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Nov 18, 2015 - area of 7000 ha of land (Mott MacDonald, 2002). 2.2. Features of Koga irrigation system. Koga irrigation system comprises of 19.7 km of lined ...
Agricultural Water Management 170 (2016) 26–35

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Evaluating and enhancing irrigation water management in the upper Blue Nile basin, Ethiopia: The case of Koga large scale irrigation scheme Sisay B. Asres Department of Irrigation Engineering, College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar, Ethiopia

a r t i c l e

i n f o

Article history: Received 27 July 2015 Received in revised form 16 October 2015 Accepted 27 October 2015 Available online 18 November 2015 Keywords: Irrigation water management Demand and supply Performance evaluation

a b s t r a c t This paper deals with the objective of evaluating and enhancing irrigation water management of Koga large scale irrigation scheme located in the Blue Nile basin of Ethiopia. Disturbed and undisturbed Soil samples were collected from selected irrigation blocks within the irrigation system. Soil moisture, texture, field capacity, permanent wilting point and bulk density data were obtained from laboratory analysis of the samples. Results of demand versus supply analysis of the scheme showed that there was excess supply at the beginning of reservoir release and upto 7.13 MCM of excess flow water was estimated in year 2015. Results also showed that crop water requirement value varied for each block and for different crops in the same block, assuming the climatic conditions of the site constant. The crop water requirement variations were caused by differences in soil water holding capacity of each block. Based on crop water demand analysis result with appropriate crop water provision of 50% efficiency, the maximum irrigable area which could be accommodated by the reservoir storage was 5635.8 ha as compared to the design command of 7000 ha. The paper also investigated the status of reservoir water availability as compared to the demand and annual release. The findings of this research will have greater implications in creating awareness to the water user associations, farmers and gate operators of Koga irrigation scheme on how to measure the amount of water they are using during the whole crop growth so that optimum irrigation water shall be delivered to a crop for maintaining water management. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Irrigation is the reliable method of increasing agricultural production and productivity and has greater impacts in solving food security problems in many parts of Ethiopia. Realizing its importance for food production, the country has been allocating huge investments for irrigation infrastructure development over the last two decades (Abate, 1994). This investment, together with improved crop production technologies has enabled the country to move towards achieving self-sufficiency in food production. Nevertheless there is also an issue in which many irrigation schemes do not perform according to the design expectations (Checkol and Alamirew, 2008). Towards solving such issues, performance evaluation studies and management of the application of water to irrigable land should be important within a given irrigation scheme for achieving more benefits. In this regard irrigation performance includes the result of variety of activities such as planning, design & construction, operation of facilities, maintenance and application

E-mail address: [email protected] http://dx.doi.org/10.1016/j.agwat.2015.10.025 0378-3774/© 2015 Elsevier B.V. All rights reserved.

of water to the land. Basically issues of the application of the right amount of water to the land at the right time are more common in various irrigation schemes available in Ethiopia (Hagos et al., 2009), which requires local solutions. The objective of this study is mainly focused in evaluating and enhancing irrigation water management of Koga large scale irrigation scheme in the upper Blue Nile basin of Ethiopia. The study also assesses basic procedures used to support sustainable irrigation water management practices. Evaluation studies are crucial in such large scale community owned scheme not only to enhance the future irrigation water management but also to primarily deal with investigations related to the imbalance between the water supply and the crop water demand. Evaluation of an irrigation system will provide the necessary information for scientific irrigation scheduling (Gorantiwar and Smout, 2005). It will also tell us if the scheme is experiencing excessive application losses, in which case the irrigation system needs service or improvement to increase application efficiency. The goal of system evaluation is to determine how much water is being applied for which the ultimate result is water saving. In other words evaluating and improving an irrigation system will help to operate irrigation systems near their design limits to achieve peak design

S.B. Asres / Agricultural Water Management 170 (2016) 26–35

efficiencies. Evaluating and selecting improved and efficient irrigation water management practices are essential for enhancement of farm profitability by increasing the water productivity in the period of limited water supplies(Ali, 2010). Efficient irrigation water management measures will increase crop production by increasing the availability of more irrigation water (Ali, 2010). Irrigation water management is also concerned with how the available reservoir water can efficiently be allocated and utilized to fulfill the required crop demand per irrigation period. In dealing with irrigation scheme evaluation, demand side evaluation and management is the most important intervention commonly implemented for water loss minimization and protection of water resources for efficient and effective use of water. Demand side management includes measures that aim to increase the efficiency of water use. Different approaches of demand management can be proposed depending on the results of this study as well as the potential of an irrigation scheme. Different authors found various forms of irrigation water saving mechanisms among which deficit irrigation can primarily be mentioned. Experimental results suggest that water use can be decreased by almost 33% by using alternate furrow irrigation approach depending on the crop type and soil properties (Kang et al., 2000; Zongsuo et al., 2000). Thus, increasing field irrigation efficiencies enable to grow more crops to be produced with less water (Raine and Bakker, 1996). Improved scheduling of irrigation increases water management based on soil water measurements by using estimates of daily evapotranspiration rates using climatologically methods and evaporation pans (Kirda, 2002). The use of seedlings could also reduce crop duration. Experiment had showed that total crop duration in the field (from transplanting to maturity) can be reduced by 30 days, which obviously reduces the crop water requirement, and increases water productivity (Ali, 2010). All the above and newly emerged water management technologies can be applied for future enhancement after irrigation scheme evaluation studies. On the other hand, supply side management options refer to the actions that affect the quantity and quality of water at the entry point to the distribution system. The approach includes considerations of conceptual water balance, influence of sedimentation and the stakeholder involvement. In places where dam reservoirs are the major supply sources like Koga large scale irrigation scheme, the issues of sedimentation and evaporation will be pronounced and hence supply side management evaluation shall be a great strategy to be conducted to meet the downstream irrigation demand. 2. Study area and data collection

