Irrigation Treatments Research Articles

Drought resistance and resilience of rhizosphere communities in forest soils from the cellular to ecosystem scale - insights from 13C pulse labeling.

The link between above- and belowground communities is a key uncertainty in drought and rewetting effects on forest carbon (C) cycle. In young beech model ecosystems and mature naturally dry pine forest exposed to 15-yr-long irrigation, we performed 13C pulse labeling experiments, one during drought and one 2 wk after rewetting, tracing tree assimilates into rhizosphere communities. The 13C pulses applied in tree crowns reached soil microbial communities of the young and mature forests one and 4 d later, respectively. Drought decreased the transfer of labeled assimilates relative to the irrigation treatment. The 13C label in phospholipid fatty acids (PLFAs) indicated greater drought reduction of assimilate incorporation by fungi (-85%) than by gram-positive (-43%) and gram-negative bacteria (-58%). 13C label incorporation was more strongly reduced for PLFAs (cell membrane) than for microbial cytoplasm extracted by chloroform. This suggests that fresh rhizodeposits are predominantly used for osmoregulation or storage under drought, at the expense of new cell formation. Two weeks after rewetting, 13C enrichment in PLFAs was greater in previously dry than in continuously moist soils. Drought and rewetting effects were greater in beech systems than in pine forest. Belowground C allocation and rhizosphere communities are highly resilient to drought.

Deficit irrigation strategy to sustain available water resources using guar

The gradual depletion of irrigation water is a major threat to the agricultural economy in the arid and semi-arid regions of the world. Drought–tolerant crops and improved irrigation management practices may help the sustainability of agriculture in the region. A three–year field experiment (2018–2020) was conducted to assess the effects of pre–and in–season irrigations on the physiology, biomass production, yield, and yield components of two diverse guar (Cyamopsis tetragonoloba) cultivars. A split-split plot design was used, where the main plot was pre–irrigation, subplots consisted of in–season irrigation treatments, and sub-sub plots were cultivars. Each treatment was replicated four times each year. Pre–irrigation improved the averaged final seed yield by 32% compared to plots with no pre–irrigation. Seed yield was significantly affected by in–season growth stage based irrigation treatments. Averaged over the years, full irrigation achieved the highest seed yield, although not significantly different from no irrigation during the vegetative growth stage in any study year. Cultivar Kinman performed better than Monument in biomass production and seed yield. Overall, pre–irrigation improves guar growth and development, leading to greater seed yield and biomass production. The most sustainable strategy to utilize the available water resources will be to grow guar with restricted irrigation during the vegetative growth stage, save 22% of water, and maintain a similar seed yield to plots that received irrigation throughout the crop season.

Water Stress Index and Stomatal Conductance under Different Irrigation Regimes with Thermal Sensors in Rice Fields on the Northern Coast of Peru

In the face of the climate change crisis, the increase in air temperature negatively impacts rice crop productivity due to stress from water scarcity. The objective of this study was to determine the rice crop water stress index (CWSI) and stomatal conductance (Gs) under different irrigation regimes, specifically continuous flood irrigation treatments (CF) and irrigations with alternating wetting and drying (AWD) at water levels of 5 cm, 10 cm, and 20 cm below the soil surface (AWD5, AWD10, and AWD20) in an experimental area of INIA-Vista Florida and in six commercial areas of the Lambayeque region using thermal images captured with thermal sensors. The results indicated that AWD irrigation generated more water stress, with CWSI values between 0.4 and 1.0. Despite this, the yields were similar in CF and AWD20. In the commercial areas, CWSI values between 0.38 and 0.51 were obtained, with Santa Julia having the highest values. Furthermore, a strong Pearson correlation (R) of 0.91 was established between the CWSI and Gs, representing a reference scale based on Gs values for evaluating water stress levels.

Calibration and evaluation of the FAO AquaCrop model for canola (Brassicanapus) under full and deficit irrigation in a semi-arid region

Field experiments were conducted in the cropping years 2012 and 2013 with four irrigation treatments, I1, I2, I3, and I4, respectively, corresponding to full irrigation, 20, 35, and 50% less than the crop water requirement in a semi-arid region of Iran. Data were used for calibration (2012) and validation (2013) of biomass (B), grain yield (GY), soil water content (SWC), canopy cover (CC), and evapotranspiration (ET) of canola by the AquaCrop model. Model coefficients were calibrated for I1 treatment in 2012, and the calibrated parameters were used for other treatments in both years. Based on the results, the NRMSE value for SWC ranged from 10 to 20%. Simulated and measured SWC for the four water treatments showed that AquaCrop has good accuracy. Overall, the results of the model in simulating SWC for both years were satisfactory. AquaCrop provided overestimation in the calibration phase of ET and underestimation in the ET validation phase. The results also showed that AquaCrop is overestimated in the simulation and prediction of CC and is less satisfactory under water stress. The model evaluation showed that AquaCrop can simulate B and GY with high accuracy. In general, AquaCrop was identified as a good tool for studying irrigation management, crop B and GY, CC, SWC, and other factors affecting the canola growth in the studied fields and climate.

