Research Report Indicators for Comparing Performance of Irrigated Agricultural Systems David Molden, R. Sakthivadivel Christopher J. Perry, Charlotte de Fraiture and Wim H. Kloezen International Water Management Institute INTERNATIONAL WATER MANAGEMENT INSTITUTE P O Box 2075, Colombo, Sri Lanka Tel (94-1) 867404 • Fax (94-1) 866854 • E-mail IIMI@cgnet.com Internet Home Page http: //www.cgiar.org/iimi ISSN 1026-0862ISBN 92-9090-356-2 20 Research Reports IWMI’s mission is to foster and support sustainable increases in the productivity of irri- gated agriculture within the overall context of the water basin. In serving this mission, IWMI concentrates on the integration of policies, technologies and management systems to achieve workable solutions to real problems—practical, relevant results in the field of ir- rigation and water resources. The publications in this series cover a wide range of subjects—from computer model- ing to experience with water users associations—and vary in content from directly appli- cable research to more basic studies, on which applied work ultimately depends. Some re- search reports are narrowly focused, analytical, and detailed empirical studies; others are wide-ranging and synthetic overviews of generic problems. Although most of the reports are published by IWMI staff and their collaborators, we welcome contributions from others. Each report is reviewed internally by IWMI’s own staff and Fellows, and by external reviewers. The reports are published and distributed both in hard copy and electronically (http://www. cgiar.org/iimi) and where possible all data and analyses will be available as separate downloadable files. Reports may be copied freely and cited with due acknowledgment. 1iii Research Report 20 Indicators for Comparing Performance of Irrigated Agricultural Systems David J. Molden, R. Sakthivadivel, Christopher J. Perry and Charlotte de Fraiture International Water Management Institute P O Box 2075, Colombo, Sri Lanka The authors: David J. Molden, R. Sakthivadivel, Christopher J. Perry, Charlotte de Fraiture, and Wim H. Kloezen all of whom are at IWMI are Research Leader (Performance and Im- pact Assessment Program), Senior Irrigation Specialist, Deputy Director General, Associate Expert in Irrigation Management, and Associate Expert in Irrigation Management (Mexico National Program), respectively. This work is the result of efforts of several scientists from IWMI and collaborating institu- tions. The names of contributors and the country from which they obtained and analyzed information are given below: Upali Amerasinghe, Muda System in Malaysia; Carlos Garcés-Restrepo and Charlotte de Fraiture, Colombia; Paul van Hofwegen (IHE), Morocco; Wim H. Kloezen, Carlos Garcés- Restrepo, and Sam Johnson, Mexico; Chris Perry, Egypt; Hilmy Sally, Burkina Faso; R. Sakthivadivel, India; M. Samad and Douglas Vermillion, Sri Lanka; Zaigham Habib, Paki- stan; Charles Abernethy and Kurt Lonsway, Niger; and David Molden, Turkey. This work was undertaken with funds specifically allocated to IWMI’s Performance and Impact Assessment Program by the European Union and Japan, and from allocations from the unrestricted support provided by the Governments of Australia, Canada, China, Den- mark, France, Germany, Netherlands, and the United States of America; the Ford Founda- tion; and the World Bank. Molden, David J., R. Sakthivadivel, Christopher J. Perry, Charlotte de Fraiture, and Wim H. Kloezen. 1998. Indicators for comparing performance of irrigated agricultural systems. Research Report 20. Colombo, Sri Lanka: International Water Management Institute. / irrigated farming / irrigation systems / indicators / performance indexes / land / water / financ- ing / crop production / ISBN 92-9090-356-2 ISSN 1026-0862 © IWMI, 1998. All rights reserved. The International Irrigation Management Institute, one of sixteen centers supported by the Consultative Group on International Agricultural Research (CGIAR), was incorporated by an Act of Parliament in Sri Lanka. The Act is currently under amendment to read as In- ternational Water Management Institute (IWMI). Responsibility for the contents of this publication rests with the authors. 3iiiiii Contents Summary v Introduction 1 Performance Indicators for Comparison 1 Features of the Selected Indicators 3 The Indicators 4 Application 8 Temporal and Spatial Variation of Indicators within a Project 15 Limitations of the Indicators 16 Interpretation of Results 18 Discussion 19 Annex 1. Data Requirements to Calculate Performance Indicators 20 Annex 2. Calculation Example of Performance Indicators 21 Annex 3. World Market Prices of Agricultural Products in Constant 1995 Dollars 25 Literature Cited 26 v Summary A set of comparative performance indicators is defined, which relates outputs from irrigated agriculture to the major inputs of water, land, and finance. Nine indicators are presented with the objective of providing a means of comparing performance across irrigation systems. These indicators require a limited amount of data that are generally available and readily analyzed. Results of application of the indicators at 18 irrigation systems are presented and large differences in performance among systems are shown. In spite of uncertainties in estimation of indicators, the large differences discerned by the indicators justify the approach taken. 1 Indicators for Comparing Performance of Irrigated Agricultural Systems David J. Molden, R. Sakthivadivel, Christopher J. Perry, Charlotte de Fraiture, and Wim H. Kloezen Introduction With increasing population and demand for food, sustainable production increases from irrigated agriculture must be achieved. With limited freshwater and land resources, and increasing competition for these resources, irrigated agriculture worldwide must im- prove its utilization of these resources. Few would disagree with these statements, yet we do not have a way of determining the present state of affairs with respect to irri- gated agriculture. The question—how is ir- rigated agriculture performing with limited water and land resources?—has not been satisfactorily answered. This is because we have not been able to compare irrigated land and water use to learn how irrigation systems are performing relative to each other and what the appropriate targets for achievement are. With the many variables that influence performance of irrigated agriculture, includ- ing infrastructure design, management, cli- matic conditions, price and availability of inputs, and socioeconomic settings, the task of comparing performance across systems is formidable. However, if we focus on com- monalties of irrigated agriculture—water, land, finances, and crop production—it should be possible to see, in a gross sense, how irrigated agriculture is performing within various settings. This report presents IWMI’s “compara- tive” indicators and experience with their use, based on application across several irri- gation systems. At this stage, it is hypoth- esized that through the use of these indica- tors, we are able to document and compare key performance attributes of irrigation sys- tems. If so, then it should be possible to compare performance across irrigation sys- tems in a number of settings to understand where we presently stand with respect to productive utilization of land and water, to compare relative performance of systems, and to identify where performance can be improved. Performance Indicators for Comparison It is useful to consider an irrigation system in the