College of Agriculture and Environmental Sciences

Investigation of egg quality characteristics of Ross 308 broiler breeder chickens

Tue, 28 Apr 2026 00:00:00 GMT

Investigation of egg quality characteristics of Ross 308 broiler breeder chickens Nkune, Achy Poultry products such as chicken eggs and meat continue to have an important economic impact and are important to the growth of a country`s economy. Moreover, poultry products are globally consumed in greater quantities than any other source of animal protein, because they are reasonably priced. Ross 308 broiler chicken is the best breed of chicken nationally which was bred specifically for meat. Poultry breeders focus on egg characteristics in their selection process for reproduction, because poor egg quality results in economic losses during production. This study was conducted to investigate the characteristics of egg quality traits, the correlations among these factors, how the egg weight influences egg quality traits, and to estimate the weight of the egg from egg quality traits in Ross 308 broiler breeder chickens. A total of 1000 eggs were purchased from Daybreak Farm in Bela Bela Local Municipality, Limpopo province of South Africa to conduct the study. Eight external traits of egg quality, including egg length, egg width, shell weight, shell index, shell surface area, unit surface shell weight and shell ratio, and five internal egg quality traits such as yolk weight, albumen weight, albumen ratio, yolk ratio and yolk/albumen. The descriptive statistics were used to quantify the egg quality traits, and the correlation between weight and quality traits of egg was investigated using Pearson`s correlation, One-way Analysis of Variance (ANOVA) was applied to investigate which egg quality traits are affected by egg weight and stepwise regression analysis was used to come up with best fit model to predict egg weight from egg quality traits. The descriptive statistics revealed that egg weight had a minimum and maximum value of 32.59 g and 98.25, g respectively. Egg length had a minimum value of 46.61 mm and a maximum value of 70.36 mm, and yolk weight had a minimum value of 5.31 g and a maximum value of 34.02 g. One-way ANOVA revealed that egg weight has a substantial effect (p < 0.05) on egg length, egg width, shell weight, shell index, shell surface area, unit surface shell weight, shell ratio, yolk weight, albumen weight, albumen ratio, yolk ratio and yolk/albumen. Pearson’s correlation findings displayed that egg weight had a highly positive, remarkable association (p < 0.01) with egg length, egg width, shell weight, unit surface shell weight, albumen weight, yolk weight, yolk ratio and yolk/albumen. In addition, negative correlation (p > 0.05) was identified between egg weight and shell index, shell ratio and albumen ratio. Stepwise regression findings revealed that the model, including yolk weight, yolk ratio, albumen ratio and albumen weight, was the best-fitted model (R2 = 1.00 and RMSE = 0.05) for estimation of egg weight. The model including yolk weight, albumen weight and yolk ratio was the second best (R2 = 1.00 and RMSE = 0.34). The study concludes that the improvement of yolk weight, yolk ratio, albumen weight and albumen ratio might improve the egg weight of the Ross308 broiler chicken breed. The results of this study may be useful to farmers in considering egg quality traits during breeding to improve the egg weight of Ross 308 broiler breeder chickens.

Determinants of biogas adopton and its impact on livehoods: evidence from Domboshava, Zimbabwe

Fri, 01 Aug 2025 00:00:00 GMT

Determinants of biogas adopton and its impact on livehoods: evidence from Domboshava, Zimbabwe Chawarika, Admire This study investigates the socio-economic determinants of biogas technology adoption among livestock farmers in Domboshava, Zimbabwe and examines its impact on rural livelihoods. Despite Zimbabwe's potential for renewable energy technologies, biogas adoption remains limited, particularly in rural areas facing persistent energy poverty. This research addresses key knowledge gaps regarding technology uptake barriers and livelihood outcomes in developing country contexts. The study employed a mixed-methods targeting randomly selected 368 respondents, however 370 livestock farming households were interviewed and formed the basis for the analysis utilizing descriptive statistics, binary logistic regression, multinomial logistic regression and multiple linear regression for analysis. Qualitative methods, comprising institutional mapping and stakeholder analysis, were employed to complement the quantitative findings, drawing on data from 25 key informant interviews. Socio-economic variables included gender, age, education, remittances, asset ownership, non-farm income, land size, livestock units, energy costs, access to credit and extension services. A composite livelihood index was developed to measure multidimensional welfare outcomes. Analysis using binary and multinomial logistic regression revealed that off-farm income, land size, access to credit, gender, livestock units, energy costs, and extension services significantly influenced biogas adoption, while energy costs and livestock ownership were relatively weak predictors. Similarly, farmers’ plans to adopt biogas were strongly affected by income, land size, credit access and the availability of information. Multiple linear regression further indicated that biogas adoption significantly enhances household livelihoods, particularly in terms of energy security, income diversification and overall welfare. Based on these findings, the study proposes a tailored institutional framework that emphasizes coordinated roles for government, NGOs, financial institutions and local communities to promote biogas adoption and its contribution to sustainable livelihoods. Policy recommendations include targeted financial mechanisms, improved extension services, gender-sensitive strategies and cross-sectoral coordination. These insights offer valuable guidance for scaling renewable energy solutions and advancing sustainable rural development in Zimbabwe and Africa.

