Crop Irrigation Research Articles

Cyanobacterial Cultures, Cell Extracts, and Individual Toxins Decrease Photosynthesis in the Terrestrial Plants Lactuca sativa and Zea mays

Cyanobacterial harmful algal blooms (cHABs) are increasing due to eutrophication and climate change, as is irrigation of crops with freshwater contaminated with cHAB toxins. A few studies, mostly in aquatic protists and plants, have investigated the effects of cHAB toxins or cell extracts on various aspects of photosynthesis, with variable effects reported (negative to neutral to positive). We examined the effects of cyanobacterial live cultures and cell extracts (Microcystis aeruginosa or Anabaena flos-aquae) and individual cHAB toxins (anatoxin-a, ANA; beta-methyl-amino-L-alanine, BMAA; lipopolysaccharide, LPS; microcystin-LR, MC-LR) on photosynthesis in intact plants and leaf pieces in corn (Zea mays) and lettuce (Lactuca sativa). In intact plants grown in soil or hydroponically, overall net photosynthesis (Pn), but not Photosystem-II (PSII) electron-transport yield (ΦPSII), decreased when roots were exposed to cyanobacterial culture (whether with intact cells, cells removed, or cells lysed and removed) or individual toxins in solution (especially ANA, which also decreased rubisco activity); cyanobacterial culture also decreased leaf chlorophyll concentration. In contrast, ΦPSII decreased in leaf tissue vacuum-infiltrated with cyanobacterial culture or the individual toxins, LPS and MC-LR, though only in illuminated (vs. dark-adapted) leaves, and none of the toxins caused significant decreases in in vitro photosynthesis in thylakoids. Principal component analysis indicated unique overall effects of cyanobacterial culture and each toxin on photosynthesis. Hence, while cHAB toxins consistently impacted plant photosynthesis at ecologically relevant concentrations, the effects varied depending on the toxins and the mode of exposure.

Influence of Farmer’s Income Level on Adoption of Agricultural Water Management Practices among Smallholder Farmers in Rongai Sub-county, Kenya

Water is a resource that cannot be replaced and can only be renewed if it is well managed. It is basic for all forms of life, for every aspects of socio-economic development, and for the maintenance of a healthy ecosystem. Agriculture is the biggest water user globally, accounting for 70 percent of total water withdrawals on average. There are different practices that can aid in managing water used for agriculture. Some of these agricultural water management practices are; rainwater harvesting, irrigation, organic farming, and use of drought resistant crops. Farmer’s decision to either adopt or reject these practices can be affected by several factors, some of which are socio-economic. Some of the socio-economic factors that may influence adoption of these practices include farmer’s educational level, income level and farm size. This study examined the influence of farmer’s income level on adoption of agricultural water management practices (Rainwater harvesting, Irrigation Drought resistant crops). Cross-sectional survey design was adopted, while proportionate and simple random sampling technique was used to obtain the respondents. The accessible population was 6,230 smallholder farmers from the target population of 26,804 smallholder farmers in Rongai sub county Kenya. The study was done in August 2023 to November 2023.The study included 120 smallholder farmers in Rongai Sub County. The study used questionnaire to collect data while binary logistic regression was used to analyze the data. The results indicated that adoption of agricultural water management practices is low, only 36% of smallholder farmers had adopted agricultural water management practices. The P value calculated for the 120 smallholder farmers was P=.033 which is < than .05 and therefore the null hypothesis was rejected concluding that in this study income level had statistically significant influence on adoption of agricultural water management practices among smallholder farmers in Rongai sub-county in Nakuru ,Kenya. The findings may help in emphasizing the necessity of assisting farmers in removing financial obstacles that may hinder adoption of agricultural water management practices.

