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Data Quality Assessment (DQA) Morocco Economic Competitiveness Program

Data Quality Assessment (DQA) Morocco Economic Competitiveness Program. Data Quality Assessment Objectives. Refine the steps needed for each sub-indicator Three years of data for primary data (crop area survey, yield survey)

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Data Quality Assessment (DQA) Morocco Economic Competitiveness Program

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  1. Data Quality Assessment (DQA)Morocco Economic Competitiveness Program Helene Kiremidjian

  2. Data Quality Assessment Objectives • Refine the steps needed for each sub-indicator • Three years of data for primary data (crop area survey, yield survey) • Double check availability and quality of data planned to be collected in the PMP • Aggregate water indicator used to push activities forward • Monitoring progress on project activities and objectives • Trigger discussion with and among partners • If issue with sub-indicators, there are two options: • Modify the sub-indicator, or • Re-Orient program activities to better reach program objectives (and in turn sub-indicators)

  3. Calendar Monday: • Meeting with ORMVA-D to explain DQA and request data (during program’s restitution day for C2) Tuesday: • Work sessions with M&E specialist (Edgar) and Irrigation specialist (Mustapha) to review primary data and preliminary issues • Identification of data needed from partners and planning Wednesday: • Visit of pumping station of Boulaouaneand work session with local representation of ORMVA-D • Visit of farmers from the association “El Hassina” at Boulaouane • Work session with ORMVA-D Thursday: • Work session with ABH-Moulouya at Oujda Friday: • Work session at ORMVA-Moulouya at Berkane • Visit of “DomaineKarima” at Berkane • Work session with Omar Nejjari on the SMS Advisory Service • Meeting with Mr. Abderrahman from CMGP (CompagnieMarocaine de Goutte a goutte et de Pompage) Monday: • Work session with program staff to review primary dataand discuss preliminary findings

  4. Data Needed Partner data (ORMVAs and ABH): • Water allocation process • Water allocation in volume (disaggregated by crop type, farm/parcel) • Water used by farmers • Water requirement per crop • Cost of water pumping • List of water pumping permits • Volume of water in reservoirs (in normal, dry and wet year) • Volume and timing of snowmelt • Rainfall forecast and control • Flood forecast and control Farmers: • Water allocation based on advisory services • Decision of crop mix by farmers based on water allocation Primary data (gathered by the program): • Crop survey • Yield survey • Market prices • Grant reporting • Water requirement per crop • SMS advisory survey Secondary data: • Formulas for water pricing • Formulas for efficiency gain of irrigation technologies

  5. SUB-INDICATOR (a): Water savings based on upgrading in irrigation systems and practices (1/5) – Definition of scenarios Findings • Remove scenario 1. In Oriental, basins are always linked to change to drip irrigation, and in D-A, stand alone basins are anecdotal • Refine the definition of water rotation and scenario 2; water savings can be assessed through the switch from gravity or sprinkler to drip irrigation. Proposed scenarios in PMP • Switch from tour d’eau (water rotation) to basin • Switch from water rotationto drip irrigation and basin • Use of irrigation advisory services Recommendations The three scenarios should be: • Switch from gravity to drip irrigation • Switch from sprinkler to drip irrigation • Use of irrigation advisory services

  6. SUB-INDICATOR (a): Water savings based on upgrading in irrigation systems and practices (2/5) – Calculation method Findings • Data on number of ha cultivated disaggregated by irrigation techniques are available at ORMVAs but not the crops associated with it; MEC will use the crop survey • Annex 4 on water allocation per crop from ORMVA-M is theoretical and does not provide a valid way to convert number of ha in cubic meters; • Efficiency gain formulas are widely accepted and used internationally and refined by ORMVAs based on Moroccan context (follow-up needed) • Water costs charged to farmers have been refined during the DQA; data is available and reliable • Significance of this sub-indicator: reconversion rate of 2,500 ha per year in Moulouya perimeters • Validity issue: can the program claim attribution of the overall sub-indicator?

