Agricultural Water Management最新文献

Although afforestation plays a crucial role in reducing global warming, the survival of forests is increasingly threatened by the regular occurrence of extremely high-temperature (EHT) events. Currently, an innovative technology known as supplementary irrigation with ceramic emitter (SICE) was developed to maintain soil moisture and promote tree growth under EHT. Nevertheless, it is still unclear how SICE affects tree growth under EHT. In this study, a field experiment was conducted to monitor the growth status of Platycladus orientalis (P.orientalis) and analyze the physicochemical properties of the rhizosphere under EHT with SICE. The α diversity of soil bacterial communities in the rhizosphere was analyzed using high-throughput sequencing of 16 S rRNA and 18 S rRNA genes. The results indicated that SICE consistently maintained the soil water content (SWC) range from 0.26 cm³ cm⁻³ to 0.54 cm³ cm⁻³ during the entire experimental period under high-temperature conditions. Furthermore, SICE marginally increased the relative abundance of Actinobacteriota, Acidobacteriota, Chloroflexi and Methylomirabilota by 1.61 %, 0.99 %, 2.33 % and 4.31 % compared with CK, respectively. In comparison, SICE significantly decreased the relative abundance of Proteobacteria by 11.93 %, including α-Proteobacteria and γ-Proteobacteria, respectively. Additionally, SICE improved the absorption of SOC and nitrogen nutrients in P.orientalis, which were 41.20 %, 41.17 % and 28.35 % higher than CK, respectively and had a significant positive effect on the conversion from SOM to SOC and accelerated the absorption of soil nutrients for P.orientalis under EHT, resulting in increasing stem diameter, tree height, crown breadth and branch length of P.orientalis by 104.80 %, 81.67 %, 47.59 % and 94.68 %. Overall, this work provides direct evidence that SICE promoted tree growth by indirectly increasing α diversity of soil bacterial communities (e.g. Chloroflexi and Methylomirabilota) in the rhizosphere and accelerating the absorption of soil nutrients under EHT during the study period, which could offer a promising implication for advancing and implementing SICE technology under EHT.

Deficit irrigation is a common strategy to reduce water use and improve the sustainability of cotton production. However, the effects of water deficits on crop productivity and quality are subject to genotype by management by environmental interactions. This study investigated effects of water deficits and frequency of irrigation on cotton performance grown in semi-arid region, Xinjiang, the main cotton-growing area in China. Two field trials (2020 and 2021) with split experimental design, including main factors of three irrigation levels (moderate-deficit, mild-deficit and full-irrigation) and split factors of three irrigation frequencies (4, 8 and 12 days) were conducted. Results from two trials both showed little negative influence of irrigation levels on yield, and higher irrigation frequency improved yield under same irrigation level. Significant effects of irrigation levels on yield components were found in 2021, with a 22 % increase in boll number and an 18 % reduction in boll weight under moderate-deficit irrigation compared with those under full-irrigation. Interactions between irrigation levels and frequencies significantly affected harvest index (HI), showing that reduced irrigation might be beneficial for improving HI. However, decreased fibre length while increased fibre micronaire were found under deficit irrigation. A strong association between radiation use efficiency (RUE) and boll growth rate was observed, suggesting that RUE might be the driving force of yield formation. A tight correlation between both biomass and transpiration efficiency versus delta temperature between air and canopy (ΔTair-canopy) was observed, suggesting ΔTair-canopy could be used as an efficient tool to assess plant production under deficit irrigation. This study provided an improved understanding of the physiological basis of cotton yield formation and further identified a high-throughput and instantaneous method to monitor effects of deficit irrigation on crop productivity.

