Irrigation Zone Research Articles

Application of knowledge graph in smart irrigation district management decision making

Regarding the traditional irrigation and drainage management methods in China's irrigation districts, there are problems such as complex management processes, dispersed information systems, difficulty in resource sharing, diverse data sources, and insufficient intelligent auxiliary decision-making. In order to further realize refined intelligent management decision-making on issues related to smart irrigation districts, this article uses the basic information of 28 Yellow River diversion irrigation districts in Henan Province, as well as the overall plan reports and related issue management manuals of 7 management offices of the first and second phase projects of Zhaokou Irrigation District as data sources. Collect and organize data from irrigation areas, and use these to build a knowledge graph for management of issues in irrigation areas diverted from the Yellow River. At the same time, the BERT + BiLSTM + CRF model is used to intelligently identify entities such as irrigation projects and problem events in the Zhaokou Irrigation District inspection log text, and entity alignment technology is used to match the inspection text with entities in the knowledge graph. Finally, combined with the graph retrieval function, the intelligent generation of smart irrigation district management decision-making solutions is realized. The validation of specific irrigation district examples and analysis of the evaluation indexes of relevant models demonstrate the reliability of the problem management decision-making scheme proposed in this paper. This application effectively facilitates the integration of information data in the Yellow River diversion irrigation area and enables visual management of intelligent irrigation zones, presenting a novel concept for the informationization construction of China's irrigation areas.

Aerial dispersal of Venturia inaequalis ascospores with under-canopy sprinkler irrigation for apple scab management

AbstractSprinkler irrigation systems can release ascospores of Venturia inaequalis, the cause of apple scab, from infected leaves on the ground under conditions unsuitable for infection, and thus reducing the primary inoculum. Under-canopy irrigation was carried out for two hours in the middle of the day over overwintered apple leaves heavily infected with scab, either in a wind-protected enclosure or in a wind-exposed orchard. Ascospores were captured with rotating-arm spore traps at heights ranging from 0.3 m to 3.0 m above the ground. Ascospores dispersed above the irrigated layer and were detected at all heights above the sprinklers. Wind played a critical role in spore transport, evident from the set-up where wind interference was minimised by a wind fence, resulting in higher airborne spore numbers across all measured heights compared with the orchard exposed to unrestricted wind conditions. Furthermore, vertical temperature gradients significantly correlated with spore distributions, particularly where negative gradients at heights between 0.3 m and 0.05 m and positive gradients at heights between 1.0 m and 0.3 m led to spore retention within the irrigated zone. The findings highlight that ascospores, dispersed above the irrigated layers, could settle on susceptible tissues. It thus becomes imperative to ensure a rain-free period of at least 24 h post-irrigation and, if a rainfall shortly occurs after irrigation, the application of curative fungicides becomes essential following unexpected rain. Reliable weather forecasts are therefore crucial in determining the effectiveness of under-canopy irrigation to reduce apple scab incidence.

Partial root zone irrigation and K application improves summer maize production and salt resistance in saline soil

Irrigation patterns and K fertilization are the key measures used to improve the salt tolerance of crops in saline land. A pot experiment was carried out to assess the effects of partial root-zone irrigation and K fertilization on maize in saline land. For the study parameters, we used a split plot design with three irrigation treatments (conventional irrigation (CI), fixed root-zone irrigation (FRI) and alternate root-zone irrigation (ARI)) and two K application rates (0 (K0) and 120 (K1) kg K2O ha−1). Compared with the CI treatment, the FRI and ARI treatments reduced the transpiration rate (Tr) and MDA content but increased the root length density (RLD), root surface density (RSD), root volume density (RVD), root dry weight density (RDWD), proline content, plant K/Na ratio, photosynthetic rate (Pn), yield, water use efficiency (WUE) and K use efficiency (KUE). Compared with the FRI treatment, the ARI treatment increased RLD, RSD, RVD, RDWD, the Tr, the proline content, the plant K/Na ratio and the Pn but decreased the MDA content; these factors increased yield, WUE and KUE. K application decreased the MDA content; improved root growth; and increased the stomatal conductance, Tr, proline content, Pn, yield and WUE. Of all the treatments, the ARI-K1 treatment maximized RLD, RSD, RVD, RDWD, the Pn and yield while minimizing the MDA content. Therefore, ARI combined with K application can maximize resistance to salt stress and osmotic stress, delay senescence, and improve photosynthesis, yield and WUE.

