Soil Water Content Measurements Research Articles

Soil microbial activity as influenced by crusted runoff strip length and mulch cover under in-field rainwater harvesting (IRWH)

A field experiment was designed to provide insight into the system of in-field rainwater harvesting (IRWH) in determining the effect of microbial activities. The treatments that were evaluated include narrow bare (NB) narrow mulch (NM), wide bare (WB) and wide mulch (WM) runoff strips. Soil samples were collected at depths of 5 and 15 cm from the center section of the runoff area and directly from the rhizosphere of the maize roots around basins. From fresh soil samples, Colony Forming Unit (cfu) readings were taken by using a colony counter and total microbial activity was estimated using the fluorescein diacetate (FDA) hydrolysis method. Community-level diversity physiological profiles of the soil samples were assessed by using Biolog EcoPlates™. Results revealed that the mean values of microbial activity indicated the greatest was found under NM rhizosphere (1.611 ± 0.038) and lowest under WB (0.079 ± 0.005) at 5cm depth. Microbial activity under NM at 5 cm was higher (63% increase) than at the rhizosphere (8% increase). However, there was a decrease in the microbial activity of 17% at 15 cm for WM compared to WB. FDA values were positively correlated with measured soil water content across both 5 and 15 cm depths (r = 0.61 and 0.67) and highly significant (r = 90 at P-value ≤ 0.01) for rhizosphere. Some interesting positive correlations between yield and microbial activity were also found however further investigation on microclimatic aspects in association with microbial diversity and activity on soil surface treatments are important for non-uniform canopy structures of IRWH.

Effects of paramagnetic material on the characteristics of low field nuclear magnetic resonance signals and water measurement in different textured soils.

Nuclear magnetic resonance (NMR) technology has been applied in soil science due to the characte-ristics of high efficiency, rapidity, no damage to soil structure, and harmlessness to the human body. However, the effect of the presence of paramagnetic materials in soils on the characteristics of NMR signals was still unclear. In this study, we investigated the effects of paramagnetic material on the low field nuclear magnetic (LF-NMR) signals and soil water content measurement in soils with different texture. The results showed that the LF-NMR signal of soil water could reach about 150, while that of all the solid materials including soil minerals, organic matter and microbes was less than 0.3, which was relatively negligible. Compared with the NMR signals produced by solid materials in soils, soil texture and paramagnetic material had stronger impact on the measured LF-NMR signals of soil water. LF-NMR equipment had a relaxation time monitoring blind area, and the loss of NMR signal was mainly due to the acceleration of the relaxation process of hydrogen protons in water by magnetic materials, resulting in extremely fast LF-NMR signals feed back by water in small pores that could not be captured by monitoring equipment. For loamy fluvo-aquic soil (1.2%) and clay loamy black soil (1.3%) with low paramagnetic material contents, the loss of LF-NMR signals was not large, which was linearly related to soil water content. For clayey red soil with high content of clay (45.3%) and paramagnetic materials (4.0%), a part of the LF-NMR signals would be lost in the measurement, and the monitored LF-NMR signal was not linearly related to the soil water content. In addition, external addition of paramagnetic materials (3.0 g·L-1 MnCl2 solution) would further reduce the LF-NMR signals that could be monitored in black and red soils. The maximum signal loss rates of black soil and red soil were 41.0% and 46.7%, respectively, which greatly changed the quantitative relationship between it and soil water content. Therefore, the influence of paramagnetic materials on the LF-NMR signals should be reduced first through correction when using LF-NMR to measure the water content of clay soil with rich internal paramagnetic materials (>1.3%) or external addition of paramagnetic materials. Our results would provide valuable insights into the study of soil water content measurement and soil pore structure analysis using low field nuclear magnetic resonance technology.

Inverse Modelling to Estimate Soil Hydraulic Properties at the Field Scale

Field-scale estimation of soil hydraulic parameters is important for simulating water movement, particularly in the vadose zone. The accuracy of soil hydraulic parameters significantly affects the output of models using these parameters as input variables. A new approach for an inverse method, often referred to as inverse modelling, was presented to calibrate soil hydraulic parameters. The new method is based on the use of uniform design theory to generate uncertainty estimates of the soil hydraulic parameters. The uncalibrated and calibrated soil hydraulic parameters were evaluated by comparing the predicted and measured soil water contents using the HYDRUS-1D model. The results show that the calibrated soil hydraulic parameters that were inversely estimated by uniform design theory were accurate and reasonable. In addition, the results provided recommendations for improving the estimation of soil hydraulic parameters when there are no available observation data for calibrating and optimizing soil hydraulic parameters.

