Improving global food security will be made more challenging by a changing climate (Turral et al. 2011, Wheeler and von Braun 2013, Bell et al. 2016). Because climate change will alter water supplies, irrigated agriculture in particular will suffer (Gleick 2003, FAO 2012). This is especially true for irrigators who rely on snowmelt (Vicuña et al. 2012, Villamayor-Tomas 2012). This is problematic because irrigation is expected to be responsible for meeting growing demands for food and already accounts for 40% of the world’s food supply (UNESCO 2012). Worldwide, about three quarters of irrigated cropland and one quarter of all cropland relies on self-governed irrigation systems (Mabry 1996), and 90% of all irrigation systems are small-scale and self-governed (Cifdaloz et al. 2010). An irrigation system, i.e., the diversion dam, diversion structure, canals, weirs, sluices, and other infrastructure, is self-governed when farmers with rights to access the water conveyed by the system own, operate, maintain, and manage the system. Thus, irrigators themselves will be tasked with the vast majority of adaptation, yet scholars, policymakers, and irrigators themselves may not know enough to be adequately prepared for climate change (Kramer et al. 2017).
Farmers in the state of Colorado are a good test case for questions related to climate change adaptation in irrigated agriculture. Just over half of the farmland in Colorado is irrigated by self-governing irrigation systems (Sax et al. 2006, USDA 2014), and about four fifths of stream flow start as snow (CCC, n.d.). Although potentially offset by CO2 fertilization and an extended growing season (Wiltshire et al. 2013, Deryng et al. 2016), farmers nevertheless face mounting climate change challenges: more severe forest fires and forest composition changes (Lukas et al. 2014), declines in snowpack volume and earlier spring melt (Llewellyn and Vaddey 2013, Koirala et al. 2014), and increased crop water demand due to rising temperatures (Lukas et al. 2014). The part of the Rio Grande Basin commonly called the San Luis Valley (SLV), in which a community of 50,000 irrigates between 140,000 and 200,000 hectares, may be the most negatively impacted in Colorado and has already seen climatic changes (Mix et al. 2011, 2012, Lukas et al. 2014). Early signals of climate change have already prompted responses from irrigators, especially those dependent on groundwater (Cody et al. 2015, Smith et al. 2016).
Many scholars, policymakers, and irrigators see improvements to irrigation systems as important to adaptation (FAO 2012, Lee et al. 2014). Some of the improvements envisioned are institutional changes; that is, changes to laws, policies, rules, and norms (Kenney 2005, Ostrom 2005, Huntjens et al. 2012, Meinzen-Dick 2014). Particular attention has been paid to property rights to water (Cody 2018). However, property rights can be very difficult to change, and government-imposed rule changes are often resisted (Poteete et al. 2010). To compensate, irrigators on self-governing systems have developed local adaptations to shortage that manipulate two of the major influences on water use they control: rules governing water allocation and water distribution among members (Dinar et al. 1997, Joshi et al. 1998). My study investigates the effectiveness of two common drought responses of irrigation systems, (1) water delivery via rotation and (2) shortage sharing, in four configurations: rotation with shortage sharing, rotation without shortage sharing, simultaneous delivery with shortage sharing, and simultaneous delivery without shortage sharing. Effectiveness is evaluated at different levels of water shortage and for water users at different points along the irrigation system’s water conveyance network. Rotation is defined as water delivery that occurs in turns, regardless of the duration of the turns or the sequence of delivery. Shortage sharing is defined as the alteration water allocations between users of the same irrigation system in times of drought, regardless of the original criteria used to allocate water (land owned, private rights held, historical use, etc.).
