Abstract
With emerging water scarcity and rising fertilizer prices, optimising future water use while maintaining yield and nutrient efficiency in irrigated rice is crucial. Alternate wetting and moderate soil drying irrigation (i.e., re-irrigation when the water level reaches 15 cm below the soil surface) has proven to be an efficient water-saving technology in semi-arid zones of West Africa, reducing water inputs without yield penalty. Alternate wetting and severe soil drying (AWD30), by re-irrigating fields only when the water table reaches 30 cm below the soil surface, may further reduce water inputs compared to farmers’ irrigation practices (FP). However, acute soil drying may impair fertilizer use efficiency and reduce the bio-availability of some key nutrients. This study assessed the potentials and risks associated with AWD30 for smallholder rice farmers in the semi-arid zones of West Africa. We conducted 30 on-farm field trials over three seasons (wet and dry seasons of 2019, and dry season of 2020), in Kou Valley, Burkina Faso. We assessed yield, water productivity, nutrient uptake, and use efficiency under AWD30 and FP. In FP, farmers maintained their fields submerged as frequently as possible according to the scheme-dependent water provision schedule. With AWD30, irrigation frequency was reduced by 30%, however, soils were seldom completely dried due to a shallow groundwater table. Compared to FP, AWD30 reduced irrigation water input by 37% with no significant effects on grain yields (average of 4.5 Mg ha−1), thus increasing average water productivity by 39%. Both irrigation management practices provided comparable crop uptake of N, P, and K, and use efficiencies of applied N and P. However, the N content in straw and the P concentration in grain generally increased with total water input (rain + irrigation). We conclude that at locations with a shallow groundwater table, AWD30 can effectively save irrigation water without significantly reducing the grain yield and the use efficiency of applied mineral nutrients.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Introduction
Water is a key input for producing rice, the most important food crop in many developing countries and the staple food consumed by more than half of the world’s population (Mishra et al. 2022). Irrigated lowland rice farming plays a crucial role in global food security, providing 75% of the world's rice supply (Global Rice Science Partnership 2013). However, irrigated rice also requires more water (40% of the global irrigation water) than any other staple crops (Surendran et al. 2021). In addition to water, nitrogen (N) is another essential input limiting rice production (Johnson et al. 2019; Saito et al. 2019). Most cultivable soils contain insufficient amounts of N, and therefore, N accounts for the largest share of fertilizer consumption (Aulakh and Malhi 2005; Johnson et al. 2023b).
With increasing competition for water resources between domestic, industrial, and agricultural sectors, water scarcity has become one of the main issues faced by poorer communities, particularly in arid areas. Over 2.3 billion people live in water-scarce areas and their number will increase (UN-Water 2021) due to rapid demographic growth. In West Africa, and mainly in semi-arid and arid zones, water scarcity threatens the sustainability of irrigated rice production (Johnson et al. 2023a).
The rice crop takes up only ~ 40% of applied N fertilizers, indicating that about 60% of N applied is not assimilated and may be lost from the soil–plant system (Wopereis et al. 1999; Yu et al. 2022), making it a potential source of environmental (water and air) pollution (Tilman et al. 2002). In addition, N fertilizer represents the second-highest input cost in rice production for smallholder farmers in sub-Saharan Africa, and its inefficient use thus contributes significantly to economic losses (Chidiebere-Mark et al. 2019; Saito et al. 2023b). The sharp rise in fertilizer prices following the COVID-19 pandemic and the Russia-Ukraine war further increased concerns in the Global South and emphasized the relevance of improved nutrient management (Arndt et al. 2023). Furthermore, water scarcity may also affect nutrient use efficiency (Datta et al. 2017) and reduce crop N uptake (Lassaletta et al. 2023).
In this context, reducing water input while maintaining grain yield and nutrient use efficiency is of critical importance for sustaining the production of irrigated rice. To tackle this challenge, several water-saving technologies have been advocated. Among these, alternate wetting and drying irrigation (AWD) has become one of the most prominent and widely recommended water-saving technologies in Asia (Cheng et al. 2022; Chu et al. 2018). However, in the semi-arid zones of West Africa, AWD is largely unknown to smallholder farmers (Johnson et al. 2023a). This water-saving irrigation technology involves intentionally allowing the paddy fields to dry out until the field water level reaches a threshold, before re-irrigation, rather than keeping them continuously submerged (Lampayan et al. 2015). In practice, AWD is categorized into two groups according to either the soil water potential or the soil water table threshold: (1) alternate wetting and moderate soil drying (AWD15), which involves re-irrigation when the water level reaches 15 cm below the soil surface (or soil water potential ≥ -20 kPa); and (2) alternate wetting and severe soil drying (AWD30), where re-irrigation occurs when the field water level drops below 15 cm and up to 30 cm below the soil surface (or soil water potential < -20 kPa) (Carrijo et al. 2017). Globally, alternate wetting and moderate soil drying irrigation, considered as “safe” AWD procedure, reduced the water input by 23% without yield penalty compared to continuous submerged field practices (Carrijo et al. 2017). A recent study, comparing it to farmers’ irrigation practices, demonstrated its effectiveness as a water-saving technology in semi-arid areas of sub-Saharan Africa (SSA) (Johnson et al. 2020). Compared to farmers’ irrigation practices, it reduced water input by up to 30% without compromising yield (Johnson et al. 2020). Alternate wetting and severe soil drying has the potential to further reduce water inputs (Carrijo et al. 2017). However, extended periods of aerobic soil conditions may stimulate the germination and growth of terrestrial weeds, thereby increasing the demands for weed control management (Rodenburg et al. 2011). The extended dry periods during AWD implementation may lead to NO3--N losses due to nitrification and denitrification, further affecting the nitrogen use efficiency (De Datta et al. 1990). In addition, ammonia (NH3) volatilization in alkaline soils may enhance the N losses during drying events (Ishfaq et al. 2020; Moormann and van Breemen 1978). On the other hand, some studies reported no increase in N losses under AWD or even an enhanced N use efficiency due to reduced N leaching, being one of the primary routes of N losses in paddy fields (Ghosh and Bhat 1998; Peng et al. 2011). Furthermore, AWD may also potentially reduce the bioavailability of macronutrients such as P and K and some micronutrients (mainly Fe and Mn) (Ishfaq et al. 2020). To date, it is still unclear how AWD affects nutrient uptake and use efficiency.
Most previous studies assessing the effect of AWD on nutrient uptake and use efficiency were conducted in Asia and were obtained from on-station trials (Cheng et al. 2022). Among the limited number of studies providing insights from the Sahelian region in West Africa (de Vries et al. 2010; Djaman et al. 2018), the focus has primarily been on N use efficiency, comparing AWD to continuous submergence. However, continuous submergence is not the predominant water management practice implemented in irrigated rice schemes in West Africa where water is made available on a scheme-depended irrigation schedule (Johnson et al. 2023a). Comparisons between AWD and actual farmers’ irrigation practices are limited (Krupnik et al. 2012b). Furthermore, no previous study in the region assessed the effect of AWD on P use efficiency and the concentration of macro and micronutrients in straw and grain. There is a need to evaluate the impact of AWD in dry climatic zones in West Africa under farmers’ field conditions to assess its feasibility and sustainability. To address this knowledge gap, we quantified and compared water input, growth parameters, grain yield, water productivity, N and P use efficiency, and nutrient uptake (N, P, K, Mn, Fe, and Zn) under the farmers’ irrigation practices (FP) and alternate wetting and severe soil drying irrigation (AWD30). We hypothesized that due to increased N losses during the drying phases and reduced availability of P under aerobic soil conditions, AWD30 may reduce the N and P use efficiency compared to FP. To test this hypothesis, two-year on-farm trials were conducted in Burkina Faso, with the following objectives: (i) to compare the effects of alternate wetting and severe soil drying irrigation (AWD30) and farmers’ irrigation practices (FP) on growth parameters, weed infestation, grain yield, water productivity, nutrient uptake and use efficiency; (ii) to assess the effect of water input and anaerobic conditions on N, P and K uptake; and (iii) to evaluate the synergies and trade-offs between water productivity and N and P use efficiency.
