Research Paper Why AWD isn’t taking off: Understanding barriers and pathways for scaling in gravity-fed irrigation systems in rice landscape Gio Karlo Evangelista a, Kristine Samoy-Pascual b, Romeo J. Cabangon a,d, Manuel J. Regalado b, Yuji Enriquez a,c, Rubenito Lampayan d, Arnel Rala a, Sudhir Yadav a,e,* a Sustainable Impact through Rice-based Systems Department, International Rice Research Institute, Los Baños, Philippines b Rice Engineering and Mechanization Division, Philippine Rice Research Institute Central Experiment Station, Nueva Ecija, Philippines c Department of Agriculture and Food, World Bank, Washington, DC, USA d College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, Philippines e Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, Australia H I G H L I G H T S G R A P H I C A L A B S T R A C T • AWD reduced irrigation by 21–50 % in gravity-fed rice systems without yield loss. • Soil texture and elevation can impact AWD scalability at a landscape scale. • Water governance affects large-scale AWD adoption in gravity-based systems. • Considering governance with agro- environmental variability is vital for AWD scaling. A R T I C L E I N F O Editor: Jagadish Timsina Keywords: Water governance Spatial variability Scaling Impact pathway Decision-making Rice productivity A B S T R A C T CONTEXT: Alternate Wetting and Drying (AWD) offers considerable potential to reduce water use and methane emissions in irrigated rice systems without compromising yields. However, despite decades of promotion, AWD adoption remains limited, especially in gravity-fed irrigation systems where institutional and agro-environmental complexities pose challenges to implementation. OBJECTIVE: This study assessed the biophysical, socio-economic, and institutional determinants of AWD adop tion at the turnout level in a gravity-fed irrigation system in Nueva Ecija, Philippines. The aim was to identify key barriers and opportunities for scaling AWD under spatially heterogeneous and rotationally scheduled irrigation conditions. METHODS: Six turnouts within the Lateral G canal of the Upper Pampanga River Integrated Irrigation System were selected. Data were collected on plot elevation, soil texture, ownership patterns, water application, and grain yield. Water governance structures were analysed through focus group discussions and interviews with stakeholders. A decision logic framework was used to classify AWD adoption based on field-level water depth measurements. * Corresponding author at: Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia, Australia. E-mail address: sudhir.yadav@uq.edu.au (S. Yadav). Contents lists available at ScienceDirect Agricultural Systems journal homepage: www.elsevier.com/locate/agsy https://doi.org/10.1016/j.agsy.2025.104491 Received 4 April 2025; Received in revised form 19 August 2025; Accepted 21 August 2025 Agricultural Systems 231 (2026) 104491 Available online 2 September 2025 0308-521X/© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by- nc-nd/4.0/ ). mailto:sudhir.yadav@uq.edu.au www.sciencedirect.com/science/journal/0308521X https://www.elsevier.com/locate/agsy https://doi.org/10.1016/j.agsy.2025.104491 https://doi.org/10.1016/j.agsy.2025.104491 http://creativecommons.org/licenses/by-nc-nd/4.0/ http://creativecommons.org/licenses/by-nc-nd/4.0/ RESULTS AND CONCLUSIONS: AWD reduced irrigation input by 21 % in the dry season and 50 % in the wet season while maintaining yields. However, adoption was constrained by elevation-driven water flow patterns, clay distribution, tenant-operated plots, and rigid rotational schedules. AWD adoption was more feasible during the wet season due to reduced irrigation risk. Weak farmer engagement in decision-making limited field-level adaptability. SIGNIFICANCE: Scaling AWD requires reconfigured irrigation governance, integration of real-time water monitoring technologies, and economic incentives such as carbon financing. Context-specific, multi-level in terventions are essential to enable large-scale AWD implementation in gravity-fed systems. 1. Introduction The impact of climate change on global agriculture presents signif icant challenges to food security, water resources, biodiversity, and human health (Muluneh, 2021). The agricultural sector in Asia, which is a primary source of livelihood for millions, is particularly vulnerable to the impacts of climate change. According to the Intergovernmental Panel on Climate Change (IPCC), the region is projected to experience increased frequency and intensity of droughts, floods, heat waves, and storms, alongside changes in pest and disease patterns, crop yields, and water availability (IPCC, 2023). This necessitates the development and implementation of adaptation and mitigation strategies to secure food supply in the region. Rice is the staple food for over half of the global population (Seck et al., 2012) and accounts for about 35 % of the total agricultural share of irrigation water consumption (Bouman et al., 2007a). In the Philippines, rice holds particular significance as both a primary staple and a major contributor to economic growth. Yet, the sector faces pressing challenges, including water scarcity, high irrigation demand, and its status as the primary anthropogenic source of methane emissions within agriculture (Cobb et al., 2013; Fitton et al., 2019). The Philippines has the highest CH4 emissions per unit area and yield, driven by high temperatures and the continuous flooding of most rice paddies (Qian et al., 2023). These pressures are compounded by more frequent El Niño events and the increasing variability of water availability, highlighting the critical need for sustainable water management prac tices in rice production. Alternate wetting and drying (AWD) is a mature technology designed to enhance water management in rice production. The technology in volves irrigating rice fields intermittently, allowing periods of dryness after the disappearance of ponded water (Bouman et al., 2007b). Over four decades of research (Bhuiyan, 1992; Bouman and Tuong, 2001; Kukal et al., 2005; Sandhu et al., 1980) have shown that AWD can reduce irrigation input by reducing hydrostatic pressure and water loss through seepage and percolation while maintaining sufficient moisture in the root zone (Bouman et al., 1994; Kukal and Sidhu, 2004; Lampayan et al., 2004). To facilitate AWD implementation, the International Rice Research Institute (IRRI) introduced the field water tube, a practical tool to monitor water depth and schedule irrigation. This “safe AWD” prac tice includes shallow flooding for the first two weeks after transplanting, shallow ponding during heading and flowering to avoid water stress, and AWD during other growth stages when the perched water table falls to approximately 15 cm below the soil surface (Bouman et al., 2007b). Among Asian countries, the Philippines was the first country to conduct significant research on AWD, followed by Bangladesh, Indonesia, Lao PDR, Myanmar, and Vietnam (Rejesus et al., 2013). Pilot testing has been conducted in various irrigation systems, including deep wells (Rejesus et al., 2011) and a gravity-fed system (Sibayan et al., 2010). Studies reported irrigation savings of 5–30 % without or with minimal yield reduction (Carrijo et al., 2017; Lampayan et al., 2015b; Palis et al., 2004) and lower labour costs (Siopongco and Wassmann, 2013). However, some research noted increased labour costs due to frequent weed management (Neogi et al., 2018). Early AWD research in the Philippines involved a multi-stakeholder partnership between IRRI, the Philippine Rice Research Institute, the Bureau of Soil and Water Management (BSWM), and the National Irrigation Administration (NIA) (Palis et al., 2017). This collaboration led to policy advancements, such as the Department of Agriculture’s Administrative Order 25 and the National Irrigation Administration’s Memorandum Circular 35, which advocated AWD adoption in national irrigation systems. Despite these efforts, the adoption and impact of AWD in the Philippines have been limited and inconsistent. Lampayan et al. (2014) reported an approximately 2 % adoption rate of AWD in the Philippines in 2011 (82,000 farmers and 93,000 ha), with a projection of an increase in adoption rate of 10 % by 2016. However, a subsequent impact study by Rejesus et al. (2017) reported a decline in adoption to 69,000 farmers (referred to as official estimates from NIA) as of September 2017. Recent figures indicate that only 12 % of the national irrigation system area practices AWD (NIA, 2020). While these figures lack rigorous quanti tative validation, they certainly highlight inconsistent trends in AWD adoption. Most of the research on AWD has predominantly focused on plot-scale studies, with only limited evaluations conducted at lateral or service area levels. Even at these scales, most studies use plot-scale ob servations as representative points (Samoy-Pascual et al., 2022). Addi tionally, most research has centred on pumped irrigation systems, with gravity-fed systems receiving less attention due to flat irrigation fees, which provide limited incentives for water savings (Siopongco et al., 2013). There is also a paucity of research exploring the influence of agro-environmental characteristics, hydrological boundaries, and water governance within basins on AWD adoption at landscape scales. More over, the Free Irrigation Service Act of 2018 has further complicated water-saving efforts by providing free irrigation water nationwide (Briones, 2022; Briones et al., 2019). As seen in broader technology adoption literature, scaling innovation is not simply a matter of transfer, but involves complex “translation” processes where institutions like public research organisations play key roles in adapting technologies to new settings (Vilas-Boas et al., 2024). This study seeks to address these gaps in AWD research and practice in the Philippines by focusing on four key research questions (a) What factors in technology development, packaging, and dissemination hinder large-scale AWD adoption in canal-based irrigation systems across diverse agro-environmental and socio-economic contexts? (b) How do agro-environmental characteristics, such as land elevation, soil texture, plot size, and land ownership patterns, influence the scalability of AWD practices at landscape levels? (c) How do current water governance structures and irrigation scheduling practices affect AWD adoption at the turnout scale, and what modifications could enhance adoption rates and water productivity? (d) What social, economic, and institutional incentives could improve the willingness of farmers, irri gators’ associations, and other stakeholders to adopt AWD practices in gravity-based irrigation systems? 2. Methodology 2.1. Study site The research was conducted in 2017–2018 in Bantug, Muñoz, Nueva Ecija, Philippines. The site is managed by the Bantug-Bakal Irrigators’ Association, which sources its irrigation from the Upper Pampanga River Integrated Irrigation System (UPRIIS) through its Lateral G channel G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 2 (Fig. 1). The main source of water for UPRIIS is the Pantabangan Dam, which has a reservoir area of 8420 ha (UPRIIS) and provides irrigation water to 119,640 ha, covering four provinces. The climate of Muñoz is characterised by distinct dry and wet seasons, with rice being planted twice a year. Based on 20 years of data (Fig. 2), the annual rainfall av erages 1747 mm, with the dry season lasting 4 to 5 months (December to April) and receiving an average of 66 mm of rainfall, while the wet season (July to November) accumulates 995 mm. The maximum tem perature during both seasons is nearly identical at 31.5 ◦C. However, the minimum temperature varies, averaging 22 ◦C in the dry season and 23.7 ◦C in the wet season. Relative humidity is higher during the wet season at 88 %, compared to 83 % in the dry season. Solar radiation is recorded at 22.5 MJ/m2 in the dry season and 19.5 MJ/m2 in the wet season. The study was conducted along the upstream toposequence of the lateral canal, assuming a reliable water supply at that level and with the potential to save irrigation water for downstream use. The lateral canal was selected according to a predefined protocol that included the following criteria: a) toposequence position, b) canal water as the sole source of irrigation, and c) presence of an inlet to the turnout service area (TSA) or turnout (TO). A turnout is composed of contiguous pieces of plots, often owned by different farmers, ideally sharing only one inlet for irrigation. The lateral G canal consisted of 13 turnouts (TO), and out of these, six turnouts were selected for this research study, mainly based on the receptiveness of the community to participate in progressing their management practices. The turnouts were tagged as TO-A to F. In our study, a turnout gate refers to an irrigation inlet that distributes irriga tion water to a group of adjacent rice fields. Each turnout services multiple plots, which are individually managed fields. In total, six turnouts (labeled TO A–F) were selected for inclusion in the study. These were chosen to represent the range of hydrological and toposequence conditions within the irrigation system. The plots within each turnout were managed by different farmers. A total of 84 plots per season were monitored, managed by 43 farmers (some farmers managed more than one plot). The selection of plots and farmers was purposive, based on active rice cultivation during both cropping seasons, farmer willingness to participate in the monitoring activities and overall receptiveness to project activities. This approach ensured that each turnout had representation across water management practices and seasonal conditions. The rationale for selecting the turnouts and farmers was primarily based on the community’s receptiveness to participating in improving their management practices. Further, the Irrigators’ Association’s members are progressive farmers who have been briefed with AWD at a plot level before, and served as partner stakeholders to evaluate a landscape-level implementation of AWD as they understand the in tricacies of the methodology implemented (e.g. assist in surveying observation well installation points, tube installation and maintenance, water level measurements, etc). The site layout with the boundaries of different plots in each turnout is presented in Supplementary Fig. 1. 