Open Access

Spatial variations of household food insecurity in East Gojjam Zone, Amhara Region, Ethiopia: implications for agroecosystem-based interventions

  • Zewdie Aderaw Alemu1, 2Email author,
  • Ahmed Ali Ahmed2,
  • Alemayehu Worku Yalew2 and
  • Belay Simanie3
Agriculture & Food Security20176:36

DOI: 10.1186/s40066-017-0113-9

Received: 2 February 2017

Accepted: 26 March 2017

Published: 11 July 2017

Abstract

Background

In Ethiopia, food insecurity remains a major public health challenge. Agroecosystem has a potential to determine food insecurity. Therefore, this study aimed to determine the spatial pattern of household food insecurity across different agroecosystems in East Gojjam Zone. An agroecosystem-linked cross-sectional survey was done among 3108 households after selecting using multistage cluster sampling. The study area is divided into hilly and mountainous highlands, midland plains with black soil, midland plains with brown soil, midland plains with red soil and lowlands of Abay valley. Data were collected on sociodemographic variables, household food access and geographical location after five days training and pretesting of the tool to maintain data quality. Data were entered using Epi Info version 3.5 and exported to SaTScan and SPSS 20 for further analysis. To identify the most likely clusters using SaTScan software, the log likelihood ratio (LLR) and P less than 0.05 were considered as the level of significance.

Results

The overall prevalence of household food insecurity was found to be 65.3% (95% CI 63.5, 67.00). The lowlands of the Abay valley (70.6%, 95% CI 66.9, 74.2) and hilly and mountainous highlands (69.8%, 95% CI 65.9, 73.3) showed a significantly higher prevalence compared to midland plains with black soil (61.7%, 95% CI 58.1, 65.6), midland plains with red soil (63.5%, 95% CI 59.9, 65.0) and midland plains with brown soil (61.5%, 95% CI 57.4, 65.3). SaTScan spatial analysis identified hilly and mountainous highlands (LLR: 11.64; P 0.0088) and lowlands of the Abay valley (LLR: 8.23; P 0.025) as the most likely primary and secondary clusters, respectively.

Conclusions

The lowlands of Abay valley and hilly and mountainous highlands were the most vulnerable areas of food insecurity. Concerned bodies that are working to mitigate food insecurity shall consider microlevel food insecurity variations during planning interventions. Further research is needed to determine the temporal variation of household food insecurity. Also, it is very important to validate the spatial analysis results applicability to design geographically targeted interventions.

Keywords

Food insecurity Agroecosystem Ethiopia

Background

Food security is one of the necessary conditions for nutrition security, and the concept has been defined variously over the years [1]. According to Food and Agriculture Organization (FAO), food security exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life [2]. Household food insecurity is recognized as a major public health problem in both developing and developed nations since it leads to poor health outcomes through a complex networking of factors [3, 4]. Food security remains a serious challenge for many households in East Africa [5], and Ethiopia is one of the most food insecure countries in East Africa [6, 7]. More than half of the African’s food insecure population lives in Ethiopia and six other countries [6, 7]. In the country, food insecurity remains as a major development challenge due to the synergetic effects of land degradation, rapid population growth and climate change [8].

Inadequate quality and quantity of food supply to the household members affect the nutritional status of the community [9]. Also, it limits growth and development of young children and infants [1012], increases adolescent school absenteeism, lowers educational attainment [13], lowers cognitive and academic performance among children and adolescents and affects psychosocial interaction [14, 15]. Food insecure individuals have elevated experience of anxiety, depression and other symptoms of common mental disorders compared with food secure individuals [14, 15]. This can be true in three major pathways: Initially, food insecure households may have diets that lack critical micronutrients and micronutrient deficiency may lead to anxiety and depression [16]. Secondly, food insecurity may lead to uncertainties to food supply which leads to stress. Finally, food insecurity may affect individual well-being through creating differences in the community [16].

In Ethiopia, there are efforts to improve overall livelihood of the community with an emphasis to reduce or eliminate food insecurity [17]. The Ethiopian Government has designed strategies to address food insecurity of vulnerable households, including Productive Safety Net Program (PSNP), Household Asset Building Program (HABP), Complementary Community Investment (CCI) project and Voluntary Resettlement Program (VRP) in addition to the existing agricultural extension packages [18].

Type and amount of crop produced differ from area to area due to various factors [19]. Agroecosystem characteristic is one of the factors that may create a spatial variation of household food insecurity [20]. A study from Kenya indicated that households from lower midland zones with high crop diversity were more food secure compared with those from the upper midland zones with low crop diversity [21].