Comprehensive Review of Crop Water Requirement Estimation Techniques: Approaches, Challenges, and Future Directions

In order to meet the growing demand for water, water scarcity is a significant issue that needs attention. Agriculture crops are not getting enough water because of this problem. Consequently, figuring out how much water a given crop needs requires using the right method. Accurate crop water requirements must be measured in order to schedule irrigation effectively, which in turn leads to efficient crop water management. To restore the lost moisture and promote the best possible growth for plants, irrigation is used. Water management and irrigation scheduling fundamentally depend on an accurate calculation of the crop's crop water requirements (ETc). Accurate evapotranspiration measurements are necessary for effective irrigation water management. Evapotranspiration (ET), a process that measures the amount of water lost from soil and crops through transpiration and evaporation processes, respectively, is dependent on a variety of meteorological factors. A significant factor in determining crop water requirements and irrigation schedules is reference evapotranspiration. There exist multiple theories and methodologies for estimating reference evapotranspiration, ranging from empirical to physical based. Reference Evapotranspiration is referred to the idea behind ET is to calculate ET based on a reference surface that is comparable to a deep surface of green grass that is consistently growing, completely covering the surface with enough water, and looking stable. The goal of irrigation futures is to choose a suitable model for estimating reference crop evapotranspiration. In order to calculate the amount of water needed for a crop, multiply ETo by the crop coefficient (Kc), which is dependent on the phases and length of a crop's growth. Regression, fuzzy logic, Penman-Monteith, Blaney-Criddle, Hargreaves, ANN and WNN, and other conventional and non-traditional methods are used to estimate ETo.

Persistence of Microcystin in Three Agricultural Ponds in Georgia, USA

Cyanobacteria and their toxins can have multiple effects on agricultural productivity and water bodies. Cyanotoxins can be transported to nearby crops and fields during irrigation and may pose a risk to animal health through water sources. Spatial and temporal variations in cyanotoxin concentrations have been reported for large freshwater sources such as lakes and reservoirs, but there are fewer studies on smaller agricultural surface water bodies. To determine whether spatiotemporal patterns of the cyanotoxin microcystin occurred in agricultural waters used for crop irrigation and livestock watering, three agricultural ponds on working farms in Georgia, USA, were sampled monthly within a fixed spatial grid over a 17-month period. Microcystin concentrations, which ranged between 0.04 and 743.75 ppb, were determined using microcystin–ADDA ELISA kits. Temporal stability was assessed using mean relative differences between microcystin concentrations at each location and averaged concentrations across ponds on each sampling date. There were locations or zones in all three ponds that were consistently higher or lower than the average daily microcystin concentrations throughout the year, with the highest microcystin concentrations occurring in winter. Additionally, microcystin patterns were strongly correlated with the patterns of chlorophyll, phycocyanin, and turbidity. The results of this work showed that consistent spatiotemporal patterns in cyanotoxins can occur in produce irrigation and livestock watering ponds, and this should be accounted for when developing agricultural water monitoring programs.

Intercropping increases plant water availability and water use efficiency: A synthesis

Intercropping, involving the incorporation of annual crops with alternative crops or non-crop cash crops, has the potential to enhance water conservation and stabilize agroecosystems. However, few studies have comprehensively explored the effects of intercropping on water cycling. Here, we investigated the impacts of intercropping on five crucial water cycle processes (states): soil water content (SWC), runoff (RO), soil evaporation (E), leaf transpiration (LT), and water use efficiency (WUE). To this end, a meta-analysis was carried out utilizing a global dataset comprising 1285 paired observations from 64 publications. We found that intercropping reduced SWC (1.31 %), RO (29.17 %), and E (10.30 %), but increased LT (9.85 %) and WUE (29.46 %). The effects of intercropping on SWC, E, and RO did not exhibit significant fluctuations over the course of a year, but SWC initially decreased then increased in multi-year planting durations. Moreover, the intercropping effect was contingent upon climatic conditions (mean annual precipitation and temperature), soil characteristics (organic matter content, bulk density, and total nitrogen content), and agricultural practices (crop type, fertilization, and irrigation). We determined that resource complementarity, abiotic facilitation, and biotic feedback mechanisms may underlie the effect of intercropping on the water cycle. This research underscores the potential of using intercropping to improve plant water usage and the sustainability and productivity of cropping systems.

When does group chat promote cooperation in shared resource governance?

When people use shared resources, overextraction can occur. While deliberation tends to mitigate shared resource exploitation problems, the question remains: under what conditions does group chat improve cooperation in shared resource dilemmas? This study analyzes chat and game data from about 1500 rounds of gameplay involving 143 groups across 4 resource types using Sentiment Analysis and Structural Topic Model. We find that, despite their fundamental differences, the 4 games tend to have similar discussions, including strategizing actions, coordinating choices, and socialization, but they differ in which topics explain cooperation within each game. Discussion topics promoting cooperation include coordination in the foraging game (FOR) and long-term goals in the groundwater game (GG). However, discussion topics negatively associated with cooperation include off-topic/socialization in FOR and the irrigation game (IRR) and crop choice affirmation in GG. We suggest that the context in which communication occurs matters, and biophysical characteristics, rules of the game, and levels of uncertainty explain some variations of our findings.