Effect of Water Conservation and Nitrogen Reduction on Root Growth and Yield in Spring Maize in Typical Sand Interlayered Soil

Given the low water and fertiliser use efficiency and the extensive distribution of sand interlayered soil in the Hetao irrigation district (HID), this study aimed to investigate the effects of different irrigation and fertilisation regimes on root parameters and yield in spring maize grown in sand interlayered soil. A two-year field plot experiment was conducted using the spring maize “Ximeng 3358” under three irrigation and nitrogen levels. Root length (RL), surface area (RS), diameter (RD), volume (RV), and length density (RLD), grain yield, and water use efficiency (WUE) were examined. Root growth was inhibited at the sand layer, with approximately 72.46–87.37% of the roots concentrated in the 0–40 cm soil layer. Notably, the proportion of roots in the bottom layer was 24.61–87.37% higher than that in the sub-bottom layer. Moreover, RL, RS, RD, and RV peaked in the medium irrigation and nitrogen fertilisation (I2F2) treatment. Furthermore, correlation analysis showed that the root parameters were significantly positively correlated with yield and WUE, with RS being most correlated to yield and WUE. Roots at a narrow row spacing of 20 cm (NR20) and at a depth of 10–20 cm were strongly correlated with yield and WUE. Conclusively, the I2F2 treatment can be used as the optimal combination of water and nitrogen for sand interlayered soil farmlands.

Experimental drought reduces the productivity and stability of a calcareous grassland

Abstract Grasslands, which encompass 40% of terrestrial ecosystems, hold global significance for food production, carbon storage and other ecosystem services. However, grasslands across the biosphere are becoming increasingly exposed to both wet and dry precipitation extremes resulting from climate change. Therefore, understanding how grasslands will respond to precipitation extremes is a pressing issue for managing changes to biodiversity and ecosystem service provision. Here, we use experimental manipulations of precipitation (50% increase and 50% decrease in growing‐season precipitation) over 6 years to investigate the stability and resistance of both productivity and community diversity in a calcareous grassland community. We found that decreased growing‐season precipitation led to reductions in mean productivity (25% decrease in peak above‐ground biomass) and its temporal stability (54% increase in the coefficient of variation of biomass across years). Productivity losses were more pronounced for graminoids and legumes, but this was not reflected in species per cent cover. Community composition was resistant to the precipitation manipulations, with no clear differences in overall community compositional turnover, dissimilarity or biodiversity indices. There was also no detectible effect of increased precipitation on productivity or community composition, which may indicate a lack of efficacy of the irrigation treatment in increasing soil moisture. Finally, the precipitation manipulations had no effect on temporal trends of community change, with an overall increase in richness and shift in grassland community composition across the study period independent of treatment. Synthesis. While the diversity and composition of this calcareous grassland was resistant to precipitation extremes (at least in the short term), sustained reductions in growing‐season precipitation reduced productivity and its temporal stability, particularly for graminoids and legumes. Therefore, we highlight that different properties of grasslands can vary in their responses to changes in precipitation.

Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

A techno-economic investigation for utilizing solar energy in irrigation of palm trees in Saudi Arabia

This paper presents a techno-economic investigation for utilizing photovoltaic solar energy in water pumping applications for the irrigation of Palm trees in the Qassim region in Saudi Arabia. The Analysis has been done by applying four technical indicators and three economic indicators on a real farm of palm trees. The investigation took into account the varied water demand for palm trees over the years, meteorological data of the region, the characteristics of the borehole, and the local market prices of PV system components. The investigation has been done using two options of PV systems: grid-connected system and standalone system. The results showed the superiority of the grid-connected system in spite of the unfair price of energy exchange with the utility grid. The results achieved are the Levelled cost of energy, which is in the range from 0.013 to 0.019 $/kWh. The standardized cost of produced water is in the range from 0.011 to 0.013 $/m3, and the simple payback time is in the range from 9.65 to 12.22 years. The results are considered to encourage farmers in the region to convert to solar energy utilization.