The Common Pool Resource (CPR) literature has begun to emphasize the configurational nature of rules (Baggio et al. 2016), but there is still much to learn about how different configurations influence the outcomes of CPR governance, such as equity (Ingram et al. 2008). Fairness and equality are of utmost importance: many studies have shown that perceptions of fairness in a self-governing CPR context are important for maintaining the collective action necessary to maintain the flow of resources to users (Arnold 2008, Poteete et al. 2010, Cody et al. 2015, Pérez et al. 2016, McCord et al. 2017). In irrigation, a ubiquitous issue is the potentially highly unequal relationship between irrigators upstream (head-enders), which have the ability to withdraw water first and in the largest amounts, and downstream (tail-enders), who must wait for water to flow past head-enders and can only take what is left (Janssen et al. 2011, 2012). Additionally, different levels of water shortage should generate different irrigation outcomes even with the same rules in place, as Cody (2018) found with respect to water rights. Therefore, I evaluate the effectiveness of the aforementioned configurations at different levels of water shortage and for fields at different distances from the irrigation system’s main diversion.
To do this, a natural experiment in the use of rotation and shortage sharing during a period of drought (2011-2014) in the SLV was exploited. Drought creates the water shortage that triggers the implementation of the rotation and shortage sharing rules under study, and these rules serve as the different treatments. Hydrologic, technological, agronomic, and remotely sensed crop data (Normalized Difference Vegetation Index, NDVI) were paired with data on over 6700 individual fields nested within 60 self-governing irrigation systems for the years 2011-2014. The data are complemented by a stratified irrigation manager survey conducted in 2013 that assessed the rules in use of those systems, among other features. The study area and sampled irrigation systems are depicted in Figure 1. Because variables of interest are time invariant, the data were analyzed one year at a time using Ordinary Least Squares (OLS) linear regression and logistic regression with standard errors clustered by irrigation system. Tobit regression and spatial error and spatial lag models were performed as robustness checks.
The results show that not only are these configurations significant predictors of irrigation performance, but there is also a significant interaction between the configurations and context. The rule configurations have significantly different impacts on the degree to which head-end fields and tail-end fields have divergent levels of crop production, and these effects are further moderated by the amount of water available. In particular, rotational delivery with shortage sharing, the most frequent combination of rules in the sample, has the capacity to equalize NDVI between head-enders and tail-enders across all levels of water shortage. It also produces nearly equal marginal productivity of water, i.e., increases in crop growth per additional unit of water, between head-enders and tail-enders across all levels of water shortage. Rotation with shortage sharing consistently performs well compared to other configurations with less equal outcomes, even if in some water shortage conditions other configurations out-perform it. Because of its more equal outcomes, and because it competes well with other configurations in drought, it is likely that rotational delivery with shortage sharing best promotes the collective action necessary to adapt self-governing irrigation systems to a more arid and unpredictable future in the SLV and elsewhere. That said, the precise forms of rotation and shortage sharing cannot be prescribed from this analysis. Indeed, in some cases, only rotation, only shortage sharing, or neither rule may be warranted. Further investigation of individual irrigation systems would be necessary to determine the exact rules that would optimize performance under a diverse set of conditions (Cifdaloz et al. 2010, Pérez et al. 2016). Nevertheless, this study gives irrigators and managers more certainty about the outcomes of the options they have.
Two universal institutional features of self-governing irrigation systems are water allocation rules and water distribution rules (Ostrom 1992, Dinar et al. 1997, Joshi et al. 1998). They are especially worthy of study because of their direct and fundamental influence on water use, and therefore water demand and ecological impacts. In the context of irrigation, water allocation rules pertain to how much water each farmer within an irrigation system can use (e.g., 9200 cubic meters per hectare of land owned), and distribution rules determine how that water reaches the farmer (e.g., for sequential 12-hour turns; Ostrom 1992, Dinar et al. 1997, Joshi et al. 1998). One of each of these types of rules is the focus of this study, because they are known adaptations to shortage (Abdullaev et al. 2006, He et al. 2012) and are therefore likely to be important for adaptation to climate change. They determine (1) whether or not water allocations can be changed between individual irrigators on the same irrigation system (i.e., shortage sharing, de facto temporary transfers of the usufruct water right), and (2) whether the flow of the ditch is delivered to individual irrigators in a rotation or divided among them simultaneously. Irrigation systems in the SLV under normal water availability may or may not rotate, with some switching to rotation and some changing the rotation itself during shortage. However, although there are myriad manifestations of these rules in practice (e.g., multiple forms of rotation, multiple criteria for determining water allocations), some level of abstraction is required to make general inferences about their influence, and so they are considered binary in this study.
