Soil Water Model Research Articles

Strong sensitivity of watershed-scale, ecohydrologic model predictions to soil moisture

It is currently unclear how changes to modeled soil water simulations influence key predictions in complex, ecohydrologic models, particularly beyond hydrology. We assessed sensitivity to soil moisture in the Soil and Water Assessment Tool by artificially perturbing simulated soil moisture and analyzing targeted prediction change. Results demonstrated a strong, process-dependent sensitivity of model predictions to perturbations in soil moisture. At the basin scale, the absolute values of differences from a baseline simulation for surface runoff, evapotranspiration, and nitrate loss ranged from 42% to 107%, 29%–76%, and 19%–369%, respectively, for soil moisture changes between 0.5 and 2 standard deviations. Landscape-scale results illustrated the impact of modeled land management actions, notably fertilizer applications, on determining the magnitude of the change. The findings highlight and motivate the need to improve soil moisture simulations, suggesting possible accuracy improvement for key model predictions by more precisely simulating soil moisture.

Biocrusts and its disturbance controls on soil infiltrability in a semi‐arid ecosystem

AbstractThe biocrust occurrence and its disturbance alters infiltration in the Mu Us Desert. Knowledge of the hydrological properties of biocrusts and parameterization of soil hydraulic properties are crucial to improve simulation of infiltration and soil water dynamics in vegetation–soil–water models. Infiltration experiment was conducted to evaluate the effects of biocrusts and its disturbance on soil infiltrability in the Mu Us Desert, northwest of China. A combined Wooding inverse approach was used for the estimation of soil hydraulic parameters. The results showed that both lichen‐ and moss‐covered biocrusts had a negative influence on infiltration in comparison with the bare soil. Biocrust disturbance alters infiltration, but its effect differs depending on the biocrust and disturbance types. For high‐pressure heads, water retention on the moss‐covered soils was higher than on the lichen‐covered soils. Moreover, trampling caused a higher water retention at high‐pressure heads. However, opposite was observed at low‐pressure heads. In addition, moss‐covered soils had lower hydraulic conductivity than the lichen‐covered soils. Additionally, for each biocrust‐covered soil, both trampling and scraping resulted in a higher hydraulic conductivity when compared with the undisturbed soils. The occurrence of biocrusts and its disturbance influenced van Genuchten parameters and subsequently affected the water retention curve and thus altered water condition in the biocrust layer. The findings about the parameterization of soil hydraulic properties are crucial for the simulation of eco‐hydrological processes in arid and semi‐arid ecosystems.

Comparison of AET partitioning and water balance between degraded meadow and artificial pasture in Three‐River Source Region on the Qinghai‐Tibetan Plateau

AbstractActual evapotranspiration (AET) is the main component of water balance and comprises evaporation (E) and transpiration (T). Despite widespread recognition of the importance of water in terrestrial ecosystems, knowledge about key processes in the water balance is surprisingly limited, especially for how water is partitioned between E and T. We used a daily time step soil water model (SOILWAT2) and 10 years of data to describe AET partitioning and water balance of a degraded meadow and an artificial pasture in the Three‐River Source Region on the Qinghai‐Tibetan Plateau. Our results showed that there are two important environmental factors influencing water balance processes: evaporative demand of the atmosphere and precipitation. Due to the same evaporative demand and precipitation regime, the AET between our two sites was similar in seasonal trends and annual amounts. On an annual basis, AET and deep drainage accounted for almost 100% of the precipitation received by both the two sites. Despite the similarity of annual AET between the degraded meadow and the artificial pasture, the AET partitioning between the two sites was substantially different. Mean annual transpiration/actual evapotranspiration (T/AET) was 27% in the degraded meadow and 52% in the artificial pasture. The key controls on AET partitioning were vegetation and soil, and compared to soil, our study emphasized the influence of vegetation, and high amounts of biomass resulted in the highest values of T/AET in the artificial pasture. Additionally, we identified deep drainage as an important and previously overlooked component of water balance accounting for an average of approximately 21% of the annual precipitation in both the two study sites.

