Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda

Journal of Arid Environments xxx (2017) 1e12

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Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda D. Akwango*, B.B. Obaa, N. Turyahabwe, Y. Baguma 1, A. Egeru School of Agricultural Sciences, Makerere University, P.O Box 7062, Kampala, Uganda

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 January 2016 Received in revised form 24 May 2017 Accepted 31 May 2017 Available online xxx

Drought Early Warning Systems (DEWS) are instrumental in drought mitigation because they provide drought-prone communities with information on how to mitigate and cope with drought. This study assesses the quality of DEWS information in the Karamoja sub-region of Uganda. Data were collected from 173 households that accessed information from DEWS in Kotido and Nakapiripirit districts. A three way interaction model was used to determine the socio-economic factors associated with preference for information channels. It was established that land size and level of education significantly influenced preference for information channels. The study also found that information from DEWS was applicable, relevant, and understandable though not delivered in a timely manner. Slightly more than half (51%) of the respondents accessed information from Parish Chiefs. Although most respondents stated that they preferred radio (69%), it was the least used. The households also used traditional methods like observing the direction of wind and examining animal intestines to predict drought. The study concluded that information from DEWS was of good quality although it was poorly disseminated. There is need to enhance the timeliness of information dissemination if the system is to effectively enhance community preparedness to cope the effects of drought. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Sub-Saharan Africa Drought Rainfall prediction DEWS Agro-pastoralists

1. Introduction Drought continues to be a major setback for agricultural development in Sub-Saharan Africa (Inter- Governmental Panel on Climate Change, 2014). Drought is more than a dry spell, which is a prolonged period of dry weather. It is an extended period of months or years in which precipitation is less than the annual average and results in severe water scarcity (Alliance for water Efficiency, 2016). When droughts occur in this region, agro-pastoral and pastoral livestock keepers are very vulnerable to the effects of crop losses and livestock destruction since their mode of subsistence is extremely responsive to weather changes (IPCC, 2014; Masinde, 2015). In Sub-Saharan Africa, the Sahel region experienced major droughts between the 1960s and 1980s. In contrast, the East African region experienced droughts mainly in the 1980s and 1990s (Schreck and Semazzi, 2004; Urgessa, 2013; Zwaagstra et al., 2010). Drought Early Warning Systems (DEWS) are instrumental in drought mitigation because they provide drought-prone communities with information on how to mitigate and cope with drought.

* Corresponding author. E-mail address: [email protected] (D. Akwango). 1 National Agricultural Research organization, P.O Box 295, Entebbe-Uganda.

The aim of DEWS is two pronged. Firstly, they are designed to institutionalize data collection for monitoring drought indicators. As such the process of DEWS involves monitoring the weather indicators for the possible occurrence of drought, including onset and progression, and likely impact on water and pasture that are key resources for pastoral and agro-pastoral livelihoods. Secondly, DEWS provide timely notice for appropriate community drought response (UN/ISDR, 2013) and offer information on the effects of drought. They are particularly important in semi-arid areas with inherently low rainfall of between 250 mm and 500 mm and a high propensity to variability (Egeru et al., 2014). DEWS are therefore critical and should be integral in mitigating the effects of drought (Egeru, 2016; Masinde, 2015; Quansah et al., 2010; Tarhule et al., 2009). Information obtained from DEWS is useful in setting up databases to be utilised for local development and for planning by communities as a capacity building measure for drought mitigation (UNISDR, 2013). However, most emphasis on the functioning of DEWS has been placed on risk knowledge analysis and development of warning messages by DEWS implementers, with less attention to the content and timeliness of the dissemination of information from DEWS (Basher, 2006; Choo, 2009). In addition, uncertainty of early warning prediction often leads to unreliable information (Coffey et al., 2015). Studies such as those by Makala