Material and methods
Study area
Burkina Faso, a Sahelian country in West Africa, facing recurrent drought events and water scarcity in the last decades, was chosen as the focus of this present study. In this country, the availability of water for rice field irrigation is dwindling, especially during the dry season (Johnson et al. 2023a). The study was conducted in the Kou Valley irrigation scheme (11°23′7’’N, 4°24′20’’W, 320 m a.s.l), located in the southwest of Burkina Faso and approximately 30 km north of the town of Bobo-Dioulasso (Fig. S1). Commissioned in 1970, Kou Valley is one of the largest rice irrigation schemes in the country (Dembelé et al. 2005; Johnson et al. 2023a). Covering 1,260 ha within the Kou River watershed, this scheme relies on a run-of-river diversion intake from an upstream headwork (Wellens et al. 2013). Irrigation water is conveyed by gravity through either earthen or concrete-lined canals and then distributed to individual plots using bund breaks. Kou Valley irrigation scheme is a suitable case study, as it directly experiences water scarcity due to the reduced exploitable flow rates in the Kou River, resulting from a decrease in rainfall over the last two decades (Johnson et al. 2023a) and increasing water withdrawals for the city of Bobo-Dioulasso for industrial and households needs (Dembelé et al. 2005).
The region’s climate is classified as Sudanian with a unimodal rainfall pattern and an annual rainfall varying between 800 and 1,200 mm, primarily concentrated between June and September. The average annual minimum and maximum temperatures are 21.2 °C and 33.8 °C, respectively (Johnson et al. 2023a). The dominant soil types are Luvisols and Regosols (Johnson et al. 2023a) with generally sandy-clay-loam textured topsoils, progressively turning into clay in subsurface horizons (FAO and IIASA 2023). Farmers typically engage in two rice-growing seasons each year: the dry season (from January to May) and the wet season (from June to November). More information on the scheme is presented in Johnson et al. (2023a).
Description of the on-farm trials and settings
A series of on-farm trials were conducted during the wet and dry seasons of 2019 and the dry season of 2020, comparing alternate wetting and severe drying (AWD30) irrigation to farmers’ irrigation practices (FP). The purpose of the experiment was explained during cooperative group meetings, and farmers were chosen based on their willingness to participate. Initially, our goal was to involve 15 participating farmers per cropping season. However, logistic challenges and setbacks in on-farm trials (non-cooperative farmers, farmers’ non-compliance with harvesting data collection instructions, cattle grazing fields), led to a total sample size of 30 farmers’ fields (12 in the wet season of 2019, 12 in the dry season of 2019 and 6 in the dry season of 2020). No experiment was conducted twice in the same farmer's field.
In each field, two plots of about 500 m2 each with the same cropping histories were delimited by 30 cm high consolidated bunds of 50 cm width to prevent seepage. In these plots, two water irrigation regimes were applied, namely, i) farmer’s practice of irrigation (FP) and ii) AWD30 irrigation practice. The plots were irrigated using the scheme irrigation system. Farmer’s practice of irrigation differed from conventional continuous flooding irrigation due to the non-permanent availability of water in the canals. Irrigation frequency was further determined by both individual farmers’ water management decisions and the scheme-dependent water supply schedule, with water provision varying from every 4 to 5 days. Generally, in the absence of recent rainfall, whenever water is available, farmers irrigate their fields to reach a standing water level of 10 – 15 cm depth. In AWD30 plots, farmers were instructed to maintain a ponded water level of 2–5 cm during the initial 21 days after transplanting (DAT) to assist seedling recovery and ensure weed suppression. Thereafter, the AWD30 irrigation treatment was imposed. Following the installation of the perforated PVC tubes in every AWD plot to monitor the field water level (Lampayan et al. 2015), farmers received training in applying AWD to a threshold water level of 30 cm below the surface. As water deficit during the flowering stage of rice can increase spikelet sterility, farmers were instructed to halt the drying phase in AWD30 plots and to maintain ponded water levels of 2 to 5 cm during this critical stage (Bouman et al. 2007). We provided farmers with specific window dates corresponding to this period based on the phenological characteristics of the variety they planted. Thereafter, farmers applied again AWD30 until about 2 weeks before rice harvest. Except for irrigation management, plots in the same fields had identical cropping and land-use histories and were managed according to farmers’ cropping practices. Farmers selected their preferred rice variety and decided on the type, quantity, and timing of input (herbicides, pesticides, and fertilizer) use. For further analysis, which includes assessing nutrient concentration in plants and specific indices of N and P use efficiency, we selected a representative subset of farmers’ fields (5 in the wet season and 6 in the dry season). Within each irrigation treatment plot in these fields, we marked out and isolated a subplot of 10 m × 12.5 m with no application of mineral NPK, where we assessed rice biomass and grain yield to calculate agronomic nutrient use efficiencies. We used consolidated bunds that were 30 cm high and 50 cm wide for delimitation.
Soil and plant sampling and analysis
Before the onset of the trials, we collected approximately nine soil cores (at a depth of 0 – 20 cm) along the diagonals in each field and combined them to create composite samples. These samples were air-dried, sieved, and analyzed for soil texture (clay, silt, and sand contents), and chemical attributes such as pH (1:2.5 dry soil: water ratio), total organic C, total N, and available P (Bray-2) following the analytical methods described by Johnson et al. (2019).
Six randomly selected rice hills were sampled at maturity from each plot as well as from the nutrient omission subplot to determine yield components (tillers and panicles number), total shoot biomass, and nutrient uptake. Plant samples were separated into straw and grain and oven-dried at 60 ºC for 72 h to calculate the harvest index.
Dried grain and straw samples were fine-ground using a vibrating disc mill (Siebtechnik TS 250, Siebtechnik GmbH, Mülheim an der Ruhr, Germany) and stored in plastic containers for subsequent concentration analysis of N, P, K, Mn, Fe, and Zn. The total N in plant materials was determined using an elemental analyzer (EURO EA Elemental Analyzer series 3000, EuroVector, Pavia, Italy). Plant P, K, Mn, Fe, and Zn were determined after microwave digestion with concentrated nitric acid (HNO3) using an Inductively Coupled Plasma—Optical Emission Spectrometer (Thermo Fischer ICP-OES iCAP PRO, Thermo Fisher Scientific GmbH, Dreieich, Germany). N, P, K, Mn, Fe, and Zn concentrations in straw and grain were expressed on a dry weight basis. N, P, and K uptake were calculated as the sum of straw and grain dry matter weights, multiplied by their respective nutrient concentrations.
Field survey, observations, and data collection
Weather data (solar radiation, maximum and minimum air temperature, relative air humidity, and rainfall at daily time steps) were retrieved from “aWhere”, a cloud-based real-time data platform (aWhere, Inc., Broomfield, Colorado, United States).
We collected data on (i) farm management practices (source of seeds, straw and residue management, tillage method, land levelling, sowing date, planting density, number of seedlings per hill, weed management, fertilizer management, pests control, irrigation frequency), (ii) weed infestation at flowering, both below and above rice canopy (visually assessed, categorized and scored into four levels: 0 = No weed, 1 = weed cover ≤ 10%, 2 = weed cover > 10% and ≤ 30%, and 3 = weed cover > 30%) (Gongotchame et al. 2014), and (iii) farmers’ yield using a standardized protocol [see Niang et al. (2017) and Tanaka et al. (2017)] (Table S1). Three harvest areas of 12 m2 (~ 360 hills) each were demarcated within both FP and AWD30 plots for assessing farmers’ land and crop management practices and determining grain yield. The harvest areas were positioned along a diagonal to capture the variability of productivity within the fields. At maturity, grain yield was measured within each harvest area and expressed at a standardized moisture content of 14%.
Every three days, the field water status in each plot was visually assessed and scored using a three-point scale [1: ponded water (i.e., standing water table with a depth of more than 1 cm), 2: wet soil surface (i.e., fully saturated or partially saturated soil without standing water table), 3: dry soil surface (i.e., soil surface does not exhibit any visible signs of moisture)] (Haefele et al. 2006). Additionally, the frequency of irrigation (the number of irrigations during the growing cycle) for each irrigation management treatment was recorded. We estimated the water flow rates using the bucket method. The bucket method is a straightforward and efficient technique for measuring low to medium flow rates in channels or small streams. It entails placing a bucket of known capacity at the plot's inlet to capture all the flowing water through bund breaks while timing how long it takes for the container to fill using a stopwatch. The flow rate is then calculated by dividing the volume of the container by the filling time (Saito et al. 2023a; Trimmer 1994). To ensure accuracy, we repeated these steps at least three times and calculated the average flow rate. In each plot, the quantity of water applied per irrigation was estimated three to four times during the growing season. Total irrigation water input was calculated by multiplying the number of irrigation events by the average water quantity per individual irrigation event.
