Current Issue : April-June Volume : 2023 Issue Number : 2 Articles : 5 Articles
Honeybee colonies exhibit a wide range of variations in their performance, depending on genetic and environmental factors. However, there has been little research carried out on Apis mellifera bandasii (A. m. bandasii) populations to characterize their behavioural performance. To gain insight into the details of the behavioural performance of this local honeybee, we characterized and compared the colony performance of honeybee populations at different altitudes. Fifty honeybee colonies per site, making a total of 150 colonies, were established at Bako (mid-highland), Gedo, and Holeta (highland). The colonies were evaluated for brood-rearing activities, resource collecting, brood solidity, swarming, defensive and hygienic behaviours, and honey yield parameters. The average brood areas were determined to be 6114.13 ± 500.36, 3298.30 ± 365.92, and 2521.23 ± 244.67 cm2 per colony; the average nectar areas were found to be 3399.46 ± 738.88, 1238.78 ± 228.96, and 1883.09 ± 232.57 cm2 per colony; the average number of queen cells was determined to be 0.62 ± 0.30, 1.20 ± 0.39, and 2.19 ± 0.49 per colony; the average percent of pinkilled broods removed was determined to be 93.78 ± 1.74, 96.42 ± 1.86, and 80.09 ± 7.86 per colony; the average percent of colonies absconded was determined to be 36, 54, and 46 per site at Holeta, Gedo, and Bako, respectively. The mean differences among the locations for brood areas, nectar areas, number of queen cells, percent of pin-killed broods removed, and percent of colonies absconded were significant (p < 0.05), while the variations in the area of stored pollen, brood solidness, and honey yield were not significant. Significant variation within colonies of the same apiary of the same subspecies was observed. These results showed that A. m. bandasii at Holeta had the best performance and that Bako had the lowest performance. Therefore, the variability in colony performances indicates the possibility of improving strains of native stocks through selection and breeding strategies using the variations as an opportunity....
Information on the diversity of landraces is necessary to improve crops through selection or hybridization. This study was conducted to show the diversity of fenugreek landraces and associated traits. A total of 160 accessions including one local and four improved standard checks were evaluated in an augmented block design at the Haramaya University research site in 2016. Data collected include days to flowering, days to maturity, seed yield (kg/ha), thousand seed weight (g), the number of primary branches, plant height at flowering (cm), the number of pods per plant, the number of secondary branches, average pod length (cm), the number of seeds per pod, and seed yield per plant (g/plant) of quantitative traits. The analysis of variance revealed the existence of significant differences between accessions of all parameters. Genotypic and phenotypic coefficients of variation departed from 5.95–56.91% and 6.47–58.88%. Heritability in the broad sense and expected genetic gain as percent mean varied from 60.9 to 96.1% and from 2.5 to 70.3%. The seed yield per plant, the number of secondary branches, and the number of pod per plant had positive direct effects on the yield at both genotypic and phenotypic levels and the number of primary branches, and the average pod length via seed yield per plant, while the number of seeds per plant through the number of primary branches and the number of pods per plant had a positive indirect effect on the yield at the genotypic level, suggesting that these traits could be considered for indirect selection of genotypes for yield. The genetic distances of genotypes measured by Euclidean distance ranged from 0.07 to 10.6, and the dendrogram was constructed by using the unweighted pair group method using arithmetic mean. The presence of variability among fenugreek accessions suggested possibilities to improve the crop through the crossing of distant genotypes. This was an excellent opportunity to contribute to farmers’ food security and livelihoods by bringing about the improvement of fenugreek....