Soybean plants enhance growth through metabolic regulation under heterogeneous drought stress

Heterogeneous drought stress (HED) is prevalent in drought-resistant practices such as deficit irrigation, root zone irrigation and strip intercropping. The mechanisms and improvement of crop drought resistance under HED are not fully understood. This study used double-root grafted Nandou 12 (ND12) soybean seedlings to simulate HED treatment under controlled conditions. Seedlings were transplanted into root cups with waterproof partitions to establish different soil moisture treatments: sufficient irrigation (SI) with 80 % soil moisture on both sides, HED with 80 % on one side and 40 % on the other, and homogeneous drought stress (HOD) with 60 % on both sides. The results indicated that soybean plants treated with HED exhibited healthier growth compared to those treated with HOD. Photosynthesis rate (Pn), stomatal conductance (cond.), transpiration rate (Tr), and relative water content (RWC) decreased by 10.24 %, 43.90 %, 152.66 %, and 8.69 % in HED-treated plants, respectively, but dry biomass increased by 3.38 %. Conversely, HOD conditions led to a significant decline in these parameters. Metabolomic and transcriptomic analyses revealed significant changes in the biosynthesis and signaling pathways of key phytohormones and metabolites, including abscisic acid (ABA), gibberellin (GA), jasmonic acid (JA), isoflavones, starch, and sugars. In HED-treated plants, GmNCED downregulation resulted in 54.22 % less ABA than HOD. GA levels increased under HED with upregulation of GmGA3OX1 and GmGA3OX2. JA content in HED-treated roots was 90.90 % higher than in HOD-treated roots. Isoflavones concentration including genistein (73.01 %), genistin (63.63 %), malonylgenistin (20.58 %), malonylgenistin (65 %), diadzin (38.15 %), and malonyldiadzin (47.61 %) levels, were significantly higher in HED-treated plants. Antioxidant enzyme activities indicated a 20 % increase in peroxidase (POD) activity under HED, while malondialdehyde (MDA) content was 27 % higher in HOD-treated plants, indicating greater oxidative stress. Chlorophyll content remained stable, and starch concentration increased by 33.33 % in HED-treated plants compared to HOD-treated plants. HED enhances phytohormonal responses and metabolic adjustments in soybean plants, boosting photosynthetic efficiency, antioxidant capabilities, growth, and drought resilience. This regulatory mechanism balances growth promotion and drought resistance, highlighting HED potential in improving crop resilience.

Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff

This study aims to model the uncertainty and reliability quantification of estimating the planning irrigation water demands in the multi-canal irrigation zone, named the RA_IWD_Canal model. The proposed RA_IWD_Canal could estimate the zone-based and branch-based water demands and quantify their uncertainties and reliabilities via the weighted frequency quantile curves. The historical planning irrigation water demands and measured surface runoff from 2019 to 2024 in the Zhudong irrigation zone are utilized in the model development and application. Using the proposed RA_IWD_Canal model, the estimated branch-based irrigation water demands exhibit a significant variation (on average, from 0.02 m3/s to 1.7 m3/s) in time and space attributed to uncertainties in the historical gauged surface runoff. Also, the Zhudong Canal zone is demonstrated to be sufficiently supplied irrigation water subject to existing introduced water demands with a high reliability of 0.85; instead, the associated branches have considerable difficulty achieving the expected irrigation efficiency based on the desired water requirements with low reliability (nearly 0.25). To keep all branches in the irrigation zone consistent in irrigation efficiency, the probabilistic-based water demands could be introduced via the proposed RA_IWD_Canal model with the desired reliability.

Effect of zinc fortified, arid and irrigated wheat varieties flour upon life history parameters of red flour beetle, Tribolium castaneum (Herbest) (Coleoptera: Tenebrionidae)