In the rural part of Ethiopia, the level of agricultural productivity determines the household food insecurity, since household food energy availability is derived from household’s own agricultural production [19]. Vulnerability to food insecurity is a common problem in semiarid lowlands and mountainous high land areas including Ethiopia where rural households depend on rainfed agriculture for crop production [22]. A study from East and West Gojjam Zones indicated that households in the highland agroecological zones were more food insecure than lowland areas [23]. However, there were no statistical significant differences of household food insecurity between lowland and middle highland agroecological zones [23]. This might be explained as most highland areas are mountainous and hilly which are prone to soil erosion and degradation, and these could reduce agricultural productivity [23]. On the other side, food insecurity contributes to degradation and depletion of natural resources, migration of the community and political and economic instability [24], which are categorized as basic causes of food insecurity.

In such type of situations, World Food Programme recommends to conduct spatial analysis of food insecurity to identify the most food insecure places using geographic information system (GIS) [25]. To be effective and bring sustainable solutions and meeting the needs of the most vulnerable community [6], recognition of the spatial distribution of food insecurity in specific contexts is very crucial [4, 26]. Different studies elsewhere abroad have tried to identify certain geographical regions as highly vulnerable to household food security [2729]. For example, a study from Nigeria indicated that food insecurity showed a clear spatial pattern across different geographical locations [28].

Understanding of household food insecurity spatial patterns at microlevel using geographic information system (GIS) [30] is very important to identify the most affected geographical locations, to design local interventions, to allocate scarce resources to the most affected areas, to convince policy and decision makers and program managers using visualizing maps and to ensure equity in the community [4, 30]. However, there are limited studies on spatial variability analysis of household food insecurity based on agroecosystem characteristics in Ethiopia. Therefore, this study aimed to identify spatial patterns of household food insecurity across different agroecosystem in East Gojjam Zone, Amhara Regional State, Ethiopia. This agroecosystem-based geographical analysis of hotspot areas to household food insecurity is very important to identify the most vulnerable geographical locations and to give priority and develop microlevel interventions and allocate the scarce resources to the most affected areas.

Methods

Study area and period

This study was conducted in East Gojjam Zone, Amhara Regional State, Ethiopia, from January 2015 to April 2015. The 2015 population projection using 2007 Census, East Gojjam Zone has a total of 2,496,325 (1,221,255 males and 1,275,070 females) population [31]. East Gojjam Zone has a total of four town administrations and 16 rural districts. As indicated in Fig. 1, the area includes the Choke Mountain watersheds found in the Blue Nile Highlands of Ethiopia, which extends from tropical highland of over 4000 meters elevation to the hot and dry Blue Nile Gorge, including areas below 1000 m below sea level. Based on different parameters and characteristics like farming system, temperature, rainfall, soil type, adaptation potential and constraints, the area is divided into six agroecosystems with its respective characteristics [20].
Fig. 1

Map of agroecosystems distribution in East Gojjam Zone, 2011 [22].

Source Agroecosystem analysis in East Gojjam zone [22]

Lowlands of Abay valley (AES1) is characterized by lowland areas with unfavorable agroecological condition with extensive land degradation compared with other agroecosystems [20]. Midland plains with black soil (AES2) are characterized with a considerable high agricultural productivity potential [20]. The midland plain with red soil (AES3) is suitable for its agricultural productivity, since it has a good potential to use mechanized agriculture and irrigation [20]. The midland plain with brown soil (AES4) is characterized by low natural fertility with high level of soil acidity, sloppy terrain and higher rate of water runoff with soil erosion making the crop production potential very low [20]. The hilly and mountainous highland (AES5) was characterized with low crop productivity due to erosion and deforestation [20].

Study design

Agroecosystem-linked five-arm comparative cross-sectional study design was used to determine the spatial patterns of household food insecurity among households with a child 6–59 months of age in rural parts of East Gojjam Zone.

Sample size determination

By taking into consideration the crop productivity potential of the agroecosystem characteristics that hypothesized as it affects the spatial patterns of household food insecurity the proportion of household food insecurity prevalence difference between the highland (p 1 = 52.3%) and lowland agroecological zones (p 2 = 63.8%) from the study area was considered [23] and a double population proportion formula was used. The computation was made using the Stat Cal application of Epi Info version 3.5.1 with an inputs of 95% confidence level (Z α/2 = 1.96), 80% power of the study and one-to-one ratio between lowland and midland area samples. Finally, assuming 1.5 as design effects for its multistage cluster sampling and adding 5% none response rate, the sample size is 481 study participants from each agroecosystem. This study was aimed to compare food insecurity in five groups, it was multiplied by five and the total sample size from the five groups for the study was 2405 households. However, sample sizes of 3225 households were considered which was calculated to satisfy another objective.