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Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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(2012) and UNISDR (2013), argue that DEWS should ensure that the information generated (warning messages) is disseminated in a manner that is easily understood by the target population in order to elicit appropriate coping strategies to drought effects. The coping strategies are actions taken by the communities to prepare to manage drought effects within their existing resources. Effectiveness of DEWS requires that the information disseminated is of good quality and that it is transmitted through appropriate mechanisms. According to Mittal and Mehar (2012), Attaluri et al. (2012) and Makala (2012), quality of information is measured by its level of accessibility, availability, relevance and timeliness. In addition, studies have shown that appropriate channels are essential for effective information exchange between DEWS actors (World Bank, 2011). A variety of channels such as electronic and print media, community gatherings, telephones, extension services can be used for information sharing. However, Ayubu et al. (2012) and Rasmussen et al. (2015) point out that the effectiveness of information sharing channels is influenced by context specific factors. For example, print media is affected by low literacy levels (Lwoga et al., 2010) while electronic media results in low coverage due to a scarcity of electricity (Mercer et al., 2010). In addition, the content, format and dissemination channels used are at times not user friendly to the communities they are targeting (Masinde, 2015) limiting utilization of the information. Similarly, the United Nations Environmental Program (UNEP, 2012) noted that the major setbacks of most Early Warning Systems (EWS) in Sub-Saharan Africa are the inaccessibility of key messages and difficulty in understanding the information. This study assesses the quality of DEWS information and its effectiveness with reference to the Karamoja sub-region of Uganda, which is the only area where DEWS are implemented in this country. Drought occurrences are increasingly registered beyond Karamoja, pointing to a heightened need to upscale the application of the drought early warning system within and outside the Karamoja Sub-Region (UNDP, 2014; OPM, 2012). However, since its establishment in 2008 there have been no empirical studies undertaken to assess the effectiveness of DEWS. Particularly, there is limited information on the quality of information and dissemination mechanisms that could guide efforts to upscale and out-scale the application of DEWS. The focus of this study is guided by Basher (2006), Mittal and Mehar (2012) and Attaluri et al. (2012) who indicated that analysis of the quality of information is measured based on how timely, relevant, reliable and accessible it is. The channel should be affordable, accessible and appropriate to the user conditions. This study assessed the quality of the Karamoja DEWS information and dissemination channels using the following guiding questions: What are the community information needs? Which information is delivered by DEWS? How relevant is this information? Which channels are used by DEWS? Which channels are preferred by the community? What are the challenges and suggestions for DEWS improvement? 2. The study area The Karamoja sub-region located in the north-eastern part of Uganda, between longitudes 330 E
350 E and latitude 10 Ne 4 0 N (Fig. 1), and comprises seven districts: Nakapiripirit, Amudat, Moroto, Napak, Kotido, Kaabong and Abim. This study was conducted within the districts of Kotido and Nakapiripirit. The Karamoja sub-region is bordered by Kenya to the East and South Sudan to the North. The internal neighboring districts are Katakwi, Kapchorwa, Kumi, Lira, Pader and Sironko. Rainfall is generally limited and unpredictable, with an annual average of 400 mm and 1000 mm in the east and west of the sub-region respectively. The sub-region is mostly semi-arid with failed rain seasons, prolonged

dry seasons and droughts commonly causing failed harvests (Powell, 2010). Livestock is the major source of livelihood for most of the inhabitants who move from place to place in search for pasture and water for their livestock (Birch and Grahn, 2007). People in this region mainly feed on milk and blood as a staple meal but are increasingly dependent on maize and sorghum. Karamojong livelihoods have been transformed significantly in the past decade due to the increased military presence in the region related to the disarmament operation in the region. Overall livestock numbers have declined, and many livestock are kept in protected kraals which has diminished mobility (C&D, 2010). Fewer households have cattle, and many have converted to crop production from transhumant pastoralism. Conflict among the Matheniko, Dodoth and Jie is very common due to a struggle for resources such as rangelands, with no institutions to solve this problem (Levine, 2010). The most serious difficulties facing pastoral households are related to security and the accompanying restrictions on freedom of movement. The Karamoja sub-region is divided into three major livelihoods zones. The Karamoja livestock-sorghum-bullrush-millet livelihood zone is agro-pastoral mainly covering the districts of Moroto, Kotido and Nakapiripirit. The soils are predominantly sandy loam, with some black clay soils. Sandy clay alluvial soils are found in the valleys and plains. The Abim simsim-groundnut- sorghum-livestock livelihood zone is an agriculturally-based zone that extends across all of Abim district and a small part of Moroto district. The main ethnic group in the area is the Labwor. The zone has sandy and black clay loam soils in the plains and alluvial soils along river courses that support a wide variety of crops. The central and southern Karamoja pastoral livelihood zone is located in Karamoja region and comprises parts of Moroto and Amudat districts. 2.1. The Karamoja Drought Early Warning System (KDEWS) In the Karamoja region, the first droughts were experienced between the years 1983 and 1984. Pre- 1990s, droughts used to occur on average approximately every five years (USAID, 2011). However since the 1990s the droughts are now occurring regularly after every two to three years. For example Karamoja region experienced severe droughts in the years including: 1992, 1994, 1998, 2000, 2001, 2002, 2004, 2006, 2007, 2009 while droughts were experienced in the years 1990, 1995, 1996, 1997 and 1999 (Egeru et al., 2014; Ogwang et al., 2012; USAID, 2011). Generally, it takes two years for households to recover from the impact of drought, yet current time between the droughts has been too short such that the asset base of the communities in Karamoja has been drastically reduced. The Karamoja DEWS was set up in 2008 by the Government of Uganda and a consortium of non-governmental organisations (NGOs) that included the Agency for Technical Cooperation and Development (ACTED), United Nations Food and Agricultural Organisation (UNFAO), United Nations World Food Program (UNWFP) and the local government district heads of sectors. This was in response to the chronic food insecurity episodes precipitated by inadequate policies to mitigate the effect of recurrent prolonged dry seasons that occurred from the years 2006e2011 in the Karamoja Sub-Region (Egeru, 2012; FAO, 2009). The major objective of KDEWS was to inform all stakeholders in a timely manner about the risk of drought occurrence and its likely effects on the community. Information on broad indicators of crop, livestock and water availability was collected at household and kraal level (ACTED, 2011). This was meant to prepare the households to cope with drought effects. Data collection is done by sentinels who are parish chiefs. The parish chiefs collect most of the community livelihood related data from their parishes and submit

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Fig. 1. Map of Karamoja sub-region showing the study area.

it to the District early warning focal persons (Institute for International Cooperation and Development, C&D 2010). The district heads of units collect information on health, crop yields and nutrition. The information collected for the entire month is entered and analyzed by the district focal persons who generate EWS bulletins by using specific EWS software. The drought bulletin is then shared with the different district departments who verify and comment on the findings before sharing it with partners (C&D 2010).