Soil water status, nutrient use efficiency indices, and performance indicators
Soil water status, nutrient use efficiency indices, and other performance indicators were computed for each plot. The soil flooding index (%) was calculated as the ratio between the number of days with ponded water and the total number of recorded days during the growing season (from three weeks after transplanting to maturity) (Eq. 1).
Similarly, the soil dryness index was calculated using the number of days with a dry soil surface (Niang et al. 2018).
A soil flooding index of 0 indicates a permanently dry soil surface or wet soil conditions, while a flooding index of 100 indicates a permanent presence of ponded water.
We computed water productivity (WP, in kg grain m−3 water), irrigation water productivity (IWP, in kg grain m−3 water), and partial factor productivity of applied mineral N (PFPN, in kg grain kg−1 N), P (PFPP, in kg grain kg−1 P), K (PFPK, in kg grain kg−1 K) (Fixen et al. 2015; Sharma et al. 2015) as follows:
whereby Yield represents grain yield expressed in kg ha−1. The total volume of water input comprises both irrigation and rainfall and is expressed in m3 ha−1 during the rice-growing period, and the rates of N, P, and K applied are expressed on an elemental basis (i.e., kg N, P, or K ha−1).
In addition, specific indices of N use efficiency such as agronomic efficiency (kg grain kg−1 N), recovery efficiency (%), physiological efficiency (kg grain kg−1 N), soil N dependent (%) rate were calculated according to Ladha et al. (2005) and Ye et al. (2007) and using the following equations:
where YieldT represents the grain yield (kg ha−1) in the plot with total N supply (from both the soil and fertilizer); Yield0 is the grain yield (kg ha−1) in the plot without N fertilizer or with only the soil N supply, UptakeT (kg ha−1) is the plant N uptake measured in aboveground biomass in the plot that received N fertilizer, and Uptake0 (kg ha−1) indicates the plant nutrient uptake in the plot without N fertilizer application.
In addition, for P, we calculated the partial P balance (%) (Syers et al. 2008), expressing the total P uptake as a percentage of the P applied (Eq. 10).
Statistical analyses and visualization
Field survey data were collected using Android mobile devices, and submitted on a server, where they were downloaded, cleaned, gathered into an Excel spreadsheet, and analyzed using R. 4.3.1 (R Core Team 2023).
Descriptive statistics and Student's t-test
Weather and soil fertility attributes, and farm management practices presented as continuous variables were analyzed using descriptive statistics (mean and standard deviation) while percentage shares were calculated on categorical variables. We computed Chi-squared (χ2) tests of homogeneity to determine whether the proportions of categorical variables were the same across wet and dry seasons (Franke et al. 2012). Continuous variables related to farm management practices were compared between seasons using either the Student's t-test when the normality of residuals and homogeneity of variance were met or the Mann–Whitney-Wilcoxon test as the non-parametric alternative.
Mixed-effects models
We visualized 30 pairwise comparisons of AWD and FP for yield, water productivity, and irrigation water productivity using scatterplots. We subsequently determined the proportion of fields where AWD30 outperformed FP.
Linear mixed-effects models [LMMs, lme4 package; Bates et al. (2015)] were applied with irrigation management as fixed factors and field as a random factor (1| Field), to compare the overall performance of AWD30 and FP on different response variables (weed infestation, grain yield, yield components, water productivity, partial factor productivity of applied nutrients, water input, N and P use efficiency indices, nutrient concentration in straw and grain, total N, P and K uptake) for both wet and dry seasons. “Field” was considered as a random effect to account for repeated measurements, with two plots within each farmer’s field. We combined the data from the two dry seasons due to the absence of significant differences in terms of yield and water productivity. This approach was also adopted to prevent a reduction in the statistical power of the tests. P-values for the fixed effect (irrigation management) were estimated using type-III analysis of variance with Satterthwaite's method, implemented using the lmerTest package (Kuznetsova et al. 2017). When assumptions for the application of LMMs were violated (i.e., heteroscedasticity and deviance of residuals from normality), robust linear mixed-effects models [with Robustlmm package; Koller (2016)] were alternatively run and p-values of the fixed effect were obtained using the emmeans package (Lenth 2022). To model count variables (e.g., tiller or panicle number and frequency of irrigation), we employed Poisson generalized linear mixed-effects models (GLMMs) with the log-link function, using the package lme4 (Bates et al. 2015). After checking overdispersion concerns in the Poisson model, we estimated the p-values of the fixed effect according to the Wald test.
Correlation analysis
Correlation analyses were conducted to explore the relationships between inputs (water, N, P, and K application rates), and outputs (N, P, and K uptakes, and grain yield), regardless of irrigation treatments (FP and AWD30) and season. Furthermore, correlations between grain yield, water productivity, and N and P use efficiency indices were computed to assess potential trade-offs among these performance indicators. The pairwise associations between variables were visualized using a correlogram plotted with the package corrplot (Wei and Simko 2021). As the standard Pearson's correlation estimate is parametric, relying on assumptions of normality and homoscedasticity, it can be sensitive to extreme values. To address these concerns, we opted to compute Spearman's rank correlation coefficient (ρ) (Wilcox 2016). The effect size ρ was interpreted following Cohen’s (1992) guidelines.
Results
Weather conditions, soil attributes, and farmers’ management practices
The weather conditions during the rice growing period differed between seasons. The relative air humidity varied between 64 and 39%, and the cumulative rainfall amounted to 258 and 60 mm in the wet and dry seasons, respectively. Cumulative solar radiation and maximum air temperature during the growing period were higher in the dry season (639 kWh m−2 and 37 °C) than in the wet season (556 kWh m−2 and 34 °C) (Table S2).
The soil clay content varied between 4 and 45% and the sand content between 40 and 68%. Soil pH (H2O) ranged from 5.2 to 6.2 with an average pH of 5.7. Total N ranged from 0.06% to 0.21% and total organic C from 0.70% to 2.0% with an average of 1.19%. Available P (Bray-2) exhibited high variability (CV = 101%) among farmers’ fields, with an average of 20 mg kg−1 (Table S3).
The mean sowing date in the nursery was August 14th (± 16 days) in the wet, and January 13th (± 20) in the dry season. On average, farmers transplanted 25-day-old seedlings. Rice straw from previous seasons was removed for multiple uses (animal feeding or building of rammed earth houses with straw bales) in a quarter of the studied fields, burned in about half of the fields, and incorporated, mulched, or used for animal feeding in the field in another quarter of the fields. These proportions were similar in both seasons (Table S4). More than 80% of the farmers used animal traction for land preparation and nearly all the fields were well-leveled. Besides, non-certified seeds were used in almost 90% of the surveyed fields. The average growth duration of varieties was 109 ± 11 days and was similar in both seasons. Transplanting was the main crop establishment method. During the rice-growing period, farmers weeded their rice fields between one and five times with an average of three weeding operations. In 95% of the cases, farmers applied herbicides at least once (Table S4). While organic manure was applied in approximately 20% of fields, mineral fertilizers were utilized by all farmers. N, P, and K were applied at average rates of 125, 14, and 27 kg ha−1, respectively (Table S4).
Water input
Irrigation frequency and applied amount of water differed between irrigation treatments. In the wet season, the irrigation frequency in FP (7 ± 2) was higher than in AWD30 (4 ± 2). Similarly, in the dry season, the irrigation frequency in FP (16 ± 4) exceeded that in AWD30 (11 ± 3) (p < 0.001). AWD30 reduced irrigation water inputs by 44% in the wet season and by 35% in the dry season. On average, irrigation water contributed to 27% and 79% of the total water input in the wet and dry seasons, respectively. Frequent rainfall events resulted in a higher soil flooding index in the wet season compared to the dry season. As a result, the soil flooding index did not differ between AWD30 and FP irrigation in the wet season (50 ± 28% vs. 60 ± 20%). However, in the dry season, the soil flooding index was lower in AWD30 than in FP (35 ± 19% vs. 51 ± 19%). Regardless of the season, fields rarely fell completely dry, as indicated by the soil dryness index remaining at 0% in the wet season and 2 ± 7% in the dry season (Table 1).
