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MSc thesis topic: Can observed nutrient deficiencies be used to provide fertilizer advice to smallholder farmers in Africa?

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January 3, 2022

Contact

Dr. Jens Andersson (PPS-WUR) – jens.andersson@wur.nl

Mukoma Kilakila (PPS-WUR)

Chair group

Plant Production Systems

Timeline

Start: January 2022, timing is flexible

Background

Agricultural advisory for smallholder farmers in Africa is currently dominated by blanket fertilizer recommendations for large geographies. Yet, as farmers’ field conditions are highly diverse, site-specific nutrient management (SSNM) advice is increasingly seen as the way forward. Generating such advice is problematic though. Large-scale soil testing is unfeasible in most African countries, as fields are small and recurrent testing necessary, while current services ‘providing field-specific recommendations appear to promise more accuracy than soil analysis can realistically deliver’ (Schut & Giller 2021).

In this MSc-thesis project you will explore whether in-field observations of nutrient deficiencies can be an alternative strategy for generating field-specific fertilizer advice for smallholder farmers. To evaluate this strategy, you will use observational data on nutrient deficiencies that was collected with a mobile advisory application in southwestern Tanzania. Your findings may directly contribute to the development of new, field-specific advice protocols for advice provision to smallholder farmers.

Research

We are looking for an enthusiastic MSc student who would like to work on this topic and contribute to the development of alternative approaches to identify nutrient deficiencies in smallholder farmers’ fields. The thesis will involve literature study on the topic of nutrient deficiencies in smallholder farming systems in Africa and data analysis of farmer-observed nutrient deficiencies and input use in Songwe region, a major maize growing area in southwest Tanzania.

Requirements

Next to the required courses for doing a thesis project at PPS, you will need to have some experience in using R statistics.