context of nested systems to describe different types and uses of performance in- dicators (Small and Svendsen 1992). An irri- gation system is nested within an irrigated agricultural system, which in turn can be considered part of an agricultural economic system. For each of the systems, process, output, and impact measures can be consid- ered. Process measures refer to the pro- cesses internal to the system that lead to the ultimate output, whereas output measures describe the quality and quantity of the out- puts where they become available to the next higher system. Performance is assessed for a variety of reasons: to improve system operations, to assess progress against strategic goals, as an integral part of performance-oriented man- 2 agement, to assess the general health of a system, to assess impacts of interventions, to diagnose constraints, to better understand determinants of performance, and to com- pare the performance of a system with oth- ers or with the same system over time. The type of performance measures chosen de- pends on the purpose of the performance assessment activity. Many authors have proposed indicators to measure irrigation system performance as summarized by Rao (1993) and have given examples of their use at particular irrigation systems (Bos and Nugteren 1974; Levine 1982; Abernethy 1986; Seckler, Sampath, and Raheja 1988; Mao Zhi 1989; Molden and Gates 1990; Sakthivadivel, Merrey, and Fernando 1993; Bos et al. 1994). But, there are very few examples of cross-system com- parisons or analyses (Bos and Nugteren 1974; Murray Rust and Snellen 1993; Merrey, Valera, and Dassenaike 1994) Recent studies have attempted to standardize these indicators to allow for better comparison across systems (Bos et al. 1994). We are pres- ently at a state in the development of per- formance assessment of irrigation where we have a limited number of case studies with intensive measurements of performance, and few examples of studies of performance across irrigation systems. Much of the work to date in irrigation performance assessment has been focused on internal processes of irrigation systems. Many internal process indicators relate per- formance to management targets such as timing, duration, and flow rate of water; area irrigated; and cropping patterns. A ma- jor purpose of this type of assessment is to assist irrigation managers to improve water delivery service to users. Targets are set relative to objectives of system management, and performance measures tell how well the system is performing relative to these tar- gets. When the performance is not ad- equate, either the process must be changed to reach the target, or the target itself must be changed. These “internal” indicators aid irrigation system managers to answer the question “Am I doing things right?” (Murray-Rust and Snellen 1993). We could conclude, although it would be premature, that these internal indicators do not lend themselves well to cross-system comparison. This is due to several reasons. First, internal processes of irrigation systems vary widely from system to system, so that performance indicators are tailored to meet system-specific needs. Second, indicators re- lated to irrigation processes tend to be data- intensive and it is often difficult, time-con- suming, and expensive to obtain complete data sets. Third, assumptions about rela- tions between internal processes and out- puts may not be valid. It is often assumed that meeting a target will improve output in terms of agricultural production or net ben- efit to farmers. An approach to cross-system compari- son is to compare outputs and impacts of irrigated agriculture. “External” indicators are used to relate outputs from a system derived from the inputs into that system. They provide little or no detail on internal processes that lead to the output. For ex- ample, the critical output of an irrigation system is the supply of water to crops. This output in turn is an input to a broader irri- gated agricultural system where water com- bined with other inputs, leads to agricul- tural production. As irrigated agriculture always deals with water and agricultural production it should be possible to develop a set of external indicators for cross-system comparison. The purpose of this study is to present and apply a set of external and other com- parative performance indicators that will al- low for comparative analysis of irrigation performance across irrigation systems. The 3 indicators reveal general notions about the relative health of the irrigation system, yet they are not too data-intensive to discourage widespread and regular application. Data requirements to calculate the minimum set of indicators are given in annex 1. Such a set of indicators potentially has several purposes. The indicators will allow for comparison be- tween countries and regions, between differ- ent infrastructure and management types, and between different environments, and for assessment over time of the trend in perfor- mance of a specific project. They will allow an initial screening of systems that perform well in different environments, and those that do not. They will allow for both assess- ing impact of interventions and managers to assess performance against strategic, long- term objectives. Features of the Selected Indicators IWMI’s minimum set of external indicators was originally presented by Perry (1996). The indicators have been widely field-tested and slightly amended, resulting in this present list. The intent of presenting this set of indicators is to allow for cross-system performance. Some of the features of the in- dicators are: • The indicators are based on a relative comparison of absolute values, rather than being referenced to standards or targets. • The indicators relate to phenomena that are common to irrigation and irrigated agricultural systems. • The set of indicators is small, yet re- veals sufficient information about the output of the system. • Data collection procedures are not too complicated or expensive. • The indicators relate to outputs and are bulk measures of irrigation and irri- gated agricultural systems, and thus provide limited information about inter- nal processes. This set of indicators is designed to show gross relationships and trends and should be useful in indicating where more detailed study should take place, for ex- ample where a project has done extremely well, or where dramatic changes have taken place. This approach differs from that of us- ing ratios of actual to target in that the in- terpretation of these ratios relative to perfor- mance is not always clear (e.g., if the target value is 1, is 0.9 better than 1.1?) . A relative comparison of values at least allows us to examine how well one system is performing in relation to others. And, if we have enough samples, this approach may ulti- mately allow us to develop standards and targets. The main audience for these exter- nal indicators comprises policy makers and managers making long-term and strategic decisions, and researchers who are search- ing for relative differences between irriga- tion systems while the main audience for internal indicators comprises irrigation sys- tem managers interested in day-to-day op- erations where ratios of actual to target val- ues may be quite meaningful. As water becomes a limiting resource, an important question that arises is: What is the value of irrigated agricultural production per unit of water consumed from the hydrological cycle? Answering this question requires