Red flour beetle, Tribolium castaneum (Herbest) (Coleoptera: Tenebrionidae) is a stored grain pest. Various fortified wheat varieties have been produced but data about the impact of different wheat varieties flour on life history parameters of T. castaneum are poorly understood. This study evaluated the effect of fortified (Zincol-16 T1), arid zone [(BARS-09 T2), and (Fatehjang-2016 T3)] and irrigated zone [(Faisalabad-2008 T4), (Gold-2016 T5), (Manthar-03 T6), (Fareed-06 T7), (Mairaj-08 T8)] wheat varieties flour upon some life history parameters of T. castaneum in the laboratory using age-stage two sex life table. The egg incubation period, adult male and female longevities, and total male and female longevities of T. castaneum were shortest in T5 and T6 as compared to all other treatments. The T5 presented shorter larval and pre-adult duration. Egg incubation, larval, and pupal durations and total male and female longevities were higher in T1. Total longevity (egg to adult) of T. castaneum was lowest in T1 as compared to T3 and T4 but similar to other treatments. Pre-adult duration was longer in T1 as compared to all other treatments. Pre-adult survival was significantly lower in T1 (53%) than all the treatments in which it remained at par. Lowest proportion of female adult (Nf/N) was found in T1 as compared to T3, T4, and T7. Age stage specific survival rate (sxj) was lower on T1, whereas life expectancy (exj) was higher than other varieties. These results suggest that T. castaneum develop poorly upon zinc fortified wheat variety, Zincol as compared to other varieties thus it can be a good integrated pest management tool against this pest.

New strategy based on Hammerstein–Wiener and supervised machine learning for identification of treated wastewater salinization in Al-Hassa region, Saudi Arabia

The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

Physicochemical characterization of agricultural soils under a traditional system in the Mezquital Valley, Hidalgo

The study examines alkaline and sodic soils, characterized by a pH exceeding 7.5 and elevated exchangeable sodium, indicative of deficiencies in nitrogen, manganese, iron, zinc, and copper in crops. Its objective is to characterize agricultural soils under traditional and conventional systems using physical and chemical parameters to advocate for regenerative agriculture. Sampling and analysis adhere to Nom-021-RECNAT-2000 standards. Predominantly, the soils exhibit clay loam and sandy loam textures across Tula de Allende, Tezontepec de Aldama, Francisco I. Madero, and San Salvador municipalities. Organic matter percentages vary, classified as very high (Tula de Allende and San Salvador), high (Tezontepec de Aldama, Mixquiahuala de Juárez, and Francisco I. Madero), and low (Santiago de Anaya). The pH ranges from moderately alcaline to highly alkaline. Cation exchange capacity in irrigation zones varies from very high to high, and medium for Tezontepec and Santiago de Anaya, which rely on rainfed irrigation. Elevated nitrate (NO3-), phosphorus (H2PO4-), and potassium (K+) concentrations are noted across different municipalities. These findings prompt reconsideration of traditional tillage practices that exacerbate sodicity in agricultural soil.

Soil quality evaluation for irrigated agroecological zones of Punjab, Pakistan: The Luenberger indicator approach

AbstractThis article describes the construction of the Luenberger soil quality indicator (SQI) using data on crop yield, non‐soil inputs, and soil profile from three irrigated agroecological zones of Punjab, Pakistan, namely, rice–wheat, maize–wheat–mix, and cotton–mix zones. Plot level data are used to construct a soil quality indicator by estimating directional distance functions within a data envelopment analysis (DEA) framework. We find that the SQI and crop yield relationships exhibit diminishing returns to improving soil quality levels. Using the constructed SQI values, we estimate linear regression models to generate weights that could be used directly to aggregate individual soil attributes into soil quality indicators without the necessity of fitting a frontier to the crop production data. For wheat and rice production, we find that SQI is most sensitive to changes in soil electrical conductivity (EC) and potassium (K). The SQI has direct relevance for site‐specific decision‐making problems where policymakers need to price land resources and conservation services to achieve agricultural and environmental goals.

Quality assessment using water quality indicators with geospatial analysis of groundwater quality El-Oued region, Northern Sahara, Algeria

ABSTRACT The present study was carried out to assess groundwater quality in two layers (Albian and complex terminal) intended for irrigation, to identify their original chemical composition and overall risk by different attack techniques in the province of El-Oued. Eleven physico-chemical parameters were measured at 16 locations. Emphasis was placed on the selection of irrigation water quality index (IWQI) inputs through the application of multivariate statistical analysis, the estimation of various classical irrigation indices, and the integration of geographic information systems. The results of the IWQI, sodium adsorption ratio, Kelly index, permeability index, residual sodium carbonate and magnesium hazard showed that the majority of groundwater samples were classified for irrigation purposes in the following categories: severely restricted (100%), low sodium and excellent (100%), good (93.75%), marginally safe (100%), good (100%) and suitable (81.25%), respectively. In addition, the majority of selected samples were classified as “good to eligible for irrigation” based on %Na as shown in the distribution map. Whereas the principal component analysis results show that the four factors together explain 72.74% of the total variance according to the Kaiser rule. Cluster analysis divides the parameters into three groups. In summary, the combination of the geographic system and irrigation quality indices is useful for identifying irrigation zones.