3. Methods 3.1. Sample selection Nakapiripirit was selected for this research because it was the pilot district for DEWS. Similarly, Kotido was considered because it

was among the districts that implemented DEWS in the second phase. As noted above, the two districts are vulnerable to drought occurrences. In addition, pastoralism and crop production are the main economic activities. Hence, information on livestock and crop production was utilized in understanding the processes of DEWS. Sub-counties were selected in each district based on project coverage. The project initially, and up to the time of data collection, covered only four sub-counties from each district. Thus, four subcounties of Panyagara, Kacheri, Kotido and Regen were selected from Kotido while Nabilatuk, Namalu, Kakomongle and Loregae were selected from Nakapiripirit. At the second level, two parishes were then selected from each sub-county based on the parishes where the project was implemented, representing 50% of the parishes in each district. The lists for this households were obtained from ACTED and the parish chiefs. Initially, 10 households were selected by ACTED per parish. However, during data collection,

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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some household heads had passed on or migrated, leaving the researcher with between 5 and 8 households per parish. All 173 households participating in DEWS were included in this study; 92 and 81 from Kotido and Nakapiririt respectively. Data on the households interviewed is summarized in Table 1.

3.2. Data collection Both qualitative and quantitative data were collected using a cross-sectional survey design guided by semi-structured questionnaires. Data were specifically collected relating to DEWS including, information desired by the households, communication pathways of DEWS and other drought effects and coping strategies practiced. Data were collected from October to December 2014 among 173 households participating in DEWS in Kotido and Nakapiririt districts. The study was conducted during the dry season when the household heads (Fig. 2), were expected to be within the “manyattas” (households). Data collection for the study was only carried out between 8.00a.m and 2.00p.m before the heads of households began their social gatherings. The lead researcher was supported by research assistants who were familiar with the language of the local area of Karamoja in order to reduce interpretation and translation

Fig. 2. Lead author conducting household interviews in Kotido district, Karamoja subregion.

errors which could result in capture of incorrect data. Initially, the research assistants were oriented about the questionnaire which was pre-tested before actual data collection. During data collection there was assessment and evaluation of the field activity every evening to ensure that mistakes were corrected and not repeated.

Table 1 Socio-economic information of the households. Household Characteristics

Pooled Sample (N ¼ 173)

Kotido (N ¼ 92)

Nakapiripirit (N ¼ 81)

Gender of respondent (% female) Gender of household head (% males) Marital status of household head (%) Married Divorced Widowed Occupation of the household head (%) Farming Salaried income Self-employment off-farm Farmer worker Off-farm employment Causal Labor House keeping Schooling Type of house the respondent lives in Mud wall, grass thatched Brick wall, grass roofed Mud wall, iron roof Brick wall, iron roof

59.5 78.0

62.0 77.2

56.8 79.0

88.4 1.2 10.4

88.0 1.1 10.9

88.9 1.2 9.9

85.0 4.6 1.2 2.3 1.2 2.3 0.6

89.1 5.4 1.1 e e 1.1 e

80.2 3.7 1.2 4.9 2.5 3.7 1.2

78 9.2 6.9 5.8

65.2 16.3 10.9 7.6

92.6 1.2 2.5

Household characteristics

Pooled sample (N ¼ 173)

Kotido (N ¼ 92)

Nakapiripirit (N ¼ 81)

Average age of household head (Years) Education of households head (Years of schooling) Education of spouse in Household (Years of schooling) Average household size (Number) Average number of children less than five years Average number of people (5e9) years Average number of people (10e17) years Average number of people (18e59) years Average number of people 59 years and above Average number of people of working age (10 years and above) Distance to the nearest water source

47.13 (12.62) 3.03 (3.428) 0.49 (1.81) 9.88 (5.68) 2.20 (1.81) 2.17 (1.71) 2.23 (2.81) 3.00 (2.20) 0.29 (0.57) 5.47 (3.941) 1.14 (1.21)

44.8 (12.51) 3.07 (3.94) 0.50 (1.92) 9.3 (5.88) 1.99 (1.34) 2.17 (1.92) 2.16 (3.41) 2.85 (1.96) 0.20 (0.45) 5.13 (4.30) 1.37 (1.41)

49.7 (12.30) 3.00 (2.73) 0.48 (1.70) 10.49 (5.41) 2.44 (2.17) 2.17 (1.47) 2.31 (1.85) 3.17 (2.45) 0.40 (0.66) 5.86 (3.47) 0.88 (0.87)

Household wealth indicators; Land ownership and utilization

Pooled (N ¼ 170)

Kotido (N ¼ 89)

Nakapiripirit (N ¼ 79)

Average total land owned Average total land used

6.09 (6.697) 3.93 (3.630)

6.41 (7.854) 4.03 (3.855)

5.75 (5.164) 3.82 (3.372)

Livestock ownership and sales by the household

Pooled

Kotido

Nakapiripirit

Average number of cattle owned Average number of cattle sold per year Average earning from cattle sales per year

7.77 0.48 981,000

7.44 0.52 955,769

8.15 0.44 1,017,444

Figures in brackets represent standard deviations.

