Practical drivers of water recycling in agro-aqua systems for long-term climate adaptation in semi- sahel zone

Practical drivers of water recycling in agro-aqua systems for long-term climate adaptation in semi- sahel zone | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Practical drivers of water recycling in agro-aqua systems for long-term climate adaptation in semi- sahel zone Toundji Olivier Amoussou, Chibuye Florence Kunda-Wamuwi, Wendnso Eddie Lionel Gouem, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189278/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Water efficiency is a major concern for many agro-aqua units (AAU) in semi-sahel zone. At AAU scale, water intends to agro-aqua production is of great concern with regard to effluent-emitted during Growth Stages of Fish (GSF). The amount of water used and lost in these AAU as well as the resulting fertilizing potential are worth determining. Many descriptive and inferential approaches were used to estimate water inflows, water outflows and water reuse efficiency, and water performance indicators at AAU scales. The study demonstrated significant variability in AAU aquaculture production component according to GSF. High aquaculture water reuse efficiency was observed at proportions from 91% to 99% among AAU, with weaker statistical variations. This reflects important miletones, as adaptative options in agro-aqua production can be of concerned with regard to water recycling. Fish at adult GSF generated the most nutrient-rich effluents, particularly in terms of nitrogen and phosphorus, with high interaction between GSF, feed suppling, and organic load. High concentrations of total nitrogen, orthophosphate in juveniles, and in high concentration of potassium in fry were observed, as adding value to AAUs fertilizing potential. AAUs involving Clarias gariepinus production enhances cropping water in semi-sahel agro-aqua climatic conditions. Agro-aqua Fry Juvenile Adult fish Aquaculture effluent Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Article Highlights Agro-aqua production water are profiled according to Growth Stages of Fish (GSF) in semi-sahel farms; Water utilization volume, i.e. water productivity was optimized in many agro-aqua production sub-components; Water requirements and efficiency in agro-aqua production is profiled for semi-sahel zone. 1 Introduction Water, as a resource with multiple uses, is subject to many challenges, including population growth, climate change. The intensification of man-made activities affects water availability, quality, and accessibility. Many users experience water shortages every year, and this trend continues year after year [ 1 ]. In such a dynamic, cropping, which uses approximately 80% of freshwater resources [ 2 ], is at the core of concerns, especially since demand for consumption will require a 60 to 70% increase in production by 2050 [ 3 ]. To address such challenges, a far-reaching transformation of agricultural systems, particularly in arid and semi-arid areas, is needed in order to enhance productivity and water use efficiency while ensuring sustainable production. Surface water availability in semi-sahel zone is highly influenced by seasonal variations. Seasonal variations are mainly due to climatic factors characterized by recurring droughts, an average temperature increase of 2°C [ 4 ], and growing water needs, mainly for irrigation. Irrigated cropping uses substantial amounts of water, often above the water regeneration potential. Innovative agricultural practices are needed to make efficient use of each available quantity. Seen this way, aquaculture, and especially fish farming, seems like a good opportunity. This is the fastest growing farming system, providing about 20% of animal protein for consumption [ 3 ]. That production system still has environmental-related and sustainability-related challenges [ 5 ]. In the country, domestic fish production remains relatively modest at 30,555 tons in 2021, compared to an annual demand of 193,160 tons. Given the current depletion of resources and the need to meet ever-increasing food demands, integrating aquaculture into irrigated cropping is a strategic option. The integrated system allows nutrient-rich AWE to be used as fertilizer for cropping, while irrigation water is used to maintain the aquaculture ponds. Water dilution by replacing source water with aquaculture wastewater by up to 25% allowed productivity [ 6 ], as usual in agro-aqua production. The successful agro-aqua integration depends on several key factors. Indeed, the quality of aquaculture water, which varies according to the Growing Stage of Fish (hereafter referred to GSF), directly influences its cropping value for irrigation. Moreover, stocking density, aquaculture inputs, and water flow management also have a major impact on the overall performance of the agro-aqua system. In the sudano-sahelian zone, where every hydric asset is important, understanding these interactions is essential for designing sustainable and profitable agro-aqua systems. This research aims to assess the impact of GSF on the cropping quality of aquaculture-emitted water, on the efficiency of aquaculture-emitted water’s agro-aqua use, as well as the agro-aqua requirements in a semi-sahel zone. Especially, the aim is to identify and quantify the aquaculture inputs used according to the GSF across Agro-Aqua Units (hereafter referred to as AAU). It also aims to determine the quantities of water used and lost in the integrated agro-aqua system. The relationship between the cropping quality of water, the water efficiency of use, and the agro-aqua requirements also deserves to be examined. The first assumption of the study is to establish whether the cropping water quality varies according to the GSF. Does agro-aqua integration optimize volumes of water used and lost in its various components? Does agro-aqua integration improve water requirements and efficiency in semi-sahel zone? The study then explored two main factors including GSF and AAU. 2 Materials and methods 2.1 Climatic features of agro-aqua units The study was undertaken in Fada N’Gourma, 12°03'N, 0°21'E, where the soils used for agro-aqua production are diverse due to the topography and water regime. Soils with little gravel erosion, i.e. 53.5%, and tropical ferruginous soils, i.e. 31.3%, are largely dominant. Vertisols can be identified in low-lying areas, eutrophic soils can be found along certain slopes, and hydromorphic soils are observed in depressions. This soil diversity determines the potential for agro-aqua production, but the low organic matter content of ferruginous soils requires the addition of fertilizing nutrients, which justifies the use of Aquaculture Water Effluents (AWE) as a natural soil improver. The surface water in Fada N'Gourma comes from two watersheds namely the Niger stream to the north and the Oti stream to the south. The city itself is flowed through by lake Fada N'Gourma. The rapid silting-up of these watersheds reduces their storage capacity and limits the availability of water at the end of the dry season. In terms of fisheries, watersheds like reservoirs, lakes offer modest potential, often exploited on a small-scale basis. Most of the fish consumed comes from other locations such as Kompienga. Aquifers, located in fractures in the bedrock and weathered layers, provide a complementary but limited supply, which requires careful and sustainable management. In Fada N'Gourma, cropping focused on vegetable and fruit production in lowlands and floodplains, constitutes the main livelihood, although hampered by declining soil fertility. 2.2 Setting up agro-aqua units An Agro-Aqua Units (AAU) is a unit composed of an aquaculture pond whose water is dedicated to the production of one or more downstream horticultural crops. Each AAU comprises an aquaculture component and a horticultural component. AAUs were considered as factors in the data collection process. A total of four criteria were considered in the selection of AAUs used for data collection: (i) the presence of an aquaculture pond containing farmed fish, (ii) the presence of one or more associated cropping downstream of the aquaculture pond, (iii) the use of water from aquaculture ponds for irrigation of crops, and (iv) the possibility of obtaining data on the different components of each AAU. The aquaculture production ponds are made of above-ground cement. The aquaculture ponds are circular in shape, with an average height of 120 cm and an average diameter of 185 cm. Each pond is equipped with two drainage systems: the outlet, used to drain wastewater, and the overflow, used to regulate the water level by allowing excess water to flow out. There is also a water intake for water renewal after draining. The species farmed in these ponds are catfish. Water renewal is ensured by 10-meter-long pipes, connected to each other, running from the water intake to the aquaculture pond. The water used comes from an underground source captured by a borehole. Downstream or around aquaculture ponds, various cropping plots are set up. These crops include vegetables, fruits, fodder plants, and trees. Plant species chosen for their ability to benefit from irrigation with pond-water include onions, tomatoes, cabbage, eggplant, papaya, and moringa (Fig. 1 ). 2.3 Data collection Data was collected in the field using data-sampling sheets, and was compiled by AAU. Data concerning the integrated agro-aqua system covered the type of agro-aqua integration implemented, the nature of cropping, the type of aquaculture practiced, the growing stage of fish (GSF), the areas allocated to each agro-aqua component of the integrated system, the aquaculture inputs used, etc. Data concerning agro-aqua water efficiency included the quantities of water used for agro-aqua production, the rate of reuse of aquaculture water for irrigation, the cropping quality of the water, the monitoring of agro-aqua water inflows and outflows, the water sources and losses, etc. Data on the diet of aquaculture species includes the nature of the feed, the feed quality, the feed composition, the feeding frequency, the amount of feed provided, etc. The data collection sheets were used to record data on water inflows and outflows, feeding of aquaculture species, agro-aqua water losses, and characteristics of the integrated agro-aqua system. A Global Positioning System (GPS) was used for local positioning and recording the geographical coordinates of the agro-aqua station, horticultural plots, aquaculture ponds, and water sources or intakes. Sterile plastic bottles were used to collect water samples. Aluminum foil was used to cover the water samples to limit photosynthesis. Laboratory equipment was used to analyze the quality of the water samples. A tape measure was used to measure water levels in aquaculture ponds, as well as to measure the dimensions of aquaculture ponds and production plots for plant species. A camera was used to take photographs. A stopwatch was used to quantify water volumes. A Systamec model C600 7-in-1 digital water tester was used to measure quality parameters in situ . The measured variables include hydrogen potential (pH), electrical conductivity (EC), dihydrogen (H 2 ), salinity, total dissolved solids (TDS), oxidation-reduction potential (ORP), water temperature, and specific gravity (SpG). An electronic scale was used to measure the amount of feed consumed by the aquaculture species. 2.4 Estimates of water inflows, outflows and reuse efficiency Daily monitoring of water inflow and outflow data was undertaken using specific data collection sheets. Based on these data, the quantities of water used per AAU were estimated accurately and systematically. Measurements were therefore made daily and during each type of water inflow and outflow operation in the AAU. First, water inflows, defined as the supply of freshwater to aquaculture ponds, and water outflows, i.e., water leaving aquaculture ponds for irrigation of horticultural cropping, were measured. A tape measure was used to calculate the water level in the aquaculture ponds. Before each water renewal, the initial water level was measured. Then, after the aquaculture ponds had been drained or purged, the remaining water level was measured. The difference between these two measurements gives the height of water removed from the aquaculture pond, used for irrigating cropping. Once the draining or purging was complete, the new water height, brought in by the renewal, was measured. The difference between the height after draining and this new height gave the height of water entering the aquaculture pond. Several fixed measurement points distributed evenly throughout the aquaculture pond made it possible to obtain an average measurement and avoid errors due to local water irregularities. Once the water levels were obtained, the volumes of water entering and leaving are determined using the diameter and water depth of the aquaculture ponds, which are all circular in shape. The volume of outgoing water, i.e., the volume of AWE, the outgoing water volume or irrigation water volume (V ReUsed ), used for crop irrigation, is calculated by multiplying the outgoing water depth by πR 2 , where R is the radius of the aquaculture pond. Similarly, the volume of water entering, which corresponds to the volume used for aquaculture production, denoted V Aqua , is determined by multiplying the height of the water entering by πR 2 . The water outflows (H Outflow ) and inflows (H Inflow ) were calculated as indicated in equations 1 and 2. The calculation of V ReUsed is shown in Eq. 3. For the calculation of the incoming water volume (V Aqua ), the formula is given in Eq. 4. Water Reuse Efficiency (WRE) corresponds to the proportion of water used for aquaculture production that is effectively reused for irrigating associated cropping AAU. The WRE (in %) was calculated using the formula in Eq. 5. H Outflow = H InitialHeight – H AfterEmptying (1) H Inflow = H NovelHeight – H AfterEmptying (2) V ReUsed = H Outflow × πR 2 (3) V Aqua = H Inflow × πR 2 (4) WRE = (V ReUsed /V Aqua ) x 100 (5) 2.5 Estimation of water performance indicators The estimation of net water requirements for production, as well as the calculation of water performance indicators such as the Water Reuse Index (WRI) and External Water Dependency (EWD), were based on climate data, cropping patterns, and water volumes available in AAUs. Daily agro-climatological data for the study area were extracted from the NASA POWER [ 7 ] package. Evapotranspiration (ET₀) was calculated using the standardized Penman-Monteith equation [ 8 , 9 ], based on daily climate data provided by the NASA POWER package for the study area. The climate parameters used include minimum and maximum air temperature, in °C, average relative humidity, %, wind speed, in m/s, and global radiation, in MJ/m²/day. The calculations were performed using R software [ 10 ] and RStudio [ 11 ]. The Penman-Monteith formula used is as in equations 6. The monthly ETo (ETo month ) was obtained by multiplying the daily value by the number of days in the month. ET ₀ = [(0,408 ∆(R n −G)) + (((γ(900/(T + 273)) (u 2 (e s − e a ))] / [∆ + γ(1 + 0,34u 2 )] (6) ETo is the reference evapotranspiration, i.e. mm/day. Rn is the net radiation, in MJ/m 2 /day. G is the ground heat flux, i.e. MJ/m 2 /day. T is the average air temperature, in °C, while u 2 is the wind speed at 2 m, in m/s, e s and e a are respectively the saturated vapor pressure and the actual pressure, i.e. kPa. ∆ is the slope of the vapor pressure curve, i.e. kPa/°C. And, γ is the psychrometric constant, i.e. kPa/°C. For each associated cropping component in the AAU, i.e. tomato, eggplant, papaya, banana, cabbage, okra, the reference values for the month of sowing, the length of the production cycle (days), and the average production coefficient (K c ) were used to estimate horticultural evapotranspiration (ET h ) and the Net Water Requirements (NWR) of the crops. The NWR was estimated using the Eq. 7. The horticultural production period was defined as the succession of months covering the entire cycle or part of the cycle. The effective precipitation (P e ) was estimated monthly using the simplified method, while the NWR (in mm/cycle) of the associated cropping were determined using Eq. 8. ET h = ET o × K c (7) ETh is the evapotranspiration of production (mm/day). ETo is the monthly reference evapotranspiration (mm/day). Kc is the average cropping coefficient of over the production cycle. NWR = ET h - P e (8) P e is the effective precipitation (mm/cycle). Water levels were converted into volumes overall AAU, in m 3 /day, and used to calculate the net water requirements overall AAU as stated in Eq. 9. NWR AAU = NWR × Area Irrigated overall AAU × 10 (9) Irrigated area overall AAU is defined in ha, when 10 refers to the conversion factor to m 3 /ha. The WRI estimated at AAU scale is the proportion of water requirements for cropping covered by AWE. The WRI is defined as the ratio between the average daily V ReUsed and the NWR within AAU. Of course, V ReUsed refers to the average volume of water reused in the form of AWE per AAU, in m 3 /day. WRI = (V ReUsed /NWR AAU ) × 100 (10) Further considerations about the Eq. 10 are (i) WRI ≥ 100% means that water coverage is total or surplus (potential surplus), (ii) 20% ≤ WRI < 100% means that the water coverage is partial, and (iii) WRI < 100, means that water coverage is very low. The EWD expresses the proportion of water requirements that must be met by external water supplies for cropping irrigation in AAU. EWD, in %, is expressed by Eq. 11. EWD = [(1 – WRI) x 100] (11) In terms of the coverage rate for fertilizers derived from AWE, a quantitative assessment of the contribution of these AWEs to the nutrient requirements of vegetable production was completed. The coverage rate for major elements, i.e. nitrogen, phosphorus, and potassium, was estimated based on a combination of nutrient concentration data including total nitrogen, orthophosphate, and potassium, V ReUsed , and specific fertilizer requirements including Nitrogen, Phosphorus, and Potassium for each of the associated cropping AAU. The nutrient concentrations in AWE, in mg/l, were converted into the mass of nutrients supplied daily to the AAU plots, while considering the volume of irrigation water used, in m³/day, and the irrigated area, in ha. The said variable in consider as the Daily Nutrient Supply (DNS). Daily Nutrient Supply (DNS) = (Ci x V ReUsed ) / S (12) DNS refers to inputs of nutrients N, P, K, in kg/ha/day. Ci is the concentration of nutrients N, P, K in AWE, in kg/m 3 . V ReUsed is the volume of AWE reused for irrigation of associated cropping (m 3 /day). S is the irrigated area, in ha. The cumulative input of each nutrient was calculated by multiplying the daily input supply by the duration of the production cycle (in days) in term of Total Nutrient Supply (TNS): TNS = DNS × Production Cycle Duration (13) TNS is the total nutrient supply during the entire production cycle (kg/ha/cycle). The duration is the one associated with irrigated cropping cycle (days). The Total Nutrient Coverage (TNC) rate for nutrients constituting the fertilizer requirements for nitrogen, phosphorus, and potassium in irrigated cropping was extracted and estimated from Reference Nutrients Needed (RNN) data (Table 1 ). The TNC rate was estimated as the ratio between the cumulative input from aquaculture irrigation and the total production requirement, according to the formula: Table 1 Agroclimatic parameters and water requirements of the main cropping across the UAA Tomato Cycle length (days) Mean cropping coefficient Monthly evapotranspiration (mm/cycle) Annual evapotranspiration (mm/year) Effective rainfall (mm/cycle) 120 0.95 1350.2 1282.7 169.2 Eggplant 130 0.95 799 759 420.8 Banana 365 1.1 2673.7 2941.1 1090 Cabbage 165 0.9 1821 1638.9 340 Papaya 180 1.2 1663 1995.6 340 Okra 110 0.68 703.8 478.6 295.8 AAU Agro-Aqua Unit TNC rate i = [(TNS i / RNN i ) × 100] (14) TNC rate is the coverage rate for nutrient i, i.e. N, P, and K, supplied by AWE, as %. RNN is the estimated actual requirement for nutrient i, N, P, and K, in kg/ha/cycle. 2.6 Sampling over AAUs The quantity of feed given to fish in AAUs was determined using an electronic scale and recorded daily on collection sheets each feeding time. In other words, before adding feed to each pond, the quantity was weighed accurately using the electronic scale. The data recorded included the amount of feed provided, i.e., the amount of feed given to the fish (g/day), the feeding frequency, the frequency of feed delivery (meals/day), and the surface density, i.e., the number of fish per unit area (m²) of pond. The GSF considered are fry, juvenile, and adult for Clarias gariepinus . Based on the aquaculture pond features, three sampling points were identified in the aquaculture component: a pond with a pond with Clarias gariepinus fry, Clarias gariepinus juveniles, and a pond with Clarias gariepinus adults. Three samples were taken at each sampling point, for a total of nine samples. The water samples were collected manually in plastic bottles and were dedicated for the determination of quality control data (QCD). The bottles were filled to the top and sealed to prevent gas exchange with the atmosphere during analyses. The bottles were then labeled and wrapped in aluminum foil. Certain water QCD, such as temperature, EC, pH, and dissolved oxygen (DO), were measured in situ during sampling using a HACH HQ 4300 field multi-parameter meter. The water samples were then transported in a cooler at approximately 4°C while awaiting the various measurements and titrations. Aside from pH, temperature, conductivity, and DO, which were measured in situ, the other water-related variables were determined by dosing and titrating samples collected on-site. These included total nitrogen, orthophosphate, potassium, total carbon, Suspended Dissolved Matters (SDM), salinity, TDS, and biological oxygen demand (BOD). BOD was measured using an incubator Oxitop at 20°C under dark conditions over five days. SDM was evaluated by filtration through a 1.5 µm glass microfiber filter. Nitrogen was measured after mineralization in a BUCHI K-436 mineralizer and after distillation in a BUCHI K-355 distiller, followed by titration with 0.04 mol/l hydrochloric acid. Total carbon was estimated from organic matter using a conversion factor of 2. Orthophosphate was measured by spectrophotometry using a DR 1900 spectrophotometer. Potassium was measured by flame photometry according to standard methods. TDS was determined by gravimetric method after drying at 103°C. All the animals used were obtain from one locally-established aqua-farm. We obtained informed consent from the owner to use the animals in the study. 