Root Zone Depth Research Articles

Analytical derivation of optimal irrigation water depth for efficient irrigation scheduling.

Optimal irrigation water depth is a crucial parameter in irrigation engineering, often referred to as root zone depth. It is typically assumed to lie between 1 and 1.5m below the ground surface, depending on the crop and soil types as well as the practitioner's skill and experience. This approach can lead to inefficient irrigation scheduling. Coupling Richards' equation with the Soil Conservation Service Curve Number (SCS-CN) concept and using the three-phase diagram of soil column widely used in geotechnical engineering, this paper suggests an analytical expression for optimal irrigation water depth providing the maximum storage capacity of a soil depending on its hydraulic/storage properties. The results for winter wheat crop in different hydrologic soil groups show that the use of the proposed concept can lead to savings of 71.79% and 57.69% of irrigation water in sandy soils (HSG-A) compared to that used in traditional irrigation considering lump-sum 1.5m and 1m optimal irrigation water depths, respectively. In the case of silty loam soils (HSG-C), these savings can assume 52.42% and 28.62%, respectively. The proposed relation can also be of great help in volumetric assessment of field capacity, moisture content, maximum water storage capacity (of different agricultural soils), and avoiding the issue of waterlogging that may arise from over-irrigation and thus is useful in efficient irrigation scheduling as well as in sustainable agricultural water management.

Estimation of 100 m root zone soil moisture by downscaling 1 km soil water index with machine learning and multiple geodata

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (Topt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, Topt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Organic soil amendment effects on soil hydrology in an almond orchard evaluated using time-lapse electrical resistivity tomography

Adding organic amendments to soils in orchards has been suggested as a climate-smart agricultural practice that can increase resilience to extremes such as drought. The benefits of adding almond hulls and shells to soil include releasing potassium into the soil, improving water infiltration, reducing soil water evaporation, and enhancing water-holding capacity. In this study, soil infiltration and root water uptake (RWU) patterns of almond trees in amended and control soil treatments were investigated. An almond orchard was mulched with a mixture of almond hulls and shells used as surface-applied organic matter amendments. The combined use of time-lapse electrical resistivity tomography (ERT), stem water potential (SWP) and leaf water potential (LWP) measurements were used to evaluate RWU, soil infiltration patterns, and tree water status at different times during the study. The results of the ERT showed that the RWU patterns for the almond trees are distinct between the amended and the control treatments after the irrigation was applied. Compared to the control treatment, the amended treatment allowed the soil to store more water. Regardless of treatment, the majority of RWU patterns were observed in the top 0.5–1 m root zone depth. Almond trees began to recover from stress, as indicated by the SWP and LWP values, four hours after the start of irrigation. In addition, this was demonstrated by the ERT measurements, which revealed the RWU activity four hours after the irrigation had been applied. This research reveals that time-lapse ERT surveys combined with soil and tree water status data can infer patterns of RWU in almond trees grown with organic soil amendments and under controlled conditions. Furthermore, it was concluded that using almond hulls and shells as organic matter amendments can help almond growers improve infiltration, making almond production more resilient to climate change-related extremes such as droughts.

STABILITY ASSESSMENT OF ROOT-REINFORCED SLOPES USING FINITE ELEMENT LIMIT ANALYSIS

Slope instability poses serious threats to infrastructure and environmental sustainability. As a result, numerous reinforcement techniques have been used as disaster mitigation attempts to prevent slope failure. Among various traditional slope reinforcement methods, the use of vegetion root is more cost-effective and environmentally friendly. This paper presents stability assessment of root-reinforced slopes. Firstly, slope models were built for case of bare and root-reinforced slopes. The slope angles were varied in the range of 15º ~ 55 º. The root cohesion and depth of root zone were adjusted within the ranges of 0 ~ 20 kPa and 0 ~1.5 m, respectively. The strength reduction finite element limit analysis method was applied to evaluate the stability of the slopes in this study. The results indicate that factor of safety increases with the increasing of root cohesion and depth of root zone. The best factor of safety was obtained for the case with root cohesion and depth of root zone of 20 kPa and 1.5 m, respectively. Shear dissipation contours of the slope models also show that root reinforcement reduces shear dissipation energy along the failure surface, consequently lowering the possibility of slope failure.