Increasing drought severity and evaporative demand in Mediterranean areas makes it necessary to implement irrigation systems with high water and nutrient supply efficiency. The combined management of drip irrigation burial depth and different nitrogen (N) sources, thus far unexplored, predicting these effects using proximal and spectral vegetation indices. A 2-year field experiment was conducted comparing maize yield and N uptake from four N fertilization treatments: ammonium sulfate (AS), AS with the nitrification inhibitor DMPP (AS+INH), calcium nitrate (CN) and a control without N fertilization combined with surface or subsurface (30 cm depth) drip fertigation. Multispectral data were collected to calculate various vegetation indices, while the chlorophyll content was measured with a soil plant analysis development (SPAD) sensor in the second year. Subsurface drip and AS+INH increased maize grain yields compared to surface drip and AS-only (by 12 % and 18 %, respectively, P < 0.05). However, this was observed only in the second season, as were increases in grain N content. The results show that the use of CN performed better in surface drip, while the use of NH₄⁺-N-based fertilizers were recommended for subsurface irrigation. Regarding the spectral data, at the flowering-milky kernel and dent kernel phenological stages Normalized Difference Red Edge (NDRE) and the canopy chlorophyll content index (CCCI) were the two vegetation indices that best estimated agronomical parameters and were able to discriminate the phenological differences between irrigation systems. This study highlights the potential for (i) predicting yield and N uptake using proximal and multispectral sensors in drip-fertigated maize and (ii) optimizing crop performance by combining drip burial depth and N source (DMPP combined with subsurface irrigation), with relevant implications for climate change adaptation (i.e., potential improvements in crop phenology and water saving).

Irrigation practices and sources can have significant impacts on water resources and the hydrologic fluxes that control these resources. To better manage water resources and future water supply, the influence of irrigation practices and management on these hydrologic fluxes should be quantified in time and space at varying scales, under potential irrigation management practices. To fulfill this objective, a surface-subsurface modeling approach was applied to simulate watershed-scale hydrologic processes in the Cache la Poudre River Basin, Colorado, USA (4824 km²), in which both surface water irrigation and groundwater irrigation are prevalent. The model chosen for this study is the watershed model SWAT+, using the spatially distributed, physically based groundwater module gwflow, in which unconfined groundwater storage, flows, and interaction with land surface features are simulated using a collection of grid cells that represent control volumes of the aquifer. Major groundwater inflows and outflows include pumping, recharge, groundwater-channel exchange, groundwater-lake exchange, and tile drainage outflow. To investigate the impact of irrigation practices, detailed surface and groundwater irrigation routines and canal-aquifer interactions were added to the SWAT+ source code, requiring information of irrigation sources and irrigation canal locations throughout the river basin. Model calibration and testing was performed using monthly stream discharge and groundwater head. The calibrated model is used to quantify the impact of surface water and groundwater irrigation scenarios on water availability and hydrologic fluxes within the river basin. A total of 22 scenarios were conducted and grouped into five main groups: irrigation source, irrigation amount, irrigation type, canal bed thickness, and partial or full sealing of earthen irrigation canals. Using groundwater as the only irrigation source decreases groundwater discharge to streams (by 14 %) due to lowering groundwater levels; converting flood irrigation to sprinkler irrigation throughout the basin decreases surface runoff by 22 %; and sealing earthen canals leads to a lowering of groundwater levels, which decreases groundwater discharge to streams by 9 %, leading to an overall decrease in streamflow in the Cache la Poudre River and changes to temporal patterns in streamflow. Overall, irrigation amount and type and canal sealing have a small impact on total groundwater storage, compared to changes in the percent of fields irrigated by groundwater pumping. Results are helpful for informed decision-making in agriculture water management and can lead to sustainable, efficient, and equitable use of water resources, helping to address the challenges posed by water scarcity and environmental conservation.

Soon, water scarcity is expected to worsen due to several factors including the population growth and the climate change. To address this, the European Water Framework Directive (WFD) mandates an increase in the water use efficiency of agrosystems. In this context, the aim of the study was to provide a novel methodological approach, based on the use of satellite-based classification algorithms (i.e., artificial neural networks, ANN, and the Optical Trapezoid Model, OPTRAM), agro-hydrological modelling (i.e., satellite-based ArcDualK_c model versus traditional FAO-56 approach) combined with different sources of agrometeorological data (i.e., ground-based versus ERA5 Land data), for mapping the irrigated crops and determining their irrigation water requirements (IWR) at the irrigation district level. The study was carried out, during the period 2019–20, in an irrigation district, named “Quota 102,50” (Eastern Sicily, Italy) and managed by the local reclamation consortium. The use of ANN and of OPTRAM allowed to obtain an accurate detection of the irrigated crops, with overall accuracy of 82 % and 88 %, respectively during 2019–20. The IWR retrieved with the ArcDualK_c model and the standard FAO-56 approach were generally underestimated in comparison to the volumes supplied by the farmers. The best performance resulted when the ArcDualK_c model was implemented with ERA5 Land data, with average values of coefficient of determination, residual standard error and slope of 0.99, 975.31 m³ and 0.78, respectively, during 2019–20. The outputs at the district scale compared to the data declared by the reclamation consortium resulted in overestimations in terms of both irrigated areas and IWR, with absolute errors of about 1539 ha and 1431 ha, and of about 9 10⁶ m³ and 12 10⁶ m³, respectively, during 2019–20. Finally, the study provided a useful methodological framework for supporting the water management authorities to better planning and monitoring the irrigation water uses under the current WFD.

