Irrigation Reliability And The Productivity Of Water: A Proposed Methodology Using Evapotranspiration Mapping. Chris Perry.

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1 Irrigation Reliability And The Productivity Of Water: A Proposed Methodology Using Evapotranspiration Mapping Chris Perry. Abstract Irrigation is the dominant user of water world wide, but provision of potable water and water for industry are higher priorities and give higher social and economic returns. Irrigation will continue to lose water to competing sectors and the productivity of irrigation systems (since food demand continues to grow) remains a central issue in water management. Performance assessment of irrigation has traditionally been difficult when based on field measurements of flows, deliveries and depths over large areas. Furthermore, performance measures have shifted from narrow engineering indicators to broader productivity issues of production achived per unit of water consumed. Remote sensing, applied to the measurement of ET over large areas, provides analysts of irrigation systems with extraordinary new tools for the objective assessment of consumption and production constituting a quantum leap in the assessment of irrigation system performance. Utilisation of these tools is spreading rapidly, but important areas remain to be converted from traditional approaches that rely on an array of estimated parameters. The next challenge for remote sensing will be to map the frontier between the reliability of the irrigation service and the productivity achieved. Reliability provides the inducement for farmers to invest in higher productivity to the benefit of themselves and society and understanding better how the individual maximises profits within an uncertain irrigation environment can provide important guidance to managers and system designers. Introduction Irrigation is the dominant user of water. Worldwide about 70% of water use is for agriculture, with a much higher figure (85%) in low and middle income countries, where agriculture is a major economic sector (World Bank, 1992). But in most countries, irrigation is not the highest priority for water use: provision of potable water and water for industry give the highest social and economic returns. In most countries water for fisheries, navigation, power and commercial uses also take precedence over irrigation, and in many wealthy countries environmental uses are now in substantial competition with irrigation. Thus, paradoxically, irrigation is both the biggest user, but has to make do with what no-one else needs.. Visiting Professor of Irrigation Economics, Cranfield, University, Silsoe, Bedfordshire MK45 4DT, UK c.perry@cranfield.ac.uk Perry, 2003 Page 1

2 In consequence, as populations rise and economies grow, water is transferred from irrigation to other uses, and irrigation will continue to lose water to competing sectors. The performance of irrigation systems (since food demand continues to grow) remains a central issue in water management. Performance, in this context, is focused essentially on productivity: how much water is consumed and how much crop is produced. We may also seek social objectives but it is a question for social scientists and politicians as to how much production we are ready to lose to increase equity or meet other socially desirable objectives. Quantifying physical and productive performance of irrigation systems through the development and application of indicators has been an active field for many years, but the performance of the indicators has often been as difficult to assess as the performance of the irrigation systems they were applied to. Indicators of Performance: Where have we been? Some ten years ago, the International Water Management Institute published a review of the indicators of irrigation performance that had been proposed at that time (Rao, 1993). There were some 57 indicators, the vast majority of which were to be based on measurements of physical parameters (flow rates, irrigation schedules, yields, irrigation depths, canal seepage) at field, subproject, project or wider scales. One of the simplest of these (Levine 1992) was measured as: Irrigation Delivery + Effective Rainfall Evapo-transpiration + Seepage & Percolation Measurement of these variables at various points in an irrigation system, at regular intervals, provides an indication of whether water availability exceeds or falls short of demand an indicator of management effectiveness. In the case of most irrigation systems in developing countries, even these basic data are exceptionally difficult to measure: irrigation deliveries are rarely uniform over time or space (but an average figure is meaningless); effective rainfall is as much a matter of opinion as scientific fact (and rainfall itself may vary significantly within a large irrigation project); evapotranspiration, if computed using the conventional Penman-Monteith approach, requires a data set that is rarely available; and seepage and percolation vary dramatically, depending on prior conditions, maintenance and construction standards, at all points in the system. So this simple indicator of management effectiveness presents severe difficulties of implementation and measurement in large, spatially and temporally diverse systems. More complex performance indicators relating to equity, reliability and productivity present even greater challenges to field measurement, so that realistic estimates of the reliability of such indicators would in most cases have shown such wide confidence intervals as to be essentially meaningless. Perry, 2003 Page 2