2.7 Statistical analysis Statistical methods were performed using R [ 10 ] and RStudio [ 11 ]. Quantitative variables are expressed as mean ± standard deviation. The normality of distributions was tested using the Shapiro-Wilk test [ 12 ], while the homogeneity of variances was verified using Levene's test [ 13 ]. Given the non-normality of certain variables and the ordinal nature of certain factors such as GSF and feed grain size, comparisons between groups were performed using the nonparametric Kruskal-Wallis test [ 14 ]. In a simpler way, the Kruskal-Wallis test, also known as single factor ANOVA on ranks, was used as a nonparametric method to test variability according to the GSF. When significant differences were detected (p < 0.05), a Dunn post-hoc test with Bonferroni correction was used to identify statistically distinct groups. The relationships between feed grain size, QCD of water, nutrient loads of N, P, and K, as well as indicators of WRE and EWD, were explored by calculating Spearman's correlation coefficient, which is better suited to non-normal distributions. A Principal Component Analysis (PCA) was also performed to reduce the dimensionality of the data and visualize the underlying structures linking the variables. This PCA was run on the centered and reduced values of the water QCD and aquaculture subcomponent variables, using the FactoMineR package. The PCA was graphically plotted using factoextra to highlight correlations between variables and groupings between ponds of AAUs. R packages such as FSA for Dunn's test, psych and Hmisc for correlations, ggplot2 for visualizations, as well as FactoMineR and factoextra for PCA were employed. All statistical tests were interpreted at the respective significance levels of α = 0.05, β = 0.01, and γ = 0.001. 3 Results 3.1 Farm-scale description of AAUs The integrated agro-aqua system investigated is structured into AAUs consisting of one to two circular aquaculture ponds, located above ground and intended for the production of Clarias gariepinus . The pond diameters vary from one AAU to another, reflecting adaptation to spatial constraints and production capacities. Each pond is equipped with a complete piping system including a drainage device, an overflow to regulate the water level during rainfall or excessive inflows, and a purge outlet for periodic sediment removal. The water from the ponds, rich in organic nutrients and minerals, i.e., aquaculture effluent, is systematically reused for irrigation without undergoing any prior treatment. The agro-aqua coupling is indirect because the effluents do not flow continuously to the cropping production plots, but are discharged by gravity when water is needed, from the drainage system. The water supply for the ponds comes mainly from boreholes distributed throughout the agro-aqua site. These boreholes are connected to polytank reservoirs, powered by solar pumps, which provide buffer storage before transfer to the ponds via a network of mobile pipes. Cropping plots vary in size and are used to grow vegetables such as tomatoes, i.e. Solanum lycopersicum , okra, i.e. Abelmoschus esculentus , cabbage, i.e. Brassica oleracea , and eggplant, i.e. Solanum melongena . The production consists of bananas, i.e. Musa spp., and papayas, i.e. Carica papaya . Furthermore, various agroforestry species are planted there, including the baobab ( Adansonia digitata ), pigeon pea, i.e. Cajanus cajan , custard apple, i.e. Annona squamosa , moringa, i.e. Moringa oleifera , cashew, i.e. Anacardium occidentale , shea, i.e. Vitellaria paradoxa , fig, i.e. Ficus spp., flamboyant, i.e. Delonix regia , acacia, i.e. Acacia nilotica , and shea, i.e. Vitellaria paradoxa . Irrigation is provided via surface gravity, using simple equipment such as buckets, bowls, and flexible hoses. Across all AAUs, two cropping cycles are practiced per year, ensuring continuous plant production throughout the year. In the aquaculture component, production exclusively involves the farming of Clarias gariepinus , fed with commercial complete pellets. No hormonal or medicinal treatments are used in the AAUs. AAU monitoring focuses on monitoring water temperature and turbidity. In the event of sporadic fish mortality, a sodium chloride, i.e. NaCl, solution is applied as a prophylactic measure. In addition to taking advantage of the organic quality of agro-aqua water, and organic fertilizers, i.e., compost, manure are used across all AAUs. In addition, several sustainable agricultural practices such as mulching, crop rotation and crop succession, cover cropping, composting, and agroforestry plantations were identified across all AAUs. 3.2 AAUs aquaculture component description Aquaculture practices in terms of daily feed intake, feeding frequency, pellet size breakdown, and pellet nutritional composition were presented according to GSF, i.e., fry, juveniles, and adults. Many aquaculture practices were considered in AAUs according to Clarias gariepinus specimens GSF. At the fry stage of development, fish were fed 378.9 ± 195.5 g of feed per day, with a frequency of 1.19 ± 0.54 meals/day. At this stage, the average surface density was highest, reaching 1064 ± 705 specimens/m 2 . At the juvenile stage, the average daily feed intake was the highest of all three stages, at 714 ± 397.4 g/day, while feeding frequency remained relatively stable at 1.16 ± 0.47 times/day. The surface density decreases significantly, with an average of 220 ± 116 individuals/m². In adult fish, the amount of feed provided is reduced to 486.8 ± 340.8 g/day, with a frequency of 1.12 ± 0.52 meals/day. At this stage, aquaculture density was also the lowest, with an average of 64 ± 13 specimens/m 2 . Figure 2 A and Fig. 2 E show the daily amounts of feed allocated to the ponds of the five AAUs, based on the developmental stage of the Clarias gariepinus specimens. Adult stage ponds, particularly AAU 1 and AAU 3, receive an average feed amount of between 438 and 585 g/day, with a maximum observed amount of 2,235 g/day in AAU 1. The adult stage showed significant variability in feed intake. The juvenile stage AAU 2 showed the highest average intake, estimated at 714 g/day, and a peak of 1630 g/day. This value reflects a phase of active growth, characterized by significant nutritional needs. For fry present in AAU 4 and AAU 5, the daily quantities distributed are lower, with averages between 338 and 421 g/day, and maximums of up to 824 g/day, particularly in AAU 5. The minimum values recorded for all AAUs reached 0 g/day, indicating that no feed was provided some days. Figure 2 B and Fig. 2 F illustrate the daily feeding frequency observed in the ponds of the different AAU, depending on Clarias gariepinus GSF. Frequencies are expressed in terms of the number of meals per day (meals/day). The average feeding frequency was generally stable across the AAUs, with values ranging from 1.12 meals/day for adults, i.e. observed in AAU 3, to 1.20 meals/day for fry observed in AAU 4. The maximum frequency observed is 2 meals/day in all AAUs. However, minimum frequencies of 0 meals/day were recorded in several AAUs, indicating the absence of feeding certain days. Figure 2 C illustrates the distribution of feed grain sizes (mm) used in the different ponds of AAUs, depending on Clarias gariepinus GSF. Fine grain sizes, i.e. 1 mm and 2 mm, are mainly used for the fry stage, with peak usage at 2 mm, with 65 fish specimens. The juvenile stage also uses 2 mm pellets, but in smaller proportions, i.e. n = 22, and remains poorly represented for other sizes, i.e., 1 mm, 3 mm, and 4.5 mm. Among adult fish, the 4.5 mm size fraction is by far the most dominant, with a number of specimens exceeding 160, making it the most common size fraction across all stages. Finally, the 6 mm particle size is exclusively reserved for adults, but is used only marginally. The nutritional composition of the feed pellets (Fig. 2 D) used in the AAUs is presented according to their particle size, as mm. These floating pellets, from the Raanan Fish Feed®, are formulated from various ingredients such as fish meal, poultry by-products, oilseeds, cereals, as well as vitamin and mineral supplements. The protein content decreases as the particle size increases. In fact, 1 mm pellets, i.e. intended for fry, contain up to 55% protein, while 4.5 mm and 6 mm pellets, used for more advanced stages, i.e., juvenile and adult, contain only 38% to 42%. Lipids range from 10% to 13%, with the highest values observed in smaller granules. Fiber, i.e. 2%, and phosphorus, i.e. 1.3%, remain constant regardless of pellet diameter. However, ash content shows a slight upward trend with pellet size, rising from 9% to 10%. Diets also contain vitamins A and C. 3.3 Loading capacities and degrees of connection within AAUs The surface density as well as the volume density are presented by Clarias gariepinus specimens GSF. Load capacities and their degree of connection with feeding practices were presented, too. Figure 3 shows the average surface density of Clarias gariepinus fish, i.e. specimens/m², in relation to their AAUs in AAU aquaculture ponds. Variations were observed between GSF. The juvenile stage has the highest density, with an average of 1,064 ± 705 specimens/m² and a maximum of 2,387 specimens/m². At the juvenile stage, density is intermediate, with an average of 220 ± 116 specimens/m² and a maximum of 503 specimens/m². A reduction in the stocking density of the ponds was observed as the fish grew. Finally, the adult stage recorded the lowest density, with an average of 64 specimens/m² and a maximum of 84 specimens/m². The Fig. 3 B illustrates the density of fish (individuals/m³) in Clarias gariepinus aquaculture ponds in different AAUs according to the GSF. A logical trend-based increase in volumetric densities was observed depending on GSF. In other words, the earlier age fish are, the higher their density tends to be. In the adult stage, the lowest densities are observed, with averages of 62.52 specimens/m³ for AAU 1 and 48.63 specimens/m³ for AAU 3. Aquaculture practices are much less frequent for fish at the stage of sexual maturity. The juvenile stage, in AAU 2, has a higher density, with an average of 183.66 individuals/m³ and a maximum of 418.85 individuals/m³. The highest densities are recorded among fry, in both AAU 4 and AAU 5. AAU 4 recorded an average of 979.39 specimens/m³ with a maximum of 1,352.82 specimens/m³, while AAU 5 recorded an average of 794.53 specimens/m³ with a peak of 1,989.44 specimens/m³. The amounts of affinity between the three key variables of aquaculture practices, namely the amount of feed distributed per day, as in g/day, feeding frequency, i.e. in meals/day, and stocking density, i.e. in individuals/m², are shown in Table 2 . A weak but positive correlation was observed between the amount of feed distributed and feeding frequency, i.e. ρ = 0.38, suggesting that ponds where fish are fed more often also receive larger overall amounts of feed. However, no correlation was observed between surface density and the two feed variables, with coefficients close to zero, i.e. ρ ≈ 0. This showed that the amount of feed and the frequency of meals are not adjusted to the stocking density, which could reflect a lack of a feeding strategy proportional to the number of fish found in AAU aquaculture ponds. Table 2 Spearman correlations (ρ) between stocking density in aquaculture ponds and feeding practices across AAUs Fish feed quantity (g/day) Fish feed quantity (g/day) Feeding frequency (meals/day) Areal density (specimens/m²) 1 Feeding frequency (meals/day) 0.38 1 Areal density (specimens/m²) -0.04 0.06 1 3.4 Influence of GSF on aquaculture practices The Table 3 presents three key variables of the integrated agro-aquaculture system, i.e., the amount of feed distributed per day, i.e. in g/day, the feeding frequency, i.e. in meals/day, and the stocking density, i.e. in specimens/m², according to Clarias gariepinus GSF, i.e., fry, juvenile, and adult. The amount of feed distributed varied significantly between GSFs, i.e. p = 1.56 × 10⁻⁷, confirming that nutritional requirements change markedly as fish grow. On average, juveniles received the highest amounts of feed, followed by adults, while fry received the lowest rations. Moreover, the surface density of fish stocking shows a highly significant difference depending on the stage, i.e. p < 2.2 × 10⁻¹⁶, illustrating a wide variation in stocking practices depending on the size and the fish GSF. The fry are raised at very high densities, while the adults occupy less densely populated ponds. However, feeding frequency differs only marginally according to GSF, i.e. p ≈ 0.05, which may indicate a tendency to apply a quasi-uniform frequency across all AAUs, regardless of GSF, despite theoretical variations in feeding demands. Table 3 Variation in aquaculture practices according to Clarias gariepinus specimens GSF, based on the Kruskal-Wallis test Quantity of feed (g/day) ꭓ² ddl p -value Statistical relevance 31343 2 1.56 × 10⁻⁷ *** p -value < 0.001 Feeding frequency (meals/day) 5988 2 0.0501 * p -value ≈ 0.05 Areal density (specimens/m²) 268 2 < 2.2 × 10⁻¹⁶ *** p -value < 0.001 ꭓ² Khi square test overall AAUs ddl number of degrees of freedom 3.5 Drain frequency, agro-aqua water management, and efficiency of water reuse in AAUs The effectiveness of reusing aquaculture effluent for irrigation was presented by AAU and based on GSF, i.e., fry, juveniles, and adults. The variation in the volumes of water used and reused was also presented as a function of GSF, fry, juveniles, and adults. The relationship between V Aqua and V ReUsed was calculated according to AAUs. The average time intervals between two complete drainings of aquaculture ponds varied moderately from one AAU to another. Aquaculture ponds are emptied completely every 3 to 5 days on average, at least during the experimental period (Fig. 4 C). AAU 1 indicated an average interval of 3 days between two drainages. AAU 2, AAU 3, and AAU 5 reported an average emptying interval of 4 days. AAU 4 reported the longest interval, with an average of 5 days between two drainings. No partial draining or purging is undertaken with regard to aquaculture ponds, i.e., only complete draining of these ponds was undertaken. Figure 4 A illustrates the average daily water volumes (m³/day) measured in the five AAUs, distinguishing between the volume used for aquaculture production and the volume reused for irrigation from effluents from aquaculture ponds. The calculated reuse efficiency, as %, therefore corresponds to the ratio of the volume of water reused from aquaculture pond effluent to the volume of water used for aquaculture production. In AAU 1, dedicated to eggplant and okra production, the average water volume used is 0.85 m³/day, while 0.84 m³/day is reused for irrigation, representing an efficiency rate of 98.8%. AAU 2, with no irrigated production, has an aquaculture volume of 0.69 m³/day and a reused volume of 0.67 m³/day, representing 97.1% efficiency. In AAU 3, comprising two adult-keeping ponds irrigating banana trees, 0.58 m³/day are pumped into the aquaculture component, compared to 0.53 m³/day reused for cropping irrigation, representing an efficiency of 91.4%. AAU 4, which grows tomatoes, cabbage, and papaya, uses 0.53 m³/day for l'aquaculture and reuses 0.48 m³/day, yielding an efficiency of 90.6%. Finally, the AAU 5 cropping papaya recorded a water usage volume of 0.86 m³/day, with a reused water volume slightly above 0.89 m³/day, resulting in an apparent efficiency of 103.5%. Figure 4 B shows the average daily volumes of water, as m³/day, used for aquaculture and reused for horticultural irrigation, relative to Clarias gariepinus GSF, namely fry, juveniles, and adults. The water used for irrigation in the cropping component corresponds to the water delivered to the aquaculture ponds within each AAU. The calculated reuse efficiency, as %, therefore corresponds to the ratio of the volume of water reused (from effluent from aquaculture ponds) to the volume of water used for aquaculture production. For the fry GSF, the volume of water used for aquaculture production amounted to 0.70 m³/day, with an average reuse of 0.69 m³/day for cropping irrigation, for an efficiency of 98.6%. At the juvenile GSF, the volume used for aquaculture production was 0.69 m³/day, with 0.67 m³/day reused for irrigation of associated crops, representing a reuse efficiency of 97.1%. Finally, at the adult GSF, the volume pumped into aquaculture ponds is 0.67 m³/day, compared to 0.63 m³/day reused, representing a reuse rate of 94%. Table 4 illustrates the variations in water volumes used for aquaculture production and those reused from aquaculture effluents as irrigation water, across the five AAUs and across all three GSF. The volumes of water used in the aquaculture component did not vary significantly between AAUs, i.e. p -value = 0.216. The volumes of water reused for cropping irrigation did not vary significantly among AAUs, i.e. p -value = 0.113, as well. The same trend was observed with regard to the volumes of water used in aquaculture and the volumes of water reused from aquaculture effluents for cropping irrigation, according to the three Clarias gariepinus GSF, namely fry, juveniles, and adults. In fact, these two data points related to water volume capacity didn't change much between the GSF, whether it was the amount of water used for aquaculture production, i.e. p -value = 0.579 > 0.05, or for the volumes of water reused from aquaculture effluents and directed towards cropping production, i.e. p -value = 0.519 > 0.05. Table 4 Change in water volumes used for aquaculture production and water volumes reused from aquaculture effluent for irrigating cropping in AAUs V Aqua V ReUsed AAU GSF AAU GSF χ² 5.78 1094647 7.47 130966 ddl 4 2 4 2 p -value 0.216 0.578 0.113 0.519 Statistical relevance NS NS NS NS V Aqua Volume of water used for aquaculture production (m³/day) V ReUsed Volume of aquaculture effluent reused for crop irrigation (m 3 /day) AAU Agro-Aqua Unit GSF Growing Stage of Fish NS Non Significant The relationship between the daily volumes of water used for aquaculture production and the volumes reused for cropping irrigation, according to the Clarias gariepinus GSF in the different AAU, is shown in Fig. 5 . This relationship is linear and strong, i.e. τ = 0.9054111, p -value < 2.20e-16, and the volume of water used for aquaculture production increases with the volume of water used from aquaculture effluents and reused for cropping irrigation. Thus, the greater the volume brought into aquaculture ponds for aquaculture production, the greater the volume of water reused for associated cropping irrigation. In other words, the correlation is positive and very strong, with τ = 0.91. This trend is clearly visible in the scatter plot, according to the Clarias gariepinus GSF, which are fry, juveniles, and adults (Fig. 5 ). 3.6 Cropping component description and water requirements in AAUs irrigated production Figure 6 shows the irrigated areas (in m²) and the types of cropping associated with each AAU. These areas correspond to the areas actually irrigated using aquaculture effluent. Significant variability in cultivated areas and diversity in the cropping type choice are observed between AAUs. AAU 5, entirely devoted to papaya production, has the largest irrigated area in the agro-aqua system, covering 1,250 m², an indicator of the focus on long-term production requiring high water demand. Although AAU 2 had a surface area of 1,228 m², no active cropping took place there. The AAU 4, which combines three vegetable species, e.g., tomatoes, cabbage, and papaya, covers a large area of 1,135 m², making one of the most diverse in terms of plant species and offering high potential for enhancement. AAU 1, dedicated to eggplant and okra production, covers 900 m², while AAU 3, dedicated exclusively to bananas, has the smallest area at 400 m². The estimated coverage of production water supply needs through recycling aquaculture effluent is shown in Table 5 . This concerns the average daily volumes of water reused from aquaculture ponds, the water requirements for cropping in each AAU, and two agro-related water performance indicators: the WRI and the EWD. WRI refers to the proportion of water needed to meet production water requirements, which is effectively supplied by irrigation using aquaculture effluent. EWD is the proportion of water supplied from a source other than aquaculture effluent, i.e. 1 – WRI. Insufficient coverage of irrigation needs was observed across all AAUs, despite good reuse of aquaculture effluent water. In fact, WRI values varied between 1.6% and 26.17%. AAU 4, consisting of tomato, cabbage, and papaya production, has a high requirement of 29.91 m³/day for a reused volume of only 0.48 m³/day, resulting in an extremely low WRI of 1.62%, with a critical EWD of 0.98. AAU 5, under papaya production, has a WRI of 7.77% despite a relatively high reuse volume of 0.89 m³/day. Conversely, AAU 1, eggplant and okra production, and AAU 3, banana production, recorded the best WRIs in the system, at 21.85% and 26.17% respectively, although still well below full water self-sufficiency. None of the AAUs are therefore able to fully meet the water requirements of production using recycled water volumes as a sole source. Dependence on an external water source, namely borehole water, remains necessary and varies from 0.74 to 0.98 depending on the units. Table 5 Average volumes of water reused per day for irrigation, estimated daily requirements for vegetable production, water requirement coverage ratio, external water dependency, and associated cropping pattern for each AAU AAU 1 Associated irrigated cropping Volume of aquaculture effluent reused for crop irrigation (m 3 /day) Net daily water requirements for production (m 3 /day) Coverage of water needs (%) Coverage status External water dependency Cropping sustainability status Eggplant and Okra 0.84 3.84 21.85 Low coverage 0.78 Moderate dependence AAU 2* - 0.67 - - - - - AAU 3 Banana 0.53 2.03 26.17 Low coverage 0.74 Moderate dependence AAU 4 Tomato, Cabbage, and Papaya 0.48 29.91 1.62 Very low coverage 0.98 Critical dependence AAU 5 Papaya 0.89 11.5 7.77 Very low coverage 0.92 Critical dependence *No production is associated with AAU 2 AAU Agro-Aqua Unit The Water Reuse Index (WRI) represents the proportion of water requirements covered by the reuse of aquaculture effluents; External water dependency represents the remaining portion of water requirements that must be met by external sources, including well water Coverage status and dependency level are defined according to established thresholds: low or very low coverage, i.e., < 30%; critical dependence, i.e., ≥ 0.9 3.7 Emissions and water quality in AAUs The daily variation in the water quality of aquaculture ponds is shown in Fig. 7 . The effect of GSF on water quality was assessed, as well. The degree of correlation between the water quality variables was also presented on the basis of a correlation matrix plot (Fig. 8 ). Daily measurements were used to assess the characteristics of the water in the aquaculture ponds in each of the AAUs. The parameters measured include pH, temperature, EC, TDS, salinity, water SpG, ORP, and H ₂ concentration. These parameters indicated relatively low variability between AAUs, reflecting overall homogeneity in water quality in the ponds during the observation period. The pH varied very little, between 6.44 and 6.48, indicating slightly acidic water but still close to neutral, which is optimal for the production of Clarias gariepinus . The water temperature remained stable at around 24°C, typical of a tropical environment and favorable for fish growth. EC ranges from 594 to 652 µS/cm, reflecting moderate mineralization of the aquatic environment in AAUs. TDS were concentrated between 302 and 328 ppm, reflecting salinity values ranging from 303 to 328 ppm. ORP is negative in all ponds, with values ranging from − 84 to -63 mV, indicating a reducing environment typical of a system rich in organic matter. Finally, concentration in molecular hydrogen (H₂) was relatively stable between the ponds, with values ranging from 1396 to 1421 ppm. The main water quality-related parameters monitored in the aquaculture ponds of the AAUs, as a function of Clarias gariepinus GSF, i.e., fry, juveniles, and adults, are presented in Table 6 , as mean ± standard deviation. Some parameters varied depending on Clarias gariepinus GSF. In fact, the water temperature varied from 21.03°C for fry to 25.03°C for juveniles, with an inter-mean value of 23.2°C for adults. The pH remains generally neutral to slightly basic, ranging from 7.23 for fry to 7.49 for adults. EC remains relatively stable across the three GSF factors, with values ranging from 668 to 686 µS/cm. Similarly, TDS varied very minimally between stages, i.e., 658.67 mg/l in adults, 683 mg/l in fry, and 678.33 mg/l in juveniles. However, the concentration of DO remained very low in all cases, between 0.19 and 0.31 mg/l. Suspended solids (SS) clearly increase with GSF. In fact, MES levels are 37.17 mg/l in adults, 61.67 mg/l in fry, and reach a maximum of 95.5 mg/l in juveniles. Total carbon and BOD were highest in fry, with values of 7.98 mg/l and 130 mg/l, respectively. Total nitrogen concentrations remain stable between the fry and adult GSF, but peak in juveniles at 28.67 mg/l. Orthophosphate was also higher in juveniles, at 32.2 mg/l, compared to approximately 24.5 mg/l in both fry and adults. Potassium (K) was highest in fry, at 13.25 mg/l, and lowest in juveniles, at 6.8 mg/l. Table 6 Aquaculture effluent water quality according to Clarias gariepinus GSF Temperature (°C) Inf..Sup Fry Juvenile Adult ANOVA 20.9 ... 