Mobile smartphones, open-source set tools, and mobile applications have provided vast opportunities for timely, accurate, and seamless data collection, aggregation, storage, and analysis of agricultural data in sub-Saharan Africa (SSA). In this paper, we advanced and demonstrated the practical use and application of a mobile smartphone-based tool, i.e., the Open Data Kit (ODK), to assemble and keep track of real-time maize (Zea mays
L.) phenological data in three SSA countries. Farmers, extension agents, researchers, and other stakeholders were enlisted to participate in an initiative to demonstrate the applicability of mobile smartphone-based apps and open-source servers for rapid data collection and management. A pre-installed maize phenology data application based on the ODK architecture was provided to the participants (n = 75) for maize data collection and management over the maize growing season period in 2015–2017. The application structure was custom designed based on maize developmental stages such as planting date, date of emergence, date of first flowering, anthesis, grain filling, and maturity. Results showed that in Ethiopia, early maturing varieties took 105 days from sowing to maturity in low altitudes, whereas late-maturing varieties took up to 190 days to complete developmental stages in high altitude areas. In Tanzania, a similar trend was observed, whereas in Nigeria, most existing varieties took an average of 100 days to complete their developmental stages. Furthermore, the data showed that the durations from sowing to emergence, emergence to flowering, flowering to maturity were mainly dependent on temperature. The values of growing degree for each phase of development obtained from different planting dates were almost constant for each maize variety, which showed that temperature and planting time are the main elements affecting the rate of maize development. The data aggregation approach using the ODK and on-farm personnel improved efficiency and convenience in data collection and visualization. Our study demonstrates that this system can be used in crop management and research on many spatial scales, i.e., local, regional, and continental, with relatively high data collation accuracy.
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