Real-time irrigation scheduling of maize using Degrees Above Non-Stressed (DANS) index in semi-arid environment

Irrigation scheduling methods have been used to determine the timing and amount of water applied to crops. Scheduling techniques can include measurement of soil water content, quantification of crop water use, and monitoring of crop physiological response to water stress. The aim of this study was to evaluate the performance of a simplified crop canopy temperature measurement (CTM) method as Irrigation Principles. Soil and Water Conservation Engineera technique to schedule irrigation for maize. Specifically, the Degrees Above Non-Stressed (DANS) index, which suggests water stress when canopy temperature exceeds the non-stressed canopy temperature (Tcns), was determined by estimating Tcns from a weather based multilinear regression model. The modeled Tcns had a strong correlation with observed Tcns with a pooled R2 values of 0.94 across the 2018, 2019, and 2020 growing seasons. This DANS index was also highly correlated with the conventionally used Crop Water Stress Index (CWSI) with R2 values of 0.67, 0.59, and 0.76 in 2018, 2019, and 2020, respectively. Furthermore, DANS had a strong linear relationship with soil water depletion above 60% in the 0.60 m soil profile with an R2 of 0.78. The CTM method was also compared to more commonly used scheduling methods namely: soil moisture monitoring (SMM) and crop evapotranspiration modeling (ETM). Grain yield was significantly lower for the CTM method than for the ETM method in 2018 and 2020 but not in 2019. No significant differences were observed in Irrigation Water Productivity (IWP) in 2018; however, all treatments were significantly different with the CTM method having the greatest IWP in 2020. For attempting to trigger full irrigation with the CTM method, a fixed DANS threshold of 0.5 °C was found to be more appropriate than the literature value of 1.0 °C, but consideration of crop growth stage would further improve scheduling.

Concurrent data assimilation and model-based optimization of irrigation scheduling

Crop models can be combined with optimization algorithms in order to develop management tools. However, such model-based tools are inherently affected by the imperfectness of the model on which they are based. In this paper we describe a procedure in which data assimilation and partial re-parametrization of the model embedded in the optimization procedure used to determine irrigation scheduling are performed before each optimization run. Furthermore, sensitivity analysis is performed before performing data assimilation, which ensures that only influential parameters are adjusted. The procedure was tested via simulation with DSSAT-CROPGRO for a hypothetical processing tomato crop in Davis, CA, in 2010–2019. Several scenarios that differed in terms of the measurements assumed to be available for assimilation (leaf area index, biomass and/or soil water content) were simulated. The results were compared to a benchmark scenario involving a perfect model, as well as a scenario in which data assimilation was not performed. The analysis focused on the overall performance of the irrigation schedule (yield vs. irrigation amount) derived using the model rather on the accuracy of the estimated model parameters. Assimilating weekly measurements of leaf area index led to overall performance that was within 3% of the benchmark performance. Adding weekly measurements of biomass or daily measurements of soil water content did not improve the performance. On the other hand, assimilating only daily soil water content measurements led to poorer results (5% decrease compared to benchmark) and also affected the repeatability of the results. Defining dynamically the subset of parameters for calibration via sensitivity analysis rather than calibrating a fixed subset of parameters or all parameters was beneficial, both in terms of overall performance and repeatability of the results. Overall, concurrent data assimilation and model-based optimization has potential to enhance irrigation scheduling decision making, particularly in water limited environments.

A Bayesian perspective on the information content of soil water measurements for the hydrological characterization of the vadose zone

Transient measurements from soil water monitoring installments are frequently coupled with Richards-based solvers to inversely estimate soil hydraulic parameters (SHPs) and numerically describe vadose zone water fluxes, such as groundwater recharge. To reduce model predictive uncertainty, the experimental setup should be designed to maximize the information content of observations. However, in practice, this is generally done by relying on the a priori expertise of the scientist/user, without exploiting the advantages of model-based experimental design. Thus, the main aim of this study is to demonstrate how model-based experimental design can be used to maximize the information content of observations in multiple synthetic scenarios encompassing different soil textural compositions and climatic conditions. The hydrological model HYDRUS is coupled with a Nested Sampling estimator to calculate the parameters’ posterior distributions and the Kullback-Leibler divergences. Results indicate that the combination of seepage flow, soil water content, and soil matric potential measurements generally leads to highly informative designs especially for fine textured soils, while results from coarse soils are generally affected by higher uncertainty. Additionally, the propagation of parameter uncertainties in a contrasting (dry) climate scenario strongly increased prediction uncertainties for sandy soil, not only in terms of the cumulative amount and magnitude of the peak, but also in the temporal variability of the seepage flow. A complementary real-world application with a sandy soil lysimeter identified a combination of seepage data and matric potential as the most informative design and confirmed findings of the synthetic scenarios, in which matric potential proved to be more informative than soil water content measurements.