Delivery and allocation rules interact with key human and hydrologic behaviors, specifically the incentive to overuse or steal water and the fact that water is lost to seepage and some evapotranspiration down the length of an earthen canal. Irrigation managers in the SLV reported anywhere from 5-15% losses depending on the distance a farmer’s headgate is from the diversion structure, the slope of the ditch, soils, ditch lining (e.g., concrete, bentonite, nothing), vegetation along the ditch, height of the water table, etc. This implies that seepage loss can become a major factor for irrigators to consider during shortage. Most irrigation managers also reported some level of water theft, usually more damaging during shortage and on larger systems in which monitoring is difficult.
Important for this study, water rights in the SLV are administered by the state of Colorado at the point in which water is diverted from the natural water source through a human-made diversion structure and into the human-made irrigation network. Beyond the diversion structure, the state does not directly interfere with how water is allocated on the irrigation system. The allocation and distribution rules being investigated were adopted by irrigators themselves based on the coevolution of contextual factors, such as law and geography, and irrigator preferences (Ostrom 2014). The decision to implement institutional adaptations to shortage such as rotation or shortage sharing is generally based on snowpack or streamflow and determined by an informal dialogue between irrigators, though it may be taken to a vote. Irrigators on the same irrigation system usually interact almost daily during irrigation season, and nearly all systems have an annual meeting prior to the season to discuss the ongoing needs of the system, potential changes, and whether to implement adaptations to shortage. The process of rule adoption is deeply historical; irrigators in this region have been continuously operating their systems as far back as the 1850s and pass land down largely from fathers to sons. Therefore, in a highly path-dependent process (North 1990), these distribution and allocation rules have evolved slowly over time to accommodate new users, new technologies, changes in water law, changes in the hydrologic context, and other influences. Depending on the collective choice rules of the irrigation systems, operational rules of allocation and delivery are selected through majority vote, consensus, inherited tradition, hegemonic behavior of a few powerful irrigators, or some other decision process. Whatever the case, each configuration is ultimately the product of coevolving contextual factors and irrigator preferences (Ostrom 2014). Irrigators report that changes to their rules have produced meaningful changes in crop production in the past, and among irrigators the importance of allocation and distribution rules is widely acknowledged.
Looking first at delivery rules, under rotation users can easily monitor each other at the main canal as they take turns diverting water. Monitoring in the SLV is conducted almost exclusively by eye. There are very few irrigation systems in which electronic ditch gates and gages are in use. Under rotation, the next farmer in turn will be at the ditch, engaged in de facto monitoring, sometimes in the middle of the night. Although requiring increased negotiation, management, and operational costs, rotation thus helps prevent “stationary bandit” behavior (Janssen et al. 2011, 2012), wherein head-enders take advantage of being first in line to extract water and deprive tail-enders of their full allocation (Ostrom 1992, Lam 1998). If left unchecked, stationary bandits eventually cause tail-enders to become helpless to match the elevated extraction of head-enders, and in extreme cases, tail-enders get no water at all. In contrast, rotation creates an affirmative requirement to deliver water to all users, potentially improving collective action through time (Dayton-Johnson 2000, Pérez, et al. 2016).
Because rotation generally allows the full flow of the ditch to reach each irrigator, it has four importantly different hydrologic impacts compared to simultaneous delivery: (1) rotation generates enough hydraulic head to push sufficient water the full length of the ditch (Lam 1998); (2) rotational pulses more quickly saturate the root zone over a given area of land when compared to continuous application of the same volume; (3) during very high flows, rotation can overwhelm and damage infrastructure, waste water, overwhelm crops, and erode soils; and (4) depending on how turns are taken, rotation may cause water to flow over a dry ditch bed at the start of turns, increasing seepage losses compared to simultaneous delivery, which keeps the ditch bed wet constantly.