Streptomyces spp. Strains as Potential Biological Control Agents against Verticillium Wilt of Olive.

Verticillium wilt of olive (VWO) caused by Verticillium dahliae is considered a major olive (Olea europaea) disease in Mediterranean-type climate regions. The lack of effective chemical products against VWO makes it necessary to search for alternatives such as biological control. The main goal of this study was to evaluate the effect of six Streptomyces spp. strains as biological control agents (BCAs) against VWO. All of them were molecularly characterized by sequencing 16S or 23S rRNA genes and via phylogenetic analysis. Their effect was evaluated in vitro on the mycelial growth of V. dahliae (isolates V004 and V323) and on microsclerotia (MS) viability using naturally infested soils. Bioassays in olive plants inoculated with V. dahliae were also conducted to evaluate their effect against disease progress. In all the experiments, the reference BCAs Fusarium oxysporum FO12 and Aureobasidium pullulans AP08 were included for comparative purposes. The six strains were identified as Streptomyces spp., and they were considered as potential new species. All the BCAs, including Streptomyces strains, showed a significant effect on mycelial growth inhibition for both V. dahliae isolates compared to the positive control, with FO12 being the most effective, followed by AP08, while the Streptomyces spp. strains showed an intermediate effect. All the BCAs tested also showed a significant effect on the inhibition of germination of V. dahliae MS compared to the untreated control, with FO12 being the most effective treatment. Irrigation treatments with Streptomyces strain CBQ-EBa-21 or FO12 were significantly more effective in reducing disease severity and disease progress in olive plants inoculated with V. dahliae compared to the remaining treatments. This study represents the first approach to elucidating the potential effect of Streptomyces strains against VWO.

Adjustment of the AquaCrop model in maize with different levels of irrigation in southern Uruguay temperate climate conditions

The AquaCrop model allows evaluating and designing irrigation strategies that improve the use of irrigation water. The objective of this research was to calibrate and validate the AquaCrop model for maize to the climatic conditions of southern Uruguay, with different irrigation water management. This model was calibrated and validated for corn using experimental data from irrigation trials with different deficit levels in three seasons, 2015-16 and 2016-17 (calibration) and 2014-15 (validation). Three maximum irrigation depths were evaluated: 3, 6 and 9 mm day-1, and rainfed (rainfall only). The crop was parameterized for local conditions and water stress coefficients were adjusted. The calibration simulated the yield, biomass and soil moisture in the irrigated treatments with good performance. All the statistic indexes used to evaluate the adjustment between the observed and simulated data model indicated a good model performance, with the exception of the efficiency coefficient of the Nash-Sutcliffe (EF) model. The model underestimated the yield in the rainfed treatment (EF of -0.52) when root depth was limited to 0.7 m. However, the test soil allowed for greater radical exploration than the initially used. At 0.90 m root depth, the model was good at simulating the yields in the rainfed treatment, mainly in dry years (EF of 0.79). The model predicts the yield with good adjustment in different irrigation and rainfall situations if the stress coefficients are adjusted and the crop is properly parameterized, mainly the root depth.

New challenges for Uruguayan viticulture

Climate scenarios in the medium and long term (2010-2070) foresee increased summer rainfall for Uruguay and the region, with increased water deficits and excess episodes. Although at the international level irrigation in viticulture has a long experience and tradition, at the local level (Uruguay), only 10% of the vineyard surface area implements a fixed or complementary system for water supply in their crops. This work aimed to model the crop water requirements for a vineyard in southern Uruguay based on pedo-climatic variables. In addition, the plant response to controlled deficit irrigation was evaluated in two consecutive seasons. The experiment was conducted in a 1.1 ha commercial vineyard in Canelones, Uruguay (34°36'S, 56°14W), during two successive seasons (2020-2021). The additional irrigation (I) treatment was compared against a control (C) without irrigation. A controlled water deficit was established from flowering to harvest. The adjustment in the demand was made as a function of a percentage of crop evapotranspiration. The Kc of the crop was estimated using digital tools. The simulation of the water balance made it possible to evaluate the vineyard water needs. Plants subjected to controlled deficit irrigation showed higher vegetative growth, positively impacting yield and the accumulation of sugars and anthocyanins in the berry. Based on our results, a supplementary water supply, at the right doses and time, allows us to face water deficit situations, positively impacting the productive and economic variables. Knowing the variability in a vineyard is necessary to achieve proper irrigation scheduling and optimize water use. New technologies applied to irrigation are an opportunity for winegrowers to obtain more sustainable vineyards and production.