Enhancing precision of root-zone soil moisture content prediction in a kiwifruit orchard using UAV multi-spectral image features and ensemble learning

Accurate and real-time monitoring of soil moisture content (SMC) is of utmost importance for effective field irrigation and maximizing crop water productivity. However, a comprehensive investigation into the inversion study for determining suitable combinations of unmanned aerial vehicle (UAV) image features and enhancing the precision of SMC model prediction has yet to be fully validated within a kiwifruit orchard setting. This study addresses this gap by employing a pre-processing method and an optimal band combination algorithm to assess the impact of various combinations of kiwifruit canopy reflectance and fraction vegetation coverage (FVC) features on the sensitivity of root-zone SMC. Furthermore, an optimal ensemble learning (EL) framework was developed to monitor SMC at various root-zone depths (0–10 cm [SMC10], 0–20 cm [SMC20], 0–30 cm [SMC30], 0–40 cm [SMC40], 0–50 cm [SMC50], 0–60 cm [SMC60]). The key findings of this research highlight the successful derivation of 10 wavebands and FVC features, exhibiting a strong correlation with SMC at different root depths. The gradient boosting (GBDT) model demonstrated the exceptional accuracy in estimating SMC10, with an impressive R2 value of 0.963 ± 0.030 and low RMSE values of 0.238 ± 0.111. Similarly, the eXtreme Gradient Boosting (XGBoost) model outperformed in estimating SMC20 to SMC60, with R2 and RMSE values of 0.963 ± 0.024 and 0.117 ± 0.053, respectively. Additionally, the utilization of the optimal EL model allows for digital mapping of SMC at different depths across fruit growth stages, showcasing superior adaptability for SMC30 to SMC60 (with R2 and RMSE of 0.782 ± 0.090 and 0.037 ± 0.011) compared to SMC10 and SMC20 (with R2 and RMSE of 0.765 ± 0.097 and 0.056 ± 0.024). These results underscore the potential of the EL estimation framework in characterizing the spatial distribution of root-zone SMC at the individual kiwifruit plant level.

A simple method for evaluating the relative water uptake rate of drip‐irrigated crops

AbstractDrip irrigation is widely acknowledged for its water use efficiency, yet evaluating relative water uptake rates (RWURs, ratios between the water uptake rates and the irrigation rates) remains pivotal for effective system design and management. This article presents a novel method employing straightforward measurements of wetted soil surfaces around emitters or perpendicular to driplines, both with and without water uptake, emphasizing simplicity and practicality. The proposed method offers valuable insights into agronomic water use efficiency, facilitating the optimization of drip irrigation for both annual and perennial crops. While effective for intensively irrigated crops, the method does have limitations for smaller wetted areas and longer irrigation cycles, depending also upon a reasonable determination of the active root zone depth and the soil capillary length. Despite relying on a simplified water uptake model, the accessibility and cost‐effectiveness of the method render it a valuable tool for assessing RWURs in diverse agricultural settings, contributing to the sustainable utilization of water resources in drip irrigation.

Study the Physical Properties of Fertilizers and Plant Basin Parameters to Design the Tractor Operated Round Basin Maker cum Fertilizer Applicator for Orchard Crop