Sampling procedures

Based on the agroecosystem characteristics, multistage cluster sampling technique was used to select study participants. In the initial phase, from 16 rural districts, 5 districts were purposively selected taking the five agroecosystem types into consideration. In each district, all kebeles with similar agroecosystem were listed. In the second phase, from each agroecosystem, 6–8 kebeles were selected using a simple random sampling technique with lottery method. In the third step from each selected kebele, one “got” was selected as a cluster using simple random sampling method and all eligible households in the cluster “got” were considered, which made the total number of clusters included 38.

Data collection

An interviewer-administered questionnaire was used to collect data on sociodemographic characteristics of study participants. Household food insecurity status was measured using Household Food Insecurity Access Scale (HFIAS) of Food and Nutrition Technical Assistance (FANTA) questionnaire developed in 2006 with a recall period of 30 days [32]. The study participants were asked about the amount and variety of meals eaten, and the occurrence of food shortage, of the household members, causing them not to eat the whole day or eat at night only, in the past four weeks preceding the survey [32]. All “Yes” responses were coded as one and “No” responses were coded as zero, and the responses were summed to produce an index of household food insecurity [32]. Georeference coordinates data were collected at household level using a handheld global positioning system (GPS).

Quality control

To assure the quality of the study, content validation of the questionnaire with local experts was done before adapting the FANTA food insecurity access scale. The local language questionnaire terminologies were corrected based on the discussion with local experts. Five days training with pretesting was given to data collectors and supervisors to ensure that all research team members are kebele to administer the questionnaires properly, read and record measurements accurately. All necessary corrections were made based on the pretest finding before the actual data collection process. At the end of every data collection date, each questionnaire was checked for its completeness and consistency by the supervisors and the principal investigator and pertinent feedbacks were given to the data collectors and supervisors to correct it in the next data collection day. Wrongly filed questionnaires were excluded from the analysis to assure the quality of data.

Data management and analysis

Data were entered coded, entered and cleaned into Epi Info version 3.5 and exported to excel, SaTScan, Arc map 10.1 and SPSS 20 for further analysis. Descriptive frequency statistics were used to clean the data. The overall and respective agroecosystem prevalence with 95% CI was determined. To identify the spatial patterns of household food insecurity, SaTScan™ software version 9.1 (http://satscan.org/) using the Kulldorf method was used after identifying food insecure households as cases and food secure households as controls. The discrete Bernoulli model was used for such type of data (cases and controls) to analyze the spatial scan statistics. The number of food secure and insecure households in each location has Bernoulli distribution, and the model requires data with a disease (cases) or without a disease (controls) [33]. Therefore, food insecure households were considered as cases and food secure households were considered as controls for the SaTScan spatial analysis purpose.

Spatial scan statistics were used to explore the spatial patterns of household food insecurity, which can be used to identify significant spatial clusters of household food insecurity. SaTScan software uses a circular window moved systematically throughout the study area to identify significant clusters of food insecure households. Cluster analysis was performed with default maximum spatial cluster size of <50% of the population. This fifty percent was specified as the upper limit, which allowed both small and large clusters to be detected and ignored clusters that contain more than 50% of the population.

The likelihood ratio test (LLR) was used to test the hypothesis that there is elevated food insecurity risk inside the circular window compared with the risk outside the circular window. The window sizes and locations with the maximum likelihood were defined as the most likely cluster(s) for household food insecurity. Monte Carlo replications of the dataset determined the distribution and P value of the most likely and secondary clusters. A standard of “no geographical overlaps” was used to report secondary clusters in the SaTScan spatial statistical analysis. The P value was created using a combination of standard Monte Carlo, sequential Monte Carlo and Gumbel approximation and used 999 replications of Monte Carlo [33].

The result is presented with proportions at 95% CI and the location of the identified clusters using geographical locations (Northing and Easting location) with the circular window radius. Also, the result presents the ratio of expected and observed values ratio, the relative risk and the log likely hood ratio with study participants. Finally using the shape file created from the SaTScan output, the circular windows location was created using the ArcMap 10.1 and the shape file map.

Ethical consideration

Ethical clearance was sought from Addis Ababa University Institutional Review Board of College of Health Sciences, ethics and research committee of school of public health. Also, permissions were secured from Amhara Regional Health Bureau, East Gojjam Zone Health Office and from the selected districts. Participants of the study were on a voluntary basis, and informed consent was sought from study participants to confirm their willingness after detailed explanations were provided on the possible benefits and risks in participating in the survey. Privacy and confidentiality were maintained.

