Irrigation District Research Articles

An Optimisation–Evaluation Framework for the Sustainable Management of the Water–Energy–Food Nexus for an Irrigation District under Uncertainty

The synergistic regulation of the water–energy–food nexus in irrigation districts is important for promoting the sustainable management of agricultural resources in irrigation districts. In this paper, a new integrated optimization–evaluation modelling framework for the water–energy–food nexus in agricultural irrigation areas is developed. It can measure the synergistic effects of economic, social and environmental multidimensional objectives on the sustainable management of agricultural resources in irrigation areas. The model couples an optimisation module and an evaluation module, combines a multiobjective nonlinear planning model with an opportunity-constrained planning model and uses an entropy-weighted TOPSIS assessment approach to sustainably assess the multidimensional indicators of the water–energy–food nexus in irrigation districts, with full consideration given to the effects of uncertainty in agricultural water and soil resources and social systems. The feasibility of the constructed model is verified through a study of the Jinxi irrigation district. The results show that compared to the actual area, the optimised surface water and groundwater availability increased by 23.5% and 22.7%; the optimised total area increased by 4%, whereas corn decreased by 40%, rice increased by 34.6% and soybean decreased by 33.8%; the energy consumption decreased by 17.6% and the total recycled resources amounted to 8.97 × 109 kg, with a combined net economic benefit of CNY 1.25 × 109 more than the actual current amount. The synergistic development of the water–energy–food nexus (WEFN) in the district is relatively harmonious, suggesting that the district should focus on developing agricultural mechanisation and balancing economic benefits with environmental and ecological protection; furthermore, the model constructed should provide decision-making support for the efficient use of agricultural resources in the irrigation district.

Agricultural ditches are hotspots of greenhouse gas emissions controlled by nutrient input

Agricultural ditches are pervasive in agricultural areas and are potential greenhouse gas (GHG) hotspots, since they directly receive abundant nutrients from neighboring farmlands. However, few studies measure GHG concentrations or fluxes in this particular water course, likely resulting in underestimations of GHG emissions from agricultural regions. Here we conducted a one-year field study to investigate the GHG concentrations and fluxes from typical agricultural ditch systems, which included four different types of ditches in an irrigation district located in the North China Plain. The results showed that almost all the ditches were large GHG sources. The mean fluxes were 333 μmol m-2 h-1 for CH4, 7.1 mmol m-2 h-1 for CO2, and 2.4 μmol m-2 h-1 for N2O, which were approximately 12, 5, and 2 times higher, respectively, than that in the river connecting to the ditch systems. Nutrient input was the primary driver stimulating GHG production and emissions, resulting in GHG concentrations and fluxes increasing from the river to ditches adjacent to farmlands, which potentially received more nutrients. Nevertheless, the ditches directly connected to farmlands showed lower GHG concentrations and fluxes compared to the ditches adjacent to farmlands, possibly due to seasonal dryness and occasional drainage. All the ditches covered approximately 3.3% of the 312 km2 farmland area in the study district, and the total GHG emission from the ditches in this area was estimated to be 26.6 Gg CO2-eq yr−1, with 17.5 Gg CO2, 0.27 Gg CH4, and 0.006 Gg N2O emitted annually. Overall, this study demonstrated that agricultural ditches were hotspots of GHG emissions, and future GHG estimations should incorporate this ubiquitous but underrepresented water course.

Effect of Autumn Irrigation on Salt Leaching under Subsurface Drainage in an Arid Irrigation District