3 It is no exaggeration to say that at the time of IWMI s review there were fewer complete and reliably measured data sets than there were suggested indicators. This is not surprising: it is easy to define a mathematical formula for an indicator of performance, but the difficulties of measuring spatially and temporally variable data were insurmountable at reasonable cost, especially in the conditions of many developing countries, where farm sizes are often less than 1 ha, and overall levels of water control are poor. Even in most developed countries there were few complete data sets to enable assessment of irrigation system performance, or to determine the scope for meeting the challenge of increasing water productivity. Indicators of Performance: Where are we? During the subsequent ten years, the need for indicators of performance has become increasingly pressing. As already noted, competition for water is increasing; water is being transferred from agriculture to non-agricultural uses; the need to detect and address non-beneficial uses of water is critical; and investments to meet such objectives are often being made in the absence of adequate analysis. Furthermore, emphasis has shifted from technical estimation of efficiency of water delivery to a crop per drop perspective less concerned with physical measurement of water flows in relation to targets, and more concerned with the productivity of water consumed, and the extent of non-productive consumption. The advent of remote sensing as a tool to measure such fundamental parameters as evapotranspiration and biomass formation has radically changed our abilities in this area. Spatial coverage is available at the various scales needed field, project, basin. Temporal coverage is vastly superior, at minimal cost, compared with the field measurement of data. Thus it is now possible to conceive of management tools that inform system operators of the temporal and spatial pattern of water use, how productively it is being used, and where it is being wasted through non-beneficial evapotranspiration. In short, while the field-level difficulties of measuring water supplies remain, remote sensing allows us to do the accounts, in water consumption terms, in ways that were unthinkable a decade ago, dramatically reshaping the way in which researchers and planners can assess systems. Suffice it to say that in his excellent paper on water accounting Molden (1997) does not even refer to his own earlier and seminal work with Gates (Molden and Gates,1990). We are in a new era of water management and remote sensing will be at the heart of activities, yet some key players have still to join the club. The FAO (AQUASTAT, 2003) has undertaken an extensive (and important) review of water withdrawal and consumption. This review is based on traditional computations of reference ET, crop growth stage coefficients, effective rainfall, etc. From these data even though we know that our estimates of these variables are dubious at the best of times, and particularly uncertain Perry, 2003 Page 3

4 when we are assessing low-performance irrigation systems conclusions are drawn about the extent to which a country s resources are utilised, how much water remains for further development, and how much can be saved by more efficient management. Burt et al. (2001) in assessing evaporation for irrigated agriculture in California (across multiple crops, climatic districts and technologies) base the analysis entirely on the computations of physically based models. Much better, and more objective, to use standardised, direct measurements of the key physical parameters on which estimates of consumption and production can be uniformly based, thus establishing more objectively how much water irrigated agriculture is consuming, where, when, and with what productivity. Indicators of Performance: What next? The remainder of this paper sets out what the author believes is the next key issue where remote sensing can offer insights for irrigation professionals that cannot be obtained from conventional field measurement. The challenge goes beyond water accounting (which provides us with records of what went where and what levels of productivity were achieved) and addresses a component of the why issue of variation in water productivity. Analyses of yields (be it yield per hectare or yield per unit of water) typically show wide variations between poor, average and good farmers within the same agro-climatic and agro-economic environments. We know too that tail-end farmers are often less productive than head-end farmers in irrigation systems. In part this is simply because they get less water (which is directly reflected in lower irrigation intensity). Also, it is because their water supply is less secure (reflected indirectly through lower yields per unit of land or water). The literature on the relationship between yield and ET (Doorenbos and Kassam, 1979) typically defines yield to be linear function of Potential ET, Actual ET and Potential Yield. By implication, the objective of irrigation in this formulation is to get Actual ET as close as possible to Potential ET and hence achieve Potential Yield. But Potential Yield is itself a variable whose impact is intuitively obvious. In choosing what crops to grow an irrigator makes a myriad of complex trade-offs related to his known resource availability (land, labour, machinery, money), his expectations about markets and prices, and his expectations of water availability. Other things being equal, the farmer will be ready to invest more in the chosen crops if the availability of water is more secure: he will be ready to select high yielding but water sensitive varieties, prepare the land carefully, plant intensively, and invest in high quality seeds, fertilisers and pesticides to ensure a healthier crop. Conversely, if water supplies are uncertain, he will choose a drought-tolerant crops and invest little extra to try to improve on a low but secure yield. Perry, 2003 Page 4

