Optimum Soil Moisture Research Articles

Soil bacterial habitat generalists strengthen depth-dependent organic carbon accrual in coastal reclaimed lands after afforestation

The microbially driven soil organic carbon (SOC) turnover and accrual at the aggregate scale are crucial to reveal potential SOC sequestration and stabilization. However, information about how afforestation in reclaimed lands influences the bacterial contribution to SOC within soil aggregates remains unclear. Here, we investigated the bacterial taxonomic and functional composition and habitat associations of species (i.e., generalists and specialists) in different aggregates. Soil aggregates were isolated using an optimal soil moisture method. Bacterial taxonomic and functional composition were determined using high-throughput sequencing and PICRUSt2, respectively. We evaluated the effect of bacterial traits on SOC accrual within aggregates along the soil profile in unreclaimed tideland and two poplar plantations at different stages of reclamation (i.e., reclaimed 24 and 64 years prior to data collection) in East China. The bacterial traits and SOC concentration did not differ significantly among different-sized aggregates and that bacterial alpha diversity and SOC within aggregates increased with reclamation time, but this was only pronounced in surface soil. The relative abundance of generalists increased, specialists decreased in the soil profile with reclamation time, and the magnitude of variation declined with soil depth. The random forest model and variation partitioning analysis confirmed that generalists and specialists dominated the variation in SOC. Moreover, some subgroups from Proteobacteria, Acidobacteria, Actinobacteria, and Chloroflexi phyla were rich in generalists and may play crucial roles in SOC accrual. These findings suggest that the bacterial traits and SOC in coastal coarse-textured soil were not affected by aggregate sizes and might rely on other aggregate attributes (e.g., stability). Furthermore, SOC accrual was strongly affected by an increase in habitat generalists. We provide new insights into the variation pattern in SOC and bacterial traits at the aggregate scale and illustrate that bacterial generalists have the greatest impact on SOC accrual in coastal reclaimed lands after afforestation.

Influence of selected grass mulches on soil physical properties under sprinkler irrigation system

Mulching is a vital agricultural practice that affects soil moisture retention, temperature regulation, and overall soil health. Three grass mulching materials (Gamba (Andropogon Gayanus), Jema (Chrysopogan Zizanioides) and Kachala (Typha Angustifolia)) at four mulching depths (0, 5, 10 and 15 cm) were examined to assess their effects on soil moisture content, temperature, bulk density and porosity. The study was conducted on onion plots irrigated with sprinkler system at a constant operating pressure (100 kPa). The study was carried out at the irrigation field laboratory of the Yelwa campus of Abubakar Tafawa Balewa University, Bauchi. Data were analysed using SPSS software version 23. The results indicated a significant combined impact of mulch type and depth on all studied parameters, except for bulk density. Results showed that Kachala grass, particularly at 15 cm depth, demonstrated superior moisture retention capabilities with 16.8% moisture content, leading to optimal soil moisture levels for plant growth. While the lowest soil moisture (8.81%) was conserved under the control (Un-mulched plot). Soil temperature recorded beneath the mulch was lowest (19.1oC) under 10 cm Kachala grass, while the highest (28.0 oC) was recorded under 15 cm Gamba grass. Highest soil bulk density (1.78 g/cm3) was recorded under the control, while 15 cm depth of Kachala grass gave the lowest (1.5 g/cm3) value of bulk density. The highest value of soil porosity (46.8%) was observed under 15 cm Kachala grass and the lowest (34.2%) was observed under the control. In conclusion, grass mulching has a positive effect on soil physical properties under sprinkler irrigation system in the fadama areas of Bauchi, Nigeria.

Automatic water table control system with remote telemetry and control unit

A newly developed automatic control system effectively manages water tables using perforated pipes for both irrigation and drainage. Water levels are monitored by sensors connected to a Remote Telemetry Control Unit (RTCU), which operates irrigation and drainage valves. The system links to a cloud server, providing remote access through a user dashboard. This enables users to observe water level data and adjust the water table as needed. The system’s adaptability allows for regulation of rice field water levels based on cultivation requirements, enhancing water use efficiency, sustainability, and environmental friendliness. Operating at a laboratory scale for several months, the system’s performance in controlling water tables has been assessed using recorded data. The data reveal that the system automatically provides irrigation when water tables decrease and initiates drainage when rain increases water levels or when a lower level is set. This control ensures optimal soil moisture preservation, readily available to plant roots. Overall, the system demonstrates strong performance in maintaining water tables and soil moisture levels