Coupled bio-solar photocatalytic treatment for reclamation of water polluted with pharmaceutical and pesticide residues. Impact on tomato irrigation

Reusing reclaimed water for crop irrigation can mitigate water scarcity in agriculture; however, contaminants such as pharmaceuticals and pesticides in wastewater pose risks. This study investigated the impact of a coupled bio-solar photocatalytic treatment on the reclamation of water polluted with seven pharmaceuticals and seven pesticides for irrigation of two tomato crop cycles. Pollutant residues were removed using natural sunlight and TiO2/Na2S2O8 in a pilot plant located in Murcia, Spain. Efficient removal (> 96%) of all target pollutants was achieved in the effluent after coupled treatment. Reclaimed water was then used to irrigate the tomato crops, and several yield and quality parameters were analysed to evaluate the effects on the harvested tomatoes. No significant differences were observed in the total yield, number and mean fruit weight, size, pericarp firmness, external colour, and nutritional data between the crops irrigated with reclaimed, control, and polluted water. However, differences in the degree of ripeness were observed. None of the investigated pollutants was detected above the limit of quantification in tomato samples irrigated with reclaimed water, except for venlafaxine (0.028µgkg-1) in the second crop cycle. When the crop was irrigated with polluted water, different pollutant residues were detected in soil (10) and tomato (4) samples. The results suggest that coupled bio-solar photocatalytic treatment is an effective method for reclaiming water polluted with pharmaceutical and pesticide residues, and the reclaimed water can be safely used for tomato irrigation without compromising crop yield and quality.

Optimization of border irrigation variables based on a correction factor for irrigation quota

The optimization of surface irrigation variables, i.e., the selection of the optimal combination of the inflow rate per unit width (q) and cutoff time (tco), is essential for obtaining high performance. The main objective of this study was to optimize irrigation variables by considering different irrigation requirements and the total loss model, which includes the irrigation water loss and crop yield loss. A correction factor for the irrigation quota (Cf) was introduced to achieve this objective. Cf considers different irrigation requirements, as it represents the ratio of the actual irrigation quota for certain irrigation to the designed irrigation quota (Dreq). Uniform design theory was used to determine random combinations of q and Cf from a selected range. The q value ranged from 3 to 9 L.m-1. s-1. Because the actual irrigation quota does not greatly deviate from the design irrigation quota, Cf should reach approximately 1.00. The selected Cf ranged from 0.80 to 1.38. To obtain higher crop yields, lower economic losses, and more reasonable design variables for surface irrigation, a total loss model for uneven border irrigation was established, which was defined as the objective function to optimize border irrigation design—the total loss model was combined with uniform design theory and the WinSRFR model to analyze different scenarios. The results showed that Cf has a clear meaning and exerts a favorable application effect on the irrigation performance evaluation indicators and can be used to design border irrigation systems. Based on the total loss model for uneven border irrigation, the optimal irrigation variables q and Cf for design irrigation quotas of 60, 80, and 100 mm are q=6.22 L.m-1. s-1 and Cf=1.17; q=4.60 L.m-1. s-1 and Cf=1.14; and q=3.80 L.m-1. s-1 and Cf=1.10, respectively. Compared with the conventional design results, q in the optimal design results based on the loss model decreased, Cf increased, and the total loss significantly decreased. Optimization of irrigation variables based on the border irrigation loss model could ensure favorable irrigation performance evaluation indicators, improve the water use efficiency, provide higher crop yields, and minimize the total economic losses caused by uneven irrigation.

