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Aims Plant-available water is determined by soil matric and osmotic potential. The effect of salinity is a combination of the osmotic potential, the plant’s capacity to osmotically adjust, and the specific toxicity of the salt. Our aim was to better understand the relative importance of these components in a soil where the relationship between soil solution composition and soil water content had been characterized. Method The capacity of wheat ( Triticum aestivum L.) and chickpea ( Cicer arietinum L.) to extract water from a saline soil was examined by imposing water stress on established plants, which were then grown until permanent wilting point (PWP) was reached. Results Wheat extracted soil moisture to lower potentials (−1.2 MPa) than chickpea (−0.80 MPa) in 0 NaCl treatments. Where salinity was low to moderate, plants extracted water to a PWP determined by the combined total of matric and osmotic potentials. Wheat extracted water to PWP in salinity treatments producing saturated-paste electrical conductivity (EC se ) of up to 5.3 dS/m, and chickpea to 2.9 dS/m. Conclusions As salinity increased, the effects of specific ion toxicity dominated, and water extraction by plants was significantly lower than that predicted on the basis of the total soil water potential.

Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field.

Soil hydraulic properties are important for irrigation scheduling and proper land-use planning. Field capacity, permanent wilting point and infiltration rate are the three vital hydraulic properties which determine the availability and retention of water for crop growth. These properties are difficult to measure and time-consuming, but can be easily predicted from the available information like soil texture, bulk density, organic carbon content, etc. through pedotransfer functions (PTFs). PTFs were developed for field capacity and permanent wilting point for two different regions of Karnataka, viz. Northern Karnataka Plateau (512 soil samples) and Southern Karnataka Plateau (228 soil samples), separately. PTF for infiltration rate was developed using 100 soil samples for the entire Karnataka. Cross-validation techniques were used to validate the PTFs, and the results are satisfactory with low RMSE and higher R². The developed PTFs are useful in determining soil hydraulic properties of the semi-arid regions of southern India.

Climate change has intensified drought events, compromising popcorn production, particularly in tropical regions. This study aimed to identify popcorn inbred lines with superior water-use efficiency and responsiveness, and to examine the relationships among morpho-agronomic traits associated with expanded popcorn volume per hectare (VP). Fifty inbred lines were evaluated under well-watered (WW) and water-stressed (WS) conditions across two cropping seasons (2020 and 2021). Water deficit was imposed at pre-anthesis, with the permanent wilting point occurring during early reproductive stages in 2020 and during grain filling in 2021. Principal component analysis and efficiency/responsiveness classification were used to characterize line performance. Significant genotype × water condition × season interactions affected all traits. Water stress reduced VP by 75% in 2020 and 46% in 2021, reflecting the differing timing of stress. Line L477 showed high efficiency and responsiveness, while genotypes such as L213, L221, and L222 were inefficient and non-responsive in both years. Under WW, VP was mainly associated with hundred-grain weight, ear length, and grain number per row, whereas under WS, ear diameter and number of rows per ear were the strongest contributors, indicating that the available genetic variability is more effectively exploited through selective morpho-agronomic criteria tailored to each water scenario. Contrasting crosses between efficient and non-responsive lines (L325 and L481) and inefficient but responsive lines (L513, L625, and L689) are recommended to support the development of hybrids that combine high yield under irrigation with resilience under water-stress conditions.

Climate scenarios predict more frequent and longer drought periods, potentially threatening agricultural yield. The water holding capacity of soils is crucial in controlling drought stress intensity for plants. Recently, amorphous silica was suggested to increase soil water holding capacity and availability. The objective of this study was to explore the potential impact of Si application to soils on the retention and flow of water in soils and their consequence on plant access to water under soil drying conditions. Two sandy soils were mixed with varying contents (0, 1 and 5% g/g) of some selected ASi amendments. The soil water retention and soil hydraulic conductivity were determined using evaporation measurement device implemented in a commercial device called HYPROP. For both soils, an application of ASi at rates of 1 or 5% increased the water holding capacity and soils treated with ASi maintained a higher hydraulic conductivity under soil drying conditions than the control soil. Simulation demonstrated that soils treated with ASi could longer sustain the transpirational demand of plants during a soil drying cycle. These first results confirm expected positive crop-growth effect of silica amendments on hydraulic properties of coarse-textured soils mainly by longer keeping up capillary flow during water extraction by plant roots.