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

&lt;p&gt;Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Classification of cotton water stress using convolutional neural networks and UAV-based RGB imagery

&lt;p&gt;Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;

Exogenous Melatonin Enhances Moisture Stress Tolerance in Mango (Mangifera indica L.) through Alleviating Oxidative Damages.

In subtropical regions, April to June represents a temporary moisture stress for mango trees, leading to huge economic loss. Although water is available in the deep root zone, the upper soil surface, which has fibrous roots, is dry, and the tree transpiration rate is high. Moisture stress causes an increased oxidation state, which is detrimental to fruit growth and development. Finding substitutes for moisture stress management is important for sustainable mango production. To manage this moisture stress in mango, we tested if foliar application of 20, 50, 100 and 150 μM melatonin helped to maintain a reduced oxidation state in the cells. Applications were made at three phenological stages of fruit development (marble, egg and mature fruit stages) in 16-year-old trees and the same plants for each treatment were followed over three years. Melatonin application indeed improved the fruit yield of mango. Moisture stress decreased yield by 55.94% compared to irrigated trees but only by 7.5% in melatonin treatment. Also, more 'A' grade fruits were harvested in irrigated and melatonin-treated conditions than in non-irrigated and non-treated conditions. Indeed, the total chlorophyll content in the leaves of moisture-stressed melatonin-treated trees (12.58 mg.g-1 fresh weight) was well above non-treated trees (6.77 mg.g-1) and similar to irrigated trees (12.50 mg.g-1). A dose-dependent increase in the chlorophyll content of melatonin-treated plants was found. Similarly, the activities of catalase, peroxidase, superoxidase dismutase enzymes in leaves of irrigated and melatonin-treated trees were lower than in non-irrigated condition, and superoxide free radial formation was lower in moisture-stressed melatonin-treated trees (0.77 nmol H2O2.mg-1 protein) and irrigated trees (0.65) than moisture-stressed non-treated trees (4.27). Significant variations was found in antioxidants (total, reduced and oxidized glutathione and ascorbate) content and antioxidant enzymes' activities (i.e., glutathione reductase and ascorbate peroxidase) in irrigated, melatonin-treated and non-irrigated conditions. Overall, 150 μM exogenous melatonin applied three times at different fruit development stages may be a sustainable and useful approach to manage transient moisture stress in mango trees thanks to its positive action on the antioxidant system.

Effects of increasing drip irrigation at different maize growth stages on soil microorganisms.

To investigate the effects of different drip irrigation periods on soil microbial communities and functions. Increasing drip irrigation amount at the seedling (S), jointing (J), bell (B), tasseling (T) and grain filling (G) stages of maize were studied using no increase in irrigation amount as control (CK). Principal component analysis was conducted to comprehensively evaluate soil microbial quality following the different drip irrigation treatments. In addition, the characteristics of the community structure and the potential functional composition of soil bacteria and fungi were comparatively analyzed by combining amplicon sequencing and functional prediction methods. The results indicated that MBNT15 was the most important genus for the classification of soil bacterial samples, Saitozyma was the most important genus for the classification of soil fungal samples, and fungi were more important than bacteria for the classification of soil microbial samples. Compared with fungal communities, bacterial communities exhibited high levels of functional diversity. The proportion of metabolism was the highest in the prediction of bacterial primary functions, and carbohydrate metabolism and amino acid metabolism were important functions in the prediction of bacterial secondary functions. BugBase phenotype prediction results showed that soil bacteria under B treatment had a higher number of aerobic bacteria and greater resistance to disease and stress. The J treatment had the highest number of bacteria with biofilm forms, and the J, S, and G treatments contained more potentially pathogenic bacteria but fewer stress-tolerant bacteria compared with the CK treatment. The number of Saprotroph was the largest and the number of Symbiotroph was the least. The relative abundances of Saprotroph, Pathotroph and Symbiotroph were 68.60%~74.33%, 15.76%~20.60% and 9.16%~11.13%, respectively. The findings provide a reference for conserving water resources, improving maize yield, and predicting soil microbial metabolic potential and function by reflecting the richness of the soil microbial community structure in maize fields.

