INFLUENCE OF CLIMATE VARIABILITY AND CHANGE ON FOOD UTILIZATION AT HOUSEHOLD LEVEL A CASE STUDY OF MWINGI SUB COUNTY IN KITUI COUNTY, KENYA BY

Transcription

1 INFLUENCE OF CLIMATE VARIABILITY AND CHANGE ON FOOD UTILIZATION AT HOUSEHOLD LEVEL A CASE STUDY OF MWINGI SUB COUNTY IN KITUI COUNTY, KENYA BY CASSIM JIDALI ZUBERI I54/65323/2013 A RESEARCH DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTERS OF SCIENCE IN CLIMATE CHANGE OF THE UNIVERSITY OF NAIROBI JUNE, 2016

2 Supervissors 1. Dr F.Karanja Ms Emily Bosire

3 Introduction

4 Introduction continued Kitui County is situated in the former Eastern Province of Kenya, and borders Taita Taveta County to the South, Makueni to the West, Machakos to the Northwest, Tana River to the East, Embu and Tharaka Nithi Counties to the North. The County has eight sub counties namely; Kitui Central, Kitui South, Kitui East, Kitui Rural, Kitui West, Mwingi North, Mwingi West and Mwingi Central. It is located between latitudes and South and longitudes and East. The County covers an area of 30, km 2 of which 6,369 km 2 is occupied by Tsavo East National park. The County has a total population of 1,012,709 (approx. 205,491households) according to 2009 Kenya population census, with 2.1% growth rate. Figure 1 below is the map of Kitui County classified by livelihood zones.

5 Introduction cont One of the outcomes of food insecurity in the County has been on poor food utilization specifically at the household level (ACF Mwingi nutrition survey 2009). Whereas most of the focus by food security stakeholders has been on impacts of drought on food availability and access, there has been little attention on the impacts of drought on food utilization. Impacts of poor food utilization by the affected population can be seen in terms of increase in prevalence of malnutrition cases and disease outbreaks among the vulnerable population. According to the Kenya Demographic Health Survey of 2014 (KDHS 2014), approximately 46% of the children of under five years of age have stunted growth (Against a national prevalence rate of 26%). Of this number, 13% were reported to suffer from severe stunting. Stunting is a malnutrition indicator, which is a manifestation of presence of chronic food insecurity problems. Chronic food insecurity is a product of the existence of high risk recurrent food security hazards such as drought

6 Statement of the problem Mwingi region of Kitui County has persistently experienced cases of malnutrition particularly for the under five children. Moreover, incidents of disease outbreak particularly the water borne disease are quite common. This can be linked to household consumption of food that is characterized by poor quality diets, a direct outcome of effects of climate change and variability on food production, which in turn influences the type of diet to be consumed by individual household members. The situation of malnutrition and disease prevalence is further compounded by consumption of water that is inadequate and of poor quality, especially during periods of extreme droughts. Frequent droughts in the region have made it difficult to have proper water access and availability, a situation that compromises hygienic food preparation procedures. This is a big concern for food safety and health of individual household members.

7 Statement of the problem Cont The consequences of malnutrition and ill health particularly among the children will eventually have an adverse impact on their future potential. It will have a negative impact on the cognitive development abilities of the child thus affecting their performance in education. Moreover, economic productivity of such individuals is highly compromised. Increase in household resource diversion to address malnutrition and health issues is evident which in turn affects expenditures in other equally important sectors such as education, farming among others. The net effect of this is economic underdevelopment thus contributing in perpetuating the vicious poverty cycle in the region.