Uncertain multi‐objective programming approach for planning supplementary irrigation areas in rainfed agricultural regions

AbstractRainfed agriculture is crucial for ensuring global food and water security, and supplementary irrigation is an effective means of improving the economic benefits in rainfed agricultural regions. This study proposes a novel uncertain optimization approach to optimize supplementary irrigation areas in rainfed agricultural regions. The approach incorporates multi‐objective linear programming, interval linear programming, fuzzy goal programming and stochastic expected value programming. In the proposed interval multi‐fuzzy goal stochastic expected‐value programming, uncertainties are expressed in the form of discrete intervals, probability distributions and fuzzy goals. This approach, which considers the randomness of precipitation during the optimization process and allocates limited irrigation water resources to different subareas and crops, was applied to a case study of crop irrigation area planning in Guyuan City, Ningxia Hui Autonomous Region, northwestern China. The maximum economic benefits and the minimum sum of the Gini coefficients among the different subareas and crops were regarded as the planning objectives, and a series of optimal irrigation areas with different crops and subareas under different water levels were obtained. The optimization results revealed that vegetables and fruits consumed large amounts of irrigation water to increase their economic benefits. In addition, allocating a large amount of irrigation water to wheat is essential to decrease the Gini coefficient and meet food security constraints, particularly at extremely low water levels. Compared with current management, the optimized irrigation area decreased by 30%, the crop water deficit index increased by 9%, the economic benefits increased by 3% and the total Gini coefficient of crops decreased by 17%, indicating that the optimization approach could fairly and reasonably allocate irrigation water resources. Our research provides a mathematical approach for decision‐makers to plan supplementary irrigation areas in rainfed agricultural regions.

Heavy metals phytoextraction potential of Medicago sativa L. irrigated with waste and groundwater

Wastewater is generally used for crop irrigation purposes it since provides nutrients and organic matter to the soil. However, it can also add contaminants such as heavy metals. The objective of the present study was to evaluate the absorption and accumulation of lead and copper in the aerial and root parts of alfalfa (Medicago sativa L.) irrigated with raw wastewater, treated wastewater, and groundwater using the bioconcentration (BCF) and translocation (TF) factors. Three treatments (raw wastewater, treated wastewater, and groundwater) were tested in a randomized block design with four replicates. The results showed significant differences between treatments. The highest concentrations of lead and copper occurred in the aerial part of the alfalfa irrigated with raw wastewater. The TF factor for lead and copper was higher than one in all treatments; something similar was observed for the BCF, except in the groundwater treatment, indicating that alfalfa has the potential for phytoextraction.

Optimizing yield and water productivity in summer mung bean (Vigna radiata L.) through crop residue management and irrigation strategies

A multi-season research trial entitled ‘crop residue management effects on yield and water productivity of summer mung bean (Vigna radiata L.) under different irrigation regimes in Indian Punjab’ was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Bathinda, during rabi 2020 and 2021. The field experiment was conducted in a split-plot layout with nine treatment combinations and replicated thrice. The treatments consisted of T1 (no wheat residue along with tillage), T2 (leftover wheat residue with zero tillage), and T3 (incorporated wheat residue along with tillage) in main plots and irrigation regimes viz., I1 (vegetative growth and flowering stage), I2 (vegetative growth, flowering, and pod filling stage) and I3 (vegetative growth, flowering, pod formation and pod filling stage) in sub-plots, respectively. The growth and yield attributing characters were significantly higher under T3 than T1 but statistically at par with T2 during both years. An increase of 24.1% and 19.0% in grain yield was found in residue incorporation (T3) and residue retention (T2) over residue removal (T1), respectively. Maximum crop and irrigation water productivity was observed under T3 due to reduced water use and increased yield. Among the irrigation regimes, the I3 recorded significantly higher grain yield (0.70 and 0.79 t ha− 1) than I1. It was at par with I2 during both years due to higher irrigation frequency at the pod formation and pod filling stage. Crop water productivity (CWP) was higher under I3, whereas irrigation water productivity (IWP) was higher under I1 during both years. Additional irrigation at the pod-filling stage increased the grain yield by 36.5%, and two additional irrigations at the pod-formation and pod-filling stage further increased yield by 46.2% compared to only two irrigations at the vegetative and flowering stages. The treatment combinations of T2I2 and T3I2 outperformed T1I3 in terms of growth and yield attributing characters viz. plant height, dry matter accumulation (DMA), leaf area index (LAI), pods plant− 1, seeds pod− 1, and 1000-seed weight, which resulted in higher grain yield in these treatment combinations over T1I3. Applying crop residue can help minimize water use and increase crop water productivity. So, retaining crop residue in summer mung bean resulted in saving irrigation water due to lesser evapotranspiration from the soil surface.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

