Adapting innovation of information and communication technologies to climate change risks for agriculture sustainability in central Tanzania

Abstract

Climate change is one of the most challenging and alarming global issues for threatening the existence of the living entities of ecosystems. Manifestations of climatic aberrations are evidently diverse and extensive in nature. Unfortunately, agricultural sector is heavily vulnerable to the vagaries of climate putting global food security at stake. Farming communities rarely have access to up-to-date weather and climate information, which hinders their ability to practice climate-smart agriculture. Extreme weather events variations in rainfall and temperature can result in reduced yields or even the destruction of crops, affecting farmers’ incomes and livelihoods. Unsustainable agricultural practices compound the problem by reducing in soil fertility and the soils’ capacity to retain water, contributing to soil erosion. This can drive ecosystem degradation and habitat loss, with a resulting loss of biodiversity. To cope up with this changing scenarios, strategic innovation is a must, especially for the highly populous developing countries like Tanzania. By virtue of the generative and systemic tools of Information and Communication Technologies (ICTs) and its efficient knowledge management system, ICTs offers a great potential to record, transform and disseminate information while strengthening adaptability to the changing situations in multifarious ways. Remote sensing tools and corresponding technological innovations can ensure a sustainable meal to the ever-surging population. Climate change can only be confronted when these future ready technologies will be coupled with efficient agricultural extension protocols. This paper was an attempt to snowball adapted ICTs tools to climate change adaptation strategies as an innovating way to sustain food security and climate resilience community. The recommended tools should cover agronomic data capture, on-farm data visualization, machine learning analysis, meteorological data curation, crop modeling and seasonal climate forecasting.