Prediction of groundwater level changes based on machine learning technique in highly groundwater irrigated alluvial aquifers of south-central Punjab, India

Groundwater serves as a vital resource for all living organisms. In regions extensively reliant on groundwater irrigation, hydro-climatic factors, groundwater extraction, and the flow of surface water exhibit an indirect interdependence. This study primarily aims to anticipate GWL in such highly irrigated zones using the Machine Learning (ML) approach. To achieve this, the widely employed Random Forest (RF), Bagging-Reduce Error Pruning Tree (Bagging-REPTree), and Bagging-Decision Stump Tree (Bagging-DSTree) models have been employed for the accurate forecasting of groundwater levels. The long-term pre-monsoon and post-monsoon (fourteen locations) data set of South-Central Punjab state has been applied for the model calibration/training and validation/testing. Seven statistical indices were used such as percent bias (PBIAS), root mean square error (RMSE), normalized root mean square error (nRMSE), RMSE-observation standard deviation ratio (RSR), mean absolute error (MAE), Nash Sutcliffe efficiency (NSE) and correlation coefficient (CC) for the model performance analysis. The results revealed that the RF model outperformed in pre-monsoon (testing phase) (RMSE = 0.682, NSE = 0.958) as well as the post-monsoon (testing phase) (RMSE = 0.150, NSE = 0.997) compared to the other two models in the station Ahmadapur and the similar trend is observed in all the stations. Overall, the RF model demonstrates superior performance in predicting groundwater levels during both pre-monsoon and post-monsoon seasons, particularly in highly groundwater-irrigated alluvial aquifers of the southern region.

Soil water dynamics and deep percolation in an agricultural experimental area of the North China Plain over the past 50 years: Based on field monitoring and numerical modeling

The unregulated irrigation systems used in the late 20th century have led to increasingly severe deep percolation (DP) in the agricultural irrigation areas of the North China Plain. This has become an important factor limiting the efficient utilization of water resources and sustainable environmental development in these irrigation areas. However, the thick vadose zone is hydrodynamically exceptionally complex. The soil hydrological cycle is constantly changing under the influence of major climate change and human activity, thereby causing changes in DP that are difficult to quantify accurately. Here, the Luancheng Agricultural Irrigation District in North China was selected for a continuous 20-year in situ experiment. Soil-water dynamics were monitored using neutron probes and tensiometers, to determine the complete annual soil-water cycle and the hydrodynamic properties of the thick vadose zone irrigation district. For 1971–2021, DP was simulated using the HYDRUS-1D model and was verified by fitting observed values. Soil water content (SWC) exhibited similar trends in years that differed in terms of the amounts of irrigation and precipitation. The 0–100 cm soil layer was significantly affected by precipitation and other factors, and recharge >60 mm/d caused percolation. DP occurred mostly after irrigation or during the period of intensive precipitation in July–October. The maximum percolation rate was 16.9 mm/d under the present irrigation method. The main factors leading to DP were soil water storage capacity (R2 = 0.86) and precipitation (R2 = 0.54). Under the evolution of irrigation measures in the last 50 years, the average DP has gradually decreased from 574.2 mm (1971–1990) to 435.5 mm (2005–2021). However, a substantial amount of precipitation and irrigation water infiltrated the soil and percolated into the deep soil layer without being utilized by the crop. Therefore, there is an urgent need to consider measures to reduce DP to improve water-use efficiency in agriculture.

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Coupled Calculation of Soil Moisture Content and PML Model Based on Data Assimilation in the Hetao Irrigation District

Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simultaneously, dynamical soil evaporation coefficients are generated based on the assimilated SM to improve the simulation accuracy of the PML model. The new scheme was validated at a typical irrigation zone in north China and showed obvious improvements in both SM and ET simulations. Moreover, the effect of the assimilation of SM on the simulation accuracy of ET for different crop growth periods is further analyzed. This research provides a new idea for the coupling simulation of the SM and PML models.

Granulometric composition irrigated soils of Bukhara region

The article describes the characteristics of common soil types and their particle size distribution in the Bukhara oasis. In the Bukhara oasis, various types of automorphic, hydromorphic, and semihydromorphic soils, which differ in grading composition, are mainly distributed. In the old irrigated zone of the oasis, old-irrigated meadow heavier and medium loamy soil varieties are common.