8 Objectives Main objective To investigate the influence of climate change and variability on aspect of food utilization at household level in Mwingi region of Kitui County

9 Specific objectives 1. To investigate the historical profile of increasing drought events as a consequence of climate change and variability 2. To determine the influence of drought on the quality and quantity of households diets 3. To investigate the influence of drought on household decisions made on choice of crop enterprise and food purchases at household level

10 Justification Kitui County has a history of food insecurity. Because of reliance on rain fed agriculture (both crop and livestock production), the County is quite vulnerable to drought impacts. In the year 2004, the County was placed under Emergency Operation Food Programme (EMOP) whereby the most vulnerable populations were placed under food aid (KFSSG LRA report-2005). Since then, the County has been on food aid with beneficiary numbers being adjusted according to recommendations of food security assessments reports. The food assessments reports are normally prepared after the end of both long rains and short rains season with the aim of finding out the impacts of the season on food security in the County. One of the outcomes of food insecurity in the County has been on poor food utilization specifically at the household level (ACF Mwingi nutrition survey 2009). Whereas most of the focus by food security stakeholders has been on impacts of drought on food availability and access, there has been little attention on the impacts of drought on food utilization.

11 Justification cont Impacts of poor food utilization by the affected population can be seen in terms of increase in prevalence of malnutrition cases and disease outbreaks among the vulnerable population. According to the Kenya Demographic Health Survey of 2014 (KDHS 2014), approximately 46% of the children of under five years of age have stunted growth (Against a national prevalence rate of 26%). Of this number, 13% were reported to suffer from severe stunting. Stunting is a malnutrition indicator, which is a manifestation of presence of chronic food insecurity problems. Chronic food insecurity is a product of the existence of high risk recurrent food security hazards such as drought

12 Literature review Roughly over a billion people in the world are food insecure (Burke et al 2010). The food insecurity arises because the food is either unavailable to them, it is not accessible, or because these people are too unhealthy to effectively utilize the available food. They further argue that food security is a product of complex natural and social systems in which agricultural yields play only one part. In order to understand full impacts of climate change on food security, one requires knowledge of potential effects of climate change on proximate causes of food insecurity (such as low agricultural yields) as well as poor economic progress that is hindering food access. Majority of the food insecure people are to be found in Sub Saharan Africa. According to Food and Agriculture Organization (FAO) report of 2001, nearly 35% of the Sub Saharan population is food insecure and the numbers are rapidly increasing. Smith et al (2006) suggests that the prevalence rate of food insecure people in the Sub Saharan African region could be as high as 40% of the population. Table 5 below shows levels of food insecurity in some selected African countries in Sub Saharan region.

13 Literature review cont Even if climate change had minimal impacts on food availability and access issues, it may still affect the other pillar of food security, i.e. food utilization. This pillar of food security is the least studied aspect of food security. It generally involves matters of nutritional aspects of food consumption. A review article on research done on food security issues by Tim Wheeler and Von Braun in August 2013 (Climate change impacts on global food security, August 2013) describes the research that has been done so far on food security globally. The review identifies that global research on food security picked up in the mid 90s and got more attention from the year 2008 and beyond. Most of the research dwells on the pillar of food availability (70%) while the other 30% is left for the other 3 pillars of food security i.e. access (11.9%), stability (4.2%) and utilization (13.9%). The article advances some reasons why this is the case among them being that most studies have tended to focus on areas that are easy to investigate and avoided complex and multi layered features of food security which would require the integration of biophysical, economic and social factors.

14 Literature review cont There is growing evidence that insufficient intake of micronutrients plays an important role in global illness and death from infectious diseases (Black 2003). More importantly, percentages of global micronutrient deficiencies are much higher than those for energy deficiencies. This is posing a major concern for global public health. Table 7 below highlights major global micronutrient deficiencies, their health effects and their respective global prevalence rates. Climate change has the potential to affect consumption of micronutrients in 3 ways. By changing the yield of important food crops that are main sources of micronutrients. By altering the nutritional content of a specific crop By influencing the decisions of farmers to grow or purchase crops of different nutritional value.

15 Literature review cont There is growing evidence on how farmers are being influenced by climate change in making decisions on the type of crop to be grown ( Enete et al 2011). Food utilization deals with the ability of individuals to make use of nutrients available to them. Therefore the overall safety of the food itself as well as the health status of an individual is critical in determining food utilization. There is a strong linkage between malnutrition and diseases, whereby malnutrition leads to increased disease infection which leads to energy loss, reduced productivity and poor access to food (Schaible et al 2007). Confalonieri et al (2007), notes that most manifestations of climate change such as drought and floods have the potential to negatively affect health in such a way that it affects food utilization. There is growing evidence that shows that global warming is playing a significant role in food safety, as pathogens enjoy warmer climates. For instance, global warming is responsible for increased incidence of salmonella related food poisoning in Europe and Australia (Kovats et al 2004, McMichael et al 2006). The same trend has been observed here in Kenya and particularly in Kitui County and other Ukambani areas whereby increased incidents of afflatoxin poisoning in maize harvest has been observed during episodes of warmer and wet weather (Lewis et al, 2005).