On systems that distribute simultaneously, the flow is divided among users according to de facto rights between them at the same time. Three key features of simultaneous delivery include: (1) greater transaction costs to establish monitoring and more difficulty in monitoring because of the need to monitor many water users at once, and thus less monitoring overall; (2) no affirmative delivery requirement to tail-enders (or anyone, for that matter); and (3) decreases water supply reliability to tail-enders because of divided hydraulic head and thus worse seepage losses relative to rotation. Together, these features encourage theft and hegemony by head-enders as well as potentially more severe seepage losses. However, they may also ensure predictability of flow for most users most of the time. Simultaneous delivery is also inexpensive to organize and administer in terms of time and labor. Finally, the lack of turn-taking simplifies transfers, infrastructure needs, and maintains a consistently saturated ditch bed over the distance that water flows, eliminating the need to repeatedly saturate the bed when rotating turns.
The other rule in use under consideration, shortage sharing, implies flexibility in the ownership of de facto water rights. This flexibility should allow for more efficient allocations of water, enabling irrigators to improve the vigor of already planted crops, reliably plant more area, and earn revenue on unused water (He et al. 2012). Shortage sharing should improve marginal productivity in most cases by allocating water to lands with greater needs. Without the flexibility of shortage sharing, there is likely to be lower aggregate production than could otherwise be achieved. However, this flexibility can also increase the costs of monitoring water use by creating ambiguous water rights. Higher monitoring costs introduced by shortage sharing could lead to lower levels of monitoring, thus encouraging water theft and reduced irrigation performance, especially for tail-enders. However, if rotation is in place, this effect may be mitigated. Furthermore, shortage sharing will alter the hydraulic head, seepage loses, and return flows from irrigation applications not fully consumed. This would harm tail-enders relative to head-enders and would be more damaging under simultaneous delivery because of the lack of an affirmative delivery requirement.
The consequences of one rule depend on the adoption of the other rule, leading to hypothesis 1 (H1): the effect of shortage sharing on irrigation performance depends on how water is being delivered, and the effect of the delivery method on irrigation performance depends on whether shortage sharing is practiced. Using rotation, with higher monitoring and higher hydraulic head, shortage sharing will be helpful because of increased flexibility, thus allowing water to flow to fields most in need. Under simultaneous delivery, with lower monitoring and lower hydraulic head, shortage sharing will be harmful due to stationary bandit behavior and seepage losses. Without shortage sharing’s increased flexibility, rotation may be harmful because of the rate of flow being variable and thus water may be insufficient for or overwhelm infrastructure, soils, or crop demand. Finally, without shortage sharing, simultaneous delivery will tend to produce stationary bandit behavior and, in severe shortage, difficulty generating enough hydraulic head to move water the full length of the irrigation system.
The effects of the rules in use will depend on water availability and how far water has had to flow from the diversion structure to the field, leading to hypothesis 2 (H2): the effects of shortage sharing and delivery rules on irrigation performance are moderated by the amount of water diverted by the irrigation system and a field’s distance from the diversion. Rotational delivery should equalize irrigation performance between head-enders and tail-enders regardless of water availability and shortage sharing rules, but without shortage sharing could prove inflexible to changes in water availability, leading to worse performance as higher levels of water availability overwhelm the system. Shortage sharing should harm tail-enders under simultaneous delivery, especially in extreme drought. But with rotation, shortage sharing should stabilize irrigation performance at higher levels of water availability through more efficient transfers. Rotation with shortage sharing, however, should harm performance at low levels of water availability due to the hydrologic inefficiencies of turn taking and the agronomic problems associated with very few pulses of irrigation water. Under simultaneous delivery without shortage sharing, there should be high inequality between the head and tail-ends and marginal productivity of crop per unit of water should be higher for head-enders who will capture the water ahead of tail-enders.
Careful case selection for natural experiments is increasingly encouraged when research questions cannot be answered using a laboratory, field experiments, or modeling; when study systems involve many different biophysical and social data; and when data are difficult or impossible to acquire or aggregate (Poteete et al. 2010, Cox 2015). Natural experiments, such as this study, require data about numerous potentially confounding variables, and these data are seldom available at the same unit of analysis or resolution. However, the SLV overcomes many of these challenges because of the richness of its public data, the stability of the units of analysis, and six years of site visits by the author to ground truth the data and analysis. A period of drought in the SLV from 2011-2014 enables an evaluation of the rules in use during shortage, which serve as the different treatments in the study design.