Results

From the total 3225 household study participants, 3108 responded to the questionnaire which made the response rate of 96.4%. Of the total study participants, 616 (19.8%) were from lowlands of Abay valley, 629 (20.2%) from midland plains with black soil, 631 (20.3%) from midland plains with red soil, 623 (20%) from midland plain with brown soil and 609 (19.6%) from hilly and mountainous highlands.

Sociodemographic characteristics

As indicated in Table 1, the majority (91.7%) of household heads’ were males, married (90%), followers of Orthodox Christianity (99.9%) and Amhara ethnicity (99.8%). The study indicated that 2492 (80.2%) of the women and 1490 (47.9%) of the men had no formal education. 2856 (91.9%) of the women and 2895 (93.1%) of men were farmers. In the study, 1589 (51.1%) of the households have less than five family size and 2683 (86.3%) women participated in household decisions.
Table 1

Sociodemographic characteristics of study participants in East Gojjam Zone, Amhara Regional State, Ethiopia, 2015

Variables

Category

Frequency

Percentage

Household head sex

Male

2851

91.7

Female

257

8.3

Mother marital status

Married

2823

90.0

Divorced

187

6.0

Separated

67

2.2

Widowed

31

1.0

Religion

Orthodox

3096

99.6

Othera

12

0.4

Ethnicity

Amhara

3102

99.8

Otherb

6

0.1

Father education

No formal education

1490

47.9

Primary (1–6 grade)

1202

38.7

Secondary and above

416

13.4

Mother education

No formal education

2492

80.2

Primary (1–6 grade)

304

9.8

Secondary and above

312

10.0

Mother occupation

Farmer

2856

91.9

Housewife

99

3.2

Merchant

77

2.48

Daily Laborer

36

1.2

Employed

16

0.5

Other

24

0.8

Father occupation

Farmer

2895

93

Merchant

86

2.8

Daily Laborer

58

1.9

Employed

45

1.4

Otherc

24

0.8

Average family size

<5 members

1589

51.1

 

≥5 members

1519

48.9

Number of under five children

One

2593

83.4

Two and above

515

16.6

Women participation in decision making

Yes

2683

86.3

No

425

13.7

aProtestant and Muslim

bOromo and Tigrie

cHave no job totally

Household food insecurity access domains

As indicated in Table 2, 76.1% of households had feelings of uncertainty and anxiety about the household food supply and 53.7% perceived that the household food supply was insufficient quality and not a preferred type. Also, 26.8% of the households were taking insufficient quantity of food in the last 30 days. Spatial inequality in the domains of household food insecurity access was observed based on agroecosystem. A higher proportion of anxiety/uncertainty in household food supply was reported from the lowlands of the Abay valley (81.8%, 95% CI 78.75, 84.85) and hilly and mountainous highlands (88.7%, 95% CI 86.2, 91.2) agroecosystems. Similarly, higher proportion (41.8, 95% CI 37.88, 45.71) of insufficient quantity household food supply was observed hilly and mountainous highlands, and higher proportion of insufficient food quality supply was observed in the lowlands of the Abay valley (57.6% (57.6, 95% CI 53.7, 61.5)).
Table 2

Household food insecurity access domains at different agroecosystems in East Gojjam Zone, Amhara Regional state, Ethiopia, 2015

Agroecosystem type

Anxiety and uncertainty

Insufficient quality

Insufficient quantity

Lowlands of Abay valley

81.8 (78.75, 84.85)

57.6 (53.7, 61.5)

20.8 (17.6, 24.01)

Midland plains with black soil

67.2 (63.53, 70.87)

47 (43.1, 50.9)

24.2 (20.85, 27.55)

Midland plains with red soil

71.9 (68.40, 75.40)

47 (43.1, 50.89)

20.7 (17.54, 23.86)

Midland plains with brown soil

71.0 (67.44, 74.56)

53 (49.0, 56.9)

26.9 (23.4, 30.4)

Hilly and mountainous highlands

88.7 (86.20, 91.20)

54 (50.0, 57.96)

41.8 (37.88, 45.71)

Prevalence of household food insecurity

In this study, using Household Food Insecurity Access Scale, 65.3% (95% CI 63.5, 67.00) of the households were found to be food insecure. From the total sample households, 38.1% (95% CI 36.4, 39.7), 23.1% (95% CI 21.6–24.6) and 4.1% (95% CI 3.3, 4.8) households were mildly, moderately and severely food insecure, respectively.

