(C) PLOS One This story was originally published by PLOS One and is unaltered. . . . . . . . . . . Yield, water productivity and nutrient balances under different water management technologies of irrigated wheat in Ethiopia [1] ['Alebachew Enyew Tiruye', 'Soil', 'Water Research Directorate', 'Adet Agricultural Research Center', 'Bahir Dar', 'Sisay Asres Belay', 'Faculty Of Civil', 'Water Resource Engineering', 'Bahir Dar University', 'Bahir Dar Institute Of Technology'] Date: 2022-12 Development of irrigation technologies and agricultural water management systems holds significant potential to improve productivity and reduce vulnerability to climate change. Our study dealt with the behavior of irrigation water productivity, partial nutrient balance and grain yield of wheat under the application of different irrigation water management technologies in the Koga irrigation scheme in Ethiopia. For our analysis, we considered three nutrient fluxes entering and leaving farmers’ fields. Our experimental design had three irrigation blocks with three different irrigation water management practices (wetting front detector, Chameleon soil moisture sensor and farmers’ practice as control) on three farm plots replicated in each block. To calculate irrigation water productivity and grain yield of wheat, the amount of irrigation water applied and the agronomic attributes of wheat yield were recorded during the irrigation period. Further, three input and output variables were considered to determine the partial nutrient balances of nitrogen (N), phosphorus (P) and potassium (K). The results showed that the amount of irrigation water used was 33% and 22% less with a wetting front detector and Chameleon sensors, respectively, compared to the farmers’ practice. The wetting front detector (WFD) and Chameleon sensor (CHS) treatments gave a 20% and 15.8% grain yield increment, respectively, compared to the farmers’ practice plot. The partial nutrient balances of N and K were negative for the wetting front detector and chameleon sensor practices while it was positive for P in the control (farmers’ practice) treatment. We conclude that irrigation water management with appropriate technologies can improve yield, water productivity and the nutrient utilization. However, further research needs to be conducted on the suitability of irrigation management technologies to achieve full nutrient balance. Copyright: © 2022 Tiruye et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The objective of this research is to study the impact on water productivity, grain yield and partial nutrient balance of wheat under application of various novel water management technologies in Ethiopia’s Koga irrigation scheme. The technologies we considered in this study were the use of wetting front detectors and Chameleon sensors, which are produced by the Virtual Irrigation Academy in Australia. These technologies were studied in comparison with farmers’ practices as control. As irrigation expands in Ethiopia, there is pressure to come up with new strategies to increase its efficiency. Water management for instance could be improved by implementing various practices to increase soil moisture retention capacity and water-use efficiency [ 8 , 9 ]. Without decreasing productivity, on-farm water use can be substantially reduced through improved irrigation technologies and more efficient water management systems [ 10 ]. In addition, such technologies can be important tools to manage the depletion of nutrients in farmers’ fields through runoff and percolation. Plant nutrient loss due to practices that do not consider the status of moisture availability, overutilization of essential nutrients, or underutilization of irrigation water result in constraints like ineffective water use, yield reduction, waterlogging, salinization, leaching of agrochemicals following contamination of groundwater and soil degradation in general. In the more modern irrigation systems, technologies are being used that enable farmers to grow additional crops [ 6 ]. Smallholder farmers in the Ethiopian highlands, however, have not received the benefits of irrigation technologies that could enable them to save water for additional crops, manage plant nutrients and reduce percolation. On the one hand, irrigation in Ethiopia is poorly developed in relation to the potential available; on the other hand, as most of the water is used up to irrigate a limited amount of land, there is little left to serve potentially irrigable land [ 7 ]. While the irrigated area is indeed rapidly expanding, water-use efficiency remains as low as 46% within the existing irrigation infrastructure [ 3 ]. Substantial water management is therefore needed to ensure efficient utilization of water and other resources [ 4 ]. Proper application of irrigation is necessary for high water and crop productivity, better nutrient use by plant roots and protection against