Conventional wisdom in many agricultural systems across the world suggests that farmers cannot, will not, or should not pay the full costs associated with surface water delivery, i.e., the value of the water as well as the canal infrastructure to deliver it, and that it ought to be viewed and treated as a free public good. Even in systems with active water users’ associations (WUAs), the collection of even modest water-use fees is very low, and WUAs are seemingly powerless to enforce fee collection or to provide a credible threat of enforcement that would induce voluntary payment. Among the countries of the Organisation for Economic Co-operation and Development (OECD), only a handful can claim complete recovery of operation, maintenance, and capital costs (OECD 2013). Agricultural responsibility for water is capped at the equivalent of a few U.S. pennies per cubic meter in Brazil (Formiga-Johnsson et al. 2007). Across Central and South Asia, fees are lower still, with farmers in Nepal, India, and Kazakhstan paying fractions of a U.S. penny for a cubic meter of water (Rogers et al. 2002, Cornish et al. 2004, Ray 2011). Fees are similarly low in Pakistan, host to the Indus Basin Irrigation System (IBIS), the world’s largest gravity fed irrigation system (Khan 2009). Per-area fees in the IBIS range from 85 to 200 Pakistani Rupees (PKR), an amount roughly equivalent to US$1-2 per acre per season, depending on the crop and the season (GP-FAS 2012).
Despite these low fees, farmers in Pakistan spend orders of magnitude more for diesel fuel to pump groundwater each season, suggesting a latent willingness to spend for water that, under the right conditions, could potentially be directed toward water-use fees (known as abiana in Pakistan) for surface water supply (WSTF 2012). In a previous study using a discrete choice experiment for irrigation water supply, we measured this willingness to pay for reliable surface water to be a smooth function of surface water reliability, rising to the order of PKR 23,000 per acre per season (Bell et al. 2014) for a 100% reliable supply. Importantly, this finding presents a clear potential link between the asymmetry of resource access in irrigation systems and cost recovery. Where asymmetry in system design leads some users to have less reliable access to water, they in turn will be less willing to contribute to maintaining the system.
In the present study, we developed a modeling framework to explore the system-level implications of this observation from the field. Specifically, we examined how the outcomes of overall productivity in the system (economic efficiency) as well as the distribution of wealth accumulation (equity) could be shaped by assessment of higher water-use fees along the IBIS in the Punjab district of eastern Pakistan. We developed an agent-based model (ABM) of a small irrigation command, representing a small part of a large-scale irrigation system fed solely by surface water, in which farmers choose their cropping pattern based on expectations of water receipt and interact via a voluntary market for the exchange of water allocations. The water allocation market provides a rational basis to identify the benefits that could accrue from the exchange (i.e., who might wish to use more or less water than their base allocation, and who might benefit from such transactions), and considers the implications of a similar exchange that is nonmarket or inequitable. We examined efficiency and equity outcomes across a range of different possible cost structures for the maintenance of the system (distinguishing both local watercourse and global system costs), levels of market development, asymmetric access to water, and assessed water charges. We drew on our empirical findings from previous work (Bell et al. 2014) for a simple characterization of what farmers will pay, based on what they are charged and what they are receiving.
We found that, robust to a range of different hypothetical (although realistic) conditions, increased water charges lead to gains in both efficiency and concomitant improvements in equity as investments in canal infrastructure and system maintenance improve the conveyance of water resources further downstream. These findings suggest that, under conditions in which (1) farmers are currently spending money to pump groundwater to compensate for a failing surface water system and (2) there is possibility through initial investment to provide perceptibly better or more reliable water supply, genuine win-win solutions can be attained through assessing and collecting higher water-use fees from beneficiary farmers.
The IBIS is a large, publicly maintained system of canals feeding branch canals, branch canals feeding distributaries and minors, and these, in turn, feeding lower-level watercourses. Irrigators along the lowest-level watercourse in the IBIS receive water according to a fixed-turn system (known as warabandi) in which one farmer appropriates all water entering the watercourse for some fixed interval before yielding flow to the next farmer, with a typical cycle taking about 10 days (Bandaragoda 1998). Farmer choices for water appropriation (e.g., opening and closing of gates, committing labor to maintain watercourse) shape a local commons dilemma of the kind well studied elsewhere in small-scale irrigated systems in which the variability in water receipt is largely a function of local actions (e.g., Janssen et al. 2011, D’Exelle et al. 2012). Large-scale public irrigation schemes such as Pakistan’s differ from such systems in that water receipt to terminal watercourses is shaped by investment and appropriation decisions at higher scales (Bell et al. 2015a), to which farmers’ major connection is the water-use fee that is paid. With some variation across the country, generally some component of this fee is retained locally, for local maintenance, whereas the remainder is collected centrally and applied (somehow) toward the maintenance of the larger IBIS system. The focus of our study is on these latter processes, linking broader investments in system maintenance to the performance of local watercourses, rather than on the local watercourse commons themselves. However, we are mindful in our design and discussion to consider the possible implications of the local dilemma on our broader findings.