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905 km of unlined quaternary canals and 11 lined Night Storage Reservoirs (NSRs). The main canal was designed to provide irrigation water for 24 h during irrigation period. There are 12 secondary canals designed for 12 h irrigation supply each covering an area of irrigated land ranging from 220 ha to over 1000 ha. Tertiary canals are designed for 12 h irrigation supply. The area irrigated by a tertiary canal range between 20 ha to 65 ha. The quaternary canals have a capacity of irrigating 8–16 ha of land while field canals will serve an area of 2.0 ha within the quaternary unit. The maximum field canal design capacity is 30 l/s. (Mott MacDonald, 2002). 2.3. Data sources and data collection 2.3.1. Secondary data collection Secondary data sources were primarily selected before proceeding to the collection of primary data. Ministry of water, irrigation and energy (MoWIE), Abay Basin Authority (ABA), Tana sub-basin Organization (TaSBO) and National Meteorological Agency (NMA) were primarily selected secondary data sources. Crop information such as cropping calendar and cropping intensity data were collected from Koga irrigation development management unit (KIDMU). KIDMU has a release database system on the main canal and the release data from year 2011 to 2015 were collected. 2.3.2. Primary data collection Primary data was collected by investigating the overall command area of 11 secondary irrigation blocks. Six major secondary blocks with a total irrigable area of 3576 ha (51%) have been selected for this study. Soil texture was taken at depths of 10 cm, 30 cm and 60 cm in the plant root medium. Disturbed and undisturbed soil samples were collected from each block. Undisturbed soil sample data was collected for testing bulk density; moisture content, field capacity and permanent wilting point. Undisturbed soil samples were collected by means of core sampler at depths of 30 cm and 60 cm to investigate the differences in density and Total Available Water (TAW) at each soil layers of each Block. It is presumed that the depths represent the depth of the effective root zone of the most likely selected irrigated crops and vegetables. For example, the maximum effective root zone of small vegetables, like onion, is 60 cm (Allen et al., 1998). Tube infiltration tests were also conducted at the field conditions for selected irrigation secondary blocks. The discharges of secondary, tertiary and quaternary canals were measured by using equipment called propeller type current meter velocity measurement. The canal cross section data was obtained through direct measurement using graduated staffs and meter tape.

2.1. Description of the study area Koga River is a tributary of Gilgel Abay River located in the headwaters of the upper Blue Nile catchment in Ethiopia. Its catchment area is about 220 km2 including a reservoir area of 17 km2 . Koga dam is located in geographic coordinates of 11.35◦ N latitude & 37.14◦ E longitude, at an altitude of 1900 m above sea level. The scheme is specifically located Near Merawi town which is located 35 km from the city of Bahir Dar. Location map of the project area is shown in Fig. 1 below. The detail design of Koga irrigation scheme was completed in the year 2002 and its construction completed after 8 years in 2010. The reservoir impounding capacity is 83.1 million cubic meter (MCM) and planned to irrigate a maximum area of 7000 ha of land (Mott MacDonald, 2002). 2.2. Features of Koga irrigation system Koga irrigation system comprises of 19.7 km of lined main canal, 52 km of lined secondary canals, 156 km of unlined tertiary canals,

2.3.3. Meteorological data Merawi meteorological station which is available near the project site is non synoptic station which has only temperature and rainfall data records. Hence temperature (minimum and maximum) and rainfall data were collected from this station. For other meteorological variables, Dangila and Bahir Dar Meteorological stations were additionally selected. Dangila meteorological station has data records from 1997 to 2012 years. The monthly total rainfall data recorded from 1997 to 2012 was taken for analysis. Bahir Dar station is also a synoptic station containing necessary climatic data used for irrigation crop water requirement computation. It has all elements of climatic data from 1960 to 2012 record years. Koga scheme is located at 40 km from Bahir Dar and 39 km from Dangila. Therefore the average of the two nearby synoptic stations has been used for Koga scheme computation of crop water requirement. The data is organized in Table 1 below. The data was used for Koga crop water requirement calculation is the average of Dangila and Bahr Dar synoptic stations.