The physical form of fertilizer production is of extremely importance, both agronomically and in regard to satisfactory handling, transport, storage, and finally application to the field. The improvement of equipment for handling, storage, and application of granular fertilizer require a proper understanding of material properties and flow behavior. The most common problems encountered in fertilizers were caking, poor flow ability, segregation, dustiness and excessive hygroscopicity due to differences of physical properties. The physical properties such as moisture content, angle of repose and bulk density as important in designing the hopper and metering roller of fertilizer plays an important role in the storage, transport and application of fertilizer. The urea, Ammonium phosphate Sulphate, complex fertilizers and fertilizer ratio (10 26 26) per one orchard tree should be applied yearly trice based on crop requirement. The angle of repose value ranges 28° -37° the hopper angle was decided. For that, to flow the fertilizer freely from the hopper, the inclined angle of the hopper should be greater than 40° respectively. The lowest bulk density and tapped density observed 0.648 kg cm-3 and 0.708 kg cm-3 respectively. Coefficient friction for galvanized Iron (GI) material is very high So, based on the obtained results, a fertilizer applicator for orchard crops has been designed. The basin diameter of orchard palm varies from 1500 mm to 1750 mm the mean of 1650 mm. The root zone depth of orchard crop varies from 150 to 180 mm at radius of 1800 mm from the base of palm. The mean 171.66 mm hence depth of operation of machine was selected as 150 mm such that the machine does not interfere with root zone of orchard trees.

Application of Compost and Manure in the Biopore Infiltration Hole to Improve Saturated Hydraulic Conductivity (Shc) of Soil in the Coffee Root Zone

Coffee is one of the primary sources of foreign exchange in Indonesia. The factors causing fluctuations in coffee production are climate change, high soil density, less organic material, which causes coffee roots to be unable to absorb water and nutrients optimally. The soil water movement measured by saturated hydraulic conductivity which represents the soil ability to transmit water. The application of compost and manure in the biopore infiltration hole (BIH) is an effective technology in reducing surface-runoff and organic wastes management, as well as soil characteristics improvement. This study was conducted using a Randomized Block Design with four treatments and four replications. The treatments were P1 as control (without BIH), P2 (BIH without compost), P3 (BIH+compost), and P4 (BIH+goat manure). Data analysis was carried out using the Ftest (ANOVA), and continued with Least Significant Difference (LSD) test at 5% level, when there was a significant difference. Observations of soil characteristics were carried out every two months in three depth of root zone, 0-20 cm, 20-40 cm, and 40-60 cm. While the observation of the Auger hole at a depth of 0-30 cm and 0-60 cm. Research variables include soil texture, soil bulk density, Saturated Hydraulic Conductivity of soil by Auger-Hole, Soil Organic Matter (SOC), pH, Cation Exchange Capasity (CEC), and coffee yield. Results showed that treatment of BIH+goat manure gave the best results, measured by improving soil hydraulic conductivity up to 40%. The highest coffee yield was found in the BIH+manure treatment to 3.29 t ha-1.

MATHEMATICAL MODELS OF METHANE CONSUMPTION BY SOILS: A REVIEW

MATHEMATICAL MODELS OF METHANE CONSUMPTION BY SOILS: A REVIEW

Investigating Correlations and the Validation of SMAP-Sentinel L2 and In Situ Soil Moisture in Thailand.

Soil moisture plays a crucial role in various hydrological processes and energy partitioning of the global surface. The Soil Moisture Active Passive-Sentinel (SMAP-Sentinel) remote-sensing technology has demonstrated great potential for monitoring soil moisture with a maximum spatial resolution of 1 km. This capability can be applied to improve the weather forecast accuracy, enhance water management for agriculture, and managing climate-related disasters. Despite the techniques being increasingly used worldwide, their accuracy still requires field validation in specific regions like Thailand. In this paper, we report on the extensive in situ monitoring of soil moisture (from surface up to 1 m depth) at 10 stations across Thailand, spanning the years 2021 to 2023. The aim was to validate the SMAP surface-soil moisture (SSM) Level 2 product over a period of two years. Using a one-month averaging approach, the study revealed linear relationships between the two measurement types, with the coefficient of determination (R-squared) varying from 0.13 to 0.58. Notably, areas with more uniform land use and topography such as croplands tended to have a better coefficient of determination. We also conducted detailed soil core characterization, including soil-water retention curves, permeability, porosity, and other physical properties. The basic soil properties were used for estimating the correlation constants between SMAP and in situ soil moistures using multiple linear regression. The results produced R-squared values between 0.933 and 0.847. An upscaling approach to SMAP was proposed that showed promising results when a 3-month average of all measurements in cropland was used together. The finding also suggests that the SMAP-Sentinel remote-sensing technology exhibits significant potential for soil-moisture monitoring in certain applications. Further validation efforts and research, particularly in terms of root-zone depths and area-based assessments, especially in the agricultural sector, can greatly improve the technology's effectiveness and usefulness in the region.

