Integrated Context Analysis ICA Kenya - WFP GeoNode

Integrated Context Analysis ICA

Kenya

Photo: Fabio Bedini

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A review of existing information to inform new programme development

Background:

The 2011 Long Rains Assessment (LRA) indicated emergency food insecurity levels in the north-eastern pastoral

livelihood zones in the country, and crisis levels for the remainder of the country (with the exception of the major

cereal producing areas of Kenya). In March 2012, FEWSNET released a food security update, indicating an improved

situation in the first quarter of this year, with most of the country classified as stressed, with some remaining

pockets of crisis. Shortly after, a FEWSNET Special Report for the Horn of Africa was released, forecasting a poor

rainy season possibly as low as 60-85% below the average, and representing a significant deterioration from earlier

forecast analyses on food security status. Data collection for the Short Rains Assessments (SRA) was conducted in

the first quarter of 2012, and findings are expected to be released shortly.

Such variations in food security statements within this short time period pose challenges in planning for medium to

long-term programmes. A qualitative review of historical food security, nutrition, and other data was conducted to

provide a deeper understanding of recent trends that would contribute to determining rationales and the objectives

in the development of new programmes. It would also provide greater confidence in the identification of those

geographical areas that face recurring food and nutrition insecurity for longer term programming efforts.

The time frame selected to review trends was the last 5 years – i.e. from 2007 to 2011. This time frame was selected

because it is long enough to show more recent trends on which to build arguments – for example, justifying

assistance to populations affected by a drought 7 years ago would be difficult, etc. However, by demonstrating that

people have been exposed to constant food insecurity over the last 5 years, and have experienced one or two major

shocks during this time is more convincing and defendable. Understanding more recent events also identifies where,

and to what depth, resilience building efforts may be required and provides some level of prioritization and focus in

programming.

Overall, this information review aims to identify:

trends in numbers of food insecure populations to assist in planning

areas of recurring food insecurity, undernutrition, and shocks, and where these converge as a contribution to

identifying if and where relief, early recovery, DRR and resiliency building efforts, and development may be

required

linking the above to landcover and livelihoods to better inform response and resilience

any seasonal variations that should be considered in programme design, to better align, complement, and

harmonize programme responses and interventions

The data sets used for this synthesis were:

LRA’s and SRA’s from 2007 to 2011 (2 assessments per year, thus 10 assessment records)

FEWSNET Quarterly Outlooks from 207 to 2011 (4 outlooks per year, thus 20 quarterly outlook records)

Nutrition surveys (total of 21 nutrition surveys, 3 MIC surveys and three compilation documents from WFP

Kenya), Kenya Nutrition Bulletins (4 quarterly bulletins from 2010-2011), and the Kenya Demographic and Health

Survey (DHS) 2003 and 2008-9.

Normalized Deviation Vegetation Index (NDVI) imagery from 1999 to 2011 (thus, a 12 year record – note that

although a longer time frame is more desirable, NDVI data was only available for this period)

UNEP/World Bank Flood risk modelling - Global Risk Data Platform Project 2009

Disaster Risk Reduction Strategy (DRRS) Government of Kenya flood hotspots

Land cover data from European Space Agency (ESA) 2009

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Land degradation data from GLASOD : The GLASOD project (1987-1990) for Human-induced soil degradation

Markets: Ministry of Agriculture (MoA) for wholesale prives and the Arid Lands Resource Management Project

(ALRMP) for retail prices

Note on shocks: The Disaster Risk Reduction Strategy for Kenya lists droughts and desertification, floods, landslides,

earthquakes and volcanic activity as the main natural shocks that occur in the country – with droughts,

desertification (or land degradation), and floods as the most common and frequently occurring shocks. Although

there are records of where these events have occurred in various reports, no database of shock data - particularly at

localized levels – that would allow for a rapid trend analysis to determine the frequency that populations have been

exposed to these shocks was found. Thus, a set of assumptions and proxy analyses was done, namely:

using the number of times that people in need of food assistance were found in the LRA’s and SRA’s as an

indication of the frequency (or how often) people have been exposed to food insecurity

using NDVI to determine the number of times that vegetation growth was below the average, as a proxy to

water stress (i.e. drought)

The strengths in conducting such historical reviews from multiple data sets is that they will, in part, mitigate

concerns by some stakeholders when target areas and programme types are selected from a single assessment,

particularly if there are biases towards the assessment methodology. Additionally and importantly, greater buy-in

and consensus on findings can be reached, as all relevant information derived from partner efforts has been

acknowledged, considered and used.

Caveats:

The following information synthesis was pulled together by a Programme, VAM, and Emergency Preparedness team

and compiled through a mix of technical (e.g. NDVI analyses) and qualitative (e.g. reclassifying landcover types)

approaches. The interpretation and overlays of various findings were also done qualitatively according to the way in

which the team approached the problem – therefore additional or different ways in which the data can be put

together can be done. Given the short time available for this work, qualitative compilation of information (e.g. the

FEWSNET Quarterly Outlooks) and the setting of classes for mapping (e.g. population percentages) were done

collectively, through discussions and agreements within the team.

Thus, the findings of this synthesis represent a joint yet internal WFP body of work, and would benefit from a

presentation to and consultation from partners to (i) verify and build consensus around the understanding of the

context that these findings provide; (ii) determine additional information gaps, and areas that would require deeper

exploration; and (iii) identify further ways in which the information can be synthezised to further inform programme

design of WFP, partners, and other stakeholders, including Government strategy and policy development.

Ideally, and for future reference, such work could be done with partners from the start, replicating the approach

used by the WFP team and ensuring a common understanding and consensus from the outset.

Lastly, and most importantly, this information should be used as a background to understanding trends and for

planning and programme design , and as a contextual backdrop against which to review the most recent assessment

information available – in this case the Long Rains Assessment (LRA) 2011 and the compilation of nutrition data from

2011 nutrition surveys. All necessary adjustments – such as numbers of people in need of food assistance, or areas

that may have not been found as food insecure in this synthesis, should be included. In summary, this synthesis

should be seen as a complement to, and not a replacement of, recent food security and nutrition assessments. it is

also recommended that that as new assessment information comes in, that it be incorporated and this trend

document is kept updated for future reference.

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Trend analyses of numbers of people in need of food assistance

The key difference between reviewing historical data and those from the most recent assessments is that the former

incorporates changes over time, whilst the latter reflects a more current situation. Broadly:

1. Historical data used for broad planning and strengthening of longer-term programme justifications; and

2. Recent assessment data used to update and adjust caseload estimates based on prevailing conditions on the

ground.