Research on Underground Drip Irrigation and Soil Redistribution - Take Heshan District of Yiyang City as an Example

Abstract: The drip flow and the buried depth of drip irrigation zone are not only important parameters of subsurface irrigation design and operation management, but also important factors affecting the migration and redistribution of soil water and fertilizer. Crop growth is closely related to soil water and nitrogen leaching, water and fertilizer transport patterns and redistribution characteristics. It is of great significance to study the migration and redistribution of soil water and fertilizer under subsurface drip irrigation for improving the utilization efficiency of water and fertilizer and improving the system of subsurface drip irrigation. In this paper, the migration mode and redistribution characteristics of water and nitrogen in subsurface drip irrigation were studied and analyzed by means of laboratory soil box experiment, considering the two factors of drip flow and the buried depth of drip irrigation belt. The main research conclusions are as follows: Ⅰ. Different drip flows had effects on soil moisture front, soil moisture content and soil water redistribution. If other conditions remain unchanged, increasing the drip flow can increase the moist body area of soil and increase the moisture content of soil. The distance between vertical and horizontal directions increased with the large drip flow, and the soil water content decreased with the increase of distance. Large drip flow can promote the redistribution of soil water after drip irrigation. Ⅱ. Different drip depth had effects on soil moisture content and soil water redistribution. When the drip depth was 0cm, the surface shape of the wet body was a "horizontal" semi-ellipse after drip irrigation. When the drip depth was 10cm and 20cm, the surface of the wet body was an "upright" semi-ellipse after drip irrigation. The larger the buried depth of the drip can make the distribution of soil moisture more uniform, and the larger the buried depth of the drip can promote the migration of water in the soil. Ⅲ. Different drip discharge had an effect on soil nitrogen redistribution, and large drip discharge promoted soil nitrogen redistribution after drip irrigation. The Nitrate Nitrogen (NO3--N) content in the moist body is lower than that near the edge of the wetting front. The content of Ammonium Nitrogen (NH4+-N) near the edge of the wetting peak is higher than that inside the wetting body. Ⅳ. The redistribution of soil nitrogen was affected by different buried depth of drip head, and the greater buried depth of drip head could promote the redistribution of soil nitrogen. In terms of vertical and horizontal migration of ammonium nitrogen and nitrate nitrogen, on the day after drip irrigation, the cumulative effect of Nitrate Nitrogen (NO3--N) buried at a depth of 20cm was more significant near the wetting front edge at a larger vertical downward distance, and the cumulative effect of Nitrate Nitrogen (NO3--N) buried at a depth of 20cm was more significant inside the wetting body at a larger vertical downward distance. At the same time, the drip buried depth of 20cm can spread the cumulative effect inside the wet body and near the edge of the wet front to a longer horizontal distance.

Assessing the Influence of Flow Variability on Irrigated Agriculture in the Central Gharb Plain, Morocco

The lower main watercourse of the Sebou Basin in Mechra-Bel-Ksiri is experiencing adverse effects attributed to climate change, notably drought and rising temperatures. In this study, we evaluated the impact of these changes on the irrigation zone using a comprehensive methodology involving break, trend, and low-water analyses. Our assessment, incorporating Land Use and Land Cover Change (LULCC) considerations, reveals detrimental effects on the expansion of irrigated agriculture along the Sebou wadi and its Baht tributary. Consequently, farmers are compelled to tap into underground water sources to fulfill irrigation needs for their farms. This research sheds light on the pressing challenges posed by climate change in the region and the consequent adjustments farmers are forced to make in their water sourcing practices. Keywords: Flow variability; break; trend; low water; land use land cover.

Photosynthetic characteristics, yield and quality of sunflower response to deficit irrigation in a cold and arid environment.