Assessing the Effect of Irrigation Levels and Hydrogel on Growth and Yield of Wheat (Triticum aestivum L.)

One of the most essential inputs for agriculture is water. Moisture stress at critical growth stages in wheat severely effects the growth and yield. Hydrogel (water-absorbing polymer) can keep the appropriate moisture level at the root zone depth and protects the crop from adverse effect of moisture stress. The present trial was conducted during rabi season of 2020-21 to assess the performance of different hydrogels under different levels of irrigations on growth, yield, and water use efficiency of wheat. Results revealed that application of 3 irrigations recorded significantly maximum number of tillers per m2 at 90 Days After Sowing and at harvest. The application of Nano hydrogel @ 20 kg ha-1 significantly increased the number of tillers per m2 at 90 DAS and at harvest over control. Significantly maximum grain (26.1%) and straw (24.5%) yield were obtained with 3 irrigation levels over one irrigation. The Nano hydrogel increased grain (33.6%) and straw (22.9%) yield significantly over control. Water use efficiency significantly improved with one irrigation over 3 irrigation levels, application of Nano hydrogel @ 20 kg ha-1 significantly increased WUE.

Modelling of mechanical roots on slope stability

Root system mechanical reinforcement through root-soil cohesion on slope stability is important. However, the root cohesion of <em>Tectona grandis</em>, <em>Maesopsis eminii</em>, and shrubs (<em>Chromolaena odorata</em>) on slope stability is rarely studied and modelled. This study aimed to model the mechanical effect of vegetation through root cohesion, namely teak (<em>Tectona grandis</em>), <em>Maesopsis eminii</em>, and shrubs (<em>Chromolaena odorata</em>). The study was conducted in a simultaneous landslide on January 1, 2020, that dominantly occurred on vegetated slopes of Sukajaya District, Bogor Regency, West Java. The Wu model's root cohesion (<em>C<sub>R</sub></em>) was modelled on slope stability using a modified Bishop model. The modelling used the data from field and laboratory-measured. The study found that the presence of a root system increases slope stability's factor of safety (FOS). The root system of young <em>Maesopsiss eminii</em> produces the largest effect of FOS compared to the root system of shrubs, teak, and old <em>Maesopsis eminii</em>. The slope stability of vegetated slopes is a function of the <em>C<sub>R</sub></em> and the effective root zone depth. The highest total <em>C<sub>R</sub></em> of vegetation was teak with 0.398 kPa, followed by shrubs, young <em>Maesopsis eminii, </em>and<em> </em>old <em>Maesopsis eminii</em> with 0.202 kPa, 0.191 kPa, and 0.087 kPa, respectively. The effective root zone of teak, young <em>Maesopsis eminii</em>, and shrub were 500, 230, 140, and 66 cm, respectively.

Design of Basin Irrigation System using Multilayer Perceptron and Radial Basic Function Methods

The common use of an artificial neural network model has been in water resources management and planning. The length, width, and discharge of a basin were measured in this study utilizing field data from 160 Dashti Hawler existing projects. Multilayer Perceptron (MLP) and Radial Basic Function (RBF) networks were employed in the basin irrigation assessment. Input factors included the soil type, the conveyance system effectiveness, and the root zone depth. 130 projects were used for calibration, while the remaining 30 were used for validation. When developing the basin irrigation system, the models’ aforementioned indicators’ performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), relative error (RE), and Nash Sutcliff efficiency (NSE). For the basin's length, width, and discharge, the (R2) values for the MLP model were determined to be 0.97, 0.97, and 0.96, respectively, whereas the corresponding values for the RBF model were 0.88, 0.89, and 0.89. Compared to the RBF model, the values of (MAE) for basin length, width, and discharge for the MLP model were determined to be 8.99, 8.52, and 42.58, respectively. However, the (NSE) values for the models mentioned above were 0.95, 0.96, and 0.94, as well as 0.65, 0.66, and 0.66 for the basin’s length, width, and discharge, respectively. When it comes to building the irrigation system for the basin, the MLP is more precise than RBF depending on the values of (R2), (MAE), and (NSE). Finally, the ANN approach uses additional design options quickly examine which model is computationally efficient.