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S.B. Asres / Agricultural Water Management 170 (2016) 26–35

Fig. 1. Location map of Koga irrigation scheme. Table 1 Koga irrigation scheme climate input data. Country

Ethiopia

Koga irrigation scheme CROPWAT 8.0 climate input data for consumptive crop water use/demand computation

Station Altitude Latitude Longitude

Koga 1900 m 11.41 N 37.42 E

Month

Min. temp. (◦ C)

Max. temp. (◦ C)

Humidity (%)

Wind (km/day)

Sunshine (hour)

Rainfall (mm)

January February March April May June July August September October November December

5.9 7.9 9.5 11.1 13.6 12.7 13.5 13.3 12.6 11.5 8.7 6.0

28.7 30.0 30.3 29.9 27.9 25.5 23.6 23.8 25.1 26.6 27.3 27.3

53.4 45.2 45.8 47.3 58.1 73.5 76.9 81.4 76.9 69.5 62.7 55.9

65.14 72.41 84.14 87.55 87.71 84.31 77.08 75.06 67.09 61.33 53.5.0 53.77

9.4 9.3 8.7 8.6 8.0 6.6 4.4 4.4 6.4 7.6 9.0 9.3

0.2 0.9 26.6 15.6 147.3 358.9 418.4 334.9 287.2 119.9 24.9 3.7

S.B. Asres / Agricultural Water Management 170 (2016) 26–35

2.3.4. Laboratory data Amhara Design & Supervision Works Enterprise soil and Geo-technique Laboratory center was selected for conducting Laboratory analysis of soil samples. Bulk density, FC and PWP were very critical for irrigation water management studies of schemes more specifically for irrigation scheduling. Infiltration test was also conducted at selected fields using tube infiltrometer and was found to be in the ranges of 30–34 mm/day. The laboratory analysis results for each parameter per each irrigation block are shown in Table 2 below. 2.3.5. Crop data Major crops practiced in Koga scheme were wheat, barley, bean, maize, cabbage, potato, tomato, onion, shallot and pepper. Crop coefficient data was collected from FAO recommendations for each of selected crops. The crop coefficient integrates the effect of characteristics that distinguish a typical field crop from the grass reference crop which has a constant appearance and a complete ground cover. Different crops have different crop coefficients (Kc) and the CROPWAT 8.0 model requires crop data to be entered for each crop and growth stage. The study used the crop patterns which were practiced in the past 3 years including year 2015. The major crop characteristics collected from field investigation and FAO-56 reference (Allen et al., 1990) are organized in Table 3 below. 2.3.6. Reservoir water release Koga dam has computerized control operating system for main canal release. Reservoir water from the dam has been released on the basis of farmers demand for each irrigation block. A time series release processed data from 2011 to 2015 was collected from Koga irrigation development management unit (KIDMU). A time series plot of the release is shown in Fig. 2 below. As shown in the figure, the peak release was recorded between the months of January and February where peak demands were expected. in year 2012, mode of release was bimodal and the crop area was divided in two periods. Some irrigation blocks were planted in December and the rest blocks were planted late in January where the peak release fall in February whereas, in 2013 irrigation year, there was higher release in the month of December due to higher irrigation water requirement for land preparation. The different release patterns from year to year indicated that the irrigation operators were practicing flexible release schedule due to variations in crop water demand caused by cropping patterns and difference in planting time. 2.3.7. Measures of scheme performance evaluation Conflicts between water users have been very common in Koga irrigation scheme specially during flowering stages of crop growth. Usually such conflicts would be solved primarily through increasing irrigation water efficiencies so that farmers would irrigate fields with less water. In addition, increased irrigation efficiencies generally mean better water management practices which, in turn, often produce high water productivity. In this regard it is advisable to evaluate the status of water delivery by comparing it with the expected design flows and the demands. It can be evaluated in terms of water supply-requirement ratios and other indices such as cropping intensity. Efficiencies related to water supply requirement ratios include secondary/tertiary unit ratio, equity and adequacy. The objective of adequacy states the desire to deliver the required amount of water over the command area served by the system. A measure of performance relative to adequacy (PA ) for a scheme or blocks served by the system is proposed as (David et al., 1990): PA =

QR QD

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Where QD = the actual amount delivered by the system, QR = the amount of water required for consumptive use, farm application and conveyance losses downstream of the delivery point. The performance of Koga irrigation system relative to adequacy and due to structural characteristics (PAS ) shall also be checked by the ratio of the amount deliverable or capacity (Qd ) to the amount scheduled (Qs ) over the irrigation scheme or blocks and time period of interest (David et al., 1990). It is given by the equation: PAS =

Qd Qs

Where Qd = the amount deliverable, Qs = the amount scheduled/planned. Deliverable amounts (Qd ) less than scheduled amounts (Qs ) will indicate the overflows in the system showing greater inadequacy. Similarly, the performance due to adequacy and management characteristics (PAM ) can be determined by the ratio of the amount delivered to the amount deliverable (David et al., 1990): PAM =

QD Qd

Delivered amounts (QD ) less than deliverable amounts (Qd ) indicated a breach of operating policy, causing the value of PAM to be less than unity. This might mean that management practices were contributing directly to inadequate delivery or that management was attempting to compensate for an inefficient scheduling policy or structural design.

