines (RILs) of wheat genotypes derived from traditional landraces and modern cultivars (C518/2*PBW343) based on various morpho-physiological traits. A total of 175 RILs were selected for this study based on various tolerance indices. The genotype by trait (GT) biplot analysis was applied to data from seven high-yielding RILs grown under irrigated (E1) and rainfed environments (E2). The GGE biplot explained 100% of the total variation for chlorophyll content, grain filling period, peduncle length, water-soluble carbohydrates, grain number, grain yield, and 95.1% for canopy temperature, 94.9% for thousand-grain weight. GT-biplots indicated that the relationships among the studied traits were not consistent across environments, but they facilitated visual genotype comparisons and selection in each environment. RIL 84 and RIL108 were close to the average environment (ideal genotype) for all traits studied except chlorophyll content. A well-performing genotype with great environmental stability is called an "ideal genotype. Among all entries, these genotypes performed well. Therefore, among the traits studied, grain filling period, peduncle length, canopy temperature, water soluble carbohydrates, and 1000 grain weight contributed to grain yield under a stress environment. Furthermore, it may be used as a donor material in breeding programs and QTLs mapping.

Differentiation and identification of winter bread wheat verieties according to a complex of baking quality indicators

Purpose. Reveal the features of the formation of a quali­ty indicator complex in winter bread wheat depending on the growing seasons, preceding crops and sowing dates, as well as differentiate and identify genotypes with high and stable levels of manifestation. Methods. Field, laboratory, statistical. Results. A different share of the influence of the year conditions, the preceding crop, the sowing date and their interactions on the quality indicators of some varie­ties was determined. A different reaction of varieties in terms of quality indicators, depending on the investigated factors was revealed. The variation was very low for test weight, water absorption ability of flour, crumb porosity. Strong variation was observed for flour strength after sunflower and soybean as preceding crops, alveograph configuration ratio after sunflower and soybean, index of elasticity dough after corn, valorimetric value after mustard, dough dilution degree after green manure, sunflower, corn and especially after mustard and soybeans. The varieties, which on average for 2016/17–2018/19 reliably exceeded the standard both in individual indicators and in general in terms of physical indicators of grain and flour quality and dough rheological properties. GYT biplot analysis identified the genotypes ‘MIP Vidznaka’ and ‘MIP Assol’ with a more optimal combination of increased yield and a complex of quality indicators in terms of different years, preceding crops and sowing dates. Some varieties, namely, ‘Estafeta myronivs’ka’, ‘Trudiv­nytsia myronivs’ka’, ‘MIP Valensiia’, ‘MIP Yuvileina’, ‘Balada myronivs’ka’, ‘Vezha myronivs’ka’ were inferior to them, but were significantly superior the others. Conclusions. The selected by quality indicators varieties as genetic sources can be used in breeding process. A more stable level of yield and quality indicators at different sowing dates after different preceding crops should be expected for growing varieties ‘MIP Vidznaka’, ‘MIP Assol’, as well as ‘Estafeta myronivs’ka’, ‘Trudivnytsia myronivs’ka’, ‘MIP Valensiia’, ‘MIP Yuvileina’, ‘Balada myronivs’ka’, ‘Vezha myronivs’ka’. The peculiarities obtained in the research should be taken into account when evaluating and differentiating genotypes in breeding process, as well as developing basic elements of technology for growing the varieties of winter bread wheat.