PhD student name: Peter Kimani Muchina
PhD project title: Unraveling genetic diversity and population structure of black soldier fly (Hermetia illucens) in Kenya and Uganda
Enrolment University: Jomo Kenyatta University of Agriculture and Technology (JKUAT)
Supervisors: Prof Johnson Kinyua (JKUAT), Dr. Fathiya Khamis (ICIPE), Dr. Chrysantus MBI Tanga (ICIPE), Goutam Sahana (AU), Grum Gebreyesus (AU).
Abstract
The black soldier fly (Hermetia illucens) has been shown to significantly contribute to the circular bio-economy due to its ability to convert organic waste into high-quality livestock protein. Despite the potential benefits, the limited knowledge of the genetic diversity of the wild and mass-reared BSF populations in Africa raises concerns about their long-term sustainability. This study seeks to address this gap by investigating the genetic diversity and population structure of BSF populations in Kenya and Uganda. The findings will guide colony management, selective breeding programs, and genetic improvement efforts, as well as guide BSF genomic studies in the future, ensuring continued growth and sustainability of BSF.
PhD student name: Sarah Nawoya
PhD project title: A computer vision-based system for large scale identification and phenotyping of black soldier fly (BSF) in managed breeding sites within Uganda and Kenya
Enrolment University: Makerere University, College of Engineering, Designs, Art and Technology, Uganda
Supervisors: Dr. Grum Gebreyesus, Dr. Roseline N Akol, Dr. Andrew Katumba, Dr. Cosmas Mwikirizze
Abstract
The growing interest for insect farming as a sustainable protein alternative has given rise to the commercial production of key species like the Black Soldier Fly (BSF). Despite the heightened interest in BSF production, there is a need for increased efficiency, particularly in the context of large-scale measurement of various traits for selective breeding as well as management optimization. Larval size and weight are important traits in BSF production, including selective breeding. Manual measurement of these traits for management decisions and selective breeding is time-consuming and limits the scale of production. Computer vision has increasingly shown to be a promising technology for automatic measurement and monitoring of different livestock traits including growth and health performances.
The goal of the PhD project is to:
1. To explore the potential of computer vision (CV) in predicting the larval sex and body traits of BSF, offering a non-invasive, rapid, and automated methods for trait measurement.
2. To develop image-based methods to enable family-level prediction of the BSF body traits for family-based selective breeding.
PhD student name: Maweu Annastacia Nduku
PhD project title: Characterization and trait preferences of black soldier fly in small holder farmer production system in Kenya and Uganda
Enrolment University: University of Nairobi, Kenya
Supervisors: Dr. Rawlynce Bett
Abstract
The growing human population is threatening global food security, particularly the proteins resulting to food scarcity and protein deficiency. Conventional food production systems such as livestock rearing are unable to cope with the raising demand. In addition, efforts to boost productivity through intensification generates huge tonnage of organic wastes leading to loss of biodiversity due to increased utilization of natural resources and climate change. This has necessitated the exploitation of alternative sources of protein to meet the growing demands. Among the alternatives is the utilization of insect’s species which has gained increased attention as feedstock for food, feed, and industrial applications. Among such species is black soldier fly (BSF) Hermetia illucens, whose larvae can convert low-value organic waste into valuable fat- and protein-rich biomass. Despite the growing interest on BSF farming, the production systems and traits preferences are not clear to most farmers, and little has been done to improve the quantity and quality of BSFL resulting to low productivity. Therefore, this study will contribute to sustainable production of insects as alternative protein source of livestock feeds in Kenya and Uganda through a) characterizing Black Soldier Fly (BSF) production system and trait preferences in selected regions in Kenya and Uganda b) determining social economic characterization of small holder BSF producers in selected regions of Kenya and Uganda c) Assessing farmer’s preferences for BSFL in selected regions in Kenya and Uganda through choice experiment. Participatory approach, choice experiment and questionnaire-based surveys will be administered to respondents from selected farms in Kenya and Uganda. Data will be analyzed using SPSS version 20 and Minitab statistical software. The findings of these study will help in raising awareness among BSF producers on the rearing and management to improve productivity and efficiency.