Modeling of soil moisture and water fluxes in a maize field for the optimization of irrigation

Precision irrigation is becoming more and more important in agricultural crop production due to the limited water resources resulting from the negative effects of climate change. Modeling of irrigation can support decision makers in the maintenance of the quantitative and qualitative parameters of the cultivated crops, while improving the efficiency of water consumption. In the present paper, a 3D hydrodynamic model was created in the HYDRUS environment to support the modeling of the temporal changes and spatial variations in the water balance (WB) of a maize cultivated Hungarian study site, thereby providing inputs for irrigation scheduling and variable rate irrigation for stakeholders. Beside soil physical parameters, crop evapotranspiration (ETc) values were also considered as model inputs for the phenological crop development stages of the maize from the sowing to the harvesting. This period was between the 3rd of May and the 10th of September in 2020 and the 20th of May and 14th of September for the year 2021. The performance of the model for soil moisture conditions were validated by on-field soil moisture measurements. The overall performance (full vegetation period) of the model was good (r2 = 0.88 for 2020 and r2 = 0.91 for 2021), but slightly varied among different phenological stages. Based on the model, the water balance of the investigated area was determined without any irrigation, alongside the cumulative water fluxes (CWF) through the boundaries including the root water uptake (RWU) of the maize as well. The results revealed that the incoming precipitation was not sufficient to supplement the water content in the soil to the optimal soil moisture range, except when the precipitation level is high enough to balance it. It was concluded that the water balance was negative for the investigated time periods without any irrigation. The specific water deficit (WD) values were calculated considering the area of the study site, which is 1439 m3/ha for 2020 and 2068 m3/ha for 2021. From the obtained results, optimal irrigation schedules were presented to keep the soil moisture content in the optimal range (20.92–14.41 V/V%) in the modeled area for two different vegetation periods. It was found that due to the irrigation the crop water productivity (CWP) increased by 6% and 4% in 2020 and 2021. Overall, the model is able to cope with changing circumstances that could help to mitigate the negative effects of climate change with the reasonable use of limited water resources in the future.

Smart Irrigation Systems using (IoT) – A Survey

In India a very vast population is dependent on farming activities and most of them still uses traditional methods of irrigation. Water availability plays main role in this sector. As the world is advancing and population of India is on the rise scarcity of water is becoming a common problem in most part of the country. This survey paper provides a comprehensive overview of smart irrigation systems, focusing on their integration with IoT, deep learning, sensors, and AI to optimize agricultural practices. The paper conducts an extensive literature review, exploring challenges and opportunities in the domain, including IoT applications in agriculture, deep learning techniques, sensor technologies, and AI-driven decision-making. The methodology involves developing and integrating key components like sensor deployment, data collection, deep learning model training, and AI-based decision-making algorithms. Performance evaluation compares the system's effectiveness with traditional methods, analyzing irrigation decisions, water consumption, crop yield, and water conservation. The results demonstrate how the smart irrigation system optimizes water usage, maintains optimal soil moisture levels, and improves crop health, leading to increased agricultural productivity and reduced water waste. Overall, this survey paper contributes valuable insights for researchers, practitioners, and policymakers, emphasizing the potential of IoT, deep learning, sensors, and AI in revolutionizing agriculture and promoting sustainable and efficient irrigation practices to address water scarcity challenges. Keyword:- IoT(internet of things), ML (Machine Learning), LDR(light dependent register), DHT (Dihydrotestosterone)

Estimation of High-Resolution Soil Moisture in Canadian Croplands Using Deep Neural Network with Sentinel-1 and Sentinel-2 Images

Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3/m3 and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.

Optimizing Soil Moisture Conservation and Temperature Regulation in Rainfed Jujube Orchards of China’s Loess Hilly Areas Using Straw and Branch Mulching