3. Crop water demand estimation model 3.1. CROPWAT 8.0 model Efficient models are necessary for estimation of crop water demand for Koga irrigation scheme. The usefulness of an estimate of past, current and future irrigation water demand largely depends on how closely prediction can be made of areas under different crops, cropping sequences and crop calendar. The water need of a crop consists of transpiration plus evaporation, together called evapo-transpiration. The effect of the major climatic factors such as sunshine, temperature, humidity and wind speed on crop water needs is also important so that such climatic element data are required as inputs for selected computational models (Allen, 1998; Allen et al., 1998). CROPWAT 8.0 model was developed for the water resources development and management service of FAO (Allen et al., 1990). The model allows the user to either enter measured ETo data values, or provide input data such as temperature, humidity, wind speed, sunshine hours and other relevant data so that the model is able to calculate ETo. The Model also requires the Total soil Available Water (TAW) which represents the total amount of water available to the crop. It is the difference in soil moisture content between Field Capacity (FC) and Wilting Point (WP) since there is no water available for the plants above the FC level due to the force of gravity. Likewise, plant roots cannot extract water below WP level as it is retained at high pressures within the soil matrix. All these necessary variables were determined in soil laboratory. Another most important input to the model is critical depletion fraction denoted by (p). According to Ali (2010), values are expressed as a fraction of Total Available Water (TAW) and normally vary between 0.4 and 0.6, with lower values taken for sensitive crops with limited rooting systems, and higher values taken for deep and densely rooting crops. In this study initial depletion fraction has been assumed to be 0.50.

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Table 2 Soil Laboratory results. No.

Irrigation block name

FC (%)

PWP (%)

TAW (%)

TAW mm/m depth of soil

Infiltration (mm/day)

1 2 3 4 5 6

Kudmi Chihona Ambo Mesk Adbera Mariam Enguti Tagel

25.89 24.1 26.02 26.64 27.44 26.46

20.15 18.51 19.21 20.26 22.19 21.52

5.74 5.59 6.81 6.38 5.25 4.94

57.40 55.90 68.10 63.80 52.50 49.40

34 33 30 30 33 32

Table 3 Crop characteristics data for model input. No.

1 2 3 4 5 6 7 8 9 10

Crop type

Initial stage

Wheat Barley Maize Bean Potato Cabbage Tomato Shallot Onion Pepper

Dev’t stage

Middle season

Late season

Crop height (m)

(days)

(Kc)

(days)

(Kc)

(days)

(Kc)

(days)

(Kc)

15 15 25 20 30 40 30 5 5 25

0.70 0.30 0.50 0.50 0.70 0.70 0.50 0.50 0.60 0.60

25 25 40 30 35 60 40 25 25 35

0.93 0.73 0.85 0.78 0.93 0.83 0.73 0.78 0.78 0.78

50 50 45 30 40 50 45 40 40 40

1.15 1.15 1.20 1.05 1.15 0.95 0.95 1.05 0.95 0.95

30 30 30 10 25 15 30 30 30 25

0.32 0.25 0.50 0.90 0.75 0.70 0.70 1.00 0.75 0.60

1.00 1.00 2.00 0.40 0.60 0.30 0.40 0.40 0.40 0.70

Nov,12,2011 Dec,06,2011 Dec,11,2011 Dec,27,2011 Jan,19,2012 Jan,25,2012 Jan,29,2012 Feb,22,2012 Apr,25,2012 Jul 1,2012 Nov 6,2012 Dec 3,2012 Dec 30,2012 Jan 12,2013 Mar 16,2013 Apr 21,2013 Jun 1,2012 Nov 4,2013 Nov 24,2013 Dec12,2013 Jan 1,2014 Mar 11,2014 Apr 11,2014 May 18,2014 Jul 1,2012 Nov 17,2014 Dec 7,2014 Mar 6,2015

Release in MCM

Koga Reservoir Water Release from 2011-2015 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 -

Release Dates Fig. 2. Koga reservoir water release for irrigation.

3.2. Running the model

4. Result and discussion

Crop water demand was computed using this model for 10 selected irrigable crops repeatedly practiced from 2013 to 2015 production years. The crop planting date and the growth period was collected from recorded data and field investigation. Interview was also conducted to find the actual planting date for each crop. Climate data was kept constant for all irrigation blocks because for small areas climate spatial variations can be considered negligible. An overall irrigation efficiency of 50% was selected. Using all CROPWAT 8.0 model input values, the software was run and results were collected on daily basis. The values such as planting date, growth stage, rain, crop coefficient, evapotransipiration, percent depletion, net irrigation, deficit, loss, gross irrigation and flow duty (l/s/ha) were exported to excel for further analysis. Daily demand in units of liters per second per hectare(l/s/ha) was collected for each crop in columns and multiplied by each crop area, and then rows summed up to get the total daily inflow demand required in liters per second (l/s). Actual irrigated area of each block per year was collected from KIDMU database. At the end, the daily flow requirement for all crops was added up to similar time scale as the release.