2.2. Characterisation of agro-environment Landscape-level irrigation is reliant on a rotational scheduling sys tem rather than crop and farmer demand. To assess potential factors influencing conveyance across turnouts, the study focused on land elevation, plot size, plot ownership, and soil texture as indicators to understand the agro-environment characteristics. A DJI Phantom 2 Pro drone equipped with a Hasselblad L1D-20C camera was used to capture aerial imagery of the entire Lateral G landscape. The camera had a 35 mm-equivalent focal length of 28 mm, a maximum lens aperture of f/2.8, and a field of view of approximately 84◦. A series of photogrammetry exercises were conducted to process the maps and generate orthomosaic and elevation outputs. ArcGIS 10.7 software was also used to further process the images and classify the elevation bands into different categories and grouped them into six elevation classes using the Jenks Natural Breaks Classification. Lastly, the software was used to establish boundaries and calculate areas of landholdings. These maps were used to identify representative sections of high and low elevations within each turnout and to measure the in dividual plot sizes per turnout. Ground-truthing was conducted to ensure the accuracy of plot sizes by comparing the drone-based and manually measured plots. A survey was conducted in 2018 among members of the Bantug-Bakal Irrigators’ Association to collect basic farmer profiles, including land ownership information. Land ownership was classified into three categories: a) the owner is the farmer, b) land is leased from the owner, and c) land is rented from a leaser. Using QGIS software, individual plots for each farmer were identified and mapped according to the type of landholding. Soil samples were collected at predetermined points identified using a topographic map developed through photogrammetry. The 84 sam pling sites were representative of elevation variability within each turnout. The soil samples were analysed for sand, silt, and clay content at a depth of 0–15 cm using particle size analysis by the hydrometer method. Fig. 1. Lateral G under UPRIIS located in Nueva Ecija. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 3 2.3. Understanding the water governance structure and decision-making process To effectively strategies the implementation of AWD over a land scape scale, the water governance structure and the standard operating procedure under different types of irrigation systems were assessed, with a focus on the scheme of the National Irrigation System (NIS). Focus group discussions and key informant interviews were conducted in November 2016, with the following stakeholders: a) farmers, b) presi dents and vice presidents of Irrigators’ Associations (IA) and Small Water Impounding Systems Associations (SWISA), c) gatekeepers/water masters, d) selected National Irrigation Administration-Upper Pam panga River Integrated Irrigation Systems (NIA-UPRIIS) technical staff, and e) Bureau of Soils and Water Management (BSWM) staff. 2.4. Monitoring water management at the turnout scale Monitoring points for data observation were determined through the analysis of elevation maps, allowing strategic positioning of observation tubes for AWD to ensure representation across elevation variability within the field plots (Fig. 3a). Field water tubes, as described by Bou man et al. (2007b), were used to measure daily field water levels at specific times (usually in the morning), following the protocol of AWD implementation (2 weeks after transplanting until terminal drainage) to estimate water depth and irrigation frequency. The water level in each plot was measured using a perforated observation tube (commonly referred to as a field water tube) installed at one meter away from the bund. Measurements were taken manually on a daily basis using a graduated ruler inserted into the tube to record the depth of standing Fig. 2. Long-term seasonal trends in rainfall, temperature, relative humidity, and solar radiation in Muñoz, Nueva Ecija, Philippines for dry and wet seasons. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 4 water or the depth below the soil surface when the field was not flooded. This method has been widely used in previous studies to monitor actual field water levels in farmers’ fields (i,e., Lampayan et al., 2015a; Belder et al., 2004). Along with these tubes, several discharge-measuring equipment, such as cutthroat flumes and rectangular weirs, were stra tegically positioned at selected canals of the turnouts to account for the volume of irrigation per turnout service area. Total discharge was computed using the flow formula of Manning’s equation (Chow, 1959) for flume (36″ x 8″ and 36″ x 16″): Q = C ((Ha − Hb) n ) ( − logS) where; Q – discharge (L/s). C - coefficient of discharge. Ha - depth of water measured upstream of the flume (cm). Hb - depth of water measured downstream of the flume (cm). S - quotient between Ha and Hb. n - exponent based on the length of the flume. The amount of irrigation water used was expressed as the cumulative irrigation water depth for the whole cropping season. Seasonal rainfall was obtained through an automatic weather station installed at the experimental site. The amount of water used was calculated as the sum of irrigation water use and cumulative rainfall for the whole cropping season. A decision logic framework (Fig. 3b) was developed to decide the AWD status at the TO level. If the water level is >0 cm (which means standing water) in ≥70 % of the season, then the plot is classified as Continuous flooding, CF; otherwise, it is classified as AWD (if between 0 to − 15 cm), and Severe drying if beyond − 15 cm. After each plot has been classified, if ≥50 % of plots within the turnout are classified as AWD, then it is considered that AWD was adopted at the turnout scale. The aforementioned assumptions were established for making a conclusion about the adoption of AWD in the command area of an inlet. 2.5. Grain yield sampling and water productivity A 6 m2 crop cut at the centre of each sampling plot was collected during harvest. A total of 84 plots were harvested for grain yield esti mation. The harvested grain yields were threshed, cleaned, and sun- dried. The moisture content was measured for each sample, and the grain weight was calculated based on the adjusted 14 % moisture content. Two measures of water productivity (kg/m3) were computed: irrigation water productivity (WPI)- the ratio of grain yield to the amount of irrigation inputs per turnout, and input water productivity (WPI + R)- the ratio of grain yield to the amount of irrigation water plus rainfall. 