Effect of Different Irrigated Crop Successions on Soil Carbon and Nitrogen–Phosphorus–Potassium Budget Under Mediterranean Conditions

Sustainability in agroecosystems relies on the optimized use of resources to achieve consistent yields while maintaining or improving soil health. The monitoring of soil quality is crucial when changes from rainfall-fed to irrigated crop systems occur. The objective of this study was to assess the impact of different crop successions in the Mediterranean area under irrigation and different technical practices. The soil nitrogen–phosphorous–potassium (NPK) and soil organic carbon (SOC) balances were observed in four fields with irrigated annual crops in a two-year succession timeframe, namely, sunflower–maize (P1), sunflower–clover (P2), maize–sunflower (P3), and alfalfa–alfalfa (P4). The SOC and nutrient balance, integrating the total irrigation, mineral fertilizers, and exported yield, was calculated for each farm. Except for maize–sunflower succession (P3), all fields presented a negative SOC balance at the end of the two-year crop succession, indicating losses from 2.84 to 4.91 Mg SOC ha−1 y−1. While in N-fixing plants the soil N decreased, in the remaining crops a surplus was observed, possibly leading to future N losses. The continuous depletion of soil P revealed a potential underestimation of this nutrient. Soil K appears to be related to specific crop management practices, namely, crop residue incorporation after harvest. In annual irrigated crops under Mediterranean conditions, crop succession can induce soil fertility degradation if conservation practices are absent.

Trihalomethanes (THMs) formation in chlorine-treated and -untreated wastewater and its horizontal and vertical distribution through the irrigation of agricultural fields

ABSTRACT This study examines the formation of trihalomethanes (THMs) in wastewater intended for crop irrigation. The presence of dissolved organic carbon (DOC) in the water and its release from the soil make it crucial to understand the relationship between THM formation, DOC aromaticity, and residual chlorine. The research takes a dual approach: assessing spatial distribution through field samples from canals now distributing treated, chlorinated wastewater and analysing vertical percolation of irrigation wastewater through soil previously irrigated with untreated, non-chlorinated water for over 100 years. Spatial analysis reveals that THMs are present in wastewater before treatment, with concentrations increasing post-treatment. Although DOC levels decrease after treatment, their aromaticity remains unchanged, suggesting the persistence of labile DOC. The presence of THMs in treated wastewater from irrigation canals may result from shorter travel distances or increased chlorine use during the COVID-19 pandemic. Vertical soil column experiments indicate a correlation between THM generation, water source, and soil type. Results show that THMs are retained in the soil, where labile DOC is trapped, reducing its concentration while potentially releasing DOC from humic substances. Despite this, THM concentrations do not pose a risk to aquifers, though ongoing monitoring is important should soil and/or treatment methods be altered.

Wastewater desalination for irrigation in inland regions

ABSTRACT Desalination using membrane processes is increasingly being implemented in coastal regions of the world to meet the water needs of the growing population. The use of this water treatment option to meet the growing water needs in inland regions, however, is impeded by the cost and regulatory challenges associated with responsible disposal of the concentrate brine. For inland regions of Australia, the use of evaporation ponds (EPs) is the only available brine management option. The high cost associated with construction and the high land requirements for EPs necessitates low waste brine volume production. This desktop study investigated the feasibility of achieving very high water recoveries and very low waste brine volumes in the reverse osmosis desalination of municipal wastewater for irrigation of crops near the inland town of Horsham, Victoria, Australia. Interstage lime and soda ash softening was selected as the means of removal of the scalants that prevent the achievement of high water recovery. It is hoped that this study will provide valuable information to aid in the planning of water reclamation operations for agricultural applications in inland areas at a time of increasing demand for water for food production and forecasts of population growth and climate change.