A Low-Power IoT Device for Measuring Water Table Levels and Soil Moisture to Ease Increased Crop Yields.

The simultaneous measurement of soil water content and water table levels is of great agronomic and hydrological interest. Not only does soil moisture represent the water available for plant growth but also water table levels can affect crop productivity. Furthermore, monitoring soil saturation and water table levels is essential for an early warning of extreme rainfall situations. However, the measurement of these parameters employing commercial instruments has certain disadvantages, with a high cost of purchase and maintenance. In addition, the handling of commercial devices makes it difficult to adapt them to the specific requirements of farmers or decision-makers. Open-source IoT hardware platforms are emerging as an attractive alternative to developing flexible and low-cost devices. This paper describes the design of a datalogger device based on open-source hardware platforms to register water table levels and soil moisture data for agronomic applications. The paper begins by describing energy-saving and wireless transmission techniques. Then, it summarizes the linear calibration of the phreatimeter sensor obtained with laboratory and field data. Finally, it shows how non-linear machine-learning techniques improve predictions over classical tools for the moisture sensor (SKU: SEN0193).

Determination of soil bulk density dynamic in a Vertisol during wetting and drying cycles using combined soil water content and thermal property sensors

The dynamics of soil bulk density (BD) play an important role in soil water, heat, and gas transport, which is more complicated in Vertisols due to the shrinking and swelling process. Previous studies showed BD can be estimated from soil thermal conductivity (λ) and water content (θv) measurements. Thus we comprehensively evaluated the applicability of the commercial sensors (5TE/TP01) in determining soil BD dynamic affected by wetting and drying cycles in a Vertisol. The repacked and intact soil cores with various BD and θv values were used to evaluate the accuracy of the two sensors for determining BD. We also assessed the potential of two thermal greases (MX-4 and TFX) in improving probe-soil contact and in determining BD dynamic of the Vertisol during wetting and drying (WD) cycle by different BD estimation models (Tian-de Vries model and Lu model). Results showed that the 5TE/TP01 combination gave accurate BD estimates with RMSEs of 0.0179–0.120 Mg m−3. For the WD cycle experiment, soil shrinkage led to a large bias in BD estimation. Thermal grease enhancing contact between the probes and soil matrix improved the accuracy of BD estimates, in which the TFX grease performed better (RMSE: 0.031–0.082 Mg m−3) than the MX-4 grease (RMSE: 0.067–0.204 Mg m−3). In regard to BD estimation models, the Lu model gave more accurate BD estimates (RMSE: 0.031–0.082 Mg m−3) than the Tian-de Vries model (RMSE: 0.057–0.187 Mg m−3). The high bias of BD estimation during WD cycles mainly occurred at the dry end side of soil shrinkage and at the beginning of wetting, which may result from soil cracking and the hysteresis of soil swelling. Our results demonstrate that the commercially available sensors in measuring soil water content and thermal property (e.g., 5TE/TP01) with the aid of thermal grease is a promising method to estimate the BD dynamics in Vertisols.

Estimating the GPP of olive trees with variable canopy cover by the use of Sentinel-2 MSI images

The estimation of gross primary production (GPP) is a key step towards the simulation of crop yield which can be performed by feeding the light use efficiency approach with remotely sensed estimates of absorbed photosynthetic active radiation (fAPAR). The application of this approach, however, is problematic for bi-layer ecosystems, such as olive groves, which have tree canopy cover (CC) variable in space and time due, for example, to pruning or leaf growing. The current paper investigates on this issue concerning a small olive grove in Florence (Central Italy), which, during the 2021 growing season, was monitored by standard meteorological and soil water content measurements; additionally, observations of fAPAR were taken in four plots differently affected by spring pruning. These ground observations allowed to calculate daily GPP references for the four plots, which were used to assess corresponding estimates obtained from interpolated weather data and Sentinel-2 MSI NDVI imagery. In particular, the fAPAR and GPP estimates were obtained both considering the NDVI values of the entire plots and separating the contribution of olive tree canopies from that of grasses. The results of the experiment indicated that the GPP of the most intensively pruned plots could be estimated correctly only by taking into consideration the temporally variable fAPAR contribution of olive tree canopies.