Non-growing season irrigation and farmland subsurface drainage play a crucial role in salt leaching and salinization control in arid irrigation areas. This study aimed to investigate the reduction of autumn irrigation quotas and drainage discharge while maintaining soil moisture retention and reducing soil salinization. Field experiments were conducted with different autumn irrigation quotas (160 mm for SD1, 180 mm for SD2, and 200 mm for SD3) combined with subsurface drainage (1.5 m drain depth and 45 m spacing). A control treatment (referred to as CK) without subsurface drainage received 200 mm of irrigation. The results showed that, after 31 days of autumn irrigation, the groundwater depth in all three subsurface drainage plots stabilized to 1.5 m, with the CK being 0.2–0.3 m shallower compared to the SD plots. The mean soil water content in the 0–150 cm soil layer of the SD1, SD2, SD3, and CK after autumn irrigation was 0.36, 0.39, 0.41, and 0.42 cm3cm−3, respectively. The combination of autumn irrigation and subsurface drainage significantly reduced the soil salt content. The mean desalination rates in the root zone (0–60 cm) soil layer were 57.5%, 53.7%, 51.9%, and 45.1% for the SD3, SD2, CK, and SD1, respectively. The mean desalination rate of 60–150 cm was not significantly different between the SD2 and SD3 (p > 0.05), and both were significantly higher than that of the SD1 and CK (p < 0.05). The drainage discharge was 31, 36, and 40 mm in the SD1, SD2 and SD3, respectively. The amount of salt discharge through the drain pipe increased with increasing irrigation quota, which was 1.22 t/ha, 1.41 t/ha, and 1.50 t/ha for the SD1, SD2, and SD3, respectively. Subsurface drainage is an effective way to prevent salt accumulation in the soil, and an autumn irrigation quota of 180 mm is recommended for leaching of salinity in the Hetao Irrigation District. These findings provide valuable insights into optimizing irrigation practices and managing soil salinization in arid regions.

Analysis of Social Networks of Water-Use-Related Information in the Rio Mayo Irrigation District (038) in Northern Mexico: Ethnicity, Land Tenure and Land Use

(1) Background: In Mexico, 76% of water consumed is used for crop irrigation, and close to half of this is used in 86 irrigation districts for agroindustry throughout the nation. The present study combines a political ecology approach with social networks analysis to identify how water-use-related information networks are structured according to the ethnicity (indigenous and non-indigenous) of the users of the Rio Mayo Irrigation District 038 (RMID) and how these networks are influenced by users’ type of land tenure and land use. (2) Methods: The study involved three stages: identification of social actors that influence water management (SAIWM); application of 118 structured interviews with users of RMID; and ethnographic fieldwork. (3) Results: Thirty SAIWM were identified. Only 11.8% of RMID users interviewed were indigenous farmers and only 5% were indigenous holders of collective landholdings. The information network metrics indicate that indigenous users have less access to information than non-indigenous users. (4) Conclusions: Ethnicity as well as land tenure and land use influence the structure of information networks and determine whether RMID users work as land-holding farmers or as hired labor.

Ecological degradation in the Inner Mongolia reach of the Yellow River Basin, China: Spatiotemporal patterns and driving factors

The ecological conservation and high-quality development of the Yellow River Basin (YRB) has been the prime strategy of China. Wherein, the Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a critical ecological barrier in northern China, playing an unshakable position in ecological and food security in the YRB. However, evaluation of ecological conditions and relevant driving factors have not aroused considerable concerns. Here we propose an ecological degradation index system based on the Pressure-State-Response (PSR) multi-scale comprehensive assessment framework and evaluate the ecological environment degradation intensity in the IM-YRB during 2000 to 2018. We find continual degradation of the ecological environment with increasing degradation intensity in the IM-YRB during 2000 to 2018. Moreover, we quantify that 24.16% of the IM-YRB had highly significant degradation (99% confidence level) of the ecological environment. The areas with significant degradation (95% confidence level) of the ecological environment accounted for 57.19% of the IM-YRB. The population density, land use, coal mining and fires are the main influencing factors behind ecological environment degradation in the IM-YRB. In addition, there occurred widespread land use conversion (>50% of the study region in area) amongst grassland, arable land, sands, construction land and unused land and these land conversions can be found mainly in the Yinshan Mountains (from grassland to abandoned land, the Hetao Irrigation District (from grassland to arable land) and the Kubuqi Desert (from sand, arable land and unused land to grassland). This study acts as the theoretical ground for sustainability and conservation of the regional ecological environment.

