Publications

This user evaluation report focuses on extension workers’ experiences with using the Maize- Nutrient-Manager (MNM) mobile phone application for field-specific advice provision in the period November 2020 to January 2021. It provides a systematic overview of user experiences to inform adaptations and re-design of MNM data collection and advice protocols to enhance its usability and scaling potential.
For two consecutive seasons (2019-20 and 2020-21), field-specific fertiliser management advice was provided to smallholder maize growers in the Songwe region, Tanzania. In 2019-20, about 1,000 farmers were advised (Andersson et al., 2020). In the 2020-21 season, the number was raised to 1,500 farmers. About 150 farmers received MNM advice in the two seasons consecutively. Besides provision of field-specific advice, MNM collects field-level data on nutrient management practices, landscape, and soil conditions (Andersson et al., 2020). Field-specific management advice includes:
1. Quantities of locally available fertilizers required for balanced nutrient supply, adjusted to farmers’ investment capacities, and observed phosphorous and potassium deficiencies;
2. Manure input use (if available to the farmer);
3. Fertilizer timing and splitting advice;
4. Seed requirement advice for the measured field area and farmer preferred variety
Early November 2020, we trained 49 extensionists who were equipped with smartphones and deployed in their respective operational areas to provide MNM advice. On 26 and 27 January 2021, 46 extensionists participated in an evaluation workshop. Workshop activities included a survey, filled out by each participant, and a group discussion. Both the survey and group discussion aimed at gaining insights into extensionists’ experiences with using the MNM application.
This report is organized as follows: Chapter 2 elaborates on the methods of data collection for this report. Chapter 3 presents survey and workshop findings for extensionists and advised farmers. Each section contains a discussion of findings and ends with the implications of these findings for the future design of the MNM app.