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Focus group discussions, (Fig. 3), key informant interviews, document review, participant observations were conducted for purposes of triangulation and clarification of key issues of the study. The primary social-economic unit of analysis was the household that accessed information from DEWS. The term household is difficult to define; its meaning rather overlaps with that of the family and domestic sphere (Ellis, 2000). However in this context, the household referred to “the basic unit of society involved in production, reproduction, consumption and socialization” (O’Laughlin, 1999). A household therefore shares a residence and meals, and makes coordinated decisions, resource allocation and income pooling in some cases (Ellis, 2000). In addition, they should recognize the authority of a single head of the household in major decisions relating to drought preparedness and response actions. Direct field observations were made as to how data were collected, analyzed, and disseminated. In addition, the respondents’ views on the current communication pathways for the drought key messages and the quality of the content disseminated were assessed. This assessment put into consideration the context in which communication took place and the actual information that was communicated. The factors influencing the effectiveness of the communication process were also considered especially the sources and types of information, that the people receive plus how they act on it and provide feedback (accessibility, affordability, coverage and timeliness). In addition, we also reviewed relevant archival information and records of technical project reports and communication exchanges relating to DEWS. 3.3. Data analysis Questionnaire responses were coded and analyzed using SPSS version 18 for Windows. Descriptive statistical analyses were carried out to generate information on the socio-economic characteristics of the respondents, drought effects, major coping strategies, community information needs and information disseminated from DEWS. To determine perception of the quality of information, a five point Likert scale were used to tap respondents perception on the study variables. A mean score of lesser than 3 indicated poor quality rating on information and a mean score closer to 5 indicated a favorable rating on quality of information (Gilem and Gilem, 2003). To test for the effect of socio-economic characteristics of the farmers on the choice of information channels preferred for use, a Loglinear model was used. The Hierarchical Approach in Loglinear Modeling employed in this paper used a multi-way contingency table with three variables, each with two levels. A hierarchy of models exists whenever a complex multivariate relationship present in the data necessitates inclusion of less complex interrelationships (Knoke and Burke, 1980). For

Fig. 3. Focus group discussions in Nakapiripirit District, Karamoja sub-region.

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example, in the equation below if a three-way interaction is present (ABC), the equation for the model must also include all two-way effects (AB, AC, BC) as well as the single variable effects (A,B,C) and the grand mean (m). The equation below illustrates the hierarchical approach to the Loglinear models in this study:

LnðFijÞ ¼ m þ liA þ ljB þ lkC þ lijAB þ likAC þ ljkBC þ lijkABC 1

Ln (Fij) ¼ is the log of the expected cell frequency of the cases for cell ij in the Contingency table. m ¼ is the overall mean of the natural log of the expected frequencies l ¼ terms each represent “effects” which the variables have on the cell frequencies A, B and C ¼ the variables (A and B ¼ variables from socio-economic characteristic) ¼ Education level, Arable land, Age and Gender. (C ¼ Variable from channel preferred for use) ¼ Radio, Parish chiefs, Sub-county notice board, Mobile phone and Drama. I, j and k ¼ refer to the categories within the variables Therefore:

liA ¼ the main effect for variable A ljB ¼ the main effect for variable B lkC ¼ the main effect for variable C lijAB ¼ the interaction effect for variables A and B likAC ¼ the interaction effect for variables A and C ljkBC ¼ the interaction effect for variables B and C lijkABC ¼ the interaction effect for variables A, B and C The main interest of this model is to estimate:

likAC, ¼ whether socio-economic character A has influence on choice of information channel used by C;

ljkBC ¼ whether socio-economic character B has influence on choice of information channel used by C; lijkABC ¼ whether a combination of socio-economic character A and B has influence on choice of information channel used by C. The above model is considered a Saturated Model because it includes all possible one-way and two-way effects and the model begins to delete higher order interaction terms until the fit of the model to the data becomes acceptable. A combination of three variables is used because inclusion of so many variables in Loglinear models often makes interpretation very difficult. Thus, an interaction between two socio-economic characteristic and one channel were considered for the study to avoid misinterpretation of where the real effect of the interaction is occurring. The formula for the L2 statistic is as follows: L2 ¼ 2Sfij ln(fij/Fij). L2 follows a chi-square distribution with the degrees of freedom (df) equal to the number of lambda terms set equal to zero. Therefore, the L2 statistic tests the residual frequency that is not accounted for by the effects in the model (the l parameters set equal to zero). The model generated by the three-way interaction of factors; that is, the saturated model (Initial stage interaction), is considered. This model also includes all lower-order interactions and the main effects. The three-way interaction is tested for significance by deleting it from the model. The change in chi-square from the saturated model to the model without the three-way interaction is tested and if found to be not significantly different from