Precision Agriculture using ML for Soil and Weather Prediction

Agriculture is the backbone of many economies and plays a critical role in global food security. However, with growing challenges posed by climate change, erratic weather patterns, and soil degradation, the need for precise and predictive techniques in agriculture has never been more urgent. This paper focuses on the application of machine learning (ML) techniques in soil fertility prediction and weather forecasting, two critical components that can significantly impact agricultural productivity. By analysing soil properties, such as moisture, pH, and nutrient levels, and combining them with accurate weather predictions, our system aims to help farmers make informed decisions regarding crop selection, irrigation scheduling, and pest control. Leveraging algorithms like Naive Bayes for soil classification and Long Short-Term Memory (LSTM) networks for weather prediction, we provide a comprehensive solution for precision agriculture. This system not only enhances productivity but also promotes sustainable agricultural practices by optimizing resource use and reducing wastage

Evaluation of gridded cropland phosphorus budget and use efficiency in China

Changes in soil phosphorus (P) distribution and budget critically impact the sustainability of agricultural systems. Yet, few studies have examined the long-term evolution of cropland (and crop-specific) P budget and use efficiency (PUE) at a grid level. Here, we conducted a comprehensive evaluation of the cropland P budget and PUE and their human-environmental drivers in China during 1992–2018 at a spatial resolution of 0.1°. The results reveal a significant shift in China's cropland P budget from a deficit of −3.70 Tg P yr−1 in 1992 to a surplus of 0.31 Tg P yr−1 in 2018, mainly driven by increased fertilizer application and decreased soil erosion by water. The concurrent national average cropland PUE initially decreased from 0.51 in 1992 to 0.34 in 2003, but afterwards increased to 0.39 in 2015. An environmental-Kuznets-curve-like (EKC) relationship was identified between the national average P budget (inverted U-shaped) or PUE (U-shaped) and per capita GDP in China, with the turning point occurring in 2013 for the previous and at per capita GDP of US$8.85 k (constant 2017 US$) for the latter. But PUE had been well below a threshold of 0.40 at the national level after crossing the turning point and showed a considerable trend divergence among crops, particularly for cash ones. Spatially, northern and northwestern China exhibited high positive P budget but achieved relatively low PUE in the 2010s. Crop leaf area, irrigation, fertilizer input, and precipitation were identified as the most important factors determining the multi-year spatial pattern of P budget, while fertilizer input, temperature, and residue return played a dominant role in regulating PUE. Our findings highlight the need for a long-term commitment to regionalized and crop-specific synthetic management practices for controlling P inputs and minimizing P loss in the context of global change in China.

Experimental and numerical evaluation of soil water and salt dynamics in a corn field with shallow saline groundwater and crop-season drip and autumn post-harvest irrigations

In areas with shallow saline groundwater, soil salts inevitably accumulate in the root zone during the growth period due to irrigation and upward movement of salts from the groundwater. In Northern China, autumn irrigation (AIR) with large amounts of water is commonly employed post-harvest to mitigate soil salt stress on crop growth in the subsequent year. Optimizing the total irrigation depth during both crop-growth and non-growth periods is challenging because of the movement of soil salts, which is influenced by their two-dimensional distribution around drippers and the impact of the winter freeze-thaw cycles, significantly affecting water flow and solute transport during winter. In this study, the HYDRUS-1D and HYDRUS-2D models were integrated and calibrated using experimental data collected from 2021 to 2023 in China's Ordos south bank irrigation area. This model integration was conducted to assess soil water and salt dynamics during the non-growth and corn-growth periods under different irrigation strategies: a) AIR with high (AH) and low (AL) irrigation depths, and b) drip irrigation (DIR) with high (DH), medium (DM), and low (DL) irrigation depths. The results indicated that HYDRUS effectively modeled the electrical conductivity of the saturation paste extract (ECe) across different irrigation strategies, yielding an average coefficient of determination (R2) and the root mean square errors (RMSE) of 0.87 and 0.53 dS m−1, respectively. Generally, ECe increased during the growth period with DIR and decreased during the non-growth period with AIR. For the 0–40 cm soil layer, ECe decreased by 5.7 % and 12 % for every 100 mm increase in the AIR and DIR depths, respectively. Compared with the AHDM and AHDL treatments, reducing an AIR depth and increasing a DIR depth resulted in lower ECe in the 0–40 cm layer during the growth period and higher crop yield (CY) and irrigation water productivity (WPI). Specifically, the average ECe in the 0–40 cm layer decreased by 4.8 % during the growth period in the ALDH treatment compared to the AHDM treatment, and CY and WPI increased by 7.2 % and 10.3 %, respectively. Additionally, the irrigation strategy was the most effective in reducing ECe when AIR accounted for 35 % of the total irrigation. This study suggested that combining low AIR and high DIR could enhance water and field productivity.

Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in Northeast China

The accurate prediction of reference crop evapotranspiration (ET0) is essential to better manage crop irrigation water consumption and improve crop water use efficiency. To effectively improve the accuracy of ET0 simulated by machine learning models, five meteorological stations in Hailaer, Harbin, Hohhot, Changchun, and Dalian were taken as representative stations, daily and monthly ET0 data from 1952 to 2020 were used, and empirical mode decomposition (EMD) and wavelet threshold denoising (WD) were considered. The convolutional neural network (CNN) and long short-term memory network (LSTM) models were improved, and two new hybrid neural network models (EMD–WD–CNN and EMD–WD–LSTM) were constructed. Using the ET0 calculated using the FAO-56 Penman–Monteith (P–M) formula as the standard value, the applicability of the improved machine learning model was evaluated. Results showed the following: i) the daily ET0-PM minimum values of five stations were close to 0, the average values were not significantly increased, and the maximum values significantly fluctuated (the fluctuations in Hailaer and Hohhot showed an upward trend, and the fluctuations in Harbin, Changchun, and Dalian showed a downward trend). The annual average monthly ET0-PM varied seasonally, with the peak in June in the Hailaer station and May in all other stations (the peak in Hohhot was the largest, and the peak in Dalian was the smallest). ii) The daily and monthly ET0 values predicted by the proposed EMD–WD–CNN and EMD–WD–LSTM models were highly consistent with the calculated results of the P–M model, showing high accuracy on the daily and monthly ET0 of the simulated five stations (daily: mean absolute error (MAE), 0.30–0.41 mm/day; root mean square error (RMSE), 0.46–0.60 mm/day; R2, 0.86–0.95; monthly: MAE, 5.66–13.71 mm/month; RMSE, 8.97–18.04 mm/month; R2, 0.91–0.95). iii) The EMD–WD–CNN model was suitable for daily scale ET0 simulation and prediction in Northeast China and monthly scale in Harbin, Changchun, and Hohhot. The EMD–WD–LSTM model was suitable for monthly ET0 simulation and prediction in Hailaer and Dalian in Northeast China. The mixed models of EMD–WD–CNN and EMD–WD–CNN can effectively improve the prediction accuracy of ET0 and can provide a new method for agricultural development and irrigation regulation in Northeast China.

Agricultural Valorization of Treated Effluent from Korhogo Wastewater Treatment Plant

Aims: The main objective is to evaluate the effluents of Korhogo’s wastewater treatment plant for irrigation and fertilization in agriculture. Study Design: Characterization of outlet treated wastewaters has been firstly done. Then their suitability for irrigation has been determined and finally their fertilizing power has been assessed. Place and Duration of Study: Department of geosciences, Korhogo’s wastewater treatment plant and laboratory. Sampling and analyses were carried out in January 2023, before agricultural field implementation started in February 2023. Methodology: Firstly, Physico-chemical and microbiological analyses, allowed characterization of outlet treated wastewaters. A determination of their suitability for irrigation was then done through Sodium Adsorption Ratio calculation and water classification with Diagram of Richards. An assessment of their fertilizing capacity was finally carried out using these waters for irrigating and fertilizing crop plots (3x2 m), and consisting of determining firstly their biodegradability index, then their nutrient contents (Nitrogen, Phosphorus and potassium), and the influence of their use on crop development and yield. Results: The results show that several parameters meet standards for discharge of treated wastewater except total nitrogen, suspended matters and total phosphorus (67.67 mg/l, 271.33 mg/l and 18.30 mg/l). Sodium Adsorption Ratio (SAR) value=3.78 and C3S1 class indicate that these treated effluents can be used for irrigation without salinization risk. As fertilizer, treated effluents brought to crops respectively 603 kg/ha of nitrogen, 162 kg/ha of phosphorus and 99 kg/ha of potassium for maize; and 368.5 kg/ha of nitrogen, 99 kg/ha of phosphorus and 60.5 kg/ha of potassium respectively for Okra. A yield of 12.2 kg of fresh maize and 3.15 kg of fresh okra was obtained for the plot irrigated by treated effluents, compared with 4.80 kg of fresh maize and 0.36 kg of fresh okra for the plot irrigated by well water. Conclusion: This study has shown that outlet treated wastewater from Korhogo’s wastewater treatment plant is suitable for crops irrigation, contains sufficient nutrients enhancing crops development and yield.