Crop growth, weed infestation, grain yield, and water productivity
Across water management practices, grain yields ranged from 1.6 to 8.2 Mg ha−1, with a mean of 4.5 Mg ha−1, and did not differ between seasons. Total water productivity ranged from 0.28 to 4.1 kg grain m−3 of water, with a mean of 1.6 ± 0.7 kg grain m−3 of water, and was higher in the dry season (1.8 ± 0.9 kg grain m−3 of water) than in the wet season (1.4 ± 0.3 kg grain m−3 of water). Conversely, irrigation water productivity was lower in the dry season (2.6 ± 1.7 kg grain m−3 of water) than in the wet season (6.4 ± 3.5 kg grain m−3 of water) (Table S5).
AWD30 performed better than FP in 63%, 97%, and 97% of the pairwise observations for grain yield, total water productivity, and irrigation water productivity, respectively (Fig. 1a, b, and c). Grain yields did not differ between AWD30 and FP in both wet and dry seasons (Table 2). The same trend was apparent for the number of tillers and panicles (Table 3). Water irrigation management did not affect weed infestation in the fields (Table 3). On the other hand, AWD30 improved total water productivity by 20% and 50% compared to farmers’ irrigation practices in wet and dry seasons, respectively (Table 2).
Scatter plots of (a) grain yield, (b) total water productivity, and (c) irrigation water productivity of plots under farmers’ irrigation practices (FP) and alternate wetting and severe soil drying irrigation (AWD30) in Kou Valley, Burkina Faso, in 2019–2020. The rug lines (i.e., short lines along the axes) indicate the range of the data points plotted and provide insights into the distribution of the data along each axis. The red solid line is the identity line or line of equality
In non-fertilized plots, grain yields ranged from 1.3 to 4.4 Mg ha−1 with a mean of 2.1 Mg ha−1 in the wet season and from 1.6 to 4.6 Mg ha−1 with a mean of 3.1 Mg ha−1 in the dry season. In these plots, grain yields, total water productivity, and irrigation water productivity did not differ between AWD30 and FP in both wet and dry seasons (Table S6).
Nutrient use efficiencies
Across seasons and irrigation management practices, the partial factor productivity of applied N (PFPN) ranged from 15 to 82 with a mean of 37 kg grain kg−1 N. The PFP of applied P ranged from 138 to 624 with a mean of 331 kg grain kg−1 P and was similar in both wet and dry seasons. Similarly, the PFP of applied K varied from 72 to 328 kg grain kg−1 K (Table S5). Furthermore, in both wet and dry seasons, PFPN, PFPP, and PFPK did not differ significantly between AWD30 and FP (Table 2).
The agronomic use efficiency (13 ± 5 kg grain kg−1 N), the recovery efficiency (23 ± 11%), the physiological efficiency (66 ± 30 kg grain kg−1 N) of N, and the soil N dependent rate (54 ± 17%) were comparable between AWD30 and FP in both seasons. Similarly, there were no significant differences in partial P balance between AWD30 and FP in both seasons with a mean value of 131 ± 63% (Table 4).
Nutrient content and uptake
With coefficients of variation (CV) ranging from 13 to 62%, straw, and grain samples exhibited a wide range of nutrient concentrations. Across seasons and irrigation management practices, the N concentration in straw (CV of 18%) and the N, P, K, and Zn concentration in grain (CV of 13%, 18%, 14, and 14%, respectively) were less variable than the Mn and Fe concentrations in straw (CV of 62% and 58%, respectively). In both seasons, water management practices (AWD30 vs. FP) did not significantly affect nutrient concentrations in both straw and grain, with few exceptions. In the wet season, the N concentration in straw was reduced by 24% with AWD30 compared to FP (p = 0.01). Similarly, in the dry season, the P, K, and Zn concentrations in grain were reduced by about 10–20% compared to FP (Table 5).
In the wet season, N, P, and K uptake values were 68 ± 18 kg N ha−1, 18 ± 5 kg P ha−1, and 101 ± 36 kg K ha−1 with farmers’ irrigation practices and 57 ± 15 kg N ha−1, 14 ± 3 kg P ha−1, and 84 ± 14 kg K ha−1 with AWD30. In the dry season, N, P, and K uptake values were 67 ± 21 kg N ha−1, 18 ± 8 kg P ha−1, and 103 ± 41 kg K ha−1 with farmers’ irrigation practices and 64 ± 25 kg N ha−1, 16 ± 8 kg P ha−1, and 107 ± 31 kg K ha−1 with AWD30. Differences between AWD30 and FP were not significant (p > 0.05) except for N uptake during the wet season, which was 16% lower in AWD30 compared to FP (p = 0.045) (Table 6).
Productivity trade-offs and synergies
The N application rate had a large positive effect (0.5 < ρ < 0.7; p < 0.05) on K concentration in straw and K uptake. While total water input had no significant effect on grain yield and N, P, and K uptake, increasing the soil flooding index led to higher N and P uptake by rice (Fig. 2a). There was a strong positive correlation (ρ = 0.8; p < 0.05) between P concentration in grain and total water input, suggesting that reducing water input may lead to a decrease in P concentration in grains. Conversely, K concentration in straw showed a negative correlation (ρ = -0.48; p < 0.05) with total water input.
Relationships (a) between inputs (water, N, P, and K application rates), and outputs (N, P, and K uptakes, and grain yield) and (b) between grain yield, water productivity, and N- and P- use efficiencies (n = 22) in Kou Valley, Burkina Faso, in 2019–2020. The values displayed in the matrix are Spearman's rank correlation coefficients (ρ). Positive correlations are displayed in green and negative correlations in golden brown. The colour intensity (see colour bar) and the square size are proportional to the correlation coefficients. Significant correlation coefficients are indicated by asterisks (* p < 0.05, ** p < 0.01, and *** p < 0.001), while coefficients without asterisks are not statistically significant (p > 0.05)
Significant positive correlations were observed between grain yield and the agronomic use and recovery efficiencies of N, and the partial P balance. Agronomic use and recovery efficiencies of N were not negatively correlated with the total water productivity (p > 0.05) (Fig. 2b).
Discussion
To the best of our knowledge, this is the first on-farm study conducted in the semi-arid zone of West Africa, investigating the effect of AWD irrigation on nutrient uptake in irrigated rice. It showed in locations with a shallow groundwater table, compared to the farmers’ irrigation practices (FP), alternate wetting and severe soil drying irrigation (AWD30) can reduce water input without compromising grain yield and the use efficiencies of applied N and P. However, the observed positive correlation between soil flooding index and N and P uptake raises concerns about possible trade-offs between water-savings and nutrient uptake in case of acute soil dryness. The following discussion of key findings is organized into three parts: (i) the water-saving potential of AWD irrigation and its impacts on productivity; (ii) the impact of AWD irrigation and anaerobic conditions on nutrient use; and (iii) the study's limitations and outlook.
AWD reduced water input without grain yield penalty
Alternate wetting and severe soil drying irrigation (AWD30) reduced total water input by 14% and 28% in the wet and dry seasons, respectively, without compromising the grain yield. This led to a 20% increase in water productivity during the wet season and a 50% increase during the dry season. This water-saving is attributed to the reduction in unproductive water outflows, including percolation, evaporation, and seepage. The water savings recorded in this study are lower than those reported in previous studies (30%-36%), comparing alternate wetting and severe drying regime to continuous submerged field conditions (Carrijo et al. 2017). This could be explained by the reference for the comparison, which differs from the continuous submerged field conditions. Indeed, in the present study, farmers’ irrigation practices provided a water layer only on ~ 60% of the days during the growing season. Compared to alternate wetting and moderate drying regime (AWD15) in the same scheme in the dry season (Johnson et al. 2024), AWD30 did not increase water savings. Thus, despite a reduced irrigating frequency, irrigation water inputs tended to be higher for individual irrigation events to compensate for the more extensive water losses from the soil water storage reservoir in AWD30 compared to AWD15. This raises questions about the relevance of severe drying in the dry season. Moreover, alternate wetting and severe soil drying irrigation presents higher risks and can entail a yield penalty of about 22% (Carrijo et al. 2017). In our study, AWD30 did not compromise grain yield likely because the soil did not completely dry out due to the capillary rise of water from the shallow groundwater table (Table 1). Additionally, partial soil drying also occurred in farmers' irrigation practices (FP). The assessment of the field water status was exclusively done through visual observation, limiting a comprehensive comparison of the effect of the two water management irrigation regimes (FP and AWD30) on soil dryness levels. Using appropriate instruments, such as tensiometers, could have improved the precision of measurements, resulting in more accurate and reliable insight into the soil moisture content.