The implementation of the “Returning Farmland to Forest” project in the loess hilly region of China has led to the establishment of large-scale economic forests, which have become the dominant industry driving local economic development. However, the region faces challenges such as drought, water shortages, and an uneven distribution of precipitation, which have a severe impact on the growth of economic forests, including jujube trees. Water stress significantly reduces yield and efficiency, posing a threat to the sustainable and healthy development of jujube ecological and economic forests. Therefore, this study aimed to address these issues by implementing straw mulching (SM) and jujube branch mulching (BM) measures in the mountainous jujube economic forests. Through long-term monitoring and statistical analysis, the study investigated the effects of different mulching treatments on soil moisture and soil temperature. The research findings reveal that both SM and BM significantly increased soil moisture in the 0–280 cm soil layer during the jujube growing season (p < 0.05). In both normal precipitation (2014) and drought (2015) years, SM increased average soil moisture content by 5.10% and 4.60%, respectively, compared to the uncovered treatment (CK). SM also had a positive impact on the soil moisture content in each layer of the soil profile. However, BM only increased soil moisture content in the 40–100 cm and 220–280 cm soil layers. Additionally, SM and BM reduced the variation of soil moisture, with SM showing a more significant effect in regulating soil moisture and achieving more stable moisture levels. During the jujube growing seasons in 2014 and 2015, SM and BM decreased soil temperature in the 0–10 cm soil layer. The temperature difference compared to CK decreased with increasing soil depth. SM had an overcooling effect, while BM reduced the temperature before the fruit expansion period and maintained warmth afterward. Both SM and BM also reduced the daily range and variation range of soil temperature, with SM having a more pronounced effect. The temperature of the 0–20 cm soil layer exhibited the strongest correlation with air temperature, and SM showed the weakest response. In conclusion, adopting straw mulching and jujube branch mulching in rain-fed jujube orchards in the loess hilly region not only saves materials and reduces costs but also contributes to water retention and temperature regulation. Straw mulching, in particular, plays a more significant role in moisture retention and temperature regulation and is advantageous for soil management in rain-fed jujube orchards. These research findings provide a scientific basis for optimizing water and heat management in orchards with limited water resources.

Rice-Fallow Targeting for Cropping Intensification through Geospatial Technologies in the Rice Belt of Northeast India

Rice-fallow areas have significant potential to sustainably increase agricultural intensification to address growing global food demands while simultaneously increasing farmers’ income by harnessing the residual soil moisture in rainfed ecologies. Assam is the largest rice-growing belt in northeast India during kharif; however, for the next rabi season, an average of 58% of the rice areas remain uncultivated and are described as rice-fallow (Kharif, rabi and zaid are the crop seasons in the study area. The kharif season refers to the monsoon/rainy season and corresponds to the major crop season in the region extending from June to October. The rabi season refers to the winter season extending from November to April, and the zaid season is the summer crop season from April to June). Unutilized rice-fallow areas with optimum soil moisture for a second crop were identified over three consecutive years using multiple satellite data (optical and radar) for the state of Assam and an average accuracy of 92.6%. The reasons governing the existence of rice-fallow areas were analyzed, and an average of 0.88 million ha of suitable rice-fallow areas, based on soil moisture availability, were identified. Targeted interventions were carried out in selected locations to demonstrate the potential of sustainable cropping intensification. Maize, with best management practices, and a yield between 5.5 and 6 t/ha, was demonstrated as a successful second crop during the rabi season in selected areas with optimum residual soil moisture after the kharif paddy harvest. This study highlights the significance of geospatial technology to effectively identify and target suitable rice-fallow areas for cropping intensification and to enhance productivity and profitability.

Effect of Long-Term Use of Fertilizers on Soil-Dwelling Micromycetes of Meadow Chernozem Soil in Wheat Crops

The effect of long-term systematic application of fertilizers on the mycobiota of meadow chernozem soil in a stationary field experiment (Amur region) was studied for the first time. Information about cultivated soil microscopic fungi is given, an annotated list including 26 species is compiled. It was revealed that the application of nitrogen fertilizers (N30) for wheat against the background of long-term use of fertilizers caused an increase in the total number of soil fungi relative to the control variant by an average of 30–68%, whereas with their aftereffect, a less significant increase in the number of mushroom germs was observed (23–29%). The intervals of the optimal content of macronutrients for the active increase of the pool of microscopic fungi in meadow chernozem soil were determined: phosphorus – 50–65, potassium – 195–210, nitrogen – ≥10 mg/kg with optimal soil moisture of 22–24%. The aftereffect of long-term application of fertilizers, especially organo-mineral fertilizers, as well as the application of mineral fertilizers (N30) directly in the year of the study had a positive effect on species diversity, frequency of occurrence and dynamics of the number of fungal germs in the soil. Negative rearrangements of micromycete complexes as a result of prolonged use of mineral fertilizers in the agrocenoses of meadow chernozem soils were not detected, the introduction of nitrogen-phosphorus fertilizers contributed to a significant decrease in the pool of phytopathogenic fungi.