2.6. Statistical analysis To analyse the effects of water management, crop season, turnout, and variety on grain yield, a Generalised Least Squares (GLS) model was performed in RStudio software (Ver. 2024.04.2), using the nlme package for GLS modelling and the car package for assumption diagnostics. GLS was selected because it accounts for potential correlation within experimental groups, in this case, repeated or clustered observations within turnouts. The dependent variable, grain yield, was square root- transformed to stabilise variance and improve normality. The model included water management (AWD vs. CF), cropping season (dry vs. wet), turnout (six levels: A–F), and variety as fixed effects. An autore gressive order 1 (AR(1)) correlation structure was specified to account for potential autocorrelation within levels of turnout. The model was fitted using the Restricted Maximum Likelihood (REML) method, which produces unbiased estimates of variance components (Pinheiro and Bates, 2000). Preliminary analysis showed that the interaction between water management and cropping season was not statistically significant; thus, it was excluded from the final model to focus on the main effects and improve interpretability. The final model was mathematically expressed as: Yijkl = μ+ αi + βj + γk + δl + εijkl Where, Yijkl is the square root-transformed grain yield (kg/ha), μ is the intercept, and αi, βj, γk, and δl represent the effects of water man agement, cropping season, turnout, and variety, respectively, where the subscripts i, j, k, l represent the levels of each factor. The residual εijkl was modelled with an AR(1) structure within turnout. This formula is consistent with the approach outlined by Pinheiro and Bates (2000) and implemented in the nlme package of the RStudio software. Diagnostic tests were performed to evaluate model assumptions. The Shapiro-Wilk test was used to assess the normality of residuals, while Levene’s test examined the homoscedasticity (equal variance) of re siduals across groups. Autocorrelation in residuals was tested using the Durbin-Watson statistic. Because irrigation measurements were conducted at the turnout Fig. 3. Spatial distribution of installation points within turnouts for AWD tubes and flumes in various canals (a) and framework used for assessment of AWD adoption at turnout scale (b). G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 5 level rather than at the plot level, a separate analysis was performed for irrigation, total irrigation (irrigation + rainfall), grain yield, irrigation water productivity (WPI), and total water productivity (WPT). A One- Way Analysis of Variance (ANOVA) was performed to determine whether significant differences existed between AWD and CF, regardless of cropping season. Before conducting ANOVA, the assumptions of normality (Shapiro-Wilk test) and homogeneity of variances (Levene’s test) were assessed. If assumptions were violated, a Wilcoxon Rank Sum Test was used as a non-parametric alternative. The statistical signifi cance was set at α = 0.05. Logistic regression was conducted to assess the impact of plot size, ownership type, or elevation class on AWD. 3. Results 3.1. Understanding the water governance structure and decision-making process The irrigated area in the Philippines is approximately 3.4 million hectares (PSA, 2023), with ~66 % served by river diversion systems, 12 % by storage/reservoir systems, and 22 % by pump irrigation systems. The river diversion system operates under two primary management categories: national irrigation systems (NIS) and communal irrigation systems (CIS). NIS, managed by the National Irrigation Administration (NIA), serves areas exceeding 1000 ha, while CIS, constructed by NIA and operated by irrigators’ associations (IAs), covers service areas of less than 1000 ha. Together, NIS and CIS account for 46 % and 35 % of the total irrigated area, respectively. The Bureau of Soils and Water Man agement (BSWM) oversees Small Water Irrigation Systems Associations (SWISAs), which utilise small-scale irrigation facilities like Small Water Impounding Projects (SWIP) with a service area of 20–50 ha, Small Diversion Dams (SDD) with a service area of 160 ha on average, Small Farm Reservoirs (SFR) with a service area of 20 ha, and Shallow Tube Wells (STW) with at least 0.5 ha. The water governance structure for different irrigation systems is illustrated in Fig. 4, which details the information flow of water demand requests from farmers to various actors involved in water release de cisions. Based on focus group discussions and key informant interviews, the processes of information flow and decision-making were categorised into eight systems: one for NIS, three for CIS (depending on the service area), and four for SWISA. In NIS, the water demand process begins with farmers notifying the IA President or Vice President through the Turnout Service Area Group (TSAG) Leader. Upon receiving water requests for most plots within the IA’s turnout service area, the IA President communicates the demand to the NIA’s Water Resources Facilities Technician (WRFT). The WRFT collaborates with the NIA Hydrologist, who submits a water release request to the NIA Operations Engineer. Once water availability is verified, the hydrologist directs the gatekeeper to open the lateral gate for distribution among turnouts. In cases of water scarcity, allocation decisions are escalated to the Regional Manager or Reservoir Division Fig. 4. Water governance in a) National Irrigation Systems (NIS), b-d) Communal Irrigation systems (CIS) serving areas of <200 ha, 200–700 ha and 700–1000 ha, respectively; and in Small Water Irrigation Systems Associations (SWISA) including e) Small Diversion Dams (SDD), f) Small Water Impounding Projects (SWIP), g) Small Farm Reservoirs and h) Shallow Tube Wells (STW). G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 6 Manager for reservoir-type systems. All six turnouts of the study area under the Lateral G are classified under the NIS water governance structure. While farmers, through their irrigators’ association TSAG Leader, President, and Vice President, can relay their respective irriga tion demands and requirements, the distribution in the end still relies on the predetermined irrigation schedule set by the National Irrigation Administration (NIA). CIS management is similar to NIS but is not under the control of NIA. CIS can be divided into three subtypes based on service area: CIS-small (<200 ha), CIS-medium (200–700 ha), and CIS-large (>700 ha). While the fundamental steps are consistent, farmers notify Sector Leaders, who then communicate with Water Tenders and the Vice President of Op erations; however, the differences emerge in responsibilities. For instance, in CIS-small, the Vice President of Operations makes final water release decisions, while in CIS-medium, this role is assumed by the Labour Committee Chairman. cIS-Large systems involve additional roles, such as Dam Caretakers and Gatekeepers, to ensure efficient water distribution. SWISAs, managed by BSWM, follow simplified coordination pro cesses compared to NIS and CIS. These systems are further classified by irrigation facility type: Small Diversion Dam (SDD), Small Water Impounding Project (SWIP), Small Farm Reservoir (SFR), and Shallow Tube Well (STW). Commonly, farmers initiate requests through Cluster Leaders, who work with Water Masters and System Committees to for ward the demand to the SWISA Boards of Directors (BOD). If water is available and the SWISA President approves, the Water Master is instructed to release water. In scarcity scenarios, top management for mulates allocation and scheduling plans. Variations exist across SWISA subtypes; for example, SFR farmers can directly access reservoirs, while STW farmers may rent pumps or request water through SWISA leadership. This structured yet hierarchical decision-making process highlights the complex interplay of stakeholders and the critical need for stream lined communication and equitable allocation mechanisms to optimise water governance in the Philippines. 