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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0 (significance value > 0.05). The interaction term is dropped from the model and those found to be significant are entered into two way interaction model. The model generated by all two-way interactions is considered. This model also includes the main effects. Each two-way interaction is tested for significance by deleting it from the model. When the significance value for the change in chisquare for each effect is less than 0.05, these terms should be kept in the model otherwise dropped. All significant values from the two-way iteration are included in the final model. When none of the two-way interactions are removed from the model, there are no more terms to test for significance. Thus, the final model includes the main effects of the two-way interaction terms. Then the final result is presented in the table. The following parameters and descriptions were used in the Log linear model: education (number of years in school), land owned (number of acres utilized by the household), and age category (average number of respondents in age category 10e17, 18e59, and above 59 years of age), Gender of the household head (male or female) as recommended by Hinkle et al. (1988). Xenarios et al. (2012) used similar socio-economic characteristics to determine cropping patterns in India.

4. Results 4.1. Demographic and socio-economic characteristics of the respondents The majority (85%) of the respondents interviewed were agropastoralists and most (78%) of the household heads were male (Table 1). The average household size was registered as 10 members per household which is quite higher than the national average size which is currently at 6 members per household (Uganda Bureau of Statistics, 2016). Majority of the respondents and household heads had an average of only three years in school, while the women had no education at all. Most (78%) of the households lived in mudwalled and grass thatched houses while 5.8% lived in brick-walled and iron roofed houses (Table 1). On the household wealth indicators, there was no significant difference between the average total lands owned and total land that is utilized by households in the two districts. The average number of cattle owned was at 7.7 per household with most households selling one or no cattle per year.

4.2. Major drought effects and household coping strategies in Karamoja sub-region Before analysing the quality of information it was relevant to first understand the effects of drought on the households in Karamoja sub-region and how they cope. A review of documents indicated that the Karamoja region was severely affected by drought in 2006 followed by prolonged dry spells in 2008 and 2009. This led to 70% crop failure and water shortage for both people and livestock (C&D 2010). To date, drought has continued to occur in Karamoja sub-region with more people being affected (Egeru, 2016). The respondents (DEWS participating households) were asked what they perceived as the major effects of drought since 2004 to 2014. In their view, 74% mentioned that drought had led to hunger and malnutrition, 53% mentioned crop failure and poor harvest and 23% indicated livestock death and lack of water and pasture. To cope with the major effects of drought, the Karamoja households indicated that they implemented coping strategies to enable them earn a livelihood during the drought events (Fig. 4). In most cases these coping strategies were implemented after receiving information about the likely occurrence of drought and or onset of rains from the drought early warning systems.

Fig. 4. Strategies of coping with the effects of drought.

4.3. Quality of DEWS information disseminated to the community 4.3.1. Timeliness of information The results from Fig. 5 and the study (Table 2) show that information from DEWS was not timely (mean score ¼ 2.68). This was re-enforced by findings from Focus Group Discussions (FGD) where it was mentioned that late dissemination of information constrained the effectiveness of DEWS. During data collection, one member reported that they had taken a period of six months without receiving information of DEWS. 4.3.2. Understandability of information Although the households rated the information highly understandable at a mean score of (4.21) (Table 2), during the focus group discussions, some (40%) of the participants complained that information was mainly in English. The research team did not find any bulletins translated into the local languages. During key informant interviews, one parish chief indicated that they (parish chiefs) are expected to translate the messages as they disseminate the information. However some terminologies in the bulletins are difficult to translate to local languages. As shown in Table 1, the number of educated respondents was very low making it difficult for most of the target households to easily understand the information. 4.3.3. Usefulness/relevance of information The respondents were asked if the information disseminated by DEWS was in line with the information they needed in responding to the effects of drought (Figs. 6 and 7). The respondents indicated that most information provided by DEWS was predominately on food security (55%), drought occurrence (46%) food storage (24%), and planting of short maturing crops (17%) (Fig. 6). Food security information comprised a combined summary of information on production outcomes, consumption and utilization. The key information needs of the communities were: information on the onset of rainfall (39%), followed by information on access to market (22%), the need for irrigation facilities (18%), with the least being information on pasture preservation at 6% (Fig. 7), Yet the DEWS mainly gave information on food security (55%) as per (Fig. 6). There was also a gap of lack of information on pasture preservation and access to credit that was required by the households. During focus groups discussions, the respondents unanimously agreed that information on the onset of rains was the most important information that the farmers needed as one member put it: “without information on the onset of rains, then all the other DEWS information is useless, all that we need is reliable information on when the rains will begin”. The same focus group highlighted the inaccuracies in the information that was disseminated by DEWS. It emerged during FGDs that there were seasons when radio announcements were made telling people to open up land and plant,

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Fig. 5. Household rating of quality of information from DEWS.