Variables were drawn from CPR theory and organized using the Institutional Analysis and Development Framework (Ostrom 2005). Variables were also selected in part because of their use to previous studies of irrigation in this region (Cox 2010, Smith 2016). The overall approach to the study is depicted in Figure 2. Water flows from left to right, being influenced by the variables in the diagram along the way. All the variables shown in Figure 2 are used in the regression analyses. Table 1 also provides this information with more detail about the variables.
Data were collected from various public sources, primarily the Colorado Department of Natural Resources’ Rio Grande Decision Support Systems (RGDSS), the United States Geologic Survey, and GoogleEarth Engine (e.g., NDVI and elevation rasters). For the purposes of the regressions, irrigation system-level data were applied to the field observations to assess irrigation outcomes for individual fields. Figure 3 illustrates how NDVI raster data from GoogleEarth Engine, July 2013, were overlaid by individual fields and irrigation system boundaries from RGDSS, shown as vectors. When calculating the average NDVI value for each field, the vector data were converted into raster data to compute zonal statistics in ArcGIS 10.5.
Data were also collected using surveys of a stratified sample of 60 irrigation system leaders in 2013 to assess rules in use and other irrigation system features. Stratification was done by groundwater access (access/none), water right priority (senior/junior), geographic location (upstream/downstream), geographic location (four major watersheds), and cultural heritage (founded by the Spanish or by the Americans). The sample may therefore not be representative of the SLV overall, but it will be better able to determine whether underlying effects exist that would otherwise go undetected if the sample were not balanced on these key variables. Surveys were administered in English and conducted in person at a location of the interviewee’s choosing by two to three researchers at a time. One researcher led the questioning and recording of responses, and the others took notes and confirmed accuracy. To ensure that questions were asked and answers recorded consistently, the groups of researchers were mixed each day.
There are some limitations to the data. For example, the study lacks farm-level data and therefore cannot account for farm-level effects. However, fields nearer to each other are more likely to be owned by the same farm, and so the spatial regressions take some farm-level effects into account passively. The study also lacks any direct data on the wealth available to irrigation systems or individual farmers. That said, the area of an irrigation system is a proxy for the wealth and labor available to that irrigation system for operations and maintenance. Distinct patterns of natural resource use can be the product of distinct economic relationships (Kininmonth et al. 2016). Therefore, a dichotomous variable indicating whether an irrigation system was founded by the Spanish (acequia) is included. This provides socioeconomic and demographic information because those systems tend to be more collectivist, less capital intensive, physically smaller, less market oriented, utilize animal fertilizers, grow heirloom crops, and have historically been persecuted, oppressed, and excluded from governance processes (Rivera 1998, Rodriguez 2006, Cox and Ross 2011, Cox 2014). The installation of sprinkler irrigation and irrigator-reported infrastructure quality are also proxies for the cost structure and capital available to individual farmers and the irrigation system (Bell et al. 2016). Irrigation system area also correlates with the number of irrigators, a key variable important for the extent of and difficulty of solving collective action problems faced by irrigators. Finally, for systems using rotational delivery, the data lack information on the rotation itself, e.g., the location on the canal of each irrigator, the order in which they may take water, the duration of each farmer’s turn, etc.
Finally, over time, irrigation systems adopt rules based on the feedback irrigators receive from past performance (Ostrom 2014). It could therefore be argued that the effects for different rules in use actually reflect past irrigation performance and/or the factors shaping past irrigation performance and not the current rules. However, the parsimonious explanation that arises from the data is that the causal explanations are straightforward: the rules cause the outcomes (see Appendix 1, Section 9). Most importantly, the selection story and endogeneity argument presume that rules have real-time effects on performance, i.e., if performance responds to certain rules, irrigators will presumably alter rules to take advantage of these effects. Without that causal link, there is no selection pressure on the rules and there is no endogeneity. This study embraces that causal link, but argues that the feedbacks take too long and are too weak in the near-term to overwhelm the carefully designed analyses reported here. Irrigators report a highly cautious and slow-moving approach to institutional change at the level of the irrigation system, as well as the active influence of rules on outcomes of the system (especially in drought and on the equality of head and tail-enders). Furthermore, because rules assessed in 2013 can’t have been shaped by performance in 2014, because the regressions account for other drivers of performance and rule choice, because the sample was stratified for important factors that drive performance and rule choice, and because of the content of the 60 interviews and other conversations with key stakeholders and informants, it is reasonable to interpret the results in a straightforward way.