Assessing the feasibility of applying remote sensing technique in calculating water consumption

Abstract Remote sensing techniques are currently used in different fields including irrigation and water management fields. One of the important fields is the calculation of water consumption (WC). Remote sensing techniques can be used to estimate actual evapotranspiration and it can also be used to estimate crop coefficients based on spectral reflectance of vegetation indices (VIs), and from a relation with normalized difference vegetation index (NDVI), which is an indicator for the absorption and the reflection ratios by the green plant. The current study used remote sensing data to calculate WC for El-Bostan irrigation district. Crop coefficient values were calculated from NDVI and reference evapotranspiration values were calculated using CROPWAT program. The obtained results were verified by comparing them with field measurements. There was a strong linear correlation between the measured and calculated values with r2 of 0.90, while the root-mean-square error (RMSE) was 0.68. The results were also verified by comparing irrigation efficiencies obtained from remote sensing and from field measurements. The results were very close to each other. The study illustrated the importance and the reliability of using remote sensing techniques in calculating WC values, and which could improve water management and water use efficiency.

Deep Learning-based Near-real-time Monitoring of Autumn Irrigation Extent at Sub-pixel Scale in a Large Irrigation District

Flood irrigation is widely applied in harvested croplands of arid regions and can be classified as autumn/winter irrigation (AI) depending on the time of application. Due to its high water consumption, real-time monitoring of the AI extent is crucial to improve its scheduling. We proposed a remote sensing-based long short-term memory (LSTM) model for near-real-time monitoring of AI extent at sub-pixel scale. The model loosely coupled MODIS data with Sentinel-2 data to solve the mixed pixel issue of MODIS data, and calibrated Sentinel-2 thresholds for AI identification by a random forest (RF) module to extract large-scale reference data with high temporal frequencies. The variable importance estimated by RF is used as a reference for feature screening in the LSTM model. As Sentinel-2 images are not available daily, LSTM models trained with incomplete sequences were validated using multiple validation approaches. The model was applied to the Hetao Irrigation District, the largest irrigation district in arid region of China, and delivered reasonable performance with coefficient of determination of over 0.82 and mean absolute error of around 10.7%. Classification of irrigation patterns using simulated time series of irrigation area fractions revealed eight different irrigation patterns from 2010 to 2020 in the study region. Results indicate that the maximum fraction of AI area upstream is closely related to the cropland distribution pattern. The AI patterns changed significantly over the 11 years, with a more pronounced reduction in irrigation duration for individual pixels in the downstream area. These changes are associated with two important land policies implemented in many regions of China, land consolidation and land transfer. The proposed model demonstrates the potential for near-real-time monitoring of autumn irrigation extent within large irrigation districts, which can aid in AI scheduling and provide insight into irrigation patterns and practices.

New memory-based hybrid model for middle-term water demand forecasting in irrigated areas

The energy demand and their associated costs in pressurized irrigation networks together with water scarcity are currently causing serious challenges for irrigation district’s (ID) managers. Additionally, most of the new water distribution networks in IDs have been designed to be operated on-demand complexing ID managers the daily decision-making process. The knowledge of the water demand several days in advance would facilitate the management of the system and would help to optimize the water use and energy costs. For an efficient management and optimization of the water-energy nexus in IDs, longer term forecasting models are needed. In this work, a new hybrid model (called LSTMHybrid) combining Fuzzy Logic (FL), Genetic Algorithm (GA), LSTM encoder-decoder and dense or full connected neural networks (DNN) for the one-week forecasting of irrigation water demand at ID scale has been developed. LSTMHybrid was developed in Python and applied to a real ID. The optimal input variables for LSTMHydrid were mean temperature (°C), reference evapotranspiration (mm), solar radiation (MJ m−2) and irrigation water demand of the ID (m3) from 1 to 7 days prior to the first day of prediction. The optimal LSTMHybrid model selected consisted of 50 LSTM cells in the encoder submodel, 409 LSTM cells in the decoder submodel and three hidden layers in the DNN submodel with 31, 96 and 128 neurons in each hidden layer, respectively. Thus, LSTMHybrid had a total of 1.5 million parameters, obtaining a representativeness higher than 94 % and an accuracy around of 20 %.