Importance of short-term temporal variability in soil physical properties for soil water modelling under different tillage practices

Soil properties are often assumed to be static over time in hydrological studies, especially in hydrological modelling. Although it is well appreciated that soil structure and its impact on hydraulic properties are time-variable, particularly on cultivated land, very few studies have focused on quantifying the influence of such changes on soil hydrology, especially at the short term (i.e. seasonal). This study explored the value of incorporating such short-term time-variable soil properties in hydrological models. It is based on soil hydraulic properties from temporal field data under no-till done by direct seeding and under conventional cultivation done by ploughing to 0.2 m and harrowing. It uses a controlled tillage experiment in Scotland, on a soil with very good structural stability that experiences gentle rainfall in a temperate oceanic climate (Köppen Cfb). Water retention data were collected from intact soil cores sampled at 0.025, 0.095 and 0.275 m depth at three times between April and August 2013; (i) immediately following tillage, (ii) at barley crop establishment 1 month later and (iii) after harvest. Soil structure varied over time, with no-till soils gaining porosity and ploughed soils losing porosity. We hypothesised that no-till soils would have less seasonal temporal variability, but found it to be comparable to ploughed soils, albeit with pore structure changes following different trends. These changes were reflected in Van Genuchten fitting parameters, which if accounted for in 1-D HYDRUS modelling, had a marked impact on modelled soil water content over time if contrasted to predictions assuming a static pore structure. Using data from multiple sampling events, as opposed to one sampling event, resulted in up to a 44 % difference in soil water content predictions and increased the temporal variability by a factor of 1.5. Hence, our results have demonstrated that it is important to account for short-term temporal variability in soil physical properties in soil water modelling studies, and should not be ignored as a default, particularly on cultivated agricultural soils.

Identifying Within-Field Spatial and Temporal Crop Water Stress to Conserve Irrigation Resources with Variable-Rate Irrigation

Addressing within-field and within-season variability of crop water stress is critical for spatially variable irrigation. This study measures interactions between spatially variable soil properties and temporally variable crop water dynamics; and whether modelling soil water depletion is an effective approach to guide variable-rate irrigation (VRI). Energy and water balance equations were used to model crop water stress at 85 locations within a 22 ha field of winter wheat (Triticum aestivum L.) under uniform and spatially variable irrigation. Significant within-field variability of soil water holding capacity (SWHC; 145–360 mm 1.2 m−1), soil electrical conductivity (0.22–49 mS m−1), spring soil water (314–471 mm 1.2 m−1), and the onset of crop water stress were observed. Topographic features and modelled onset of crop water stress were significant predictors of crop yield while soil moisture at spring green-up, elevation, and soil electrical conductivity were significant predictors of the onset of crop water stress. These results show that modelling soil water depletion can be an effective scheduling tool in VRI. Irrigation zones and scheduling efforts should consider expanding to include temporally dynamic factors, including spring soil water content and the onset of crop water stress.

Two-layer material point method for modeling soil–water interaction in unsaturated soils and rainfall-induced slope failure

Two-layer material point method for modeling soil–water interaction in unsaturated soils and rainfall-induced slope failure

Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions.

ABOUT THE PARAMETERS OF THE DIELECTRIC MODEL OF SOILS USED IN THE SMOS ALGORITHM

The features of the four-component refractive dielectric model of moist soils are considered. Typical values of the parameters of the Debye model of bound water obtained by processing experimental data using this model are given. The reasons why the parameters of the Debye model of free soil water in this model differ from the true values of these parameters are explained. It is shown that the excess of the static permeability and specific conductivity values relative to the true values is caused by the Maxwell – Wagner relaxation process with a relaxation time of sev-eral nanoseconds.

Improved hydrological modeling with APEX and EPIC: Model description, testing, and assessment of bioenergy producing landscape scenarios

A Richards-based soil water model was implemented in the APEX and EPIC terrestrial ecosystem models to improve their hydrologic modeling capabilities. The Richards model together with two existing soil water models were calibrated and evaluated to assess their performance for simulating watershed-level hydrology under scenarios of landscape conversion to bioenergy crop production. The Richards model was shown to better reflect observed soil-water dynamics in grain (corn) and cellulosic (switchgrass) bioenergy agroecosystems, whereas all three models simulated historic streamflows comparably. Application of the models to understand the impacts of widespread landscape conversion from traditional agriculture to bioenergy producing landscapes indicated disparate conclusions, with the Richards-based simulations indicating a modest 1.0% reduction in streamflow whereas the existing models simulated sizable reductions of 10.6–16.1%. This study clearly demonstrates the impact of model methodology on system understanding and contextualizes the wide range of simulated streamflow impacts from bioenergy conversions reported in the literature.