3.2. Understanding biophysical and social determinants of water management 3.2.1. Land elevation and texture Land elevation affects water distribution under gravity irrigation systems. Variability in land elevation across turnouts influences water advancement and recession times (Devkota et al., 2021), with notable implications for irrigation efficiency. Elevation analysis revealed vari ability within service areas of turnouts, directly affecting water move ment. Soil particle size analysis indicated an increasing trend in clay content from the head to the tail ends of the turnouts, further impacting water dynamics. At the head, the clay content averaged 25.6 %, increasing to 36.3 % in the middle and 39.5 % at the tail ends. Such gradations influence infiltration rates and water retention, particularly in downstream plots (Fig. 5). For example, the increasing clay content could explain the observed fluctuations in field water depth, as finer- textured soils at the tail end tend to slow water percolation and enhance water retention, resulting in a more stable field water depth compared to the head, where greater variability is observed (Fig. 6). Using TO-A as an example (Fig. 6), the average trend of water level was more variable closer to the inlet, while water level became more stabi lized as water moved away from the inlet. The stable water level was due to the impounding of irrigation caused by elevation gradient and higher clay content in the downstream section of the turnout. The transition Fig. 5. Characteristics of different turnouts: a) elevation differences of different turnouts along the canal relative to the highest turnout, and within turnouts from the highest to lowest points, and b) soil texture distributions per turnout. The table indicates the average clay content (%) with standard deviation in the head, middle and tail sections of each turnout. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 7 from coarser soils at the head to finer soils at the tail plays a critical role in shaping the overall water distribution pattern. These findings reflect the necessity for tailored water management strategies to address hy drological variability within and among turnouts. 3.2.2. Plot size and land ownership Turnout sizes within the Lateral G service area ranged from 8 to 19 ha, with significant variation in plot numbers and sizes (Fig. 7). Plot sizes ranged from as small as 6 m2 to 4165 m2, shrinking toward the tail ends. The number of plots varied widely, with TO-B having 162 plots over 8 ha, while TO-C contained 366 plots across 16.96 ha. TO-A, the largest turnout with 19.06 ha, had 205 plots, compared to TO-E, which had fewer plots but was entirely tenant-operated. Land ownership showed a distinct pattern, with 66 % of plots cultivated by tenants and 34 % by owners (Fig. 8). Certain turnouts, such as TO-B, had plots exclusively managed by owners, while TO-E was entirely tenant- operated. This heterogeneity in land tenure and plot distribution may influence farmers’ decisions to adopt AWD, particularly under rotational irrigation regimes. Based on logistic regression analysis (Supplementary Table 2) of 84 sampling points across six turnouts, neither plot size nor ownership type shows a statistically significant effect on AWD adoption. 3.3. Seasonal effects on water management practices The study found that the perception of risk associated with AWD varied depending on the season. Most farmers perceived AWD as less risky during the wet season due to higher rainfall and reduced irrigation demand. Conversely, during the dry season, irrigation supply uncer tainty and higher water requirements heightened risk perceptions. The study found that most of the turnouts, with the exception of one, were classified as CF in the dry season. Although AWD was implemented in different plots in other turnouts (10–47 %), it fell short of the 50 % rule required for classification at the turnout level (Table 1). The GLS model showed that while the estimated effect of water management (AWD vs. CF) on grain yield was positive (Estimate = 2.91), it was not statistically significant (p = 0.081), indicating no clear yield advantage of AWD over CF under the study conditions (Table 2). In contrast, cropping season had a strong and significant effect on yield, with substantially lower yields in the wet season (Estimate = − 23.66, p < 0.001). Variety also had a significant effect on yield (Estimate = − 4.82, p = 0.036), with the inbred variety yielding significantly less than the reference hybrid. Turnout-level differences were not significant, indicating spatial consistency after controlling for treatments. During the wet season, however, all turnouts met the criteria for AWD classification, showcasing the feasibility of adoption under favourable climatic conditions (Table 1). The recorded average precip itation for the dry and wet seasons was 14 mm and 1497 mm, respec tively (Table 3). The water availability during the wet season could have contributed to the relatively higher adoption of AWD in the study area. The lower grain yields observed during the wet season compared to the dry season (Fig. 9) could be attributed to climatic factors such as lower solar radiation (Fig. 2) (Zhong et al., 2022). While cropping season had a Fig. 6. Average field water level across the distance from the inlet of a turnout (A). The grey area represents the standard deviation, indicating the variability of water levels at each distance. Fig. 7. Characteristics of different turnouts: a) the step chart indicates the area of each turnout (ha) as a dark line and the total number of plots as a dotted line, and b) the scatter plot with individual values represents the plot size variability in each turnout. The whisker represents the interquartile range, and the numbers near the whiskers show the median value. 0% 20% 40% 60% 80% 100% A B C D E F C ul tiv at ed ar ea Owner By Lessee By Tenant from Lessee Fig. 8. Stacked bar of land ownership characteristics per turnout. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 8 strong main effect on grain yield (p < 0.001), the lack of a significant interaction suggests that the relative difference in grain yield between AWD and CF remained consistent across both seasons. However, a separate one-way ANOVA conducted at the turnout level showed a significant effect of water management on grain yield (Table 3). This analysis was performed to align with how irrigation measurements and associated water productivity metrics were collected at the turnout rather than the plot level. The significant differences suggest that, at the scale where water delivery and management are actually implemented, AWD may influence yield outcomes more noticeably. Factors such as water distribution efficiency, field condi tions, and collective farmer practices likely contribute to variability that the plot-level GLS model does not fully capture. In the same analysis, AWD significantly reduced irrigation requirements (p < 0.001); how ever, total water use (irrigation + rainfall) did not differ significantly between AWD and CF (Table 3). The result underscores the heavy reli ance on rainfall during AWD-managed turnouts in the wet season, which compensated for the reduced irrigation. 4. Discussion In alignment with many other studies and global meta-analyses (Bo et al., 2024; Carrijo et al., 2017; Gao et al., 2024), this study also highlights the potential of AWD in effectively reducing irrigation inputs and improving water productivity in both wet and dry seasons without significant yield penalties. Key findings include the descriptive associ ations of agro-environmental factors, such as land elevation and soil texture, on water management practices and the scalability of AWD. Institutional constraints, rotational irrigation schedules, and limited farmer decision-making power emerged as significant barriers to adop tion (Jayasiri et al., 2023; Samoy-Pascual et al., 2022). Tenant-operated plots, which constituted 66 % of the study area, demonstrated unique adoption challenges due to their dependency on landowners and governance structures (Alauddin et al., 2020; Oostendorp and Zaal, 2012). Seasonal variability in water availability also influenced farmers’ risk perception and willingness to adopt AWD (Yamaguchi et al., 2017). 4.1. Technology development and adoption barriers The development and scaling of AWD technology have historically prioritized controlled validation settings, neglecting the complexities inherent in agro-environmental and institutional contexts. Early efforts focused on understanding plant response to water stress and point-level water monitoring, enabling farmers to assess water depths for AWD implementation (Bouman et al., 2007b). However, these efforts largely overlooked the complexities of agro-environmental and institutional contexts, especially in gravity-fed irrigation systems where water flows across turnouts depend on varying elevations and soil properties (Enriquez et al., 2021). Gravity-fed systems, which dominate the study area, pose unique challenges due to uneven water distribution across and within turnouts. Plots at lower elevations and with higher clay content tend to retain water longer, while upstream plots dry faster, creating inconsistencies that impede collective AWD adoption. These challenges are exacerbated under rotational irrigation schedules that do not account for intra- turnout variability (Regalado et al., 2019). Socio-economic factors, including land tenure and plot size, further constrain AWD adoption. In this study, the results showed that plot size, ownership type, and elevation class did not have statistically significant effects on AWD adoption. This finding, however, must be interpreted within the context of the study’s limited scope of only six turnouts within a single irrigation canal system and a uniform rotational irriga tion schedule that potentially minimised operational variation across plots. Such homogeneity likely reduced the observable variability needed to detect differences in adoption behaviour. Recent studies have demonstrated that farm characteristics such as plot fragmentation (Wang et al., 2020), tenure security (Ruzzante et al., 2021), and microtopography (Alauddin et al., 2020) significantly influence tech nology adoption, including AWD, when analysed across broader spatial and institutional heterogeneity. The lack of tangible incentives to farmers and the absence of concrete economic feedback on their water use further lessens the appeal for technology uptake. Tenant farmers, who comprise a significant portion of the study area, often lack auton omy in irrigation decision-making and are reluctant to adopt AWD without explicit approval from landowners or irrigation associations (Palis et al., 2017). Short-term lease agreements further discourage tenant farmers from investing in technologies with perceived risk to harvest (Mariano et al., 2012). Additionally, small and fragmented plots, a common characteristic in many gravity-fed irrigation systems, create logistical challenges for synchronising water management practices across fields (Jayasiri et al., 2022; Yamaguchi et al., 2017). Current irrigation governance frameworks often lack mechanisms for incorpo rating farmer feedback, limiting opportunities for field-level variability to inform rotational schedules effectively (Gupta et al., 2022). Table 1 Percentage of Field plots (n = 84) Adopting Different Water Management Practices per Turnout (CF: Standing water >0 cm above the soil surface; AWD: Water depletion between 0 and 15 cm below the soil surface; Severe drying: Water depletion beyond 15 cm below the soil surface). Turnout Dry Season Wet Season CF (%) AWD (%) Severe drying (%) CF (%) AWD (%) Severe drying (%) A 86 10 5 0 95 5 B 33 67 0 33 67 0 C 60 33 7 0 100 0 D 83 17 0 25 67 8 E 67 33 0 17 83 0 F 53 47 0 0 100 0 Table 2 Generalised least square model estimates and significance of treatments on grain yields (n = 145). Predictor Estimate Std. Error t-value p-value (Intercept) 101.43 3.47 29.2 <0.001 *** Water Management 2.91 1.66 1.76 0.081 Cropping Season − 23.66 2.48 − 9.55 <0.001 *** Turnout B 1.84 2.77 0.66 0.509 Turnout C 1.45 2.3 0.63 0.528 Turnout D − 1.86 2.29 − 0.81 0.418 Turnout E 3.58 2.26 1.58 0.116 Turnout F − 3.52 2.22 − 1.58 0.115 Variety − 4.82 2.27 − 2.12 0.036 * Significance levels: *** p < 0.001, * p < 0.05. Table 3 Turn-out level means of irrigation, water productivity and grain yields of water management for rice (n = 5 for CF and 7 for AWD). Treatments Irrigation Rainfall Total Irrigation (Rainfall+Irrigation) Grain yield Water Productivity Total Water Productivity mm mm mm kg/ha kg/m3 kg/m3 CF 1446 14.0 1460 9668 0.72 0.99 AWD 337 1497 1834 5909 2.46 0.24 p- values 0.00059 0.00054 0.16437 0.00756 0.02880 0.00055 G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 9 Consequently, irrigation decisions often fail to align with the diverse agro-environmental and socio-economic realities at the field level (Jayasiri et al., 2022), further deterring the widespread adoption of AWD (Regalado et al., 2019). 