AWD did not impair nitrogen and phosphorus use efficiencies
Regardless of the seasons, the partial factor productivity and the agronomic use and recovery efficiencies of applied mineral N were comparable for both irrigation management practices. However, the mean agronomic use efficiency of 13 kg grain kg−1 N and the N recovery of 23% were lower than the global average values of 22 kg grain kg−1 N, and 46%, respectively (Ladha et al. 2005). Such low N use efficiency values may well reflect poor N fertilizer management in farmers’ fields, a common issue in rice production in sub-Saharan Africa (Chivenge et al. 2021). Thus, site-specific nutrient management has been advocated for optimizing fertilizer use, enhancing the synchrony of N supply and demand, and improving N use efficiency (Saito et al. 2023b). Synergistic interaction effects between site-specific N management and AWD could well enhance grain yields while also increasing N-use efficiency (Liu et al. 2013).
There is no consensus in the scientific debate on whether AWD improves N-use efficiency or not. Thus, some studies indicated low N use efficiency under AWD irrigation due to increased NO3−-N losses (Dunn and Gaydon 2011). Other studies demonstrated improved N-use efficiency with AWD (Djaman et al. 2018; Liu et al. 2013), while yet others showed no significant difference in N-use efficiency between continuous submergence and AWD irrigation (Cabangon et al. 2004; Islam et al. 2022; Pan et al. 2017). These studies explained that AWD irrigation may decrease N losses due to the decrease in N leaching (Wang et al. 2011), and NH3 volatilization (Xu et al. 2012). On the other hand, AWD may potentially enhance NO3-–N losses through denitrification during severe soil drying and subsequent soil flooding (Yang et al. 2017), which may, in turn, negatively affect N use efficiency (Zhang et al. 2009). However, in the present study, agronomic use, recovery, and physiological efficiencies of N were not impaired by alternate wetting and severe drying. The shallow groundwater table may have mitigated the effects of severe drying, reducing the NO3--N formation and the subsequent N losses (Wang et al. 2016). This aligns with suggestions from studies in China and the Philippines conducted in environments with shallow groundwater tables (Belder et al. 2005; Cabangon et al. 2004). On the other hand, we found a positive correlation (ρ = 0.53; p < 0.05) between the soil flooding index and N uptake supporting that soil submergence fosters N uptake. This result is somewhat consistent with Atwill et al. (2018) who demonstrated that N uptake were reduced for aerobic irrigated rice compared to other irrigation treatments having higher soil flooding index. Soil submergence can buffer pH extremes. Thus, Narteh and Sahrawat (1999) surmised that the convergence of soil pH in the neutral range following soil submergences benefits the lowland rice crop through an improved plant availability of NH4+.
It is still unclear whether AWD irrigation improves P-use efficiency (Ishfaq et al. 2020). Contrary to the results of pot experiments conducted by Deng et al. (2021) and Acosta-Motos et al. (2020), we found no evidence of altered P-use efficiency with AWD30 compared to farmers’ irrigation practices. In the dry season, AWD30 even decreased grain P concentrations compared to farmers’ irrigation practices as reported before (Norton et al. 2017a; Song et al. 2018). Overall, sustaining a perched water table fosters rice P uptake (Fig. 2a). This is primarily because anaerobic reducing conditions in submerged soils may increase the concentration of plant availability P in the soil solution (Young and Ross 2001) due to the supplementary release of organic P via redox-sensitive dissociation from iron/manganese oxides. In contrast, aerobic conditions may reduce the amount of available P because of P precipitation with oxidized Fe and Mn, or by P immobilization in the soil microbial biomass (Adhikary et al. 2023). Therefore, increased P fertilizer application rates may be required under AWD, especially in acid soils (Johnson et al. 2024).
Although inconsistent across seasons, alternate wetting and severe soil drying irrigation negatively affected nutrient concentrations in rice grains. Thus, in the dry season, P and K concentrations in rice grain decreased under AWD30 as reported before by Norton et al. (2017b). In contrast to P, Zn is usually less available in anaerobic (low redox potential) than in aerobic (high redox potential) soils (Johnson-Beebout et al. 2009). Yet, we observed lower Zn concentration in rice grains under AWD30 in the present study (Table 5). This contrasts also with previous studies that showed AWD significantly increased available soil Zn but did not affect grain Zn concentration (Rubianes et al. 2018). Further investigations are needed to explore the effect of AWD on soil zinc availability and grain zinc concentration.
Limitations of the study and outlook
This on-farm-based study improved our understanding of the impact of AWD on performance indicators of irrigated rice in fields with a shallow groundwater table (Cabangon et al. 2004). We were able to demonstrate the feasibility of the AWD technology and its implementation in the local context of Burkina Faso. However, uncertainties persist due to the relatively small sample size, variabilities in farmers' implementation of the AWD technology, and site-specific conditions of water availability. Additionally, obtaining farmers' approval to make significant changes to the setting or layout of their fields was challenging, especially for this series of on-farm farmer-managed trials, where participation was voluntary and farmers did not receive any incentives. Consequently, we were unable to implement additional measures, such as those carried out in on-station trials (Krupnik et al. 2012a), to reduce underground seepage, apart from using large consolidated bunds. Therefore, the water savings and water productivity gains attributed to AWD30 may have been overestimated and should be considered with caution.
A larger study is needed to confirm the effect sizes on performance indicators (Hackshaw 2008) and to further investigate the interaction between water management and fertilizer application rates. Subsequent investigations using locally calibrated and validated models (Grotelüschen et al. 2021, 2022) can help draw insights into a wide range of climatic and hydro-edaphic conditions, as well as their interactions. In future on-farm trials, we recommend monitoring the field water level and soil water potential to assess their impact on grain yield, nutrient uptake, and use efficiency (Tuong and Bouman 2003). Furthermore, expanding the study to contrasting environments will be crucial in determining suitable niches for scaling the AWD irrigation technology to other irrigation schemes in the semi-arid zone of West Africa.
Conclusion
Compared to actual farmers’ irrigation practices, alternate wetting and severe soil drying reduced irrigation water input by 37% (mean across seasons) without yield penalty while maintaining N- and P-use efficiencies. This improved irrigation water productivity by about 39%. This study did not reveal any evident trade-offs between water productivity and agronomic use and recovery efficiencies of N. We conclude that alternate wetting and severe soil drying irrigation can efficiently increase water productivity and serve as a water-saving technology for sustainable rice production in locations with shallow groundwater tables, as observed in the case of the Kou Valley. Therefore, water management institutions and policymakers should incentivize smallholder rice farmers to adopt this water-saving technology in these locations in Burkina Faso and beyond. Furthermore, this study underscores the importance of the hydrological characterization of irrigation schemes in West Africa, especially in terms of percolation rate and groundwater table depth. Future investigations in contrasting environments should also explore the influence of alternate wetting and severe soil drying irrigation on greenhouse gas emissions and its long-term effects on soil chemical properties, as well as nitrogen and carbon storage.
Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
References
Acosta-Motos JR, Rothwell SA, Massam MJ, Albacete A, Zhang H, Dodd IC (2020) Alternate wetting and drying irrigation increases water and phosphorus use efficiency independent of substrate phosphorus status of vegetative rice plants. Plant Physiol Biochem 155:914–926. https://doi.org/10.1016/j.plaphy.2020.06.017
Adhikary PP, Mohanty S, Rautaray SK, Manikandan N, Mishra A (2023) Alternate wetting and drying water management can reduce phosphorus availability under lowland rice cultivation irrespective of nitrogen level. Environ Monit Assess 195:1420. https://doi.org/10.1007/s10661-023-12052-5
Arndt C, Diao X, Dorosh P, Pauw K, Thurlow J (2023) The Ukraine war and rising commodity prices: Implications for developing countries. Glob Food Sec 36:100680. https://doi.org/10.1016/j.gfs.2023.100680
Atwill RL II, Krutz LJ, Bond JA, Reddy KR, Gore J, Walker TW, Harrell DL (2018) Water management strategies and their effects on rice grain yield and nitrogen use efficiency. J Soil Water Conserv 73:257–264. https://doi.org/10.2489/jswc.73.3.257
Aulakh MS, Malhi SS (2005) Interactions of nitrogen with other nutrients and water: effect on crop yield and quality, nutrient use efficiency, carbon sequestration, and environmental pollution. Adv Agron. Elsevier: Amsterdam, Netherlands, 341–409. https://doi.org/10.1016/S0065-2113(05)86007-9
Bates D, Mächler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67. https://doi.org/10.18637/jss.v067.i01
Belder P, Spiertz JHJ, Bouman BAM, Lu G, Tuong TP (2005) Nitrogen economy and water productivity of lowland rice under water-saving irrigation. Field Crop Res 93:169–185. https://doi.org/10.1016/j.fcr.2004.09.022
Bouman BAM, Lampayan RM, Tuong TP (2007) Water management in irrigated rice: Coping with water scarcity. International Rice Research Institute, Los Baños, Philippines
Cabangon RJ, Tuong TP, Castillo EG, Bao LX, Lu G, Wang G, Cui Y, Bouman BAM, Li Y, Chen C, Wang J (2004) Effect of irrigation method and N-fertilizer management on rice yield, water productivity and nutrient-use efficiencies in typical lowland rice conditions in China. Paddy Water Environ 2:195–206. https://doi.org/10.1007/s10333-004-0062-3
Carrijo DR, Lundy ME, Linquist BA (2017) Rice yields and water use under alternate wetting and drying irrigation: a meta-analysis. Field Crop Res 203:173–180. https://doi.org/10.1016/j.fcr.2016.12.002
Cheng H, Shu K, Zhu T, Wang L, Liu X, Cai W, Qi Z, Feng S (2022) Effects of alternate wetting and drying irrigation on yield, water and nitrogen use, and greenhouse gas emissions in rice paddy fields. J Clean Prod 349:131487. https://doi.org/10.1016/j.jclepro.2022.131487
Chidiebere-Mark N, Ohajianya D, Obasi P, Onyeagocha S (2019) Profitability of rice production in different production systems in Ebonyi State, Nigeria. Open Agric 4:237–246. https://doi.org/10.1515/opag-2019-0022
Chivenge P, Sharma S, Bunquin MA, Hellin J (2021) Improving nitrogen use efficiency—a key for sustainable rice production systems. Front Sustain Food Syst 5:737412. https://doi.org/10.3389/fsufs.2021.737412
Chu G, Chen T, Chen S, Xu C, Wang D, Zhang X (2018) Agronomic performance of drought-resistance rice cultivars grown under alternate wetting and drying irrigation management in southeast China. Crop J 6:482–494. https://doi.org/10.1016/j.cj.2018.04.005
Cohen J (1992) A power primer. Psychol Bull 112:155–159. https://doi.org/10.1037/0033-2909.112.1.155
Datta A, Ullah H, Ferdous Z (2017) Water management in rice. In: Chauhan BS, Jabran K and Mahajan G (eds) Rice production worldwide. Springer International Publishing, Cham, Switzerland, 255–277. https://doi.org/10.1007/978-3-319-47516-5_11
De Datta SK, Buresh RJ, Mamaril CP (1990) Increasing nutrient use efficiency in rice with changing needs. Fertil Res 26:157–167. https://doi.org/10.1007/BF01048753
de Vries ME, Rodenburg J, Bado BV, Sow A, Leffelaar PA, Giller KE (2010) Rice production with less irrigation water is possible in a Sahelian environment. Field Crop Res 116:154–164. https://doi.org/10.1016/j.fcr.2009.12.006
Dembelé Y, Kambiré H, Sié M (2005) Gestion de l’eau et de l’azote en riziculture irriguée au Burkina Faso. Cah Agric 14:569–572
Deng Y, Qiao S, Wang W, Zhang W, Gu J, Liu L, Zhang H, Wang Z, Yang J (2021) Tolerance to low phosphorus was enhanced by an alternate wetting and drying regime in rice. Food Energy Secur 10:e294. https://doi.org/10.1002/fes3.294
Djaman K, Mel V, Diop L, Sow A, El-Namaky R, Manneh B, Saito K, Futakuchi K, Irmak S (2018) Effects of alternate wetting and drying irrigation regime and nitrogen fertilizer on yield and nitrogen use efficiency of irrigated rice in the Sahel. Water 10:711. https://doi.org/10.3390/w10060711
Dunn BW, Gaydon DS (2011) Rice growth, yield and water productivity responses to irrigation scheduling prior to the delayed application of continuous flooding in south-east Australia. Agric Water Manag 98:1799–1807. https://doi.org/10.1016/j.agwat.2011.07.004
FAO, IIASA (2023) Harmonized world soil database version 2.0. Food and Agriculture Organization of the United Nations (FAO), International Institute for Applied Systems Analysis (IIASA), Rome, Italy; Laxenburg, Austria. https://doi.org/10.4060/cc3823en
Fixen P, Brentrup F, Bruulsema TW, Garcia F, Norton R, Zingore S (2015) Nutrient/fertilizer use efficiency: measurement, current situation and trends. In: Drechsel P, Heffer P, Magen H, Mikkelsen R and Wichelns D (eds) Managing water and fertilizer for sustainable agricultural intensification. International Fertilizer Industry Association (IFA), International Water Management Institute (IWMI), International Plant Nutrition Institute (IPNI), and International Potash Institute (IPI), Paris, France, 8–37
Franke TM, Ho T, Christie CA (2012) The chi-square test: often used and more often misinterpreted. Am J Eval 33:448–458. https://doi.org/10.1177/1098214011426594
Ghosh BC, Bhat R (1998) Environmental hazards of nitrogen loading in wetland rice fields. Environ Pollut 102:123–126. https://doi.org/10.1016/S0269-7491(98)80024-9
Global Rice Science Partnership (2013) Rice almanac: source book for the most important economic activities on Earth. International Rice Research Institute (IRRI), Los Baños, Philippines
Gongotchame S, Dieng I, Ahouanton K, Johnson J-M, Alognon AD, Tanaka A, Atta S, Saito K (2014) Participatory evaluation of mechanical weeders in lowland rice production systems in Benin. Crop Prot 61:32–37. https://doi.org/10.1016/j.cropro.2014.03.009
Grotelüschen K, Gaydon DS, Langensiepen M, Ziegler S, Kwesiga J, Senthilkumar K, Whitbread AM, Becker M (2021) Assessing the effects of management and hydro-edaphic conditions on rice in contrasting East African wetlands using experimental and modelling approaches. Agric Water Manag 258:107146. https://doi.org/10.1016/j.agwat.2021.107146
Grotelüschen K, Gaydon DS, Senthilkumar K, Langensiepen M, Becker M (2022) Model-based evaluation of rainfed lowland rice responses to N fertiliser in variable hydro-edaphic wetlands of East Africa. Field Crop Res 285:108602. https://doi.org/10.1016/j.fcr.2022.108602
Hackshaw A (2008) Small studies: strengths and limitations. Eur Respir J 32:1141–1143. https://doi.org/10.1183/09031936.00136408
Haefele SM, Naklang K, Harnpichitvitaya D, Jearakongman S, Skulkhu E, Romyen P, Phasopa S, Tabtim S, Suriya-arunroj D, Khunthasuvon S, Kraisorakul D, Youngsuk P, Amarante ST, Wade LJ (2006) Factors affecting rice yield and fertilizer response in rainfed lowlands of northeast Thailand. Field Crop Res 98:39–51. https://doi.org/10.1016/j.fcr.2005.12.003