5 Understanding the relationship between reliability and productivity has implications for the productivity of water in agriculture, and hence the water required to meet specific levels of production, and the design and management of irrigation systems: while the FAO 33 (Doorenbos and Kassam) and other models can estimate the ex post impact of a 10% shortfall in water availability at a certain time and for a given crop and treatment we currently have no basis for predicting what the ex ante impact on farmer choice of (say) a 20% chance of a 50% shortfall will be. Figure 1 shows the relationship between yield and ET for two production strategies for a typical field crop. In one scenario the potential a low-yield, low-input crop and the other a high-yield, high-input crop. The assumed relationship between ET and actual yield for a specific crop technology is that proposed by Hargreaves (1997), which is conveniently clearly specified as: Yield = 0.8 * ET * ET * ET 3 In fact, the analysis presented below holds for any relationship between yield and ET whether linear, quadratic or other. Most of the literature shows linear relationships. In FAO 33, for example, we see various sets of linear relationships between yield and ET but the slopes of those relationships vary significantly between crop growth stages. For convenience here it is assumed that the relationship is curvilinear rather than piece-wise linear (i.e. that the crop switches instantly between stages from one slope to another). In addition, the Hargreaves relationship is pleasing to an economist in its demonstration of varying marginal returns, and different optima for returns to water and returns to land. The expression (which was derived from a large volume of data for field crops) evaluates to unity for yield when ET is also at unity (that is, Potential Yield is achieved at Potential ET). We may thus evaluate a family of yield/et outcomes for the same crop under different conditions of husbandry as shown in Figure 1. The top curve represents the Yield/ET frontier for a crop planted at high density, with high yield potential and all necessary nutrient and other chemical support. The lower curves represent the potential frontier for a range of lower input options though the total seasonal ET for a fully matured crop is essentially constant. The range of yields presented (with a factor of five between the highest and lowest) is not extreme: wheat yields within Europe varied from 1.5t/ha (Portugal) to 8.6t/ha (The Netherlands) in 1995 (CIMMYT, 2003). Clearly the farmers in these populations are following different strategies for a variety of reasons but both operate within the European Common Agricultural Policy, so that significant aspects of their decision framework are common. Perry, 2003 Page 5

6 Evapotranspiration Figure 1: Yield vs ET for a range of Crop Husbandry Conditions Focusing now on irrigation as a key variable in determining farmer strategy, we can note from Figure 1 that for a given quantity of ET (say 0.5 potential), yield can range from about 0.1 of potential to 0.6 of potential. Why would any farmer choose a low yield strategy when the ratio of productivity for a fixed level of ET (or water consumption) is so high? If we convert our indicator from yield to net value by assuming the following cost data, the issue becomes clear: Low yield crop High yield crop Potential yield 1 unit/ha 5 units/ha Fixed cost (seed, fertiliser, 0.2 units 3 units pesticide) Variable cost (harvesting) 0.1 units * Yield 0.1 units * Yield Net income at maximum = 0.7 units = 1.5 units yield/ha While the potential net income from the high yield crop is more than double the income from the low yield crop, the low yield crop gives a better outcome (higher profit, or lower loss) for any level of ET lower than about 0.65 (see Figure 2). Perry, 2003 Page 6

7 Figure 2: Income vs ET for low and high yield crop options Thus if a farmer is not confident that the irrigation service will provide more that 65% of full irrigation requirement, he will in this example switch from the high to the low yield potential option. In terms of irrigation management, the issue is reliability of supply: and this is illustrated in the figures below. A simple irrigation schedule (ten days on; ten days off for a number of rotations) is used as the basis for description. It can be unreliable in terms of three parameters: rate of supply; duration of supply; and periodicity between successive start dates. Figure 3 shows sample delivery schedules compared with targets. The left graph is relatively reliable the pattern of actual deliveries clearly related to the target. The right graph (which may be familiar to most researchers who have tried to gather data for evaluation of traditional performance indicators) introduces significant variation into all three parameters, and consequent lack of correspondence between target and actual delivery. Perry, 2003 Page 7

8 Target Q Target Q Figure 3: Declining reliability of supply Clearly, a farmer faced with progressively less reliable supplies will opt for a cropping strategy closer to the low income option of Figure 2 an option which provides a much higher probability of a positive outcome for the farmer, and the guarantee of an less-bad negative outcome. Essentially this is obvious, and it is exactly what we observe in subsistence rainfed environments. The question is how to characterise the relationship between declining reliability of supply and declining Potential Yield so as to choose design and management options that increase total productivity. Mapping the reliability/productivity frontier through remote sensing It is broadly estimated that some 300 Mha may eventually be irrigated, of which 270 Mha are already developed. Irrigation development in global terms is over (though some significant local schemes, such as Narmada river development in India and the Three Gorges and South- Perry, 2003 Page 8