environmental degradation [ 5 ]. However, in practice, irrigation water is usually applied with limited knowledge, without considering crop water requirements and the soil moisture content. Agriculture is the leading sector in the economy of Ethiopia, accounting for about 46% of the gross domestic product (GDP) and almost 90% of export earnings. The agricultural system is based on smallholder production and entirely dependent on rainfed agriculture. So, irrigation has come to play an important role in maintaining food security even though only about 3% of the cultivated land is irrigated due to limited access to water, limited extension and the absence of widespread technology adoption [ 1 , 2 ]. We obtained ethical approval for this study from the Bahir Dar Institute of Technology of the Bahir Dar University. The lead author also obtained administrative clearance from his employer, the Amhara Agricultural Research Institute. Written informed consent was obtained from all the institutions that participated in this study. Participating farmers were informed that involvement in the study was completely voluntary and that they could withdraw from it at any time without any consequences. By using unique codes instead of names, all our data collection tools were designed to ensure confidentiality. All the information gathered was treated with confidentiality by the research team and would only be used for reporting or publication purposes. We used the software GenStat (version 18.0) to carry out statistical tests for this study. Prior to data analysis, we performed a normal Q-Q test using this software. Two-way ANOVA analysis was used to assess the interaction effects of the technology treatments and irrigation blocks at the P<0.05 level of significance. Mean comparisons using the LSD (5%) test were done to observe the differences among treatments to identify the effect of irrigation water management technologies on crop and water production and productivity. The other outputs considered were biomass and grain yield. The amounts of N, P and K concentration in grain yield and biomass were calculated by multiplying the total grain yield and biomass obtained from the experimental plots. The leachate volume of water was determined based on the dimensions of the WFD funnel buried in the soil. The volume of water leached on the plot was calculated as the product of leaching depth and the surface area of the plot. The leaching depth was obtained from the volume of water collected and the surface area of the funnel. The amount of leached nutrients per area was calculated from data on nutrient concentration in soil water and the amount of water applied. Available nutrients in the water samples were expressed as kg ha -1 whereas the total amount of water applied was expressed in terms of m 3 . N, P and K added to the soil in the form of wet deposition were estimated as the product of nutrient concentration in the precipitation sample and the total amount of rainfall during the growth period of wheat under irrigation conditions. The concentrations of N, P and K in the irrigation water samples were determined and found to be 0.38 mg L -1 , 3.6 mg L -1 and 2.65 mg L -1 respectively. These values were used to calculate the nutrient loading from the irrigation water. The participating farmers applied only two types of fertilizer during our experiment: urea (46% N) at the rate of 161 kg ha -1 and DAP (18% N, 46% P 2 O 5 ) at 100 kg ha -1 . One-third of the nitrogen dosage was applied at the sowing stage and two-thirds at the tillering stage. Nutrient availability in the applied fertilizer was calculated as the product of the nutrient in the sample and the total amount of applied fertilizer. The nutrient balances of nitrogen (N), phosphorus (P) and potassium (K) were calculated as the difference between the sum of inputs and outputs, as shown in Eq 4 . (4) where, ∑Nin represents the input (kg ha -1 ) of N, P and K Irrigation water productivity is the total yield produced divided by the amount of irrigation water applied during the irrigation period. While several factors—such as crop management, soil preparation, soil type, variety of crop and climate—influence water productivity [ 13 ], for the purposes of this research, they were taken as constant for all the treatments in our study. The depth of water application by each farmer was calculated as the volume of irrigation water applied using a V-notch weir divided by the area of the experimental plot. Mathematically, this can be stated as: (2) where h = depth of water application (m) Sampling for above-ground biomass and wheat grain yield was randomly carried out. We used a 0.5 x 0.5 m quadrant to mark three sampling points in each farm plot, thereby collecting a total of 27 composite samples. Grain yield was determined by converting the bag measure smallholder farmers use into kilograms per hectare. The weight of the