4.1. Demand and supply Demand and supply analysis results showed that supply was greater than the demand during early irrigation periods before the maximum water demand. Crop water demand versus supply through main canal is plotted as shown in Fig. 3 below. From observation of local practices, two reasons have been noted for such excess supply beyond the crop demand. Firstly, reservoir water was released to some irrigation blocks for the purpose of land preparation. Secondly, there was inadequate canal clearance which could increase seepage and percolation losses in unlined earth canals. The crop water demand and the actual main canal flow results were also compared with the peak design inflow values allowed for the critical period. The peak releases were recorded in Dec 27, 2011, January 12, 2013, January 1, 2014 and February 4, 2015 with releases of 7500, 6200, 6500 and 7300 l/s respectively. However those values of release were very far from peak period design release values which were between 8500–9100 l/s (Mott MacDonald, 2004). This showed that the peak design release and computed peak demand curves matched well but the peak demand was higher than the actual peak supply for all irrigation years. The

S.B. Asres / Agricultural Water Management 170 (2016) 26–35

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60 50 40 30 20 10 0

Supply Oct,20,2012 Nov 16,2012 Dec 3,2012 Dec 26,2012 Jan 6,2013 Feb 22,2013 Mar 16,2013 Apr 12,2013 May 13,2013 JUL Nov 1,2013 Nov 16,2013 Nov 25,2013 Dec12,2013 Dec 18,2013 Mar 6,2014 Mar 17,2014 Apr 11,2014 Apr 26,2014 JUL Nov 3,2014 Nov 25,2014 Dec 15,2014 Mar 6,2015 Apr 12,2016

Release in MCM

Koga scheme

Demand

Irrigaon period Fig. 3. Koga scheme reservoir water release versus demand (2013–2015).

result also showed that over-irrigation has been seen during early stages of crop growth where as under-irrigation was observed during flowing stage of crop growth. Demand versus supply analysis was also performed for 2015 irrigation year to investigate whether there was an overflow in each secondary irrigation blocks. As shown in Table 4 below, among the six blocks, Ambo-Mesk, Tagel-Wedefit and Chihona overused 0.83, 0.64 and 0.60 Million cubic meters (MCM) of reservoir water. This showed that in year 2015, a total of 7.13 MCM of water (which would have approximately irrigate an area of about 700 ha) had been overused either for the purpose of land preparation or due to losses resulted from inadequate canal clearance. On the basis of farmers’ experience, millet was the most prominent rain fed crop within the scheme. Dry plowing of millet land was hard to practice with conventional draught power (oxen drawn) unless a heavy duty tractor would be engaged. Since tractors were not used during irrigation land preparation, farmers were highly practicing irrigation for the purpose of land preparation. Similarly overflow volume of irrigation water was analyzed for 2013 and 2014 years as shown in Table 5 below. The result indicated that proper cropping calendar of rain fed agriculture has to be prepared by considering the activities of the incoming irrigation periods. On the other hand, irrigation technology should be supported with proper and appropriate agricultural mechanization (pre-harvest and post-harvest) not only to prepare the land and save water but also to save time for irrigation activities.

4.2. Evaluating reservoir water volume with respect to demand The impounding capacity of Koga reservoir is about 83.1 MCM including the dead storage volume of 0.393 MCM. The canal outlet level was fixed to an elevation of 2005.4 masl and the bottom outlet crest level was located 3.50 m below the crest level of the canal outlet at 2001.90 masl (Mott MacDonald, 2004). A constant volume of sediment deposition was expected to occur in the reservoir in spite of sediment flushing mechanisms through the bottom outlet and over the spillway. In this regard, reservoir capacity at the end of the rainy period of 2014 became about 82.419 MCM with the design annual sediment accumulation of 48,000 m3 ; and 82.203 MCM with bathymetry annual sediment accumulation of 84,000 m3 conducted in 2012. The analysis result for the last 6 practicing years showed that a total capacity reduction due to reservoir sedimentation was about 0.288 MCM according to the design and 0.502 MCM according to bathymetry survey. Reduction in capacity of the reservoir enhances the spillage of the sediment laden runoff as a result of reduced trap efficiency. Reduction of reservoir capacity due to reservoir sedimentation is directly associated with reduction in irrigable land. In line with this discussion, the total irrigable