Assessing the impact of landfill sites on soil and water quality in neighbouring communities : a case study of Ga-Rankuwa Township, Gauteng Province of South Africa

Fri, 28 Nov 2025 00:00:00 GMT

Assessing the impact of landfill sites on soil and water quality in neighbouring communities : a case study of Ga-Rankuwa Township, Gauteng Province of South Africa Matlakala, Icaboth Tshwarelo Globally, solid waste generation is increasing due to accelerated population growth, rapid urbanisation, and economic activities. Consequently, the inadequate, absent, and poor planning and implementation of waste management in Ga-Rankuwa results in more waste in landfills. Although landfills carry a huge amount of waste, unmanaged waste can cause an increase in greenhouse gases, a reduction in the aesthetic of the environment, and untreated leachate pollutes surrounding water and soils, especially when proper procedures and maintenance are not followed. Regardless of how much research and awareness are done on this waste management method, environmental contamination is still evident due to inadequate/improper monitoring. The study aimed to assess the environmental impacts of the landfill site on soil and water quality in Ga-Rankuwa township. Soil samples were collected at the landfill perimeter in all 4 cardinal points and at 1 km and 2 km away from the 1st point, etcetera. Heavy metals, including Chromium, Mercury, and Lead, were assessed per sample for possible contamination by the landfill. Water samples were collected by directly dipping the containers in the nearby stream to collect water samples (upper river, mid river and lower river sections). The samples were taken to a laboratory for analysis, and further statistical tests and indices were used, and further analysis was conducted using XLSTAT. The water salinity and pollution indicators were assessed using pH, major cations (Na+, K+, Ca2+), phosphorus and heavy metals in soil and river samples near a landfill. Results showed significant variability in soil pH, with more acidic conditions closer to the landfill, likely due to leachate migration. Although heavy metal concentrations were elevated at certain sites, all values remained below WHO permissible limits and were classified as uncontaminated according to Müller's Geo-accumulation Index. River water showed slightly alkaline conditions, however, nutrient enrichment led to poor water quality. Trophic State Index (TSI) values for phosphorus exceeded 100 across all sites, classifying the river as hypereutrophic and identifying it as a pollution hotspot. While sodium values remained within acceptable limits for irrigation, microbial analysis revealed elevated amounts of E. coli and total coliforms downstream, indicating potential public health risks. It is recommended that landfill operators adopt advanced engineering solutions, such as state-of-the-art leachate collection and treatment systems, to minimise the release of harmful contaminants into adjacent soil and water bodies. Furthermore, the integration of waste segregation at source and comprehensive recycling programs should be prioritised to reduce the volume of non-biodegradable and hazardous waste entering landfills. vi

LC–MS/MS profiling and machine learning–guided virtual screening of dual EGFR/HER2 inhibitors from Ceratonia siliqua L. pod extract for colorectal cancer

Mon, 01 Sep 2025 00:00:00 GMT

LC–MS/MS profiling and machine learning–guided virtual screening of dual EGFR/HER2 inhibitors from Ceratonia siliqua L. pod extract for colorectal cancer Oku, Deli-Bright Nii Tettey Colorectal cancer (CRC), a malignancy that develops in the colon or rectum, is frequently associated with epidermal growth factor receptor (EGFR) overexpression, observed in up to 85% of cases, whereas human epidermal growth factor receptor 2 (HER2) amplification or overexpression is present in a smaller subset (approximately 2–6%, with increased prevalence in selected molecular subgroups). These molecular alterations highlight the therapeutic relevance of targeting EGFR and HER2 pathways in CRC management. Despite progress in targeted therapy development, most existing treatment approaches focus on inhibiting either EGFR or HER2 individually. These single-target therapies frequently demonstrate reduced efficacy because of alterations in downstream effectors such as the Kirsten rat sarcoma viral oncogene homolog (KRAS) and the activation of alternative signalling pathways that promote continued tumour growth. Consequently, designing therapeutic approaches that can concurrently block both EGFR and HER2 represents an essential and promising area of research. In this research, an innovative machine learning (ML)-driven stacking ensemble framework was established to accurately identify dual EGFR and HER2 inhibitors based on Simplified Molecular-Input Line-Entry System (SMILES) representations. A comprehensive benchmark dataset comprising active and inactive compounds targeting EGFR and HER2 was compiled from the ChEMBL database. Utilising this dataset, forty baseline models were developed and fine-tuned using various molecular descriptors and ML algorithms. The predictions from these models were then integrated through logistic regression (LR) to produce a highly reliable stacking ensemble classifier. This predictive model was further applied to natural bioactive compounds obtained from liquid chromatography–tandem mass spectrometry (LC–MS/MS) profiling of Ceratonia siliqua L. pod 9 extract, which were annotated using established spectral libraries and subsequently subjected to machine learning–guided virtual screening. The cytotoxic activity of the Ceratonia siliqua L. pod extract was confirmed experimentally using the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide) assay against human colorectal carcinoma cell line (HCT116) and non-cancerous Vero cells. The extract exhibited an IC₅₀ (half maximal inhibitory concentration) value of 13.32 ± 1.09 μg/mL in HCT116 cells, underscoring its notable anti-cancer potential. To support the experimental outcomes, molecular docking and in silico ADMET (absorption, distribution, metabolism, excretion, and toxicity) evaluations were carried out on the compounds identified from the LC–MS/MS dataset using the stacking model, alongside four Food and Drug Administration (FDA) approved anticancer drugs for comparative analysis. Among all screened molecules, NCGC00385704-01, identified from LC–MS/MS spectral data through library matching, exhibited strong dual inhibitory potential against both EGFR and HER2. Overall, this study highlights Ceratonia siliqua L. as a valuable source of potential lead molecules for colorectal cancer therapy through dual EGFR/HER2 inhibition and underscores the power of integrating computational and experimental approaches in natural product-based drug discovery.