Agroecosystem-based prevalence of household food insecurity showed geographical variation. The highest prevalence of food insecurity was observed in the lowlands of Abay valley and hilly and mountainous highlands compared with midland areas. A maximum proportion of household food insecurity was observed in the lowlands of the Abay valley (70.6%, 95% CI 66.9, 74.2). The second highest prevalence of household food insecurity was observed from hilly and mountainous highlands (69.8%, 95% CI 65.9, 73.3). There were no variations among the midlands plains with brown soil (61.7%, 95% CI 58.1, 65.6), midland plains with red soil (63.5%, 95% CI 59.9, 65.0) and midland plains with black soil (61.5%, 95% CI 57.4, 65.3) agroecosystems.

Spatial SaTScan analysis of household food insecurity

As indicated in Table 3 and Fig. 2, SaTScan cluster analysis indicated a cluster with geographical location of 10.573N, 37.817E, and a radius of 3.53 km from hilly and mountainous highlands was detected as the most likely primary cluster with the maximum LLR of 11.64 and a P value of 0.0088. The cluster location includes samples taken from Sinan Mariam, Abazazi Webeyegn and Zelen Amisitegna kebeles.
Table 3

Household food insecurity SaTScan spatial analysis identified clusters in East Gojjam Zone, Amhara Regional state, Ethiopia, 2015

Cluster

Identified kebeles

Agroecosystem

Coordinate/radius

LLR

P value

1

S/Mariam, Abazazi and Zelen

Hilly and mountainous highlands

(10.57N, 37.82E)/3.53 km

11.64

0.008

2

Mushrit Dengaye

Lowlands of Abay valley

(10.16N, 38.25E)/1.73 km

8.218

0.025

3

Kurar, Gelgele and Minejena

Lowlands of Abay valley

(10.11N, 38.15E)/3.21 km

5.983

0.898

4

Desse Asame Abo

Midland plains with brown soil

(10.32N, 37.58E)/0.42 km

5.973

0.958

5

Tegoderi

Hilly and mountainous highlands

(10.63N, 37.75E)/0.41 km

5.545

0.993

5

Goffichima

Midland plains with red soil

(10.33N, 37.37E)/0.49 km

5.545

0.993

6

Yegodena

Midland plains with black soil

(10.24N, 38.02E)/0.75 km

5.517

0.993

7

Teden

Lowlands of Abay valley

(10.15N, 38.11E)/0.33 km

5.198

0.995

Fig. 2

SaTScan spatial distribution of household food insecurity at cluster level in East Gojjam Zone, Amhara Regional State, Ethiopia

In the SaTScan spatial analysis, there were significant secondary most likely clusters detected in the lowlands of the Abay valley with LLR of 8.218 and a P value of 0.025. The cluster location includes samples taken from Mushirit Dengaye kebele. Table 3 summarizes the most likely clusters with their LLR and probability value (study participants), its geographical location with radius and identified kebeles in the clusters.

As indicated in Fig. 2, clusters with the maximum LLR (11.64) were the most likely primary cluster, which is colored with red. The most likely primary cluster is located from the hilly and mountainous highlands. The most likely secondary cluster with the second maximum LLR (8.22) is shaded with orange color, which is located in the lowlands of Abay valley.

Discussion

The current study has tried to assess the spatial patterns of household food insecurity based on agroecosystem characteristics. Overall, high proportions of households were food insecure in the study community. This study finding was lower than a study from Farta district (70.7%) [34], but higher than from studies conducted in East Gojjam Zone (56%) [19] and West and East Gojjam Zones (55.3%) [23] and Sidama Zone (54.1%) [35]. This variation might be explained as household food insecurity status varies from area to area since it is the product of many predictor variables including agroecosystem characteristics [35]. Also, the magnitude of the problem depends on the local crop productivity, which might fluctuate seasonally based on the suitability of the harvest period [36]. Those findings indicated that food and nutrition insecurity is a common problem, including areas with surplus crop production [23]. This implies that food insecurity interventions are essential including surplus crop producing areas.

Another important finding of the study is that more than three-fourth of the households had feelings of uncertainty and anxiety about the household food supply. This condition affects not only household members nutritional status [23], but also their mental health status [37]. The problem is more severe among women who are responsible to feed the family which leads to maternal anxiety, depression and other related mental illness [5, 37]. This might exert negative influence on parent to child interaction and care practices [37].

In the moderate stage of household food insecurity, households manage shortage of food with reduction in diet quality [37], and in this study more than half of the households made adjustments to household food insecurity by reducing in diet quality, which may lead to micronutrient deficiencies [37]. As the severity of household food insecurity increases, households start to reduce the quantity of food that served to the family members [32]. In the current study, about one-fourth of the households compromised the quantity of food served to the family due to shortage of food. This situation leads to protein energy malnutrition to the household members [32]. The effect of food shortage at household level is more severe to women [5] and children due to biased food distribution within household members [23].