16 DATA AND METHODOLOGY For objective 1, secondary data and literature from authoritative sources was used to highlight the historical profile of Mwingi region of Kitui County as a drought prone area. These sources include; 1. Kenya Meteorological Department: Mwingi town rainfall station; Boko University via Nation Drought Management Authority: VCI data; Rainfall data spanning 1961 to 2011 was in the analysis. Standardized Precipitation Indices (Spi) were generated from rainfall data over the area of study.

17 Data and methodology.cont Rainfall data quality was controlled using the single mass curve technique. Rainfall averages were calculated and standard deviation by month. Using rainfall data from Mwingi station, SPI was then calculated. Standardized Precipitation Indices (Spi) were computed using the relationship in equation (1).

18 Data and methodology.cont

19 Data and methodology.cont Mckee et al drought classification thresholds (1995) were then applied to characterize drought phases of Mwingi region. Drought Class McKee et al.(1995) SPI Value Extreme drought -2 Severe drought -1.5 Moderate drought -1.0 Mild drought 0 No drought 0

20 Vegetation condition index Processed Satellite imageries (Vegetation Condition Index) from Boko University (Via National Drought Management Authority) was used to track drought events since the year This data was used to complement spi information. Health vegetation has a different behavior in terms of reflection of electromagnetic radiation in the wavelength of red (R) and nearinfrared (NIR). The Normalized Difference Vegetation Index (NDVI) is usually calculated on a pixed by pixel basis as picked out by satellites from space. NDVI = (NIR-R)/(NIR+R) VCI assesses changes in the NDVI signal through time due to weather changes. It is usually calculated by the following formulae in equation 2

21 VCI Cont

22 VCI cont Where VCI j is the image of vegetation condition index values for date j NDVI j is the image of NDVI values for date j; NDVI max and NDVI min are images of maximum and minimum NDVI values from all images within the data set;

23 Data and methodology obj 2&3 Objective two and three were analysed by conducting a survey in the field. Cross sectional data from systematic sampling technique was collected from at least 270 households grouped into 9 sentinel sites each containing 30 households. The data contained responses on household food groups consumption over the recall period of the last seven days and their corresponding sources The data was collected in September This is the driest period in Mwingi when food security situation is at its lowest level most of the times due to effects of drought (CIDP 2014). The rationale behind the choice of this month is to demonstrate how food utilization at household level is influenced by changes in seasonality and more specifically drought episodes with respect to climate change. The sentinel sites were distributed across the Mixed farming and the Marginal mixed farming livelihood zones whereby five and four sentinel sites were used respectively. A questionnaire was administered to the sampled households. The questionnaire tracked all the food items consumed by household members in the last 7 days prior to the date of interview and their corresponding sources.

24 Food consumption score FCS is a composite score based on dietary diversity, food frequencies and relative nutrition importance of different food groups. FCS is a proxy for household food security and it is designed to reflect the quality and quantity of people s diets. It is considered as an outcome measure of household food security

25 FCS calculation

26 FCS calculation cont Whereby FCS= Food consumption score Xi= Frequency of food consumption- No of days for which each food group was consumed during the past 7 days ai =Weight of each food group Food consumption groups were created. Households were assigned to their groups based on their FCS values and thresholds. The thresholds have been assigned to household consumption groups as follows.

27 FCS Cont NO Food consumption group 1 Poor food consumption 2 Borderline food consumption 3 Acceptable food consumption Threshold Over 35.5

28 Household Dietary Diversity (DDS) Household dietary diversity refers to the number of food groups consumed by a household over a reference period All the food items were grouped into specific food groups. For each food group, a new variable was created which has a value of either 1(Yes the household did consume that specific food group) or 0 (No, they did not consume).all the variables were summed up in order to create a DDS. The new variable had a range from zero through the maximum number of food groups collected (7).