26.1 21.03a ± 0.12 25.03a ± 0.95 23.2a ± 0.52 NS Hydrogen Potential (pH) 7.22 ... 7.55 7.27a ± 0.05 7.23a ± 0.01 7.49a ± 0.06 NS Electrical Conductivity (EC in µS/cm) 664 ... 690 685.67a ± 5.86 676a ± 2 668a ± 3.61 NS Dissolved Oxygen (DO in mg/l) 0.19 ... 0.38 0.31a ± 0.1 0.31a ± 0.03 0.21a ± 0.03 NS Total Dissolved Solids (TDS in mg/l) 657 ... 687 683a ± 4 678.33a ± 0.58 658.67a ± 2.08 NS Suspended Dissolved Matters (SDM in mg/l) 25 ... 107 61.67a ± 2.02 95.5a ± 9.96 37.17a ± 11.36 NS Total Carbon (mg/l) 5.1 ... 8.13 7.98a ± 0.24 5.3a ± 0.2 5.6a ± 0.45 NS Total Nitrogen (mg/l) 24 ... 29 24.67a ± 0.58 28.67a ± 0.58 25a ± 1 NS Orthophosphate (mg/l) 24.2 ... 32.7 24.73a ± 0.46 32.2a ± 0.46 24.53a ± 0.32 NS Biological Oxygen Demand (BOD in mg/l) 70 ... 130 130a ± 0 73.33a ± 5.77 113.33a ± 5.77 NS Potassium (K in mg/l) 6.7 ... 13.84 13.25a ± 0.61 6.8a ± 0.11 9.17a ± 0.41 NS ANOVA Single factor analysis of variance Usual units are shown in brackets NS Non Significant The quality of the water effusing from aquaculture ponds, as measured by parameters such as BOD, K, SDM, TDS, EC, and temperature, varied significantly depending on Clarias gariepinus GSF. The quality derived from pH, total nitrogen, and orthophosphate varied moderately significantly depending on Clarias gariepinus GSF. In addition, three other parameters showed a relativity in statistical significance, with 0.05 < p < 0.06. In turn, total carbon, i.e. p = 0.0608 and OD, i.e. p = 0.1481, significantly differed between GSF. The fry and juvenile GSF showed the most significant differences, particularly in terms of BOD, K, salinity, and mineralization, i.e., TDS, EC. The Spearman correlation matrix between the various water quality-related parameters measured in the aquaculture ponds of the agro-aqua system revealed several insignificant degrees of correlation (Fig. 8 ). EC was positively correlated, i.e. ρ = 0.908, with TDS in agro-aqua waters. By the same logic, orthophosphate was also positively correlated, i.e.ρ = 0.876, with total nitrogen. The same trend was observed between suspended solids (SS) and orthophosphate, i.e. ρ = 0.777, between BOD and potassium, i.e. ρ = 0.923, and between total nitrogen and SS, ρ = 0.605. However, negative correlations were observed between temperature and BOD, i.e. ρ = -0.944, and between temperature and potassium content, i.e. ρ = -0.916. pH was also negatively correlated with DO, i.e. ρ = -0.789, and negatively correlated with EC (ρ = -0.745). Total carbon was negatively correlated, i.e. ρ = -0.590, with total nitrogen. Finally, BOD was also correlated, i.e. ρ = -0.693, with total nitrogen. Temperatures ranged from 20.9 to 26.1°C, with an average of 23.09°C, well below the critical level of 35°C set for cropping irrigation. The pH, between 7.22 and 7.55, indicates neutral to slightly alkaline water, in line with accepted agro-aqua standards of between 6.5 and 8.4. The average EC was 676.56 µS/cm, very close to the upper limit of 700 µS/cm. Also, TDS concentration averaged 673 mg/l, well within the tolerable range for irrigation, which is 450 to 2000 mg/l. However, many other parameters indicated high concentrations, which could affect the agro-aqua quality of the water. DO value was very low, with an average of 0.28 mg/l. Suspended solids (SS), with an average of 64.78 mg/l, slightly outstripped the standard of 50 mg/l. Total carbon is measured at 6.29 mg/l, with no specific standard references, but indicating a significant presence of organic matter. Total nitrogen reached an average of 26.11 mg/l, well above the recommended standard of 10 to 15 mg/l. Orthophosphate, with an average value of 27.16 mg/l, greatly exceeded the threshold of 5 mg/l set for irrigation. BOD, a key indicator of biodegradable organic load, has an average value of 105.56 mg/l, which is more than three times the permitted limit, i.e. < 30 mg/l. Meanwhile, potassium averages 9.74 mg/l, close to the upper limit of 10 mg/l. 3.8 Aquaculture effluent structuration at different GSF in irrigated AAUs The water quality of aquaculture effluent intended for fertilizing cropping in AAU was structured depending on Clarias gariepinus GSF in aquaculture ponds (Fig. 9 A). The structure was presented according to two main factorial dimensions. These two dimensions alone explained 92.8% of the total variance, with 52.9% for the first factorial component and 39.9% for the second factorial component. These two factor dimensions were therefore selected for interpretation. Dimension 1 is strongly correlated with parameters related to organic and mineral load, including BOD, i.e. -0.98, potassium, i.e. -0.88, total nitrogen, i.e. +0.94, orthophosphates, i.e. +0.97, suspended solids (+ 0.78), and temperature, i.e. +0.91. Dimension 1 thus made it possible to compare organically enriched waters with less enriched waters. Dimension 2 is mainly structured by EC, i.e. +0.96, TDS, i.e. +0.98, DO, i.e. +0.82, and total carbon, i.e. +0.60. Conversely, pH is negatively correlated with this dimension, i.e. -0.89. This is indication that dimension 2 mainly reflects the mineral properties and redox state of the water. Clear discrimination was observed on both factor dimensions, i.e. Dimension 1 = 52.9% × Dimension 2 = 39.9%, between aquaculture ponds according to the GSF. The ponds containing adult specimens are projected in the lower right quarter, strongly associated with temperature, total nitrogen, and orthophosphate. The ponds containing fry GSF specimens are located in the upper left part of the plan, linked to BOD, potassium, and total carbon. The ponds containing juvenile specimens occupy the lower left section, mainly associated with lower pH and moderate DO and SDM values. The representation of specimens on the two factor dimensions, i.e. Dimension 1 × Dimension 2, reveals a clear distinction between ponds based on the GSF. The juvenile ponds marked with pink dots are grouped in the lower right-hand corner of the graph, associated with high temperatures, orthophosphate, and total nitrogen values, reflecting increased mineral inputs and more pronounced biological activity. The fry ponds marked with green triangles are located in the upper left quarter, associated with high concentrations of BOD, total carbon, and potassium, indicating a high organic load. Adult ponds marked in blue squares are projected in the lower left quadrant, associated with lower pH levels and a more moderate influence of other variables, reflecting relative fertilizing stability. The fertilizing potential of aquaculture waters was assessed by comparing nitrogen (N), phosphorus (P), and potassium (K) inputs, expressed as a percentage of actual cropping requirements, according to the GSF, i.e., adult, juvenile, fry (Fig. 9 ). Effluents from adult fish ponds provide high to very high phosphorus coverage for all croppings, > 60% for tomatoes, eggplants, bananas, and cabbage. Nitrogen is particularly well covered for bananas, i.e. 149.52%, but remains moderate for other croppings, ranging from 30 to 44% for tomatoes, okra, and cabbage. Potassium remains the least covered factor, with levels ranging from 4.38% for papaya to 16.22% for tomatoes. At the juvenile GSF, fertilizer coverage rates, e.g., N, P, and K generally decrease. Given that the juvenile GSF in AAU 2 did not contain irrigated cropping by aquaculture effluent, the coverage rate was estimated on the basis that all cropping was present in the other AAUs. In these AAUs, nitrogen was still best supplied to bananas, i.e. 70.51%, followed by tomatoes, i.e. 20.86%. Phosphorus values are moderate for eggplant, i.e. 45.70%, and tomato, i.e. 35.15%, and low for papaya and okra, i.e.< 10%. Potassium did not exceed 5% for the majority of AAU cropping. Effluents from ponds containing fry have similar nutrient input rates to those in the juvenile GSF, but with slightly higher potassium values. Bananas remain the cropping with the highest nutrient requirements, requiring 79.42% nitrogen, 40.06% phosphorus, and 10.73% potassium. Tomatoes and cabbage also benefited from significant nitrogen and phosphorus coverage (Fig. 9 B, 9 C, 9 D). 4 Discussion The present study has demonstrated significant variability in the aquaculture production parameters of Clarias gariepinus depending on GSF. The fry GSF is characterized by a very high production density of 1,064 specimens/m 2 on average, with a maximum of 2,387 specimens/m 2 , compared to 220 specimens/m 2 for juveniles and 64 specimens/m 2 for adults. This high concentration, often associated with an intensive fry rearing phase, aims to maximize the number of fish produced in a smaller area. However, such a strategy increases the organic load in aquaculture ponds, even when daily rations remain moderate, i.e., approximately 370 g/day. Cumulative waste consisting of fish excreta and uneaten feed can quickly deteriorate aquaculture water quality if drainage and purging operations are not carried out frequently enough. Juveniles receive the highest amounts of feed, e.g., 714 g/day, reflecting a phase of rapid growth and intense metabolism. Adjusting feed intake at this stage is crucial for optimizing feed conversion ratio. But adults get more stable feed, e.g., about 560 g/day, at a lower density, which shows they are in the finishing GSF phase where their metabolic needs are lower. However, observing days without feeding, i.e., with 0 g/day, reveals a lack of coordination or temporary unavailability of feed, which can adversely affect the regularity of aquaculture production performance. In terms of grain size, feed pellet sizes generally comply with recommendations of 1 to 2 mm for fry GSF, 2 to 3 mm for juveniles GSF, and 4.5 to 6 mm for adults GSF. However, discrepancies were noted, particularly with regard to the use of 4.5 mm pellets for fry GSF and the low presence of 6 mm pellets for adults GSF. An unsuitable size can lead to feed losses and increase organic pollution. The effectiveness of feeding depends on the match between grain size and the size of the mouth cavity of fish. These discrepancies are thought to be due to limited availability of feed on the local market, requiring fish farmers to use whatever formats are available, even if they are unsuitable. The fish feed pellets used have a rich composition, with 42% to 55% protein, 10 to 13% lipids, and approximately 1.3% phosphorus, which corresponds to the nutritional requirements of Clarias gariepinus at different GSF. This nutritional profile supports GSF, but requires careful management to avoid excessive nitrogen and phosphorus runoff. Unabsorbed nutrients are mainly excreted in dissolved and particulate form, altering water quality. Feed composition is a strategic lever for managing effluent quality. Furthermore, analyzing correlations between fish density and the amount of feed distributed (ρ ≈ 0) suggests a lack of rational adjustment, which could lead to either underfeeding or overfeeding. These imbalances compromise both GSF and water quality. Integrated management of density, feeding frequency, feed distribution, and rations is therefore essential for optimizing aquaculture and cropping performance. Pond emptying frequency varies depending on AAU, with an average interval of 4 days. AAU 1 has a short purge frequency, i.e., 3 days, while AAU 4 is drained every 5 days. These differences reflect varying practices depending on organic loads and logistical constraints. Regular replacement is essential to limit waste accumulation. However, emptying the pond too frequently can be counterproductive. Aquaculture ponds are completely drained, contrary to recommendations that advocate partial drainage to limit stress on the fish and improve water quality. Exceptionally high aquaculture water reuse efficiency was observed, ranging from 90.6% to 98.8% with a peak of 103.5% in AAU 5. This efficiency is measured by the proportion of water volumes pumped for aquaculture production that are effectively reused for irrigation. The absence of statistically significant variation between volumes used and reused, whether between GSFs or between AAUs, reflects relatively consistent water management in the system being studied. These high levels of efficiency illustrate effective coordination between aquaculture and cropping, a criterion often cited as the basis for successful integrated systems. Careful synchronization of agricultural and aquaculture cycles made it possible to optimize water regulation. The apparent efficiency of over 100% observed in AAU 5 could be explained by external water inputs or unplanned withdrawals from aquaculture ponds, which are not included in the monitoring system. This anomaly highlights the importance of rigorous monitoring of water balance in the integrated system. Furthermore, a slight decrease in reuse efficiency is observed as the fish grow. In fact, it drops from 98.6% in fry to 97.1% in juveniles and 94% in adult fish. This decline is partly due to the gradual reduction in the volume of water pumped into aquaculture ponds with older fish, which require lower water depths due to their metabolism and lower production density. The extremely high-ratio positive correlation between the volumes of water injected for aquaculture production and those reused (τ = 0.91) confirms efficient water management, with few losses in the system. The uniformity of practices between AAUs suggests consistency in water management, including drainage frequency, drainage method, and effluent reuse. However, technical uniformity masks certain structural limitations. The case of AAU 2 is particularly revealing due to the lack of assignation of aquaculture effluents to production, reflecting a lack of functional integration. However, successful agro-aqua integration relies on the operational complementarity of animal and plant components, beyond simple technical connections [ 15 ]. A comparison of the volumes of water reused versus the water requirements for cropping shows that the WRI remains largely insufficient. In all AAUs, water use remains below 30%, ranging from 1.6% to 26.17%, confirming a significant dependence on external inputs, as also evidenced by a water dependency ratio of over 70%. In several agro-aqua integrated systems in West Africa, aquaculture effluents cover only a small portion of production needs due to an imbalance between aquaculture production and cropping demands. AAU 4 and AAU 5 illustrate this unbalanced pattern. Despite the presence of demanding crops such as tomatoes, cabbage, and papaya, the volumes of reused water are largely insufficient, amounting to 0.48 m 3 /day for a requirement of 30 m 3 /day in AAU 4, and 0.89 m 3 /day for a requirement of 11.5 m 3 /day in AAU 5. AAUs with large areas under cultivation or species with high water requirements have the lowest WRIs, even though the volumes available at the outlet of the aquaculture pond are comparable to other AAUs. Meanwhile, AAU 2, without irrigated production, illustrates the limitations of good hydraulic performance that is not exploited for cropping purposes. The overall efficiency of integrated systems depends as much on technical synchronization as on productive planning. Although standardizing the volumes of water injected into aquaculture ponds facilitates daily management, restrictions on the AAU ability to adjust to fluctuating needs, i.e., the frequency of drainage, water quality, production density, and DO vary according to specimens GSF, as do the water requirements of production according to its cycle, climatic conditions, and soil properties. More precise modulation of irrigation and water supply, taking these water parameters into account, would strengthen the AAU livelihood consistency. Integrated aquaculture irrigation takes advantage of the fact that fish filter water, making it a reusable resource for irrigation. However, such complementarity can only be fully realized if there is close coordination between aquaculture and cropping components, in order to ensure real water savings and the achievement of agro-aqua objectives. Effluents from aquaculture ponds at AAUs showed high concentrations of nutrients. The total nitrogen content, ranging from 25.1 to 28.7 mg/l, orthophosphate content, up to 32.2 mg/l in juveniles, potassium content, 13.25 mg/l in fry, and BOD content, ranging from 73.3 to 130 mg/l, indicate a significant organic and mineral load. These concentrations are significantly higher than the prescribed thresholds for irrigation, such as 5 mg/l of phosphorus or 30 mg/l of BOD. They indicate high fertilizing potential, but also risks of pollution if effluents are poorly managed. In terms of temperature, measurements varied depending on GSF, with 21°C for fry, 25°C for juveniles, and 23.2°C for adults. These values remain within the thermal tolerance range of Clarias gariepinus , which is 8°C to 30°C. Temperatures around 25 to 26°C promote optimal growth, which could explain a slowdown in fry exposed to cooler waters during the dry season. The pH values observed, ranging from 7.23 to 7.49, are optimal for Clarias gariepinus . However, DO levels, which are very low, ranging from 0.21 to 0.31 mg/l at all GSF, are cause for concern. Although the species can relatively tolerate hypoxia, DO values below 3.5 mg/l cause chronic stress, reduced feeding, decreased feed conversion, and increased vulnerability to disease [ 16 ]. Total nitrogen, although present in high concentrations, exceeds levels compatible with optimal aquaculture development, which range from 0.2 to 10 mg/l [ 17 ]. These high levels are comparable to those observed in many other aquaculture effluents and can be explained by the accumulation of feed residues and metabolic waste. Similarly, high levels of SDM, BOD, and orthophosphate can lead to increased ecotoxicity and impaired quality of irrigated soils. The composition of these agro-aqua waters reflects a combination of factors including high aquaculture density, nitrogen- and phosphorus-rich protein feed, incomplete nutrient assimilation, water renewal frequency [ 18 ], and GSF. These features give aquaculture wastewater significant fertilizing potential. There is a clear interest in taking advantage of these aquaculture effluents rich in nitrogen, phosphorus, and potassium, in order to reduce dependence on other types of synthetic fertilizers [ 19 ]. The use of aquaculture effluent from Oreochromis niloticus significantly increases the growth of lettuce and tomatoes [ 20 ]. However, su richness can, as well, have negative effects, including soil clogging, root asphyxiation, acidification, and pollution from leaching, especially in gravity irrigation or on poorly permeable soils. The relationship between GSF and the quality of water emitting from aquaculture subcomponent, expressed as an estimate of the fertilizer coverage rate for irrigated cropping from aquaculture effluents, was determined. The adult GSF generates the most nutrient-rich effluents, particularly in terms of nitrogen and phosphorus, thereby implying that during a growing season characterized by intensive feeding, organic aquaculture effluents reach particularly high levels, indicating a direct relationship between the GSF, their dietary requirements, and organic load. The exceptionally high nitrogen coverage observed for bananas, up to 149.52%, highlights that in some cases, aquaculture effluents may be sufficient to meet or even exceed nitrogen requirements, particularly for tomatoes in tropical areas. However, this requires special attention, as over-fertilization with nitrogen can lead to nutritional imbalance or even leaching. Phosphorus is generally well covered for cabbage and eggplant production, at 60 to 85% in the adult GSF, but this rate drops to less than 35% in the juvenile and fry GSFs. Juveniles