Identifying drivers for variability in maize (Zea mays L.) yield in Ghana: A meta-regression approach

CONTEXTMaize is the main cereal crop in Ghana, but its production is adversely affected by various biotic and abiotic factors. OBJECTIVEThis study aimed to highlight the factors related to maize yield variability. To this end, yields from 978 data points within 3 agro-ecological zones (AEZs) were used in crop-based and statistical modelling. METHODSThe QUantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model, the Linear Mixed Effects Model (LMM), and the Random Forest (RF) model were used to evaluate multiple effect sizes. RESULTS AND CONCLUSIONSAnalyzing an entire set of yield data points with QUEFTS, and LMM explained 19%, and 26% of yield variability, respectively. Considering all data points in the RF model, nitrogen fertilizer (NF) rate, temperature, root zone depth, rainfall, and variety accounted for 27%, 15%, 13%, 10%, and 9% of yield variation, respectively. In Guinea Savanna (GS), Transition Zone (TZ), and Deciduous Forest (DF), QUEFTS explained 30%, 20%, and 4% of yield variability, respectively. LMM, however, explained 47%, 51%, and 79% of yield variability in those AEZs. LMM showed that the phosphorus fertilizer (PF) rate was very important and exceeded the importance of the NF rate in GS. LMM showed also that yield variability was significantly related to maize variety at the AEZ scale. In DF, soil chemistry (marginal R2 = R2m = 0.48) and environmental variables (R2m = 0.43) contributed more to explaining yield variability, whereas in GS and TZ, fertilizer rates (R2m = 0.35 in GS and 0.26 in TZ) and variety (R2m = 0.04 in GS and 0.20 in TZ) played a much larger role. In GS, TZ, and DF, the RF model explained 74%, 79%, and 84% of the variance in yield, respectively. These findings suggest low impact of fertilization on yield on the inherently fertile soils in the DF, while fertilization drives yield increase in the less fertile TZ and GS AEZs. We may conclude that QUEFTS was unable to capture yield variability and, according to RF and LMM analysis, the NF rate was the most important factor in explaining yield variability in the data. It can also be concluded that the factors responsible for yield variability are AEZ dependent. SIGNIFICANCEWe discuss the implications of these findings to uncover factors driving maize yield variability. It also provides information to guide and prioritize actions to be taken based on the importance of these factors in contributing to yield variability.

Study of Soil and Crop Parameters of Coconut Orchard for Design of Tractor Operated Coconut Basin Lister Cum Fertilizer Applicator

Aims: To study the soil and crop parameters of coconut orchard for the design of tractor operated coconut basin lister cum fertilizer applicator.
 Place and Duration of Study: The study was conducted in Instructional Farm, KCAET in the Year 2021.
 Methodology: Soil parameters such as moisture content, bulk density, cone index and shear strength; crop parameters such as trunk diameter and root zone depth of coconut palm were studied using standard procedures to design tractor operated coconut basin lister cum fertilizer applicator.
 Results: The soil moisture content varied from 13.2 to 15.6% with mean of 14.66%. The soil bulk density ranged from 1615 to 1865 kg m-3 with mean of 1696.6 kg m-3. The soil cone index varied from 0.428 to 1.506 N mm-2 with mean of 1.041 N mm-2. The soil shear strength varied from 1.47×10-3 to 3.13×10-3 N mm-2 with mean of 2.31×10-3 N mm-2. The trunk diameter of coconut palm varied from 32.3 to 38.1 cm with mean of 34.74 cm. The root zone depth of coconut palm varied from 13.4 to 15.2 cm with mean of 14.3 cm.
 Conclusion: Soil moisture content, bulk density, cone index and shear strength helped in the design of power requirement, rotor shaft, cutting blades, main shaft etc. of the machine. The trunk diameter of the coconut palm aided in deciding the working width of the machine while the root zone depth assisted in finalizing the operational depth of the machine.

