Contextual data: Kenya - Barley - All years

‹ 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Kenya total 37,530 37,530 37,530 37,530 37,530 37,530 59,537 34,953 37,530 37,530
Central   218 218 218 218 218 218 23 125 218 218  
Coast   0 0 0 0 0 0 0 0 0 0  
Eastern   5,279 5,279 5,279 5,279 5,279 5,279 11,664 14,465 5,279 5,279  
Nairobi   0 0 0 0 0 0 0 0 0 0  
North-Eastern   0 0 0 0 0 0 0 0 0 0  
Nyanza   0 0 0 0 0 0 0 0 0 0  
Rift Valley   32,033 32,033 32,033 32,033 32,033 32,033 47,850 20,363 32,033 32,033  
Western   0 0 0 0 0 0 0 0 0 0  

Data version 2.19.3

Contextual data

Collected data

Values obtained from primary and secondary sources

Approximated data

Values approximated based on other data for same location

–No data

Not applicable or no data available

LGB Larger Grain Borer (Prostephanus truncatus) read more ›

Storage beetle pest of maize and dried cassava

Contextual data

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 household-level storage.

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APHLIS network and team members collect this data from official sources (e.g. ministries of agriculture and statistics offices), published research studies and national surveys etc, or by interviewing farmers, extension workers or other key informants. 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 contextual dataset includes production quantities (t), whether it rained at harvest, the percentage of the crop marketed during first 3 months after harvest, household-level storage duration, and presence of specific pests (for example the larger grain borer, LGB, Prostephanus truncatus) which are used to contextualise the loss profile for each year. The importance of different types of contextual factors may vary between crops, and data for the most relevant contextual factors is shown and used in the loss estimate calculations.

For specific crop, location, year combinations where contextual data is not available, approximated data has been used. This is calculated based on the contextualised data which has been provided for the same location. Approximated contextual data is shown in grey. 

Read more about how APHLIS estimates losses ›

Contextual factors

Production quantity (t) the quantity of grain produced on small and large farms for each growing season.

Rain at harvest whether or not there is damp weather at time of harvest which would make drying the grain difficult. If there is rain at harvest then the value for the harvesting link in the PHL profile is increased.

% sold during first 3 months the proportion of grain that is marketed within the first three months, i.e. that will not enter farm storage for any significant time. This proportion of the production will not be subject to farm storage losses but instead will be affected by transport and market storage losses.

Household-level storage duration (months) the length of time that grain was stored at household-level. If household-level storage is less than 3 months then household-level storage loss is reduced to 0%, if 3 to 6 months it will be only 50% of the loss profile figure, or if 6 months or more than 100% of the household-level storage loss is applied.

LGB presence in the case of maize, whether or not LGB (the larger grain borer, Prostephanus truncatus) is expected to be a significant pest. If LGB is a serious pest in that particular season then storage losses are multiplied by 2.

Season

In locations where there is more than one growing season each year, seasonal production data for each of the growing seasons is inputted. These are labelled as first, second, third season.

Farm type

In locations where there are both smallholder farms and large-scale commercial farms, the contextual data is shown for each of these types.

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