and fry excrete a higher proportion of nutrients such as nitrogen and phosphorus into aquaculture water, reflecting reduced assimilation despite high feed consumption, unlike adults. Water quality varies depending on the GSF, i.e., fry, juvenile, and adult. The fry, raised at very high densities, are associated with higher concentrations of BOD and K, reflecting a high organic load. In juveniles, the rapid growth observed correlates with high phosphorus and suspended solids emitted. Inversely, ponds containing adult specimens generate more diluted water due to lower feed rations and lower density. SDM, in particular, pose a problem at this stage in the AAUs due to their concentrations. The accumulation of SDM can irritate the gills and affect the respiratory and osmoregulatory functions of fish, in addition to polluting the water and impacting cropping. The Spearman correlation matrix confirms these observations. Indeed, strong positive correlations were observed between BDO, SDM, total nitrogen, EC, and TDS, indicating that the most beneficial waters are also the most loaded with undegraded organic matter. Conversely, temperature and DO are negatively correlated with these variables, highlighting the risks of environmental depletion. High nutrient loads also promote pH elevation, ammonia production, and bacterial proliferation, resulting in a critical decline in DO. The strong correlation between EC and TDS can be explained by their interdependence, as dissolved minerals directly influence conductivity. Despite apparent compliance with certain parameters such as pH, EC, and TDS, excessive levels of BOD, TSS, nitrogen, and phosphorus place these aquaculture waters outside acceptable cropping limits unless they are diluted or treated prior to use. Most of the variability observed was summarized as follows according to Clarias gariepinus GSF. The first two dimensions cumulate 92.8% of the total inertia, reflecting a strong structuring of the data in the factorial plane. The first principal component explains 52.9% of the variance and is mainly positively correlated with BOD, potassium, total nitrogen, and orthophosphate. The first component represents an organic and mineral load gradient, contrasting waters that are highly enriched in nutrients and biodegradable matter with waters that are low in load. This component can be interpreted as a nutrient load dimension. The second component, representing 39.9% of the remaining variance, is strongly influenced by EC, TDS, total carbon, and DO. The second main component expresses a gradient of mineralization and redox conditions, allowing aquaculture effluents to be classified by oxidation state, salinity, and biological stability. The projection of aquaculture ponds on the factorial design reveals a clear discrimination of effluents according to GSF, confirming the hypothesis of a link between aquaculture practices and water composition. The juvenile GSF ponds are located in an area characterized by high levels of total nitrogen, orthophosphate, and higher temperatures. This reflects a phase of active GSF, with a sustained metabolism and significant excretion of mineral nutrients. Such a pattern suggests that aquaculture effluents produced at this stage have significant cropping potential, particularly in terms of nitrogen and phosphorus. As for the fry GSF ponds, they are located in a quadrant strongly correlated with BOD, potassium, and total carbon, indicating a marked organic load. Such a composition is consistent with high fish density, protein-rich food inputs, and incomplete nutrient assimilation, leading to the accumulation of organic matter. These waters, although nutritious, pose a challenge in terms of DO management and effluent stabilization. The adult GSF ponds, instead, show a more moderate fertilization signature, with lower nutrient levels and a slight decrease in pH. This setting reflects a terminal growth or maintenance phase, with lower feed intake, reduced density, and more stable metabolic activity. The aquaculture effluents produced are thus more well-balanced and easier to use for irrigation, although less concentrated in AAU fertilizers. Multivariate analysis therefore highlights a functional stratification of aquaculture effluents, where each GSF is associated with a distinct AAU fertilizing profile. Such differing patterns are determined by the interaction between fish physiology, stocking density, feed composition, and management practices, including feeding frequency and pond emptying frequency. Among other things, the report highlights the importance of the water quality of irrigation water for agro-aqua production health. 4 Conclusion The study determined the interactions within integrated AAUs Clarias gariepinus production and cropping in the easthern agroecological climatic condition in semi-sahel, with perspectives involving water reuse from agro-aqua effluents. Aquaculture practices vary significantly depending on Clarias gariepinus GSF specimens. The fry GSF is characterized by extremely high density and fractional feeding, resulting in high production of organic matter. Juveniles, although raised at lower densities, receive the largest amounts of feed, resulting in a significant accumulation of nitrogen and phosphorus in the effluent. Alternatively, adults are raised at low density, generating less organic waste while producing more stable and less concentrated water. High concentrations of total nitrogen, orthophosphate in juveniles, potassium in fry, and BOD were observed. These levels indicate significant AAUs fertilizing potential. However, this wealth comes with risks of soil clogging or organic pollution if waste is poorly managed, particularly on poorly permeable or steeply sloping soils. The hydro-flow assessment within AAUs showed a water reuse rate of over 90%, reflecting encouraging structural hydraulic performance. However, aquaculture water reuse remains low across all AAUs, highlighting a persistent reliance on external inputs from drilling and rainfall for irrigation. This shortfall can be explained by a mismatch between available volumes and existing high water production requirements, but also by incomplete synchronization between aquaculture drainage cycles and cropping calendars, in which reused water was not recycled for cropping. A clear structure has been identified with regard to aquaculture effluents depending on GSF. Waters in fry and juvenile ponds appear to be highly enriched in nutrients and organic matter, while waters in adult ponds are more diluted and stable, indicating differentiation according to aquaculture stages and structured optimization for agro-aqua use. The use of aquaculture effluents makes its possible to meet nitrogen and phosphorus requirements for certain market gardening activities. However, potassium remains limiting and targeted supplementation is necessary to balance intake. This approach reinforces the idea of partial substitution for mineral fertilizers, provided that the two agro-aqua components are carefully combined. The quality of aquaculture water, as measured by BOD, orthophosphate, SDM, etc., varied significantly depending on GSF. Although water reuse rates are satisfactory, indirect losses persist due to a lack of agro-aqua recovery in certain AAUs. Ultimately, agro-aqua integration improves yields and water use efficiency, while reflecting only partial coverage of actual production needs by aquaculture effluents uniquely. Declarations Competing interests The authors declare that they have no known competing financial or non-financial, professional, or personal conflicts that could have appeared to influence the work reported in this paper. Ethics, Consent to Participate, and Consent to Publish declarations Not applicable. Funding The study received no funding. Author Contribution Toundji Olivier Amoussou and Nawroz Kareem conceptualized the study. Toundji Olivier Amoussou, Chibuye Florence Kunda-Wamuwi, Wendnso Eddie Lionel Gouem, Aïcha Edith Soara and Nawroz Kareem performed the formal analysis and validated the outputs. Toundji Olivier Amoussou and Wendnso Eddie Lionel Gouem investigated the study protocols. Toundji Olivier Amoussou wrote the original draft of the manuscript. Chibuye Florence Kunda-Wamuwi, Wendnso Eddie Lionel Gouem, Aïcha Edith Soara, Vinsoun Millogo and Nawroz Kareem edited and reviewed the manuscript. Acknowledgement We are very grateful to agro-aqua farmers of our research program for their assistance during the field work. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Mekonnen MM, Hoekstra AY. Four billion people facing severe water scarcity. 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Sparks AH. nasapower: A NASA POWER Global meteorology, surface solar energy and climatology data client for R. J Open S Soft. 2018;3:1–3. McNaughton KG, Jarvis PG. Using the Penman-Monteith equation predictively. Agric Water Manag. 1984;8:263–78. Allen RG, Pereira LS, Raes D, Smith. M. Crop evapotranspiration—Guidelines for computing crop water requirements. Rome, Italy: Food and Agriculture Organization of the United Nations (FAO); 1998. Core Team R-D. R. R: A language and environment for statistical computing Version 4.5.1. R Foundation for Statistical Computing. Vienna, Austria: 2025. RStudio Team. RStudio: Integrated development for R. RStudio, Posit Software, PBC: 2025. Venables WN, Smith DM, The R Core Team. An Introduction to R (Version 4.5.1). R Foundation for Statistical Computing; 2025. Nordstokke DW, Zumbo BD. A new onparametric Levene test for equal variances. Psicológica. 2010;31:401–30. Okoye K, Hosseini S, Mann–Whitney U. Test and Kruskal–Wallis H test statistics in R. In R Programming. 225–246. Springer: 2024. Chary K, Jaeger C, Jansen HM, Harchaoui S. Evaluating nutrient circularity in integrated aquaculture systems: Criteria and indicators. J Clean Prod. 2025;504:1–13. Brougher DS, Douglass LW, Soares JH Jr. Comparative oxygen consumption and metabolism of striped bass Morone saxatilis and its hybrid M. chrysops ♀ x M. saxatilis ♂. J W Aquac Soc. 2007;36:521–29. Gross A, Boyd CEA. Digestion procedure for the simultaneous determination of total nitrogen and total phosphorus in pond water. J W Aquac Soc. 1998;29:300–3. Ahmad A, Sheikh Abdullah SR, Hasan HA, Othman AR, Nur’ Izzati I. Aquaculture industry: Supply and demand, best practices, effluent and its current issues and treatment technology. J Environ Manag. 2021;287:1–7. Stevenson KT, Fitzsimmons KM, Clay PA, Alessa L, Kliskey A. Integration of aquaculture and arid lands agriculture for water reuse and reduced fertilizer dependency. Exp Agric. 2010;46:173–90. Delaide B, Goddek S, Gott J, Soyeurt H, Jijakli MH. Lettuce ( Lactuca sativa L. var. Sucrine) growth performance in complemented aquaponic solution outperforms hydroponics. Water. 2016;8:1–11. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 19 May, 2026 Reviews received at journal 19 May, 2026 Reviews received at journal 10 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9189278","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622809510,"identity":"5a245612-e914-4038-93fa-2fe35c3c90fc","order_by":0,"name":"Toundji Olivier 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05:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9189278/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9189278/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107018418,"identity":"c0e2f527-c68d-48b0-8f15-3f3daf809b88","added_by":"auto","created_at":"2026-04-15 20:33:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241717,"visible":true,"origin":"","legend":"\u003cp\u003eTrial design indicating water sources as well as aquaculture and cropping components of AAUs\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/4351f177ead36029e11bd3bc.png"},{"id":107480409,"identity":"c3c8ff9f-9255-47f8-abbe-e9e3d98ae8f6","added_by":"auto","created_at":"2026-04-22 02:10:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":303023,"visible":true,"origin":"","legend":"\u003cp\u003ePattern of aquaculture practices, shown according to the amount of feed in g/day (A), feeding frequency (B), grain size distributions of 1 mm, 2 mm, 3 mm, 4.5 mm, and 6 mm (C), and bromatological composition as a percentage of dry matter (D) of Raanan Fish Feed\u003csup\u003e®\u003c/sup\u003e supplied to \u003cem\u003eClarias gariepinus\u003c/em\u003e specimens, as well as aquaculture density (E) and feeding frequency (F) according to the GSF of \u003cem\u003eClarias gariepinus\u003c/em\u003e, namely fry, juvenile, and adult in AAUs (mean ± standard deviation)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/85416628d26a1c77287349be.png"},{"id":107480378,"identity":"f5d33ad2-ea93-4246-b88f-0ff430fb152e","added_by":"auto","created_at":"2026-04-22 02:09:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128475,"visible":true,"origin":"","legend":"\u003cp\u003eSurface density in specimens/m\u003csup\u003e2\u003c/sup\u003e (A) and volumic density in specimens/m\u003csup\u003e3\u003c/sup\u003e of fish based on GSF fry, juvenile, and adult in AAUs\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/17bd4d1fa0f7206347f70045.png"},{"id":107018419,"identity":"d3852ca8-430c-4556-84e9-c353d8ac4e0f","added_by":"auto","created_at":"2026-04-15 20:33:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":214579,"visible":true,"origin":"","legend":"\u003cp\u003eFrequency of pond drainage (A) and comparison of average daily volumes of water used for aquaculture and reused for cropping irrigation at different GSF in UAAs\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/bb6a2872be7c3cd8de3933e4.png"},{"id":107480450,"identity":"cf656861-03f6-4ab9-af8a-a09b091defad","added_by":"auto","created_at":"2026-04-22 02:10:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161620,"visible":true,"origin":"","legend":"\u003cp\u003eEquation relating the volume of water used for aquaculture production to the volume of water reused for cropping. Ƭ = Kendall's correlation coefficient\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/fd526177b7053299c2ff8e18.png"},{"id":107480663,"identity":"604e801f-1f37-491c-a6b6-5661ea321ff5","added_by":"auto","created_at":"2026-04-22 02:12:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25627,"visible":true,"origin":"","legend":"\u003cp\u003eProduction areas allocated to each agro-aqua unit\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/1cc1a4ab0d6fb9f1c284802f.png"},{"id":107018424,"identity":"3cd9703c-7c50-47e1-9f36-ef967bba0bc6","added_by":"auto","created_at":"2026-04-15 20:33:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":239243,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in agro-aqua water quality across all AAU\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/2377529df835816c8e0c79ea.png"},{"id":107704826,"identity":"81b25030-b1e3-4fef-af5a-4a50194bf21c","added_by":"auto","created_at":"2026-04-24 08:59:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":125243,"visible":true,"origin":"","legend":"\u003cp\u003eMatrix of Spearman correlation between the water quality data of aquaculture ponds and aquaculture effluents for all AAUs. Temperature, Hydrogen Potential (pH), Electrical Conductivity (EC), Dissolved Oxygen (DO), Total Dissolved Solids (TDS), Suspended Dissolved Matters (SDM), Total Carbon, Total Nitrogen, Orthophosphate, Biological Oxygen Demand (BOD), and Potassium\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/07ee4eb7640d733c33288428.png"},{"id":107018426,"identity":"5e2035ac-8e11-467e-b14e-fba0ea949c02","added_by":"auto","created_at":"2026-04-15 20:33:35","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":246200,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate plot (A) of aquaculture effluent water quality (B, C, and D) emitted from AAUs as well as coverage rate (%) of fertilizing nutrients emitted by these effluents according to GSF: Fry stage, Juvenile stage, Adult stage. pH: Hydrogen Potential; EC: Electrical conductivity; DO: Dissolved Oxygen; TDS: Total Dissolved Solids; SDM: Suspended Dissolved Matters; BOD: Biological Oxygen Demand; K: Potassium\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/d46812f201378eb837c758fd.png"},{"id":108006045,"identity":"6b9da127-6fb1-4cf8-8e7a-1fae8cf83d57","added_by":"auto","created_at":"2026-04-28 12:52:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2225023,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9189278/v1/b1aef7a6-72e6-417e-9d07-534c50506f3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Practical drivers of water recycling in agro-aqua systems for long-term climate adaptation in semi- sahel zone","fulltext":[{"header":"Article Highlights","content":"\u003cul\u003e\n \u003cli\u003eAgro-aqua production water are profiled according to Growth Stages of Fish (GSF) in semi-sahel farms;\u003c/li\u003e\n \u003cli\u003eWater utilization volume, i.e. water productivity was optimized in many agro-aqua production sub-components;\u003c/li\u003e\n \u003cli\u003eWater requirements and efficiency in agro-aqua production is profiled for semi-sahel zone.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1 Introduction","content":"\u003cp\u003eWater, as a resource with multiple uses, is subject to many challenges, including population growth, climate change. The intensification of man-made activities affects water availability, quality, and accessibility. Many users experience water shortages every year, and this trend continues year after year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In such a dynamic, cropping, which uses approximately 80% of freshwater resources [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], is at the core of concerns, especially since demand for consumption will require a 60 to 70% increase in production by 2050 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. To address such challenges, a far-reaching transformation of agricultural systems, particularly in arid and semi-arid areas, is needed in order to enhance productivity and water use efficiency while ensuring sustainable production.\u003c/p\u003e \u003cp\u003eSurface water availability in semi-sahel zone is highly influenced by seasonal variations. Seasonal variations are mainly due to climatic factors characterized by recurring droughts, an average temperature increase of 2\u0026deg;C [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and growing water needs, mainly for irrigation. Irrigated cropping uses substantial amounts of water, often above the water regeneration potential. Innovative agricultural practices are needed to make efficient use of each available quantity. Seen this way, aquaculture, and especially fish farming, seems like a good opportunity. This is the fastest growing farming system, providing about 20% of animal protein for consumption [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. That production system still has environmental-related and sustainability-related challenges [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the country, domestic fish production remains relatively modest at 30,555 tons in 2021, compared to an annual demand of 193,160 tons.\u003c/p\u003e \u003cp\u003eGiven the current depletion of resources and the need to meet ever-increasing food demands, integrating aquaculture into irrigated cropping is a strategic option. The integrated system allows nutrient-rich AWE to be used as fertilizer for cropping, while irrigation water is used to maintain the aquaculture ponds. Water dilution by replacing source water with aquaculture wastewater by up to 25% allowed productivity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], as usual in agro-aqua production. The successful agro-aqua integration depends on several key factors. Indeed, the quality of aquaculture water, which varies according to the Growing Stage of Fish (hereafter referred to GSF), directly influences its cropping value for irrigation. Moreover, stocking density, aquaculture inputs, and water flow management also have a major impact on the overall performance of the agro-aqua system. In the sudano-sahelian zone, where every hydric asset is important, understanding these interactions is essential for designing sustainable and profitable agro-aqua systems.\u003c/p\u003e \u003cp\u003eThis research aims to assess the impact of GSF on the cropping quality of aquaculture-emitted water, on the efficiency of aquaculture-emitted water\u0026rsquo;s agro-aqua use, as well as the agro-aqua requirements in a semi-sahel zone. Especially, the aim is to identify and quantify the aquaculture inputs used according to the GSF across Agro-Aqua Units (hereafter referred to as AAU). It also aims to determine the quantities of water used and lost in the integrated agro-aqua system. The relationship between the cropping quality of water, the water efficiency of use, and the agro-aqua requirements also deserves to be examined. The first assumption of the study is to establish whether the cropping water quality varies according to the GSF. Does agro-aqua integration optimize volumes of water used and lost in its various components? Does agro-aqua integration improve water requirements and efficiency in semi-sahel zone? The study then explored two main factors including GSF and AAU.