A trend analysis of the last 10 LRA’s and SRA’s was conducted to assist in deriving beneficiary estimates for planning

purposes, and to explore whether any distinctions could be made between potentials for relief, resilience building,

and recovery programmes, and development.

It is assumed that the higher or lower the numbers of food insecure in specific years will reflect times when food

security conditions may have been ‘worse’ (e.g. during a drought, and as a consequence more people would be food

insecure), or ‘better’ (e.g. during exceptionally good harvests, and thus less food insecure populations). As such, this

analysis tried to determine:

What could be an estimate of food insecure population even in a normal or typical year?

This was done by taking the average of all food insecure people from the data set. The assumption is that this

average will reflect those people that, at one point or another, will have been identified as food insecure in the

last 5 years, and will represent those people that are either always food insecure and/or recovering from

shock(s) and with reduced coping in the event of a new shock.

How high have the numbers of food insecure people reached in crisis years?

This was done by taking the average of the two highest emergency peaks. The assumption is that this average

will reflect the top range of the number of food insecure people in the event of a major crisis. When deducting

the overall average (above) from these two highest peaks, the difference should reflect the additional number of

people at risk to slipping into food insecurity in the event of a major shock.

What could be a core group of food insecure people even in good or better years?

Similarly, the lowest numbers found from the assessments should indicate those people that are still food

insecure in spite of what may be better conditions, and therefore represent the most food insecure group.

Overall, trends showed the following (2 highest peaks highlighted in orange, 2 lowest peaks in green):

In summary:

5 year average = 1,900,000 – of which 770,000 represent a core group (2 lowest numbers)

2 highest emergency peaks = 3,775,000

Difference between average and emergency peaks = 1,875,000

Assessment SRA LRA SRA LRA SRA LRA SRA LRA SRA LRA

Season

March to

September

2007

October

2007 -

February

2008

March to

September

2008

October

2008 -

February

2009

March to

September

2009

October

2009 -

February

2010

March to

September

2010

October

2010 -

February

2011

March to

September

2011

October

2011 -

February

2012

Total people in

need922,735 652,216 887,394 1,062,331 2,606,402 3,799,400 1,620,018 1,228,127 2,469,253 3,750,764

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Plotting these out, it is apparent that people have faced 2 major shocks in the last 5 years – the 2009/10 and

2011/12 droughts - and that there would have been insufficient time for populations to recover between them.

The maps below are plotted out at intervals of 6,000 people (i.e. 1,000 HH’s) to show the spread across the divisions

within Districts. The left map shows the average number of people in need of food assistance, whilst the one on the

right shows the range of people estimated at risk to slipping into food insecurity during a major crisis.

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Proportions of people in need of food assistance:

Understanding the proportion that food insecure people represent in an area is important. Implications of 10,000 food insecure people in a population of 40,000 people differ from those of 200,000 people, in communities ability to absorb and support food insecure people in their midst (i.e. the higher the proportion the less kinship support can be assumed), and in considering targeting approaches within communities (i.e. a large or smaller part of the community?). Findings were categorized considering what they could mean in the area or for a community. For example, <15% implies 3 out of every 20 people; 16%- 30% implies 2 to 3 out of 5 people, whilst >30% is 1 or more out of every 3 people (or more than a third of HH’s in a community). The proportion that these represent for the average number of food insecure people in the last 5 years are shown in the top left map, whilst the proportion at risk to slipping into food insecurity in the event of a crisis is shown on the bottom left. The map below has combined the two data sets to show the relative proportions of the two data sets (i.e. the average and those at risk to falling into food insecurity) to each other.

Note: The proportion of food insecure HH’s may not be evenly spread throughout an area or across communities – indeed, lower proportions could mean higher concentrations of food insecure people in localized areas. It was not possible to determine this during this review, so areas where these proportions are lower will be discussed through partner consultations to ensure that no pockets of concentrated food insecurity have been missed.

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Food Security and Nutrition trends

The Kenya Short and Long Rains Assessments Two food security assessments are conducted each year in Kenya – the Short Rains Assessments (SRA) in February and March, and the Long Rains Assessments (LRA) in July and August. These assessments show a number of divisions where food insecure populations are found each year, whilst in others the frequency in which they occur varies. This suggests that there will be areas where people experience food insecurity every year, whilst others could become food insecure as the result of a shock or at times when conditions deteriorate. The numbers of times that the last 10 assessments identified food insecure people were added up for each division, showing the frequency in which they were found over the last 5 years for each division. This was used as a proxy to understand how many years a population was assessed as food insecure - for example, 1 to 2 times would suggest one year out of 5, whilst 7 to 8 times would suggest 4 years out of 5, etc. Although this does not show the depth of food insecurity that people have faced, it contributes to understanding where food insecurity is recurring and where it is less frequent. It can be used to consider the amount of time people have faced food insecurity and how often they may need to use coping strategies. This also shows the low level of resilience of food insecure people.

FEWSNET Outlooks FEWSNET quarterly outlooks since 2

nd quarter 2007 to 1

st quarter

2012 (total of 20) were qualitatively compiled to indicate the most frequent food security classifications that each division, within each District, had been classified in the last 5 years. For example, the classification of ‘Fluctuate between Moderately to Highly Food Insecure’ in a particular area means that out of the 20 outlooks, it was classified as Moderately food insecure around half the time and Highly Food Insecure for the remainder.

Considerations:

Looking at the trend analysis using these FEWSNET quarterly outlooks since 2007, the following can be determined:

Recurring areas that have been classified with higher levels of food insecurity - i.e. those that fluctuate between moderately to highly and extremely food insecure (the last 3 categories). These areas show the lowest level of resilience to shocks, and may suggest major rebuilding of assets and resilience.

Areas that for the most part are moderately food insecure, and that at times can experience periods of deteriorations – i.e. those that are mostly moderately food insecure (the middle two categories. Resilience will rapidly erode in these areas.

Areas that for the most part have been classified as food

secure, although some can experience times of deterioration

into moderate, and occasionally high, food insecurity (first two

categories). Resilience is moderate, and would need attention.

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The map below shows these findings applied to a population density map (> 6 people per km

2) to indicate

the overall areas where these food insecure populations are likely to be found.

