Spatially-Explicit Farmgate Price Data in Africa: Why We Should Care and What to Do About It

Posted on Blogs, December 8, 2017

The use of inorganic fertilizer and other purchased inputs remains very low amongst African smallholder farmers. A central hypothesis about why this is the case, is that fertilizer use is simply not profitable, given the effective price of fertilizer at the farm gate, as well as the farm gate price of farm outputs (e.g. maize grain).

In a very stylized way, input-output price ratios are known to increase as one moves further away from markets (Figure below). Input prices increase with distance as they incorporate the price of movement from the port or blending facilities to farm locations. Output prices, in contrast, decrease as one moves away from market centers, reflecting the costs of transport and intermediation by traders. Even if fertilizer use is profitable in areas with relatively good access to markets, as one moves further from market centers this profitability can be expected to decrease and beyond some threshold of remoteness it no longer makes economic sense to participate in input and output markets.

Looking for a needle in a haystack: The trials and tribulations of spatial sampling frames

Posted on Blogs, November 2, 2017

Collecting and using large amounts of geo-referenced data, all part of so-called ‘big data’, is all the rage these days.  In TAMASA we have a spatial sampling frame for collecting household and farmer yield data, which on a map looks very neat and tidy, and eminently ‘doable’.  For example, in Tanzania this frame covers 650 households located within 25 randomly located 10×10 km grids spread across Tanzania’s major maize growing areas in the Southern Highlands and Northern zone. Within each grid, surveyed households are drawn from a further randomly defined set of three 1×1 km grid cells.

Why is farmer yield so hard to predict spatially?

Posted on Blogs, November 2, 2017

There is increasing interest, and use, for spatial and temporal estimates of on-farm or farmer yield. These data are most obviously needed for food security assessments and national or regional planning (i.e. greatest need and greatest opportunity for investment). Sustainable intensification and closing the yield gap are very much in vogue, and farmer yield is the baseline that defines the potential opportunity. Furthermore, many applications or models need this baseline to make, for example, nutrient recommendations. One could argue, and I would certainly do so, that we know technically how to close the yield gap in any given location. So, can we estimate farmer yields with any degree of confidence spatially and temporally, especially in sub-Saharan Africa?