Smart Soil Water Sensor with Soil Impedance Detected via Edge Electromagnetic Field Induction.

To address the problems in the calibration of soil water content sensors, in this study, we designed a low-cost edge electromagnetic field induction (EEMFI) sensor for soil water content measurement and proposed a normalized calibration method to eliminate the errors caused by the measurement sensor’s characteristics and improve the probe’s consistency, replaceability, and calibration efficiency. The model calibration curve-fitting coefficients of the EEMFI sensors were above 0.98, which indicated a significant correlation. The experimental results of the static and dynamic characteristics showed that the measurement range of the sensor varied from 0% to 100% saturation, measurement accuracy was within ±2%, the maximum value of the extreme difference of the stability test was 1.09%, the resolution was 0.05%, the delay time was 3.9 s, and the effective measurement diameter of the EEMFI sensor probe was 10 cm. The linear fit coefficient of determination of the results was greater than 0.99, and the maximum absolute error of the measurement results with the drying method was less than ±2%, which meets the requirements of soil water content measurement in agriculture and forestry fields. The field experiment results further showed that the EEMFI sensor can accurately respond to changes in soil water content, indicating that the EEMFI sensor is reliable.

The impacts of climate change and cropping systems on soil water recovery in the 0–1500 cm soil profile after alfalfa

Alfalfa (Medicago sativa L.) has played an important role in the “Grain-for-Green” program across the Chinese Loess Plateau but has characteristically high water consumption. Long-term growth significantly reduces soil water storage in the 0–1500 cm profile and results in an obvious decline of alfalfa yield. Fields planted with alfalfa for more than 8 years are often converted to annual crops (winter wheat or corn), but these annual crops planted after alfalfa suffer from low yield due to the reduced soil water content. The objectives of this study were to determine the time required for soil water recovery in the 0–1500 cm profile after fields cropped for 15 years with alfalfa are converted to annual crops and to evaluate the impacts of cropping systems, inter-annual climatic variation, and future climate change on the recovery time. Hydraulic parameters for six soil materials in the 0–1500 cm soil profile were calibrated using the measured soil water content in alfalfa fields from 2005 to 2020 and validated using soil water stable isotopic measurements in 2020. Using the calibrated soil hydraulic parameters, the Hydrus-1D model was used to simulate soil water dynamics in the 0–1500 cm profile and determine the recovery time in two cropping systems (alfalfa-wheat and alfalfa-corn) with 53 climatic realizations generated using random non-repeat sequences drawn from 53 years of observed climatic data (1968–2020) and 20 future climate data series projected by 10 GCM models under RCP 4.5 and 8.5 scenarios (2021–2100), respectively. The results showed that, under the 53 climatic realizations, the recovery time ranged from 14 to 44 years with a median of 27 years for the alfalfa-wheat system and from 11 to 31 years with a median of 18 years for the alfalfa-corn system. Under the RCP 4.5 and 8.5 scenarios, the median recovery times were 37.5 and 42.5 years for the alfalfa-wheat system and 30 and 31.5 years for the alfalfa-corn system, respectively. Considering the long-term effects of alfalfa growth on land productivity, planting areas and growing time of alfalfa should be restricted to maintain sustainable agriculture in the study region.

Temperature compensated and soil density calibrated soil moisture profiling sensor with multi-sensing point for in-situ agriculture application

In this work, we have fabricated a dielectric based soil moisture sensor with multi-sensing points (SMSMSP), which comprises of five interdigitated electrodes on both side of printed circuit board (PCB). Thickness of the fabricated SMSMSP is about 1.6 mm and light weight (0.1 kg), hence easy to carry for the field measurements. Further, we calibrated the sensor for different soil dry bulk density and observed that the time taken by the soil moisture to penetrate from soil surface till 25 cm is around 10 mins and 57 mins when density is maintained at 1.1 gm/cc and 1.3 gm/cc, respectively. We observed that the SMSMSP output changes only by 2.7 % when the ambient temperature varies from 20 °C to 60 °C. Both lab and field deployments results indicate that measured soil water content using SMSMSP at different depths is within ± 3% when compared with the gravimetric method.