land of about 7000 ha had been reduced to 6957.3 ha according to bathymetry sediment estimation as shown in Table 6 below. The worst sedimentation condition in the reservoir has an implication to the reduction of irrigable land. The rate of irrigable land reduction per year for the last 6 years was 7.11 ha/yr. Based on the land holding size (ha) of 1.0 ha/HH(house hold) allocated for each farmer in the scheme, the reduction in irrigable land is equivalent to a loss of 7 farmers land each year. It can be observed that under the 50% efficiency operation practices, actual reservoir live storage is much greater than the demand for all the 3 years as shown in Table 7 below. But under scheme efficiency of 60% or less, the total demand was less than the live storage indicating that the scheme was working in attaining a maximum goal of 60% efficiency not 50% as stated in the design document. This also implied that with an efficiency of 50% or less, the command area of 5132.04, 5828.79 and 6318 ha was in peak period deficit irrigation in the years 2013, 2014 and 2015 respectively. In other words, with appropriate crop water provision (no deficit) of 50% efficiency, the maximum irrigable area which could be accommodated by the reservoir storage was 5635.8 ha as compared to the design command of 7000 ha. 4.3. Evaluation and enhancement of irrigation water management Net crop irrigation water requirement per period was computed for production years of 2011–2015 of Koga scheme as shown in Table 8 below. It was found that the result of crop water requirement value varied for each block and for different crops in the same block, assuming the climatic conditions of the site constant. The crop water requirement variations were caused by differences in soil water holding capacity of each block. In other words, a crop can require less net irrigation water in a block than other blocks showing that adjustment of cropping pattern based on this knowledge is crucially important for water use enhancement regardless of other crop limiting factors. To summarize results, potato consumed less irrigation water per period in Inguti irrigation block (261.60 mm) than Chihona (269.8 mm). Likewise wheat, maize, pepper, shallot, cabbage and bean required less irrigation water per period in the blocks of Kudmi, Ambo Mesk, Adbera Mariam, Tagel Wedefit, Adbera Mariam and Inguti respectively. This implied that irrigation water management can be improved through selection of appropriate crop for each irrigation block. 4.4. Evaluation of cropping preference In Koga irrigation scheme the relative advantage of cropping patterns varied from year to year depending on the market

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Table 4 Overflow volume in MCM of water released in each irrigation block in year 2015. Release dates-2015

Kudmi

Chihona

Ambo Mesk

Adbera M.

Inguti

Tagel

All

November 3, 2014 November 17, 2014 November 18, 2014 November 25, 2014 November 27, 2014 December 7, 2014 December 15, 2014 December 24, 2014 February 4, 2015 March 6, 2015 March 24, 2015 April 12, 2015 Total

0.02 – – – 0.20 0.06 0.04 0.04 – – – – 0.37

0.02 – – – 0.32 0.11 0.10 0.05 – – – – 0.60

0.04 – – – 0.23 0.17 0.26 0.14 – – – – 0.83

0.02 – – – 0.38 0.14 0.12 0.01 – – – – 0.67

0.02 0.00 – – 0.20 0.12 0.05 – – – – – 0.39

0.03 0.00 – – 0.31 0.03 0.05 0.22 – – – – 0.64

0.32 0.00 – – 3.47 0.32 0.59 2.43 – – – – 7.13

Table 5 Three years overflow volume (MCM) of irrigation blocks in Koga irrigation scheme. Years

Kudmi

Chihona

Ambo Mesk

Adbera M.

Inguti

Tagel

All

2013 2014 2015

0.10 0.35 0.37

0.08 0.49 0.60

0.31 0.58 0.83

0.25 0.25 0.67

0.09 0.06 0.39

0.26 0.32 0.64

1.97 5.13 7.13

Table 6 The influence of sedimentation on Koga irrigation water management. Years

Annual net capacity (design case) (MCM)

Annual net capacity (bathymetry case) (MCM)

Area reduction-design case (ha)

Area reductionbathymetry case (ha)

Actual area irrigated (ha)

Modified cropping intensity

2009 2010 2011 2012 2013 2014 2015 Difference

82.707 82.659 82.611 82.563 82.515 82.467 82.419 0.288

82.707 82.623 82.539 82.455 82.371 82.287 82.203 0.504

7000.0 6995.9 6991.9 6987.8 6983.7 6979.7 6975.6 24.38

7000.0 6992.9 6985.8 6978.7 6971.6 6964.5 6957.3 42.66

– 694.88 1808.00 5115.00 5132.04 5828.79 6318.00 –

0.000 0.099 0.259 0.732 0.735 0.835 0.906 –

Table 7 Total demand versus reservoir capacity (in MCM) from 2013 to 2015 irrigation years. Years

Actual reservoir volume (MCM)

Selected efficiency and corresponding demands computed (MCM)

2013 2014 2015

0.50

0.60

0.70

89.49 83.11 8.48

74.57 69.26 67.90

63.92 59.36 58.20

Remarks

82.37 82.29 82.20

Table 8 Net irrigation water requirement of crops per period for 6 blocks of Koga scheme. No.