If we consider some part of the water-use fee to contribute to shaping the inlet conditions (i.e., water receipt reliability) to a local watercourse of irrigators paying the fees, then farm-level improvements in productivity with improved water infrastructure seem reasonable and intuitive. If cost recovery is higher, and these revenues are spent on maintenance and appropriate capital investments, at a minimum some farms should experience improved water supply. Water volumes reaching these farm outlets would be higher and more predictable, permitting farmers to better match cropping patterns to available water and to possibly select for more water-intensive, higher-valued crops. Although crop choice is ultimately mediated by a myriad of factors, evidence suggests that water reliability is one of them: our previous work across multiple sites in the region found significant cropping of water-intensive rice and sugarcane only at sites where reliable supply was available via low-salinity groundwater (Bell et al. 2014). Despite this possible mechanism for improved efficiency, it remains an open question whether actual increases in water-use fees measurably lead to improved efficiency across the system. Thus, the first test to which we apply our modeling framework is whether our field observations, i.e., a steady increase in willingness to pay water-use fees with improved reliability, translate meaningfully into potential efficiency improvements, measured as production across the system, via the following hypothesis (in null form):
• H10: Irrigation system efficiency is not improved under increased water fees
A second open question, given the unequal access to water that canal irrigation systems provide, is whether the benefits of any improvement would be equitably shared among all farmers in the system. Improved infrastructure and reduced leakage likely mean that at least some farmers will receive more water, but it is not at all obvious how far down the system of canals, of distributaries within canals, of minors within distributaries and so on, that benefits from a given investment would be perceived. To the extent that improved water supply reaches a greater number of farmers, equity might be improved. However, if the benefits are not equitably distributed, then it is possible that those farmers with the least access to canal irrigation (e.g., those toward the tail end of a canal or distributary) might actually be made worse off, paying higher water-use fees yet not reaping any of the benefits of the improved infrastructure, which are instead captured by those farmers closer to the head. Perhaps more likely, given our previous empirical work, they would simply not pay at all, remaining entirely unaffected, and no more engaged in the shared system. It is not obvious a priori whether system equity might be improved, worsened, or left unchanged under a given change to cost recovery, and thus, whether system investments might yield equity-efficiency trade-offs. Our second and third hypotheses thus examine the system outcome of equity (measured by distribution of accumulated wealth) in response to increased water fees, and its relationship with the outcome of efficiency:
• H20: Irrigation system equity is not worsened under increased water fees
• H30: Improvements to irrigation system efficiency do not come at the expense of irrigation system equity
Several factors complicate this analysis for Pakistan. One is that many systems are conjunctive use, relying on both surface and groundwater (and, to a lesser degree, rainfall), where both groundwater quality and pumping costs can be highly heterogeneous (Mahmood et al. 2001, Qureshi et al. 2010). A second factor is that (as introduced above in the context of local commons dilemmas) actual water allocations in practice often depart from official allocations, sometimes through voluntary trades but also in some cases motivated by more influential individuals, or otherwise enabled by unequal access to water resources, leading to less equitable divisions (Bandaragoda 1998). A third is that the cost structure of maintaining and developing irrigation infrastructure is not well reported; rather, the literature reports only charges assessed and recovered, and expenditures, which are known to not cover true operation and maintenance costs (e.g., Wolf 1986, Habib 2002). In all of these cases, we lack empirical data on the profile of groundwater salinity and pumping cost, on the degree to which water allocations are stolen or coerced, or on the true costs of maintaining irrigation infrastructure in the region, to describe them meaningfully within the model context.
