Storage-Related Post-Harvest Losses
English: % of target farmers who experienced post-harvest losses due to poor storage of crops after the [specify the season]
French: % des agriculteurs ciblés qui ont subi des pertes après récolte en raison d'un mauvais entreposage des cultures après [précisez la saison]
Czech: % cílových farmářů postižených posklizňovými ztrátami způsobené nevhodným skladováním plodin po [určete sezónu]
What is its purpose?
The indicator measures the proportion of supported households whose stores of certain crops were affected by pests or diseases. Inadequate storage is one of the main causes of post-production losses, accounting for up to 10% of losses to a household’s harvest.
How to Collect and Analyse the Required Data
Determine the indicator's value by using the following methodology:
1) Specify the crop(s) your survey focuses on. If required, provide further specification of what exactly is stored (e.g. maize cobs versus maize grains).
2) In consultation with the extension workers, local farmers and the project staff, list for the given crop(s) a limited number (2-4) of the most common causes of storage-related loses.
3) Collect the following data by conducting individual interviews with a representative sample of the target farmers:
Q1: After the [specify the season], did you store any [specify the crop/product]?
A1: yes / no
(ask the following question only if the previous question is YES)
Q2: After the [specify the season], did you lose a part of the stored [specify the crop/product] due to [specify the cause of storage-related losses]?
A2: yes / no
4) Consider also assessing the amount of the stored harvest that was lost due to the assessed cause. Do so either by asking for specific quantities (use locally known units) or by using the 1-5 scale, as explained under Q3 in the Production Losses indicator.
5) Calculate the indicator’s value by dividing the number of respondents who incurred storage-related loses by the number of respondents who stored the given crop(s) and multiplying it by 100. For example, 100 affected respondents divided by 400 respondents who stored the crop, multiplied by 100 = 25% households affected.
Repeat the process for other common, severe causes and the crops you focus on.
1) In order to analyse the data and allow for useful disaggregation, also collect data on the type of storage facilities the interviewed household members use.
2) Do your best to secure photos of the diseases affecting stored crops and ask the data collectors to show them to the respondents to prevent any misunderstandings.
3) Disaggregate the data by wealth.