an in- dicator that measures the contribution of the irrigation activity to the economy in relation to consumption of the increasingly scarce re- source, water. Answering this question also requires better understanding than we often have of cropping activities—the output com- 4 ponent of the basic indicator, and water bal- ances which indicate the input. The basic in- dicators here are the output of irrigated ag- riculture per unit land and per unit water. The Indicators Nine indicators are developed related to the irrigation and irrigated agricultural system. The main output considered is crop produc- tion, while the major inputs are water, land, and finances. Indicators of Irrigated Agricultural Output The four basic comparative performance indicators (see box) relate output to unit land and water. These “external” indicators provide the basis for comparison of irrigated agriculture performance. Where water is a constraining resource, output per unit water may be more important, whereas if land is a constraint relative to water, output per unit land may be more important. guish this from another important water ac- counting indicator—output per unit total consumption, where total consumption in- cludes water depletion from the hydrologic cycle through process consumption (ET), other evaporative losses (from fallow land, free water surfaces, weeds, trees), flows to 1For example, consider an irrigated area that nominally is to serve 1,000 ha. During the rainy season, 800 ha are irrigated, and during the dry season, 400 ha are ir- rigated. In this case, the irrigated cropped area is 1,200 ha. The command area is 1,000 ha. 1. 2. 3. 4. Output per cropped area ha = Irrigated cropped area (Acropped) Production Output per unit command ha = Command area (Vdiv) Production Output per unit irrigation supply m3 = Diverted irrigation supply (Vdiv) Production Output per unit water consumed m3 = Volume of water consumed by ET (Vconsumed) Production $ ha     $ ha     $ m3     $ m3     Output per unit of irrigation water sup- plied and output per unit of water con- sumed are derived from a general water ac- counting framework (Molden 1997). The water consumed in equation 4 is the vol- ume of process consumption, in this case evapotranspiration. It is important to distin- where, Production is the output of the irrigated area in terms of gross or net value of produc- tion measured at local or world prices (see below), Irrigated cropped area is the sum of the areas under crops during the time period of analy- sis, Command area is the nominal or design area to be irrigated,1 Diverted irrigation supply is the volume of surface irrigation water diverted to the com- mand area, plus net removals from groundwater, and Volume of water consumed by ET is the actual evapotranspiration of crops. 5 sinks (saline groundwater and seas), and through pollution (Keller and Keller 1995; Seckler 1996). We are interested in the measurement of production from irrigated agriculture that can be used to compare across systems. If only one crop is considered, production could be compared in terms of mass. The difficulty arises when comparing different crops, say wheat and tomato, as 1 kg of to- mato is not readily comparable to 1 kg of wheat. When only one irrigation system is considered, or irrigation systems in a region where prices are similar, production can be measured as net value of production and gross value of production using local val- ues. The Standardized Gross Value of Pro- duction (SGVP) was developed for cross- system comparison as obviously there are differences in local prices at different loca- tions throughout the world. To obtain SGVP, equivalent yield is calculated based on local prices of the crops grown, compared to the local price of the predominant, locally grown, internationally traded base crop. The second step is to value this equivalent pro- duction at world prices. To do this we are presently using World Bank prices for 1995 (see annex 2 for the list). This should not be adjusted for free on board/cost insurance freight and internal transport since we are interested in the productivity of irrigation, rather than the efficiency of markets, trans- port system, and project location. For example, if the local price of tomato is three times the local price of wheat, we consider the production yield of 10 tons/ha of tomato to be equivalent to 30 tons/ha of wheat. Total production of all crops is then aggregated on the basis of ‘wheat equiva- lent’ and the gross value of output is calcu- lated as this quantity of wheat multiplied by the world market price of wheat. The point of this is to capture local prefer- ences—for example, specialized varieties that may have a low international price, but are locally highly valued—and also to cap- ture the value of non-traded crops. where, SGVP is the standardized gross value of production, Yi is the yield of crop i, Pi is the local price of crop i, Pworld is the value of the base crop traded at world prices, Ai is the area cropped with crop i, and Pb is the local price of the base crop. It could be argued that the indicator should be net value added rather than gross. There are two reasons to work with the gross figure. First, it is far easier to measure—many of the deductions that must be made to get from gross to net value added are susceptible to distortions (subsidies and taxes on inputs, credit, and irrigation services, for example) or otherwise very difficult to measure (appropriate prices for family labor, and the opportunity cost of land and water). Second, we note that the most common indicator of agricultural performance (yield per unit land, or more commonly just ‘yield’) is itself a gross indicator, unqualified by indications of input levels, soil type, or even variety. Despite this simplicity, yield serves many agriculturists as a fundamental indicator of performance. SGVP A Y P P Pi i i bcrops world =      ∑ , 6 Other Comparative Indicators Five additional indicators were identified in this minimum set for comparative purposes. These are meant to characterize the indi- vidual system with respect to water supply and finances. Relative water supply as presented by Levine (1982) and relative irrigation supply as developed for this indicator set (Perry 1996) are used as the basic water supply in- dicators: where, Total water supply = Surface diversions plus net groundwater draft plus rainfall. Crop demand = Potential crop ET, or the ET under well-watered conditions. When rice is considered, deep percolation and seepage losses are added to crop demand. Irrigation supply = Only the surface diversions and net groundwater draft for irrigation. 5. 6. Relative water supply = Crop demand Total water supply Relative irrigation supply = Irrigation demand Irrigation supply 7. Water delivery capacity (%) = Peak consumptive demand Canal capacity to deliver water at system head Irrigation demand = The crop ET less effec- tive rainfall. Relative irrigation supply is the inverse of the irrigation efficiency presented by Bos (1974). The term relative irrigation supply was presented to be consistent with the term relative water supply, and to avoid any con- fusing value judgements inherent in the word efficiency. Both RWS and RIS relate supply to demand, and give some indication as the condition of water abundance or scarcity, and how tightly supply and demand are matched. Care must be taken in the interpretation of results: an irrigated area upstream in a river basin may divert much water to give adequate supply and ease management, with the excess water providing a source for downstream users. In such circumstances, a higher RWS in the upstream project may indicate appropriate use of available water, and a lower RWS would actually be less desirable. Likewise, a value of 0.8 may not represent a problem, rather it may provide an indication that farmers are practicing deficit irrigation with a short water supply to maximize returns on water. The water delivery capacity (WDC) is given below: where, Capacity to deliver water at the system head = The present discharge capacity of the canal at the system head, and Peak consumptive demand = The peak crop irrigation requirements for a monthly period expressed as a flow rate at the head of the irrigation system. 