biomass was measured using a sensitive balance in each quadrant and converting into the standard unit. Analysis of the concentration of nitrogen, phosphorus, and potassium was conducted at ADSWE and the laboratory of the Bahir Dar Institute of Technology (BIT) following standard lab procedures. A total of 27 irrigation water samples were taken from the inlet of the irrigated plots on the date of sowing and during the mid-growth stage of wheat. Using a wetting front detector, leaching water samples were taken based on the growth stage of wheat. The act of sucking up the leaching water below the root zone began when the participating farmers ended irrigation and the deep detector WFD indicator started popping up. A total of 108 such samples were collected during the irrigation season. Rainwater data were collected from the meteorological station whenever it rained in the experiment area during the irrigation period. All the water samples—irrigation water, leaching water and rainwater—were transported to the laboratory within 24 hours. They were analyzed for total nitrogen, phosphorus and available potassium concentration at the Amhara Design and Supervision Works Enterprise (ADSWE) laboratory using an Aqualab photometer integrated with a Palintest analysis system. The amount of irrigation water applied was measured using V-notch weirs, which were designed and manufactured from a 3 mm thick sheet of metal with standard dimensions. The V-shaped notch in the vertical thin plate was installed perpendicular to the sides and bottom of a straight canal. The line which bisects the angle of the V-notch was set vertically at the same distance from both sides of the channel [ 11 ]. The V-notch weirs were installed with a notch height (h) ranging from 2 cm to 5 cm in order to avoid side flows due to the back water flow effects of the weir blockage. These notches were installed on 9 selected quaternary canals just near the respective channel outlets. As per [ 12 ], the following equation was applied to measure the flow rate: (1) where Q = outlet discharge (m 3 /s), H = effective water (m), Ɵ = angle of the V-notch Quantitative data were systematically gathered from the experimental plots. This included climatic data (such as maximum and minimum temperature, sunshine hours, relative humidity and rainfall) collected from the meteorological station, the amount of water applied to the plots through quaternary canals, harvested grain yield, above-ground biomass and plant height. A PR2 Profile (Delta-T Devices Ltd) soil moisture probe was installed at 1 m depth to detect moisture at depths of 0.1 m, 0.2 m, 0.3 m, 0.4 m, 0.6 m and 1.0 m in the experimental plots. The moisture profiler was duly calibrated. The PR2 Profile soil moisture reading was used to determine soil moisture at various depths, and the WFDs and Chameleon sensors were used to determine the irrigation schedule so that farmers would know when to stop irrigation. The WFDs were also used to take leaching water samples in all experimental fields for partial nutrient balance analysis. The wetting front detectors and Chameleon sensors were installed as appropriate to the root depth of wheat. To guide the participating farmers, Full Stop WFDs were placed at depths of 20 cm and 40 cm as the first treatment to selected group of farmers in each block while the Chameleon soil moisture sensors were installed at depths of 20 cm, 40 cm and 60 cm as second treatment in all blocks. Both wetting front detectors and Chameleon soil moisture sensors were placed at a point three-fourths of the furrow length. For this experiment, we selected three irrigation blocks (Chihona, Adibera and Teleta) from the 12 available blocks ( Table 1 and Fig 2 ), using a systematic sampling technique that ensured inclusion of different reaches in the study, one block from the head represented by Chihona, the middle represented by Adibera, and the tail represented by the Teleta reach. Twenty-seven farmers representing the three irrigation water management treatments in each of the three irrigation blocks were selected based on interactions with the community during the farmers’ assembly. These user farmers were identified for intensive monitoring of the on-farm experiment. The area of the farm plots varied from 0.13 ha to 0.63 ha across each block due to different landholding sizes. The Kekeba variety of wheat (Triticum aestivum L.) was used for this study. Fertilizer application ranged from about 60 kg ha -1 to 260 kg ha -1 of diammonium phosphate (DAP) and 80 kg ha-1 to 210 kg ha-1 of urea. Split application of nitrogen was followed, using half at sowing and half at tillering. Each participating farmer was allocated to one of the three irrigation treatments being studied: wetting front detectors (WFD), Chameleon soil moisture sensors (CHS) or control (farmers’ practice, FP). Thus, three farmers participated in each of the three treatments in