Table 2 Responses on the quality of information from DEWS. Parameters Information Information Information Information Information

comes in time is useful to people's needs is easily understandable presented in local language is delivered through accessible channels

Mean Score

Std deviation

2.68 4.33 4.21 4.28 4.05

1.12 0.53 0.74 0.70 0.81

Fig. 6. Information provided by DEWS to households.

as rains were expected. However, as soon as the crops germinated, the rains disappeared and destroyed the crops. 4.3.4. Accessibility and preferred channels of information Households received information on drought early warning mainly through parish chiefs (51.3%), followed by extension staff (18.8%) and drama (14.9%) (Fig. 8). During monthly monitoring, the Parish Chiefs gave feed-back to the households on early warning

Fig. 7. Perceived information needed by the households.

messages. Despite the assumed great potential for information dissemination through radio, radio was among the least used by the households to access information from DEWS as indicated by (6.5%) of the study respondents. Drama was the third most used though it was limited by the problem of distortion of information during the process of

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Fig. 10. Challenges facing households in accessing information from DEWS.

translating information into drama. One respondent to the FGD noted that: “We enjoy the people acting but sometimes we cannot pick out exactly what they are saying from the drama”. The most preferred channel of accessing information by the respondents was radio mentioned by 69% of the study respondents followed by the Parish Chiefs (53%) and drama at (46%) (Fig. 9). The reasons given for radio, was that it could easily be accessed through friends and neighbors. Another explanation for the preference for radio was because the households thought that by mentioning radio, they would get free radios from ACTED since the organization had previously given out free radios. Parish chiefs were ranked in second position as the most accessible channel of accessing information about DEWS. This is because parish chiefs frequently visited the communities to collect information or accompany development workers. None of the households under the study in either of the two districts reported owning a television set. As clearly indicated in Fig. 10, the main challenges facing households in accessing information from DEWS include; unreliable information about the onset of rains mentioned by 62% of the study respondents and lack of functional radios as mentioned by 24% of the study respondents. The study respondents noted that most of the radios from ACTED were non-functional.

beginning with data collection by the parish chiefs; data entry using Nokia phones; data upload to the server by the District Early Warning Focal Person (DEWFP); Data validation and analysis by the district heads; developing of warning messages and finally disseminating of information to the community (ACTED, 2012). However, due to logistical constraints the approach to data collection was changed as reported by one respondent to the key informant interview: ‘'Initially we were to use Nokia phones to collect data from the households monthly. When the phones were brought they did not work completely possibly due to a compatibility issue with the software. Yes phones were delivered but we have never used them to collect data and send it to the district as planned (18th December 2014, Parish Chief). There were also partnership challenges: from project roll out it was mainly ACTED that undertook many of the implementation activities of DEWS. It was also acknowledged in one of the workshops that we participated in that the District Disaster Management Committees and food security working group workshops that were meant to review and validate the DEWS information bulletins before their release on a monthly basis rarely took place due to resource constraints. There was less involvement of other actors including Local Government Officers, and NGOs (Appendix 1). At the time of data collection, households had not received information for a period of five months due to a delay in approval of bulletins.

4.4. Analysis of DEWS implementation processes

4.5. Traditional mechanisms used by households to predict a prolonged drought

A review of the DEWS implementation documents and key informant interviews indicated that there was alteration from what was initially planned and what was implemented. The plan was to have the standard procedures for the implementation of DEWs

The respondents were asked if they had other means of predicting drought other than information from DEWS. Most (73.4%) of the respondents mentioned existence of traditional ways of predicting drought. Among the most popular means cited was the

Fig. 8. Most accessible channels for accessing information as perceived by respondents.

Fig. 9. Main channels preferred by households for accessing information. Challenges facing households access to information from DEWS.

Fig. 11. Some of the traditional methods of predicting drought used by households.

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Table 3 Effects of interaction between socio-economic characteristics of households and information channels in A Three -way interaction model. Variables

Chi-squared

Significance

Odd Ratio

Land*Radio, Age category Age Category*Radio, Education Level Age Category*Parish Chief, Education Level Gender*Parish Chief, Land Gender*Drama, Land Education level*Mobile phones, Age Category Education level*Sub-county Notice, Age Category

12.576

0.401

6.318

5.149

1.000

8.070

12.671 2.784 1.782 3.146 5.236

1.000 0.835 0.939 0.790 0.514

5.073 4.894 2.744 6.010 7.704

A *B or A*C or B*C means there is a significant influence.

reading of signs in animal intestines and entrails to predict drought and onset of rains, drought and water (Fig. 11). The households revealed that traditional knowledge is key for coping with the effects of drought. 4.6. Relationship between socio-economic characteristics of farmers and use of information channels Further analysis using the three way interaction model (Table 3) showed that the amount of land the farmers have, influences the use of radio in the three way interaction. The possible explanation for this is that agro-pastoralists with large areas of land are likely to be better off and are more likely to possess a radio which they listen to (Matinum, 1991). Age had no influence on the ownership of land. In the combination of education and age, age influenced the choice for radio while education level did not. This could be as a result of low education level for most of the agro-pastoralists. Age influences the use of radio as the elderly tend to be immobile and cluster around social places or community meeting places where radio is their common source of information. The elderly also preferred use of Parish Chiefs as their source of information because Parish Chiefs usually go to their villages and meeting places. The result further showed that female headed holds preferred use of information passed through Parish Chiefs and drama as they were always within the households and markets while the men travel distances to herd the animals. The information disseminated is on farming, rain onset as well as food storage and settling domestic issues which mainly affect women (Table 3). The respondents with higher educational levels obtained information from notice boards which are channels accessible to the literate. 4.7. Ways of improving the dissemination of DEWS information The respondents presented suggestions on how to improve the quality and dissemination process of DEWS. They mentioned the need for: mobilization and sensitization of community members about DEWS (51%), availing routine timetables on when the information is expected (19%) such that they could plan to move to the centers where radios are available; plan to attend community meetings to improve on the awareness of the program. Other suggestions included use of village leaders (27%), use of motorcycle/ bicycles to disseminate information by Parish Chiefs (5.7%), encouraging community members to join DEWS groups (2.8%), and 3% suggested the need to translate DEWS messages into all the local languages which are understood by the community. About 1.3% suggested the need for government to train all the DEWS implementers and work towards the reduction of the bureaucracy involved in generating the information and approval for dissemination. In addition, the focus group discussions with the households in the two districts, called for early provision of information “ tell us early when it will rain, let it be real, sometimes we are given wrong information and when we plant then the crops dry at