However, we note that a good system model is not necessarily the one that incorporates all possible variables and factors, but rather, the simplified model whose findings would remain robust to inclusion of such additional factors. We present a simple modeling framework, making use of sensitivity analyses where possible to identify effects that are robust to unknown inputs such as true maintenance costs, and discussing how these effects would persist under processes we don’t incorporate, such as groundwater supply or other modes of water exchange among users. Our framework employs an agent-based model (ABM) approach, which treats decision-making agents (such as farmers, drivers, deliberative bodies, governments, etc.) as the basic unit of analysis and allows system-level outcomes (such as land cover, traffic, or in our case, irrigation performance) to emerge out of the interactions agents have with each other and their environments, and the decisions they make (Matthews et al. 2007, Bruch and Atwell 2015). There is a wealth of literature applying ABM to study agricultural decision making (e.g., Deadman et al. 2004, Robinson and Brown 2009, Bell 2011, Bell et al. 2016) and several models built to consider irrigators specifically, including labor allocation across farming and fishing in an Asian irrigation context (Schlüter et al. 2009), or rules for collective use in a West African irrigation context (Barreteau et al. 2004). The great strength of ABMs in analyzing resource use is the ability to represent highly context-specific decision processes. The trade-off, however, is that such models are often difficult to share or apply to new contexts, and new lines of inquiry often require new models (as does ours).
We developed an ABM of a small irrigation command consisting of 24 farm agents, i.e., farmers. The complete model description adhering to the Overview, Design concepts, and Details (ODD) protocol for ABMs (Grimm et al. 2006, 2010) is included in Appendix 1. In this section, we summarize the model structure, parameters, and key assumptions. The complete model is published via OpenABM.org (Bell 2015). Additionally, to make some of the toolkits developed for this model more available (Bell et al. 2015b), individual submodels for (1) the irrigation channel model, (2) the genetic algorithm for land-use decision making, and (3) the water market model can be downloaded directly from the model’s OpenABM page (https://www.openabm.org/model/4727/version/1/view).
We model the irrigation command area as a node-and-channel model with an exogenous upstream water source (Fig. 1). In each water time step, water entering the system at the upstream inlet propagates through the entire system, with seepage losses along each channel segment according to the level of maintenance and potential withdrawal (in the absence of market transactions) at each node according to the allocation of farms located at the nodes. Any water not withdrawn drains from the final node in each channel. The modeled time scale is thus equivalent to a complete irrigation turn cycle (typically 10 days in Punjab), without the need for explicitly modeling the flow rate of water along the channels or the active opening and closing of irrigation gates at the farm nodes. Without adequate investments in maintenance and upkeep, infrastructure of both the command-area channels and the upstream inlet degrade during each water time step, leading to higher seepage losses in the former, and a more irregular inlet flow in the latter.
We approximate a year as 36 time steps or roughly 360 days (hereafter the decision time step). In each decision time step, farmers choose the optimal land-use portfolio for their farm (consisting of a subdivision of land into plots, each with its own water allocation and crop rotation), the level of the assessed water-use fee they are willing to pay, and whether to trade (purchase or sell) water allocations with other farmers in their immediate watercourse. Additionally, within each decision time step, revenues collected though water-use fees are applied to maintain both upstream inlet and local channel infrastructure.
Farmers’ decisions on crops are based on expected water receipt given their memories of historical water receipts and universally (in the model) known functions for crop yields on water, based on the Jensen crop water production function (Kipkorir and Raes 2002).
Farmers decide on the best land-use portfolio for their farm (see Table 1, for an example), maximizing expected utility on net income using a genetic algorithm. The genetic algorithm employed in this model treats the land-use portfolio as a gene, and individual rotations (together with their area and fractional water allocation) as traits. The algorithm follows the same design used by Manson (2005) and Manson and Evans (2007), with reproduction by elite tournament selection, via (1) crossover between two parents, (2) mutation of a single parent, or (3) direct reproduction without change. Crossover is performed as a simple shuffling of crop rotations, i.e., the set of all crop rotations belonging to the two parent portfolios is pooled, and then each rotation is randomly allocated to one of the two child portfolios. Mutation is allowed to occur in any part of the portfolio: (1) area mutation, (2) water mutation, or (3) crop mutation.
For the current study, we were able to obtain reliable estimates of costs, water requirements (at different points throughout the crop growth cycle), and yields for eight of the most important, in terms of area, crops in our region of interest (Pakistan Punjab): plain rice, basmati rice, wheat, sugarcane, cotton, potato, maize, and onion. All crop data and sources are included as Appendix 2. Crop data can potentially include not just yields, prices, and variable input costs, such as labor, fertilizer, etc., but also fixed costs, such as machines or crop-specific land prep, that can be shared across multiple crops to capture the imperfect and lumpy ability of farmers to switch between different cropping systems (for the current study, we do not include any fixed costs in our data).