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.

Testing of a commercial vector network analyzer as low-cost TDR device to measure soil moisture and electrical conductivity

Time Domain Reflectometry (TDR) is a non-destructive technique to determine the soil apparent dielectric constant, εa, the volumetric water content, θ, and bulk electrical conductivity, σ. However, the high cost of TDR devices may limit its use. This study evaluates two different low-cost Vector Network Analyzers (VNA) commercially available (NanoVNA), with 1.5 (VNA1.5) and 3.0 (VNA3.0) GHz maximum operating frequency. NanoVNA can be used for measurements of Frequency Domain Reflectometry (FDR) or, after suitable post-processing, for θ and σ TDR measures. Although FDR and TDR are dual procedures, TDR is easier to interpret for soil experiments. The TDR waveforms and εa measured with NanoVNA connected to 10 and 20 cm length three-rod probes immersed in air, distilled water, and a soil column with different θ were compared to those measured using a TDR100 (Campbell Sci.) instrument. The capacity of VNAs to measure σ was evaluated by immersing a 10 cm length three-rod probe in different NaCl-water solutions. Measurements obtained with the VNA and TDR100 were compared in a field test using two-rod 22 cm length TDR probes inserted in soil plots with increasing water content. A robust fit was observed between TDR waveforms registered with the two VNAs and the TDR100. Although VNA3.0 doubles the frequency range of VNA1.5, both devices allowed for good estimates of εa (εaVNA1.5, 3.0 = 1.001 εaTDR100 – 0.2125; R2 = 0.999). These results indicate that the low-cost VNA devices can measure soil water content with similar accuracy and precision as the TDR100. A good agreement (σVNA1.5, 3.0 = 0.999 σCM + 0.0023; R2 = 0.999) was also observed between the σ measured using a conductivity meter (CM) and that estimated with the VNAs. Finally, a good correlation was also observed between θ measured in the field experiment with TDR100 and the VNA1.5 and VNA3.0 devices.

Managing spatial irrigation using remote-sensing-based evapotranspiration and soil water adaptive control model

Irrigation has traditionally been managed as uniform applications where an entire field receives the same depth of water. Motivation to improve current irrigation practices has led to different approaches utilizing remotely-sensed images to inform variable rate irrigation management. This study conducted in 2019 and 2020 implemented the Spatial EvapoTranspiration Modeling Interface (SETMI), a remote-sensing-based evapotranspiration (ET) and water balance model, for managing variable rate irrigation of a maize and soybean field. This model tracked soil water content through the estimation of daily ET and tracking of various water fluxes entering and leaving a field. SETMI was used in two different irrigation treatments informed using Planet satellite (SETMI-SAT) and unmanned aerial system (UAS, SETMI-UAS) remotely-sensed images. A uniform irrigation approach managed by a professional crop consultant and a non-irrigated approach were used as the baseline in comparing irrigation management approaches. The irrigation treatments were evaluated on dry grain yield, gross irrigation, actual ET, deep percolation, change in soil water content, and water productivity. The uniform irrigation approach managed by the crop consultant applied the highest irrigation in 2019 and 2020 for maize (2019: 155 mm, 2020: 213 mm) and soybean (2019: 124 mm; 2020: 183 mm) while the SETMI irrigation treatments applied less irrigation for maize (2019: 131, 132 mm; 2020: 154, 140 mm) and soybean (2019: 116, 94 mm; 2020: 154, 175 mm). Maize yield was highest for the uniform irrigation approach in 2019 (14.9 Mg ha−1) and 2020 (13.3 Mg ha−1). The highest soybean yield was produced by the SETMI-SAT irrigation management approach in 2019 (5.0 Mg ha−1) and 2020 (4.8 Mg ha−1). Significant differences (p-value < 0.05) in applied irrigation between the uniform and SETMI irrigation management approaches were observed while there were no significant differences in dry grain yield for both maize and soybean in 2019 and 2020. At least one of the SETMI irrigation treatments produced higher crop, irrigation, and ET water productivity values in comparison to those produced by the uniform irrigation treatment for all crop-years. A post-season analysis was completed using the SETMI-UAS and SETMI-SAT treatments to evaluate the accuracy of estimated rootzone soil water depletion provided by SETMI. Rootzone depletion calculated from neutron probe volumetric soil water content measurements were compared to the modeled depletion from the SETMI-UAS and SETMI-SAT treatments. The 2020 modeled and measured depletion comparison produced better agreement resulting in a root mean squared error (RMSE) < 17 mm compared to 2019 (RMSE < 27 mm). The VRI center pivot malfunctioned during the 2019 season which caused unresolved discrepancies between actually applied irrigation and what the system was programmed to apply. The VRI system was fixed before the 2020 season.