Ishfaq M, Farooq M, Zulfiqar U, Hussain S, Akbar N, Nawaz A, Anjum SA (2020) Alternate wetting and drying: a water-saving and ecofriendly rice production system. Agric Water Manag 241:106363. https://doi.org/10.1016/j.agwat.2020.106363
Islam SMM, Gaihre YK, Islam MdR, Ahmed MdN, Akter M, Singh U, Sander BO (2022) Mitigating greenhouse gas emissions from irrigated rice cultivation through improved fertilizer and water management. J Environ Manage 307:114520. https://doi.org/10.1016/j.jenvman.2022.114520
Johnson J-M, Becker M, Dossou-Yovo ER, Saito K (2023a) Farmers’ perception and management of water scarcity in irrigated rice-based systems in dry climatic zones of West Africa. Agron Sustain Dev 43:32. https://doi.org/10.1007/s13593-023-00878-9
Johnson J-M, Becker M, Dossou-Yovo ER, Saito K (2024) Enhancing on-farm rice yields, water productivity, and profitability through alternate wetting and drying technology in dry zones of West Africa. Agric Water Manag Under review
Johnson J-M, Ibrahim A, Dossou-Yovo ER, Senthilkumar K, Tsujimoto Y, Asai H, Saito K (2023b) Inorganic fertilizer use and its association with rice yield gaps in sub-Saharan Africa. Glob Food Sec 38:100708. https://doi.org/10.1016/j.gfs.2023.100708
Johnson J-M, Kabore JPE, Ottnad SAM, Ronald E, Becker M (2020) Potentials and risks of alternate wetting and drying in rice production of the dry Savannah zone of West Africa. In: Tielkes E, Asch F, Chagunda M, Haussmann B, Müller J, Rennert T and Asare E (eds) Food and nutrition security and its resilience to global crises. paper presented at the Tropentag. DITSL, Witzenhausen, Germany, p 13. https://www.tropentag.de/2020/abstracts/links/Johnson_SMuBsQWA.php
Johnson J-M, Vandamme E, Senthilkumar K, Sila A, Shepherd KD, Saito K (2019) Near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for assessing soil fertility in rice fields in sub-Saharan Africa. Geoderma 354:113840. https://doi.org/10.1016/j.geoderma.2019.06.043
Johnson-Beebout SE, Lauren JG, Duxbury JM (2009) Immobilization of Zinc fertilizer in flooded soils monitored by adapted DTPA soil test. Commun Soil Sci Plant Anal 40:1842–1861. https://doi.org/10.1080/00103620902896738
Koller M (2016) robustlmm: An R package for robust estimation of linear mixed-effects models. J Stat Softw 75:1–24. https://doi.org/10.18637/jss.v075.i06
Krupnik TJ, Rodenburg J, Haden VR, Mbaye D, Shennan C (2012a) Genotypic trade-offs between water productivity and weed competition under the system of rice intensification in the Sahel. Agric Water Manag 115:156–166. https://doi.org/10.1016/j.agwat.2012.08.016
Krupnik TJ, Shennan C, Settle WH, Demont M, Ndiaye AB, Rodenburg J (2012b) Improving irrigated rice production in the Senegal River Valley through experiential learning and innovation. Agric Syst 109:101–112. https://doi.org/10.1016/j.agsy.2012.01.008
Kuznetsova A, Brockhoff PB, Christensen RHB (2017) Imertest package: tests in linear mixed effects models. J Stat Softw 82. https://doi.org/10.18637/jss.v082.i13
Ladha JK, Pathak H, J. Krupnik T, Six J, van Kessel C (2005) Efficiency of fertilizer nitrogen in cereal production: retrospects and prospects. Adv Agron 85–156. https://doi.org/10.1016/S0065-2113(05)87003-8
Lampayan RM, Samoy-Pascual KC, Sibayan EB, Ella VB, Jayag OP, Cabangon RJ, Bouman BAM (2015) Effects of alternate wetting and drying (AWD) threshold level and plant seedling age on crop performance, water input, and water productivity of transplanted rice in Central Luzon, Philippines. Paddy Water Environ 13:215–227. https://doi.org/10.1007/s10333-014-0423-5
Lassaletta L, Einarsson R, Quemada M (2023) Nitrogen use efficiency of tomorrow. Nat Food 4:281–282. https://doi.org/10.1038/s43016-023-00740-x
Lenth RV (2022) emmeans: estimated marginal means, aka least-squares means (R package version 1.7.3). R language. https://CRAN.R-project.org/package=emmeans
Liu L, Chen T, Wang Z, Zhang H, Yang J, Zhang J (2013) Combination of site-specific nitrogen management and alternate wetting and drying irrigation increases grain yield and nitrogen and water use efficiency in super rice. Field Crop Res 154:226–235. https://doi.org/10.1016/j.fcr.2013.08.016
Mishra AK, Pede VO, Arouna A, Labarta R, Andrade R, Veettil PC, Bhandari H, Laborte AG, Balie J, Bouman B (2022) Helping feed the world with rice innovations: CGIAR research adoption and socioeconomic impact on farmers. Glob Food Sec 33:100628. https://doi.org/10.1016/j.gfs.2022.100628
Moormann FR, van Breemen N (1978) Rice: soil, water, land. International Rice Research Institute (IRRI), Los Baños, Philippines
Narteh LT, Sahrawat KL (1999) Influence of flooding on electrochemical and chemical properties of West African soils. Geoderma 87:179–207. https://doi.org/10.1016/S0016-7061(98)00053-6
Niang A, Becker M, Ewert F, Dieng I, Gaiser T, Tanaka A, Senthilkumar K, Rodenburg J, Johnson J-M, Akakpo C, Segda Z, Gbakatchetche H, Jaiteh F, Bam RK, Dogbe W, Keita S, Kamissoko N, Mossi IM, Bakare OS, Cissé M, Baggie I, Ablede KA, Saito K (2017) Variability and determinants of yields in rice production systems of West Africa. Field Crop Res 207:1–12. https://doi.org/10.1016/j.fcr.2017.02.014
Niang A, Becker M, Ewert F, Tanaka A, Dieng I, Saito K (2018) Yield variation of rainfed rice as affected by field water availability and N fertilizer use in central Benin. Nutr Cycl Agroecosyst 110:293–305. https://doi.org/10.1007/s10705-017-9898-y
Norton GJ, Shafaei M, Travis AJ, Deacon CM, Danku J, Pond D, Cochrane N, Lockhart K, Salt D, Zhang H, Dodd IC, Hossain M, Islam MR, Price AH (2017a) Impact of alternate wetting and drying on rice physiology, grain production, and grain quality. Field Crop Res 205:1–13. https://doi.org/10.1016/j.fcr.2017.01.016
Norton GJ, Travis AJ, Danku JMC, Salt DE, Hossain M, Islam MdR, Price AH (2017b) Biomass and elemental concentrations of 22 rice cultivars grown under alternate wetting and drying conditions at three field sites in Bangladesh. Food Energy Secur 6:98–112. https://doi.org/10.1002/fes3.110
Pan J, Liu Y, Zhong X, Lampayan RM, Singleton GR, Huang N, Liang K, Peng B, Tian K (2017) Grain yield, water productivity and nitrogen use efficiency of rice under different water management and fertilizer-N inputs in South China. Agric Water Manag 184:191–200. https://doi.org/10.1016/j.agwat.2017.01.013
Peng S-Z, Yang S-H, Xu J-Z, Luo Y-F, Hou H-J (2011) Nitrogen and phosphorus leaching losses from paddy fields with different water and nitrogen managements. Paddy Water Environ 9:333–342. https://doi.org/10.1007/s10333-010-0246-y
Rodenburg J, Meinke H, Johnson DE (2011) Challenges for weed management in African rice systems in a changing climate. J Agric Sci 149:427–435. https://doi.org/10.1017/S0021859611000207
R Core Team (2023) R: A language and environment for statistical computing. R Foundation for Statistical computing. R language. Vienna, Austria. https://www.R-project.org/
Rubianes FHC, Swamy BPM, Johnson-Beebout SE (2018) Irrigation management risks and Zn fertilization needs in Zn biofortification breeding in lowland rice. Exp Agric 54:382–398. https://doi.org/10.1017/S0014479717000084
Saito K, Johnson J-M, Hauser S, Corbeels M, Devkota M, Casimero M (2023a) Guideline for measuring agronomic gain key performance indicators in on-farm trials (Version1). Excell Agron Sustain Intensification Clim Change Adapt Initiative.https://hdl.handle.net/10568/134668
Saito K, Senthilkumar K, Dossou-Yovo ER, Ali I, Johnson J-M, Mujawamariya G, Rodenburg J (2023b) Status quo and challenges of rice production in sub-Saharan Africa. Plant Prod Sci 26:320–333. https://doi.org/10.1080/1343943X.2023.2241712