Irrigation block name

TAW (mm/m)

1 2 3 4 5 6

Kudmi Chihona Ambo mesk Adbera Mariam Enguti Tagel

57.40 55.90 68.10 63.80 52.50 49.40

Net irrigation requirement computed in mm Wheat

Barley

Maize

Bean

Potato

Cabbage

Tomato

Shallot

Onion

Pepper

347.4 353.5 351.7 353.2 352.6 353.9

334.7 334.6 330.4 333.1 332.8 329.4

491.6 497.9 476.9 489.2 494.2 495.8

285.6 280.4 275.6 275.7 274.6 276.5

263.0 269.8 265.7 261.9 261.6 266.0

509.3 513.6 506.7 501.5 505.4 515.0

369.8 358.8 367.9 370.0 360.2 372.3

325.8 325.7 328.4 322.8 324.9 320.9

320.9 324.6 323.5 328.1 319.7 330.3

354.9 365.0 361.0 348.8 359.5 361.8

availability. According to direct interview of farmers and KIDMU managers, annual crop plan was governed mainly by market availability in the area. The result indicated that potato crop had more coverage at the beginning of the scheme and then reduced from year to year due to the lack of market in the area. Information on market availability was gathered from the past experiences of water users in the scheme. However, the coverage of wheat was

increasing from year to year mainly due to same reason as shown in Fig. 4 below. Currently, Wheat market was insured by the local Improved Seed Enterprise where both seed and fertilizer support were provided to each interested farmer. In addition to this, planting wheat had more advantage than other crops in consuming less labor for irrigation. In this regard irrigation water management and

S.B. Asres / Agricultural Water Management 170 (2016) 26–35

33

Fig. 4. cropping pattern situations of Koga scheme from 2010 to 2015 years.

planning should also be designed and compromised with the local market system. 4.5. Evaluation of scheme performance Comparison of the water demand (QR ) for three years with the annual main canal release (QD) showed that the overall efficiencies of Koga irrigation scheme was found to be 0.65, 0.66 and 0.70 in the years 2013, 2014 and 2015 respectively. In addition, performance of irrigation system relative to adequacy and structural limitation (PAS ) values less than unity indicated that the scheduled amount has not been delivered due to the structural limitations as shown in Table 9. The PAS values for Chihona was less than 1 showing that in this irrigation block, over flows were observed. PAS value also showed that there was greater inequity between the secondary blocks. Similarly only 19 Tertiary canals have PAS > 1 out of 33 tertiary canals in four secondary blocks. This implies that flows in most tertiary blocks were inadequate. Performance due to adequacy and management characteristics (PAM ) values less than unity indicated that the release policy of the scheme was violated as shown in Table 9. The PAM values for Kudmi, Ambo Mesk and Inguti was less than 1 showing that in these irriga-

tion blocks, performance in terms of adequacy was low as compared to Chihona where PAM was much greater than 1. Similarly only 14 Tertiary canals were having PAM > 1 out of 33 tertiary canals. The analysis result also showed that the equity and adequacy aspects were not right in all the selected irrigation blocks. The majority of the delivery was not attaining the required discharge. Some blocks receive higher supplies and other run short of it, which proved that the equity term was not fulfilled. It was also observed that some TCs received less discharge due to the rise in bed crest elevation of the offtakes indicating that the adequacy was not right due to structural limitations. Indications showing the inadequacy and inequity of the irrigation supply were investigated during the investigation period. In the 1st place users were talking hardly with the gate operators in that gate operators were not releasing the right amount of supply to each block. Another evidence of scarcity was that the night storage reservoir (NSR) structures were designed to function for 12 h during peak crop water demand. However, the NSR were observed emptied at reduced time in 7–8 h. This showed that the total main canal release/inflows into the 11 night storage reservoir were delivered to the system at the rate of 5580. 13 l/s as compared to the design inflow required which were 8370.0 l/s. This

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S.B. Asres / Agricultural Water Management 170 (2016) 26–35

Table 9 Values of PAM and PAS values of tertiary canals. No.

Name of blocks

Irrigable area (ha)

Design flows, Qd )(l/s)

Measured flows, QS = QD (l/s)

PAM

PAS

NSR capacity (m3 )

1

Kudmi TC-01 TC-02 TC-03 TC-04 TC-05 TC-06

389 54 26 36 48 112 113

466.8 64.8 31.2 43.2 57.6 134.4 135.6

309.0609 36.2262 86.0308 80.6539 5.4989 19.9972 80.6539

0.6621 0.5590 2.7574 1.8670 0.0955 0.1488 0.5948

1.510 1.789 0.363 0.536 10.48 6.721 1.681

20,165.76

2

Chihona TC-01 TC-04 TC-05 TC-06 TC-07 TC-08 TC-09

653 22 84 57 60 80 73 149

783.6 26.4 100.8 68.4 72 96 87.6 178.8

1168.4395 50.1859 387.2914 101.0219 153.0140 309.0725 83.9269 83.9269

1.4911 1.9010 3.8422 1.4769 2.1252 3.2195 0.9581 0.4694

0.671 0.526 0.260 0.677 0.471 0.311 1.044 2.130

33,851.52

3

Ambo Mesk TC-01 TC-02 TC-03 TC-04

814 144 78 48 54

976.8 172.8 93.6 57.6 64.8

726.5743 109.6058 112.1948 61.8759 67.5435

0.7438 0.6343 1.1987 1.0742 1.0423

1.344 1.577 0.834 0.931 0.959

42,197.76

No.