A simulation-based multi-objective two-level optimization decision-making approach for supporting balanced irrigation water management

In this study, a simulation-based multi-objective two-level optimization decision-making approach is developed for optimal irrigation water allocation, improving irrigation water productivity and controlling regional accumulated salts. Techniques of multi-objective programming, two-level programming and simulation model of water and salt physical movement process are incorporated into the modeling framework. The simulated processes are parameterized and calibrated with field experimental data while the optimization model is used to generate optimal solutions though predefined objectives and the associated constraints. This model is applied to a case study on irrigation water allocation in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Firstly, the study area is delineated into several homogeneous irrigation decision-making units (IDMUs) for better characterizing their spatial variability because it’s spatially heterogeneous. Afterwards, decomposition-coordination algorithm is introduced to solve such an integrated simulation-based multi-objective two-level optimization model. Finally, optimal solutions of irrigation water allocation for different crops during crop growth periods in different IDMUs can be obtained for supporting sustainable strategies of irrigation. The results can achieve balanced tradeoffs between different stakeholders (i.e., the upper-level decision-makers and the lower-level farmers) and between conflicting economic objectives and environmental objectives. Moreover, optimal solutions have a slight increase in economic returns over the baseline scenario (i.e., status quo), but the irrigation water productivity is increased by nearly 60% due to less irrigation water used. Regional accumulated salts can be controlled because the soil salinity is constrained within the predetermined salt accumulation constraint. Therefore, these findings can provide evidence for efficient use of irrigation water resources and further support of sustainable irrigated agriculture.

Rice irrigation water efficiency improvement: An AquaCrop-based optimization modeling approach

Rice is the cereal crop that consumes the largest amount of water in the world. To improve the water use efficiency of rice farming and promote the sustainable development of rice production, this study proposes a novel AquaCrop-based optimization modeling approach. First, an AquaCrop-based multiobjective model is constructed to generate dynamic water production functions (WPFs). The NSGA-II algorithm is employed to solve the model, and quadratic dynamic WPFs for rice under different hydrological years (wet, normal and dry) are generated based on the obtained Pareto fronts, which can improve the modeling efficiency and help reflect the complicated reality of water flows. Second, a fuzzy credibility-constrained stochastic multiobjective programming (FCC-SMOP) model is developed for irrigation water allocation based on the acquired WPFs. The model addresses the randomness of precipitation and runoff, the fuzziness of decision-makers' preferences, and conflicting objectives of water savings, benefit increments, and water demand during the critical period of rice growth. The proposed simulation-optimization approach was applied to the Changgang irrigation district in northeast China. Optimal irrigation amounts under different decision-making preference scenarios (including conservative, moderate and positive) and different hydrological years were simultaneously generated. The following conclusions can be drawn: (1) The AquaCrop-based multiobjective method is capable of generating WPFs of different hydrological years with sparse discontinuous field experiments; (2) the decision-making preferences of decision-makers significantly impact the economic benefits, water use efficiency, and practicality of schemes; and (3) the FCC-SMOP model successfully makes tradeoffs among the three objectives under multiple uncertainties and can further improve the system efficiency and increase planting income in the Changgang irrigation district when its irrigation facilities become optimal.