Modeling Soil Water Content and Reference Evapotranspiration from Climate Data Using Deep Learning Method

In recent years, deep learning algorithms have been successfully applied in the development of decision support systems in various aspects of agriculture, such as yield estimation, crop diseases, weed detection, etc. Agriculture is the largest consumer of freshwater. Due to challenges such as lack of natural resources and climate change, an efficient decision support system for irrigation is crucial. Evapotranspiration and soil water content are the most critical factors in irrigation scheduling. In this paper, the ability of Long Short-Term Memory (LSTM) and Bidirectional LSTM (BLSTM) to model daily reference evapotranspiration and soil water content is investigated. The application of these techniques to predict these parameters was tested for three sites in Portugal. A single-layer BLSTM with 512 nodes was selected. Bayesian optimization was used to determine the hyperparameters, such as learning rate, decay, batch size, and dropout size.The model achieved the values of mean square error values within the range of 0.014 to 0.056 and R2 ranging from 0.96 to 0.98. A Convolutional Neural Network (CNN) model was added to the LSTM to investigate potential performance improvement. Performance dropped in all datasets due to the complexity of the model. The performance of the models was also compared with CNN, traditional machine learning algorithms Support Vector Regression, and Random Forest. LSTM achieved the best performance. Finally, the impact of the loss function on the performance of the proposed models was investigated. The model with the mean square error as loss function performed better than the model with other loss functions.

Moment Analysis for Modeling Soil Water Distribution in Furrow Irrigation: Variable vs. Constant Ponding Depths

Despite increasing use of pressurized irrigation methods, most irrigation projects worldwide still involve surface systems. Accurate estimation of the amount of infiltrating water and its spatial distribution in the soil is of great importance in the design and management of furrow irrigation systems. Moment analysis has previously been applied to describe the subsurface water distribution using input data from numerical simulations rather than field measured data, and assuming a constant ponding depth in the furrow. A field experiment was conducted in a blocked-end level furrow at Maricopa Agricultural Center, Arizona, USA, to study the effect of time-variable ponding depths on soil water distribution and the resulting wetting bulb under real conditions in the field using moment analysis. The simulated volumetric soil water contents run with variable and constant (average) ponding depths using HYDRUS 2D/3D were almost identical, and both compared favorably with the field data. Hence, only the simulated soil water contents with variable ponding depths were used to calculate the moments. It was concluded that the fluctuating flow depth had no significant influence on the resulting time-evolving ellipses. This was related to the negligible 10-cm variation in ponding depths compared to the high negative matric potential of the unsaturated soil.

Analysis of the response of the Epitácio Pessoa reservoir (Brazilian semiarid region) to potential future drought, water transfer and LULC scenarios

The Brazilian semiarid region (BSAR) is affected by the high precipitation variability and severe drought events over time, promoting recurrent water scarcity. The water supply in the BSAR has suffered since 2012 from severe incidents of rainfall reduction, creating a complex scenario of water scarcity, for instance, in Campina Grande city, which is supplied by the Epitacio Pessoa reservoir (EPR). This study analyzed the future impacts on the volume of the EPR based on potential future droughts, water transfer from the Sao Francisco River, population growth and recent changes in land use and land cover (LULC). Several procedures were performed, including (a) simulation of LULC scenarios with the LCM algorithm, (b) simulation of future rainfall scenarios for the period from 2020 to 2030, (c) streamflow simulation using the Soil Water and Assessment Tool (SWAT) model and (d) prediction of water storage in the EPR for the period from 2020 to 2030 using three rainfall scenarios (− 40%, − 45% and − 50%). The results indicated that the SWAT calibration and validation results were considered satisfactory based on the R2, Nash, and PBIAS values. The results were assumed from the rainfall scenarios for the period from 2020 to 2030 together with the LULC scenario for 2030, including the estimated outflow of water transfer from the Sao Francisco River. The results showed, based on the three adopted rainfall scenarios, that the reservoir would reach its critical volume in the first two years of the analyzed period, and the reservoir volume would then increase until 2024 and subsequently return to decrease, reaching the critical volume in 2030.