7 Water dilivery capacity is meant to give an indication of the degree to which irrigation infrastructure is constraining cropping intensities by comparing the canal conveyance capacity to peak consumptive cated to irrigation. The cost of the distribu- tion system can either be estimated from original costs, or estimated by using present costs of similar types of infrastructure devel- opment. Financial self-sufficiency tells us what percent of expenditures on O&M is generated locally. If government subsidizes O&M heavily, financial self-sufficiency would be low, whereas if local farmers through their fees pay for most of the O&M expenditures, financial self-sufficiency would be high. Financial self-sufficiency does not tell us the O&M requirement, only the expenditures. A high value of financial self-sufficiency does not automatically indicate a sustainable system as the O&M expenditures might be too low to meet the actual maintenance needs. demands. Again, a lower or higher value may not be better, but needs to be interpreted in the context of the irrigation system, and in conjunction with the other indicators. Policy makers are keenly interested in the returns to investments made. Similarly, researchers would like to be able to recom- mend systems that yield acceptable returns within a given environment. Large irriga- tion investments are made in irrigation in- frastructure, thus returns compared to in- vestment in infrastructure are presented here. We focus on water delivery infrastruc- ture to be able to analyze differences be- tween various types of delivery systems such as structured, automated, lined, and unlined canal sections. Infrastructure related to river diversions, storage, and drainage is not included here, because of the desire to be able to compare different methods of water delivery. Also, diversion and storage works often serve other nonirrigation pur- poses so their costs cannot be entirely allo- Financial Indicators Two financial indicators that are used are given below: where, Cost of irrigation infrastructure considers the cost of the irrigation water delivery system referenced to the same year as the SGVP, Revenue from irrigation, is the revenue generated, either from fees, or other locally gen- erated income, and Total O&M expenditures are the amount expended locally through O&M plus outside subsidies from the government. 8. 9. Gross return on investment (%) = Cost of irrigation infrastructure SGVP Financial self-sufficiency = Total O&M expenditure Revenue from irrigation 8 The minimum set of external indicators pro- posed by IWMI was tested in 18 systems, or parts of irrigation systems located in 11 countries: Burkina Faso, Colombia, Egypt, India, Malaysia, Mexico, Morocco, Niger, Pakistan, Sri Lanka, and Turkey. The sites are those at which IWMI is involved through either their field offices or collabo- rative efforts with research partners. The major features of the systems used for com- puting the indicators are indicated in table 1. These features suggest that the data used for computation come from a wide range of agro-climatic regions and systems having different characteristics, crops and cropping patterns, water distribution patterns, water resource availability, and management style. Data on water supply, agriculture, rev- enue, and irrigation costs were collected. Most of the data used for analysis are sur- vey data derived from official statistics and measurements or collected and compiled by IWMI and collaborating scientists working in different countries. Although much of the data used comes from secondary sources such as irrigation departments, agricultural departments, revenue departments, and state statistical departments, IWMI has put in much effort by way of initiating survey and field observations to acquire reliable data and to check the secondary data for their consistency. The actual data collection procedures adopted in different countries are documented in IWMI’s country reports. Table 2 gives the results of the performance indicators computed for 18 schemes throughout the world. SGVP Per Unit Command The SGVP per unit command varies be- tween US$679 and $2,888 per ha with a variation ratio of 1 to 4.25 (figure 1). The systems at the low end of the spectrum (less than US$1,500/ha) are those which mostly grow rice with low cropping intensity. Middle range values of SGVP per ha (US$1,500 to $2,000) are produced by those which grow rice with high cropping inten- sity of the order of 200 percent. Those at the high end (US$2,000/ha and above) include orchards, industrial crops, and some cereals. These initial results indicate that the two important factors contributing to higher gross value of output per unit command are the cropping intensity of rice and the type of crop grown, especially those of orchards and industrial crops. SGVP Per Unit Cropped Land The SGVP per unit cropped land, in figure 2, presents two broad classes of irrigation systems. Rice producing irrigation systems have their gross value of output per unit cropped land roughly equal to US$1,000 and below while systems producing non- rice crops including industrial and orchard crops have their gross value of production per unit crop land between $2,000 and $3,500. This parameter between these two types of systems varies between a ratio of 1:2 and 1:3.5. In other words, non-rice pro- ducing irrigation systems can be more pro- ductive than the rice producing irrigation system by 100 to 200 percent. SGVP Per Unit Irrigation Supply The SGVP per unit irrigation supply in fig- ure 3 varies between a ratio of 1 and 15 and can be grouped into three classes. Purely rice-based systems give a gross value of output per unit volume of irrigation water varying between US$0.04 and $0.10. Irriga- tion systems which grow rice during rainy Application 9 TABLE 1. Salient features of the studied irrigation schemes. No. Country System name Type of system Command Cropping Climate Cropping Annual Annual Type of Water area pattern intensity rainfall evaporation manage- availability (ha) (mm) (mm) ment 1 Burkina Faso Gorgo Tank storage 50 Rice, potato, Sudano 0.93 400 to 2,600 Village Water-short 2 Mogtedo Village irrigation scheme 93 Tomato, bean Sahelian 2.00 1,200 cooperatives systems 3 Savili Pumping scheme 42 Agroclimatic zone 0.94 4 Colombia Coella Diversion 25,600 Rice, maize, sorghum Temporate and 1.01 1,000 to 1,800 Transferred Water-short 5 Saldana Diversion 13,975 Fruit and vegetables tropical 1.61 1,500 to WUAs Water-abundant 6 Samaca Storage 3,000 Onion and potato 1.60 700 1,100 Sufficient water 7 Egypt Nile Delta Storage 3,100,000 Wheat, maize, Arid 2.00 10 to 500 – Agency- Sufficient Rice, sorghum, managed surface water, Egyptian cloves, groundwater, Cotton drainage water 8 