Following Gujarati and Porter (2009), OLS and logit regressions were run in R 3.2.2 (R Core Team 2015) for all years in the study period for three dependent variables: irrigated vs. fallowed/not irrigated (logit), percentage irrigated (OLS), and NDVI (OLS). Using Primo et al. (2007) as a guide, the analysis is not a hierarchical model but instead uses OLS and logit regression with clustered standard errors at the level of the irrigation system. This is because the data exist only at two levels (field and irrigation system), the measure of interest is the average effect of the rules in use across systems, and fixed effects would obscure the rules in use. The models are run for each year as robustness. To specify the model, an iterative process was conducted between consulting theory and running correlations, pair-wise regressions, and analysis of variance to assess which of the available variables to include in the regressions. Variables that were not deemed sufficiently explanatory or were not especially warranted by theory were excluded from the final regression. The model without interactions takes the form:
Interactions between shortage sharing (SHR_SRC) and rotational delivery (ROT_SRC) were conducted to assess the first hypothesis. To assess the second hypothesis, a categorical variable with five categories (the four potential institutional configurations plus seven systems owned entirely by one farmer) was interacted with volume diverted by the irrigation system per irrigable unit area on that system (AFDIV_PERDACRE) and percent of the maximum field distance from the diversion (DIV_DIST). South facing aspect (SOUTH), a ditch-level variable, which captures the intensity of direct sunlight, was only included in models using NDVI as the dependent variable. Data for irrigation method (SPRINK), crop grown (CROP), and groundwater access (GROUND) were for the most recent observation for that field given the year under analysis (i.e., fields not irrigated were given the most recent data available, usually the previous year). Data for volume diverted per irrigable unit area and percent average flow at the upstream gage (PERAVAFGAGE) were for the year under analysis. One ditch system lacked diversion volume data for 2014, and so it was excluded from the 2014 analysis.
Because percentage area irrigated and NDVI are censored variables, Tobit regressions were run to confirm the significance, size, and direction of the effects found using OLS. To explicitly account for spatial autocorrelation, spatial lag and spatial error models were also run (Bivand and Piras 2015). These robustness checks confirmed the OLS and logit results. Because the OLS and logit results are easier to interpret, they are reported below.
Shortage sharing has a significantly different effect on outcomes under rotational delivery when compared to simultaneous delivery, and rotational delivery has a significantly different effect on outcomes under shortage sharing than under fixed allocations (p < 0.05). When rotating, shortage sharing has no significant effect on outcomes in any year studied for any dependent variable measured. That said, the predicted values for rotation and sharing is higher than rotation alone for the vast majority of years and dependent variables. When not rotating, shortage sharing significantly harms outcomes in all years studied for all dependent variables measured, supporting predictions. Conversely, when sharing shortage, rotation improves outcomes in all years studied for all dependent variables measured, as predicted. However, when there is no shortage sharing, rotation significantly harms the probability of being irrigated in 2012 and 2014. In other years and for other dependent variables, there is no significant difference, though the model’s predicted values are higher for simultaneous delivery and no shortage sharing in all years. This suggests agreement with the prediction that rotation without shortage sharing could have ambiguous effects.
Figure 4 depicts the probability of a field being irrigated in 2012 under the four different institutional configurations. The year 2012 is representative of the overall results. The probability of being irrigated is shown here because irrigating a field reflects a large commitment on the part of an irrigator that is often made prior to the beginning of the irrigation season. It is therefore a more conservative measure of the influence of rules on outcomes.