Name of Blocks

Irrigable area (ha)

Design flows, Qd )(l/s)

Measured flows, QS = QD (l/s)

PAM

PAS

NSR capacity (m3)

TC-05 TC-06 TC-07 TC-08 TC-09 TC-10 TC-11 TC-11L TC-11R TC-11QC TC-12 TC-14 TC-16

32 42 16 22 78 32 130 50 64 16 32 58 48

38.4 50.4 19.2 26.4 93.6 38.4 156 60 76.8 19.2 38.4 69.6 57.6

44.4947 13.9771 – 33.2528 41.9315 41.8663 73.8105 43.1124 24.1600 6.5381 35.4422 44.4202 46.1592

1.1587 0.2773 – 1.2596 0.4480 1.0903 0.4731 0.7185 0.3146 0.3405 0.9230 0.6382 0.8014

0.863 3.606 – 0.794 2.232 0.917 2.114 1.392 3.179 2.937 1.083 1.567 1.248

Inguti TC-01 TC-03 QC-01 TC-05

391 149 152 16 74

469.2 178.8 182.4 19.2 88.8

444.5409 214.4477 132.9314 14.1000 83.0618

0.9474 1.1994 0.7288 0.7344 0.9354

1.055 0.834 1.372 1.362 1.069

4

gave a reduction in release of 0.33% during peak period crop water requirement. 5. Conclusions and implications It can be concluded that in Koga irrigation scheme, overirrigation of the land was practiced at early stages of crop development stage while underirrigation was conducted during the period of peak crop water demand. This can support that deficit irrigation was practiced at critical crop flowering stage where productivity could probably decrease as a result of low water application. Overflows of over 7.13 million cubic meter obtained in 2015 have an indication of poor water release policy of the scheme as well as poor water application to the required land. It was also investigated that there were differences in the crop irrigation water requirement of crops growing in different irrigation blocks. This difference was mainly due to differences in characteristics of soil water holding capacity of each block as well as due to the inherent characteristics of crops (such as root depth). Growing selected crop in the block where it needs the application of less net irrigation water should be an appropriate scheduling measure for saving water selection of appropriate crop combination, selection of improved crop and vegetable varieties, enhancement of canal efficiencies, establishment of the irrigation water monitoring sites are the major future interventions used to implement proper irrigation water management for Koga irrigation scheme.

20,269.44

Acknowledgement I would like to Thank Blue Nile Water Institute of Bahir Dar University for providing me enough funds, offices and other facilities for completion of the study. A special gratitude is also given to the Amhara Design and Supervision Works Enterprise for making timely and quality soil sample testing. References Abate, Z., 1994. Water Resources Development in Ethiopia: An Evaluation of Present Experience and Future Planning Concepts. Ithaca Press, U.K. Ali, M., 2010. Crop water requirement and irrigation scheduling. Fundamentals of Irrigation and On-Farm Water Management, vol. 1. Springer. Richard, G., Allen., et al., 1990. FAO irrigation and drainage paper No-56, Utah, U.S.A. Allen, R.G., 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Checkol, G., Alamirew, T., 2008. Technical and Institutional Evaluation of Geray Irrigation Scheme in West Gojjam zone, Amhara Region. David, J., et al., 1990. Performance measure for evaluation of irrigation water delivery systems. J. Irrig. Drain. 116 (6), 804–823. Gorantiwar, S., Smout, I.K., 2005. Performance assessment of irrigation water management of heterogeneous irrigation schemes: 1. A framework for evaluation. Irrig. Drain. Syst. 19, 1–36. Hagos, F., Makombe, G., Namara, R.E., Awulachew, S.B., 2009. Importance of Irrigated Agriculture to the Ethiopian Economy: Capturing the Direct Net Benefits of Irrigation. IWMI.

S.B. Asres / Agricultural Water Management 170 (2016) 26–35 Kang, S., Liang, Z., Pan, Y., Shi, P., Zhang, J., 2000. Alternate furrow irrigation for maize production in an arid area. Agric. Water Manag. 45, 267–274. Kirda, C., 2002. Deficit irrigation scheduling based on plant growth stages showing water stress tolerance. Deficit Irrig. Pract. Mott MacDonald, 2004. Feasibility Study and Design Document of Koga Irrigation Project , Addis Ababa. Raine, S., Bakker, D., 1996. Increased furrow irrigation efficiency through better design and management of cane fields. In: Proceedings-australian Society of

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Sugar Cane Technologists, Watson Ferguson and Company, pp. 119–124. Zongsuo, L., Shaozhong, K., Peize, S., Yinghua, P., Liji, H., 2000. Effect of alternate furrow irrigation on maize production, root density and water-saving benefit. Scientia Agricultura Sinica 33, 26–32.