9 North Transfer in China, remain), and the future of irrigation development consists of making existing facilities more productive, often with less water than they have today. Returning now to Figure 1, we see first that the potential production gain to society from a farmer choosing the high yield strategy is a yield increment of 400% over the low yield strategy, an increment which exceeds the impact of irrigation itself in many circumstances. In Figure 2 we see that the income gain to the farmer is 100% much lower though still substantial. The key point is that society is the major beneficiary of a farmer s choice to invest in a high-input strategy. Second, it is interesting that the investment to make that productivity jump is funded by the farmer he trusts the irrigation system and invests in high yielding seeds, improved land preparation, fertilisers, pesticides, weeding, and so on safe in the knowledge that water at least will not limit his production. This is induced investment, knowledgably targeted and not at a cost to the taxpayer. It has exceptionally high returns. But we know nothing beyond the intuitive map presented here about the actual relationship between degree of unreliability and the farmer response; we do not in truth even know what the key parameters might be in the definition variation from target cumulative volume? Critical depletion of soil moisture? What parameters should a system manager use to minimise reliability impacts on farmer investments in productivity? Remote sensing offers the potential to address the problem: spatial monitoring of seasonal trends in ET can provide indices of reliability. Remote sensing allows us to plot, at the pixel level, the progress of ET, crop growth, and the productivity of land and water through the crop season. Thus we can conceive of computing (for a sample of plots of a specific crop) the path of transpiration over time, and the ultimate productivity of water. For a crop receiving adequate irrigation, we would expect the evaporative fraction to be close to unity for the entire season and high overall productivity of land and water. Where irrigation is reliable, but less water is available than might potentially be utilised (land being plentiful in relation to water) we might expect to observe a consistent pattern of reduced evaporative fraction in the range where yield falls less than water consumption, and overall observe a somewhat lower productivity of land, and somewhat higher productivity of water than in the first case. Where we observe random reductions in evaporative fraction, we expect to observe (as a result of the farmer s risk aversion and lower levels of inputs) significantly lower levels of land and water productivity. Quantifying the relationship between the degree of random reduction in the evaporative fraction and the overall productivity of land and water will open the way to clarifying the nature of the relationship between the unreliability of irrigation and the productivity of water. Such patterns, and far more insightful ones that will be developed as data accumulates, can be related to final yields and converted to water productivity statistics eventually tracing the frontier between reliability and productivity. Perry, 2003 Page 9

10 Measurement will difficult: we have to observe many farmers experiencing various levels of unreliability, and somehow quantify the results into patterns that can help us design and manage systems better by reducing unreliability to the extent possible. Practical outcomes include: Better understanding the extent to which complex irrigation schedules, designed to meet precise needs of water sensitive crops should be pursued at the cost of potential degradation in reliability Insights into whether erratic inflows into dams are better released as lower volumes at higher reliability saving unexpected peaks for later use as reliable, controlled supplies in a subsequent season. These and other analyses can guide those designing the management strategies for the improved irrigation facilities that will feed and clothe future generations a small challenge, with a potentially huge reward. References Burt, Charles M., Daniel J. Howes, and Andrew Mutzinger Evaporation Estimates for irrigated Agriculture in California. Paper presented to Irrigation Association Conference San Antonio, Texas, USA. ITRC paper No. P CIMMYT Production statistics for wheat. Viewed on July 14, 2003 Doorenbos, J. and A.H. Kassam Yield response to water (FAO Irrigation and Drainage Paper No. 33). Food and Agriculture Organization of the United Nations, Rome, Italy FAO 2003, Aquastat viewed on July 14, Hargreaves, George, Water requirements manual for irrigated crops and rainfed agriculture. Utah State University, Logan, Utah, USA Levine, G Relative Water Supply: an explanatory variable for irrigation systems. Technical Report No. 6. Cornell University. Ithaca, New York, USA Molden, D Accounting for water use and productivity. IWMI/SWIM Paper No. 1, International Water Management Institute, Colombo, Sri Lanka Molden, David J. and Timothy Gates Performance measures for evaluation of irrigation water delivery systems. Journal of Irrigation and Drainage Engineering, ASCE 116(6): Perry, 2003 Page 10

11 Rao, P. S Review of Selected Literature on Indicators of Irrigation Performance, International Irrigation Management Institute, Colombo, Sri Lanka The World Bank, World Development Report 1992: Development and the Environment. Oxford University Press, New York Perry, 2003 Page 11