This study was conducted on one hundred common bean landraces at the Jimma Agricultural Research Center, Melko, with the objective of assessing genetic variability and association of traits in common bean landraces collected from different parts of Ethiopia. The experiment was laid out in a simple lattice design with two replications. Analysis of variance showed significant differences among genotypes for all traits. This highly significant difference indicates the existence of large variability among genotypes. High phenotypic coefficients of variation and genotypic coefficients of variation were obtained for plant height (19.43, 11.73), pod length (11.27, 10.69), and 100-seed weight (15.42, 12.74). High heritability in the broad sense was found for days to 50% flowering (66.98), days to 90% maturity (87.43), pod length (90.03), pod width (78.23), harvest index (98.67), and 100-seed weight (68.31). High genetic advance as a percentage of mean with high heritability was obtained for pod length, pod width, harvest index, and hundred seed weight. Grain yield had a positive and significant association with pod length (rp 0.153∗, rg 0.282∗∗) and 100-seed weight (rp 0.294∗∗, rg 0.492∗∗). Hundred seed weight exerted the highest positive direct effect (0.294) on grain yield at genotypic level. The D2 classified landraces into 7 clusters and one solitary, which makes them moderately divergent. The highest inter-cluster distance was observed between clusters VII and IV. The first five principal components with eigenvalues greater than one altogether explained about 79.56% of the total variation. In conclusion, the top high-yielding landraces, namely, P#1247, P#1092, P#1077, P#861, P#990, P#763, P#58, and P#857, should be included in the next breeding program. 100-seed weight had the highest direct effect and a positive significant association with grain yield. Thus, it should be considered as the selection criteria for further common bean yield improvement. However, the current result is merely indicative and cannot be used to draw definite conclusions. Therefore, the experiment should be replicated in different locations and seasons for greater consistency....
Strawberry (Fragaria × ananassa Duch) plants are vulnerable to climatic change. The strawberry plants suffer from heat and water stress eventually, and the effects are reflected in the development and yields. In this investigation, potential chlorophyll-fluorescence-based indices were selected to detect the early heat and water stress in strawberry plants. The hyperspectral images were used to capture the fluorescence reflectance in the range of 500 nm–900 nm. From the hyperspectral cube, the region of interest (leaves) was identified, followed by the extraction of eight chlorophyll-fluorescence indices from the region of interest (leaves). These eight chlorophyllfluorescence indices were analyzed deeply to identify the best indicators for our objective. The indices were used to develop machine-learning models to assess the performance of the indicators by accuracy assessment. The overall procedure is proposed as a new workflow for determining strawberry plants’ early heat and water stress. The proposed workflow suggests that by including all eight indices, the random-forest classifier performs well, with an accuracy of 94%. With this combination of the potential indices, namely the red-edge vegetation stress index (RVSI), chlorophyll B (Chl-b), pigment-specific simple ratio for chlorophyll B (PSSRb), and the red-edge chlorophyll index (CIREDEDGE), the gradient-boosting classifier performs well, with an accuracy of 91%. The proposed workflow works well with a limited number of training samples which is an added advantage....
Rice fraud is one of the common threats to the rice industry. Conventional methods to detect rice adulteration are costly, time-consuming, and tedious. This study proposes the quantitative prediction of rice adulteration levels measured through the packaging using a handheld near-infrared (NIR) spectrometer and electronic nose (e-nose) sensors measuring directly on samples and paired with machine learning (ML) algorithms. For these purposes, the samples were prepared by mixing rice at different ratios from 0% to 100% with a 10% increment based on the rice's weight, consisting of (i) rice from different origins, (ii) premium with regular rice, (iii) aromatic with non-aromatic, and (iv) organic with non-organic rice. Multivariate data analysis was used to explore the sample distribution and its relationship with the e-nose sensors for parameter engineering before ML modeling. Artificial neural network (ANN) algorithms were used to predict the adulteration levels of the rice samples using the e-nose sensors and NIR absorbances readings as inputs. Results showed that both sensing devices could detect rice adulteration at different mixing ratios with high correlation coefficients through direct (e-nose; R = 0.94–0.98) and noninvasive measurement through the packaging (NIR; R = 0.95–0.98). The proposed method uses lowcost, rapid, and portable sensing devices coupled with ML that have shown to be reliable and accurate to increase the efficiency of rice fraud detection through the rice production chain....
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