The second hypothesis is supported overall, though there are some circumstances in which interactions are not significant and in which results are unexpected. That said, the results illustrate similar trends across all outcome variables and across years, implying robust results. Rotational delivery mitigates inequality of irrigation performance between head-enders and tail-enders regardless of water availability and with or without shortage sharing. Rotational delivery without shortage sharing improves marginal productivity at the tail-end but is easily overwhelmed by increases in water availability without shortage sharing, leading to negative marginal productivity (i.e., more water decreases crop production). This exceeds predictions, which expected no or low positive marginal productivity and suggests physical damage. Shortage sharing increases marginal productivity under rotation but diminishes marginal productivity to near zero under simultaneous delivery, especially at the tail-end. Shortage sharing also makes inequality worse under simultaneous delivery when compared to under rotation, especially in extreme shortage at the tail-end. The weakest differences between performance of the different configurations emerge at the head-end under extreme scarcity, with the tail-ends under minor and slight scarcity generating the largest differences in performance between institutional configurations. More detailed findings for each institutional configuration are given in Table 2.
Figures 5 and 6 illustrate the results by showing the interaction between rules in use, field distance from diversion, and volume diverted per unit area for 2012 using NDVI as the outcome. Figure 5 illustrates performance as one moves from the head-end to the tail-end of the system for different levels of water availability, whereas Figure 6 illustrates the marginal productivity of water at different points along the irrigation network. For these results, seven irrigation systems owned by only one farmer are included as counterfactuals to systems reliant on collective action. Results for NDVI are given here because NDVI represents a proxy for the other outcomes: lower NDVI values also correspond to lower irrigated area, including no irrigation. The NDVI represents a better approximation of total crop growth, and therefore income and potential for subsistence, than irrigated area because NDVI also includes information about the intensity of crop growth and therefore the weight of sellable or consumable crop. In brief, NDVI gives a sense of both how extensive and intensive irrigation was.
This study advances the literature by considering the combined effects on irrigation performance of shortage sharing and delivery method. There are numerous studies that separately investigate shortage sharing (Torell and Ward 2010, D’Exelle et al. 2012, He et al. 2012, Ward et al. 2013) and rotation (Turral et al. 2002, Abdullaev et al. 2006, Janssen et al. 2012). However, complicating this literature, there is not agreement as to what constitutes shortage sharing. D’Exelle et al. (2013) investigated instances in which head-enders forego diversions with the intention of enabling tail-enders to irrigate (thus reducing the head-ender diversions disproportionately), finding that although this reduced efficiency, it improved equality. Ward et al. (2013) and Torell and Ward (2010) assessed various shortage sharing arrangements, finding that an equal percentage reduction in diversions by all irrigators was flexible, easily understood, and enhanced crop production when compared to shortage arrangements that applied unequal risk burdens. He et al. (2012) also studied several mechanisms of shortage sharing under Prior Appropriation in Alberta, Canada where changes to water allocations were made through various inflexible rules as well as markets. They found that all modes of shortage sharing were efficiency improvements over Prior Appropriation, with market exchanges being the most efficient (these findings were for intersystem sharing, not intra-system sharing as in the present study). The overall message from the literature regarding shortage sharing is that it is beneficial, especially when it is congruent with contributions to system maintenance, allocates shortage risk equitably, and is agreed upon in a transparent manner between all members of the irrigation system (Dayton-Johnson 2000, Torell and Ward 2010, Bernard et al. 2013, Ward et al. 2013). However, the present study draws a contrasting finding; shortage sharing can actually result in worse performance overall, and for tail-end users in particular, if rotation is not also employed. However the present study finds that shortage sharing produces benefits overall when coupled with rotational delivery.
As for rotation, the literature has largely found that rotation accomplishes the goals it is implemented to achieve: it improves equality between the head-end and tail-end (Turral et al. 2002, Abdullaev et al. 2006). Indeed, irrigators in the SLV directly stated in interviews that this was the intention of rotation. Although Janssen et al. (2012) did not make this finding in an experimental setting, the rotational delivery mechanism was not accompanied by enforcement of any maximum diversion duration or amount, was not negotiated by the irrigators, and the effect of rotation was not the focus of the study. Additionally, rotation was selected by 2/3 of the experimental groups of real-world irrigators in Janssen et al. (2012), possibly because irrigators understand that rotation is effective and equitable. Similar to other studies, the findings of the present study do not find that rotation is necessarily efficiency enhancing, only that under a well-functioning rotational system the most vulnerable irrigators, i.e., tail-enders, are spared from the worse consequences of drought, particularly when shortage sharing is also allowed.