4.2. Institutional and governance challenges Water governance, particularly in gravity-fed irrigation systems, involves hierarchical and complex decision-making processes (Hosseinzade et al., 2017). These systems cover a significant proportion of irrigated land, representing 75 % of the total irrigated area in the Philippines, compared to only 12 % for pump-based systems (Delos Reyes, 2017; Inocencio and Inocencio, 2020. Despite this dominance, farmers in gravity-fed systems often lack influence on irrigation sched ules, which are dictated by preset rotational plans that do not account for field-level variability (Jayasiri et al., 2023; Yadav et al., 2020), contrary to pump-based systems, where farmers have a direct incentive mechanism to conserve water use as they have full autonomy over irrigation management. This study highlights the necessity of rede signing governance frameworks to incorporate farmer input and syn chronise irrigation timing with site-specific water demand (Samoy- Pascual et al., 2022). For example, the limited adoption of AWD during the dry season highlights the practical challenges of its implementation, particularly when canal water availability does not align with crop water requirements. Addressing these challenges requires adaptive governance strategies, such as integrating real-time data feedback from Internet of Things (IoT)-based monitoring systems. This approach could enhance responsiveness and promote more equitable water allocation (Parra-López et al., 2024). Although these technologies offer practical tools to support the transition, their adoption in the Philippines remains in its early stages (Kato et al., 2023). Nevertheless, the use of remote sensing applications and mobile-based advisory platforms is steadily gaining traction, primarily through innovative smart farming initiatives (e.g., Alonzo et al., 2023; Panganiban, 2018). The role of public research and technology institutions in technology adoption has shifted from passive dissemination to active translation, including knowledge brokerage, capacity building, and context-specific adaptation (Glover et al., 2019; Vilas-Boas et al., 2024). Public in stitutions can be seen as potential “orchestrators” or “supporters” in AWD translation ecosystems, terms describing how such actors either steer or enable innovation adoption processes. However, the private sector, particularly agri-businesses, irrigation service providers, or technology firms, can also play instrumental roles in innovation trans lation, either by enabling bundled service delivery or by co-investing in irrigation infrastructure. In surface irrigation systems where water management is typically coordinated at the sub-canal or turnout level, adoption decisions are often made at the cluster scale, rather than by individual farmers. In such settings, models like small farmers–large field (Baruah et al., 2022), which could promote shared scheduling and infrastructure, offer a promising framework for synchronising AWD practices across fragmented holdings. This collaborative approach could enhance field-level efficiency while addressing socio-technical con straints such as plot heterogeneity and land tenure fragmentation. 4.3. Rethinking of pathways of scaling AWD Scaling AWD requires a multidimensional approach that addresses interconnected biophysical, institutional, and socio-economic factors (Fig. 10). While traditional plot-scale demonstrations and training pro grams have effectively highlighted water productivity benefits, they fail to address the complexities of scaling within gravity-fed irrigation sys tems (Delos Reyes, 2017; Lampayan et al., 2015a). To achieve wide spread adoption, a shift toward lateral and system-level approaches is imperative. This includes the development of adaptive rotational irri gation schedules, which integrate spatial variability in elevation, soil texture, and hydrological patterns. The impact pathway for improving AWD scaling and adoption must address technology development, capacity building, and policy influ ence to overcome existing barriers (Douthwaite et al., 2003). Techno logical advancements, including remote sensing and IoT-based water monitoring systems, can facilitate more precise AWD implementation. These tools enable targeted interventions, reduce reliance on manual processes, and ensure resource allocation aligns with local needs (Regalado et al., 2019). For example, IoT systems can automate alerts for re-irrigation thresholds, reducing the risk of under- or over-irrigation, which is particularly useful in poorly draining soils. In tenant- operated plots, where labour and decision-making are often decentral ised, such tools can also help cooperatives or irrigators’ associations coordinate water delivery more efficiently across multiple users. Simultaneously, capacity-building initiatives are essential for improving farmers’ awareness of AWD benefits and establishing robust monitoring and reporting systems. Institutional frameworks must integrate training programs that address climate change and environmental degradation while building capacity for sustained AWD adoption. This is particularly important in areas where tenant-operated plots dominate, as these farmers often face unique constraints, such as limited access to financial incentives and technical support. Tailored mechanisms, such as sub sidies for observation wells, flow control gates, clustered implementa tion, and other infrastructure improvements, can alleviate upfront costs and encourage broader adoption. Policy reform, including support for mainstreaming AWD and linking it with carbon financing mechanisms such as the Clean Development Mechanism or the provisions under Article 6 of the Paris Agreement, presents an opportunity to monetise its greenhouse gas mitigation benefits (Zimm and Nakicenovic, 2020). Strengthening institutional frameworks, establishing robust Monitoring, Reporting, and Verification (MRV) systems, and providing capacity- building initiatives can facilitate AWD’s integration into climate finance. Such measures can enhance farmers’ access to financial in centives, promote long-term adoption, and ensure compliance with Fig. 9. Comparison of grain yields between dry and wet seasons and the water management of all plots regardless of turnouts (n = 145). The edges of the box represent the first and third quartiles (25th and 75th percentiles). The lines extending from the box (whiskers) show the range of the data within 1.5 times the interquartile range (IQR). The black circle represents the average yield. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 10 additionality criteria (Gao et al., 2019; Siopongco et al., 2013). Streamlined processes for carbon credit verification and equitable access to financial incentives are critical to ensuring inclusivity and sustain ability. Institutional reforms, including gender inclusivity and modern isation of irrigation policies, further strengthen the operational framework for AWD adoption. Experiences from other agricultural sys tems suggest that successful scaling of innovations requires navigating the delicate process of decontextualising and recontextualising tech nologies (Vilas-Boas et al., 2024). This involves aligning technical fea tures with local socio-economic conditions and institutional structures, often through multi-actor translation ecosystems that orchestrate adaptation, experimentation, and learning. Collaborative governance, which involves farmers, policymakers, and private sector stakeholders, is vital for creating inclusive and context-specific strategies. Such cooperation facilitates institutional alignment, addresses socio-economic barriers, and ensures the long- term sustainability of AWD practices. By adopting these multidimen sional strategies outlined in the impact pathway, AWD can transition from plot-level interventions to systemic, large-scale adoption. 