Farms choose to pay water-use fees in this model according to the following schedule:
where the value 23,000 corresponds to the maximum cumulative willingness to pay of approximately PKR 23,000 per hectare for a reliable water supply estimated by Bell et al. (2014). This model structure reflects our maintained assumption that farmers would be willing to pay up to PKR 23,000 per hectare for an assured water supply, but would be equally happy to pay less if the assessed fee is such.
Fees received are allocated to separate channel accounts, with farms contributing only to channels through which they receive water, and with a proportional allocation of the fees across the inlet and other channels fixed by the irrigation system parameters described in Table 2.
A rural water market can be difficult to resolve because farms have simultaneous potential to be buyers or sellers of their water allocation. For instance, a farm with more than enough water to grow wheat but not enough water to grow sugarcane might have a low marginal value for a small additional amount of water (as they could not use it to their advantage) but a high marginal value for a larger amount of water, if it enables them to transition from wheat to sugarcane. At the same time, they may be quite interested in selling water.
The water market submodel acts as a clearinghouse, receiving a list of all bids that farms in a market are willing to make on increments of δ through nδ of water allocation and a separate list of prices at which the same farms would be willing to sell increments of δ through nδ of water allocation. These bid and ask prices are evaluated on a farmer-by-farmer basis by estimating the expected change in utility to the farmer arising from an additional allocation (and actual receipt) of δ through nδ (to calculate the bids for purchasing) or from a reduction in allocation by δ through nδ (to calculate the ask prices for selling). Note that a change in water allocation of δ is not the same as a change in water receipt of δ; the submodel looks at the actual water receipt histories of neighboring farms to determine what change in actual water receipt would be expected to occur with a change in allocation of δ.
The list of all bids across all farms in the market is ordered from greatest to least, and a standard “knapsack” combinatorial optimization problem (e.g., Strandmark 2009) is solved for each one in turn, until there are no more possible transactions. A transaction is possible if there is a set of increments for sale such that the total price for the increments offered is below the willingness to pay for the total set of increments, e.g., a bid of 18 for 4δ could be met by 3δ offered for 12 and δ from another farm offered for 5. Once a farm has participated in a transaction, either as a buyer or a seller, it leaves the market for this time step and does not participate in further transactions until the next decision time step at the earliest.
We ran a full factorial experimental design over the costs of maintaining upstream inlet reliability (3 levels), the costs of local channel maintenance (3 levels), the degree of permissible market participation of farmers (i.e., the limit on allocation increments that farmers could buy or sell in the market; 3 levels), the structure of the canal command (i.e., the number of branches across which the 24 farms were evenly distributed; 3 levels), and the level of water-use fee assessed (4 levels). We repeated this sweep of conditions with 3 random seeds, for a total of 972 modeling runs.
At initialization in each simulation run, farms have no previous memory of water receipt or candidate land-use portfolios for consideration. At Δtw = 0 (i.e., the water time step is 0), the random seed for the simulation is set, the landscape is initialized, and the simulation is run for a spin-up period of 10 full decision time steps (in our simulations, 360 Δtw) without the farms taking any action, to accumulate a memory of water receipt. The first decision time step ΔtD thus occurs at Δtw = 361.
Table 2 summarizes model parameterization for our chosen set of experiments. Genetic algorithm parameterization is based on that of Manson (2005). True costs for maintenance of irrigation systems in South Asia are not well known, because at best only revenues and spending are recorded, rather than indication of actual maintenance and repair needs (Malik et al. 2014); in our experiments we choose local and global irrigation maintenance costs to cover a range of conditions from insignificant to limiting cost levels. Assessed water-use fee levels in these experiments are chosen to span a range of conditions, but notably this range begins at a level above current water-use fee assessments for Pakistan, as a representative of large-scale public irrigation in Asia, and stops well below farmers’ measured willingness to pay for reliable canal water (Bell et al. 2014).
Our principal interest is in examining outcomes of economic efficiency, measured as value of production (VOP), and equity, measured by the wealth GINI coefficient across a simulation, as water-use fees are increased, over a range of different cost and market access conditions. Here, farm wealth includes both agricultural income and income from the sale of water allocation. A complete set of experimental outcomes is included in Appendix 3; in the current section we draw a subset of these to illustrate the overall narrative that the full experiment conveys.