29 DDS Cont The DDS score is used to describe how diverse is the household diet. The same food groups used in calculating FCS were also used in generating DDS. The higher the DDS, the better the quantity and quality of diet taken at the household level. A livelihood zone with a higher DDS and FCS translates into better food utilization at the households. By proxy, this should translate to a better food security situation in my region of interest.

30 Micro nutrient intake (MNI) Micro nutrient based food groups were created for calculation of MNI. I then summed the days of consumption of each food item within each micronutrient group. I Truncated the value of each group total to 7. I then categorized the households in three categories for each group as follows.

31 MNI Cont 1. No consumption (0days) 2. Some consumption (1-5 days) 3. Frequent consumption (6-7 days) Prevalence per group and average days of consumption per group were reported. Just like in FCS, food items were categorized into 6 groupings as follows i.e. protein, iron, vitamin A, Oil, staples, fruits and vagetables.

32 Objective 3 This particular objective is intended to show how drought events are influencing households in making decisions with regard to the type of crop enterprises to be undertaken within their farms. The objective also highlights how drought is influencing decisions to be made with respect to food purchases that are being consumed at household level. In both cases, the objective shows that decisions made by household will ultimately have a bearing on food quality being consumed at household level. As I sourced for information on all the food items consumed by the household in the last 7 days prior to the date of interview, the questionnaire also tracked the source of the food being consumed (i.e. either from own production, purchased, relief, gifts etc). All the sampled households were then distributed across the food source categories with the aim of finding out the proportions. The analysis was done in terms of the number of times a particular source of food was employed over the recall/reference period. The percentages have been computed out of the total number of frequencies used for all the food sources over the recall period.

33 RESULTS AND DISCUSSION Temporal Characteristics of Drought The following figures shows temporal characteristics of drought during the June-July-August (JJA), December- January-February (DJF), March-April-May (MAM) and October-November-December (OND) seasons respectively. Drought episodes were considered every time the spi was negative. The analysis done returned three drought characteristics namely the severe, mild and moderate drought conditions classified according to Mckee et al (1995). The findings reveal a seasonal trend in Drought events.two-tail p-value t-test gave a value of p= and t=3.2 and mean of 0.1±0.07 at 95% confidence level. The test also gave a Pearson s correlation coefficient of 0.2.

34 Temporal Characteristics of drought during the JJA season

35 JJA The above figure shows the seasonal characteristics of SPI over the area of study during the JJA season. It can be seen that droughts occured in the years 1963, 1965, 1970, 1975, 1983, 1984, 1986, 1999,2000,2005,2007,2008 during the June-July-August (JJA) season,including severe cases of drought events in 1970,1975,1983,1999 and 2005 respectively.this can be explained by the fact that the area of study experiences relatively lower precipitation amounts during this period.it can also be observed that drought events have been increasing both in frequency and intensity during the most recent past over the area of study.this characteristic can be attributed to increasing Climate Variability and/or change,over the area of study.

36 Temporal Characteristics of drought during the DJF season

37 DJF The above figure shows the seasonal characteristics of SPI over the area of study during the DJF season. It can be seen that droughts occured in the years 1965,1973,1974,1980,1987, ,2005,2009 respectively.however,the intensity of the droughts was moderate.this can be attributed to December rainfall cessation period where precipitation reduces towards January over the area of study.

38 Temporal Characteristics of drought during the MAM season

39 MAM The Figure above shows the seasonal characteristics of SPI over the area of study during the MAM season. It can be seen that droughts occurred in the years 1965,1973,1980,1983,1998,2001, ,2011 respectively. It can be seen that the drought events have been increasing.this can be attributed to increasing climate variability and/or change over the area of study. However, the drought events in this season have been moderate, a phenomena that can be attributed to significantly observed rainfall amount during the months of March and April.