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Climatic features of agro-aqua units\u003c/h2\u003e \u003cp\u003eThe study was undertaken in Fada N\u0026rsquo;Gourma, 12\u0026deg;03'N, 0\u0026deg;21'E, where the soils used for agro-aqua production are diverse due to the topography and water regime. Soils with little gravel erosion, i.e. 53.5%, and tropical ferruginous soils, i.e. 31.3%, are largely dominant. Vertisols can be identified in low-lying areas, eutrophic soils can be found along certain slopes, and hydromorphic soils are observed in depressions. This soil diversity determines the potential for agro-aqua production, but the low organic matter content of ferruginous soils requires the addition of fertilizing nutrients, which justifies the use of Aquaculture Water Effluents (AWE) as a natural soil improver. The surface water in Fada N'Gourma comes from two watersheds namely the Niger stream to the north and the Oti stream to the south. The city itself is flowed through by lake Fada N'Gourma. The rapid silting-up of these watersheds reduces their storage capacity and limits the availability of water at the end of the dry season. In terms of fisheries, watersheds like reservoirs, lakes offer modest potential, often exploited on a small-scale basis. Most of the fish consumed comes from other locations such as Kompienga. Aquifers, located in fractures in the bedrock and weathered layers, provide a complementary but limited supply, which requires careful and sustainable management. In Fada N'Gourma, cropping focused on vegetable and fruit production in lowlands and floodplains, constitutes the main livelihood, although hampered by declining soil fertility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Setting up agro-aqua units\u003c/h2\u003e \u003cp\u003eAn Agro-Aqua Units (AAU) is a unit composed of an aquaculture pond whose water is dedicated to the production of one or more downstream horticultural crops. Each AAU comprises an aquaculture component and a horticultural component. AAUs were considered as factors in the data collection process. A total of four criteria were considered in the selection of AAUs used for data collection: (i) the presence of an aquaculture pond containing farmed fish, (ii) the presence of one or more associated cropping downstream of the aquaculture pond, (iii) the use of water from aquaculture ponds for irrigation of crops, and (iv) the possibility of obtaining data on the different components of each AAU. The aquaculture production ponds are made of above-ground cement. The aquaculture ponds are circular in shape, with an average height of 120 cm and an average diameter of 185 cm. Each pond is equipped with two drainage systems: the outlet, used to drain wastewater, and the overflow, used to regulate the water level by allowing excess water to flow out. There is also a water intake for water renewal after draining. The species farmed in these ponds are catfish. Water renewal is ensured by 10-meter-long pipes, connected to each other, running from the water intake to the aquaculture pond. The water used comes from an underground source captured by a borehole. Downstream or around aquaculture ponds, various cropping plots are set up. These crops include vegetables, fruits, fodder plants, and trees. Plant species chosen for their ability to benefit from irrigation with pond-water include onions, tomatoes, cabbage, eggplant, papaya, and moringa (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eData was collected in the field using data-sampling sheets, and was compiled by AAU. Data concerning the integrated agro-aqua system covered the type of agro-aqua integration implemented, the nature of cropping, the type of aquaculture practiced, the growing stage of fish (GSF), the areas allocated to each agro-aqua component of the integrated system, the aquaculture inputs used, etc. Data concerning agro-aqua water efficiency included the quantities of water used for agro-aqua production, the rate of reuse of aquaculture water for irrigation, the cropping quality of the water, the monitoring of agro-aqua water inflows and outflows, the water sources and losses, etc. Data on the diet of aquaculture species includes the nature of the feed, the feed quality, the feed composition, the feeding frequency, the amount of feed provided, etc.\u003c/p\u003e \u003cp\u003eThe data collection sheets were used to record data on water inflows and outflows, feeding of aquaculture species, agro-aqua water losses, and characteristics of the integrated agro-aqua system. A Global Positioning System (GPS) was used for local positioning and recording the geographical coordinates of the agro-aqua station, horticultural plots, aquaculture ponds, and water sources or intakes. Sterile plastic bottles were used to collect water samples. Aluminum foil was used to cover the water samples to limit photosynthesis. Laboratory equipment was used to analyze the quality of the water samples. A tape measure was used to measure water levels in aquaculture ponds, as well as to measure the dimensions of aquaculture ponds and production plots for plant species. A camera was used to take photographs. A stopwatch was used to quantify water volumes. A Systamec model C600 7-in-1 digital water tester was used to measure quality parameters \u003cem\u003ein situ\u003c/em\u003e. The measured variables include hydrogen potential (pH), electrical conductivity (EC), dihydrogen (H\u003csub\u003e2\u003c/sub\u003e), salinity, total dissolved solids (TDS), oxidation-reduction potential (ORP), water temperature, and specific gravity (SpG). An electronic scale was used to measure the amount of feed consumed by the aquaculture species.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Estimates of water inflows, outflows and reuse efficiency\u003c/h2\u003e \u003cp\u003eDaily monitoring of water inflow and outflow data was undertaken using specific data collection sheets. Based on these data, the quantities of water used per AAU were estimated accurately and systematically. Measurements were therefore made daily and during each type of water inflow and outflow operation in the AAU. First, water inflows, defined as the supply of freshwater to aquaculture ponds, and water outflows, i.e., water leaving aquaculture ponds for irrigation of horticultural cropping, were measured. A tape measure was used to calculate the water level in the aquaculture ponds. Before each water renewal, the initial water level was measured. Then, after the aquaculture ponds had been drained or purged, the remaining water level was measured. The difference between these two measurements gives the height of water removed from the aquaculture pond, used for irrigating cropping. Once the draining or purging was complete, the new water height, brought in by the renewal, was measured. The difference between the height after draining and this new height gave the height of water entering the aquaculture pond. Several fixed measurement points distributed evenly throughout the aquaculture pond made it possible to obtain an average measurement and avoid errors due to local water irregularities. Once the water levels were obtained, the volumes of water entering and leaving are determined using the diameter and water depth of the aquaculture ponds, which are all circular in shape. The volume of outgoing water, i.e., the volume of AWE, the outgoing water volume or irrigation water volume (V\u003csub\u003eReUsed\u003c/sub\u003e), used for crop irrigation, is calculated by multiplying the outgoing water depth by πR\u003csup\u003e2\u003c/sup\u003e, where R is the radius of the aquaculture pond. Similarly, the volume of water entering, which corresponds to the volume used for aquaculture production, denoted V\u003csub\u003eAqua\u003c/sub\u003e, is determined by multiplying the height of the water entering by πR\u003csup\u003e2\u003c/sup\u003e. The water outflows (H\u003csub\u003eOutflow\u003c/sub\u003e) and inflows (H\u003csub\u003eInflow\u003c/sub\u003e) were calculated as indicated in equations 1 and 2. The calculation of V\u003csub\u003eReUsed\u003c/sub\u003e is shown in Eq.\u0026nbsp;3. For the calculation of the incoming water volume (V\u003csub\u003eAqua\u003c/sub\u003e), the formula is given in Eq.\u0026nbsp;4. Water Reuse Efficiency (WRE) corresponds to the proportion of water used for aquaculture production that is effectively reused for irrigating associated cropping AAU. The WRE (in %) was calculated using the formula in Eq.\u0026nbsp;5.\u003c/p\u003e \u003cp\u003eH\u003csub\u003eOutflow\u003c/sub\u003e = H\u003csub\u003eInitialHeight\u003c/sub\u003e \u0026ndash; H\u003csub\u003eAfterEmptying\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003eH\u003csub\u003eInflow\u003c/sub\u003e = H\u003csub\u003eNovelHeight\u003c/sub\u003e \u0026ndash; H\u003csub\u003eAfterEmptying\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003eV\u003csub\u003eReUsed\u003c/sub\u003e = H\u003csub\u003eOutflow\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;πR\u003csup\u003e2\u003c/sup\u003e (3)\u003c/p\u003e \u003cp\u003eV\u003csub\u003eAqua\u003c/sub\u003e = H\u003csub\u003eInflow\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;πR\u003csup\u003e2\u003c/sup\u003e (4)\u003c/p\u003e \u003cp\u003eWRE = (V\u003csub\u003eReUsed\u003c/sub\u003e/V\u003csub\u003eAqua\u003c/sub\u003e) x 100 (5)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Estimation of water performance indicators\u003c/h2\u003e \u003cp\u003eThe estimation of net water requirements for production, as well as the calculation of water performance indicators such as the Water Reuse Index (WRI) and External Water Dependency (EWD), were based on climate data, cropping patterns, and water volumes available in AAUs. Daily agro-climatological data for the study area were extracted from the NASA POWER [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] package. Evapotranspiration (ET₀) was calculated using the standardized Penman-Monteith equation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], based on daily climate data provided by the NASA POWER package for the study area. The climate parameters used include minimum and maximum air temperature, in \u0026deg;C, average relative humidity, %, wind speed, in m/s, and global radiation, in MJ/m\u0026sup2;/day. The calculations were performed using R software [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and RStudio [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Penman-Monteith formula used is as in equations 6. The monthly ETo (ETo\u003csub\u003emonth\u003c/sub\u003e) was obtained by multiplying the daily value by the number of days in the month.\u003c/p\u003e \u003cp\u003eET\u003csub\u003e₀\u003c/sub\u003e = [(0,408 ∆(R\u003csub\u003en\u003c/sub\u003e\u0026minus;G)) + (((γ(900/(T\u0026thinsp;+\u0026thinsp;273)) (u\u003csub\u003e2\u003c/sub\u003e(e\u003csub\u003es\u003c/sub\u003e \u0026minus; e\u003csub\u003ea\u003c/sub\u003e))] / [∆ + γ(1\u0026thinsp;+\u0026thinsp;0,34u\u003csub\u003e2\u003c/sub\u003e)] (6)\u003c/p\u003e \u003cp\u003eETo is the reference evapotranspiration, i.e. mm/day. Rn is the net radiation, in MJ/m\u003csup\u003e2\u003c/sup\u003e/day. G is the ground heat flux, i.e. MJ/m\u003csup\u003e2\u003c/sup\u003e/day. T is the average air temperature, in \u0026deg;C, while u\u003csub\u003e2\u003c/sub\u003e is the wind speed at 2 m, in m/s, e\u003csub\u003es\u003c/sub\u003e and e\u003csub\u003ea\u003c/sub\u003e are respectively the saturated vapor pressure and the actual pressure, i.e. kPa. ∆ is the slope of the vapor pressure curve, i.e. kPa/\u0026deg;C. And, γ is the psychrometric constant, i.e. kPa/\u0026deg;C.\u003c/p\u003e \u003cp\u003eFor each associated cropping component in the AAU, i.e. tomato, eggplant, papaya, banana, cabbage, okra, the reference values for the month of sowing, the length of the production cycle (days), and the average production coefficient (K\u003csub\u003ec\u003c/sub\u003e) were used to estimate horticultural evapotranspiration (ET\u003csub\u003eh\u003c/sub\u003e) and the Net Water Requirements (NWR) of the crops. The NWR was estimated using the Eq.\u0026nbsp;7. The horticultural production period was defined as the succession of months covering the entire cycle or part of the cycle. The effective precipitation (P\u003csub\u003ee\u003c/sub\u003e) was estimated monthly using the simplified method, while the NWR (in mm/cycle) of the associated cropping were determined using Eq.\u0026nbsp;8.\u003c/p\u003e \u003cp\u003eET\u003csub\u003eh\u003c/sub\u003e = ET\u003csub\u003eo\u003c/sub\u003e \u0026times; K\u003csub\u003ec\u003c/sub\u003e (7)\u003c/p\u003e \u003cp\u003eETh is the evapotranspiration of production (mm/day). ETo is the monthly reference evapotranspiration (mm/day). Kc is the average cropping coefficient of over the production cycle.\u003c/p\u003e \u003cp\u003eNWR\u0026thinsp;=\u0026thinsp;ET\u003csub\u003eh\u003c/sub\u003e - P\u003csub\u003ee\u003c/sub\u003e (8)\u003c/p\u003e \u003cp\u003eP\u003csub\u003ee\u003c/sub\u003e is the effective precipitation (mm/cycle). Water levels were converted into volumes overall AAU, in m\u003csup\u003e3\u003c/sup\u003e/day, and used to calculate the net water requirements overall AAU as stated in Eq.\u0026nbsp;9.\u003c/p\u003e \u003cp\u003eNWR\u003csub\u003eAAU\u003c/sub\u003e = NWR \u0026times; Area Irrigated overall AAU \u0026times; 10 (9)\u003c/p\u003e \u003cp\u003eIrrigated area overall AAU is defined in ha, when 10 refers to the conversion factor to m\u003csup\u003e3\u003c/sup\u003e/ha.\u003c/p\u003e \u003cp\u003eThe WRI estimated at AAU scale is the proportion of water requirements for cropping covered by AWE. The WRI is defined as the ratio between the average daily V\u003csub\u003eReUsed\u003c/sub\u003e and the NWR within AAU. Of course, V\u003csub\u003eReUsed\u003c/sub\u003e refers to the average volume of water reused in the form of AWE per AAU, in m\u003csup\u003e3\u003c/sup\u003e/day.\u003c/p\u003e \u003cp\u003eWRI = (V\u003csub\u003eReUsed\u003c/sub\u003e/NWR\u003csub\u003eAAU\u003c/sub\u003e) \u0026times; 100 (10)\u003c/p\u003e \u003cp\u003eFurther considerations about the Eq.\u0026nbsp;10 are (i) WRI\u0026thinsp;\u0026ge;\u0026thinsp;100% means that water coverage is total or surplus (potential surplus), (ii) 20% \u0026le; WRI\u0026thinsp;\u0026lt;\u0026thinsp;100% means that the water coverage is partial, and (iii) WRI\u0026thinsp;\u0026lt;\u0026thinsp;100, means that water coverage is very low.\u003c/p\u003e \u003cp\u003eThe EWD expresses the proportion of water requirements that must be met by external water supplies for cropping irrigation in AAU. EWD, in %, is expressed by Eq.\u0026nbsp;11.\u003c/p\u003e \u003cp\u003eEWD = [(1 \u0026ndash; WRI) x 100] (11)\u003c/p\u003e \u003cp\u003eIn terms of the coverage rate for fertilizers derived from AWE, a quantitative assessment of the contribution of these AWEs to the nutrient requirements of vegetable production was completed. The coverage rate for major elements, i.e. nitrogen, phosphorus, and potassium, was estimated based on a combination of nutrient concentration data including total nitrogen, orthophosphate, and potassium, V\u003csub\u003eReUsed\u003c/sub\u003e, and specific fertilizer requirements including Nitrogen, Phosphorus, and Potassium for each of the associated cropping AAU.\u003c/p\u003e \u003cp\u003eThe nutrient concentrations in AWE, in mg/l, were converted into the mass of nutrients supplied daily to the AAU plots, while considering the volume of irrigation water used, in m\u0026sup3;/day, and the irrigated area, in ha. The said variable in consider as the Daily Nutrient Supply (DNS).\u003c/p\u003e \u003cp\u003eDaily Nutrient Supply (DNS) = (Ci x V\u003csub\u003eReUsed\u003c/sub\u003e) / S (12)\u003c/p\u003e \u003cp\u003eDNS refers to inputs of nutrients N, P, K, in kg/ha/day. Ci is the concentration of nutrients N, P, K in AWE, in kg/m\u003csup\u003e3\u003c/sup\u003e. V\u003csub\u003eReUsed\u003c/sub\u003e is the volume of AWE reused for irrigation of associated cropping (m\u003csup\u003e3\u003c/sup\u003e/day). S is the irrigated area, in ha.\u003c/p\u003e \u003cp\u003eThe cumulative input of each nutrient was calculated by multiplying the daily input supply by the duration of the production cycle (in days) in term of Total Nutrient Supply (TNS):\u003c/p\u003e \u003cp\u003eTNS\u0026thinsp;=\u0026thinsp;DNS \u0026times; Production Cycle Duration (13)\u003c/p\u003e \u003cp\u003eTNS is the total nutrient supply during the entire production cycle (kg/ha/cycle). The duration is the one associated with irrigated cropping cycle (days).\u003c/p\u003e \u003cp\u003eThe Total Nutrient Coverage (TNC) rate for nutrients constituting the fertilizer requirements for nitrogen, phosphorus, and potassium in irrigated cropping was extracted and estimated from Reference Nutrients Needed (RNN) data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TNC rate was estimated as the ratio between the cumulative input from aquaculture irrigation and the total production requirement, according to the formula:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgroclimatic parameters and water requirements of the main cropping across the UAA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTomato\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCycle length (days)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean cropping coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMonthly evapotranspiration (mm/cycle)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnual evapotranspiration (mm/year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEffective rainfall (mm/cycle)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1350.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1282.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEggplant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e420.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2673.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2941.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCabbage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1638.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePapaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1995.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOkra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e703.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e478.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e295.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eAAU\u003c/em\u003e Agro-Aqua Unit\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTNC rate\u003csub\u003ei\u003c/sub\u003e = [(TNS\u003csub\u003ei\u003c/sub\u003e / RNN\u003csub\u003ei\u003c/sub\u003e) \u0026times; 100] (14)\u003c/p\u003e \u003cp\u003eTNC rate is the coverage rate for nutrient i, i.e. N, P, and K, supplied by AWE, as %. RNN is the estimated actual requirement for nutrient i, N, P, and K, in kg/ha/cycle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Sampling over AAUs\u003c/h2\u003e \u003cp\u003eThe quantity of feed given to fish in AAUs was determined using an electronic scale and recorded daily on collection sheets each feeding time. In other words, before adding feed to each pond, the quantity was weighed accurately using the electronic scale. The data recorded included the amount of feed provided, i.e., the amount of feed given to the fish (g/day), the feeding frequency, the frequency of feed delivery (meals/day), and the surface density, i.e., the number of fish per unit area (m\u0026sup2;) of pond. The GSF considered are fry, juvenile, and adult for \u003cem\u003eClarias gariepinus\u003c/em\u003e. Based on the aquaculture pond features, three sampling points were identified in the aquaculture component: a pond with a pond with \u003cem\u003eClarias gariepinus\u003c/em\u003e fry, \u003cem\u003eClarias gariepinus\u003c/em\u003e juveniles, and a pond with \u003cem\u003eClarias gariepinus\u003c/em\u003e adults. Three samples were taken at each sampling point, for a total of nine samples. The water samples were collected manually in plastic bottles and were dedicated for the determination of quality control data (QCD). The bottles were filled to the top and sealed to prevent gas exchange with the atmosphere during analyses. The bottles were then labeled and wrapped in aluminum foil. Certain water QCD, such as temperature, EC, pH, and dissolved oxygen (DO), were measured in situ during sampling using a HACH HQ 4300 field multi-parameter meter. The water samples were then transported in a cooler at approximately 4\u0026deg;C while awaiting the various measurements and titrations.\u003c/p\u003e \u003cp\u003eAside from pH, temperature, conductivity, and DO, which were measured in situ, the other water-related variables were determined by dosing and titrating samples collected on-site. These included total nitrogen, orthophosphate, potassium, total carbon, Suspended Dissolved Matters (SDM), salinity, TDS, and biological oxygen demand (BOD). BOD was measured using an incubator Oxitop at 20\u0026deg;C under dark conditions over five days. SDM was evaluated by filtration through a 1.5 \u0026micro;m glass microfiber filter. Nitrogen was measured after mineralization in a BUCHI K-436 mineralizer and after distillation in a BUCHI K-355 distiller, followed by titration with 0.04 mol/l hydrochloric acid. Total carbon was estimated from organic matter using a conversion factor of 2. Orthophosphate was measured by spectrophotometry using a DR 1900 spectrophotometer. Potassium was measured by flame photometry according to standard methods. TDS was determined by gravimetric method after drying at 103\u0026deg;C. All the animals used were obtain from one locally-established aqua-farm. We obtained informed consent from the owner to use the animals in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical methods were performed using R [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and RStudio [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Quantitative variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. The normality of distributions was tested using the Shapiro-Wilk test [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], while the homogeneity of variances was verified using Levene's test [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Given the non-normality of certain variables and the ordinal nature of certain factors such as GSF and feed grain size, comparisons between groups were performed using the nonparametric Kruskal-Wallis test [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In a simpler way, the Kruskal-Wallis test, also known as single factor ANOVA on ranks, was used as a nonparametric method to test variability according to the GSF. When significant differences were detected (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), a Dunn post-hoc test with Bonferroni correction was used to identify statistically distinct groups. The relationships between feed grain size, QCD of water, nutrient loads of N, P, and K, as well as indicators of WRE and EWD, were explored by calculating Spearman's correlation coefficient, which is better suited to non-normal distributions. A Principal Component Analysis (PCA) was also performed to reduce the dimensionality of the data and visualize the underlying structures linking the variables. This PCA was run on the centered and reduced values of the water QCD and aquaculture subcomponent variables, using the FactoMineR package. The PCA was graphically plotted using factoextra to highlight correlations between variables and groupings between ponds of AAUs. R packages such as FSA for Dunn's test, psych and Hmisc for correlations, ggplot2 for visualizations, as well as FactoMineR and factoextra for PCA were employed. All statistical tests were interpreted at the respective significance levels of α\u0026thinsp;=\u0026thinsp;0.05, β\u0026thinsp;=\u0026thinsp;0.01, and γ\u0026thinsp;=\u0026thinsp;0.001.