Organic amendments effects on soil aggregation and carbon sequestration in saline‐alkaline croplands in China

AbstractSaline‐alkaline soils are wide‐spread and approximately 6.5% of the world's land area is reported as being saline and sodic. Increasing soil organic carbon (SOC) concentration and aggregate stability in saline‐alkaline soils has been challenging due to the deleterious effects of soil salinity and sodicity. To address this issue, a 2‐year filed experiment was conducted to evaluate the effects of biochar (BC), farmyard manure (FM), corn straw (CS), and sheep manure (SM) in combination of chemical fertilizer (CK) on soil aggregation and C sequestration in mildly and moderately alkaline soils in Hetao Irrigation District, China. For mildly alkaline soil, the proportion of macroaggregates (>0.25 mm) was significantly higher under FM, CS, and SM (34.6%–51.2%) compared with CK (13.2%). The mean weight diameter (MWD) and geometric mean diameter (GMD) of water‐stable aggregates significantly increased under FM, CS, and SM by an average of 172.7% and 58.7% over CK. In addition, FM, CS, and SM enhanced the distribution of organic carbon (OC) in macroaggregates (>0.25 mm) and accelerated the accumulation of particulate organic matter within macroaggregates. Treatment BC enhanced OC distribution in microaggregates (0.053–0.25 mm) and exhibited a 7.40 g kg−1 SOC content, followed by SM and FM which exhibited SOC contents of 7.10 and 6.76 g kg−1. For moderately alkaline soil, data obtained in this study showed a slight influence from organic amendments on soil aggregates and C sequestration, suggesting that the moderately alkaline soil was not as sensitive to organic amendments as the mildly alkaline soil. Overall, the results of this study demonstrated that FM and SM combined with chemical fertilizer were the top organic amendments to improve soil aggregation and C sequestration in the mildly alkaline soil but not the moderately alkaline soil over the short term.

Agricultural irrigation water price apportionment and sharing

Abstract Studying the apportionment and sharing of agricultural water prices is necessary for clarifying the relationship between agricultural water price apportionment and government responsibility and ensuring the benign operation of irrigated areas. From the perspective of the versatility of irrigation water, irrigation benefits are classified as internal or external; the degree of benefit is measured using the proportion of irrigation water per cubic metre of internal and external benefits. This is used as an allocation coefficient to reasonably share prices based on farmers’ water price tolerance, build agricultural water price apportionment and sharing models, and calculate the range of water prices borne by farmers and the government in the Baojixia Irrigation District in the Shaanxi Province of China. Results showed that the apportionment coefficients of farmers and government were 0.85 and 0.15, respectively. For grain crops, the farmers' price range was 0.115–0.508 yuan/m3, while that of the government was 0.516–0.909 yuan/m3; for cash crops, these values were 0.566–3.009 and 0.154–0.458 yuan/m3, respectively. The results of this study support the formulation of agricultural water prices and provide a theoretical reference for reducing the water burden of farmers and promoting the high-quality development of the regional economy

Geotechnical Zoning for the Construction of an Irrigation District in the Department of Atlántico - Colombia

For the construction of an irrigation district, the geotechnical conditions of the existing soils in the municipality of Ponedera (Atlántico, Colombia) were analyzed, by means of manual sampling and standard permeability test (SPT). This information was processed in a database for analysis. The soil types were obtained, most of which were classified as inorganic clays and silts (CL and ML), to a lesser extent, silty and clayey sands (SM and SC), and maps of soil types zoning and allowable capacity were drawn in GIS. No water table was found in the investigated soil area. Following the guidelines of the Colombian Earthquake Resistant Regulation (NSR-10) and the RAS, the usefulness for future studies has been concluded. Finally, the soil explored can support load capacities at a depth of 3.0 m of 31.8 ton/m2.

Impact of chlorine or peracetic acid on inactivation of Salmonella, Escherichia coli, and Listeria monocytogenes in agricultural water