Combining SRA’s/LRA’s & FEWSNET

Triangulating results from the FEWSNET trends and the LRA’s / SRA’s show overall similar patterns – i.e. a broad overlapping of areas where repeated food insecure populations (SRA’s/LRA’s) and higher food insecure classifications (FEWSNET) converge. Broadly, the previous LRA/SRA exercise only shows the number of times that a population was found to be food insecure in each of the divisions, yet gave no indication of the depth of this food insecurity. The FEWSNET exercise on the other hand gave a qualitative indication of the depth of this food insecurity, based on the frequency of certain food security classifications for an area. Combining these two data sets bring together the overall food security classification of an area and the frequency in which food insecure populations were identified – or, how often people would have experienced which level of food insecurity. This exercise would make the distinction, for example, of those divisions where people would have been assessed to be food insecure for 3 to 4 years (LRA’s/SRA’s), yet in some areas as mostly high to extremely food insecure, whilst in others as moderately food insecure, etc. These two data sets were combined in the following manner:

FEWSNET Moderately to highly and extremely food

insecure (last 3 categories)

Mostly moderately food insecure (middle 2

categories)

LRA’s & SRA’s

9 - 10 times 7 – 8 times


LRA’s & SRA’s



LRA’s & SRA’s

(not found) 1 - 2 times

Rationales for choosing these have been categories are:

9-10 & 7-8 times represents 4 to 5 years out of 5

5-6 & 3-4 times represents 2 to 3 years out of 5

1-2 times represents 1 year out of 5

This shows where the more severe food insecure classifications overlap with greater regularity with people being identified as food insecure, qualitatively indicating areas where in the last 5 years:

high food insecurity and people in need of food assistance have been found almost every year, or only half the time or less

moderate food insecurity and people in need of food assistance have been found almost every year, around half the time, or less

Note: Excluded are the areas that are not systematically assessed by the LRA’s/SRA’s given their food secure status, and the Generally Food Secure areas from the FEWSNET trends.

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Acute Malnutrition

Source: Compilation of 2011 Nutrition Surveys

Prevalence of Global Acute Malnutrition (GAM) in 2011

Nutrition data that was extracted and compiled from the latest 21 Nutrition Surveys shows that in 2011, 8 out of 16 districts had Global Acute Malnutrition (GAM) rates above the critical level of 15% which calls for emergency intervention (based on the WHO classification). Note: The data from these 21 surveys were collected from all four quarters of the year (not-season specific). The comparison of GAM in these three maps is not ideal; as the compilation of 2011 data reflects all four quarters, and the DHS data only reflects GAM at one point of time. As a consideration of the analysis of this data, it is important to note that in the prevention of acute malnutrition the objective is to target the vulnerable populations in the time period preceding the lean season (minimum 2 months before) to minimize mortality and the incidence of new cases of acute malnutrition. Programmes must start before nutrition status begins to decline.

Past Trends in GAM from Kenya DHS data:

Data from the latest two DHS surveys (2003, 2008-09) were mapped for 5 out of 8 provinces, including 31 districts. The maps below show the trends in GAM.

GAM 2003 (KDHS)

GAM 2008 - 2009 (KDHS)

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Chronic Malnutrition

Chronic malnutrition (stunting) has been generally high throughout the country and over the past 5 years. The national prevalence of stunting is 35% in children under five years of age. [Source: KDHS 2008-09] Trends in Chronic Malnutrition from Kenya DHS data: The maps below show that from 2003 to 2008-09, chronic malnutrition increased in three districts (Mandera, Wajir, Garissa districts) in the North Eastern province and increased in one geographical area in a fourth district (Tana River district) in Coast province. It is also important to consider in the analysis, that chronic malnutrition (stunting) can exist in food secure areas because it occurs when there is limited access and/or availability of nutrient dense foods required by specific vulnerable target groups (children 6-23 months of age, PLW). Infant and young child feeding (IYCF) practices and repeated illness/health issues can also contribute to chronic malnutrition.

Stunting 2003 (KDHS)

Stunting 2008 - 2009 (KDHS)

Micronutrient deficiency (MND) among children is a major contributor to childhood morbidity and mortality, and can

contribute to chronic malnutrition. Children can obtain micronutrients from foods, food fortification, and direct

supplementation. Vitamin A, iron, iodine and zinc are some of the key micronutrients needed for healthy growth and

development among children. There is a lack of current data on the prevalence of micronutrient deficiencies. Very

few nutrition surveys have addressed this issue, and the little information that has been included has been limited to

data on vitamin A supplementation coverage. Currently there is a national micronutrient survey underway in Kneya,

however no data has been released yet.

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Shocks and natural landscapes

Shocks - Droughts and Floods

Floods: Limited historical data prevented determining the frequency and areas of flood occurrence in the last 5 years. The most recent data was sourced from the Government’s Disaster Risk Reduction Strategy (DRSS) indicating flood hotspots, from 2006. This was combined with modelled flood risk from UNEP, indicating the returning period (or the expected frequency of flood occurrence). In the arid/semi-arid lands (ASAL), flash floods will be localized events after the rains. On such landscapes, rainfall over vast, gently sloping areas with sparse vegetation can lead to the rapid accumulation of large amounts of water which concentrate in dry river beds. Primarily, it will be those settlements and any marginal agricultural lands that will be affected by flash floods.

Drought: No specific data set was available to indicate more localized areas where droughts had occurred over the last 5 years, other than references to a number of regions and districts having been affected by drought. Using NDVI imagery, vegetation performance for each of the growing seasons (2 per year) was conducted with the assumption that each time vegetation growth in a growing season was significantly below the longer-term average, it would indicate drought or water stress conditions for vegetation growth. The average vegetation growth for each of the two growing seasons in the last 12 years was calculated to act as a benchmark. The most recent 11 growing seasons (reflecting the most recent 5 and half years) were then compared against this benchmark, and a final map was created to show the number of growing seasons that were significantly below the 12-year average, as follows:

1-2 poor growing seasons represents 1 year out of 5

3-6 poor growing seasons represents 2 to 3 years out of 5

7-11 poor growing seasons represents 4-5 years out of 5 Overall, it can be seen that in the last 5 years there have been 2 to 3 years with poor vegetation growth, or growing seasons below the 12 year average, in those areas where food insecurity has been recorded. Areas where growing seasons have been below the average for almost (if not) the entire time are concentrated in Turkana, Marsabit, Isiolo, and Wajir districts, which also coincide with the highest food insecurity classification trends from the FEWSNET outlooks. In terms of livelihoods, it is the pastoralist and agro-pastoralists in the north-west, the east, and the south of the country that have been the most affected in the last 5 years, as well as marginal agriculture along the coastal belt.