Agroecosystems type has an important role in determining food insecurity status, especially for farmers with subsistence farming [38]. The current study showed nonrandom distribution of household food insecurity across different agroecosystems. The highest prevalence of household food insecurity was observed from the lowlands of Abay valley. This area was characterized with relatively unfavorable agroecologic conditions, high climate change vulnerability and low climate change adaptive capacity [19]. The second highest prevalence of household food insecurity was observed from the hilly and mountainous highlands, which is located in the Blue Nile highlands of Choke mountain [20]. This agroecosystem has lower crop production potential due to its lower temperature, lower soil fertility due to erosion, high climate vulnerability, low climate change adaptive capacity of farmers and high human-induced land degradation [20]. However, there was no statistically significant difference in prevalence of household food insecurity taken from in the midland plains.

The SaTScan spatial cluster analysis using SaTScan across different agroecosystems identified the most likely primary and secondary clusters from the hilly and mountainous highlands and lowlands of Abay valley with a statistical significant number of study participants, respectively. This study suggested that there was significant spatial clustering of household food insecurity based on agroecosystem. There were clusters within an agroecosystem at microlevel, and planning using aggregated data at macrolevel may reduce the efficiency the programs. This is because targeting food insecurity interventions based on vulnerability of the community would help to maximize the benefits from interventional programs through optimal utilization of scarce resources [39]. Spatial SaTScan cluster analysis of food insecurity can improve food insecurity intervention coverage effectiveness [40]. This SaTScan analysis of household food insecurity identified specific geographical locations that needs assistant which can improve coverage of the program through identifying the beneficiaries [40].

The cluster analysis can provide valuable information about spatial disparity of household food insecurity that may be relevant for further epidemiological research on the topic. However, this study has limitations due to its cross-sectional nature of the survey, which makes it difficult to see the temporal variation under different seasons. Also, it is limited that clusters in this method of spatial analysis are assumed to be circular and dissimilar in size to areas within given kebele boundaries. This could result in the exclusion or inclusion of districts/kebeles that register excess or less risks to household food insecurity.

Conclusion

In this study area, the overall prevalence of household food insecurity was very high. The community was exposed to anxiety and uncertainty due to food insecurity. Also, large proportions of households had tried to manage the stress of food insecurity through reduction in diet quality and then quantity based on level of severity. The magnitude of the problem showed spatial patterns by the agroecosystem characteristics. Hilly and mountainous highlands and lowlands of Abay valley agroecosystems were significantly identified as the most hotspot areas for household food insecurity compared with other midland areas. The study suggests microlevel spatial variations of food insecurity in the study community. Designing food insecurity intervention programs and plans using regional level evidence and government administration units might mask the true picture of spatial distribution of the problem at local context. So, program-level planning shall take into account agroecosystem-based microlevel variation in allocating resources for intervention. Further research is recommended to address the spatiotemporal patterns of food insecurity. Also, further study on spatial analysis of food insecurity using panel data at microlevel is recommended to validate the current study results.

Abbreviations

LLR: 

log likelihood ratio

SPSS: 

Statistical Package for Social Sciences

CI: 

confidence interval

HABP: 

Household Asset Building Program

PSNP: 

Productive Safety Net Program

CCI: 

Complementary Community Investment Program

VRP: 

Voluntary Investment Program

GIS: 

Geographical Information Program

AES: 

agroecosystem

HFIS: 

Household Food Insecurity Access Scale

Declarations

Authors’ contributions

Conception and design of the work proposal, collection of data, analysis and interpretation of data were performed by ZA, AA, AW and BS. Drafting the manuscript article, revising it critically for intellectual content and final approval of the version to be published were performed by ZA, AA, AW and BS. Finally before submission for publication. All authors read and approved the final manuscript.

Acknowledgements

We are grateful to the East Gojjam Zone and selected districts health departments and each kebele Health Extension Workers and kebele leaders for assisting during data collection. We also thank the Ethiopian Central Statistics Agency for providing census tract-shaped files for the study areas. Also, our acknowledgment goes to the Addis Ababa University School of Public Health, Addis Ababa University CNH project and DebreMarkos University. Lastly our appreciation goes to the data collectors, supervisors and the community who participated in the study.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

The data sets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Consent for publication

Information on the research objective was read to the participants, and verbal informed consent was received to collect data and for publication during data collection. The privacy and confidentiality of the study participants were also maintained.

Ethical approval

Ethical clearance was sought from Addis Ababa University Institutional Review Board of College of Health Sciences, Ethics and Research Committee of School of Public Health. Also, permissions were secured from the Amhara Regional Health Bureau, East Gojjam Zone Health Office and from the selected districts. Participants of the study were recruited on a voluntary basis, and informed consent was sought from study participants to confirm their willingness after detail explanations were given on the possible benefits and risks of participating in the survey. Privacy and confidentiality were maintained. Data collectors and supervisors gave advice about child feeding and care practice to the mothers or caregivers after completing the interview. Also, they advised to seek treatment for severely undernourished children.