In arid regions, deficit irrigation stands as an efficacious strategy for augmenting agricultural water conservation and fostering sustainable crop production. The Hexi Oasis, an irrigation zone situated in Northwest China, serves as a pivotal area to produce grain and cash crops. Nonetheless, due to the predominant conditions of low rainfall and high evaporation, the scarcity of irrigation water has emerged as a critical constraint affecting crop growth and yield in the area. In order to evaluate the effects of deficit irrigation on photosynthetic characteristics, yield, quality, and water use efficiency of sunflower, a two-year field experiment with under-mulched drip irrigation was conducted in the cold and arid environment of the Hexi Oasis region. Water deficits were implemented at sunflower seedling and maturity and consisted of three deficit levels: mild deficit (65-75% field capacity, FC), moderate deficit (55-65% FC), and severe deficit (45-55% FC). A total of six combined water deficit treatments were applied, using full irrigation (75-85% FC) throughout the entire crop-growing season as the control (CK). The results illustrated that water deficit engendered a decrease in leaf net photosynthetic rate, transpiration rate, and stomatal conductance of sunflower compared to CK, with the decrease becoming significant with the water deficit increasing. A mild water deficit, both at the seedling and maturity phases, precipitated a significant enhancement (p< 0.05) in leaf water use efficiency. Under mild water deficit, stomatal limitation emerged as the predominant factor inducing a reduction in the photosynthetic capacity of sunflower leaves, while as the water deficit escalated, non-stomatal limitation progressively assumed dominance. Moreover, a mild/moderate water deficit at seedling and a mild water deficit at maturity (WD1 and WD3) significantly improved sunflower seed quality under consistent yield conditions and significantly increased irrigation water use efficiency, with an average increase of 15.3% and 18.5% over the two years, respectively. Evaluations utilizing principal component analysis and membership function methods revealed that WD1 attained the highest comprehensive score. Consequently, a mild water deficit at both seedling and maturity (WD1) is advocated as the optimal deficit irrigation strategy for sunflower production within the cold and arid environment of Northwest China.

Modeling GA-derived optimization analysis for canal-based irrigation water allocation under variations in runoff-related and irrigation-related factors

This study proposes an optimal analysis model for carrying out the canal-based irrigation water allocation (OPA_IWA_Canal) for a schedule-based irrigation zone under consideration of the variations in the runoff-related factor and irrigation-related factors. Coupled with the modified genetic algorithm based on the sensitivity of the model parameters (GA-SA) with a nonlinear objective function relying on the branch-based supplying satisfaction index, the proposed OPA_IWA_Canal model could optimally allocate the irrigation water supply under a desired demand. The Zhudong Canal irrigation zone with 15 branches is selected as the study area with the upstream inflow from the Shanping weir and two intake-water hydraulic structures (Baoshan Reservoir and Yuandon water treatment plant). The results from the model application on a variety of upstream inflow and water demands indicate that the proposed OPA_IWA_Canal model can provide the branch-based irrigation water supplies and water intake of the hydraulic structures to enhance the irrigation efficiency significantly, on average, from 0.25 to 0.7 under the insufficient upstream inflow. Additionally, by proceeding with the proposed OPA_IWA_Canal model under the various combinations of the unirrigated branches, all reaming branches could be availably allocated irrigation water supplies with a high irrigation efficiency (nearly 0.8).

Responses of Soil Microbial Survival Strategies and Functional Changes to Wet-Dry Cycle Events.

Soil microbial taxa have different functional ecological characteristics that influence the direction and intensity of plant-soil feedback responses to changes in the soil environment. However, the responses of soil microbial survival strategies to wet and dry events are poorly understood. In this study, soil physicochemical properties, enzyme activity, and high-throughput sequencing results were comprehensively anal0079zed in the irrigated cropland ecological zone of the northern plains of the Yellow River floodplain of China, where Oryza sativa was grown for a long period of time, converted to Zea mays after a year, and then Glycine max was planted. The results showed that different plant cultivations in a paddy-dryland rotation system affected soil physicochemical properties and enzyme activity, and G. max field cultivation resulted in higher total carbon, total nitrogen, soil total organic carbon, and available nitrogen content while significantly increasing α-glucosidase, β-glucosidase, and alkaline phosphatase activities in the soil. In addition, crop rotation altered the r/K-strategist bacteria, and the soil environment was the main factor affecting the community structure of r/K-strategist bacteria. The co-occurrence network revealed the inter-relationship between r/K-strategist bacteria and fungi, and with the succession of land rotation, the G. max sample plot exhibited more stable network relationships. Random forest analysis further indicated the importance of soil electrical conductivity, total carbon, total nitrogen, soil total organic carbon, available nitrogen, and α-glucosidase in the composition of soil microbial communities under wet-dry events and revealed significant correlations with r/K-strategist bacteria. Based on the functional predictions of microorganisms, wet-dry conversion altered the functions of bacteria and fungi and led to a more significant correlation between soil nutrient cycling taxa and environmental changes. This study contributes to a deeper understanding of microbial functional groups while helping to further our understanding of the potential functions of soil microbial functional groups in soil ecosystems.