The Correction Method of Water and Fresnel Reflection Coefficient for Soil Moisture Retrieved by CYGNSS

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data.

A Model and Its Performance of Evapotranspirative Irrigation Tested to Grow Water Lettuces

Efficient use of irrigation water is crucial to face the uncertain trend of a declining water resource. Ensuring a supply of water that matches crop evapotranspiration (ETc) while optimizing soil moisture is challenging. This study aimed to come up with a model of an irrigation system that can supply water immediately to meet ETc without using electricity. To test the proposed water balance model a trial was established in water lettuces. The system consisted of 15 pots connected in serial using water pipes and hoses through the bottoms of the pots. The first pot was used as a water level controller and the last pot used as a drain water collector. Irrigation water would flow immediately to the pots that had water levels lower than the setting level. Testing carried out on rainy days resulted in considerably higher rainwater (99.8%) being utilized compared to the irrigation water (0.2%). For water lettuces, the yield was 258.9 g/m2, water use efficiency reached 95% while the economical and physical water productivity were 2.17 g/L and 2.28 g/L, respectively. The water level in the pots could be maintained within the expected range while the soil moisture maintained an unsaturated condition. Further tests however are still needed, which currently is underway.

Verticulture Plant Monitoring System Using Internet of Things Based on Blynk Application

The development of technology is now very rapid. One of them is the use of the internet of things in various fields. One of the applications of technology that is developing in the world including in Indonesia is in agriculture. In this field, there is a model of how to grow plants verticulturally that utilizes limited land. In agriculture, the verticulture cropping technique known as vertical cropping requires optimal soil moisture through proper watering. So an automatic monitoring process is needed to measure soil moisture, air humidity to plant temperature. In this study focused on optimizing the automatic monitoring system using the blynk application compared to ordinary monitoring. This research also encourages further exploration of the use of micro controllers and internet of things technology in agriculture.

Willow communities at technogenic habitats in the middle-taiga subzone of north-east of the European part of Russia

The paper highlights the diversity of willow communities at technogenic habitats of the middle taiga subzone of north-east of the European part of Russia. Willow communities at technogenic habitats normally develop at places with excessive soil moisture content. At places with optimal soil moisture content, they are found in relatively rich loams. Four new associations are described. They are mesophytic willow communities in middle loamy, as well as residual carbonate soils – Calamagrostio arundinaceae–Salicetum capreae, forest communities surrounding them –Calamagrostio arundinaceae–Piceetum obovatae, willow communities in excessively moist meso- and oligotrophic acid soils – Polytricho communis–Salicetum phylicifoliae, willow communities in meso- and eutrophic waterlogged soils confined to groundwater outlets – Drepanoclado adunci–Salicetum myrsinifoliae. For the latter association, 3 new subassociations are described (typicum, bryetosum pseudotriquetri, philonotietosum fontanae). Mesophytic willow communities at anthropogenically disturbed territories belong to the new alliance Betulo pubescentis–Salicion capreae. Willow communities at technogenic habitats with excessive soil moisture content are described within the alliance Alno incanae–Salicion pentandrae Kielland-Lund 1981. The communities dominated with willows at technogenic habitats mainly include early-succession species. The floristic composition of communities largely depends on the particular soil type. Comparing the species composition of willow communities and forest communities surrounding them, we diagnose certain signs of restoration even at the first succession stages. For example, the willow communities include species being typical of syntaxa of forest ecosystems at advanced successional stages.

Rancang Bangun Sistem Monitoring KelembapanTanah dengan Konsep Smart Farming untuk Budidaya Tanaman Cabai Rawit Berbasis Internet of Things (IOT)

The design of a Soil moisture monitoring system with the Smart Farming Concept for IoT-based Chili Cultivation is a system design tool used to monitor Soil moisture in cayenne pepper cultivation. The monitoring system can determine Soil moisture conditions by monitoring Soil moisture information using IoT-based Microcontroller technology so that farmers can monitor the growth of cayenne pepper plants without the need to come to the Greenhouse location to monitor. The design of this monitoring systemuses a Soil moisture sensor. Where the Soil moisture sensor works is to monitor in real-time the state of Soil moisture in a Greenhouse. The classification of Soil moisture on soil quality so that the growth of cayenne pepper plants does not experience problems for dry soil and 61% to 100% for wet soil. The optimal Soil moisture content for cayenne pepper plant growth is in the range of 60% to 70% moist. The program flow to create a Soil moisture monitoring system for IoT-based chili cultivation goes through 3 main stages, namely the initialization, setup, and loop stages so that it can produce a display on the Soil moisture monitoring system application in real time. The results of the design of a Soil moisture monitoring tool based on trials show the optimal Soil moisture value at an average humidity of 63.5% and automatic watering will be carried out by the system when the Soil moisture level decreases from 60% so that it can be concluded that the IoT- based Soil moisture monitoring system can work well.