PhD student name: Frank Ssemakula
PhD project title: Non-destructive, high-throughput technologies to assess the nutritional value of black soldier fly larvae
Enrolment University: Makerere University, College of Engineering, Designs, Art and Technology, Uganda
Supervisors: Dr. Grum Gebreyesus, Dr. Roseline N Akol, Dr. Andrew Katumba, Dr. Cosmas Mwikirizze
Abstract
The Black Soldier Fly larvae (BSF: Hermetia illucens) has gained considerable attention as a sustainable alternative protein and fat source for animal feeds, attributed to their high levels of protein and fat. In this work we propose the use of NIR spectroscopy and chemometric methods to predict the protein and fat content in BSF. Chemometric method is for providing the ground truth which can be extended to other technologies such as computer vision.
NIRS provides quick, simple and non-destructive chemical nutritional composition analysis. Chemometric analysis is commonly used as a benchmark to compare results with other methods and has become the primary method for estimating protein and fat content due to its high precision, outstanding consistency, and universality. The experiment involves rearing BSF half-sibling families on different substrates, analyzing larvae protein and fat using cameras, NIRS, and chemometric methods. This research will examine the effect of substrate type and genetic distribution on the nutritional quality of BSF larvae, providing important information for improving rearing techniques using new emerging technology hence positively contributing to selective breeding and nutritional composition quality.
PhD student name: Chesang Sumukwo
PhD project title: Estimation of genetic parameters for economically important Black Soldier Fly (BSF) traits
Enrolment University: University of Nairobi, Kenya
Supervisors: Rawlynce Bett, Joel Winyo Ochieng, Grum Gebreyesus and Laura S. Hansen
Abstract
The growing human population is threatening global food security, particularly protein-source food for both animals and humans. This has necessitated the exploitation of alternative sources of protein to meet the growing demand. Among the alternatives is the utilization of insects, Black Soldier Fly (BSF) is of particular interest because of its protein content and climate-friendly bio-waste conversion ability. Despite growing interest in BSF farming, commercialization of BSF has not been embraced by most farmers, and little has been done to improve the quantity and quality of its products through selective breeding. The project aims at improving the quantity and quality of BSF products through selective breeding by studying genetic parameters of prioritized traits in Kenya and Uganda. The traits measured will include larval traits (larval weight, larval length, developmental time until pupation) and nutritional content traits. To achieve these 200 families will be formed and used for recording phenotype which will include both manual and sensor-based phenotyping for each of the prioritized BSF traits. Genetic parameters estimation including heritability, repeatability, and genetic correlations between the traits will be done using various statistical models and the analysis will be undertaken using linear-mixed models. The study will generate new knowledge in BSF genetics and will inform the designing of breeding schemes.
PhD student name: Hulunim Gatew Tariku
PhD project title: Driving Economic Values and Optimizing Alternative Breeding Scheme for Important Traits in Black Soldier Fly (Hermetia illucens)
Enrolment University: Makerere University, Uganda
Supervisors: Hanne-Marie Nielsen (PhD), Sadhat Walusimbi (PhD), Donald Kugonza (PhD) and Rawlynce Bett (PhD)
Abstract
A major challenge in the multi-traits selection breeding approach is deciding how best to prioritize certain breeding objectives and optimize strategies to get the best possible result while maximizing resource utilization. Efficient alternative BSF breeding schemes are crucial for boosting the selection response of prioritized traits. The goal of this project is designing and implementing an efficient multi-traits selective breeding scheme to optimize the production of Black Soldier Fly (Hermetia illucens) larvae. Initially, the bio-economic model with stochastic modelling approach will be used to estimate the economic values (EV) of traits that are critical for the production of BSF larvae. The estimation of economic values will take into account main traits of interest in BSF larvae production, such as growth rate, weight of the larva on day 15, feed conversion ratio, feed intake, dry mass, protein, and fat content, development time, number of eggs per fly, rate of egg hatching, and larval mortality. Secondly, computer simulation will be used in investigating optimal BSF selection strategy that would allow the highest possible rate of genetic improvement while preserving genetic diversity and minimizing breeding costs for selective breeding programs that will be implemented, in both Uganda and Kenya.