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Livelihoods and Landcover

Broadly, the livelihoods found in the food insecure areas are pastoralists, agro-pastoralists primarily practicing agriculture in marginal lands, and agrarian communities in the hinterlands of the coast. Specific details of these livelihoods are found in the Livelihood Zoning Profile report and are not elaborated here. The main land uses for these livelihoods will be for grazing (pastoralists and agro-pastoralists) and for agriculture (agro-pastoralists and agrarian farmers). The key landcover types for these livelihoods would be:

Grasslands to shrublands: for pastoralists with browsing livestock (camels and goats)

Grasslands: for pastoralists with grazing livestock (cattle and sheep)

Agriculture: for farmers and agro-pastoralists

Sparse vegetation: a significant landcover type in arid/semi-arid lands, that would be used for opportunistic grazing

Relevant detailed land cover data was reclassified into the 4 broad classes outlined above (top left map). Results also match closely to the livelihood zone profiles – pastoralists in the northern and northwestern pastoral zones have a combination of browsing and grazing livestock given the mix of grasslands and shrublands; those in the northeastern areas own only grazers, whilst in the southern pastoral zones only grazers are owned. The previous analyses of vegetation growth performance over the last 5 years were applied to this reclassified landcover (bottom left map) to show which vegetation type – and hence livelihoods – would have been affected by poor growth seasons. Unsurprisingly, these findings closely match and triangulate with the trends found from the FEWSNET and LRA’s/LRA’s information. Lastly, existing data on soil degradation levels (source: GLASOD) were overlayed onto this final map. High soil degradation is, unsurprisingly, mostly found in densely populated agriculture areas. Similarly, high degradation is also found in parts of the pastoral zones where population density is greater. It is noted though that in arid/semi-arid lands, changes in or loss of natural vegetation growth and cover is a better indicator of land degradation than soil erosion. This is because soils in arid lands, which are often affected by droughts, repeatedly over-grazed, and are compacted to extents that limit rainfall infiltration rates, result in limited biomass (e.g. pasturelands) growth and shift to a rapid dominance of coarser and unpalatable vegetation. Understanding the context in an area – i.e. the prevailing livelihood, the land cover type and status, the typologies of the shocks and risks, and the numbers and proportions of food insecure people – all contribute to identifying the types of appropriate programmes that may be required and tailored to local conditions.

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Seasonality (harmonization and complementarities)

Given that livelihoods are closely linked to seasonal events which will determine periods of production and scarcity,

a broad review of seasonal factors across the country was conducted to determine whether there were any key

differences to better inform programming decisions and design.

In Kenya, there are two rainy seasons – the long and short rains – with dry periods between them. Broadly, for:

Pastoralists and agro-pastoralists living in arid/semi-arid lands, the rainy periods represent better times of the

year when water and pastures are more plentiful, which improves animal conditions and hence milk

production. The most difficult periods are the dry seasons, when pasturelands are depleted and people begin

to move in search of water and grazing for their animals. In Kenya this results in the splitting of the household –

that is, the men and young adults will migrate with the herds, leaving behind (at their permanent/semi-

permanent settlement) the mothers, young children, and the elderly members of the household with a few

milking animals, until they return at the start of the rains. Lack of water during the dry season results in extreme

hardships on people, particularly for those remaining at the settlement, and especially for the women who are

responsible for looking after the children and family members, collecting water, and feeding and watering the

livestock that has been left behind. Thus, in pastoral areas understanding conditions during the dry season are

critical – it is the availability of dry season pastures and waters that is essential in this livelihood, and this will

differ depending on whether it is the pastures after the long, or the short rains, that will act as the bridge

between two the seasons.

Agrarian communities, the better periods will obviously be those after the main harvests, and whether

farmers have 1 or two harvests a year. Harvests come in once the rainy seasons have passed and conditions are

dryer. The amount they have been able to produce will determine when their stocks deplete and hence when

their lean season start. Broadly however, it is at the start of the rainy seasons when farmers begin to experience

the most difficult times as workloads increase as they prepare their lands, plant, and weed their lands, and

during the rainy season when household stocks are likely to be exhausted and crops are growing in the fields.

Understanding these elements, and how they relate to each other, are critical for programme decision making and

design. They are essential elements to determine objectives – for example: Would the placing of a FFA intervention

before a lean season contribute to resilience building? If it is during a lean season is it protective and to offset

seasonal hunger? Or if it placed after a lean season or a shock is the objective more of a recovery one?

They inform who could be targeted when, where, and with which programmes – for example: pastoral women

around their settlements during the dry season when men have moved away with the herds, and with labour-based

works to create assets that can be built during dry seasons, etc.

They also inform the alignment and harmonization of programmes and the complementarities between internal

WFP programmes and those of partners – for example, the impacts that women’s participation in FFA during lean

seasons may have on workloads, child caring practices, and child malnutrition.

Complementing the following seasonal comparison, a series of field level Seasonal Livelihood Consultations were

conducted across Kenya, highlighting seasonal differences and programming implications at more local levels (refer

to Turkana, Isiolo, Kitui and Kilifi Seasonal Livelihood Programming consultation reports; Ministry for the

Development of Northern Kenya and Other Arid Lands (MDONKOALS) & WFP; December 2011).

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To determine the overall seasonal variations across the country which would better inform programme decisions

and design, three data sets were triangulated with each other:

the FEWSNET seasonal calendar, which broadly outlines key events throughout the year that will affect pastoral

and agrarian livelihoods.

an NDVI analysis that indicates which parts of the country have more vegetation growth during the 4 seasons –

namely, the two major growing periods during the rainy seasons, and where vegetation will occur during the two

dry seasons (using a 12 year average)

the FEWSNET Quarterly Outlook, compiled seasonally to indicate seasonal food security trends in different parts

of the country over the last 5 years

The findings of these comparisons indicate that there are major differences across the country in terms of where and

when vegetation growth is greater.

In pastoral and agro-pastoral areas in the east of the country, the major – or most productive - growing period

is linked to the short rains in the last quarter of the year (October through January). There will be more dry

season vegetation growth (grazing) in the first quarter between the short and the long rains.