India Mahi Kadana Storage-cum- 212,000 Rice, wheat, Semiarid 1.20 823 1,700 Agency- Abundant groundwater Tobacco, banana, managed (conjunctive use) Vegetables 9 Malaysia Muda Storage 96,000 Rice-rice Humid 2.00 2,000 1,800 Agency- High rainfall but managed insufficient stored surface water 10 Mexico Alto Rio Lerma Storage system 107,541 Wheat, sorghum, maize Moderate 0.66 700 – Transferred Surface Cortazar Module 1,714 deep wells 18,848 and bean. Underground Subhumid 0.70 to WUA Water-short Salavatierra (conjunctive use) 15,897 water used for wheat, 0.46 project Module vegetables, alfalfa 11 Morocco Triffa Scheme Storage and 36,060 Orchards, sugarbeet, Semiarid 1.00 Average 300 – Agency- Water-short pumping Potato, wheat Mediterranean 150–450 managed 12 Niger Saga Pumping from river 407 Rice Arid 1.85 300 to 550 Agency- Water-sufficient 13 Kourani Baria I Pumping from river 425 Rice 1.76 managed 14 Kourani Baria II Pumping from river 268 Rice 1.69 15 Pakistan Chishtian Storage-cum- 70,656 Cotton, rice Arid 1.20 200 mm Agency- Water-short sub-division groundwater managed 16 Sri Lanka Nachchaduwa Storage 2,539 Rice, chili, soybean, Semiarid 2.00 981 2,000 Joint Water-short Vegetables, onion, management 17 Rajangana Storage 5,909 Rice 2.00 500 to 1,800 2,000 – do – Water-abundant Average 750 18 Turkey Seyhan Storage 120,200 Maize, cotton, oranges, Mediterranean 0.86 620 Transferred Water-abundant and many others 10 Country System Year ($/ha) ($/ha) ($/m3) ($/m3) % % Ratio Ratio Ratio Burkina Faso Gorgo 1992/93 1,205 1,065 0.10 0.91 9 42 1.6 3.5 3.5 Mogtedo 1992/93 1,204 2,499 0.09 0.14 21 79 1.4 2.7 2.1 Savili 1992/93 3,085 2,652 0.37 0.80 33 – 2.5 2.6 2.9 Gorgo 1994/95 771 679 0.08 0.12 6 35 1.9 2.7 3.5 Mogtedo 1994/95 1,403 2,384 0.11 0.15 20 78 1.4 2.5 2.1 Savili 1994/95 2,348 2,281 0.28 0.62 29 28 2.5 2.6 2.9 Colombia Coella 1993 1,290 1,303 0.14 0.20 24 114 1.8 1.8 2.2 Saldana 1993 1,125 1,811 0.12 0.17 33 127 2.2 2.9 3.2 Samaca 1993 1,472 2,462 0.63 0.34 36 109 1.2 1.1 1.7 Egypt Nile Delta 1993/94 1,510 2,594 0.12 0.11 26 – 1.6 1.6 1.3 India Mahi Kadana 1991/92 605 515 0.04 0.03 30 – 3.9 3.0 2.9 Mahi Kadana 1995/96 916 893 0.07 0.06 52 53 2.7 2.5 2.6 Malaysia Muda 1994/95 1,021 2,041 0.38 0.10 59 – 0.8 0.4 – Mexico Alto Rio Lerma Surface + Public wells 1994/95 2,227 1,464 0.18 0.24 28 80 2.2 3.3 5.1 Private wells 1994/95 3,220 2,242 0.26 0.37 64 – 1.9 2.5 – Cortazar Module Surface + Public wells 1994/95 2,615 1,827 0.22 0.25 33 133 2.1 2.3 1.2 Private wells 1994/95 3,626 2,888 0.26 0.48 66 – 2.2 2.6 – Salvatierra Module Surface + Public wells 1994/95 2,117 974 0.10 0.27 27 101 4.1 4.8 2.4 Private wells 1994/95 1,863 703 0.14 0.23 75 – 2.3 4.5 – Morocco Triffa Scheme, Sec. 22 1994/95 1,087 1,358 0.27 0.34 – 47 1.3 1.1 – Niger Saga 1993/94 1,389 2,592 0.12 0.13 – 139 2.2 1.8 – Kourani Baria I 1994 827 1,460 0.05 0.17 – – 2.9 2.4 – Kourani Baria II 1994 1,107 1,879 0.06 0.11 43 – 2.2 1.7 – Pakistan Chishtian sub-division 1993/94 384 477 0.04 0.05 – 40 1.3 1.2 0.8 Sri Lanka Nachchaduwa 1994/95 826 1,544 0.04 0.08 34 – 2.0 2.2 – Rajangana 1994/95 967 1,934 0.06 0.11 43 – – – 3.3 Turkey Seyhan 1996/97 2,167 2,526 0.21 0.19 108 88 2.07 2.15 2.62 TABLE 2. Performance indicators computed for 18 systems throughout the world. W at er -d el iv er y ca pa ci ty O ut pu t / u ni t cr op pe d la nd O ut pu t / un it co m m an d O ut pu t / un it irr ig at io n su pp ly O ut pu t / un it w at er c on su m ed G ro ss re tu rn on in ve st m en t Fi na nc ia l se lf- su ffi ci en cy R el at iv e w at er s up pl y R el at iv e irr ig at io n su pp ly 11 FIGURE 1. Standardized gross value of production per unit command. ** private wells ** surface and public wells 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 ** private wells ** surface and public wells FIGURE 2. Standardized gross value of production per unit cropped land. 12 Módulos 4 al 11 iguales a los módulos 1 al 3 Personal de Hidrología y Aforos Jefes de las Zonas Personal de Operacion Personal de Conservación Personal Administrativo Personal de Conservación Personal Administrativo Recolector de Tarifas Planificación Estacional Representantes en el Comité Hidráulico de la CNA, de los 11 módulos y del Estado de Guanajuato Jefe de Operacion Jefe de Conservación Jefe de canaleros Gerente General Directiva Asamblea de Delegados Canaleros Jefe del Distrito Comisón Nacional del Agua (CNA) O&M de presas y la red mayor Asociación 3 de Usuarios de Agua O&M de la red menor y recolección de tarifas del módulo 3 FIGURE 3. Standardized gross value of production per unit irrigation supply. ** private wells ** surface and public wells 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 FIGURE 4. Standardized gross value of production per unit water consumed. 13 seasons and other field crops during a dry season give a gross value of output per unit irrigation water varying between US$0.10 and $0.29. Systems which grow orchards, industrial crops, and vegetables yield an SGVP per cubic meter of irrigation water higher than US$0.20. The SGVP per cubic meter of irrigation tends to be higher in hu- mid regions where irrigation needs are gen- erally lower. Obviously, this also depends on the ability of farmers and system manag- ers to use rainfall effectively. SGVP Per Unit Water Consumed Consumed water is the actual evapotranspi- ration from irrigated crops (ET). The gross value of output per unit water consumed in figure 4 shows variations of 1 to 6. It is seen that purely rice-based systems with abun- dant water supply and rice-based system with cropping intensity less than 100 per- cent give a gross value of output per unit water consumed of about US$0.10 whereas water-short systems with orchard and in- dustrial crops and those systems with pri- vate-well pumping give a gross value of output per unit water consumed between $0.20 to $0.60. This parameter among these two types of systems varies over a range of 1:2 to 1:6. Relative Water Supply (RWS) Values for RWS vary between 0.80 and 4.0 (figure 5). Half of the systems have RWS ** private wells ** surface and public wells 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 FIGURE 5. Relative Water Supply (RWS). 14 values greater than 2 showing an adequate supply relative to demand. Relative Irrigation Supply (RIS) Relative irrigation supply (RIS) focuses on supply of irrigation water alone, in contrast to RWS which also includes rainfall. When ir- rigation tightly fills the gap of water require- ments after they are met by rain, RIS is near unity. The RIS values plotted in figure 6 indi- cate there is a wide variation in the RIS val- ues among the systems studied (0.41 to 4.81). In situations where return flows go to a sea or a sink, and there is a scarce water supply in the river basin, it is better to have a relative irrigation supply near 1 than a higher value. It is instructive to note that the Muda System in Malaysia which uses a real- time monitoring of water-depth in rice fields is able to use rainfall effectively and has the lowest RIS value. This is particularly impressive as the storage is about 200 km upstream from the diversion point. Water not consumed by ET in the Muda System flows to the sea, so it is important for this area to closely match supply with demand. At Muda, RIS and RWS values are minimized by using a real-time monitoring rainfall and adjusting the irrigation release from storage/diversion structures to effectively use the rainfall component of the water supply. FIGURE 6. Relative Irrigation Supply (RIS). ** private