  7. SUB-INDICATOR 1: Water savings based on upgrading in irrigation systems and practices (3/5) – Recommendations Recommendations • It is better to utilize the amount of water rotation allocated in reality by type of irrigation techniques than a hypothetical water allocation (crop water requirements); crops never get what they need (the difference can be accounted for by rainfall and groundwater pumping) • The targets will be re-calculated based on this new process as they over-estimate the amount of water savings that can be generated by the switch to irrigation techniques • Recalculate water costs based on new data gathered from ORMVAs • To strengthen the attribution of this sub-indicator to program activity, MEC will refine this sub-indicator by also capturing the reconversions that happened due to program activities, mainly through grants (477 ha will be reconverted in Oriental). To capture water savings due to grants, the program will conduct a baseline survey with each grantee and when reconversion is done (including crop mix before and after the reconversion)

  8. “Walk-Through” example for Sub-indicator (a)

  9. “Walk-Through” example for Sub-indicator (a) Rules used by ORMVAs to establish their water allocation plan to farmers – example from the agricultural campaign of 2011-2012

  10. “Walk-Through” example for Sub-indicator (a)

  11. SUB-INDICATOR (a): Water savings based on upgrading in irrigation systems and practices (4/6)– SMS Advisory Services Findings • The PMP attempts to measure the water saved by the SMS by comparing the SMS advises with the recommendations provided by one of the irrigation companies (CMPG); there are several issues: • It is hard to assess whether farmers apply those advises • The program will need to collect this scenario for each of the crops for which SMS are being provided (follow-up with CMPG) – data is available • There are 10 irrigation companies of that sort in Morocco with different requirements • The level of advisory services provided by CMGP are not as precise as the SMS advisory services • The calculation assumes that farmers use the SMS advisory service every day, which is not the case, which leads to overestimating the overall result of this sub-indicator

  12. SUB-INDICATOR (a): Water savings based on upgrading in irrigation systems and practices (5/5)– Recommendations Recommendations • To assess the effect of the use of SMS advisory services by farmers, the program should compare a control group of farmers who do not receive SMS with a group of farmers that do use it; • To improve user rate by farmers: • SMS should not be daily (fastidious to change water requirement everyday) • Be translated in Arabic • Any activities that will improve farmer’s awareness on crop water requirements and how to efficiently allocate water to crop will improve the results of the SMS advisory service (e.g. develop advisory services by irrigation companied using SMS recommendations) • MEC support to improving irrigation practices is not limited to the provision of SMS advisory services to farmers; the program has set-up weather stations at ORMVAs to improve their planning of water allocation to farmers; the program will define a scenario to capture that activity (see explanation in the DQA report)

  13. SUB-INDICATOR (b): Increased farm revenue due to changes in crop patterns Findings • Crop survey will allow us to compare crop patterns over the past three years; low coefficient of variation (less than 10% in most cases;); one caveat: change in crops that occur over less than 12 months will not be accounted for in the crop survey • Farmers are free to use any crop mix in normal and humid year but ORMVA has to follow a priority list during dry year, which will impact our result (e.g., citrus trees generate less revenue per cubic meter than vegetables although citrus trees are on the priority list for Oriental) • Other factors than water savings affect farmers’ decision when choosing crop mix (eg, labor cost, energy costs, crop rotation) • Should the program invest to improve precision of indicator?

  14. SUB-INDICATOR (b): Increased farm revenue due to changes in crop patterns Recommendations • Refine the indicator to get at attribution by designing a scenario capturing the result of training on production techniques provided to farmers in the dairy, forage and vegetable sectors by the program (see DQA report for explanation) • The effect of program activities on what we are trying to measure can be improved by promoting the use of the Excel Solver tool by ORMVAs and farmers as a decision making tool in the water allocation process; • Pilot project: provide to Water User Association in Boulaouane a computer with the Excel Solver Tool application

  15. “Walk-Through” example for Sub-indicator (b)

  16. “Walk-Through” example for Sub-indicator (b)

  17. SUB-INDICATOR (c): Additional water provided due to better modeling, planning and controls and improvement of river flows forecasting (1/4) Findings • The ABH does not have a precise system to monitor and report snow melt. Snowmelt does not intervene directly in their water allocation decision; • The idea of this scenario is that by allowing ABH to know exactly how much water they get from snowmelt in advance, they will improve their water planning and be able to better allocate water to ORMVAs; • Reliable data over the past 10 years are available to develop the baseline scenario: • Historical data on water reserve at the dams • Historicaldata on water inflow (“apport”) in the reservoirs • Historicaldata on water allocation to ORMVAs These data are available on a monthly basis; data is collected daily by the dam manager and computed monthly in a software at the ABH (“Outil pour la Gestion des Ressources en Eau et pour l’Aide a la Decision”); data quality checks are performed monthly by ABH staff Recommendations: MEC will use the historic data from water reserve, additional inflow and water dotation to ORMVA to develop a baseline scenario