Funding

This work was supported by Addis Ababa and Debre Markos Universities. The funding bodies of this study had no role in the designing of the study, in the collection, analysis and interpretation of the data, in the writing of this manuscript and in the decision to submit for publication.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Public Health Department, College of Health Sciences, Debre Markos University
(2)
School of Public Health, College of Health Sciences, Addis Ababa University
(3)
Center for Environment and Development, College of Development Studies, Addis Ababa University

References

  1. Saaka M. How is household food insecurity and maternal nutritional status associated in a resource-poor setting in Ghana? Agric Food Secur. 2016;5(1):11.View ArticleGoogle Scholar
  2. FAO. Rome Declaration on World Food Security and World Food Summit Plan of Action. World Food Summit 13–17 Rome 1996.Google Scholar
  3. Bhattacharyaa J, Currieb J, Haiderc S. Poverty, food insecurity, and nutritional outcomes in children and adults. J Health Econ. 2004;23(4):839–62.View ArticleGoogle Scholar
  4. Chagomoka T, Unger S, Drescher A, Glaser R, Marschner B, Schlesinger J. Food coping strategies in northern Ghana. A socio-spatial analysis along the urban–rural continuum. Agric Food Secur. 2016;5(1):4.View ArticleGoogle Scholar
  5. Silvestri S, Sabine D, Patti K, Wiebke F, Maren R, Ianetta M, et al. Households and food security: lessons from food secure households in East Africa. Agric Food Secur. 2015;4(1):23.View ArticleGoogle Scholar
  6. Smith LC, Haddad L. Reducing child undernutrition: past drivers and priorities for the post-MDG era. World Dev. 2015;68:180–204.View ArticleGoogle Scholar
  7. Ding M, Keiley M, Garza K, Duffy PAZ. Food insecurity is associated with poor sleep outcomes among US adults. J Nutr Community Int Nutr. 2014;19:615–21.Google Scholar
  8. Tesfai M, Adugna A, Nagothu US. Status and trends of food security in Ethiopia. 2014. https://www.researchgate.net/publication/267551178 [cited 10/2/2017].
  9. Belsky DW, Moffitt TE, Arseneault L, Melchior M, Caspi A. Context and sequelae of food insecurity in children’s development. Am J Epidemiol. 2010;172(726):1–35.Google Scholar
  10. Lee JS, Frongillo EA. Nutritional and health consequences are associated with food insecurity among U.S. elderly persons. J Nutr. 2001;131:1503–9.PubMedGoogle Scholar
  11. Stuff JE, Casey PH, Szeto KL, Gossett JM, Robbins JM, Simpson PM, et al. Household food insecurity is associated with adult health status. J Nutr. 2004;134:2330–5.PubMedGoogle Scholar
  12. Tarasuk VS, Beaton GH. Women’s dietary intakes in the context of household food insecurity. J Nutr. 1999;129:672–9.PubMedGoogle Scholar
  13. Vozoins N, Tarasuk V. Household food insufficiency is associated with poor health. J Nutr. 2003;133:120–6.Google Scholar
  14. Lynch J, Smith GD. A life course approach to chronic disease epidemiology. Annu Rev Public Health. 2005;26:1–35.View ArticlePubMedGoogle Scholar
  15. O’Brien LM, Heycock EG, Hanna M, Jones PW, Cox JL. Postnatal depression and faltering growth: a community study. Pediatrics. 2004;113:1242–7.View ArticlePubMedGoogle Scholar
  16. Quine S, Morrell S. Food insecurity in community-dwelling older Australians. Public Health Nutr. 2006;9:219–24.View ArticlePubMedGoogle Scholar
  17. Arcaya MC, Arcaya AL, Subramanian SV. Inequalities in health: definitions, concepts, and theories. Glob Health Action. 2015;8:27106.View ArticleGoogle Scholar
  18. Ethiopian Ministry of Agriculture. Productive Safety net Program. Addis Abeba. 2010.Google Scholar
  19. Mesfin D, Damtew A, Maru H. The status of food availability in the face of climate change and variability in Choke Mountain Watersheds, Central Ethiopia. J Dev Agric Econ. 2015;7(10):358–72.View ArticleGoogle Scholar
  20. Simane B, Zaitchik BF, Ozdogan M. Agroecosystem analysis of the choke mountain watershades, Ethiopia. Sustainability. 2013;5:592–616.View ArticleGoogle Scholar