Looking across all farms in all simulation runs (Fig. 2), we observed clear patterns in crop choice and efficiency as a function of the reliability of surface water (measured as the fraction of allocated water received over the duration of the simulation). Farms with low surface water reliability (the peak at the left in Fig. 2A) appear to grow more lower-value, hearty crops, such as maize or wheat; with increasing surface water reliability, there is a gradual transition toward more high-value but water-sensitive crops, which, in our dataset, includes moving toward cultivating onions and sugarcane. This is consistent with trends observed in our previous field study in Punjab (Bell et al. 2014).
Efficiency improves with increasing water-use fees across a broad range of structural conditions in the irrigation system (Fig. 3). We consider both a system in which there is high potential for increased water fees to have an impact on system performance, hereafter a “high potential” system (Fig. 3A), as well as a “low potential” system (Fig. 3B). In terms of our experimental variables, high potential is captured by (1) relatively low maintenance costs, meaning even a modest increase in use fees leads to rapid infrastructural improvement, and (2) high asymmetry, with farmers all arranged along a single watercourse. In contrast, the low-potential system has relatively high maintenance costs and a lesser problem of asymmetric access because farmers split evenly across three watercourses accessing the inlet directly. We note that this increase in efficiency is robust to changes in market development; that is, the increasing extent to which farms can sell portions of their allocation in each decision time step does not disrupt the gains in efficiency that higher water-use fees bring.
Similarly, we observe an apparent, though slight, reduction in wealth inequality measured across farms with increasing water-use fees (Fig. 4A, B). This decrease appears robust to irrigation structural conditions as well as the development of markets. However, a visualization alone does not demonstrate effect, nudging us toward formal tests of relationships between our experimental sweep variables and the outcomes of interest.
Treating each of the 972 simulation runs as an independent data point, the effects of our sweep variables emerge clearly from simple ordinary least squares (OLS) regressions of the main effects and 1st-order interactions (Table 3). System-level efficiency (VOP) increases with water-use fees (evidence to reject H10), with the development of markets for water allocation having a negative effect that could offset some of these efficiency gains. This is to say, as farms sell off their allocations, some fraction of land in the watercourse would go to lower-valued crops or fall out of use altogether (we note that our model does not explicitly model any alternative incomes) so that wealth creation across the command could decrease even as some farms produce higher-value crops and others make rational choices to sell off their allocation. Both local and inlet costs have intuitive impacts of decreasing VOP; as the system becomes more expensive to maintain, overall maintenance levels are lower, and production is lower. Breaking farmers up across multiple channels with symmetric access to the resource improves production in the system. Key interactions for VOP include that (1) the effect of local costs on VOP is higher when abiana levels are high; (2) the effect of having more channels is more important when local costs for maintenance are high; and (3) higher abiana levels and a greater number of channels have offsetting, positive effects on VOP.
Equity (wealth GINI) is also improved with increasing water-use fees (recall that a lower GINI indicates more equal distribution of wealth), though we note the overall low level of explained variance in this regression. Including 1st-order interaction terms in the regression model for wealth, GINI leads these significant effects to disappear, likely by collinearity and variance inflation. Taken together, these results suggest that if present, any effect of increasing water fees on equity is small but positive (i.e., reduces the GINI); we find no support to reject H20, with no evidence to suggest that equity is worsened under increased water fees. Further, our outcomes of GINI and production are negatively correlated (a Pearson coefficient of -0.2375, significant at 0.1%, indicating a positive relationship between efficiency and equity), and thus find no support for H30, that improvements in efficiency come at the expense of equity.
Overall, explanatory variables in the regression models reflecting the structure of the irrigation system (costs, number of channels) exhibit the effects on outcomes posited by our high-potential and low-potential categorization: higher maintenance costs tend to reduce farms’ ability to produce, whereas a higher number of channels (and thus more symmetric access to water) tends to improve both equity and efficiency. Interestingly, we observe no significant interaction effects of market development (numDeltas) with other variables in our sweep.
We present a simple results narrative, fleshed out in further detail with detailed experimental sweep results in Appendix 3. In general, our results suggest that increasing water-use fees not only increases agricultural efficiency, but does so without compromising equity in the command area. Efficiency gains emerge as farmers are better able to choose and grow water-sensitive, higher-value crops. This result is robust to a range of operation and management cost structures and symmetry in water access, capturing the variation that could be expected across small to large irrigation command areas. Additionally, this result is mostly robust to variation in the extent to which farms are able to trade their water allocations among themselves.
