An overview of genotype x environment interaction and yield stability analysis in applied plant breeding: great emphasis given to coffee (Coffea arabica L.)

The inconsistence of genotypes across location during plant breeding is the major challenges to the breeder. That is the differential response of genotypes to different environment. Meanwhile stability is the ability of a genotype to withstand stressful conditions and yet be able to produce yield. Thus, stability is an absolute and relative measure. Arabica coffee has location specific adaptation nature and that leads to highly significant instability in its breeding program. In the study of coffee bean yield stability cultivars tested at multi- locations within the domain of coffee growing ecologies of Ethiopia, showed a significant genotype x environment interaction. The review of previous research also indicated inconsistent effects of genotype x environment interaction on cup quality. Yield-stability analysis is very important in measuring cultivar stability and suitability for growing crops across seasons and agro-ecological region to identify stable genotype. The yield stability have been challenge to the plant breeders and biometricians, it complicates the selection of superior genotypes. It is important to minimize the usefulness of the genotype across environments for selecting. Since approach of plant breeding is to develop genotypes that are, optimum for the condition under which they will be grown breeders have to manage yield instability throughout formalized procedures of plant breeding. During stability measurement if the variance is found to be significant, various methods of measuring the stability of genotypes can be used to identify the stable genotype(s). Most of stability analysis parameters are briefly discussed in this review. Int. J. Agril. Res. Innov. Tech. 11(2): 117-123, Dec 2021

Feature Selection and Feature Stability Measurement Method for High-Dimensional Small Sample Data Based on Big Data Technology

With the rapid development of artificial intelligence in recent years, the research on image processing, text mining, and genome informatics has gradually deepened, and the mining of large-scale databases has begun to receive more and more attention. The objects of data mining have also become more complex, and the data dimensions of mining objects have become higher and higher. Compared with the ultra-high data dimensions, the number of samples available for analysis is too small, resulting in the production of high-dimensional small sample data. High-dimensional small sample data will bring serious dimensional disasters to the mining process. Through feature selection, redundancy and noise features in high-dimensional small sample data can be effectively eliminated, avoiding dimensional disasters and improving the actual efficiency of mining algorithms. However, the existing feature selection methods emphasize the classification or clustering performance of the feature selection results and ignore the stability of the feature selection results, which will lead to unstable feature selection results, and it is difficult to obtain real and understandable features. Based on the traditional feature selection method, this paper proposes an ensemble feature selection method, Random Bits Forest Recursive Clustering Eliminate (RBF-RCE) feature selection method, combined with multiple sets of basic classifiers to carry out parallel learning and screen out the best feature classification results, optimizes the classification performance of traditional feature selection methods, and can also improve the stability of feature selection. Then, this paper analyzes the reasons for the instability of feature selection and introduces a feature selection stability measurement method, the Intersection Measurement (IM), to evaluate whether the feature selection process is stable. The effectiveness of the proposed method is verified by experiments on several groups of high-dimensional small sample data sets.

Maize Hybrids: Mega-Environments in Java Island, Indonesia

Identification of grain yields on stable and high yielding maize hybrids in a wide environment requires high accuracy. There were many stability measurement methods used in multi-environment experiments. However, the relationship between each measurement was still difficult to understand. The objectives of this study were to: 1. identified the effect of growing season, location, hybrids (genotypes), and their interactions (GEIs) on grain yields; 2. determined the relationship between each stability measurement; 3. selected the stable and high yielding maize hybrids in a wide environment; and 4. determined favorable (discriminativeness) locations for testing. The field experiment was conducted at eight locations in Java island, Indonesia during two seasons used a randomized completed block design with three replications. The experimental results showed that the main effects of growing season, location, hybrid, and GEIs, had a significant effect on maize hybrid yields. Stability measurements S(3), S(6), NP(2), NP(3), NP(4), and CVi, belong to the concept of dynamic stability that can be used to selected maize hybrids in favorable environments, while other measurements were classified as in the concept of static stability. Two maize hybrids were successfully selected to have high and stable yields based on numerical and visual measurements, namely SC3 and SC9. The two hybrids can be used as candidates for a sustainable maize development program. GJRS and KARS were the most discriminativeness environments. Both can be used as a favorable environment for selecting the ideal maize hybrid.