  21. Mburu SW, Koskey G, Kimiti JM, Ombori O, Maingi JM, Njeru EM. Agrobiodiversity conservation enhances food security in subsistence-based farming systems of Eastern Kenya. Agric Food Secur. 2016;5(1):19.View ArticleGoogle Scholar
  22. Yirgu B. An Agro-Ecological Assessment of Household Food Insecurity in Mid-Deme Catchment, South-western Ethiopia. Global Adv Res J Geogr Reg Plan. 2013;2(8):185–92.Google Scholar
  23. Motbainor A, Worku A, Kumie A. Level and determinants of food insecurity in East and West Gojjam zones of Amhara Region, Ethiopia: a community based comparative cross-sectional study. BMC Public Health. 2016;16(503):1–13.Google Scholar
  24. Beddington JR, Asaduzzaman M, Clark ME, Bremauntz AF, Guillou MD, Jahn MM, et al. The role for scientists in tackling food insecurity and climate change. Agric Food Secur. 2012;1(1):10.View ArticleGoogle Scholar
  25. Gwatkin DR, Rutstein S, Johnson K, Suliman E, Wagstaff A, Amouzou A. Socioeconomic differences in health, nutrition and population in Cameroon HNP/poverty. Washington DC: Thematic Group of World Bank; 2000.Google Scholar
  26. Pongou R, Ezzati M, Salomon JA. Household and Community Socioeconomic and Environmental Determinants of Child Nutritional Status in Cameroon. BMC Public Heath. 2006;6(98):1–19.Google Scholar
  27. Ramu B. Spatial inequality in child nutrition in Nepal: implications of regional context and individual/household composition. Nepal University of Maryland; 2011.Google Scholar
  28. Tolulope TA, Olubukola OB, Olukemi SF. Spatial pattern of household food insecurity and childhood malnutrition in Akinyele local government area, Nigeria. Int J Health Sci. 2015;3(1):235–50.Google Scholar
  29. Feizizadeh B, Pourmoradian S, Pournoradian S. Food security assessment based on GIS spatial analysis in rural areas of East Azerbaija province. Iran. J Geogr Sci. 2015;1:474–83.Google Scholar
  30. Gwatkin DR, Rutstein S, Johnson K, Suliman E, Wagstaff A, Amouzou A. Socioeconomic differences in health, nutrition and population in Cameroon HNP/Poverty. Washington DC: Thematic Group of World Bank; 2000.Google Scholar
  31. Amhara Regional State Finance and Economic Development. Amhara Region projected population size of 2005 E.C/2013 G.C: http://www.amharabofed.gov.et/population_report.html: Amhara Regional State; 2005 [cited 2016 may 2016].
  32. Coates J, Swindale A, Bilinsky P. Household Food Insecurity Access Scale (HFIAS) for measurement of food access: Indicator Guide. 2007.Google Scholar
  33. Kulldorff M. SaTScan user guide for version 9.0. Available at http://www.satscanorg/2010.
  34. Enadle W, Birhanu Z, Atinafu A, Awoke A. Food insecurity in Farta District, Northwest Ethiopia: a community based cross sectional study. BMC. 2014;7(130):1–6.Google Scholar
  35. Regassa N. Small holder farmers coping strategies to household food insecurity and hunger in southern Ethiopia. Ethiop J Environ Stud Manag. 2011;4(1):39–48.Google Scholar
  36. Jacqueline M, Brett R, Cuong T, Sean B, Christian O, Kathryn W. Seasonal variation in the nutritional status of children aged 6 to 60 months in a resettlement village in West Timor. Asia Pac J Clin Nutr. 2013;22(3):449–56.Google Scholar
  37. Cook J, Jeng K. Child Food Insecurity: The Economic Impact on our Nation. A report on research on the impact of food insecurity and hunger on child health, growth and development commissioned by Feeding America and The ConAgra Foods Foundation. 2000.Google Scholar
  38. Atapattu S, Barron J, Bindraban P, Coates D, Descheemaeker K, Eriyagama N, et al. Ecosystems for water and food security. Nairobi: United Nations Environment Programme; Colombo: International Water Management Institute; 2011.Google Scholar
  39. Pullan RL, Sturrock HJ, Soares Magalhaes RJ, Clements AC, Brooker SJ. Spatial parasite ecology and epidemiology: a review of methods and applications. Parasitology. 2012;139(14):1870–87.View ArticlePubMedPubMed CentralGoogle Scholar
  40. Morris SS. Targeting urban malnutrition. A multicity analysis of spatial distribution childhood nutritional status. Food Policy. 2001;26:49–64.View ArticleGoogle Scholar

Copyright

© The Author(s) 2017

Advertisement