Performance of the long rains will therefore be important, as the subsequent vegetation growth will be

providing the bridge during the dry season until the start of the short rains in October. Thus, failure or poor

performance of the long rains will deepen vulnerability, leading to an earlier start of the lean season. The failure

of the short rains will have critical implications in this area – firstly, people will be entering this period already

stressed from the preceding lean season, and secondly will not benefit from the better periods and animal;

conditions that the short rains would bring. If the long rains also perform poorly, they will have no moment of

respite before the most difficult dry season before the start of the following short rains in the last quarter of the

year. When this occurs, depletion of assets and coping strategies accelerates even amongst pastoralists who in

normal times would be able to manage this period on their own, and resilience to future shocks is compromised

for many people.

For the pastoral and agro-pastoral areas in the northwest and south of the country, the major, most

productive growing period is linked to the long rains in the second quarter (April to June). Reasons for better

and worse periods are the same as those in the east of the country, yet they occur at different times – e.g. the

better times are those during the long rather than the short rains. Although not apparent on the NDVI maps, the

short rains bring about temporary and short-lived pasture flushes that provide temporary respite between the

two dry seasons and give a short boost prior to the most difficult period – which is the dry season from

December through February, prior to the start of the long rains. One key difference here is the southern

pastoral areas, where the start of the long rains results in earlier pasture growth, likely as a result of an earlier

start to the rains in these areas.

For the agricultural areas along the coast, the long rains provide the most productive growing season, and

cereals and cash crops are harvested in the third quarter (July through September). Cash crops are expected at

the start of the year, and a second cereal harvest after the short rains (March / April). As such, December

through March will represent the most difficult periods of the lean season, as this represents the longest time

between the two cereal harvests of the year, and food purchases from the markets are at their highest.

Note: When interpreting rainfall patterns and seasons, particularly against food security status, it is important to

take into account the time lags that will occur between rainfall and production. The start of the rains does not

translate into immediate vegetation growth, as this will take around 10 days or more before vegetation begins

sprouting and showing growth - although some species, particularly in arid/semi-arid lands, will flush much quicker

(e.g. pasturelands). Similarly, improvements to livestock condition and the start of milk production require some

time once pastures and browse have flushed.

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Comparing the FEWSNET Seasonal Calendar for Kenya, with the Wet (rainy) and the 12 year average Dry Season Vegetation growth analysis from NDVI (1999 – 2011):

October to January – Wet season growth

(4th

Quarter) February to March – Dry season growth

(1st

Quarter) April to June – Wet season growth

(2nd

Quarter) August to September – Dry season growth

(3rd

Quarter)

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Seasonal food security trend analyses using FEWSNET Quarterly Outlooks (2007 – 2011). These were done in the same manner as the overall one, yet for each quarter. Each quarterly map in the subsequent page represents the qualitative compilation of each outlook for that specific quarter for the last 5 years. These have been placed against seasonal trend analyses for global acute malnutrition (GAM) using compiled data from 21 nutrition surveys in 2011 only. It is noted there is a large gap in data for several of the quarters which make trend analysis challenging. Furthermore, the difference in times (i.e. a 5 year trend analysis overlayed with the a 2011 nutrition data set) should be considered.

4th

Quarter October to December

1st

Quarter January to March

2nd

Quarter April to June

3rd

Quarter July to September

In the northwestern pastoral areas, this is the period preceeding the lean season. That they are experiencing higher levels of food insecurity at this time will mean that the following lean season will be more severe. This is the period between the end of the lean and the start of the better season in the eastern pastoral areas. Conditions should be improving, and should represent the time where people recover from the lean season. That food insecurity is still high will affect this recovery and period of accumulation. The NDVI trend analysis shows vegetation growth mostly 2-3 below the 5 year average. The high levels of global acute malnutrition in the northwestern areas are likely a reflection of the high food insecurity in this area in the preceding quarter (Q3).

This period represents the main lean seasons in the northwestern and southern pastoral areas, and better conditions in the eastern pastoral belts. The high levels of food insecurity will be the result of the preceding season – for those in the northwest and south coming from a prolonged period of food insecurity before the lean season, and for those in the eastern areas that should be coming from a period of recovery from the short rains. This suggests that the short rains have performed poorly – and shown by vegetation growth having been below the average in the last 5 years.

This period should represent a better time for the northwestern and southern pastoral areas, and the period of recovery after the preceding lean season. Given the high level of food insecurity still seen, and the 2 to 3 (and at times 4-5) years below the average vegetation growth, recovery periods will be compromised and have implications for the coming dry season. Similarly, Isiolo should be having the best vegetation growth of the year and hence improved food security conditions – yet, it is the area that has the greatest concentration of 4-5 years of poor vegetation growth. Although this period shows improved food security status, the global acute malnutrition is high in the north and northwestern areas as a reflection of the preceding quarter. It is important that good nutrition status (good stores of essential vitamins and minerals) before the peak of a lean season will help to minimise mortality and nutrition decline. Programmes to prevent acute malnutrition must start minimum two months before nutritional status begins to decline.

The northwestern areas should be better due to pasture flushes after the long rains. The depth of food insecurity seen reflects the preceding season, where recovery time has been insufficient after the lean period given poor vegetation growth. This is the lean season in eastern pastoral areas. Coastal areas should be experiencing better times, and the earlier emergence of food insecurity suggests poor vegetation growth (i.e. crops) – confirmed by the 5 year NDVI trends showing up to 2-3 years – and prolonging the lean season which starts in the following quarter. The lack of data collected in this quarter make seasonal trend analysis impossible in this section.

16

4th

Quarter October to December

1st

Quarter January to March

2nd

Quarter April to June

3rd

Quarter July to September

17

Other Programmatic Considerations

Nutrition situation analysis

Micronutrient deficiencies:

Micronutrient deficiency (MND) among children is a major contributor to childhood morbidity and mortality.

Children can obtain micronutrients from foods, food fortification, and direct supplementation. Vitamin A, iron,

iodine and zinc are some of the key micronutrients needed for healthy growth and development among children.

The Kenya Demographic and Health Survey (KDHS) 2008-09 collected data on the micronutrient intake and

micronutrient supplementation among children 6-59 months. The coverage of vitamin A supplementation is

estimated around 30%. However, the latter is in contrast with some of the 2011 Nutrition Surveys reporting average

vitamin A supplementation coverage to be 60-65%. Both estimates are still well below the national target of 80%.

