Weather forecasting is an ever-challenging area of investigation for scientists. It is the application of science and technology in order to predict the state of the atmosphere for a given time and location. Rainfall is one of the weather parameters whose accurate forecasting has significant implications for agriculture and water resource management. In Zambia, agriculture plays a key role in terms of employment and food security. Rainfall forecasting is one of the most complicated and demanding operational responsibilities carried out by meteorological services all over the world. Long-term rainfall prediction is even more a challenging task. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. It is mainly done by experts who have gained sufficient experience in the use of appropriate forecasting techniques like modelling. In this paper, a rainfall forecasting model using Artificial Neural Network is proposed as a model that that can be 'trained' to mimic the knowledge of rainfall forecasting experts. This makes it possible for researchers to adapt different techniques for different stages in the forecasting process. We begin by noting the five main stages in the seasonal rainfall forecasting process. We then apply artificial neural networks at each step. Initial results show that the artificial neural networks can successfully replace the currently used processes together with the expert knowledge. We further propose the use of these neural networks for teaching such forecasting processes, as they make documentation of the forecasting process easier and hence making the educational process of teaching to forecast seasonal rainfall easier as well. Artificial Neural Networks are reliable, handle more data at one time by virtual of being computer based, are less tedious and less dependent on user experience.

Popular tools used in studies in life sciences are often costly. This often pauses challenges to researchers in spite of the fact that research continues to be a key to the successful systematic development of new knowledge and a fundamental aspect to the usefulness of all higher education. Particularly, higher education also aims to advance, create and disseminate knowledge through research. Such critical studies like mutation studies therefore require affordable and fast results yielding software. In such research, open source software tools become handy in place of expensive proprietary tools. In order to provide alternative software tools for research, we decided to use a case study of the mutation of the African Cassava Mosaic Virus (ACMV) done by researchers in Zambia. The study of ACMV mutation is hampered by fragmented and non-user-friendly tools, which are currently available. A number of the tools used also depend on network connection, especially the Internet, to access and analyze data. To help alleviate this problem this research proposes the use of open source libraries in biopython to generate cost efficient and user-friendly solutions. Additionally, we propose the use of an open standard using XML as a standard protocol to share data between applications or stages in genomic data analysis of the ACMV. In our strife to provide open source solutions we analysed various tools and noted that biopython is quite popular. During our study of biopython our initial results show that it’s possible to use free tools to analyze data in the life sciences and consequently reduce the time and cost required to analyze ACMV. Based on this case study we propose the adoption of such open source libraries in order to make research much more affordable for scientists in the life sciences for researches that operate within a constrained budget.