Saito K, Vandamme E, Johnson J-M, Tanaka A, Senthilkumar K, Dieng I, Akakpo C, Gbaguidi F, Segda Z, Bassoro I, Lamare D, Gbakatchetche H, Abera BB, Jaiteh F, Bam RK, Dogbe W, Sékou K, Rabeson R, Kamissoko N, Mossi IM, Tarfa BD, Bakare SO, Kalisa A, Baggie I, Kajiru GJ, Ablede K, Ayeva T, Nanfumba D, Wopereis MCS (2019) Yield-limiting macronutrients for rice in sub-Saharan Africa. Geoderma 338:546–554. https://doi.org/10.1016/j.geoderma.2018.11.036
Sharma B, Molden D, Cook S (2015) Water use efficiency in agriculture: Measurement, current situation and trends. In: Drechsel P, Heffer P, Magen H, Mikkelsen R and Wichelns D (eds) Managing water and fertilizer for sustainable agricultural intensification. International Fertilizer Industry Association (IFA), International Water Management Institute (IWMI), International Plant Nutrition Institute (IPNI), and International Potash Institute (IPI), Paris, France, 39–64
Song T, Xu F, Yuan W, Zhang Y, Liu T, Chen M, Hu Q, Tian Y, Xu W, Zhang J (2018) Comparison on physiological adaptation and phosphorus use efficiency of upland rice and lowland rice under alternate wetting and drying irrigation. Plant Growth Regul 86:195–210. https://doi.org/10.1007/s10725-018-0421-5
Surendran U, Raja P, Jayakumar M, Subramoniam SR (2021) Use of efficient water saving techniques for production of rice in India under climate change scenario: a critical review. J Clean Prod 309:127272. https://doi.org/10.1016/j.jclepro.2021.127272
Syers JK, Johnston AE, Curtin D (2008) Efficiency of soil and fertilizer phosphorus use: reconciling changing concepts of soil phosphorus behaviour with agronomic information. Food and Agriculture Organization of the United Nations, Rome, Italy
Tanaka A, Johnson J-M, Senthilkumar K, Akakpo C, Segda Z, Yameogo LP, Bassoro I, Lamare DM, Allarangaye MD, Gbakatchetche H, Bayuh BA, Jaiteh F, Bam RK, Dogbe W, Sékou K, Rabeson R, Rakotoarisoa NM, Kamissoko N, Mossi IM, Bakare OS, Mabone FL, Gasore ER, Baggie I, Kajiru GJ, Mghase J, Ablede KA, Nanfumba D, Saito K (2017) On-farm rice yield and its association with biophysical factors in sub-Saharan Africa. Eur J Agron 85:1–11. https://doi.org/10.1016/j.eja.2016.12.010
Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S (2002) Agricultural sustainability and intensive production practices. Nature 418:671–677. https://doi.org/10.1038/nature01014
Trimmer WL (1994) Estimating water flow rates. Extension Service, Oregon State University, Oregon State, USA
Tuong TP, Bouman BAM (2003) Rice production in water-scarce environments. In: Kijne JW, Barker R and Molden D (eds) Water productivity in agriculture: Limits and opportunities for improvement. CABI, Wallingford, UK, 53–67. https://doi.org/10.1079/9780851996691.0053
UN-Water (2021) Summary Progress: SDG 6 - water and sanitation for all. UN-Water, Geneva, Switzerland
Wang X, Suo Y, Feng Y, Shohag MJI, Gao J, Zhang Q, Xie S, Lin X (2011) Recovery of 15N-labeled urea and soil nitrogen dynamics as affected by irrigation management and nitrogen application rate in a double rice cropping system. Plant Soil 343:195–208. https://doi.org/10.1007/s11104-010-0648-z
Wang Z, Zhang W, Beebout SS, Zhang H, Liu L, Yang J, Zhang J (2016) Grain yield, water and nitrogen use efficiencies of rice as influenced by irrigation regimes and their interaction with nitrogen rates. Field Crop Res 193:54–69. https://doi.org/10.1016/j.fcr.2016.03.006
Wei T, Simko V (2021) R package “corrplot”: visualization of a correlation matrix (Version 0.92). R language. https://github.com/taiyun/corrplot
Wellens J, Nitcheu M, Traore F, Tychon B (2013) A public-private partnership experience in the management of an irrigation scheme using decision-support tools in Burkina Faso. Agric Water Manag 116:1–11. https://doi.org/10.1016/j.agwat.2012.09.013
Wilcox RR (2016) Comparing dependent robust correlations. Br J Math Stat Psychol 69:215–224
Wopereis MCS, Donovan C, Nebié B, Guindo D, N’Diaye MK (1999) Soil fertility management in irrigated rice systems in the Sahel and Savanna regions of West Africa Part I. Agron Anal Field Crops Res 61:125–145. https://doi.org/10.1016/S0378-4290(98)00154-3
Xu J, Peng S, Yang S, Wang W (2012) Ammonia volatilization losses from a rice paddy with different irrigation and nitrogen managements. Agric Water Manag 104:184–192. https://doi.org/10.1016/j.agwat.2011.12.013
Yang J, Zhou Q, Zhang J (2017) Moderate wetting and drying increases rice yield and reduces water use, grain arsenic level, and methane emission. Crop J 5:151–158. https://doi.org/10.1016/j.cj.2016.06.002
Ye Q, Zhang H, Wei H, Zhang Y, Wang B, Xia K, Huo Z, Dai Q, Xu K (2007) Effects of nitrogen fertilizer on nitrogen use efficiency and yield of rice under different soil conditions. Front Mech Eng China 1:30–36. https://doi.org/10.1007/s11703-007-0005-z
Young EO, Ross DS (2001) Phosphate release from seasonally flooded soils: a laboratory microcosm study. J Environ Qual 30:91–101. https://doi.org/10.2134/jeq2001.30191x
Yu X, Keitel C, Zhang Y, Wangeci AN, Dijkstra FA (2022) Global meta-analysis of nitrogen fertilizer use efficiency in rice, wheat and maize. Agric Ecosyst Environ 338:108089. https://doi.org/10.1016/j.agee.2022.108089
Zhang H, Xue Y, Wang Z, Yang J, Zhang J (2009) An Alternate Wetting and Moderate Soil Drying Regime Improves Root and Shoot Growth in Rice. Crop Sci 49:2246–2260. https://doi.org/10.2135/cropsci2009.02.0099
Acknowledgements
We extend our gratitude to the rice farmers of Kou Valley for their invaluable collaboration during the study. Special thanks to Mesdames Jolie B. Johnson and Jeanne F. Zerbo for their contribution in managing the post-harvest activities. We gratefully acknowledge Dr. Koichi Futakuchi for providing valuable insights into the study. We would also like to thank Mesdames Angelika Glogau, Nur Gömec, Ira Kurth, and Angelika Veits from the Institute of Crop Science and Resource Conservation (INRES) for assisting with plant analysis and Mr. Salifou Goube Mairoua from Africa Rice Center for soil analysis.
Funding
Open Access funding enabled and organized by Projekt DEAL. This study received financial support from the German Federal Ministry for Economic Cooperation and Development (BMZ) commissioned and administered through the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) Fund for International Agricultural Research (FIA), grant number: 81206679. It was conducted under the framework of “Climate-smart rice technologies to enhance the resilience of smallholder rice farmers in Burkina-Faso (CSA Rice Burkina)” project led by Africa Rice Center, University of Bonn and Institut de l'Environnement et de Recherches Agricoles (INERA). The first author (J.-M. J.) was granted a 3-month scholarship extension, funded by the University of Bonn and supported by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia (MKW) under the Excellence Strategy of the Federal and State governments.
Author information
Authors and Affiliations
Contributions
J.-M.J.: Conceptualization, Methodology, Investigation, Data collection, curation & analysis, Visualization, Writing—Original Draft, Writing-Review & Editing; M.B.: Conceptualization, Writing—Review & Editing, Project administration, Fund acquisition, Supervision; J.E.P.K.: Investigation, Data collection, Review & Editing; E.R.D.-Y.: Review & Editing, Project administration; K.S.: Review & Editing.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Johnson, JM., Becker, M., Kaboré, J.E.P. et al. Alternate wetting and drying: a water-saving technology for sustainable rice production in Burkina Faso?. Nutr Cycl Agroecosyst 129, 93–111 (2024). https://doi.org/10.1007/s10705-024-10360-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10705-024-10360-x