Identifying efficient water storage schemes for climate change adaptation in agriculture: evidences from Indris watershed in Ethiopia

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1 Identifying efficient water storage schemes for climate change adaptation in agriculture: evidences from Indris watershed in Ethiopia Stefanos Xenarios a, Matthew McCartney a and Berhanu Adenew b a International Water Management Institute, East Africa& Nile Basin Office b Ethiopian Economic Association, Addis Ababa, Ethiopia Abstract Considerable research has been conducted into the technical adaptation measures that Ethiopian farmers might adopt to mitigate adverse climate change impacts. However, relatively little is known about Ethiopian farmers perceptions of using water storage and land allocation to combat climate change. This paper assesses the major water and land related attributes which could be used to mitigate the impacts of climate change in agrarian regions of Ethiopia. The study was conducted in the Indris small irrigation system located northwest of Addis Ababa in Oromia region. The findings indicate that farmers prefer to utilize modern water canals in combination with large plots in lowland areas and high water consumption, even if they have to share half of their production in exchange. Introduction The agricultural sector in Ethiopia comprises the main employment in the country (about 80% of the total workforce) and its contribution to gross domestic product (GDP) is significant (44%, CSA, 2011). The majority of farmers are subsistence agriculturists with very small landholdings, almost no mechanical support and negligible irrigation (Namara et al, 2007). Recent climate studies for Ethiopia indicate that high rainfall variability is likely to increase as a consequence of climate change and this will most likely worsen agriculture, as a consequence of higher frequency of droughts and floods (IPPC, 2007). Some studies have questioned the causal relationship between precipitation trends and agricultural impacts in Ethiopia (Meze Hausken, 2004). However, most climate change studies propose the rapid introduction of adaptation measures in farming. The vulnerability of Ethiopian farmers towards the current and future climatic effects have been considerably examined with sound quantitative approaches (Deressa et al, 2008). Also, the socio economic factors which might contribute to the alleviation of climate change impacts in agrarian regions of Ethiopia have been explored (Deressa et al, 2010). Further, statistical and econometric models were employed to capture farmers perceptions about the adoption of adaptation strategies for climate

2 change (Deressa et al, 2009, 2011). The inclusion of key socio demographic explanatory attributes in all the aforementioned studies enabled the identification of initiatives to support the weakest farming groups. The worrying projections of increased droughts, especially in lowland and semi arid areas, have revived the funding interest of international organizations for water storage development schemes throughout the entire Sahel area (Mertz et al, 2009). To date attention is focused largely on large water storage schemes though undertaking small scale land and water related adaptation strategies has been strongly advised. Considerable emphasis has been placed on water storage and land allocation as major responses for climate change adaptation in the agrarian regions of Ethiopia by however not exploring the users (farmers ) responses in such initiatives. Against this background, the study reported here assessed the farmers preferences in relation to land size and location, water storage, water volume and availability. The study employed a choice experiment approach while the analysis run with SPSS 19 statistical software. Material and Methods The selected site is located in the Indris small watershed about 130 km North west of Addis Ababa. Administratively, the area belongs to the West Shoa zone of the Oromia regional state, within the Toke Kutaye district (woreda). The altitude of 2100m attributes to a relatively warm ecology and supports a wide range of cultivated cereals and vegetables. The main crops grown in the area are teff, barley, maize, rice, oat, potato, tomato, cabbage, dagussa, beans and paprika. A considerable part of the agricultural area is irrigated by Indris river with two main concrete low elevated dams. The dams provide water supply all over the year for about 1750 users which are connected with soil made furrows. The farming conducted before the dam sites is identified as upstream agricultural area while after the dams is acknowledged as downstream. The two dams provide water only to downstream farmers since the system acts with natural gravity. For the downstream farmers, the regional Department of Agriculture imposes a quota of maximum water provision to one fourth of a hectare. The upstream farmers and the farmers who are out of reach of the irrigation systems, often construct makeshift embankments to the tributaries of Indris river. These systems which are usually acknowledged as traditional dams, store temporarily the water in artificial ponds and then divert it with natural gravity to the upstream farms. They are mainly made of wood, soil and crop residues. There are also about 18 pump owners almost all over the watershed (upstream and downstream), who exert pumping directly from the Indris river and its tributaries. There is not any prohibition for that, except for the case when the pump is placed directly onto the dam area. The majority of the farms is discerned by small and fragmented landholdings. However, there are also a few emerging small scale private commercial activities engaged in horticultural production. In general, the use of water for irrigation is not yet intensified due to the absence of pumping devices. However, the introduction of