40 Temporal Characteristics of drought during the OND season

41 OND OND Figure above shows the seasonal characteristics of SPI over the area of study during the OND season. It can be seen that no drought events were observed during this period. This can be explained by relatively high precipitation levels observed during the months of November and December over the area of study. SPI trend that is increasingly becoming irregular during the most recent period can also be attributable to increasing Climate variability and/or Change.

42 Monthly temporal Characteristics of drought over the area of study

43 Monthly SPI time series The above Figure shows a time series graph of SPI values for Mwingi synoptic station. It can be seen that droughts experience a cyclic trend with peaks during the JJA season and troughs during the OND season. Results also show that at least 11 drought events have occurred between 1961 to 2011 ranging from severe to moderate drought phases. The frequency of drought events is also becoming more regular with an interval of five to six years between them.

44 VCI

45 VCI Cont The VCI matrix above indicates that Mwingi region has had its own share of drought events ranging from moderate to severe phases since the year Drought episodes were experienced in the year 2006, 2009, 2011, 2014 and In summary, both scenarios of using SPI and VCI indicators shows that though the region has not had an extreme drought situation, the frequency of moderate to severe drought events recurring is quite common.

46 Food Consumption Score Livelihood Food consumption groups Overall FCS group Poor Borderline Acceptable Mixed 43% 31% 26% 30 Farming Marginal 22% 50% 28% 35 Mixed Farming Mean FCS 33% 40.5% 27% 32.5

47 FCS: Mixed Farming LZ Mixed Farming FCS 26% 43% POOR BOARDERLINE ACCEPTABLE 31%

48 FCS: Mixed Farming LZ Cont The majority of the households (constituting about 43% of the total households sampled) were found to be having poor food consumption About 31% of the households were found to be having borderline food consumption About 26% of the households were found to be having acceptable food consumption.

49 FCS: Marginal Mixed Farming LZ Marginal Mixed Farming 28% 22% POOR BOARDERLINE ACCEPTABLE 50%

50 FCS: Marginal Mixed Farming LZ Cont About 22% of the sampled households were found to be having poor food consumption Almost half of the sampled households in this livelihood zone were considered to be having borderline food consumption. About 28% of the sampled households were found to be having an acceptable food consumption score and therefore considered to be food secure.

51 . Relative Micro-Nutrient intake (MNI) Consumpt Staples Protein Vit A Fruits & Iron Oil ion category vegetable s No consumptio n Some consumptio n Frequent consumptio n Total %

52 MNI Cont Overall MNI scores oil Iron Fruits&vegetables Vit.A No.Consumption Some Consumption Frequent Consump. Protein Staples

53 MNI Cont Out of the six food groups, three of them (i.e. staples, proteins and fruits & vegetables) had the highest % scores under the Some Consumption category. Majority of the households are experiencing significant deficit in consuming vitamin A and iron (54% and 90% respectively). The only food group showing significant frequent consumption is oil ( 46% )

54 % of households MNI for MMF livelihood zone Micronutrient intake in MMF FREQUENT SOME NO CONSP. 20 0

55 MNI for MMF livelihood zone Cont The most consumed food groups are staples (95%), followed by proteins (83%) and vegetables (67%) as shown in Figure 14 above.. However, the consumption of these food groups is under the Some Consumption category (one to five days in a week). About 81% of the households did not consume iron. Another 36% did not consume Vitamin A while 33% did not consume fruits and vegetables

56 MNI for MFLZ %Households(Mixed Farming) FREQUENT SOME NO./CONS Micronutrient rich foods

57 MNI for MFLZ The Figure above shows that the most consumed food groups in this zone are staples (93%), followed by proteins (83%) and fruits & vegetables (80%). These food groups are consumed one to five days a week (Some Consumption category). There is significant deficit in consumption of iron and Vitamin A with 97% and 68% of the households showing no consumption of these food groups respectively.