  18. Water Inflows (2000-2012)

  19. SUB-INDICATOR 3: ADDITIONAL WATER PROVIDED DUE TO BETTER MODELING, PLANNING AND CONTROLS AND IMPROVEMENT OF RIVER FLOWS FORECASTING (2/5) • Slide on reservoir and inflows data from ABH gathered through DQA

  20. SUB-INDICATOR (c): Additional water provided due to better modeling, planning and controls and improvement of river flows forecasting (2/4) Findings • The ABH has specific procedures for flood control that are fixed (depend on the physical and technical characteristics of each dam) • Depending on the hydrological conditions, the ABH-M, however, does not follow them (water deficit so they do not respect the respect the limit of water that can be contained in the dam – the so called “creux”) • The ABH is using soft rules to forecast the amount of water that will arrive to the dam in the event of rainfall (there have 26 stations along the river) Recommendations: -MEC will use the same baseline scenario but will be looking at a different period (the hydrological season from November to February); double counting will be avoided with scenario 5 (snowmelt) as we are looking at different months

  21. SUB-INDICATOR (c): Additional water provided due to better modeling, planning and controls and improvement of river flows forecasting (3/4) Recommendations: • MEC should develop three baseline scenarios for water allocation to ORMVAs : a. normal year, b. humid year, and c. dry year (see suggestion by ABH-M); the program will be able to measure the water freed per month with and without the new system during the hydrological period using this scenarios and calculate the difference on a monthly basis • Overall, the baseline used for the indicator can be refined using the data gathered during the DQA (average of the past 10 years) – currently defined in the PMP as “average water delivery by ORMVA-Moulouya between 1992 and 2005 were 246.4 million cubic meters” • USAID could easily continue monitoring results after program completion at a low cost (emails to ABHs)

  22. SUB-INDICATOR (c): Additional water provided due to better modeling, planning and controls and improvement of river flows forecasting (4/4) Findings • The amount of illegal water pumped will be accounted for as additional water provided • The ABHs are supposed to report the permits given for digging whereas the ORMVAs provide permits for forage and use. In practice, the ABH-M does not know how much illegal pumping is happening; the baseline used will be zero; • The satellite imaging will be piloted in a specific zone, and not the whole region (price issue); data will be hard to extrapolate Recommendations • The program will conduct a survey of pumping permits and water used in each of the aquifers (Triffa in Moulouya (2,500 farms) and Sahel in Doukkala (1,500 farms)) • The baseline scenario will be ready by September 2012; survey of illegal pumping at the end of the program

  23. Summary of Findings – Recommendations • Activities that can increase the validity of sub-indicators: • Strengthen irrigation advisory services provided by irrigation companies to farmers and refine water crop requirements used by ORMVAs based on SMS advisory services • ORMVA-D has shown a great interest in using the Excel Solver Tool as a decision making tool for water allocation, especially in the case of dry season; MEC could provide to Water User Association in Boulaouane a computer with the application • Opportunity to pilot a new model of “limited delegated water management” with the Water User Association in Boulaouane (collective agreement between ORMVA, WUA and MEC) • Negative incentives for ORMVAs to save water because they fear that ABH will not reallocate the same level the following year; improving communication and data sharing between ABH and ORMVAs will be key in increasing the effect of program activities

  24. Summary of Findings – Recommendations • Partners are interested in sub-indicators and requested assistance as follows: • ORMVA-D is interested in evaluating the cost savings due to switch to drip irrigation and has requested assistance to pilot an evaluation on the Boulaouane sector – not just in terms of water savings but also in terms of labor (not water rotation at night with drip irrigation) and energy costs • ABHs don’t have disaggregated data about the use of agricultural water by ORMVAs to measure water savings in agriculture and are eager to have that available to improve their water allocation decision to ORMVAs • MEC will account for the water saved following the water audits done by industries; the program will create an additional scenario in Sub-indicator (a)

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