Preharvest agricultural water has been recognized as one of the routes of contamination for foodborne pathogens during fruit and vegetable production. Several strategies have been proposed to reduce the risk of pathogens, including preharvest water chemigation, but literature is lacking with regards to microbiological inactivation of common bacterial foodborne pathogens associated with fresh produce contamination, Salmonella enterica, Shiga-toxigenic Escherichia coli (STEC), and Listeria monocytogenes, in surface irrigation water after exposure to chlorine and peracetic acid (PAA). Surface water supplied by a local irrigation district was collected over the summer of 2019. Water was autoclaved, divided into 100 mL samples, and inoculated with a cocktail of five Salmonella, STEC, or Listeria monocytogenes strains or a single strain non-pathogenic E. coli. Samples were then treated with 3, 5, or 7 ppm of free chlorine or PAA, and surviving populations were evaluated using a time-kill assay. A first-order kinetic model was used to fit the inactivation data and obtain the D-values. A secondary model was used to explain the changes due to the type of water, treatment, and microorganism. At a concentration of 3 ppm, the observed and predicted D-values of free chlorine treatments were higher than that of PAA treatments for ground and surface water. Results indicated that PAA was more effective inactivating bacteria than sodium hypochlorite at concentrations of 3 and 5 ppm for both water sources (surface and ground). However, at 7 ppm, the effectiveness of PAA and sodium hypochlorite showed no statistically significant difference for both surface and groundwater. Findings will provide information regarding efficacy of chemical sanitizers like chlorine and PAA for inactivation of Salmonella, Listeria, and STEC in surface water from which treatments can be derived. Ultimately benefitting growers in the selection of an appropriate method for in-field treatment of irrigation water if deemed necessary.

Agricultural water-saving potentials with water footprint benchmarking under different tillage practices for crop production in an irrigation district

Agricultural water-saving potentials with water footprint benchmarking under different tillage practices for crop production in an irrigation district

Irrigation strategy optimization in irrigation districts with seasonal agricultural drought in southwest China: A copula-based stochastic multiobjective approach

Irrigation strategy optimization in irrigation districts with seasonal agricultural drought in southwest China: A copula-based stochastic multiobjective approach

Climate suitability division of solar greenhouse in Inner Mongolia Autonomous Region, China.

To resolve the issue of scientific planning and rational layout of different vegetable greenhouses in Inner Mongolia Autonomous Region, we selected the days of low temperature in winter, sunshine hours, overcast days, extreme minimum temperature, days of monsoon disaster, days of snow cover in production season of greenhouse as the climate zoning indicators, based on ground-based observation data from 119 meteorological stations (1991 to 2020) and the growing demand of leafy and fruity vegetables in greenhouse, combined with the analysis of key meteorological factors in production season and the study of meteorological disaster indicators such as low temperature and cold damage, wind disaster, snow disaster. We analyzed the indices, classification and division of comprehensive climate suitability zoning of leafy and fruity vegetables at various slopes (35°, 40°) of solar greenhouse by the weighted sum method. The results showed that the climatic suitability zoning grades of leafy and fruity vegetables at 35° and 40° slope of greenhouse was highly consistent, and that the greenhouse climate suitability of leafy vegetables was higher than that of fruity vegetables in the same region. As the slope increased, wind disaster index decreased and snow disaster index increased. Climate suitability was different in areas affected by wind and snow disasters. The northeast of the study area was mainly affected by snow disasters, and the climate suitability of 40° slope was higher than 35°. The southeast of the study area was mainly affected by wind disasters, and the climate suitability of 35° slope was higher than 40°. Alxa League, Hetao Irrigation District, Tumochuan Plain, most parts of Ordos, southeast of Yanshan foothills and the south of West Liaohe Plain were the most suitable area for the solar greenhouse, because they had the suitable solar and hot resources and the low risks of wind and snow damage, which were also the key development areas of current and future facility agriculture. Due to the deficiency of solar and hot resources, high energy consumption in greenhouse production and frequent snow storms, the area around Khingan range in the northeast of Inner Mongolia was unsuitable for greenhouse.