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Farm-scale description of AAUs\u003c/h2\u003e \u003cp\u003eThe integrated agro-aqua system investigated is structured into AAUs consisting of one to two circular aquaculture ponds, located above ground and intended for the production of \u003cem\u003eClarias gariepinus\u003c/em\u003e. The pond diameters vary from one AAU to another, reflecting adaptation to spatial constraints and production capacities. Each pond is equipped with a complete piping system including a drainage device, an overflow to regulate the water level during rainfall or excessive inflows, and a purge outlet for periodic sediment removal. The water from the ponds, rich in organic nutrients and minerals, i.e., aquaculture effluent, is systematically reused for irrigation without undergoing any prior treatment. The agro-aqua coupling is indirect because the effluents do not flow continuously to the cropping production plots, but are discharged by gravity when water is needed, from the drainage system. The water supply for the ponds comes mainly from boreholes distributed throughout the agro-aqua site. These boreholes are connected to polytank reservoirs, powered by solar pumps, which provide buffer storage before transfer to the ponds via a network of mobile pipes. Cropping plots vary in size and are used to grow vegetables such as tomatoes, i.e. \u003cem\u003eSolanum lycopersicum\u003c/em\u003e, okra, i.e. \u003cem\u003eAbelmoschus esculentus\u003c/em\u003e, cabbage, i.e. \u003cem\u003eBrassica oleracea\u003c/em\u003e, and eggplant, i.e. \u003cem\u003eSolanum melongena\u003c/em\u003e. The production consists of bananas, i.e. \u003cem\u003eMusa\u003c/em\u003e spp., and papayas, i.e. \u003cem\u003eCarica papaya\u003c/em\u003e. Furthermore, various agroforestry species are planted there, including the baobab (\u003cem\u003eAdansonia digitata\u003c/em\u003e), pigeon pea, i.e. \u003cem\u003eCajanus cajan\u003c/em\u003e, custard apple, i.e. \u003cem\u003eAnnona squamosa\u003c/em\u003e, moringa, i.e. \u003cem\u003eMoringa oleifera\u003c/em\u003e, cashew, i.e. \u003cem\u003eAnacardium occidentale\u003c/em\u003e, shea, i.e. \u003cem\u003eVitellaria paradoxa\u003c/em\u003e, fig, i.e. \u003cem\u003eFicus\u003c/em\u003e spp., flamboyant, i.e. \u003cem\u003eDelonix regia\u003c/em\u003e, acacia, i.e. \u003cem\u003eAcacia nilotica\u003c/em\u003e, and shea, i.e. \u003cem\u003eVitellaria paradoxa\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIrrigation is provided via surface gravity, using simple equipment such as buckets, bowls, and flexible hoses. Across all AAUs, two cropping cycles are practiced per year, ensuring continuous plant production throughout the year. In the aquaculture component, production exclusively involves the farming of \u003cem\u003eClarias gariepinus\u003c/em\u003e, fed with commercial complete pellets. No hormonal or medicinal treatments are used in the AAUs. AAU monitoring focuses on monitoring water temperature and turbidity. In the event of sporadic fish mortality, a sodium chloride, i.e. NaCl, solution is applied as a prophylactic measure. In addition to taking advantage of the organic quality of agro-aqua water, and organic fertilizers, i.e., compost, manure are used across all AAUs. In addition, several sustainable agricultural practices such as mulching, crop rotation and crop succession, cover cropping, composting, and agroforestry plantations were identified across all AAUs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 AAUs aquaculture component description\u003c/h2\u003e \u003cp\u003eAquaculture practices in terms of daily feed intake, feeding frequency, pellet size breakdown, and pellet nutritional composition were presented according to GSF, i.e., fry, juveniles, and adults. Many aquaculture practices were considered in AAUs according to \u003cem\u003eClarias gariepinus\u003c/em\u003e specimens GSF. At the fry stage of development, fish were fed 378.9\u0026thinsp;\u0026plusmn;\u0026thinsp;195.5 g of feed per day, with a frequency of 1.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54 meals/day. At this stage, the average surface density was highest, reaching 1064\u0026thinsp;\u0026plusmn;\u0026thinsp;705 specimens/m\u003csup\u003e2\u003c/sup\u003e. At the juvenile stage, the average daily feed intake was the highest of all three stages, at 714\u0026thinsp;\u0026plusmn;\u0026thinsp;397.4 g/day, while feeding frequency remained relatively stable at 1.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 times/day. The surface density decreases significantly, with an average of 220\u0026thinsp;\u0026plusmn;\u0026thinsp;116 individuals/m\u0026sup2;. In adult fish, the amount of feed provided is reduced to 486.8\u0026thinsp;\u0026plusmn;\u0026thinsp;340.8 g/day, with a frequency of 1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52 meals/day. At this stage, aquaculture density was also the lowest, with an average of 64\u0026thinsp;\u0026plusmn;\u0026thinsp;13 specimens/m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE show the daily amounts of feed allocated to the ponds of the five AAUs, based on the developmental stage of the \u003cem\u003eClarias gariepinus\u003c/em\u003e specimens. Adult stage ponds, particularly AAU 1 and AAU 3, receive an average feed amount of between 438 and 585 g/day, with a maximum observed amount of 2,235 g/day in AAU 1. The adult stage showed significant variability in feed intake. The juvenile stage AAU 2 showed the highest average intake, estimated at 714 g/day, and a peak of 1630 g/day. This value reflects a phase of active growth, characterized by significant nutritional needs. For fry present in AAU 4 and AAU 5, the daily quantities distributed are lower, with averages between 338 and 421 g/day, and maximums of up to 824 g/day, particularly in AAU 5. The minimum values recorded for all AAUs reached 0 g/day, indicating that no feed was provided some days. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF illustrate the daily feeding frequency observed in the ponds of the different AAU, depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. Frequencies are expressed in terms of the number of meals per day (meals/day). The average feeding frequency was generally stable across the AAUs, with values ranging from 1.12 meals/day for adults, i.e. observed in AAU 3, to 1.20 meals/day for fry observed in AAU 4. The maximum frequency observed is 2 meals/day in all AAUs. However, minimum frequencies of 0 meals/day were recorded in several AAUs, indicating the absence of feeding certain days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC illustrates the distribution of feed grain sizes (mm) used in the different ponds of AAUs, depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. Fine grain sizes, i.e. 1 mm and 2 mm, are mainly used for the fry stage, with peak usage at 2 mm, with 65 fish specimens. The juvenile stage also uses 2 mm pellets, but in smaller proportions, i.e. n\u0026thinsp;=\u0026thinsp;22, and remains poorly represented for other sizes, i.e., 1 mm, 3 mm, and 4.5 mm. Among adult fish, the 4.5 mm size fraction is by far the most dominant, with a number of specimens exceeding 160, making it the most common size fraction across all stages. Finally, the 6 mm particle size is exclusively reserved for adults, but is used only marginally. The nutritional composition of the feed pellets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) used in the AAUs is presented according to their particle size, as mm. These floating pellets, from the Raanan Fish Feed\u0026reg;, are formulated from various ingredients such as fish meal, poultry by-products, oilseeds, cereals, as well as vitamin and mineral supplements. The protein content decreases as the particle size increases. In fact, 1 mm pellets, i.e. intended for fry, contain up to 55% protein, while 4.5 mm and 6 mm pellets, used for more advanced stages, i.e., juvenile and adult, contain only 38% to 42%. Lipids range from 10% to 13%, with the highest values observed in smaller granules. Fiber, i.e. 2%, and phosphorus, i.e. 1.3%, remain constant regardless of pellet diameter. However, ash content shows a slight upward trend with pellet size, rising from 9% to 10%. Diets also contain vitamins A and C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Loading capacities and degrees of connection within AAUs\u003c/h2\u003e \u003cp\u003eThe surface density as well as the volume density are presented by \u003cem\u003eClarias gariepinus\u003c/em\u003e specimens GSF. Load capacities and their degree of connection with feeding practices were presented, too. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the average surface density of \u003cem\u003eClarias gariepinus\u003c/em\u003e fish, i.e. specimens/m\u0026sup2;, in relation to their AAUs in AAU aquaculture ponds. Variations were observed between GSF. The juvenile stage has the highest density, with an average of 1,064\u0026thinsp;\u0026plusmn;\u0026thinsp;705 specimens/m\u0026sup2; and a maximum of 2,387 specimens/m\u0026sup2;. At the juvenile stage, density is intermediate, with an average of 220\u0026thinsp;\u0026plusmn;\u0026thinsp;116 specimens/m\u0026sup2; and a maximum of 503 specimens/m\u0026sup2;. A reduction in the stocking density of the ponds was observed as the fish grew. Finally, the adult stage recorded the lowest density, with an average of 64 specimens/m\u0026sup2; and a maximum of 84 specimens/m\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB illustrates the density of fish (individuals/m\u0026sup3;) in \u003cem\u003eClarias gariepinus\u003c/em\u003e aquaculture ponds in different AAUs according to the GSF. A logical trend-based increase in volumetric densities was observed depending on GSF. In other words, the earlier age fish are, the higher their density tends to be. In the adult stage, the lowest densities are observed, with averages of 62.52 specimens/m\u0026sup3; for AAU 1 and 48.63 specimens/m\u0026sup3; for AAU 3. Aquaculture practices are much less frequent for fish at the stage of sexual maturity. The juvenile stage, in AAU 2, has a higher density, with an average of 183.66 individuals/m\u0026sup3; and a maximum of 418.85 individuals/m\u0026sup3;. The highest densities are recorded among fry, in both AAU 4 and AAU 5. AAU 4 recorded an average of 979.39 specimens/m\u0026sup3; with a maximum of 1,352.82 specimens/m\u0026sup3;, while AAU 5 recorded an average of 794.53 specimens/m\u0026sup3; with a peak of 1,989.44 specimens/m\u0026sup3;. The amounts of affinity between the three key variables of aquaculture practices, namely the amount of feed distributed per day, as in g/day, feeding frequency, i.e. in meals/day, and stocking density, i.e. in individuals/m\u0026sup2;, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A weak but positive correlation was observed between the amount of feed distributed and feeding frequency, i.e. ρ\u0026thinsp;=\u0026thinsp;0.38, suggesting that ponds where fish are fed more often also receive larger overall amounts of feed. However, no correlation was observed between surface density and the two feed variables, with coefficients close to zero, i.e. ρ\u0026thinsp;\u0026asymp;\u0026thinsp;0. This showed that the amount of feed and the frequency of meals are not adjusted to the stocking density, which could reflect a lack of a feeding strategy proportional to the number of fish found in AAU aquaculture ponds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman correlations (ρ) between stocking density in aquaculture ponds and feeding practices across AAUs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFish feed quantity (g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFish feed quantity (g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeeding frequency (meals/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAreal density (specimens/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding frequency (meals/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAreal density (specimens/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Influence of GSF on aquaculture practices\u003c/h2\u003e \u003cp\u003eThe Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents three key variables of the integrated agro-aquaculture system, i.e., the amount of feed distributed per day, i.e. in g/day, the feeding frequency, i.e. in meals/day, and the stocking density, i.e. in specimens/m\u0026sup2;, according to \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF, i.e., fry, juvenile, and adult. The amount of feed distributed varied significantly between GSFs, i.e. p\u0026thinsp;=\u0026thinsp;1.56 \u0026times; 10⁻⁷, confirming that nutritional requirements change markedly as fish grow. On average, juveniles received the highest amounts of feed, followed by adults, while fry received the lowest rations. Moreover, the surface density of fish stocking shows a highly significant difference depending on the stage, i.e. p\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁶, illustrating a wide variation in stocking practices depending on the size and the fish GSF. The fry are raised at very high densities, while the adults occupy less densely populated ponds. However, feeding frequency differs only marginally according to GSF, i.e. p\u0026thinsp;\u0026asymp;\u0026thinsp;0.05, which may indicate a tendency to apply a quasi-uniform frequency across all AAUs, regardless of GSF, despite theoretical variations in feeding demands.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariation in aquaculture practices according to \u003cem\u003eClarias gariepinus\u003c/em\u003e specimens GSF, based on the Kruskal-Wallis test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQuantity of feed (g/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eꭓ\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eddl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical relevance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31343\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56 \u0026times; 10⁻⁷\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeeding frequency (meals/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e* \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026asymp;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAreal density (specimens/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eꭓ\u0026sup2;\u003c/em\u003e Khi square test overall AAUs\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eddl\u003c/em\u003e number of degrees of freedom\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Drain frequency, agro-aqua water management, and efficiency of water reuse in AAUs\u003c/h2\u003e \u003cp\u003eThe effectiveness of reusing aquaculture effluent for irrigation was presented by AAU and based on GSF, i.e., fry, juveniles, and adults. The variation in the volumes of water used and reused was also presented as a function of GSF, fry, juveniles, and adults. The relationship between V\u003csub\u003eAqua\u003c/sub\u003e and V\u003csub\u003eReUsed\u003c/sub\u003e was calculated according to AAUs. The average time intervals between two complete drainings of aquaculture ponds varied moderately from one AAU to another. Aquaculture ponds are emptied completely every 3 to 5 days on average, at least during the experimental period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). AAU 1 indicated an average interval of 3 days between two drainages. AAU 2, AAU 3, and AAU 5 reported an average emptying interval of 4 days. AAU 4 reported the longest interval, with an average of 5 days between two drainings. No partial draining or purging is undertaken with regard to aquaculture ponds, i.e., only complete draining of these ponds was undertaken. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA illustrates the average daily water volumes (m\u0026sup3;/day) measured in the five AAUs, distinguishing between the volume used for aquaculture production and the volume reused for irrigation from effluents from aquaculture ponds. The calculated reuse efficiency, as %, therefore corresponds to the ratio of the volume of water reused from aquaculture pond effluent to the volume of water used for aquaculture production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn AAU 1, dedicated to eggplant and okra production, the average water volume used is 0.85 m\u0026sup3;/day, while 0.84 m\u0026sup3;/day is reused for irrigation, representing an efficiency rate of 98.8%. AAU 2, with no irrigated production, has an aquaculture volume of 0.69 m\u0026sup3;/day and a reused volume of 0.67 m\u0026sup3;/day, representing 97.1% efficiency. In AAU 3, comprising two adult-keeping ponds irrigating banana trees, 0.58 m\u0026sup3;/day are pumped into the aquaculture component, compared to 0.53 m\u0026sup3;/day reused for cropping irrigation, representing an efficiency of 91.4%. AAU 4, which grows tomatoes, cabbage, and papaya, uses 0.53 m\u0026sup3;/day for l'aquaculture and reuses 0.48 m\u0026sup3;/day, yielding an efficiency of 90.6%. Finally, the AAU 5 cropping papaya recorded a water usage volume of 0.86 m\u0026sup3;/day, with a reused water volume slightly above 0.89 m\u0026sup3;/day, resulting in an apparent efficiency of 103.5%. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB shows the average daily volumes of water, as m\u0026sup3;/day, used for aquaculture and reused for horticultural irrigation, relative to \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF, namely fry, juveniles, and adults. The water used for irrigation in the cropping component corresponds to the water delivered to the aquaculture ponds within each AAU. The calculated reuse efficiency, as %, therefore corresponds to the ratio of the volume of water reused (from effluent from aquaculture ponds) to the volume of water used for aquaculture production. For the fry GSF, the volume of water used for aquaculture production amounted to 0.70 m\u0026sup3;/day, with an average reuse of 0.69 m\u0026sup3;/day for cropping irrigation, for an efficiency of 98.6%. At the juvenile GSF, the volume used for aquaculture production was 0.69 m\u0026sup3;/day, with 0.67 m\u0026sup3;/day reused for irrigation of associated crops, representing a reuse efficiency of 97.1%. Finally, at the adult GSF, the volume pumped into aquaculture ponds is 0.67 m\u0026sup3;/day, compared to 0.63 m\u0026sup3;/day reused, representing a reuse rate of 94%.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the variations in water volumes used for aquaculture production and those reused from aquaculture effluents as irrigation water, across the five AAUs and across all three GSF. The volumes of water used in the aquaculture component did not vary significantly between AAUs, i.e. \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.216. The volumes of water reused for cropping irrigation did not vary significantly among AAUs, i.e. \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.113, as well. The same trend was observed with regard to the volumes of water used in aquaculture and the volumes of water reused from aquaculture effluents for cropping irrigation, according to the three \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF, namely fry, juveniles, and adults. In fact, these two data points related to water volume capacity didn't change much between the GSF, whether it was the amount of water used for aquaculture production, i.e. \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.579\u0026thinsp;\u0026gt;\u0026thinsp;0.05, or for the volumes of water reused from aquaculture effluents and directed towards cropping production, i.e. \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.519\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange in water volumes used for aquaculture production and water volumes reused from aquaculture effluent for irrigating cropping in AAUs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eV\u003csub\u003eAqua\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eV\u003csub\u003eReUsed\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGSF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eχ\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1094647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eddl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistical relevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eAqua\u003c/em\u003e\u003c/sub\u003e Volume of water used for aquaculture production (m\u0026sup3;/day)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003e\u003cem\u003eReUsed\u003c/em\u003e\u003c/sub\u003e Volume of aquaculture effluent reused for crop irrigation (m\u003csup\u003e3\u003c/sup\u003e/day)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eAAU\u003c/em\u003e Agro-Aqua Unit\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eGSF\u003c/em\u003e Growing Stage of Fish\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNS\u003c/em\u003e Non Significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe relationship between the daily volumes of water used for aquaculture production and the volumes reused for cropping irrigation, according to the \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF in the different AAU, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This relationship is linear and strong, i.e. τ\u0026thinsp;=\u0026thinsp;0.9054111, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;2.20e-16, and the volume of water used for aquaculture production increases with the volume of water used from aquaculture effluents and reused for cropping irrigation. Thus, the greater the volume brought into aquaculture ponds for aquaculture production, the greater the volume of water reused for associated cropping irrigation. In other words, the correlation is positive and very strong, with τ\u0026thinsp;=\u0026thinsp;0.91. This trend is clearly visible in the scatter plot, according to the \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF, which are fry, juveniles, and adults (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Cropping component description and water requirements in AAUs irrigated production\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the irrigated areas (in m\u0026sup2;) and the types of cropping associated with each AAU. These areas correspond to the areas actually irrigated using aquaculture effluent. Significant variability in cultivated areas and diversity in the cropping type choice are observed between AAUs. AAU 5, entirely devoted to papaya production, has the largest irrigated area in the agro-aqua system, covering 1,250 m\u0026sup2;, an indicator of the focus on long-term production requiring high water demand. Although AAU 2 had a surface area of 1,228 m\u0026sup2;, no active cropping took place there. The AAU 4, which combines three vegetable species, e.g., tomatoes, cabbage, and papaya, covers a large area of 1,135 m\u0026sup2;, making one of the most diverse in terms of plant species and offering high potential for enhancement. AAU 1, dedicated to eggplant and okra production, covers 900 m\u0026sup2;, while AAU 3, dedicated exclusively to bananas, has the smallest area at 400 m\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe estimated coverage of production water supply needs through recycling aquaculture effluent is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This concerns the average daily volumes of water reused from aquaculture ponds, the water requirements for cropping in each AAU, and two agro-related water performance indicators: the WRI and the EWD. WRI refers to the proportion of water needed to meet production water requirements, which is effectively supplied by irrigation using aquaculture effluent. EWD is the proportion of water supplied from a source other than aquaculture effluent, i.e. 1 \u0026ndash; WRI. Insufficient coverage of irrigation needs was observed across all AAUs, despite good reuse of aquaculture effluent water. In fact, WRI values varied between 1.6% and 26.17%. AAU 4, consisting of tomato, cabbage, and papaya production, has a high requirement of 29.91 m\u0026sup3;/day for a reused volume of only 0.48 m\u0026sup3;/day, resulting in an extremely low WRI of 1.62%, with a critical EWD of 0.98. AAU 5, under papaya production, has a WRI of 7.77% despite a relatively high reuse volume of 0.89 m\u0026sup3;/day. Conversely, AAU 1, eggplant and okra production, and AAU 3, banana production, recorded the best WRIs in the system, at 21.85% and 26.17% respectively, although still well below full water self-sufficiency. None of the AAUs are therefore able to fully meet the water requirements of production using recycled water volumes as a sole source. Dependence on an external water source, namely borehole water, remains necessary and varies from 0.74 to 0.98 depending on the units.