Skill and lead time of vegetation drought impact forecasts based on soil moisture observations

Timely and skilful forecasts of vegetation drought impacts should enable more proactive drought preparedness, management, and mitigation. Numerous previous studies found a temporal correlation between soil moisture and vegetation condition. However, a correlation across the full range of soil moisture and vegetation condition does not automatically translate into skill in forecasting infrequent events such as agricultural droughts. Here, we develop a threshold- or impact-based forecasting framework to assess early warning capability (EWC). We analysed the skill and lead time achieved using soil moisture observations at multiple depths as predictors of subsequent vegetation drought impacts inferred from MODIS satellite observations at 93 sites across the United States. Forecast thresholds were expressed in terms of seasonally-adjusted standardised anomalies (z-scores) to distinguish climate-related impacts from any seasonal vegetation cycle. Different combinations of soil moisture integration depth, satellite vegetation indices and threshold levels were tested. Near-Infrared Reflectance of vegetation (NIRv) yielded a marginally better EWC than other indicators of vegetation drought impact. The optimal soil moisture integration depth varied between land cover types, from 0 to 10 cm for cropping systems to 0–100 cm for grassland and savanna. The greatest skill improvements were achieved using similar z-score threshold values for the soil moisture trigger and vegetation impact, producing typical lead times of two to four weeks. Further research is recommended to combine the framework developed here with spatially continuous soil moisture analyses or forecasts available from remote sensing and land surface models.

Optimizing Soil Moisture Station Networks for Future Climates

AbstractSoil moisture is central to local climate on land. In situ soil moisture observations are vital for observing vegetation‐relevant root‐zone soil moisture. However, stations included in the International Soil Moisture Network are sparse in regions with strong land‐atmosphere coupling. We apply a machine‐learning‐based procedure for informing future station placement using virtual soil moisture stations in future CMIP6 projections. Stations are placed where the climate is currently most under‐represented. This strategy outperforms random station placement and station placement according to geographical distance. Doubling the current number of stations using this method alleviates the uneven global distribution of stations, increases the skill in the estimation of inter‐annual variability and trends in dry‐season soil moisture, and reduces its differences across climates in future projections. Stations are predominantly placed in tropical climates, especially when optimizing for drying trends. The results can inform future station placement to support climate change mitigation efforts.

The effect of drip irrigation for potatoes on soil nitrogen dynamics

The effects of potato drip irrigation on soil nitrogen dynamics and potato yield was studied in small-plot field trials. The trials were evaluated at during three experimental times seasons (2016-2018). Four variants of irrigation were determined, ie non-irrigated, 60, 65 and 70 ASWC (Available Soil Water Capacity). At the same time, two pre-planting fertilization dates were set at a maximum of 120 kg N/ha a in divided i.e. 60 kg N/ha before planting and 60 kg N/ha by fertigation during vegetation. Pre-planting crop fertilization or in-season fertigation did not have any significant effect on the potato yields of most variants. In all studied parameters, the positive beneficial effect of irrigation was recorded and compared to the non-irrigated control. The mitigation of drought stress in plants during the growing season is the most important advantage of irrigation. In the trials, on average across all years and varieties, the yield of the irrigated variant increased by 41 % compared to the non-irrigated variant. Maintaining optimal soil moisture has a favourable effect use of applied fertilizers. The highest mineral nitrogen content in the soil was recorded for the variant without irrigation. Considering the increased use of nitrogen fertilizers, the subsurface drip line appeared to be optimal for the creation of suitable conditions for nitrogen availability to plants in the root zone.

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China.