each block, resulting in three replications per treatment. Our objective was to examine two questions in our analysis: how did nutrient balances, wheat yield and water productivity behave in relation to the three irrigation treatments, and how did the irrigation block position—head, middle or tail-end—influence the above parameters. Our study area is located in Mecha woreda (district), where the Koga irrigation scheme is situated, at 11°20ʹ57ʹʹ N and 37°02ʹ29ʹʹ E at an altitude of 1,950 m above mean sea level in the Amhara region of Ethiopia ( Fig 1 ). We conducted the study under irrigation conditions from the beginning of October 2018 to the end of April 2019. The Koga irrigation scheme has a total of 11 night storage reservoirs (NSRs) in which water is stored for a 12-hour duration at night when smallholder farmers irrigate their fields. The main canal runs a length of 19.7 km from the outlet of the dam. Along the way, it crosses incised drainage channels and tributaries of the Koga and Abay rivers. There are 12 secondary canals with a total length of 52 km that deliver water within their individual command area. Results and discussion We examined the effects of the irrigation technology treatments and irrigation blocks—and the interaction between these two criteria—on the volume of irrigation water and yield of wheat. We, however, found the interaction between treatments and blocks was non-significant as can be seen in the supplementary material presented in Table A in S1 Text and Table B in S1 Text. Thus, our analysis of variance focused on the irrigation technology treatments. Soil characteristics The average pH of the soil in the experimental treatments was 4.6 (Table 2), indicating that these are strongly acidic soils [14] with no major difference among the plots. Across the three irrigation blocks, soil in all the study plots was dominated by clay (Table 2). However, there were slight variations in some soil physical properties at the selected sites. According to the Ethiopian Soil Information System [15], concentration of P in the soil from Koga irrigation scheme was significantly high in the range of 12.4–30.3 ppm, which is above the threshold of 10 ppm (Table 2). However, total nitrogen (TN) content was very low (0.2–0.25%), which is below the threshold of 1.5% recommended for crop growth and development [16, 17]. PPT PowerPoint slide PNG larger image TIFF original image Download: Table 2. Mean, standard deviation (SD) and coefficient of variation (CV) of soil characteristics under wetting front detector (WFD), Chameleon sensor (CHS), and farmers’ practice (FP) treatments. https://doi.org/10.1371/journal.pwat.0000060.t002 Soil property variation can be expressed in terms of the coefficient of variation with a CV <10% indicating low variability [18]. Accordingly, the coefficient of variation for most of the soil physical characteristics had less variability among the blocks and medium variation within each experimental blocks. Some parameters such as total nitrogen, field capacity and pH did have a CV of less than 10%. All our findings on soil characteristics were similar to those reported by earlier studies [7] except available P, which may have been highly affected by the variation in fertilizer application in different plots. Wheat yield The irrigation water management tools we studied in this experiment had a significant effect on yield and biomass (P< 0.05 and P<0.01). The highest yields were achieved by WFD users followed by farmers in the CHS treatment. The analysis of variance presented in Table 4 shows a significant difference between these two technology treatments and the farmers’ practice. But there was no significant difference evident between the two technologies themselves (Table D in S1 Text). PPT PowerPoint slide PNG larger image TIFF original image Download: Table 4. Average yield estimates of wheat under different irrigation technology use treatments. https://doi.org/10.1371/journal.pwat.0000060.t004 There were yield increments of 20% and 15.8% in the WFD and CHS water management treatments, respectively, compared with the farmers’ practice. These yield increases were higher, especially for the WFD treatment, compared to the 13–17% increase reported by earlier research in the same area [20]. Earlier research studies with the same variety of wheat in semi-arid parts of Ethiopia have reported yields in the range of 2.1–3 t ha-1 [23]. The same range of yield values was reported by [24] under deficit irrigation in the Afar Region. Our yield results were in this same range but less than the yields of up to 3.4 t ha-1 reported in Bangladesh [25] and 7 t ha-1 reported in the Mediterranean region of Syria [26]. These reports indicate that there are opportunities to increase yields in the Koga irrigation scheme by introducing different water and soil management technologies. 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