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage volumes of water reused per day for irrigation, estimated daily requirements for vegetable production, water requirement coverage ratio, external water dependency, and associated cropping pattern for each AAU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAAU 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociated irrigated cropping\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVolume of aquaculture effluent reused for crop irrigation (m\u003csup\u003e3\u003c/sup\u003e/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNet daily water requirements for production (m\u003csup\u003e3\u003c/sup\u003e/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage of water needs (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoverage status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExternal water dependency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCropping sustainability status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEggplant and Okra\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.85\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow coverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate dependence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAU 2*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAU 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModerate dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAU 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTomato, Cabbage, and Papaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery low coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCritical dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAAU 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePapaya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVery low coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCritical dependence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e*No production is associated with AAU 2\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eAAU\u003c/em\u003e Agro-Aqua Unit\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe Water Reuse Index (WRI) represents the proportion of water requirements covered by the reuse of aquaculture effluents; External water dependency represents the remaining portion of water requirements that must be met by external sources, including well water\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eCoverage status and dependency level are defined according to established thresholds: low or very low coverage, i.e., \u0026lt;\u0026thinsp;30%; critical dependence, i.e., \u0026ge; 0.9\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Emissions and water quality in AAUs\u003c/h2\u003e \u003cp\u003eThe daily variation in the water quality of aquaculture ponds is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The effect of GSF on water quality was assessed, as well. The degree of correlation between the water quality variables was also presented on the basis of a correlation matrix plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Daily measurements were used to assess the characteristics of the water in the aquaculture ponds in each of the AAUs. The parameters measured include pH, temperature, EC, TDS, salinity, water SpG, ORP, and H\u003csub\u003e₂\u003c/sub\u003e concentration. These parameters indicated relatively low variability between AAUs, reflecting overall homogeneity in water quality in the ponds during the observation period. The pH varied very little, between 6.44 and 6.48, indicating slightly acidic water but still close to neutral, which is optimal for the production of \u003cem\u003eClarias gariepinus\u003c/em\u003e. The water temperature remained stable at around 24\u0026deg;C, typical of a tropical environment and favorable for fish growth. EC ranges from 594 to 652 \u0026micro;S/cm, reflecting moderate mineralization of the aquatic environment in AAUs. TDS were concentrated between 302 and 328 ppm, reflecting salinity values ranging from 303 to 328 ppm. ORP is negative in all ponds, with values ranging from \u0026minus;\u0026thinsp;84 to -63 mV, indicating a reducing environment typical of a system rich in organic matter. Finally, concentration in molecular hydrogen (H₂) was relatively stable between the ponds, with values ranging from 1396 to 1421 ppm.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe main water quality-related parameters monitored in the aquaculture ponds of the AAUs, as a function of \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF, i.e., fry, juveniles, and adults, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Some parameters varied depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. In fact, the water temperature varied from 21.03\u0026deg;C for fry to 25.03\u0026deg;C for juveniles, with an inter-mean value of 23.2\u0026deg;C for adults. The pH remains generally neutral to slightly basic, ranging from 7.23 for fry to 7.49 for adults. EC remains relatively stable across the three GSF factors, with values ranging from 668 to 686 \u0026micro;S/cm. Similarly, TDS varied very minimally between stages, i.e., 658.67 mg/l in adults, 683 mg/l in fry, and 678.33 mg/l in juveniles. However, the concentration of DO remained very low in all cases, between 0.19 and 0.31 mg/l. Suspended solids (SS) clearly increase with GSF. In fact, MES levels are 37.17 mg/l in adults, 61.67 mg/l in fry, and reach a maximum of 95.5 mg/l in juveniles. Total carbon and BOD were highest in fry, with values of 7.98 mg/l and 130 mg/l, respectively. Total nitrogen concentrations remain stable between the fry and adult GSF, but peak in juveniles at 28.67 mg/l. Orthophosphate was also higher in juveniles, at 32.2 mg/l, compared to approximately 24.5 mg/l in both fry and adults. Potassium (K) was highest in fry, at 13.25 mg/l, and lowest in juveniles, at 6.8 mg/l.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAquaculture effluent water quality according to \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInf..Sup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJuvenile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eANOVA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.9 ... 26.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.03a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.03a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.2a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogen Potential (pH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.22 ... 7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.27a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.23a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.49a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrical Conductivity (EC in \u0026micro;S/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e664 ... 690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e685.67a\u0026thinsp;\u0026plusmn;\u0026thinsp;5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e676a\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e668a\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDissolved Oxygen (DO in mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19 ... 0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Dissolved Solids (TDS in mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e657 ... 687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683a\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e678.33a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e658.67a\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspended Dissolved Matters (SDM in mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 ... 107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.67a\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.5a\u0026thinsp;\u0026plusmn;\u0026thinsp;9.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.17a\u0026thinsp;\u0026plusmn;\u0026thinsp;11.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Carbon (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.1 ... 8.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.98a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.3a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Nitrogen (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 ... 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.67a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.67a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25a\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrthophosphate (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.2 ... 32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.73a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.2a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.53a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological Oxygen Demand (BOD in mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70 ... 130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130a\u0026thinsp;\u0026plusmn;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.33a\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113.33a\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (K in mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.7 ... 13.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.25a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.8a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.17a\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eANOVA\u003c/em\u003e Single factor analysis of variance\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eUsual units are shown in brackets\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNS\u003c/em\u003e Non Significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe quality of the water effusing from aquaculture ponds, as measured by parameters such as BOD, K, SDM, TDS, EC, and temperature, varied significantly depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. The quality derived from pH, total nitrogen, and orthophosphate varied moderately significantly depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. In addition, three other parameters showed a relativity in statistical significance, with 0.05\u0026thinsp;\u0026lt;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.06. In turn, total carbon, i.e. p\u0026thinsp;=\u0026thinsp;0.0608 and OD, i.e. p\u0026thinsp;=\u0026thinsp;0.1481, significantly differed between GSF. The fry and juvenile GSF showed the most significant differences, particularly in terms of BOD, K, salinity, and mineralization, i.e., TDS, EC. The Spearman correlation matrix between the various water quality-related parameters measured in the aquaculture ponds of the agro-aqua system revealed several insignificant degrees of correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). EC was positively correlated, i.e. ρ\u0026thinsp;=\u0026thinsp;0.908, with TDS in agro-aqua waters. By the same logic, orthophosphate was also positively correlated, i.e.ρ\u0026thinsp;=\u0026thinsp;0.876, with total nitrogen. The same trend was observed between suspended solids (SS) and orthophosphate, i.e. ρ\u0026thinsp;=\u0026thinsp;0.777, between BOD and potassium, i.e. ρ\u0026thinsp;=\u0026thinsp;0.923, and between total nitrogen and SS, ρ\u0026thinsp;=\u0026thinsp;0.605. However, negative correlations were observed between temperature and BOD, i.e. ρ = -0.944, and between temperature and potassium content, i.e. ρ = -0.916. pH was also negatively correlated with DO, i.e. ρ = -0.789, and negatively correlated with EC (ρ = -0.745). Total carbon was negatively correlated, i.e. ρ = -0.590, with total nitrogen. Finally, BOD was also correlated, i.e. ρ = -0.693, with total nitrogen.\u003c/p\u003e \u003cp\u003eTemperatures ranged from 20.9 to 26.1\u0026deg;C, with an average of 23.09\u0026deg;C, well below the critical level of 35\u0026deg;C set for cropping irrigation. The pH, between 7.22 and 7.55, indicates neutral to slightly alkaline water, in line with accepted agro-aqua standards of between 6.5 and 8.4. The average EC was 676.56 \u0026micro;S/cm, very close to the upper limit of 700 \u0026micro;S/cm. Also, TDS concentration averaged 673 mg/l, well within the tolerable range for irrigation, which is 450 to 2000 mg/l. However, many other parameters indicated high concentrations, which could affect the agro-aqua quality of the water. DO value was very low, with an average of 0.28 mg/l. Suspended solids (SS), with an average of 64.78 mg/l, slightly outstripped the standard of 50 mg/l. Total carbon is measured at 6.29 mg/l, with no specific standard references, but indicating a significant presence of organic matter. Total nitrogen reached an average of 26.11 mg/l, well above the recommended standard of 10 to 15 mg/l. Orthophosphate, with an average value of 27.16 mg/l, greatly exceeded the threshold of 5 mg/l set for irrigation. BOD, a key indicator of biodegradable organic load, has an average value of 105.56 mg/l, which is more than three times the permitted limit, i.e. \u0026lt; 30 mg/l. Meanwhile, potassium averages 9.74 mg/l, close to the upper limit of 10 mg/l.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Aquaculture effluent structuration at different GSF in irrigated AAUs\u003c/h2\u003e \u003cp\u003eThe water quality of aquaculture effluent intended for fertilizing cropping in AAU was structured depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF in aquaculture ponds (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). The structure was presented according to two main factorial dimensions. These two dimensions alone explained 92.8% of the total variance, with 52.9% for the first factorial component and 39.9% for the second factorial component. These two factor dimensions were therefore selected for interpretation. Dimension 1 is strongly correlated with parameters related to organic and mineral load, including BOD, i.e. -0.98, potassium, i.e. -0.88, total nitrogen, i.e. +0.94, orthophosphates, i.e. +0.97, suspended solids (+\u0026thinsp;0.78), and temperature, i.e. +0.91. Dimension 1 thus made it possible to compare organically enriched waters with less enriched waters. Dimension 2 is mainly structured by EC, i.e. +0.96, TDS, i.e. +0.98, DO, i.e. +0.82, and total carbon, i.e. +0.60. Conversely, pH is negatively correlated with this dimension, i.e. -0.89. This is indication that dimension 2 mainly reflects the mineral properties and redox state of the water.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClear discrimination was observed on both factor dimensions, i.e. Dimension 1\u0026thinsp;=\u0026thinsp;52.9% \u0026times; Dimension 2\u0026thinsp;=\u0026thinsp;39.9%, between aquaculture ponds according to the GSF. The ponds containing adult specimens are projected in the lower right quarter, strongly associated with temperature, total nitrogen, and orthophosphate. The ponds containing fry GSF specimens are located in the upper left part of the plan, linked to BOD, potassium, and total carbon. The ponds containing juvenile specimens occupy the lower left section, mainly associated with lower pH and moderate DO and SDM values. The representation of specimens on the two factor dimensions, i.e. Dimension 1 \u0026times; Dimension 2, reveals a clear distinction between ponds based on the GSF. The juvenile ponds marked with pink dots are grouped in the lower right-hand corner of the graph, associated with high temperatures, orthophosphate, and total nitrogen values, reflecting increased mineral inputs and more pronounced biological activity. The fry ponds marked with green triangles are located in the upper left quarter, associated with high concentrations of BOD, total carbon, and potassium, indicating a high organic load. Adult ponds marked in blue squares are projected in the lower left quadrant, associated with lower pH levels and a more moderate influence of other variables, reflecting relative fertilizing stability.\u003c/p\u003e \u003cp\u003eThe fertilizing potential of aquaculture waters was assessed by comparing nitrogen (N), phosphorus (P), and potassium (K) inputs, expressed as a percentage of actual cropping requirements, according to the GSF, i.e., adult, juvenile, fry (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Effluents from adult fish ponds provide high to very high phosphorus coverage for all croppings, \u0026gt;\u0026thinsp;60% for tomatoes, eggplants, bananas, and cabbage. Nitrogen is particularly well covered for bananas, i.e. 149.52%, but remains moderate for other croppings, ranging from 30 to 44% for tomatoes, okra, and cabbage. Potassium remains the least covered factor, with levels ranging from 4.38% for papaya to 16.22% for tomatoes. At the juvenile GSF, fertilizer coverage rates, e.g., N, P, and K generally decrease. Given that the juvenile GSF in AAU 2 did not contain irrigated cropping by aquaculture effluent, the coverage rate was estimated on the basis that all cropping was present in the other AAUs. In these AAUs, nitrogen was still best supplied to bananas, i.e. 70.51%, followed by tomatoes, i.e. 20.86%. Phosphorus values are moderate for eggplant, i.e. 45.70%, and tomato, i.e. 35.15%, and low for papaya and okra, i.e.\u0026lt; 10%. Potassium did not exceed 5% for the majority of AAU cropping. Effluents from ponds containing fry have similar nutrient input rates to those in the juvenile GSF, but with slightly higher potassium values. Bananas remain the cropping with the highest nutrient requirements, requiring 79.42% nitrogen, 40.06% phosphorus, and 10.73% potassium. Tomatoes and cabbage also benefited from significant nitrogen and phosphorus coverage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study has demonstrated significant variability in the aquaculture production parameters of \u003cem\u003eClarias gariepinus\u003c/em\u003e depending on GSF. The fry GSF is characterized by a very high production density of 1,064 specimens/m\u003csup\u003e2\u003c/sup\u003e on average, with a maximum of 2,387 specimens/m\u003csup\u003e2\u003c/sup\u003e, compared to 220 specimens/m\u003csup\u003e2\u003c/sup\u003e for juveniles and 64 specimens/m\u003csup\u003e2\u003c/sup\u003e for adults. This high concentration, often associated with an intensive fry rearing phase, aims to maximize the number of fish produced in a smaller area. However, such a strategy increases the organic load in aquaculture ponds, even when daily rations remain moderate, i.e., approximately 370 g/day. Cumulative waste consisting of fish excreta and uneaten feed can quickly deteriorate aquaculture water quality if drainage and purging operations are not carried out frequently enough. Juveniles receive the highest amounts of feed, e.g., 714 g/day, reflecting a phase of rapid growth and intense metabolism. Adjusting feed intake at this stage is crucial for optimizing feed conversion ratio. But adults get more stable feed, e.g., about 560 g/day, at a lower density, which shows they are in the finishing GSF phase where their metabolic needs are lower. However, observing days without feeding, i.e., with 0 g/day, reveals a lack of coordination or temporary unavailability of feed, which can adversely affect the regularity of aquaculture production performance.\u003c/p\u003e \u003cp\u003eIn terms of grain size, feed pellet sizes generally comply with recommendations of 1 to 2 mm for fry GSF, 2 to 3 mm for juveniles GSF, and 4.5 to 6 mm for adults GSF. However, discrepancies were noted, particularly with regard to the use of 4.5 mm pellets for fry GSF and the low presence of 6 mm pellets for adults GSF. An unsuitable size can lead to feed losses and increase organic pollution. The effectiveness of feeding depends on the match between grain size and the size of the mouth cavity of fish. These discrepancies are thought to be due to limited availability of feed on the local market, requiring fish farmers to use whatever formats are available, even if they are unsuitable. The fish feed pellets used have a rich composition, with 42% to 55% protein, 10 to 13% lipids, and approximately 1.3% phosphorus, which corresponds to the nutritional requirements of \u003cem\u003eClarias gariepinus\u003c/em\u003e at different GSF. This nutritional profile supports GSF, but requires careful management to avoid excessive nitrogen and phosphorus runoff. Unabsorbed nutrients are mainly excreted in dissolved and particulate form, altering water quality. Feed composition is a strategic lever for managing effluent quality. Furthermore, analyzing correlations between fish density and the amount of feed distributed (ρ\u0026thinsp;\u0026asymp;\u0026thinsp;0) suggests a lack of rational adjustment, which could lead to either underfeeding or overfeeding. These imbalances compromise both GSF and water quality. Integrated management of density, feeding frequency, feed distribution, and rations is therefore essential for optimizing aquaculture and cropping performance. Pond emptying frequency varies depending on AAU, with an average interval of 4 days. AAU 1 has a short purge frequency, i.e., 3 days, while AAU 4 is drained every 5 days. These differences reflect varying practices depending on organic loads and logistical constraints. Regular replacement is essential to limit waste accumulation. However, emptying the pond too frequently can be counterproductive. Aquaculture ponds are completely drained, contrary to recommendations that advocate partial drainage to limit stress on the fish and improve water quality.