Multistep optimization of HyPix model for flexible vertical scaling of soil hydraulic parameters

Efficient simulation of water-flow processes in the vadose zone is crucial to increase agricultural productivity within environmental limits. This requires deriving detailed soil hydraulic parameters of the soil profile that is highly challenging, particularly for heterogeneous soils. We therefore developed an alternative indirect methodology to calibrate the hydraulic parameters from soil water content time series measured at multiple depths by using the new physically based hydrological model HyPix.We propose a novel, efficient, multistep optimization algorithm for layered soils that derives an optimal set of hydraulic parameters for a desired number of soil layers. For each selected soil layer, HyPix derives five physical, bimodal, Kosugi hydraulic parameters that describe the soil water retention and hydraulic conductivity by using a novel algorithm that reduces the degree of sensitivity and freedom of the parameters. The optimization algorithm upscales the soil hydraulic parameters by gradually incorporating the soil heterogeneity. This method overcomes the problems associated with optimization of the hydraulic parameters of each layer individually, which leads to poor results because it does not represent the cohesive soil water dynamics across the unsaturated zone.We tested the method using soil water content measurements at different depths at five heterogeneous experimental sites in New Zealand. We show how the accuracy of the simulated water balance components increases with the number of soil layers. The multistep optimization upscales a detailed, layered profile of soil hydraulic parameters into a model with fewer layers. The methodology developed provides an estimate of the uncertainty of using a reduced number of soil layers. We also show that a pedological description can provide an indication of the minimum soil layers of vertical discretization required to accurately compute the soil water balance components.

A Method Proposal for Throughfall Measurement in Grassland at Plot Scale in Temperate Climate: ‘Interception Tubes’

While net precipitation entering the soil is commonly measured in woody ecosystems, there is a lack of field measurements for herbaceous vegetation. Small canopy heights and fragile stem structures are the primary challenges for net precipitation sampling in grasslands under field conditions. We designed a new in situ device, “interception tubes”, for throughfall sampling in temperate grasslands. The instrument allows a natural development of grass canopy and sampling at multiple locations. Although it does not strictly separate throughfall and stemflow, the dominant part of the collected water is throughfall. We tested the interception tubes for splash loss with a drip experiment. Next, we evaluated the tubes’ measurements in a field installation at 25 locations both with and without vegetation cover. Also, we used measurements of gross precipitation, canopy height and soil water content to check the plausibility of the measurements. The experiment showed splash loss for the tubes is small ( &amp;lt; 3%) for the typical rain drop size for the growing season in the region, as well as for throughfall drops of lower falling velocity. In the uncovered period, splash loss corrected tubes’ measurements were generally smaller than classical funnel measurements. But the statistical model revealed that the slope of their relationship is close to unity (0.92) when accounting for topography and was probably related to wind effects. During the covered period, grass height systematically reduced below canopy precipitation measured by the tubes, indicating that they can capture spatial canopy drip patterns under denser grass foliage. The canopy height also altered the wind effect on the tube measurements. As in forest ecosystems, below canopy precipitation patterns were temporally stable and smaller events increased the spatial heterogeneity. The measured below canopy precipitation was between 95% and 22% that above, and grass height amplified the loss. The soil water balance showed the tubes underestimated soil water input at peak grass height, which suggests enhanced occurrence of stemflow in tall grass. Despite the underestimation of stemflow, the interception tubes are a suitable method for estimating the canopy effect on throughfall patterns in temperate grasslands, and stemflow can be quantified by additional soil moisture measurements.