The DHS data indicates that iron supplementation is generally low (5%) and nearly all children (98%) live in

households with adequately iodized salt. [Sources: KDHS 2008-2009, 2011 Nutrition Surveys]

There is a lack of current data on the prevalence of micronutrient deficiencies. Very few nutrition surveys have

addressed this issue, and the little information that has been included has been limited to data on vitamin A

supplementation coverage. Currently there is a national micronutrient survey underway, however no data has been

released yet.

Quality of the Diet in Kenya:

One study captured dietary diversity in seven districts over a reference period of seven days. Households were asked

how many days they consumed food from each food group in the week previous to the assessment. Household food

consumption score was also calculated for each of the districts, which is often used as a proxy for household food

security. *Note however that the sampled districts are not representative at the national level and do not reflect the

diet quality of young children 6-23 months of age.

[Source: Unconditional cash transfer baseline survey – Kenya 2012, (in Library)]

Food Consumption Score, % by Household, by District:

18

[Source: Unconditional cash transfer baseline survey – Kenya, WFP 2012]

Dietary Diversity, Number of Days Food Groups Consumed by District

[Source: Unconditional cash transfer baseline survey – Kenya, WFP 2012]

Infant and Young Child Feeding (IYCF) Practices:

The duration of breastfeeding in Kenya is on average quite long. However exclusive breastfeeding for children to 0-6

months of age is low at only 32%. It is recommended that children will be given solid or semisolid fortified

complementary foods in addition to continued breastfeeding from 6 months until 24 months. Adequate feeding

practices during these first 1,000 days of life (the critical window) are necessary for the promotion of optimal

growth, health, and development. According to the latest DHS only 39% of all children aged 6-23 months are fed in

accordance with all IYCF practices. Slightly over half (54%) of all children 6-23 months are fed from the requisite

number of food groups (3+ for breastfed children, and 4+ for non-breastfed children). KDHS data shows that children

in urban areas are more likely to receive foods from an adequate number of food groups as compared to children in

rural areas (67.3% compared to 50.8%).

IYCF Feeding Practices by Age (DHS 2008-9)

[Source: KDHS 2008-09]

19

HIV Prevalence

Kenya has 1.5 million living with HIV, with only 170,000 on antiretroviral therapy (ART) with ART coverage of 61%.

Women and girls bear the greatest burden, both as the infected and as caregivers. In households with sick and

bedridden parents, children, especially girls, are likely to drop out of school to take care of their families. PMTCT

coverage is not higher than 43%. HIV/AIDS is a significant contributor to food insecurity in Kenya. In Nyanza, the HIV

prevalence is 8.7 percent (10.7 female, 6.8 male), compared with 6.3 percent nationally. In Nairobi, the prevalence is

10 percent (12.1 female, 7.9 male). HIV/AIDS reduces household productivity and depletes household resources.

[Source: UNGASS, National AIDS Control Council. Country Report, 2010]

HIV prevalence (DHS 2008-9) - [Source: KDHS 2008-09]

According to projections by the Food and Agriculture Organization of the United Nations (FAO), between 1985 and

2020 the loss in agricultural labour force in Kenya attributable to AIDS is likely to be 17 percent.

[Source: www.fao.org/hiv/aids]

Currently there are 1.2 million children made orphans by AIDS (having lost at least one parent, of whom 30 percent

both parents). Most orphans live with grandparents or other relatives, increasing the dependency ratios and

overstretching their resources. Others, especially in urban slums, live in households headed by children, with few or

no resources for education, health care, food and shelter.

[Source: UNGASS, National AIDS Control Council. Country Report, 2010]

http://www.fao.org/hiv/aids

20

Stakeholders and Collaboration with Government:

In Kenya there are currently various nutrition programmes steered by the Division of Nutrition, Ministry of Public

Health and Sanitation (MoPHS). This is being done in collaboration with NGOs and UN agencies that cover a wide

area across the country. Currently the following efforts are on-going:

Responding to the current drought

Scaling up of High Impact Nutrition Interventions (HINI) countrywide

Nutrition coordination

Survey, surveillance and M&E

[Source: Kenya Nutrition Bulletin July-Oct 2011]

HIV &OVC:

Line ministries

Key line Ministries responsible for OVC interventions are Medical Services, Public Health and Sanitation and Ministry

of Gender, Children and Social Development for the OVC programme; the Key line Ministries for the nutritional

support to the ART clients are the Medical Services, Public Health and Sanitation and Ministry of Gender. To

complement the HIV, Care and treatment package, the food programme should be offered for clients on ART in

Government hospital facilities and partner clinics.

National coordination mechanisms:

The National Coordinating agency for HIV in Kenya is the National AIDS Control Council (NACC) which was

established in 1999 by the Governement of Kenya (GOK). WFP under the UN Joint team on AIDS is a member of the

technical working groups set up by the NACC to look into the various interventions addressed. WFP was involved in

the development of the KNASP 111 and made technical inputs into pillar 1 (Health Sector HIV Service Delivery) and

Pillar 3 (Community based HIV programmes) of the strategy. This was a national government led process and all HIV

interventions in the country will be aligned to the strategic plan. The key players are key government ministries,

development partners, civil society organisations and the public in general. Since this was led by government, the

process is owned nationally and the plan was launched by the Prime Minister in January 2010.

High Impact Nutrition Interventions (HINI):

The Kenyan Government has adopted 12 HINI interventions which include: breastfeeding promotion,

complementary feeding for infants after the age of six months, improved hygiene practices including hand washing,

vitamin A supplementation, zinc supplementation for diarrhoea management, de-worming, iron-folic acid

supplementation for pregnant women, salt iodization, iron fortification of staple foods, prevention of moderate

under nutrition and treatment of acute malnutrition.

[Source: Kenya Nutrition Bulletin, Jul-Sep 2010]

IYCF Practices:

In 2010, the Kenya Ministry of Health (MOH) prioritized evidence-based interventions that aimed at improving infant

and young child nutrition including:

Exclusive breastfeeding for the first six months of life;

Adequate and timely complementary feeding of young children;

Increased feeding during and after illness; and

Growth promotion and counselling on appropriate IYCN in the context of HIV/AIDS.

[Source: Kenya Nutrition Bulletin, Jan-Feb 2011]

21

In order to address the prevalence of Micronutrient Deficiencies in Kenya, the Government announced their

commitment in 2011 in the following focus areas:

1- Support integrated, multi-intervention strategies to eliminate all vitamin and mineral deficiencies for all age

and life stage groups;

2- Develop standards and support high quality commercial micronutrient fortification activities;

3- Promote guidelines developed for the distribution and sale of off-the-counter vitamin and mineral

supplements;

4- Promote cross-sectoral and public private alliances for development and operations of programmes to

deliver and promote micronutrients to all sectors of the population.