Towards moss biocrust effects on surface soil water holding capacity: Soil water retention curve analysis and modeling

As ecosystem engineers in drylands, biocrusts possibly generate great influences on surface soil water holding capacity and availability, but these effects have not yet been sufficiently investigated, especially through analyzing and modeling their soil water retention curve (SWRC). On the Loess Plateau of China, the naturally developed moss biocrusts and surface bare soil were sampled in five replicates on loess soil and aeolian sand respectively, and their SWRCs from saturation (soil water potential = 0 hPa) to dry (soil water potential = −15,000 hPa) were measured with a sandbox (low water potential range) in combination with a pressure plate (high water potential range). The measured SWRCs were compared to the van Genuchten (VG), Brooks and Corey (BC), and Log-Normal Distribution (LND) models, and the model with best performance was adopted to further determine the effects of biocrusts on surface soil water holding capacity and water availability through comparing the differences in soil water content (θ) at specific water potentials between the biocrusts and uncrusted soil. Our results showed that (i) the biocrusts always had higher θ as compared with the bare soil across the whole water potential range, and the increasing effects of the biocrusts was much stronger on aeolian sand (165%) in comparison to that on loess soil (15%). (ii) As compared with the bare soil, the saturated water content, field capacity, wilting point, and available water content of the aeolian sand increased by 52%, 256% (0.192 vs. 0.054 cm3 cm−3), 65%, and 1000% by the biocrusts, respectively; while that of the loess soil increased by 28%, 9% (0.240 vs.0.220 cm3 cm−3), 27%, and 8% by the biocrusts, respectively. (iii) The increased water holding capacity of the biocrusts was possibly attributed to their higher percentage (18% vs. 16%) of microscopic pores (0.3–5 μm) on the loess soil and their increasing (16% vs. 1%) microscopic pores and decreasing (18% vs. 50%) medium pores (30–75 μm) on the aeolian sand. (iv) The VG and LND models performed better (R2 > 0.98, RMSE ≤ 0.019 cm3 cm−3, RE ≤ 7.4%) than the BC model in simulating the SWRCs of the biocrusts and bare soil, and their fitting errors were mostly distributed in the high (−500 to −1000 hPa) and low (<−5000 hPa) water potential ranges. As indicated by SWRCs, we conclude that biocrusts greatly increase surface soil water holding capacity and water availability, leading in turn to a positive feedback mechanism of the biocrusted layer, which may affect surface hydropedological process (e.g., infiltration and evaporation) in drylands.

Land use and its effect on runoff modeled with SWAT

La importancia de los modelos de simulación para el monitoreo de los recursos naturales cada vez es mayor. El objetivo del presente estudio fue aplicar el modelo SWAT (Soil Water Assessment Tool) para evaluar el impacto del cambio de uso de suelo sobre el escurrimiento y sedimentos en la cuenca del Río Chapingo de 1999 a 2015. Se generaron dos mapas de uso de suelo mediante la fotointerpretación de imágenes Landsat y RapidEye con resoluciones de 15 y 5 m, respectivamente y recorridos de campo para verificación. Ante la falta de datos de escurrimiento confiable y reciente se utilizaron los registros mensuales de los períodos 1964-1970 y 1971-1975 para la calibración y validación del modelo. En este proceso los coeficientes de Nash-Sutcliffe fueron de 0.58 y 0.52, respectivamente. El uso de suelo de la cuenca en el período de estudio cambió: agricultura (-6.9 %) zona urbana (+2.89%), área de minería (+5.04 %) y pastizal (+7.75 %). La aplicación del SWAT calibrado y validado sólo para escurrimiento para el período 1999-2015 no detectó cambios en el escurrimiento (p = 0.2351) y sedimentos (p = 0.4430) en función del cambio de uso de suelo determinado. La correlación entre el escurrimiento y sedimentos anuales simulados con SWAT fue significativa (p = 0.0116) con un R2 bajo (0.36), atribuible a la falta de datos de campo de sedimentos para calibrar y validar el SWAT, además de la presencia de acciones de conservación de suelo y agua en la cuenca estudiada.

