Coalition of the willing powering data-driven solutions for Ethiopian agriculture

Agricultural stakeholders at all levels face decisions about investments of different kinds. Farmers must decide which agricultural inputs they should invest in or what kind of fertilizer they should apply. Producers and agro-dealers must decide which farm inputs to produce and sell to which consumers at what price. Government authorities must make policy and investment decisions to support agriculture and to improve the agricultural productivity of their nation. Donors and development partners must decide which agricultural projects where are variable to invest in. All these decisions require evidence to increase the probability of success and evidence comes from data. The amount, quality, and diversification of available data determine the quality of information that can be generated and used for decision-making.

The questions “where do we get the data to provide evidence for agricultural decisions?” and “how many data do we need to make decisions optimum?” lead to a discussion on the concept of “big data.” The use of data to improve agriculture is not an entirely new concept. People and institutions have been generating and analyzing data for various agricultural purposes. However, specific crops were often the focus of data collection with few parameters in consideration. Agriculture is a dynamic and location-specific process affected by a multitude of factors. A study of specific variables falls short of providing a complete understanding of how to increase productivity. But analyzing location-specific data factoring in numerous variables and drawing from layering the wealth of data generated from numerous single-use data collection missions can offer much deeper and richer information that can inform the best agricultural decisions. Here is where the concept of big data comes into play. The current trend undoubtedly confirms conquering the quest for sustainable and sufficient food production relying on the availability of high-quality and high-volume data (big data) as well as increased data storage and computing capability that can support the analysis of numerous and complex variables that are determinants for increasing productivity.

Desta, Lulseged; Erkossa, Teklu; Tafesse, Tsigereda; Abera, Wuletawu; Schultz, Steffen.