Background and aims We explored the hypothesis that low soil water-holding capacity is the main factor driving the monodominance of Brosimum rubescens in a monodominant forest in Southern Amazonia. Tropical monodominant forests are rare ecosystems with low diversity and high dominance of a single tree species. The causes of this atypical condition are still poorly understood. Some studies have shown a relationship between monodominance and waterlogging or soil attributes, while others have concluded that edaphic factors have little or no explanatory value, but none has accounted for soil-moisture variation other than waterlogging. This study is the first to explicitly explore how low soil water-holding capacity influences the monodominance of tropical forests. Methods We conducted in situ measurements of vertical soil moisture using electrical resistance collected over 1 year at 0–5; 35–40 and 75–80 cm depths in a B. rubescens monodominant forest and in an adjacent mixed-species forest in the Amazon-Cerrado transition zone, Brazil. Minimum leaf water potential (Ψmin) of the seven most common species, including B. rubescens , and soil water-holding capacity for both forests were determined. Results The vertical soil moisture decay pattern was similar in both forests for all depths. However, the slightly higher water availability in the monodominant forest and Ψmin similarity between B. rubescens and nearby mixed forest species indicate that low water-availability does not cause the monodominance. Conclusions We reject the hypothesis that monodominance of B. rubescens is primarily determined by low soil water-holding capacity, reinforcing the idea that monodominance in tropical forests is not determined by a single factor.

Soil water characteristic curve (SWCC) is one of the most essential hydraulic properties that play fundamental role in various environmental issues and water management. SWCC gives important information for water movement, soil behavior, infiltration, and drainage mechanism, affecting the water circle and the aquifer recharge. Since most of the world’s freshwater withdrawals go for irrigation uses, decoding SWCC is beneficial, as it affects water saving through irrigation planning. Estimation of crucial parameters, such as field capacity (FC) and permanent wilting point (PWP) is the key solution for water saving. Modelling of the SWCC and hydraulic parameters estimation are of great importance, since the laboratory experimental procedures and the experiments in the field are often time-consuming processes. In the present study, the SWCC along with FC and PWP of two soil types were obtained via specific experimental procedures in the laboratory. In order to simulate the SWCC and estimate FC and PWP, the experimental data were approximated with van Genuchten’s model. Results showed that using SWCC to estimate FC gives excellent results, while the method rationally overestimates the PWP. Hence, the presented method leads to estimation of crucial hydraulic parameters that can be used in irrigation planning and water saving practices.

The lower limit of available water, usually considered the permanent wilting point, can be influenced by soil attributes and by the specific ability of plants to use retained water at low soil water potentials (Ψ m ). This study evaluated the Ψ m at which the physiological permanent wilting point (Ψ PPWP ) of different crops occurs to identify the species most resistant to water deficit. The Ψ PPWP of three grain winter crops (wheat, barley, and rye) and three autumn cover crops (black oat, forage turnip, and vetch) were evaluated in two Oxisols, one sandy-clay and other very clayey soil. The water content in the Ψ PPWP was measured with a dew point potentiometer. The plants wilted at Ψ m ranging from − 35,900 to -79,540 hPa in the very clayed Oxisol, and from − 26,840 to -99,060 hPa in the sandy-clay Oxisol. The resistance of plant species to water deficit decreased in the following order barley = black oat > vetch = wheat = rye > forage turnip in the very clayey soil and barley = black oat > vetch > wheat = forage turnip > rye in the sandy-clay soil. We found a significant correlation between the root dry matter/shoot dry matter ratio and Ψ PPWP , indicating that the roots produced by the plants exerted influence on resistance to water deficit. Barley (cash crop) and black oat and vetch (cover crops) stood out for their greater resistance to water stress.

Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for alternative approaches. Pedotransfer functions (PTFs) are predictive functions used to estimate soil properties by easily measurable soil parameters. PTFs are popular in temperate regions, but few attempts have been made to develop PTFs in tropical regions. Regression approaches are widely used to develop PTFs worldwide, and recently a few attempts were made using machine learning methods. PTFs for tropical Sri Lankan soils have already been developed using classical multiple linear regression approaches. However, no attempts were made to use machine learning approaches. This study aimed to determine the applicability of machine learning algorithms in developing PTFs for tropical Sri Lankan soils. We tested three machine learning algorithms (artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF)) with three different input combination (sand, silt, and clay (SSC) percentages; SSC and bulk density (BD); SSC, BD, and organic carbon (OC)) to estimate volumetric water content (VWC) at −10 kPa, −33 kPa (representing field capacity (FC); however, most studies in Sri Lanka use −33 kPa as the FC) and −1500 kPa (representing the permanent wilting point (PWP)) of Sri Lankan soils. This analysis used the open-source data mining software in the Waikato Environment for Knowledge Analysis. Using a wrapper approach and best-first search method, we selected the most appropriate inputs to develop PTFs using different machine learning algorithms and input levels. We developed PTFs to estimate FC and PWP and compared them with the previously reported PTFs for tropical Sri Lankan soils. We found that RF was the best algorithm to develop PTFs for tropical Sri Lankan soils. We tried to further the development of PTFs by adding volumetric water content at −10 kPa as an input variable because it is quite an easily measurable parameter compared to the other targeted VWCs. With the addition of VWC at −10 kPa, all machine learning algorithms boosted the performance. However, RF was the best. We studied the functionality of finetuned PTFs and found that they can estimate the available water content of Sri Lankan soils as well as measurements-based calculations. We identified RF as a robust alternative to linear regression methods in developing PTFs to estimate field capacity and the permanent wilting point of tropical Sri Lankan soils. With those findings, we recommended that PTFs be developed using the RF algorithm in the related software to make up for the data gaps present in tropical regions.

Six models that can predict the thermal conductivity of medium‐ and high‐temperature soil were reviewed. Except Si‐Mo model, the other models contain a large number of parameters and have complex structures. A total of 381 soil thermal conductivity data measured by Chinese and Japanese researchers at 0°C–90°C using the probe method were collected from the published literature, and the measured database was constructed to evaluate the performance of the model. The results show that the prediction accuracy of the six models is all good, with root mean square error (RMSE) < 0.45 W/(mK) and Nash efficiency coefficient (NSE) > 0.65, and there are significant differences between the models: Chenhui Liu’s model performs best (NSE = 0.9380, RMSE = 0.1785 W/[mK]), and the deV‐1 model performs relatively weak (NSE = 0.6580, RMSE = 0.4191 W/[mK]). The deviation analysis showed that the absolute deviation of thermal conductivity prediction (| Δ λ |) tended to increase with the increase in temperature, and the maximum | Δ λ | mostly occurred at the point where the moisture content was slightly lower than the permanent wilting point ( θ P W P ). Combined with the measured data of high‐temperature matric suction of red clay, it is speculated that this phenomenon is due to the decrease of soil water holding capacity due to the increase of temperature, which leads to the increase in θ P W P calculated by the RETC software at high temperature, and thus underestimates the thermal conductivity contributed by the latent heat effect of water vapor (LHT). This paper realizes the unified evaluation of six soil thermal conductivity models in the middle and high temperature range for the first time, and the database fills the data gap of middle‐ and high‐temperature thermal conductivity of soils with East Asian characteristics, which can provide the basis for model selection for related projects.