Evaluating soil water routing approaches in watershed‐scale, ecohydrologic modelling

AbstractSoil water dynamics are central in linking and regulating natural cycles in ecohydrology, however, mathematical representation of soil water processes in models is challenging given the complexity of these interactions. To assess the impacts of soil water simulation approaches on various model outputs, the Soil and Water Assessment Tool was modified to accommodate an alternative soil water percolation method and tested at two geographically and climatically distinct, instrumented watersheds in the United States. Soil water was evaluated at the site scale via measured observations, and hydrologic and biophysical outputs were analysed at the watershed scale. Results demonstrated an improved Kling–Gupta Efficiency of up to 0.3 and a reduction in percent bias from 5 to 25% at the site scale, when soil water percolation was changed from a threshold, bucket‐based approach to an alternative approach based on variable hydraulic conductivity. The primary difference between the approaches was attributed to the ability to simulate soil water content above field capacity for successive days; however, regardless of the approach, a lack of site‐specific characterization of soil properties by the soils database at the site scale was found to severely limit the analysis. Differences in approach led to a regime shift in percolation from a few, high magnitude events to frequent, low magnitude events. At the watershed scale, the variable hydraulic conductivity‐based approach reduced average annual percolation by 20–50 mm, directly impacting the water balance and subsequently biophysical predictions. For instance, annual denitrification increased by 14–24 kg/ha for the new approach. Overall, the study demonstrates the need for continued efforts to enhance soil water model representation for improving biophysical process simulations.

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

Hydrothermal Effects of Freeze-Thaw in the Taklimakan Desert

The Taklimakan Desert, also known as the “Sea of Death”, is the largest desert in China and also the world’s second largest remote desert. The road crossing the Taklimakan Desert is the longest desert road in the world and has been the center of the Silk Road since ancient times. Based on field observation data (November 2013 to May 2014) collected from the Tazhong and Xiaotang stations, we studied the interannual and diurnal variations of soil temperature, soil moisture content, and surface heat fluxes during different freezing and thawing periods. The annual and daily changes of soil temperature, soil moisture content, and surface energy fluxes at different freezing and thawing stages were analyzed. We illustrated the coupling relationship between water and heat in freezing-thawing soil in the Taklimakan Desert. We established a coupling model of soil water and heat during freezing and thawing. During the soil freezing period, the soil temperatures at different depths generally trended downward. The temperature difference between the Tazhong station and the Xiaotang station was 4~8.5 °C. The freezing time of soil at 20 cm depth occurred about 11 days after that at 10 cm depth. The effect of ambient temperature on soil temperature gradually weakened with the increase of soil depth. With the occurrence of the soil freezing process, the initial soil moisture contents at 5 cm, 10 cm, 20 cm, and 40 cm depths at the Xiaotang station were 6%, 10%, 29%, and 59%, respectively, and those at the Tazhong station were 5%, 3.6%, 4.4%, and 5.8%, respectively. As the ambient temperature decreased, the freezing front continued to move downward and the liquid soil water content at each depth decreased. The desert highway is closely related to the economic development and prosperity of southern Xinjiang. Therefore, it is important to maintain and inspect the safety and applicability of freeze-thaw zones and avoid casualties from vehicles and personnel.

In-Situ Estimation of Soil Water Retention Curve in Silt Loam and Loamy Sand Soils at Different Soil Depths.