The fundamental contribution of this study is that the effects of delivery and allocation rules differ depending on their configuration. Baggio et al. (2016) found configurations important when looking at the design principles offered by Ostrom (2005), however this study finds configurations important for specific operational rules in use. Indeed, the impact of the same combinations of delivery and shortage sharing rules can differ between head-enders and tail-enders, and even these effects are conditional on the degree of water shortage.
The policy implications of these findings are not prescriptive. The answer to the question, “Which rule is best?” depends on which other rules are in place. The answer to the derivative question, “Which configuration of rules is best?” also depends a great deal on where the farmer asking the question is located on the irrigation system and how much water is available to that system. Therefore, water managers and irrigators alike should weigh hydrologic context, equity, and social norms heavily in determining which rules to experiment with and adopt.
Optimality depends strongly on normative assessments of equity (Ingram et al. 2008). This study implies that the optimal choice of institutions depends strongly on the normative, infrastructural, and hydrologic conditions of a given irrigation system over a period of many years. These findings have implications for an era of climate change, wherein irrigated agriculture will face serious challenges (Turral et al. 2011, FAO 2012, Lee et al. 2014, Kramer et al. 2017) and institutional changes have been proposed as potential adaptations (Huntjens et al. 2012). Moreover, the highly contextual influence of the rules in use under investigation highlight the configurational relationships between rules in use (Baggio et al. 2016), further demonstrate institutional interactions with biophysical context (Cody 2018), caution against panaceas in water resource management (Meinzen-Dick 2007), and support a diagnostic approach to institutional analysis (Ostrom 2007). Because of the delicate distribution of individual and group costs and benefits (Bell et al. 2016, Pérez, et al. 2016, McCord et al. 2017), heterogeneous market integration (Kininmonth et al. 2016); and divergent hydrology, infrastructure, ecological context, and institutions, “Institutional change needs to be seen as an organic process, building on existing norms and practices, rather than as an exercise in social engineering” (Meinzen-Dick 2014:23).
That said, this study shows rotational delivery with shortage sharing as the most robust institutional configuration examined. In addition to generating the most equality between head-enders and tail-enders overall, this configuration has positive marginal productivity up and down the canal at all levels of water availability, and therefore represents a safe bet under uncertain water supplies. This configuration also appears to be well suited for systems large and small, growing a wide array of crops, with different social and cultural norms, and various technological and infrastructural mixes. However, it does require sufficient resources and labor to engage in the necessary negotiations, monitoring, and, presumably, sanctioning. It may also have some hydrologic and agronomic limitations in severe shortage, with water being spread too thinly. Perhaps this is why, in the Hispanic acequia tradition in the SLV where rotation and shortage sharing were traditionally practiced, extreme shortage was met by growing crops on only the best land, with the surplusses shared among the community. That said, there are limitations to this study, and so future work should include direct measures of welfare, identify farm units, use simulations, and investigate different water rights and climatic regimes.
This study would not have been possible without funding from National Science Foundation grant BCS-1115009, the Colorado Section of the American Water Resources Association, and the Arkansas River Basin Water Forum. Publication of this article was funded by the University of Colorado Boulder Libraries Open Access Fund. All human subjects research conducted in compliance with CU IRB protocol #13-0181. The author thanks Krister Andersson, Steven Smith, Michael Cox, Kyle Kittelberger, and Matt Foster for their help collecting and coding the data. The author thanks Krister Andersson, Steven Smith, Tanya Heikkila, Doug Kenney, Lisa Dilling, Michael Cox, John Wiener, Nathan Lee-Ammons, Robert Patrie, Bill Cody, Dara Hill, and Lisette Arellano for their constructive input and support. Several anonymous reviewers also provided valuable advice. Last but not least, the author also thanks the farmers and water managers in the San Luis Valley for their time, especially members of the Rio Grande Basin Roundtable and the staff of the State Engineer's Office. All remaining errors are the author's.
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