Daily Water Surplus Variability between the Mediterranean, Semi-Arid and Arid Climates

Water surplus in a water balance is the fraction of precipitation which does not evaporate and not stored in the soil and includes both surface runoff and deep percolation. The annual course of the distribution of days with water surplus was estimated by a simple water balance model, which combines daily rainfall data, pan evaporation and soil water properties. The model was run using available published daily rainfall series and pan evaporation from ten Israel Meteorological Service stations, representing various climatic regions of Israel and using 21 different soil depths to simulate different root zone depths. The calculated daily water surplus series enable to evaluate the probability and amount of water surplus on each day of the year. The model output can be used in various hydrological models and be applied to other regions with similar conditions, without any special adjustment and may serve as an important tool for planners. This information is crucial for evaluation of the timing of occurrence of geomorphological and hydrological processes which depend on water surplus in various seasons. The annual aggregation of the water balance components provides a general view of the hydrological conditions of the different stations. The probabilistic representation of the results enables a comparison among stations with different rainfall regimes and soil types.

An integrative approach based on crop modeling and geospatial and statistical analysis to quantify and explain the maize (Zea mays) yield gap in Ghana

In Ghana, maize (Zea mays) is a crop crucial to achieving food and nutrition security. Maize consumption has increased exponentially over the past decades and contributes to 25% of the caloric consumption in the country. In order to assist in decision-making and guide investment in sustainable intensification of maize production, this study set out to identify the determinants of yield and to arrive at potential interventions for closing the maize yield gap. These were quantified using analytical approaches that combine a light use efficiency crop model (LINTUL-1) with statistical and geospatial analyses. Legacy data, auxiliary covariables, and maize fertilizer trials on eight experimental stations in Ghana were used in this study. Overall, the maize yield gap across the stations and trial treatments ranged from 17% to 98%. The variation in yield gap within a single station indicates a significant scope for closing the yield gap through site-specific nutrient management. Multiple linear regression models that explained 81% of the variability in maize yield gap identified soil organic matter, soil water-holding capacity, root zone depth, rainfall, sulfur fertilizer, and nitrogen fertilizer, in that order of importance, as the major determinants for closing the yield gap in the major agroecological zones of Ghana. The yield gap decreased by 1.4 t ha-1 with a 1% increase in soil organic matter. A 1 mm increase of the soil water-holding capacity reduced the yield gap by 1.06 t ha-1, while an increase in pH and in the application of potassium fertilizer widened the gap. These results suggest that both soil physical and chemical properties, together with weather data, should be taken into consideration to arrive at site-specific fertilizer recommendation and other agronomic practices.

Evaluating the Hydrus-1D Model Optimized by Remote Sensing Data for Soil Moisture Simulations in the Maize Root Zone