\u003c/p\u003e \u003cp\u003eExceptionally high aquaculture water reuse efficiency was observed, ranging from 90.6% to 98.8% with a peak of 103.5% in AAU 5. This efficiency is measured by the proportion of water volumes pumped for aquaculture production that are effectively reused for irrigation. The absence of statistically significant variation between volumes used and reused, whether between GSFs or between AAUs, reflects relatively consistent water management in the system being studied. These high levels of efficiency illustrate effective coordination between aquaculture and cropping, a criterion often cited as the basis for successful integrated systems. Careful synchronization of agricultural and aquaculture cycles made it possible to optimize water regulation. The apparent efficiency of over 100% observed in AAU 5 could be explained by external water inputs or unplanned withdrawals from aquaculture ponds, which are not included in the monitoring system. This anomaly highlights the importance of rigorous monitoring of water balance in the integrated system. Furthermore, a slight decrease in reuse efficiency is observed as the fish grow. In fact, it drops from 98.6% in fry to 97.1% in juveniles and 94% in adult fish. This decline is partly due to the gradual reduction in the volume of water pumped into aquaculture ponds with older fish, which require lower water depths due to their metabolism and lower production density.\u003c/p\u003e \u003cp\u003eThe extremely high-ratio positive correlation between the volumes of water injected for aquaculture production and those reused (τ\u0026thinsp;=\u0026thinsp;0.91) confirms efficient water management, with few losses in the system. The uniformity of practices between AAUs suggests consistency in water management, including drainage frequency, drainage method, and effluent reuse. However, technical uniformity masks certain structural limitations. The case of AAU 2 is particularly revealing due to the lack of assignation of aquaculture effluents to production, reflecting a lack of functional integration. However, successful agro-aqua integration relies on the operational complementarity of animal and plant components, beyond simple technical connections [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A comparison of the volumes of water reused versus the water requirements for cropping shows that the WRI remains largely insufficient. In all AAUs, water use remains below 30%, ranging from 1.6% to 26.17%, confirming a significant dependence on external inputs, as also evidenced by a water dependency ratio of over 70%. In several agro-aqua integrated systems in West Africa, aquaculture effluents cover only a small portion of production needs due to an imbalance between aquaculture production and cropping demands. AAU 4 and AAU 5 illustrate this unbalanced pattern. Despite the presence of demanding crops such as tomatoes, cabbage, and papaya, the volumes of reused water are largely insufficient, amounting to 0.48 m\u003csup\u003e3\u003c/sup\u003e/day for a requirement of 30 m\u003csup\u003e3\u003c/sup\u003e/day in AAU 4, and 0.89 m\u003csup\u003e3\u003c/sup\u003e/day for a requirement of 11.5 m\u003csup\u003e3\u003c/sup\u003e/day in AAU 5. AAUs with large areas under cultivation or species with high water requirements have the lowest WRIs, even though the volumes available at the outlet of the aquaculture pond are comparable to other AAUs. Meanwhile, AAU 2, without irrigated production, illustrates the limitations of good hydraulic performance that is not exploited for cropping purposes. The overall efficiency of integrated systems depends as much on technical synchronization as on productive planning. Although standardizing the volumes of water injected into aquaculture ponds facilitates daily management, restrictions on the AAU ability to adjust to fluctuating needs, i.e., the frequency of drainage, water quality, production density, and DO vary according to specimens GSF, as do the water requirements of production according to its cycle, climatic conditions, and soil properties. More precise modulation of irrigation and water supply, taking these water parameters into account, would strengthen the AAU livelihood consistency. Integrated aquaculture irrigation takes advantage of the fact that fish filter water, making it a reusable resource for irrigation. However, such complementarity can only be fully realized if there is close coordination between aquaculture and cropping components, in order to ensure real water savings and the achievement of agro-aqua objectives.\u003c/p\u003e \u003cp\u003eEffluents from aquaculture ponds at AAUs showed high concentrations of nutrients. The total nitrogen content, ranging from 25.1 to 28.7 mg/l, orthophosphate content, up to 32.2 mg/l in juveniles, potassium content, 13.25 mg/l in fry, and BOD content, ranging from 73.3 to 130 mg/l, indicate a significant organic and mineral load. These concentrations are significantly higher than the prescribed thresholds for irrigation, such as 5 mg/l of phosphorus or 30 mg/l of BOD. They indicate high fertilizing potential, but also risks of pollution if effluents are poorly managed. In terms of temperature, measurements varied depending on GSF, with 21\u0026deg;C for fry, 25\u0026deg;C for juveniles, and 23.2\u0026deg;C for adults. These values remain within the thermal tolerance range of \u003cem\u003eClarias gariepinus\u003c/em\u003e, which is 8\u0026deg;C to 30\u0026deg;C. Temperatures around 25 to 26\u0026deg;C promote optimal growth, which could explain a slowdown in fry exposed to cooler waters during the dry season. The pH values observed, ranging from 7.23 to 7.49, are optimal for \u003cem\u003eClarias gariepinus\u003c/em\u003e. However, DO levels, which are very low, ranging from 0.21 to 0.31 mg/l at all GSF, are cause for concern. Although the species can relatively tolerate hypoxia, DO values below 3.5 mg/l cause chronic stress, reduced feeding, decreased feed conversion, and increased vulnerability to disease [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Total nitrogen, although present in high concentrations, exceeds levels compatible with optimal aquaculture development, which range from 0.2 to 10 mg/l [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These high levels are comparable to those observed in many other aquaculture effluents and can be explained by the accumulation of feed residues and metabolic waste. Similarly, high levels of SDM, BOD, and orthophosphate can lead to increased ecotoxicity and impaired quality of irrigated soils. The composition of these agro-aqua waters reflects a combination of factors including high aquaculture density, nitrogen- and phosphorus-rich protein feed, incomplete nutrient assimilation, water renewal frequency [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and GSF. These features give aquaculture wastewater significant fertilizing potential. There is a clear interest in taking advantage of these aquaculture effluents rich in nitrogen, phosphorus, and potassium, in order to reduce dependence on other types of synthetic fertilizers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The use of aquaculture effluent from \u003cem\u003eOreochromis niloticus\u003c/em\u003e significantly increases the growth of lettuce and tomatoes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, su richness can, as well, have negative effects, including soil clogging, root asphyxiation, acidification, and pollution from leaching, especially in gravity irrigation or on poorly permeable soils.\u003c/p\u003e \u003cp\u003eThe relationship between GSF and the quality of water emitting from aquaculture subcomponent, expressed as an estimate of the fertilizer coverage rate for irrigated cropping from aquaculture effluents, was determined. The adult GSF generates the most nutrient-rich effluents, particularly in terms of nitrogen and phosphorus, thereby implying that during a growing season characterized by intensive feeding, organic aquaculture effluents reach particularly high levels, indicating a direct relationship between the GSF, their dietary requirements, and organic load. The exceptionally high nitrogen coverage observed for bananas, up to 149.52%, highlights that in some cases, aquaculture effluents may be sufficient to meet or even exceed nitrogen requirements, particularly for tomatoes in tropical areas. However, this requires special attention, as over-fertilization with nitrogen can lead to nutritional imbalance or even leaching. Phosphorus is generally well covered for cabbage and eggplant production, at 60 to 85% in the adult GSF, but this rate drops to less than 35% in the juvenile and fry GSFs. Juveniles and fry excrete a higher proportion of nutrients such as nitrogen and phosphorus into aquaculture water, reflecting reduced assimilation despite high feed consumption, unlike adults. Water quality varies depending on the GSF, i.e., fry, juvenile, and adult. The fry, raised at very high densities, are associated with higher concentrations of BOD and K, reflecting a high organic load. In juveniles, the rapid growth observed correlates with high phosphorus and suspended solids emitted. Inversely, ponds containing adult specimens generate more diluted water due to lower feed rations and lower density. SDM, in particular, pose a problem at this stage in the AAUs due to their concentrations. The accumulation of SDM can irritate the gills and affect the respiratory and osmoregulatory functions of fish, in addition to polluting the water and impacting cropping. The Spearman correlation matrix confirms these observations. Indeed, strong positive correlations were observed between BDO, SDM, total nitrogen, EC, and TDS, indicating that the most beneficial waters are also the most loaded with undegraded organic matter. Conversely, temperature and DO are negatively correlated with these variables, highlighting the risks of environmental depletion. High nutrient loads also promote pH elevation, ammonia production, and bacterial proliferation, resulting in a critical decline in DO. The strong correlation between EC and TDS can be explained by their interdependence, as dissolved minerals directly influence conductivity. Despite apparent compliance with certain parameters such as pH, EC, and TDS, excessive levels of BOD, TSS, nitrogen, and phosphorus place these aquaculture waters outside acceptable cropping limits unless they are diluted or treated prior to use.\u003c/p\u003e \u003cp\u003eMost of the variability observed was summarized as follows according to \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF. The first two dimensions cumulate 92.8% of the total inertia, reflecting a strong structuring of the data in the factorial plane. The first principal component explains 52.9% of the variance and is mainly positively correlated with BOD, potassium, total nitrogen, and orthophosphate. The first component represents an organic and mineral load gradient, contrasting waters that are highly enriched in nutrients and biodegradable matter with waters that are low in load. This component can be interpreted as a nutrient load dimension. The second component, representing 39.9% of the remaining variance, is strongly influenced by EC, TDS, total carbon, and DO. The second main component expresses a gradient of mineralization and redox conditions, allowing aquaculture effluents to be classified by oxidation state, salinity, and biological stability. The projection of aquaculture ponds on the factorial design reveals a clear discrimination of effluents according to GSF, confirming the hypothesis of a link between aquaculture practices and water composition. The juvenile GSF ponds are located in an area characterized by high levels of total nitrogen, orthophosphate, and higher temperatures. This reflects a phase of active GSF, with a sustained metabolism and significant excretion of mineral nutrients. Such a pattern suggests that aquaculture effluents produced at this stage have significant cropping potential, particularly in terms of nitrogen and phosphorus. As for the fry GSF ponds, they are located in a quadrant strongly correlated with BOD, potassium, and total carbon, indicating a marked organic load. Such a composition is consistent with high fish density, protein-rich food inputs, and incomplete nutrient assimilation, leading to the accumulation of organic matter. These waters, although nutritious, pose a challenge in terms of DO management and effluent stabilization. The adult GSF ponds, instead, show a more moderate fertilization signature, with lower nutrient levels and a slight decrease in pH. This setting reflects a terminal growth or maintenance phase, with lower feed intake, reduced density, and more stable metabolic activity. The aquaculture effluents produced are thus more well-balanced and easier to use for irrigation, although less concentrated in AAU fertilizers. Multivariate analysis therefore highlights a functional stratification of aquaculture effluents, where each GSF is associated with a distinct AAU fertilizing profile. Such differing patterns are determined by the interaction between fish physiology, stocking density, feed composition, and management practices, including feeding frequency and pond emptying frequency. Among other things, the report highlights the importance of the water quality of irrigation water for agro-aqua production health.\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThe study determined the interactions within integrated AAUs \u003cem\u003eClarias gariepinus\u003c/em\u003e production and cropping in the easthern agroecological climatic condition in semi-sahel, with perspectives involving water reuse from agro-aqua effluents. Aquaculture practices vary significantly depending on \u003cem\u003eClarias gariepinus\u003c/em\u003e GSF specimens. The fry GSF is characterized by extremely high density and fractional feeding, resulting in high production of organic matter. Juveniles, although raised at lower densities, receive the largest amounts of feed, resulting in a significant accumulation of nitrogen and phosphorus in the effluent. Alternatively, adults are raised at low density, generating less organic waste while producing more stable and less concentrated water. High concentrations of total nitrogen, orthophosphate in juveniles, potassium in fry, and BOD were observed. These levels indicate significant AAUs fertilizing potential. However, this wealth comes with risks of soil clogging or organic pollution if waste is poorly managed, particularly on poorly permeable or steeply sloping soils. The hydro-flow assessment within AAUs showed a water reuse rate of over 90%, reflecting encouraging structural hydraulic performance. However, aquaculture water reuse remains low across all AAUs, highlighting a persistent reliance on external inputs from drilling and rainfall for irrigation. This shortfall can be explained by a mismatch between available volumes and existing high water production requirements, but also by incomplete synchronization between aquaculture drainage cycles and cropping calendars, in which reused water was not recycled for cropping. A clear structure has been identified with regard to aquaculture effluents depending on GSF. Waters in fry and juvenile ponds appear to be highly enriched in nutrients and organic matter, while waters in adult ponds are more diluted and stable, indicating differentiation according to aquaculture stages and structured optimization for agro-aqua use. The use of aquaculture effluents makes its possible to meet nitrogen and phosphorus requirements for certain market gardening activities. However, potassium remains limiting and targeted supplementation is necessary to balance intake. This approach reinforces the idea of partial substitution for mineral fertilizers, provided that the two agro-aqua components are carefully combined. The quality of aquaculture water, as measured by BOD, orthophosphate, SDM, etc., varied significantly depending on GSF. Although water reuse rates are satisfactory, indirect losses persist due to a lack of agro-aqua recovery in certain AAUs. Ultimately, agro-aqua integration improves yields and water use efficiency, while reflecting only partial coverage of actual production needs by aquaculture effluents uniquely.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial or non-financial, professional, or personal conflicts that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe study received no funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eToundji Olivier Amoussou and Nawroz Kareem conceptualized the study. Toundji Olivier Amoussou, Chibuye Florence Kunda-Wamuwi, Wendnso Eddie Lionel Gouem, A\u0026iuml;cha Edith Soara and Nawroz Kareem performed the formal analysis and validated the outputs. Toundji Olivier Amoussou and Wendnso Eddie Lionel Gouem investigated the study protocols. Toundji Olivier Amoussou wrote the original draft of the manuscript. Chibuye Florence Kunda-Wamuwi, Wendnso Eddie Lionel Gouem, A\u0026iuml;cha Edith Soara, Vinsoun Millogo and Nawroz Kareem edited and reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are very grateful to agro-aqua farmers of our research program for their assistance during the field work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMekonnen MM, Hoekstra AY. Four billion people facing severe water scarcity. Sci Adv. 2016;2:1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFedoroff NV, Battisti DS, Beachy RN, Cooper PJM, Fischhoff DA, Hodges CN, Knauf VC, Lobell D, Mazur BJ, Molden D, Reynolds MP, Ronald PC, Rosegrant MW, Sanchez PA, Vonshak A, Zhu JK. 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Evaluating nutrient circularity in integrated aquaculture systems: Criteria and indicators. J Clean Prod. 2025;504:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrougher DS, Douglass LW, Soares JH Jr. Comparative oxygen consumption and metabolism of striped bass \u003cem\u003eMorone saxatilis\u003c/em\u003e and its hybrid \u003cem\u003eM. chrysops\u003c/em\u003e ♀ x \u003cem\u003eM. saxatilis\u003c/em\u003e ♂. J W Aquac Soc. 2007;36:521\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGross A, Boyd CEA. Digestion procedure for the simultaneous determination of total nitrogen and total phosphorus in pond water. J W Aquac Soc. 1998;29:300\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad A, Sheikh Abdullah SR, Hasan HA, Othman AR, Nur\u0026rsquo; Izzati I. Aquaculture industry: Supply and demand, best practices, effluent and its current issues and treatment technology. J Environ Manag. 2021;287:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevenson KT, Fitzsimmons KM, Clay PA, Alessa L, Kliskey A. Integration of aquaculture and arid lands agriculture for water reuse and reduced fertilizer dependency. Exp Agric. 2010;46:173\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelaide B, Goddek S, Gott J, Soyeurt H, Jijakli MH. Lettuce (\u003cem\u003eLactuca sativa\u003c/em\u003e L. var. Sucrine) growth performance in complemented aquaponic solution outperforms hydroponics. Water. 2016;8:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Agro-aqua, Fry, Juvenile, Adult fish, Aquaculture effluent","lastPublishedDoi":"10.21203/rs.3.rs-9189278/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9189278/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater efficiency is a major concern for many agro-aqua units (AAU) in semi-sahel zone. At AAU scale, water intends to agro-aqua production is of great concern with regard to effluent-emitted during Growth Stages of Fish (GSF). The amount of water used and lost in these AAU as well as the resulting fertilizing potential are worth determining. Many descriptive and inferential approaches were used to estimate water inflows, water outflows and water reuse efficiency, and water performance indicators at AAU scales. The study demonstrated significant variability in AAU aquaculture production component according to GSF. High aquaculture water reuse efficiency was observed at proportions from 91% to 99% among AAU, with weaker statistical variations. This reflects important miletones, as adaptative options in agro-aqua production can be of concerned with regard to water recycling. Fish at adult GSF generated the most nutrient-rich effluents, particularly in terms of nitrogen and phosphorus, with high interaction between GSF, feed suppling, and organic load. High concentrations of total nitrogen, orthophosphate in juveniles, and in high concentration of potassium in fry were observed, as adding value to AAUs fertilizing potential. AAUs involving \u003cem\u003eClarias gariepinus\u003c/em\u003e production enhances cropping water in semi-sahel agro-aqua climatic conditions.\u003c/p\u003e","manuscriptTitle":"Practical drivers of water recycling in agro-aqua systems for long-term climate adaptation in semi- sahel zone","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 20:33:31","doi":"10.21203/rs.3.rs-9189278/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-19T05:14:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-19T05:11:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:11:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T09:16:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135014787622800876331722209171237699721","date":"2026-04-23T08:05:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185908894173938535812807096779957458703","date":"2026-04-16T06:28:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-08T00:25:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T12:34:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T12:34:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2026-03-22T05:50:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d02066c9-da69-4740-938c-ff0586fe0d10","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-19T05:14:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-19T05:11:47+00:00","index":36,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-10T15:11:06+00:00","index":34,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T05:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 20:33:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9189278","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9189278","identity":"rs-9189278","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}