Blog entries filtered by: Productivity

The June 2012 issue of Insights, IFPRI’s quarterly magazine, talks up HarvestChoice’s increasingly popular online tool: MAPPR. So friendly to use that a writer and a graphics designer combined wits to create the MAPPR-generated visuals for the article.

The definition of risk used by the HarvestChoice project does not frame risk as inherently bad. After all, farmers and other individuals often voluntarily subject themselves to risks that can be avoided. That is, some individuals may view risk as undesirable, others may not care, while still others may view it as desirable. Individuals who find risk undesirable are commonly referred to as risk averse. Individuals who do not care are commonly referred to as risk neutral. Individuals who find risk desirable are often referred to as risk preferring, loving, or attracted.

With limited resources to cope with weather variability, smallholder farmers in Sub-Saharan Africa (SSA) are facing highly unreliable crop production from year to year. In this modeling exercise we quantified how much area is under such condition and what are their production potential under intensification. The simulation result indicates that, without further intensification, only 15% of current maize growing area has the potential to reliably produce more than 3 t/ha, a yield level suggested as being sufficient to sustain the cereal needs of a typical smallholder household. However, with well-managed intensification, 82% of the current maize area showed the potential to reliably produce 3 t/ha or more.

Crop systems models can help researchers estimate the future of food security under climate scenarios. Many crop models are known to exist around the world - for different crops with varying complexities, yet it is not easy to find the right model for the right problem. To better understand the global extent of crop model development and to identify gaps in capabilities, HarvestChoice participated in an initiative to conduct a rapid meta-analysis of crop models using on-line survey to the crop modeling community in the world. Here are the key findings.