The underlying mechanism is that, if farms are willing to pay more for reliable water supplies (e.g., Bell et al. 2014), and if water fees collected are invested in system maintenance and improvement, then higher water-use fees can stimulate a self-reinforcing system of improved cost recovery and downstream user empowerment. Improved performance leads recipients to pay some greater overall fraction of the assessed fees, further improving performance and leading potentially to greater cost recovery. As system improvements allow allocations to be met further downstream, downstream users in turn receive more reliable supplies, are able to undertake more profitable cultivation on their land either by increased productivity or by moving to higher value production, and are, as well, willing to pay more into the system. We must highlight, however, that this paragraph began with several “ifs.” In particular, this mechanism depends upon appropriate institutions in place to translate collected water fees into system improvements; our goal, in the current study, is not to discuss these in detail, but rather to demonstrate their importance, by illustrating how Pakistan’s irrigation landscape could be transformed were they properly in place.
This mechanism also presents something of a chicken-and-egg problem: this self-reinforcing mechanism of cost-recovery and system maintenance depends crucially on the investment of funds into providing a perceptible improvement in water reliability. Such an initial investment might come from farmers themselves, under the premise that their increased contributions should lead to improved performance, but for farms not currently receiving reliable water this might be a big ask. Indeed, the results in Bell et al. (2014) suggest that one of the major factors underlying farmers’ willingness to pay higher water-use fees is their perception of the reliability of their existing surface water supply. Certainly, if their contributions did not lead quickly to improved delivery, such contributions would not likely be sustained, as more broadly observed under the process of irrigation management transfer (IMT) in Pakistan: initial changes to local management led to short-term improvements in water-use fee collection that declined quickly in subsequent seasons when benefits were not readily apparent to farmers (e.g., Asrar-Ulhaq 2010, Ghumman et al. 2011). Rather, a program kick-start might need to come via external investment, with a clear commitment to invest in infrastructural repair and maintenance. This would be a departure from much of the broader history of development project investments, in which new infrastructure brings greater political capital and thus a cycle of build-neglect-rebuild (Khan 2009).
We argue, however, that the return for sustained investments in infrastructural repair and maintenance is a robust mechanism for efficient, equitable improvements in irrigation water supplies. We excluded additional sources of water from our model for simplicity, but we would expect this effect to hold under the conjunctive use systems common in Pakistan. Diesel fuel, for groundwater pumping, is a major cost for irrigators (Bell et al. 2014), such that access to groundwater is somewhat more the privilege of the wealthy. To the extent that providing surface water would be more cost-effective or otherwise preferred over pumping groundwater (because most usable groundwater in Pakistan is leakage from surface-water systems anyway, and will contain contaminating salts and minerals that the surface water does not; WSTF 2012), investments in infrastructure and repairs to improve surface water delivery should lead even wealthier farmers away from groundwater, toward greater payment of water-use fees, and the net empowerment of less advantaged farmers observed in our model.
We also excluded any treatment of exchange in water allocations other than the market submodel, which incorporated no measure of relative bargaining power. Even where deviations from design allocation exist purely because of coercion or theft, our mechanism will hold provided that there is at least some fraction of the command area that would benefit from having more reliable water enter the system (i.e., those who do not or cannot meet their needs through theft or coercion alone). As those contributors lead to improved performance, the net empowerment of tail-enders may also serve to mitigate the potential for theft or coercion in the future.
We applied an agent-based model of farms making decisions on what to plant based on expected water receipt and paying water-use fees based on the reliability of water that they had received. We found that increased water-use fees raised overall agricultural production in the system, as well as improved the distribution of wealth among farms in the system; a result that is robust to a range of irrigation structural characteristics, i.e., costs of local vs. global maintenance, and asymmetry of access. Our previous work in the region (Bell et al. 2014) challenged the wisdom that farmers were unwilling to pay greater amounts for water; and our current study demonstrates the system level benefits that could accrue, to a range of different forms of irrigation system, if greater water-use fees were levied.
The major challenge to kick-start such performance in an actual irrigation system is the initial provision of a perceptible improvement in irrigation performance. New command areas may be able to levy high fees from the start, but more generally it may be important to channel project funding or public expenditures into the operations and maintenance of existing irrigation infrastructure, rather than into new projects. Activating the mechanism for self-reinforcing cost recovery could make such investments highly valuable.
This work was facilitated by the Pakistan Strategy Support Program (PSSP) of the International Food Policy Research Institute (IFPRI), funded by USAID.
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