Mega-Environment Analysis and Identify Stable and High-Yielding of New Promising Black Soybean Lines in Indonesia

Stable and high-yielding are the major goals of black soybean breeding. Testing new lines in a mega-environment is one of the development processes in black soybean breeding. The aims of the research were (i) to identify the effects of genotype, environment, and GEIs on the grain yield of soybean lines in Java Island; (ii) to select stable and high yielding soybean lines; and (iii) to determine the discriminative environments, and (iv) to determine the concept of stability measurements on black soybean grain yields. Field trials of 10 new F8 promising lines and three check varieties were conducted under eight different environments during four years (2016–2019). The measurement results showed that the grain yield was influenced by genotype (8.35%), environment (59.49%), and GEIs (32.16%). Grain yield stability measurements showed that the four newly lines was identified had high yields and stable in eight environments, they were A-5A-PSJ (S2), DB-96-CTY (S5), UP 161 (S6), and UP 162 (S7). The Ngawi (2017) followed by Bogor (2019) and Banyuwangi (2016) has the strongest interactive capabilities, and was suitable for used as a trial environments. Grain yield (Y) was identified as having a positive and significant correlation (p < 0.05) with S(3), S(6), NP(2), NP(3), NP(4), KR, and YSI stability measurements, which indicated that they were included in the concept of dynamic stability measurement.

Stability Analysis Of Islamic Banking: Indonesia, Malaysia And Pakistan

Following the 2008 financial crisis, the global economy will continue to experience shock in the years to come. Therefore, it is vital to conduct research that can anticipate the impact of fluctuations in financial stability. This research examines the stability of the Islamic banking system in Indonesia, Malaysia, and Pakistan, using Z-Score as a proxy variable for stability measurement and Markov Switching VAR for the method. The objectives are to identify which Islamic banking has better resilience in facing crisis and identify the economic variables that have a significant effect on the stability of Islamic banking. The results showed that the stability of Indonesian Islamic banking was more stable compared to Malaysia and Pakistan. The crisis periods determined from the method show that in 2019 all countries studied entered the beginning of the crisis period, which means the world conditions tend to re-enter the crisis, repeating the 2008 financial crisis.

Crystallographic, Energy Gap, Photoluminescence and Photo-Catalytic Investigation of Cu Doped Cd0.9Zn0.1S Nanostructures by Co-Precipitation Method

The controlled synthesis of Cd0.9Zn0.1S, Cd0.89Zn0.1Cu0.01S and Cd0.87Zn0.1Cu0.03S nanostructures by simple chemical co-precipitation technique was reported. The XRD investigation confirmed the basic CdS cubic structure on Zn-doped CdS and also Zn, Cu dual doped CdS with no secondary/impurity related phases. No modification in cubic structure was detected during the addition of Zn/Cu into CdS. The reduction of crystallite size from 63 Å to 40 Å and the changes in lattice parameter confirmed the incorporation of Cu into Cd0.9Zn0.1S and generation of Cu related defects. The shift of absorption edge along upper wavelength region and elevated absorption intensity by Cu doping can be accredited to the collective consequence of quantization and the generation of defect associated states. The enhanced optical absorbance and the reduced energy gap recommended that Cd0.87Zn0.1Cu0.03S nanostructure is useful to enhance the efficiency of opto-electronic devices. The presence of Cd-S / Zn-Cd-S /Zn/Cu-Cd-S chemical bonding were confirmed by Fourier transform infrared investigation. The elevated green emissions by Cu incorporation was explained by decrease of crystallite size and creation of more defects. Zn, Cu dual doped CdS nanostructures are recognized as the possible and also efficient photo-catalyst for the removal dyes like methylene blue. The enhanced photo-catalytic behaviour of Zn, Cu dual doped CdS is the collective consequences of high density electron-hole pairs creation, enhanced absorbance in the visible wavelength, surface area enhancement, reduced energy gap and the formation of novel defect associated states. The stability measurement signified that Cu doped Cd0.9Zn0.1S exhibits superior dye removal ability and better stability even after 6 repetitive runs with limited photo-corrosion.

Gait Stability Measurement by Using Average Entropy

Gait stability has been measured by using many entropy-based methods. However, the relation between the entropy values and gait stability is worth further investigation. A research reported that average entropy (AE), a measure of disorder, could measure the static standing postural stability better than multiscale entropy and entropy of entropy (EoE), two measures of complexity. This study tested the validity of AE in gait stability measurement from the viewpoint of the disorder. For comparison, another five disorders, the EoE, and two traditional metrics methods were, respectively, used to measure the degrees of disorder and complexity of 10 step interval (SPI) and 79 stride interval (SI) time series, individually. As a result, every one of the 10 participants exhibited a relatively high AE value of the SPI when walking with eyes closed and a relatively low AE value when walking with eyes open. Most of the AE values of the SI of the 53 diseased subjects were greater than those of the 26 healthy subjects. A maximal overall accuracy of AE in differentiating the healthy from the diseased was 91.1%. Similar features also exists on those 5 disorder measurements but do not exist on the EoE values. Nevertheless, the EoE versus AE plot of the SI also exhibits an inverted U relation, consistent with the hypothesis for physiologic signals.