APHLIS provides evidence-based data on postharvest loss at a large scale that would be prohibitively expensive to obtain by direct observation. It does this by combining loss data from academic research with contextual observations from local experts. In doing so APHLIS provides researchers, practitioners and policy makers a valuable overview of the current cost of postharvest loss across sub-Saharan African and within countries, allowing them to focus on crops and areas where interventions will have the most impact. Historically, APHLIS has focused on eight cereal crops – maize, sorghum, millet, wheat, barley, rice, teff and fonio – in the countries of sub-Saharan Africa. Plans are underway to expand the range of crops over the next few years under the APHLIS+ project.
APHLIS estimates postharvest losses using two types of information - postharvest loss profiles derived from peer-reviewed literature, and contextual factors provided by local experts.
Postharvest loss profiles (PHL profiles) quantify the expected loss – as a percentage – at each point along the postharvest chain: from harvesting to storage and market. This loss data is gleaned from scientific literature and broken down by crop, type of farm and climate type (based on the Köppen-Geiger climate classification). These profiles provide percentage loss figures for the various crops throughout the value chain under varying conditions and are updated as new research becomes available.
Contextual factors relate to local conditions and practices that may affect losses on a seasonal or annual basis, such as weather, pest incidence, grain drying conditions and the length of on-farm storage. APHLIS network members collect this data from official sources (e.g. ministries of agriculture and statistics offices) or by interviewing farmers or extension workers. This data enables APHLIS to apply the relevant loss figures from the PHL profiles depending on seasonal circumstances, and convert percentage losses into absolute losses (in tonnes) using the production figures.
Based on the postharvest loss profiles and contextual factors, APHLIS estimates postharvest losses at the provincial, national and regional levels.
The APHLIS website provides open access to all loss estimates created by APHLIS, as well as their underlying references and methodologies
APHLIS loss estimates are openly available on our website as datatables or maps. Bibiliographical references and an indication of the reliability of the PHL profiles used are provided alongside the loss estimates.
The PHL calculator, a downloadable version of the algorithm APHLIS uses to calculate loss , allows users to substitute their own relevant values for the APHLIS defaults (e.g. percentage of storage loss) and to make loss estimates at whatever geographical level best suits their needs. The calculator can also be used with hypothetical data in order to model ‘what if’ scenarios.
While APHLIS loss estimates are exclusively based on robust scientific research, the limited existing body of research on postharvest loss means that sometimes data are missing or incomplete. In these cases, studies providing data on similar crops or contexts are used to fill these gaps. APHLIS is fully transparent with regards to the methodology used to create its estimates, and the academic research underlying its model - references of studies used to calculate loss are provided alongside the data itself.
To deal with similar gaps in the contextual data, our algorithm uses data from other years, or default ‘logic’ to approximate contextual data. This has been clearly marked throughout the database, where our loss estimates are marked as being based on, ‘complete’, ‘incomplete’ or ‘minimal’ contextual data. Information is provided on which contextual data was collected, and which contextual data is approximated. The contextual data itself can be viewed on its own tab in our data tables, allowing you to understand the data or approximations underlying the loss estimates.
In order to allow users to further localise and specify loss estimates, APHLIS provides a downloadable version of its algorithm - the PHL calculator - in which PHL profiles and contextual data can be modified to create customised estimates.