Response of Sunflower Yield and Water Productivity to Saline Water Irrigation in the Coastal Zones of the Ganges Delta

The intensification of cropping systems in the salt-affected coastal zones of the Ganges Delta can boost food security in the region. The scarcity of fresh water, coupled with varying degrees of soil and water salinity are however limiting factors for the expansion of irrigated cropping in that area. In this study, we assessed the potential of growing sunflowers using combinations of low and medium saline water for irrigation. The experiments were conducted at two locations with six irrigation treatments in 2016–2017 and 2017–2018. The treatments were: T1—two irrigations at early vegetative (25–30 days after sowing; DAS) and flowering stages (60–65 DAS) with low salinity water (LSW, electric conductivity, ECw &lt; 2 dS m−1); T2—two irrigations, one at the vegetative stage with LSW and one at the flowering stage with medium salinity water (MSW, 2 &lt; ECw &lt; 5dS m−1); T3—two irrigations, one at the vegetative stage with LSW and one at seed development stage (75–80 DAS) with MSW; T4—three irrigations at the vegetative, flowering and seed development stages with LSW; T5—three irrigations, at vegetative stage with LSW, and flowering and seed development stages with MSW; and T6—three irrigations, two at the vegetative and flowering stages with LSW and one at the seed development stage with MSW. Irrigation with LSW at early growth stages and MSW at later growth stages did not significantly (p &lt; 0.05) affect the yield compared to the LSW irrigation at early and later growth stages. Crop water productivity and irrigation water productivity of sunflowers (p &lt; 0.001) increased substantially with the decreasing amount of irrigation water with an average of 1.18 kg m−3 and 2.22 kg m−3 in 2017 and 0.92 kg m−3 and 1.29 kg m−3 in 2018, respectively. Grain yield was significantly correlated with root zone solute potential. The flowering and seed development stages of sunflowers in February–March were sensitive to both low and medium saline water irrigation for seed yield. Overall, the results show that irrigation with LSW (ECw &lt; 2dS m−1) at early growth stages and MSW (2 &lt; ECw &lt; 5dS m−1) at later growth stages could be an option for dry-season sunflowers in the coastal zones of the Ganges Delta which would allow double cropping in this area.

Effect of Subsurface Drainage in Regulating Water on Desalinization and Microbial Communities in Salinized Irrigation Soils

In order to achieve water conservation and salt control in saline irrigation areas and improve the soil ecological environment of farmland in irrigation areas, this study carried out a field trial in 2020–2021 on edible sunflowers planted in saline subsurface farmland in the Hetao Irrigation District. Three irrigation level treatments and a control setup under subsurface drainage were compared. The control was with no drainage and local conventional irrigation levels (the spring irrigation amount is 240 mm and the bud stage irrigation amount is 90 mm, CK); and the three irrigation levels were conventional irrigation (the spring irrigation amount is 240 mm and the bud stage irrigation amount is 90 mm, W1), medium water (the spring irrigation amount is 120 mm and the bud stage irrigation amount is 90 mm, W2), and low water (the spring irrigation amount is 120 mm and there is no irrigation in the bud stage, W3). The results showed that soil desalinization was best in the conventional irrigation (W1) treatment and lowest in the low-water treatment (W3) under subsurface drainage. The desalinization rate was 13.54% higher in the subsurface drainage than in the undrained treatment with the same amount of irrigation water. Under subsurface drainage, the medium-water treatment (W2) increased the diversity of soil microorganisms and the relative abundance of dominant phyla such as Ascomycetes, Chlorobacterium, Acidobacterium, and Ascomycetes among soil bacteria and Ascomycetes and Tephritobacterium amongst fungi. The average sunflower yield in the treatments under subsurface drainage increased by 32.37% compared with the undrained treatment, and the medium-water treatment (W2) was the most favorable for protein and essential amino acid synthesis. Structural equation modeling indicated that desalinization rate, irrigation water utilization efficiency, bacterial Chao1 abundance and Shannon diversity, and fungal Chao1 abundance and Shannon diversity were the major influences on sunflower yield. Based on the entropy weight method TOPSIS model, 15 indicators such as soil desalinization rate, soil microbial diversity, water and nitrogen utilization rate, and sunflower yield and quality were evaluated comprehensively for each water treatment of subsurface drainage farmland. It was found that the irrigation volume under tile drainage of 210 mm (W2) had the highest comprehensive score, which could improve the soil microenvironment of the farmland while realizing water conservation and salt control in salty farmland, increase the production of high-quality crops, and be conducive to the sustainable development of agriculture; it was the optimal irrigation treatment for the comprehensive effect. The results of this study are of great significance for the realization of efficient water conservation and salt control and the protection of food security and ecological safety in the Hetao Irrigation District.