Water stress is the most important factor limiting crop production in arid and semiarid regions. Cultivating crops using a plastic film mulch can significantly increase crop yields by optimizing soil hydrothermal conditions in semiarid agroecosystems. Therefore, clarifying root adaptability to plastic film mulch cultivation is crucial when attempting to produce stable and high maize yields. A two-year experiment was conducted to investigate the effects of two treatments, no mulching (NM) and plastic film mulching (FM), on the yield, water productivity (WP), and root morphology of spring maize on the Loess Plateau. The results showed that the FM yield (14.31–15.02 t ha^–1) significantly increased by 18.6–29.7 % compared to NM (11.03–12.66 t ha^–1). The FM treatment also significantly increased dry matter (51.0–61.6 %), leaf area (19.7–25.7 %), and WP (28.8–46.3 %), but decreased ET (8.6–12.8 %). In addition, soil water storage in the FM surface soil layer significantly increased compared to that of NM. Film mulching also produced more robust roots and promoted the convergence of roots towards the surface of the soil, whereas NM roots tended to grow downwards to obtain water from the lower soil layers. The regression analyses indicated that root length (R² = 0.725, P < 0.01) and biomass (R² = 0.736, P < 0.01) were positively correlated with grain yield. The results suggested that maize adapts to changes in root morphological behavior under FM. These changes contribute to soil water and nutrient capture and shoot development, which subsequently support the high yields produced under plastic film mulching. Therefore, film mulching is a promising strategy for improving yield and WP and for optimizing root morphology in dryland agriculture.

Climate change cause extreme weather effects with temperature increases and drops in humidity, such as drought and heatwaves, which will lead to more evaporation in arid and semi-arid lands. The application of biochar made from crop straw without burning to farmland can effectively improve the water retention capacity of soil. A testing program has been carried out in a climate simulation laboratory to study the effects of different straw biochars on the cracking and evaporation of soils due to drying. Biochar from wheat straw (WS), corn straw (CS) and rice straw (RS) is produced at a pyrolysis temperature of 500℃. Thermogravimetric analysis and elemental analysis are carried out to obtain the properties of the biochar. Five percent of WS, CS and RS biochar by weight is added to acidic soil. Digital camera and digital image processing technology are used to analyze the crack morphology of the samples during evaporation. The results indicate that the RS biochar has the highest ash content (32.5 %), CS biochar has the highest content of volatile solid (25.36 %) and WS biochar has the highest content of fixed carbon (55.38 %). Biochar can effectively improve the water retention capacity of soil. The final water contents of the WS, CS and RS biochar soil samples are 132.3 %, 101.0 %, and 20.7 % respectively higher than that of the soil without biochar. Moreover, biochar can effectively reduce the degree of soil cracking. The addition of WS, CS and RS biochar can reduce soil cracking by 9.21 %, 16.57 %, and 7.46 %, respectively. WS contains more total cellulose than CS and RS, so WS biochar is the best choice to improve soil water retention ability. Therefore, biochar technology helps to optimize soil water retention while avoiding environmental pollution from straw burning.