wells ** surface and public wells 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 15 Water Delivery Capacity Ratio The water delivery capacity ratio indicates whether the system design is in anyway a constraint to meet the maximum crop water requirement. Values much greater than 1 in- dicate that their capacity is not a constraint to meeting crop water demands. Values close to 1 indicate that there may be difficul- ties meeting short-term peak demands. Oftentimes, additional capacity is designed (at additional cost) to allow for more flex- ible water deliveries, or to ease manage- ment. Financial Self-Sufficiency Table 2 presents percent of self-sufficiency attained by different systems studied. The values indicate that in systems where man- agement has been turned over from govern- ment to locally managed entities, a higher percentage of O&M expenditure is gener- ated locally than in government-managed systems. While the locally managed systems achieve a self-sufficiency of nearly 100 per- cent, agency-managed systems have a finan- cial self-sufficiency of 30 to 50 percent. This result has to be interpreted cautiously as we have taken into account only two systems which have been turned over from the gov- ernment to local management. Gross Return on Investment In computing the gross return on invest- ment, computations of investment cost of distribution systems posed a problem. In many cases, we used a current estimated cost of construction per hectare prevailing in those countries where we could not get re- liable construction cost of project under con- sideration. The values of gross return on in- vestment presented in table 2 show a wide variation between 6 and 75 percent. Rice- based irrigation systems with less-abundant water give a low return on investment (6 to 30%) while private pump irrigation systems provide the highest rate of return on invest- ment (75%). Temporal and Spatial Variation of Indicators within a Project If the minimum set of external indicators is disaggregated in time and space, they serve as tools for internal management of irriga- tion systems and for evaluating impacts of interventions. These concepts are demon- strated by applying indicators to two sys- tems: Samaca in Colombia for impact as- sessment, and Alto Rio Lerma in Mexico for operational management. In Colombia, for the Samaca Irrigation Project, the indicators were computed for a period of 11 years (1986 to 1996). Two of the indicators, output per unit command and the financial self-sufficiency, are displayed in figure 7. Despite yearly fluctuations, SGVP per unit command shows a clear rising trend. This increase in SGVP is mainly attributed to a general increase in yield of the 2 main crops (potato and onion) grown in the area. Over the last decade, Colombia’s economy has been liberalized with subsidies in agriculture cut or reduced substantially. Attitudes in farming have changed from mainly subsis- tence to commercial farming. Agro-inputs and improved irrigation facilities are now widely used resulting in increased yields. Until 1991, the financial self-sufficiency averaged 35 percent indicating that 65 per- cent was subsidized by the government. In 1992, this situation altered dramatically when the government decided to turn over the system operation and management to the users’ association. From then onwards farmers had to bear the full costs to run the 16 gated with surface and public well systems. The results indicate that the Cortazar Mod- ule outperforms in all indicators compared to Salvatierra Module as well as the entire district of Alto Rio Lerma, while Salvatierra Module’s performance is less impressive. This gives some indication of differences in results of the turnover program. Limitations of the Indicators First, the major difficulty of using the indi- cators is the uncertainty involved in many of the estimates. Two major types of uncer- tainties exist: uncertainties in the source of data and uncertainties in the estimates. Many of the data come from secondary sources, not directly measured by the re- searchers. There is a wide variety in the quality of data obtained from these sources. Second, means of estimating leads to errors. For example, there are large uncertainties in estimates of actual crop evapotranspiration and effective precipitation related to the methodology of estimating these values. The largest degree of uncertainty exists in the estimation of effective precipitation. Several methods exist to estimate effective precipitation (Dastane 1974), and the results vary depending on the method chosen. We also know that differences in physical and management characteristics of irrigated ar- eas play a large role in determining how much rainfall is effective. For example, a flat area with low rainfall using bunds where farmers practice deficit irrigation will cap- ture rainfall much more effectively than a sloping irrigation system in a hilly area, with a plentiful surface supply. At present, there are inadequate methods to estimate effective rainfall under the variety of situa- tions that exist. For this study, we relied on the best judgment of the researcher to esti- mate effective precipitation. system. Water fees were raised by 170 per- cent and the financial self-sufficiency in- creased to around 100 percent. For Mexico, the entire district of Alto Rio Lerma and its two transferred subsystems Cortazar Module and Salvatierra Module were selected for comparison of indicators on a spatial basis. Figure 8 displays the com- puted indicators for these subsystems irri- FIGURE 7A. Temporal variance of external indicators: Standardized gross value of production (1986–1996) per command area, Samaca Irrigation Scheme, Colombia. U S$ /h a 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 FIGURE 7B. Temporal variance of external indicators: Financial self-sufficiency (1986–1996), Samaca Irrigation Scheme. % 0 20 40 60 80 100 120 140 160 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 17 FIGURE 8. Spatial variations of external indicators, Mexico. Similar to effective precipitation, but to a lesser extent, estimates of actual crop evapotranspiration are subject to un- certainties in their quantification. On a regional scale with varying soils, water deliveries, and farmer practices, it is quite difficult to obtain a regional estimate. It is even more difficult to get a good estimate when deficit irrigation is practiced or crops are stressed. Clearly, the variability in prices (which directly affects SGVP) is a threat to the sta- bility of SGVP as an indicator. Figure 9 shows SGVP calculated with actual annual prices (inflation adjusted) and with the 10- year average price. Although it is clear that results are more stable with the average price, it is important to note that the overall trends (an initial rise, a fall, then a recovery to the best overall productivity) are reflected in both graphs. This gives confidence in the approach, and suggests only that caution should be exercised in selecting the appro- priate price sets depending on the purpose of the analysis. Given that there are large uncertainties, can the indicators be used to show differ- ences in irrigation performance? Where the magnitude of difference is large, say greater than 50 percent, we are confident we are discerning differences. And there are many cases where the magnitude is quite large. If the difference noted is small, say less than 20 percent, then we cannot confidently say there is a difference in performance between systems. As further research, sensitivity to uncertainties in parameter estimation to re- sults is required. 