An inexact bi-level multi-objective modeling approach for managing agricultural water-food-carbon-land-ecology-nutrition nexus in an arid area

Multiple subsystems such as water, food, land, carbon, ecology, and nutrition are closely intertwined, and the resulting complexity and uncertainty pose challenges to water resource management. This study proposes an optimization framework from a novel water-food-carbon-land-ecology-nutrition (WONDER) nexus perspective for the integrated management of agricultural and ecological water resources. Firstly, the irrigation priority ranking of decision-making units (DMU) is obtained based on the remote sensing indices from the Google Earth Engine (GEE) platform. Secondly, the ecological satisfaction degree, which aggregates carbon sink satisfaction degree and ecological water demand satisfaction degree, is proposed to characterize the degree of achieving the pre-given ecological objectives. Then, the above two indicators (i.e., the irrigation priority ranking and the ecological satisfaction degree) are integrated with a fuzzy-interval credibility-constrained bi-level multi-objective programming model to realize: (1) reconciling conflicts in food nutrition, food security, economic development, and ecological conservation; (2) making trade-offs among the positions and preferences of different decision-makers; (3) dealing with the dual uncertainties of fuzzy-interval parameters; (4) obtaining optimal agricultural and ecological water allocation schemes. The applicability of the model is verified by taking Hongyashan Irrigation District of Minqin County as an example. The results show that: (1) the irrigation priority ranking for the DMUs is: DMU 10 > DMU 9 > DMU 4 > DMU 5 > DMU 1 > DMU 11 > DMU 6 > DMU 8 > DMU 7 > DMU 2 > DMU 3; (2) the ratio of agricultural to ecological water allocation in the irrigation district is about 3:1, which is consistent with the water use characteristics of this area; (3) the total water allocation during the whole growth period for different crops is mainly determined by planting area, and the ranking of the water allocation is: sunflower > maize > wheat > seed melon. Moreover, compared with the single-level models, the inexact bi-level multi-objective model can well coordinate the upper and lower decision-makers and the multiple decision objectives under the WONDER nexus. Therefore, the proposed framework can provide the scientific basis for balancing agricultural and ecological water management and conflicting objectives in similar irrigation districts through involving multiple subsystems and perspectives.

Water distribution and scheduling model of an irrigation canal system

A complete water distribution scheme should include the scheduling of the check gates and turn out gates. This study aims to resolve the water distribution problem in irrigation districts based on channel hydrodynamic simulation and using the hydraulic factor of water level increase in backwater areas. The supply-decided water distribution and scheduling model(SD-WDSM), the demand-decided water distribution and scheduling model(dd-WDSM) were established respectively. The model was applied to the Bojili irrigation district to verify it’s applicability. The results show that the SD-WDSM can adjust the length of the backwater area through the operation of the check gate, ensuring a large flow rate at the turn out gate. The dd-WDSM was established to group the water diversion canals by rotation irrigation, and the turn out gate in the same group could open and close simultaneously, which is convenient for the administrator to operate. The two models also ensure that the water is diverted as early as possible, and the operation numbers of the check gates and the turn out gates are less. The model obtains the complete scheduling scheme of the check gates and the turn out gates in the water distribution system. This model can improve agricultural water use efficiency and promote sustainable agricultural development. The proposed model is universal and can be applied to other irrigation districts.

Cytotoxic Effects of the Atrazine Herbicide on Erythrocytes and Liver Damage in Lithobates spectabilis

In Mexico, atrazine is one of the herbicides most widely authorized and used in different irrigation districts. Atrazine is a herbicide that contaminates aquatic systems. Previous studies have shown that atrazine causes damage to red blood cells and liver tissue in different aquatic species, including abnormalities in melanomacrophages. However, more information on amphibians is needed, since most of these studies have been done on fish. Furthermore, no study has determined the effect of atrazine on species native to Mexico. Therefore, in this study, we asked what the effects of atrazine are on the erythrocytes and melanomacrophages of the liver in the male frog (Lithobates spectabilis). In the present study, we analyzed (1) the cytotoxicity of atrazine using the micronucleus test, (2) the area of melanomacrophage centers and the presence of melanin, and (3) the characterization of liver damage using histological techniques. Our results show that atrazine is cytotoxic to erythrocytes, increases the area of and melanin presence in melanomacrophage centers, and causes liver damage in male L. spectabilis. Therefore, hepatotoxicity and cytotoxicity are indicators of environmental stress. We suggest monitoring Mexico’s aquatic systems and further analyzing atrazine effects and other pollutants on native species.