Fertilization- and Irrigation-Modified Bacterial Community Composition and Stimulated Enzyme Activity of Eucalyptus Plantations Soil.

Irrigation and fertilization are essential management practices for increasing forest productivity. They also impact the soil ecosystem and the microbial population. In order to examine the soil bacterial community composition and structure in response to irrigation and fertilization in a Eucalyptus plantations, a total of 20 soil samples collected from Eucalyptus plantations were analyzed using high-throughput sequencing. Experimental treatments consisting of control (CK, no irrigation or fertilization), fertilization only (F), irrigation only (W), and irrigation and fertilization (WF). The results showed a positive correlation between soil enzyme activities (urease, cellulase, and chitinase) and fertilization treatments. These enzyme activities were also significantly correlated with the diversity of soil bacterial communities in Eucalyptus plantations.. Bacteria diversity was considerably increased under irrigation and fertilization (W, F, and WF) treatments when compared with the CK treatment. Additionally, the soil bacterial richness was increased in the Eucalyptus plantations soil under irrigation (W and WF) treatments. The Acidobacteria (38.92-47.9%), Proteobacteria (20.50-28.30%), and Chloroflexi (13.88-15.55%) were the predominant phyla found in the Eucalyptus plantations soil. Specifically, compared to the CK treatment, the relative abundance of Proteobacteria was considerably higher under the W, F, and WF treatments, while the relative abundance of Acidobacteria was considerably lower. The contents of total phosphorus, accessible potassium, and organic carbon in the soil were all positively associated with fertilization and irrigation treatments. Under the WF treatment, the abundance of bacteria associated with nitrogen and carbon metabolisms, enzyme activity, and soil nutrient contents showed an increase, indicating the positive impact of irrigation and fertilization on Eucalyptus plantations production. Collectively, these findings provide the scientific and managerial bases for improving the productivity of Eucalyptus plantations.

Modeling root zone water and salt transport using matric flux potential based root water uptake distribution

It is important to delineate the root water uptake (RWU) response to salt and water stress for assessing future water availability under extreme climatic conditions. The process of RWU in salt-affected soils is highly complex and not well understood. The present study proposed a matric flux potential based non-linear RWU model capable of calculating reduced RWU in water and salt stressed conditions. The study incorporates a sensitivity analysis of the model's parameters. For the inverse estimation of these parameters, an objective function has been formulated, utilizing data derived from the highly sensitive model's response. The proposed objective function provides unique estimates for these parameters. Using this objective function in an optimization process, the model is calibrated with observed data obtained from a soil column lysimeter experiment conducted on berseem crop (Trifolium alexandrium). The parameters are optimized through the coupling of the model with a genetic algorithm-based optimizer tool. Simulated data for soil moisture, electrical conductivity, and percolation are then compared to observed data. The simulated model's responses are found to be closely aligns with the experimentally observed values. The RWU under four irrigation of different salinity levels is simulated. The results highlight a reduction in RWU compensation under high-salinity irrigation treatment.

The improvement of antioxidant contents and fruit quality of Sukkary date cultivar using various Potassium levels and Irrigation interval

Sukkary date palms (Phoenix dactylifera L.) were treated with three levels of potassium fertilizer combined with three irrigation intervals regime, five replicates were allocated per treatment, to enhance better date peel color; decrease loose crust, and subsequently, improve high market ability. Potassium sulfate (49% K2O) was applied annually at a rate of 490 g (as common fertilization applied in the farm in which this experiment was carried out, therefore, it was considered as a control treatment), 735 g, or 980 g K2O per palm tree, each level was divided into three equal doses. The first dose was added one month after pollination; the other two doses were added at two month interval, whereas the irrigation treatments were as follows: 24 hrs (as common irrigation application in the farm used for this study, and it was considered as a control treatment), 48 hrs, or 72 hrs. The results showed a great decrease in the loose crust dates at harvesting time. An inverse proportional relationship between irrigation intervals and loose crust of dates was observed. The High potassium fertilizer treatment accompanied with 72 hrs irrigation interval treatment produced a significantly higher date quality regarding with bright yellow color, less darkening, and loose crust. High potassium fertilizer treatments (735, or 980 g K2O per palm) increased total yield, improved date weight, size, and color. Moreover, increasing potassium level affected significantly the date total antioxidant. This study showed that, a potential means to control peel darkening and loose date crust and improve quality of cv. Sukkary dates, which ultimately improves its market ability, through implementing appropriate potassium and irrigation regimes.