germination” . 5. Discussion 5.1. The effects of drought and coping strategies in Karamoja subregion Drought is a common occurrence in Karamoja sub-region. Communities in this region have coping strategies to drought and the introduction of DEWS was meant to enhance these coping strategies. Understanding the effects of drought and household coping strategies to drought is necessary in analysis of the local context of Karamoja sub-region. It is a region that is consecutively faced by drought with deleterious effects. The drought has major implications on the food security situation in the sub-region since it leads to water shortage for both crop and livestock production (Opiyo et al., 2015). Like most pastoralists, coping revolves mainly around livestock and people migrated in search for water and pasture. Only a few cows are left behind to supply milk and blood for the household (Egeru, 2016). Some of the people in the Karamoja sub-region are agro-pastoralists who depend on both crops and livestock. In most cases when the crops are destroyed then they sell livestock to buy grain as a coping strategy. From this study it is evident that there are non-traditional based coping strategies such as buying and storing of food, pasture preservation, and planting of short maturing crops (improved seed varieties), which could be attributed to information obtained from DEWS. 5.2. Respondents’ information needs and what is provided by DEWS The study findings showed that the respondents required more information on the onset of rains and improved varieties to enable them to enhance their crop production. This is because the area has only one season of crop production. Once the season is missed then the households have to wait for the next year to be able to grow crops (Levine, 2010). The findings also showed that DEWS mainly provided the households with information on food security and on the likely occurrence of drought. This is due to the fact that the major aim of DEWS was to address the food security problem that was and is still rampant in the region (FAO, 2009). Another explanation could be due to the availability of the food security information from the food security early warning system network (FEWSNET) that supplies monthly information to the KDEWS. The household respondents noted that they lacked information on pasture preservation and access to credit. The explanation for this is that there is limited community participation in the implementation of the DEWS project. During one of the review processes, the community members were visibly absent at the review workshop of the indicators to be monitored by DEWS. Such findings indicate that it is critical that DEWS prioritizes and addresses the needs of the stakeholders (Masinde, 2015; Pulwarty and Sivakumar, 2014).

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This calls for reviews of the processes to suit the changing dynamics of community needs. 5.3. Quality of DEWS information and dissemination channels The results from this study show that the quality of DEWS information was relevant to the needs of the community. The information was perceived as user-friendly and easily understandable by the majority of the respondents. However, the major challenge was delayed delivery of information to the households. This is attributed to the long process in generation of DEWS information and the red tape that is involved in the approval of DEWS bulletins before dissemination. Other explanations for the untimely information could be the long process that involves lengthy data collection by Parish Chiefs from the households, manual data entry into the system, delayed data analysis, lengthy review meetings with district sector heads to determine risk levels, slow development of bulletins and late submission of reports to the Ministry of Agriculture, Animal Industry and Fisheries for approval in Kampala before the information is disseminated within the Karamoja SubRegion. This whole process is time consuming and lengthy. Hence, there is a need to review the DEWS processes for improvement. This is in agreement with Coffey et al.'s. (2015) findings from research undertaken in Ethiopia, which indicated that early warning systems fail to provide timely information to the users. Indeed, UNEP (2012) also pointed out that the major setbacks of most of the early warning systems in Sub-Saharan Africa, is the gap in the communication of key messages. Other authors, such as Basher (2006) and Pulwarty and Sivakumar (2014), argue that most of the existing DEWS place more efforts in the generation of DEWS information but less resources are placed on the dissemination process. For any information, timeliness is regarded as the most important characteristic of quality information (Nazari and Hasbullah, 2010). Mihaly (2010) agrees that when information is delivered late, it is useless no matter how relevant it could be. Although the households rated the quality of information as reliable, information from the focus group discussions and some household surveys indicated that sometimes, the information provided is not certain. Coffey et al. (2015) suggest that such uncertainty risks do happen when information sources are limited. The early warning practitioners therefore need to have multiple sources of information which are supported by historic data sets that will help improve the accuracy of seasonal predictions. The other explanation for lack of certainty concerning information of the onset of rains, is the lack of equipment within the region for rainfall data monitoring. DEWS depends on the Department of Meteorology on rainfall information. The Kenya drought mitigation authority is coping as it has over 2000 weather stations at the subcounty levels (Shilenje and Ogwang, 2015).

extension staff was found to be limited to reach all the households. Sub-county notices, sensitization messages and mobile telephones were rated low partly because the households indicated that the distance to the sub-county and the sensitization meeting venues was a limiting factor to these modes of communication. Use of mobile phones was limited by low household ownership levels. Land size influences the preferred channels for information access by households. The possible explanation for this is that size of land or farmers with large tracts of land are likely to be rich and so are more likely to possess a radio as observed by Matinum (1991).