3 small scale pumps, supported by regional government irrigation promotion, is anticipated to increase the number of pumps and the overall use of water. The data sampling was stratified into two equal groups: upstream farmers and downstream farmers. A total of 130 farmer households (50% from upstream and 50% from downstream i.e. below the dam and irrigation user community) were selected. The study was conducted in Birbirssa Kebele where approximately half the kebele s inhabitants were queried. Empirical Model and model Variables The present study attempted to identify the most efficient water storage and land related attributes for climate change adaptation, according to farmers perceptions. Initially, a descriptive analysis with key socio demographic elements was conducted. Namely, the sex, the educational level, the secondary employment and the income level factors were determined. Next, a choice modeling approach was employed, based on a contingent ranking technique, for the identification of the most efficient water storage scheme, defined in terms of utility. Contingent ranking (CR) is a survey based approach intended to assess the separate attributes which comprise an individual product (Smith and Desvousges, 1986). In our case, the product to be investigated was a set of components related to water storage and land allocation options. The attributes used in the modeling were land allocation, water source, water volume and the amount of harvesting to be exchanged in case of improved cultivating conditions. This amount of production actually portrayed the implicit economic value to be offered if the water and land related attributes would be better of. The respondents were asked to rank the described attributes under different scenarios. Each scenario presented different aspects of water and land related attributes which could be preferred by the respondents. The trade offs as derived from the differences in the respondents assessments were used to estimate the marginal utility of each attribute (Georgiou et al, 2000). Five scenarios were developed in our study to evaluate the trade offs between water and land related attributes. The scenarios were developed after field research for the capturing of the most attractive but also technically feasible water and land combinatory options. The input of the employees in the Agricultural Department of the region was also highly considered for the development of the scenarios. Attributes Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Land Place High Lands Sharing 250m 2 Sharing 500m 2 Renting Renting (current) in high lands in high lands 250m 2. in low 500m 2. in lands lowlands Water Source Makeshift embankments through furrows Makeshift embankments through furrows Makeshift embankments through furrows Concrete dams through furrows Concrete dams through furrows

4 Water Volume Giving in exchange 2hrs/week 4hrs /week 8 hrs/week 10hrs/week 14hrs/week Nothing Sharing 1/3 of Sharing 1/3 of Giving ½ of the production the production the production Table 1. Scenarios for mitigating climate change effects in Indris watershed Giving ½ of the production A random utility model was used to model the observed rankings in a way to describe the behaviour of individuals choice probabilities in response to changes in attributes that measure differences across individuals (Merino, 2003). The utility function of individual i for a scenario j consists of a deterministic part Vij and a stochastic part εi (Baarsma, 2003). For example, our case, the attributes of L (Land), WTSRC (Water source), WTVL (Water Volume) and WTAVL (Water Availability) are exhibited as below: Uij = Vij + εij = β1lj + β2wtsrcj + β3wtvlj + β4 WTRAVLj + εij...(1) Where i = individuals j = choice alternatives U ij = total utility individual i receives from choice alternative j Vij = observed or deterministic part of the utility function ε ij = stochastic portion of utility βn = weighting coefficient The β weights can be actually viewed as partworths that make all utility scales commensurate with each other, so that they can be summed to yield a single (overall) utility (Kjaer, 2005). In case of ranking different alternatives (scenarios) a respondent i might prefer scenario 5 to scenario 4, and scenario 3 to 4 and so on until scenario 1. This ranking order can be presented as follows: U...(2) i5 > Ui4 > Ui3 > Ui2 > Ui 1 Broadly, the probability of observing the rank order for a particular individual could be developed as below: Pr ob[ R ] = i n i [exp( β Lj + β WTSRCj + β WTVLj + β WTRAVLj) k= j n( β Lj + β WTSRCj + β WTVLj + β WTRAVLj) We can then establish likelihood function which defines the joint probabilities of the rank orderings as a function of the parameters of the indirect utility function. The