[Source: National Food and Nutrition Security Policy 2011]

Food Prices and Access:

“Malnutrition levels in urban areas are partly influenced by limited access to food, due to increasing food prices.

Inflation rate rose from 5.4% to 16.7% between January and August 2011, annual comparison shows that the price of

food commodities has increased by 23.9%”.

[Source: Kenya Nutrition Bulletin Jul-Oct 2011]

22

WFP Nutrition Programming in Kenya:

WFP has been providing assistance in Kenya since 1980. WFP Nutrition programming and assistance has shifted from

an emergency response focus (i.e. droughts, refugees) to a longer term developmental context including a focus on

prevention of stunting and micronutrient deficiencies.

Some of the key successes of WFP nutrition programming (past and planned) include:

Treatment of moderate acute malnutrition (MAM) in Kenya: Introduction of Plumpy’sup and Supercereal

Plus after clearance by GoK in July 2011. [More information and targeting criteria can be found in the

reference documents in the Library: IMAM guidelines and CMAM joint statement].

Prevention of acute malnutrition in Kenya: A Blanket Supplementary Feeding Programme (BSFP) was

implemented in 5 districts during the lean season in 2010 and as Emergency response to the HoA crisis in

2011. An Impact Evaluation was conducted in September 2010. [Find the reference document in the Library:

Evaluation of the emergency BSFP in Northern Kenya]. Lessons learnt from 2010 were used in 2011 BSFP

programme design and impact monitoring by CDC and communication strategy by HKI.

Food and Nutrition Support for vulnerable Populations Affected by HIV and AIDS – Care &Treatment:

treatment of malnourished ART clients and for those identified as food insecure an additional household food ration to

mitigate AIDS effect and reduce negative coping mechanisms. Mitigation & Safety Nets: support food insecure

household caring for children made orphans by AIDS (OVC).

Micronutrient powders (MNP) – government led; transition to developmental context and for prevention of

micronutrient deficiencies and stunting

Voucher programming with nutrition objectives in refugee camps (i.e. vouchers for fruit/veg/meat/milk)

with the aim to expand to urban high density areas (i.e. slums)

Fortification initiatives (i.e. partnerships with Global Alliance for Improved Nutrition (GAIN))

School health and nutrition - The aim for 2012 is to introduce MNPs in Home Grown School Feeding.

23

Markets in Kenya – Maize price dynamics

Maize accounts for 35% of households’ calorie intake in Kenya. Maize price fluctuations are thus an important

consideration for households’ access to food in a context where the food basket is composed of very few food items

as well as for programming.

Sources of Price Data

The monthly price data is extracted from the online VAM price database (http://foodprices.vam.wfp.org). Data

originates from the Ministry of Agriculture (MOA) for wholesale prices and the Arid Lands Resource Management

Project (ALRMP) for retail prices. As the MOA and ALRMP both cover different geographical locations, the

interpretation of the links between wholesale prices and retail prices would require a better knowledge of the maize

supply chain. The time series covers eight markets: Eldoret, Kisumu, Kitui and Mombasa (wholesale) and Kitui,

Lowdar (Turkana), Mandera and Marsabit (retail) from January 2006 to March 2012. The Central and Western

districts of Kenya are not covered.

Maize Price Integration

Market integration is important for transfer modality choices, targeting intervention areas and identifying supply and

price transmission risks.

Wholesale markets of maize are well integrated in Kenya, showing similar trends as shown in Figure 1. Retail markets

of maize are less integrated with each other whereas they are comparatively better integrated with wholesale

markets of maize (Table 1).

FIGURE 1. Price trends (2006-2012)

As shown in Table 1, pairwise price correlation coefficients are high (close to one) for wholesale prices, suggesting a

high level of wholesale price integration. The correlation coefficients range from 0.93 (Eldoret-Kisumu) to 0.97

(Kisumu-Mombasa). Meanwhile, retail market prices are slightly less correlated, in particular Mandera-Kitui (0.56),

and Lodwar (Turkana)-Kitui (0.69).

TABLE 1. Price Correlations

http://foodprices.vam.wfp.org/

24

Maize Price Volatility and Household Purchasing Power

Price volatility is a source of risk for poor and vulnerable households’ purchasing power. Maize price volatility can

have a significant effect on the overall cost of the basic food basket in Kenya, given the food basket of poor

households is maize-dominated.

Maize prices are generally volatile in Kenya with similar patterns for wholesale markets as opposed to retail markets.

Price volatility measured by the coefficients of variation (c.v.) shows close patterns for major wholesale markets:

40% (Eldoret), 39% (Kisumu) and 36% (Mombasa and Nairobi). This is a reflection of price integration between

wholesale markets in Kenya. By contrast, maize retail markets show substantially different patterns. Kitui prices are

more volatile (c.v. 46%) compared to Lodwar (Turkana) (c.v. 29%).

FIGURE 2. Maize price volatility (coefficients of variation)

Wholesale □ and Retail □ markets

The percentage changes in nominal and seasonally adjusted prices indicate the extent to which price changes can be considered normal or abnormal compared to 2003-2007 which is considered as a period of price stability prior to the 2008 high food price crisis.

As shown in Table 2, nominal and seasonally adjusted prices of maize have generally decreased in the 1st quarter of

2012 (Q1-2012) compared to the last quarter of 2011 (Q4-2011), except in Mandera which is probably affected by

the market dynamics in Somalia. However, the year-on-year (Q1-2012 vs. Q1-2011) maize price shows an upward

trend: between 27% and 59% increase depending on the market. This is a result of two consecutive droughts that

affected harvest seasons in the Great Horn of Africa. Compared to before the high food price crisis of 2008, maize

prices are still more than double.