6. Conclusions and recommendations Hunger and malnutrition are the major effects of drought events. Both traditional and contemporary early warning systems are utilized by households. The study has shown that the information generated and disseminated to farmers from DEWS meets the farmers’ information needs especially information on rainfall, food security and improved crop varieties. In the Karamoja subregion, DEWS is useful to the community but its quality is affected by delayed delivery. Households in the area mainly rely on Parish Chiefs to access DEWS information and radio is the least used although most preferred channel of accessing information about DEWS. Additionally, land size, gender and level of education influence the preference of certain channels of accessing information by the community. It also seems that the current DEWS have been quite effective in delivering information to the community. However this is constrained by failure to translate the information into the local languages. Consequently, DEWS implementers need to shorten the process of information generation and dissemination by reducing on the levels of decisions making and use of information and communication technologies. There is need to employ development communication staff to support the DEWS in design of simple messages that the target households can easily understand. This will enable development of targeted information products for extension and community based messages. We recommend that the DEWS invest in translation of the DEWS bulletins into all the local languages that are used in Karamoja subregion for adoption and impact. There is need to integrate DEWS with the traditional indicators of drought in order to make it more relevant. There is also need for capacity building of the DEWS implementers on data collection, analysis and reporting to enhance information dissemination timeliness. Further research is needed into early warning systems specifically on enhancing the monitored variables; integrating traditional early warning indicators into the DEWS, and building flexibility into the early warning processes to meet the changing needs of its users and local dynamics in order to improve on the quality of information.

5.4. Accessible information channels Acknowledgements Findings showed that use of Parish Chiefs was the most accessible channel of disseminating information to the community. Parish Chiefs visit households at least once a month for data collection and during the same time they give feed back to the households on DEWS messages. Radio was the most preferred channel by most households. However, the rate of ownership of radios in Karamoja is limited to 13.8% (UBOS, 2016). Nazari and Hasbullah (2010) and Rasmussen et al. (2015), also highlight radio as a promising and the most effective channel for disseminating information due to its wide coverage. During focus groups, the challenge with radio is that battery cells are not affordable to most rural poor. Parish Chiefs and extension services were identified as ideal; however, the number of

This study was funded by the World Bank through the Agricultural Technology and Agribusiness Advisory Services (ATAAS) project Credit No. IDA Cr. 4769-UG and GEF Grant No. TF097184 -UG under the National Agricultural and Research Organization (NARO). The authors thank the household members in Kotido and Nakapiripirit who willingly participated in the study.

Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jaridenv.2017.05.010.

Please cite this article in press as: Akwango, D., et al., Quality and dissemination of information from a drought early warning system in Karamoja sub-region, Uganda, Journal of Arid Environments (2017), http://dx.doi.org/10.1016/j.jaridenv.2017.05.010

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Appendix 1. Divergence in specific roles of actors in DEWS implementation

Actors

Roles specified in DEWS implementation design

District Early warning Focal persons -LG

Coordinate the Early Warning activities in his/her district, this means: monitoring All these roles are being carried out by the ACTED Early warning the parish chiefs during data collection, data entry and analysis and produce officers. The Nokia Data gathering Tool are non-existent in the drought bulletins field. The partners strongly noted the lack of EWS capacity by most of the DEWFPS. In charge of coordinating the EW activities in his/her district, this (i) Give technical and financial support to the government, in running and means carrying out all the roles of EWFPS. improving DEWS efficiency (i) Resource mobilization and capacity building and technical backstopping Facilitated the design of DEWS through provision of equipment and infrastructure and technical backstopping (i)Provide food security data to the DEWS on quarterly basis Collect Food security data for DEWS, done bi-annually. Provided at (ii)Provide weather equipment the design, by the time of data collection were found not operational (i) Facilitate data collection on health indicators within the district Done

2.ACTED Early warning Officers 3. UNFAO-Program officer Karamoja 4. UNWFP Karamoja office

What they did during implementation

5. UNICEF Program officer Karamoja 6.District Heads of (i) Data analysis, dissemination and review of the system Units (LG Technocrats) 7. Parish Chiefs (i)Data collection at household, kraal and market levels on monthly basis 8.District CAOS (i) Conduct mobilization of the relevant heads of department (ii) Approve the last version of the Drought Bulletin after it has been analyzed by the heads of department (iii) Clearance of the radio messages to be aired to the communities &participate in DEWS workshops 9.Community (i) Encourage the Parish Chiefs in carrying out the data collection monthly by leaders creating the demand. (ii)They follow up that the data are indeed sent to the district before the end of every month. 10.Households (i) Major source of information that feeds into the system and they are the (DEWS) recipients of the information disseminated

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