Water deficiency, together with soil salinization, has been seriously restricting sustainable agriculture around the globe for a long time. Optimal soil moisture regulation contributes to the amelioration of soil water and salinity for crops, which is favorable for plant production. A field experiment with five soil water lower limit levels (T1: 85% FC, T2: 75% FC, T3: 65% FC, T4: 55% FC, and T5: 45% FC, where FC is the field capacity) was conducted in southern Xinjiang in 2018 to investigate the responses of soil water-salt dynamics and cotton performance to soil moisture regulation strategies. The results indicated that in the horizontal direction, the farther away the drip irrigation belt, the lower the soil moisture content and the greater the soil salinity. In the vertical direction, the soil moisture and soil salinity increased first and then decreased with an increase in soil depth after irrigation, and the distribution was similar to an ellipse. Moreover, the humid perimeter of soil water and the leaching range of soil salt increased with a decrease in the soil moisture lower limit. Though more soil salt was leached out for the T5 treatment at the flowering stage due to the higher single irrigation amount, soil salinity increased again at the boll setting stage owing to the long irrigation interval. After the cotton was harvested, soil salt accumulated in the 0-100 cm layer and the accumulation amount followed T3 > T5 > T1 > T2 > T4. Moreover, with a decline of soil moisture lower limit, both plant height and nitrogen uptake decreased significantly while the shoot-root ratio increased. Compared with the yield (7233.2 kg·hm-2) and water use efficiency (WUE, 1.27 kg·m-3) of the T1 treatment, the yield for the T2 treatment only decreased by 1.21%, while the WUE increased by 10.24%. Synthetically, considering the cotton yield, water-nitrogen use efficiency, and soil salt accumulation, the soil moisture lower limit of 75% FC is recommended for cotton cultivation in southern Xinjiang, China.

A new technique for monitoring plant transpiration under field conditions using leaf optical dendrometry

Monitoring plant transpiration (Ec) is important for quantifying irrigation amounts and maintaining optimum soil moisture conditions for maximum growth potential. However, real-time quantification of Ec dynamics under field conditions is still challenging due to technical limitations. Here, we present a new approach for monitoring plant water use using continuous measurement of stem water potential dynamics (Ψstem) under non-limiting water conditions. According to Darcy's law, if daytime root to stem hydraulic conductance (Kr-s) remains constant and the contribution of plant capacitance to Ec is minimal or accounted for, then daytime Ec could potentially be continuously inferred from Ψstem dynamics. We investigated the viability of this approach by first quantifying the dynamics of daytime Kr-s and the transient buffering effects of plant capacitance in irrigated plants of two crop species with contrasting physiology and life history: the perennial herbaceous Tanacetum cinerariifolium and the annual monocot Triticum aestivum. Daytime Kr-s dynamics and the effects of plant capacitance were determined by continuously and simultaneously measuring Ψstem with optical dendrometers and Ec with gravimetric method under naturally variable conditions of light, temperature and humidity. Kr-s remained relatively stable throughout the day and the effects of plant capacitance were negligible in both species under field conditions implying that the dynamics of Ec can be inferred from Ψstem. This was clearly supported by the close agreement between measured Ec and that predicted from the optically derived Ψstem. We conclude that optical dendrometry has the potential to continuously monitor both Ec and plant hydration status at high accuracy and temporal resolution under naturally variable atmospheric conditions and optimum water supply. Combining these two aspects of the optically derived Ψstem should create new opportunities to monitor crop water use and maintain plant hydration at optimum levels for maximum productivity in various crops under field conditions.

Stabilization and strengthening of chromium(VI)-contaminated soil via magnesium ascorbyl phosphate (MAP) and phytase addition

Cr(VI) contamination of soil threatens the environment and reduces soil strength. Therefore, both Cr(VI) stabilization and soil reinforcement should be considered in site remediation for future construction. This study investigated a biochemical treatment process using magnesium ascorbyl phosphate (MAP) and phytase. MAP was hydrolyzed via phytase catalysis to produce ascorbic acid (AA) and MgHPO4·3H2O precipitation. The AA reduced Cr(VI) into low-toxic Cr(III), which precipitated as Cr(OH)3 and CrPO4. More than 90% of the 500 mg/kg Cr(VI) in soil was reduced by 5% MAP (wt% of soil) and 1% phytase (vol/vol of soil water) doses at the geotechnically optimal soil moisture content of 16.8%. The MgHPO4·3H2O precipitates filled soil pores and enhanced the unconfined compression strength of treated soil by more than two times. This research reports a novel and practical enzymatically induced phosphate precipitation process for the remediation of Cr(VI)-contaminated soil.