0 50 100 150 Return on investment Financial self-sufficiency % Salvatierra Module Cortazar Module Alto Rio Lerma 0 500 1,000 1,500 2,000 2,500 3,000 Output per unit land Output per unit command US$/ha Relative water supply Relative irrigation supply Water delivery capacity Ratio 0.0 1.0 2.0 3.0 4.0 5.0 6.0 0.0 1.0 2.0 3.0 4.0 Output per unit irrigation supply Output per unit water consumed US$/m3 18 FIGURE 9. Standardized gross value of production per cropped area, Samaca Irrigation Scheme. 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 U S do lla rs /h a Calculated with actual local and actual international prices cycle A cycle B U S do lla rs /h a 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Calculated with 10-year average prices cycle A cycle B Interpretation of Results With nine indicators per system, how do we interpret results? How do we say that sys- tem A is better than system B? The basic comparative indicators (indicators 1 through 4) represent the basic performance indica- tors. Where land is limiting relative to wa- ter, output per unit land may be more im- portant. Where water is a limiting factor to production, output per unit water may be more important. The water supply indicators (RWS, RIS, and WDC) are better suited to place the ir- rigation system in its physical and manage- ment context. Higher values of RWS, RIS, 19 and WDC indicate a more generous supply of water. In this case, productivity to land may be more important. Where the water supply indicators show a lower value it in- dicates a situation of a more constrained water supply and values of productivity per unit of water are more important. If performance in terms of output per unit land or water was high, what was the cost? The Gross Return on Investment indi- cator can give an idea of the costs involved to give such a return. With more data on external indicators we can ask such ques- tions as “in similar environments, can we achieve the same performance at cheaper costs?” Or, “what additional infrastructure costs are required to achieve better perfor- mance?” The comparative indicators can be used in irrigation management to assist in setting strategic objectives and measuring progress against those objectives. In this case, SGVP is not an appropriate term for output. Rather, gross or net returns from production should be used. The main purpose of SGVP is to allow comparison between systems. Discussion The indicators are able to discern large dif- ferences in performance relative to land, wa- ter, and production. The magnitude of these differences, in our view, justifies the ap- proach taken and the aggregate nature of the analysis made. We are confident that ratios of indicators of 2:1 and greater represent clear differences in levels of performance. With a larger sample, it may be possible to relate performance to key features of irri- gation systems: infrastructure (fixed, flexible), management (agency, joint, farmers), alloca- tion and distribution procedures (demand versus supply), climate (wet, dry), and socio- economic setting (large and small holdings). The performance study will allow compari- son of how well one system is performing relative to others in similar settings. This is an important tool for policy makers who want to know how and how much to invest in irrigation. The comparative assessment will give gross indications of where improve- ments can be made—in types of manage- ment, infrastructure, or water allocation. The comparative indicators should allow us to set up a screening process for selecting systems that perform relatively well, and those that do not. Based on the initial expe- rience from the external indicators, we can probe further into determinants of system performance using more refined techniques. These indicators are not meant to re- place day-to-day monitoring techniques that allow for performance-based management. They are useful in answering the question “am I doing the right thing?” (Murray-Rust and Snellen 1993). They can be used to iden- tify long-term trends in performance and to set and verify long-term strategic objectives. The next step is to proceed with gather- ing these indictors for a greater variety and number of irrigation systems. A typology will be developed for irrigation systems. The typology will allow comparison of irri- gation systems with similar settings. Addi- tionally, it will allow us to identify different aspects that lead to better performance. The comparative study will allow a screening of irrigation systems to highlight key issues relative to performance, and allow targeting of research to better understand key deter- minants of performance. 20 Climate To calculate evapotranspiration • monthly precipitation (mm) • mean daily maximum and minimum temperatures, per month (oC) • mean monthly windspeed (m/s) • mean monthly relative humidity (%) • mean daily hours of sunshine, per month (hours/day) Crops • total command area (ha) • cropping pattern of irrigated crops (planting dates, growth length in days) • area per crop, per season, or per year (ha) • yields, per season or per year (tons/ha) • local prices, per season, or per year (lo- cal currency/ton) ANNEX 1 Data Requirements to Calculate Performance Indicators • world market prices for main crop (US dollars/ton) Irrigation • total amount of irrigation water di- verted, scheme level, per season, or per year (m3) • net groundwater supply to system cal- culated by pumpage minus recharge or change in groundwater level times spe- cific yield • actual capacity of main canal and sec- ondary canals (m3/s) Finance • expenditures for operation, mainte- nance, and administration, i.e., all costs to run the system (in local currency/ year) • total income from water fees, farmers’ contributions, outstanding debt pay- ments, etc., excluding all government subsidies (local currency/year) • investment cost of irrigation infrastruc- ture (local currency/ha) 21 A. Standardized Gross Value of Production (SGVP) A1. In local currency For each season the six main tradable crops and irrigated pasture were taken into account. These crops cover more than 95 percent of the cultivated area. For example in 1995, the following data were collected: Season A (Jan. – June) Season B (July – Dec.) Crop Area Yield Price Average SGVP Area Yield Price Average SGVP (ha) (tons/ (pesos/ price (million (ha) (tons/ (pesos/ price (million ha) kg) pesos) ha) kg) pesos) Potato 498 25.0 265 221 3,299 475 18.0 171 200 1,462 Maize 95 1.3 502 380 62 80 2.0 250 346 40 Vegetable 145 20.0 189 255 548 216 20.0 194 239 838 Pea 349 4.0 1,259 978 1,758 270 4.0 762 889 823 Onion 357 25.0 488 444 4,355 455 25.0 502 467 5,710 Wheat 33 5.0 200 275 33 43 5.2 200 284 45 Pasture 655 332* 332 217 655 332* 332 217 Total 2,132 10,239 2,194 9,135 * 332,000 pesos per season per ha, four cuttings per season. The base year is 1995, inflation factor for Colombian pesos is 1.0, and total command area is 3,000 hectares. The total amount of water diverted yearly (scheme level) is 11,867 * 103 m3. SGVP per unit cultivated area 106 (10,239+9,135)/(2132+2194) = 4,478,000 pesos per ha. SGVP per unit command area 106 (10,239+9,135) / 3000 = 6,458,000 pesos per ha. SGVP per unit irrigation delivered 103 (10,239+9,135) / 11,867 = 1,633 pesos per m3. A2. In US dollars : Standardized gross value of production (SGVP) SGVP = {(yield crop 1) * (price crop 1 / price base crop) * (area crop 1 ) + + (yield crop 2) * (price crop 2 / price base crop) * (area crop 2 ) + (yield crop 3) * (price crop 3 / price base crop) * (area crop 3 ) etc. } * (world market price) base crop ANNEX 2 Calculation Example of Performance Indicators, Samaca Irrigation Project, Colombia 22 The base crop is the main tradable crop cultivated in the command area, which is taken as potato for Samaca. To eliminate distortions due to price fluctuations, for local as well as for international prices, averages are used: first, local prices per crop and per year are corrected for inflation (base year 1995), then the 10-year average over 1986-1995 is taken. The average world market price for wheat is US$149.4/ton. For the first season in 1995, the total SGVP is: {25 * 498 + 1.3 * (380 / 221) * 95 + 20 * (255 / 221) * 145 + 4 * (978 / 221) * 349 + 25 * (444 / 221) * 357 + 5 * (275 / 221) * 33 + 655 * (332,000 / 221)} * 149 = US$6,171,168 Likewise, for the second season in 1995 the SGVP is US$5,899,910 Total yearly value: US$12,071,078 SGVP per unit cultivated area: (12,071,078) / (2,132+2,194) = 2,790 US$/ha. SGVP per unit command area: 12,071,078 / 3,000 = 4,024 US$/ha. SGVP per unit irrigation delivered: 12,071,078 / 11,867,000 = 1.02 US$/m3. B. Crop Water Demand For each crop, the seasonal water demand is calculated with CROPWAT. The reference evapotranspiration (ETo) according to Penman-Monteith and the effective rainfall are cal- culated with CROPWAT (FAO 1992) (option 1 in main menu), separately for each year. In this case, the USBR-formula for effective rainfall is chosen (input: daily temperature, rela- tive humidity, windspeed, sunshine hours, total rainfall). For example, for 1995 Month Average Humidity Windspeed Daily ETo Penman- Total Effective daily temp. (%) (km/day) sunshine Monteith precipitation rainfall (oC) (hrs/day) (mm/day) (mm/ (USBR) month) mm/month January 13.8 76 171 7.0 3.0 1.3 1.3 February 14.3 77 180 10.2 3.7 65.1 56.6 March 14.8 78 169 6.1 3.2 142.8 102.0 April 14.7 77 155 4.2 2.8 37.6 34.8 May 14.2 79 142 4.9 2.8 64.1 55.9 June 14.2 76 193 4.1 2.7 51.5 46.2 July 13.5 80 174 5.1 2.7 26.5 25.1 August 14.1 73 175 5.3 3.0 52.8 47.2 September 13.5 78 149 5.4 2.9 27.8 26.3 October 14.6 78 118 3.2 2.5 60.3 53.0 November 14.3 74 145 5.2 2.8 86.5 71.5 December 14.3 80 139 3.3 2.3 82.9 69.2 Total 1043 699.2 589.1 23 Then, the net crop water requirement (CWR) and the net irrigation requirement (IR) are computed for each irrigated crop and for each growing season (option 2 in CROPWAT main menu). The crop coefficients provided with CROPWAT program are used (input: planting dates and growth length in days). For Samaca 1995, the outcomes were: Crop Area Net crop water Net irrigation Area Net crop water Net irrigation (ha) requirement: requirement: (ha) requirement: requirement: Season A Season A Season B Season B (mm/season) (mm/season) (mm/season) (mm/season) Potato 498 394.6 136.7 475 381.0 118.3 Maize 95 463.5 166.9 80 444.3 166.0 Vegetables 145 351.1 116.2 216 336.7 138.9 Peas 349 298.5 106.7 270 283.9 144.8 Onion 357 278.6 94.7 455 270.6 50.1 Wheat 33 326.3 137.4 43 329.8 131.3 Pasture 655 523.8 245.2 655 511.8 225.5 Total 2,132 2,194 The total net crop demand for season A is: CWR potato * (area potato / area total ) + CWR maize * (area maize / area total) + etc. = 394.6 * (498 / 2,132) + 463.5 * (95 / 2,132) + 351.1 * (145 / 2,132) + 298.5 * (349 / 2,132) + 278.6 * (357 / 2,132) + 326.3 * (33 / 2132) + 523.8 * (655 / 2,132) = 387.7 mm / season. In the same way, the total net irrigation requirements are computed. Results: Season Net crop water requirement Net irrigation demand A (Jan - June) 387.7 158.0 B (July - Dec) 383.2 143.4 Total 770.9 301.4 The SGVP per unit consumed could be approximated by SGVP / net CWR in pesos: 19,374 * 106 / (2,132 * 387.7 + 2,194 * 383.2)*10 = 1,162 pesos/m3 in dollars: 12,071,078/ (2,132 * 387.7 + 2,194 * 383.2)*10 = 0.72 dollar/m3 Amount of water diverted: Scheme level season A: 280.1 mm Field level season A : 193.5 mm season B: 268.7 mm season B : 198.0 mm yearly : 548.8 mm yearly : 391.5 mm 24 Relative water supply = (Irrigation derived + total precipitation) / crop water requirements2 Scheme level: (548.8 + 699.2) / (387.7 + 383.2) = 1.62 Relative irrigation supply = Irrigation applied / irrigation requirements3 Scheme level: 548.8 / 301.4 = 1.82 Water delivery capacity = Actual canal capacity/scheme peak demand4 Actual canal capacity was measured at the main reservoir outlet. The capacity is 750 /s. The scheme irrigation requirement was calculated with CROPWAT (option 4 in main menu) using the climate data, cropping pattern, planting dates, and area as mentioned above. For 1995, the scheme irrigation requirements were: Jan. Feb. March April May June July Aug. Sept. Oct. Nov. Dec. IR in l/s/ha 0.13 0.08 0.01 0.17 0.11 0.11 0.08 0.08 0.20 0.09 0.05 0.04 Peak irrigation requirements occur in September, 0.20 l/s/ha. Peak demand is 0.20 * cropped area for that month = 0.20 * 2,194 = 439 l/s. Water delivery capacity: 750 / 439 = 1.71. C. Financial Data Financial self-sufficiency = Revenue from irrigation / O&M expenditures The revenue from irrigation includes all income derived from water fees, water user association’s fees, outstanding debt and interest on debt payments but excludes all kind of government subsidies or payments. For 1995, this was : 92,032,056 Colombian pesos. The exchange rate for 1995 was 913 pesos/dollar so the revenue from irrigation was US$100,802. O&M expenditures include all expenditures to operate and maintain the system. For Samaca, they include operation, maintenance, and administration costs, totaling 86,296,340 pesos or US$94,519. Financial self-sufficiency = (100,802 / 94,519) * 100% = 107 %. In this case, income generated was more than the expenditure. Gross return on investment = Gross value of output / Cost of distribution system The cost of the distribution system is not known for the Samaca Project as the system was built over a time span of several decades. As an approximation, the investment cost of a similar system nearby (currently under construction) is taken. This amounted to US$7,000 per hectare for 1996 (figures for 1995 not available). The SGVP was US$2,976 per year per hectare of the command area. Gross return on investment is 3,096/7,000 = 42 %. 2Net crop water require- ment excluding effi- ciency losses. 3Net irrigation require- ments excluding con- veyance and application losses. 4Net peak demand ex- cluding conveyance and application losses. 25 ANNEX 3 World Market Prices of Agricultural Products in Constant 1995 Dollars Crop Unit 1980 1985 1990 1993 1994 1995 1996 Rice (Thai 5%) $ / mt 680.1 342.1 322.9 263.9 289.4 321.0 353.7 Maize $ / mt 207.4 195.0 130.3 114.4 116.3 123.5 173.1 Sorghum $ / mt 213.4 179.0 123.9 111.0 112.4 119.0 156.7 Wheat $ / mt 286.1 236.0 161.5 157.2 162.0 177.0 216.7 Soybean $ / mt 490.6 390.0 294.2 286.0 272.4 259.3 318.2 Coffee, robusta c / kg 537.3 460.2 140.9 129.8 283.4 277.1 188.5 Cotton c / kg 341.6 229.0 216.9 143.5 190.7 212.8 185.1 Source: Commodity Price Outlook, World Bank, Development Prospect Group, August 1997. 26 Literature Cited Abernethy, C. L. 1986. Performance measurement in canal water management. 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Kloezen International Water Management Institute INTERNATIONAL WATER MANAGEMENT INSTITUTE P O Box 2075, Colombo, Sri Lanka Tel (94-1) 867404 • Fax (94-1) 866854 • E-mail IIMI@cgnet.com Internet Home Page http: //www.cgiar.org/iimi ISSN 1026-0862ISBN 92-9090-356-2 20