5 identification of the maximum likelihood estimation will indicate the attributes which maximize the probability that a respondent ranks the choices in the order that they are actually selected. Results First, we provide a descriptive analysis of the main socio demographic elements encompassed in the sample (Table 2). The majority of the sample was dominated by male respondents. Also, the greatest number of respondents were school graduates while an almost equal number were illiterate. A much smaller portion had attended the first and second cycle of graduate school. Further, most of the respondents were solely occupied in farming. It is also rather interesting to note that about 60% of the respondents were below the poverty level of 2$ a day (11,680ETB 1 per year), as (poverty line) established by international organizations (World Bank, 2010). This fact highlights the likely high vulnerability of most of the farmers to potential climate change impacts. Socio demographic factors Attributes Distribution (%) Sex Male 82.3 Female 17.7 Graduate.8 Secondary 1st Cycle 11.6 Secondary 2nd Cycle 11.6 Educational level Primary 37.2 Traditional School 7.0 Read and write 3.9 Illiterate 27.9 Secondary employment Yes 15.4 No 84.6 <= 11, , , Income level (ETB) (per annum) 20, Table 2. Distribution of socio demographic attributes The results of the contingent ranking analysis are presented using the utility scores and the standard errors for each factor level (Table 3). The higher utility values indicate greater preference. As presented in table 3, there is an inverse relationship between the land harvesting to be given in exchange with the utility, as indicated by the negative values. The higher the renting or sharing of land, the more negative values are given. The same situation occurs with the production sharing where a proportional negative increase appears. It is surprising that for the water storage 1 The equivalence of United Stated dollar ($USD) with the Ethiopian Birr (ETB) is considered as 1:16 ratio.

6 options, the utility value appears to be more negative in the case of the modern compared to the traditional canal option. However, for the case of water volume, the utility increases proportionally with weekly water consumption, as might be expected. Attributes Land allocation Utility Estimate Std. Error High Lands (current) Sharing 250m 2 in high lands Sharing 500m 2 in high lands Renting 250m 2 in low lands Renting 500m 2 in lowlands Traditional Canal through furrows Water storage types Modern Canal through furrows hrs/week hrs/week Water consumption 8hrs/week hrs/week hrs/week Nothing Giving in exchange Sharing 1/3 of the production Sharing 1/2 of the production Constant Table 3. Attribute utilities for the developed scenarios In turn, the weighting factor β or else the coefficient of each attribute is estimated accordingly. As presented in table 4, all coefficients are negative except for water consumption; a fact which deteriorates the negative utility resulted in the estimates. Coefficients Estimates Coefficients Estimates Land allocation.001 Water storage types.002 Water consumption.002 Giving in exchange.012 Table 4. Coefficients of the four attributes Since all the utilities are expressed in a common unit, a simple summation of the partial utilities, multiplied by the relevant coefficients, indicates the total utility for each of the five scenarios as below: Scenarios Scoring Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Totals Table 5. Total utility accounted for each scenario

7 The results indicate that the farmers preferred Scenario 5. It appears that the leasing of large plots, the exchange of half of the production and the use of modern water storage systems could be counterbalanced with the largest weekly water consumption. It is interesting to note that although a higher negative utility was associated with the modern canal systems (i.e. compared to other water storage options) in the case of scenarios, the modern canal was preferred as a component of the overall alternative. The range of estimated utility of each factor provides a measure of the importance of that attribute in respect to the overall preference. The attributes scored with higher utility are more significant than those with lower utility. To this end, we design a significance chart in which the score given to each attribute is computed by dividing the average utility range by the sum of the utility range of all attributes (Figure 1). Figure 1. Importance of attributes in the scenarios As indicated in Figure 1, the Giving in Exchange attribute (i.e., the indirect cost that has to be paid for a better plot of land and more water from the concrete canals), is identified as the most important factor. The Water Consumption factor is acknowledged as the second most important attribute with moderate distinction from Land Allocation. Interesting is the case, that the Water Storage Type is

8 considered of least importance, thereby indicating farmers relative indifference towards the selection of water storage. Conclusions The choice modelling analysis was conducted for the identification of the most preferred scenario of water storage and land allocation types in Indris watershed by providing some interesting findings in relation to adaptation in climate change. It seems that the farmers are willing to sacrifice up to half of their yearly production in return for more land and better water provision. The attributes of water storage are not deemed to be a major determinant for climate change adaptation and farmers rate water storage much less important than other factors. Hence, from a policy perspective, it seems more attention should be given to land allocation and the amount of water to be supplied rather than the type of water storage. It should however be noted that the relatively small sample size could mean that results deviate from those that might be obtained from a larger sample. Acknowledgements This study was conducted for the project Rethinking water storage for climate change adaptation in Sub Saharan Africa, funded by the German Gesellschaft für Technische Zusammenarbeit (GTZ) as part of its programme on Adaptation of African Agriculture to Climate Change.

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