TABLE 2. Recent changes in prices of maize

Region MarketMain staple

food

Calo

ric

cont

ribut

ion

Change from

last quarter

(% change)

Seasonally

adjusted

quarterly

change (%

change)

Monthly

change from

last year (%

change)

Quarterly

change

from last

year (%

change)

Quarterly

change from

last 5-years (%

change)

Cumula

tive

impact

of the

quarter

Cumula

tive

impact

from 5-

year

average

Per

staple

Cumulative

(BLACK

arrow: all

staples, BLUE

arrow: main

caloric

contributor

A B C D E F G H I J K L M

Kenya - Rift Valley - Uasin Gishu Eldoret Maize 35 -7 -5 25 58 144 -2 50 ↓ ↓

Kenya - Nyanza - Kisumu Kisumu Maize 35 0 -8 30 59 141 -3 49 ↓ ↓

Kenya - Eastern - Kitui Kitui Maize 35 N/A N/A N/A N/A N/A #VALUE! N/A

Kenya - Rift Valley - Turkana Lodwar (Turkana) Maize 35 -8 -8 34 36 107 -3 37 ↓ ↓

Kenya - North Eastern - Mandera Mandera Maize 35 15 22 51 52 202 8 71 ↑ ↑

Kenya - Eastern - Marsabit Marsabit Maize 35 N/A N/A 17 27 151 #VALUE! 53

Kenya - Coast - Mombasa Mombasa Maize 35 -12 -17 18 40 119 -6 42 ↓ ↓

Kenya - Nairobi - Nairobi Nairobi Maize 35 -8 -18 21 39 111 -6 39 ↓ ↓

Contribution to Price trends

25

The impact of maize price increases on the cost of the basic food basket is high (Figure 4). High maize prices, combined with livestock losses, have negatively affected

pastoralists and partly fuelled conflict in pastoral areas. While a slow recovery from drought is observed in the last quarter of 2011, several parts of the country were also

adversely impacted by floods. The flood effect is not captured in the maps below:

FIGURE 4. Impact of maize price changes on the cost of the basic food basket (2011) 4

th Quarter 1

st Quarter 2

nd Quarter 3

rd Quarter

26

Maize Price Seasonality and Prospects

Price forecast is crucial for cash and voucher transfer value setting and determination of forward contract price of

local purchases. In the following paragraph, seasonal analysis takes into account only wholesale prices. Maize prices

are deflated using their annual centred moving average, to smooth out common factors including inflation.

Long term seasonal patterns of maize prices in Eldoret remain roughly around their mean during the entire year,

suggesting a limited risk in case of massive maize purchasing (Figure 5). For the other wholesale markets, there is a

common behaviour in May and partially in August, where prices usually spike in the former, and are above their

mean in the latter. Kisumu market in Nyanza, near the border with Uganda, shows a complete different trend, with

spikes occurring mainly in December and January. December is the month when purchasing maize is more

convenient in Mombasa market. In Nairobi, prices show a quite bimodal behaviour.

FIGURE 5. Grand Seasonal Index (GSI)

The monthly change in the grand seasonal index (GSI) can also be used to forecast prices given an observed price at

time t. Basically, it is assumed that the expected price is a function of the current price, corrected by the historical

seasonal fluctuation assigned to each month.

27

Table 3 forecasts 2012 prices in the four wholesale markets investigated so far, starting from April. Price bands have

their maximum width between May-July (lean season), while they are closer between September-November.

TABLE 3. Price forecasts

Eldoret E(P) High Low

observed January 2,800.00 2,800.00 2,800.00

observed February 2,625.00 2,625.00 2,625.00

observed March 2,589.00 2,589.00 2,589.00

forecasted April 2,579.93 2,757.66 2,402.21

forecasted May 2,825.19 3,057.72 2,592.65

forecasted June 2,751.99 3,160.99 2,343.00

forecasted July 2,661.29 3,118.63 2,203.94

forecasted August 2,832.97 3,091.00 2,574.95

forecasted September 2,781.08 3,188.19 2,373.98

forecasted October 2,582.52 2,926.60 2,238.44

forecasted November 2,425.87 2,665.16 2,186.58

forecasted December 2,432.37 2,720.10 2,144.64

-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

Eldoret forecasted 2012 prices

E(P)

High

Low

Kisumu E(P) High Low



observed March 3,200.00 3,200.00 3,200.00


forecasted May 3,262.89 3,813.77 2,712.02

forecasted June 3,144.95 3,763.12 2,526.78

forecasted July 2,991.96 3,525.38 2,458.54






-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

4,000.00

4,500.00

Kisumu forecasted 2012 prices

E(P)

High

Low

Mombasa E(P) High Low



observed March 2,644.00 2,644.00 2,644.00


forecasted May 2,783.88 3,026.12 2,541.63

forecasted June 2,659.41 3,046.23 2,272.59

forecasted July 2,531.36 2,923.75 2,138.97






-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

Mombasa forecasted 2012 prices

E(P)

High

Low

Nairobi E(P) High Low



observed March 2,667.00 2,667.00 2,667.00


forecasted May 2,945.32 3,207.75 2,682.88

forecasted June 2,766.52 3,076.50 2,456.53

forecasted July 2,649.56 3,028.12 2,271.01






-

500.00

1,000.00

1,500.00

2,000.00

2,500.00

3,000.00

3,500.00

Nairobi forecasted 2012 prices

E(P)

High

Low

28

Considerations for Programme

Market related information is pivotal to WFP operational needs. Besides monitoring and assessment, it is crucial to

identify threats to market functioning that can hinder the implementation of market-based interventions.

Notwithstanding the importance of in-depth analysis, including the analysis of the enabling environment of markets,

the structure of maize markets (e.g. supply chain), the conduct of supply chain actors (e.g. competition) and other

determinants of market performance (e.g. traders’ response capacity, transaction costs, etc.), the above synthetic

analysis suggest the following:

Maize markets are generally well integrated, more so between wholesale markets than retailer markets.

Retail markets are also relatively well integrated with wholesale markets. Such a context is favourable to

market-based interventions (e.g. cash and vouchers) so long as supply and price risks can be mitigated by

additional inflows of maize from wholesale market centres.

High price volatility in a context where markets are integrated poses some threats to the transfer value of

cash and voucher interventions from a household perspective. Such transfer values should account for

inflation to avoid a depletion of households’ capability to access enough food through markets.

Cash and voucher transfer value setting should also take price prospects into account by setting the future

transfer values using the upper bound of forecasted prices, as a contingency for the effect of price hikes on

both the budget and the transfer value to beneficiaries. If purchasing maize locally is being considered for

food aid interventions, the forecasted prices can also be used for setting the forward contract prices.

References:

WFP (2011): “Market Analysis Framework”, Food Security Analysis Service (ODXF).

WFP (2011): “The Market Monitor”, issues no. 11/12/13/14, Food Security Analysis Service (ODXF).

Integrated Context Analysis ICA Kenya - WFP GeoNode

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