Precision irrigation aims to deliver water and nutrients to crops at exactly the right time, in the right place and in the right amount. While surface irrigation is often perceived as less precise, accurate water distribution, wise use of resources and high efficiency can still be achieved with careful land preparation, astute irrigation management and rigorous performance monitoring. In this study, we advocate the innovative concept of Precision Surface Irrigation, centered around three key design and operational principles that are: well-organized field geometry (and microtopography), precise control of hydraulic-hydrological variables and, finally, regular performance evaluation. These pillars are then integrated into a simulation environment capable of capturing the intricacies of surface irrigation dynamics. Conducted in northern Italy's Padana plain, the study contrasts one-dimensional (WinSRFR) and two-dimensional (IrriSurf2D) irrigation dynamic modeling. Data collection includes boundary geometries, inflow rates, intervention durations, and microtopography, facilitating spatial performance assessment from both the models. The results show that the versatility of the two-dimensional modelling approach was able to reproduce well the observed water depths and the phases of water advance and depletion both in time and space within the studied border irrigation strips, even in complex situations where the strips were hydraulically connected. The RMSE between observed and simulated maximum water depth and waterfront advance time was less than 2.1 cm and 1.9 min, respectively. The two-dimensional approach was also able to detect the cross variability of irrigation dynamics, and to provide a spatial assessment of irrigation performance at high resolution. In conclusion, while the one-dimensional hydrodynamic approach to describing the hydraulic behavior of surface irrigation and field-scale irrigation performance remains valid, the two-dimensional approach provides, in our case study and reasonably elsewhere, a valid simulation environment for spatially characterizing irrigation dynamics in the context of Precision Surface Irrigation.

Soke Plain, located within the Büyük Menderes River Basin is one of the highest producers of cotton in Türkiye. The overall irrigation water supply is based on scarce conventional water resources that are being depleted at an increasing pace due to climate change impacts in B. Menderes. The inclusive objective of this research is to pave the way for a "water efficiency action plan" incorporating non-conventional (alternative) water resources for irrigation in Soke Plain to address adaptive management. Integrated Water Resources Management (IWRM) principles help decision makers (DMs) to identify and apply the most adequate alternatives among other possible ones in resource planning processes. Therefore, the preference ranking of DMs among possible water resource alternatives for irrigation is vital for implementation. This paper marks the first instance of using a multi-criteria decision-making (MCDM) method to evaluate both conventional and non-conventional water resource alternatives for cotton irrigation. The evaluation and ranking of water resource alternatives is processed using the hybrid MCDM method, integration of “Hesitant Fuzzy-Analytic Hierarchy Process” (HF-AHP) and “Hesitant Fuzzy Evaluation based on Distance from Average Solution” (HF-EDAS), namely HF-AHP-EDAS. This procedure implies several possibly contradictory qualitative and quantitative criteria, incorporates ambiguity, vagueness, and hesitancy in decision-makers’ decisions, and achieves a consistent, dependable ranking of alternatives. Eight different water resources for irrigation are evaluated by 5 experts, for 15 assessment criteria, in Soke Plain. Conventional water resources blended with drainage water is concluded to be the best irrigation water resource alternative, with HF-AHP-EDAS and also with HF-AHP-PROMETHEE II (Preference Ranking Organization Method for Enriching Evaluations II), that is used for comparison analysis. This choice aligns well with the outlined arguments, culminating in an overall result deemed compliant with the field survey.

Reducing methane (CH₄) emissions is increasingly recognized as an urgent greenhouse gas mitigation priority for avoiding ecosystem ‘tipping points’ that will accelerate global warming. Agricultural systems, namely ruminant livestock and rice cultivation are dominant sources of CH₄ emissions. Efforts to reduce methane from rice typically focus on water management strategies that implicitly assume that irrigated rice systems are consistently flooded and that farmers exert a high level of control over the field water balance. In India most rice is cultivated during the monsoon season and hydrologic variability is common, particularly in the Eastern Gangetic Plains (EGP) where high but variable rainfall, shallow groundwater, and subtle differences in topography interact to create complex mosaics of field water conditions. Here, we characterize the hydrologic variability of monsoon season rice fields (n = 207) in the Indian EGP (‘Eastern India’) across two contrasting climate years (2021, 2022) and use the Denitrification Decomposition (DNDC) model to estimate GHG emissions for the observed hydrologic conditions. Five distinct clusters of field hydrology patterns were evident in each year, but cluster characteristics were not stable across years. In 2021, average GHG emissions (8.14 mt CO₂-eq ha⁻¹) were twice as high as in 2022 (3.81 mt CO₂-eq ha⁻¹). Importantly, intra-annual variability between fields was also high, underlining the need to characterize representative emission distributions across the landscape and across seasons to appropriately target GHG mitigation strategies and generate accurate baseline values. Simulation results were also analyzed to identify main drivers of emissions, with readily identified factors such as flooding period and hydrologic interactions with crop residues and nitrogen management practices emerging as important. These insights provide a foundation for understanding landscape variability in GHG emissions from rice in Eastern India and suggest priorities for mitigation that honor the hydrologic complexity of the region.