The Hydrus-1D model is widely used for soil water content (SWC) simulations, wherein the exact configuration of soil hydraulic parameters is key to accuracy. To assess the feasibility of using “low-cost” multi-source remote sensing data to optimize the parameters of the Hydrus-1D model, five types of soil hydrodynamic parameter acquisition methods were designed for comparative evaluation, including the use of default parameters for soil texture types (DSHP), predictions from three and five soil mechanical composition parameters (NNP3/NNP5), inverse solutions from measured historical data (ISHD), and innovative introduction of historical remote sensing data (ERA-5 land reanalysis information and MODIS LAI products) instead of ground measured data for the inverse solution (ISRS). Two spring maize crops were planted in Beijing, China, in 2021 and 2022. Meteorological, soil, and crop data were collected as real measurements of the true values during the growth period. The boundary flux characteristics of the model simulation results were analyzed. The accuracy differences in the five approaches were compared from three perspectives: overall root zone, growth stage, and soil depth. The results showed that (1) evapotranspiration was the main pathway for soil water depletion in the root zone of maize; the actual total evapotranspiration accounted for 68.26 and 69.43% of the total precipitation in 2012 and 2022, respectively. (2) The accuracy of the SWC simulations in the root zone was acceptable for different approaches in the following order: NNP5 (root mean squared error (RMSE) = 5.47%) > ISRS (RMSE = 5.48%) > NNP3 (RMSE = 5.66%) > ISHD (RMSE = 5.68%) > DSHP (RMSE = 6.57%). The ISRS approach based on remote sensing data almost achieved the best performance while effectively reducing the workload and cost. (3) The accuracy of the SWC simulation at different growth stages was ranked as follows: seedling stage (mean absolute error (MAE) = 3.29%) > tassel stage (MAE = 4.68%) > anthesis maturity stage (MAE = 5.52%). (4) All approaches’ simulation errors exhibited a decreasing trend with increasing soil depth. The ISHD approach, based on the measured data, achieved the best performance at a depth of 60 cm (MAE = 2.8%). The Hydrus-1D model optimized using multi-source remote sensing data can effectively simulate SWC in the maize root zone with low working cost, which is significant for applications in areas where it is difficult to obtain field soil hydrodynamic property parameters to simulate SWC at a global scale.

The influence of riparian woody vegetation on bankfull alluvial river morphodynamics

Exploring the effects of bank vegetation on fluvial morphodynamics has long been an essential part of fluvial morphodynamic-related research. In a practical sense, a central question is: does increased vegetation density increase or decrease the channel width? Several aspects concerning the role of vegetation may result in examples of both width decrease and increase. In this study, we examined more than 170 alluvial river sections. Our goal was to detect the phenomena that ultimately determine riparian woody vegetation-induced width variation. We found that bed material is a governing factor. In the case of fine-grained material, i.e. median size D50 < 2 mm, increasingly densely forested riparian vegetation reduces the bankfull Shields number, and destabilizes the banks toward a wider bankfull channel. In the case of coarse-grained material (i.e. median size D50 ≥ 16 mm), the effect is the opposite; increased density is correlated with a higher bankfull Shields number and a narrower bankfull channel. The extent of the role of vegetation varies depending on the ratio of characteristic root zone depth to channel depth and channel width. We present an improved estimator for bankfull Shields number, which considers riparian vegetation density. The bankfull Shields number can be estimated up to 19% more accurately with our corrected estimator.

Development of Electronic Wetting Front Detector for irrigation scheduling

Water is an important for plants growth, development and generating good crop yield. The rate of consumption of water by the crops depends on the type of crop, stage, soil characteristics and local climatic conditions. Irrigation scheduling takes care of when and how much water is to be applied to a crop during the entire the growing period. This calls for optimal water management, equipment, and labor in developing countries. An Electronic Wetting Front Detector was developed for irrigation scheduling by estimating the optimum soil moisture content present at the root-zone depth of the crops. The device measures the volumetric water content using electrical resistance of the soil and alarms the growers for the need of irrigation through its LED indicator installed in the device. The device was further calibrated and validated in the laboratory as well as in the farmer's field near Central University of Jharkhand, Ranchi, India. The device was tested and calibrated in the laboratory with the soil present in a container along with the Tensiometer fixed in the same container of soil and the data of the gravimetric soil moisture content was compared for accuracy by using regression analysis. Similarly, validation was also performed in the farmer's field and the result showed that it has a higher accuracy for measuring the soil water content. The developed device interprets readings from the sensors and alarms the farmer of overirrigation or underirrigation based on the sensors data. The EWFD developed is new intervention since the device could be easily used by farmer to understand the soil water content and schedule irrigation with respect to crop needs by alarming the farming. Therefore, the device may prove a valuable contribution to the list of other scientific instruments used for measuring the soil water content. The device can be used perfectly for scheduling irrigation.