4.4. Limitations and future research directions This study monitored six turnouts along a single lateral using pur posive, not random, plot selection; power to detect effects was therefore limited. Irrigation inputs were measured at the turnout scale, whereas yields were analysed at the plot scale, necessitating careful alignment of inference across outcomes. Several potentially relevant factors were not observed, including gender and intra-household decision-making, farmer education/experience, explicit irrigation fees or pumping costs, labour/weed management costs, access to extension or digital tools, and detailed governance heterogeneity across irrigators’ associations. These unmeasured variables, combined with the modest sample size, likely reduced our ability to isolate drivers of AWD adoption statistically; future work should address these gaps. Future research should prioritise the systematic scaling of AWD by addressing technical and socio-economic dimensions. Integrating advanced technologies, such as IoT-based monitoring systems and remote sensing tools, can facilitate precise irrigation scheduling and scalability across diverse agro-environmental conditions. More research is required for understanding AWD adoption with larger numbers of turnouts to capture the variability of irrigation decision dynamics as well as biophysical conditions. Longitudinal studies are needed to evaluate AWD’s sustained impacts on water productivity, economic outcomes, and greenhouse gas emissions, considering both agronomic and socio-economic implications. Comparative research across gravity-fed and pump-based irrigation systems can provide valuable insights into the adaptability of AWD to different contexts. Developing adaptive irrigation frameworks incorpo rating risk assessment and hotspot identification will also guide the implementation of rotational schedules aligned with AWD principles. Furthermore, behavioural studies are essential to understanding farmer decision-making and fostering collective action. Policy-oriented research should evaluate existing incentives, such as carbon credits, and explore policy reforms to integrate AWD principles into national irrigation strategies. Aligning these strategies with broader water man agement and climate resilience goals is crucial for sustainable adoption. These research directions will support the transition of AWD from pilot projects to large-scale, sustainable adoption, addressing critical water management challenges and enhancing agricultural sustainability and climate resilience. 5. Conclusion The findings of this study emphasise the multifaceted nature of implementing AWD in gravity-fed irrigation systems, highlighting sig nificant implications for sustainable water management in rice pro duction. While this study reconfirms AWD’s potential to reduce irrigation inputs by 21 % in the dry season and 50 % in the wet season without compromising yields, however, the scalability of this water- saving technology is constrained by the interplay of agro- environmental, institutional, and socio-economic factors. Key chal lenges as suggested by spatial patterns include variability in soil texture and land elevation, which affect water distribution and drying patterns. The hierarchical and rigid water governance frameworks, along with the socio-economic dynamics of tenant-operated plots, which represent the majority of the study area, further complicate the adoption of AWD at the turnout scale. The results suggest that addressing these challenges necessitates tailored interventions at multiple levels. Enhancing insti tutional frameworks, incorporating farmer input and aligning irrigation schedules with site-specific conditions are crucial for fostering AWD adoption. Integrating advanced monitoring technologies, such as IoT- based systems, could provide real-time insights to optimise water Fig. 10. Proposed impact pathway framework to improve adoption of AWD. G.K. Evangelista et al. Agricultural Systems 231 (2026) 104491 11 allocation and reduce the information lag among different actors involved in water allocation and release. Moreover, policy measures, including carbon financing mechanisms and subsidies for infrastructure improvements, can incentivise the adoption of AWD, particularly among tenant farmers who are more exposed to economic constraints. Efforts to scale AWD must also consider seasonal dynamics, with greater adoption potential during wet seasons due to reduced perceived risks. Future pathways for AWD scaling should incorporate technological, institutional, and socio-economic strategies to overcome identified barriers. Investments in adaptive irrigation governance, technology dissemination, and financial incentives can facilitate the transition from plot-scale demonstrations to large-scale adoption. This approach will not only enhance water productivity but also contribute to climate resilience and sustainable agricultural development. Declaration of generative AI in the writing process During the preparation of this work, the author(s) used Microsoft Copilot and ChatGPT in order to improve the overall readability of the originally drafted manuscript by authors. After using this tool, the au thors reviewed and edited the content as needed and take full re sponsibility for the content of the publication. CRediT authorship contribution statement Gio Karlo Evangelista: Writing – original draft, Investigation, Formal analysis, Data curation. Kristine Samoy-Pascual: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Romeo J. Cabangon: Writing – review & editing, Methodology, Investigation, Conceptuali zation. Manuel J. Regalado: Writing – review & editing, Supervision, Funding acquisition. Yuji Enriquez: Writing – review & editing. Rubenito Lampayan: Writing – review & editing. Arnel Rala: Writing – review & editing, Formal analysis. Sudhir Yadav: Writing – review & editing, Writing – original draft, Visualization, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement The DA-Bureau of Agricultural Research under the project WateRice (PhilRice-RTF-002-246 and IRRI-A-2017-21) funded the project. The team also would like to extend our gratitude to Engr. 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adoption barriers 4.2 Institutional and governance challenges 4.3 Rethinking of pathways of scaling AWD 4.4 Limitations and future research directions 5 Conclusion Declaration of generative AI in the writing process CRediT authorship contribution statement Declaration of competing interest Acknowledgement Appendix A Supplementary data Data availability References