The soil water retention curve (SWRC) shows the relationship between soil water (θ) and water potential (ψ) and provides fundamental information for quantifying and modeling soil water entry, storage, flow, and groundwater recharge processes. While traditionally it is measured in a laboratory through cumbersome and time-intensive methods, soil sensors measuring in-situ θ and ψ show strong potential to estimate in-situ SWRC. The objective of this study was to estimate in-situ SWRC at different depths under two different soil types by integrating measured θ and ψ using two commercial sensors: time-domain reflectometer (TDR) and dielectric field water potential (e.g., MPS-6) principles. Parametric models were used to quantify θ—ψ relationships at various depths and were compared to laboratory-measured SWRC. The results of the study show that combining TDR and MPS-6 sensors can be used to estimate plant-available water and SWRC, with a mean difference of −0.03 to 0.23 m3m−3 between the modeled data and laboratory data, which could be caused by the sensors’ lack of site-specific calibration or possible air entrapment of field soil. However, consistent trends (with magnitude differences) indicated the potential to use these sensors in estimating in-situ and dynamic SWRC at depths and provided a way forward in overcoming resource-intensive laboratory measurements.

Inverse Estimation of Spatiotemporal Flux Boundary Conditions in Unsaturated Water Flow Modeling

AbstractAccurate estimation of boundary conditions is essential to the success of numerical schemes for modeling soil water flow processes. The flux‐type boundary condition, which is integration of many boundary fluxes and subsurface conditions, is traditionally estimated from hydrologic and meteorological information. However, such information could be rapidly variable in space and time and difficult to measure and quantify accurately. This study inversely estimates upper flux boundary conditions by calibrating interior observations of soil moisture. Unknown flux boundaries with both spatiotemporal and temporal only variabilities are studied. In the case of spatiotemporal variability, we formulate the inverse model by extending a Tikhonov regularization framework proposed in our previous study, which calibrated a macroscopic root water uptake model in one‐dimensional soil water flow. The developed method is efficient because it takes advantage of the strategy in the previous study that the inversion is transformed to a variational problem, and also because it incorporates locally one‐dimensional operator splitting technique to overcome the computation complexity when dealing with multidimensional flow processes. When flux boundaries vary with time only (special case), we have found that the inversion degenerates into a well‐posed problem and formulated the inversion explicitly without use of regularization. Numerical tests are conducted through both one‐dimensional (well‐posed) and two‐dimensional (ill‐posed) heterogeneous soil profiles to illustrate the effectiveness of the proposed inverse formulation. Particularly, the applicability of the inverse formulation in the special case is further validated in field experiments using in situ measurements in arid and semiarid northern China.

Forage yield gap analysis for tall fescue pastures in Argentina: A modelling approach

AbstractA large gap between actual and potential herbage production of tall fescue is a major limitation for livestock production systems in Argentina. The objectives of this work were to (a) calibrate and test the ability of a published pasture–soil water model to represent herbage growth dynamics of tall fescue [Lolium arundinaceum (Schreb.) Darbysh.] under different growing conditions, using data from controlled field experiments; (a) use the evaluated model to predict the magnitude and time of the year that N or soil water would constrain tall fescue from attaining its potential growth rate in the south‐eastern Pampas of Argentina; and (c) quantify herbage production gains and temporal variability for a proposed improved management practice (IMP) of N fertilizer under different meteorological and soils scenarios. After tall fescue‐specific calibration, the model accurately represented the response of tall fescue herbage mass to irrigation and N fertilization (root mean square error of prediction &lt;550 kg DM ha−1, R2 &gt; .70) observed in thirteen single‐season, controlled, field experiments. Results from a 40‐year simulation show that the gap between fertilized and unfertilized tall fescue pastures in Argentina can exceed 14 t DM ha−1 year−1. The proposed IMP maximized responses to N in autumn and early spring, reducing the annual herbage production gaps by 38%. The effect of IMP on annual herbage production was larger (4–5 t DM ha−1 year−1) than the effect due to variability in soils’ ability to store water (2–4 t DM ha−1 year−1) or annual variability in meteorological conditions (±1–2 t DM ha−1 year−1). This work provides a sound modelling approach to identify, for a particular forage species and site, most of the main gaps of herbage production; and to quantify, without the need of long‐term, expensive field studies, some of the potential gains (and risks associated with temporal variability), that could be achieved by producers. In addition, our results clearly indicate that site‐specific calibration is required for the model to provide accurate predictions and sound management advice to farmers.