Enhancing long-term no-till wheat systems with cover crops and flash grazing

Monoculture practices under conventional tillage (CT) are detrimental to sustainable soil ecosystem functions and services under intensive agriculture practices, ultimately diminishing net benefits. Conservation practices, such as no-till (NT) and cover crops (CC) can nurture sustainable soil ecosystem functions and services. The impact of introducing CC, grazing, intercropping, and reverting to tillage in a long-term NT continuous wheat system on soil properties was evaluated in three years of implementation. Treatments were CT and combinations of NT, CC, grazing, and intercropping wheat with radishes and turnips. Tillage significantly decreased large macroaggregates (33–39%), mean weight diameter (21–26%), and POX-C (21–29%) within large macroaggregates and increased small macroaggregates (40–65%) compared to all CC treatments (including grazed and intercropped) within a 3-year period. Reverting to tillage after 12 years of NT significantly increased bulk density by 19%, reduced total porosity by 21% and soil water content by 28% compared to all CC treatments. In addition, Cover crops (including grazed and intercropped) enhanced NT as evident in significant improvements in POX-C (19–32%), large macroaggregates (37–51%), mean weight diameter (22–31%), bulk density (8–13%), total porosity (10–18%), and measured soil water content (11–14%). Flash grazing CC did not result in any adverse effects compared to all other treatments for measured parameters. Ultimately, reverting to tillage in a long-term NT system significantly degraded soil physicochemical properties. In contrast, implementing CC to long-term continuous wheat systems resulted in rapid soil improvements within the 3-year study period which were reflected in enhanced stored soil moisture storage of up to 39% higher under CC compared to CT. Flash grazing CC can be successfully implemented in NT systems without adversely affecting soil physicochemical properties. Intercrops did not show pronounced effects possibly due to winterkill.

Contribution of deep soil layers to the transpiration of a temperate deciduous forest: Implications for the modelling of productivity

Climate change is imposing drier atmospheric and edaphic conditions on temperate forests. Here, we investigated how deep soil (down to 300 cm) water extraction contributed to the provision of water in the Fontainebleau-Barbeau temperate oak forest over two years, including the 2018 record drought. Deep water provision was key to sustain canopy transpiration during drought, with layers below 150 cm contributing up to 60% of the transpired water in August 2018, despite their very low density of fine roots. We further showed that soil databases used to parameterize ecosystem models largely underestimated the amount of water extractable from the soil by trees, due to a considerable underestimation of the tree rooting depth. The consensus database established for France gave an estimate of 207 mm for the soil water holding capacity (SWHC) at Fontainebleau-Barbeau, when our estimate based on the analysis of soil water content measurements was 1.9 times as high, reaching 390 ± 17 mm. Running the CASTANEA forest model with the database-derived SWHC yielded a 185 gC m−2 y−1 average underestimation of annual gross primary productivity under current climate, reaching up to 687 ± 117 gC m−2 y−1 under climate change scenario RCP8.5. It is likely that the strong underestimation of SWHC that we show at our site is not a special case, and concerns a large number of forest sites. Thus, we argue for a generalisation of deep soil water content measurements in forests, in order to improve the estimation of SWHC and the simulation of the forest carbon cycle in the current context of climate change.

Revisiting crop water stress index based on potato field experiments in Northern Germany

Different types of the Crop Water Stress Index (CWSI) have been useful for water stress monitoring and irrigation management in semi-arid regions, however little research exists on its effective application in humid regions. This study aims to assess the effectiveness of three CWSI models (CWSIe - empirical, CWSIt - theoretical, CWSIh - hybrid) for crop water stress monitoring in an experimental field for potato crops in Northern Germany. Irrigation experiments with three treatments (optimum-OP, reduced-RD and no) were conducted in the summer of 2018 and 2019. Continuous canopy temperatures (Tc) for OP and RD irrigation treatments together with meteorological measurements were used to derive CWSI from the different models. Additionally, Visible/near infrared (VNIR) and Thermal Infrared (TIR) drone images were collected on several days during the growing season to create CWSI maps. The different CWSI models were correlated with volumetric soil water content (θ) measurements for comparison and relationships were established between CWSI and θ for prediction. Results showed that CWSI accurately estimates soil water content under atmospheric conditions similar to those in semi-arid regions. The predictive performance of different CWSI models were fairly good (R2 =0.57–0.63) (situation in 2019). CWSIe and CWSIh performed better than CWSIt. CWSI-θ relations calibrated in one year effectively predicted θ in another year with errors of 1.2–2.2% absolute soil water content. CWSIh could be a promising alternative to the traditional CWSI as it combines aspects of CWSIe (empirical upper limit) and of CWSIt (theoretical lower limit) which has advantages for operational use. Finally, the drone-based CWSI and θ maps (derived from the developed CWSI- θ relations) captured well the applied irrigation patterns and could help to decide when to irrigate and how much water to apply.