58 Dietary Score Diversity (DDS) Weekly score MMF MF Food Groups

59 DDS Cont Both livelihoods recorded a DDS mean value of six as shown in the figure above. The figure shows that all the food groups used in computing DDS were consumed during the reference period

60 Food sources 80 Source of food by livelihood zones % Consumption by frequencies MMF % MF 10 0 Source of food

61 Food sources Cont The number of times households in M.F and M.M.F accessed food from own production was 11% and 13% respectively during the reference period. The number of times households in MF and MMF employed purchases as a source of food was 68% and 58% respectively over the reference period. The number of times households in MF and MMF used gathering as a source of food was 19% and 20% respectively. Other sources of food were not that significant during the reference period.

62 CONCLUSIONS From the spi analysis, we can conclude that Mwingi region has been experiencing increasing mild to moderate drought events with occasional severe cases being reported since No extreme drought event was recorded during this period. Even though most of the drought events being recorded are mild to moderate in magnitude, their increasing trend have the potential to significantly affect food productivity in the long run. It can be argued that this could be the underlying root cause of chronic food insecurity in the region, which indirectly affects food utilization pillar.

63 Conclusions cont Only about 27% (mean average) of the sampled households had an acceptable food consumption score. This implies that the rest (73%) have either borderline or poor FCS and therefore are likely to be stressed in accessing food. The high number of households with unacceptable food consumption scores could be attributed to inadequate meals (both in terms of meal sizes and frequencies) and lack of dietary diversity options available to households during dry seasons. This scenario is evident in both the two livelihood zones.

64 Conclusions cont There is no frequent consumption of nearly all the micro nutrient food groups across the two livelihood zones. Staples, proteins and fruits & vegetables are the most common food groups being consumed. There is severe deficit in consumption of iron and vitamin A across the two livelihood zones.

65 Conclusions cont On sources of food, it is evident that during times of drought, majority of the households rely on markets in accessing food. In my case, about 68% and 58% of the total number of food source frequencies in MF and MMF respectively was from purchases. About 11% in MF and 13% in MMF of the food source frequencies were from own production. Bearing in mind that 73% of the sampled households have unacceptable food consumption score, coupled with lack of frequent consumption of micro nutrient food groups, and that the majority of the households were accessing their food from the local markets through purchases, this then implies that households were merely purchasing food items that were affordable to them and not necessarily food of good dietary quality. By default, the food items that were being purchased happened to be of low dietary quality, resulting in poor food consumption scores across the two livelihood zones.

66 Conclusions cont Mild to moderate drought being a frequent recurrent event in the area of study due to climate change and/or variability (as seen from the analysis), this implies that the above observations have been common in the past and will continue to occur well into the future if no appropriate interventions are taken. This explains the prevalence of chronic malnutrition problems such as stunting (as stated by KDHS report 2014) due to long term food insecurity issues.

67 Recommendations Due to inadequate consumption of micro nutrients, there is need to initiate or upscale existing food supplementary programmes in the County. The programme should target the most vulnerable groups such as young children and pregnant women. Iron deficiency in pregnant women can result in serious anaemic conditions that can compromise the health of the mother and the unborn child. Lack of Vitamin A in children can result in poor eyesight in children thus compromising their learning capability in schools. There is a need to design Climate Proofed food and nutrition programmes that will not only increase food production in the County but will address the issue of food quality and diversity in totality. This will promote acceptable food consumption behaviour such as increase in meal frequencies and dietary diversity.

68 Recommendations cont Since majority of the households rely on food purchases during dry seasons, it is highly likely that other important social sectors such as health, education among others are starved of cash at the expense of buying food. This could be affecting the general development of the local area and the entire ASAL region and therefore undermining the achievement of vision 2030 of the Kenya Government. The Government and all other stakeholders in food security should come up with appropriate intervention measures that will promote income generation activities that will provide the much needed cash to purchase food as well as spend in other equally important welfare sectors. Last but not least, there is an opportunity to carry out further research on this topic by expanding the temporal and spatial coverage of this kind of work. This will enable us to observe changes in food utilization scores across seasons and regions within a given time frame. Another area for further investigation is to find out how climate change and/or variability is influencing alteration of nutritional content of specific crops that form the main source of micronutrients for ASAL households. Such findings will be useful in formulation of appropriate food security policies in the country.

69 THANK YOU ALL END