Explore the vast range of titles available.

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.

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.

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.

PurposeField capacity (FC) and permanent wilting point (PWP) are important physical properties for evaluating the available soil water storage, as well as being used as input variables for related agro-hydrological models. Direct measurements of FC and PWP are time consuming and expensive, and thus, it is necessary to develop related pedotransfer functions (PTFs). In this study, stepwise multiple linear regression (SMLR) and artificial neural network (ANN) methods were used to develop FC and PWP PTFs for the deep layer of the Loess Plateau based on the bulk density (BD),sand, silt, clay, and soil organic carbon (SOC) contents.Materials and methodsSoil core drilling was used to obtain undisturbed soil cores from three typical sites on the Loess Plateau, which ranged from the top of the soil profile to the bedrock (0–200 m). The FC and PWP were measured using the centrifugation method at suctions of − 33 and − 1500 kPa, respectively.Results and discussionThe results showed that FC and PWP exhibited moderate variation where the coefficients of variation were 11 and 23%, respectively. FC had significant correlations with sand, silt, clay, and SOC (P < 0.01), while there were also significant correlations between all of the variables and PWP. In addition, sand was an important input variable for predicting FC, and clay and BD for predicting PWP. The performance of the SMLR and ANN approaches was similar.ConclusionsIn this study, we developed new PTFs for FC and PWP as the first set of PTFs based on data obtained from deep profiles in the Loess Plateau. These PTFs are important for evaluating the soil water conditions in the deep profile in this region.

PurposeThe field capacity (FC) and permanent wilting point (PWP) are important soil hydraulic properties that determine the maximum available water for plants, and they are crucial parameters for biophysical models and irrigation management. However, previous estimates of the FC and PWP failed to consider their spatial correlations at the regional scale. Therefore, we estimated the FC and PWP using the state-space equation by considering the spatial correlations between soil properties.Materials and methodsWe estimated the FC and PWP using a first order autoregressive state-space equation based on the elevation (Elev), bulk density (BD), soil texture (Clay, Silt, Sand), soil organic carbon (SOC), and land use (LU) with a data set obtained from 104 in situ sampling sites across the entire Hexi Corridor.Results and discussionThe results indicated that the distributions of the FC and PWP exhibited moderate variations in the Hexi Corridor, with values of 0.127 ± 0.060 and 0.075 ± 0.034 g/g (mean ± 1 standard deviation (SD)), respectively. According to t tests, the autocorrelation coefficients for FC, PWP, Elev, LU, Silt, Sand, and SOC as well as the cross-correlation coefficients between FC, PWP, and pertinent variables were significant with one lag distance (approximately 40 km) (p ˂ 0.05). Calculations of the coefficient of determination (R2) and root mean square error (RMSE) showed that the state-space models performed better at estimating FC and PWP than multiple linear stepwise regression models (SMLRs). Bivariate state-space equations based on Silt and LU were the optimal models for estimating FC (R2 = 0.999, RMSE = 0.002 g/g) and PWP (R2 = 0.997, RMSE = 0.002 g/g). According to the coefficients in the optimal state-space models, soil texture and LU were the dominant factors that affected the spatial variability in FC and PWP. After neglecting the spatial correlations between variables, the SMLRs showed that BD and soil texture were the best variables for estimating FC and PWP.ConclusionsThe state-space approach is recommended as a useful tool for quantifying larger-scale spatial patterns in soil properties.

Considering that water is extremely important in agricultural production, but with restricted availability in some Brazilian regions, this research sought to identify the water limit for the rootstocks: Cleóptra tangerine (Citrus reshni hort. Ex Tan), Volkamer lime (Citrus Volkameriano Pasquale), Citrandarin ‘indio’ (TSK X TRENG 256), Santa Cruz Rangpur lime (Citrus × limonia) and Sunki Tropical tangerine (Citrus sunki HORT. EX TAN) grafted orange ‘Pera’ (Citrus sinensis), obtained by two methods: the traditional method of determining the permanent wilting point described by SHANTZ & BRIGGS (1912) recovery of plants with saturated environment and by irrigating recovery method. The experimental design used was in a completely randomized design with four replications totaling 20 experimental plots. It was verified that the rootstocks Cravo Santa Cruz lemon and Volkamerian lemon were the most resistant in initial conditions of water restriction, evaluated by the method of BRIGGS & SHANTZ (1912), with recording of humidity of 0.0488 and 0.0489 respectively. Under more severe conditions of water restriction, determined by the irrigation method, Volkamerian lemon presented the highest resistance, with a humidity of 0.0371. RESUMO: Considerando que a água é extremamente importante na produção agrícola, mas com restrita disponibilidade em algumas regiões brasileiras, é que esse trabalho buscou identificar o limite hídrico inferior para os porta-enxertos: tangerina Cleóptra (Citrus reshni hort. Ex Tan), limão Volkameriano (Citrus Volkameriano Pasquale), citrandarin ‘Indio’ -TSK X TRENG 256, limão Cravo Santa Cruz (Citrus × limonia) e tangerina Sunki Tropical (Citrus sunki HORT. EX TAN) enxertadas em laranja ‘Pêra’ (Citrus sinensis), obtidos por dois métodos: o método tradicional de determinação do ponto de murchamento permanente descrito por BRIGGS & SHANTZ (1912) com recuperação das plantas em ambiente saturado e o método de recuperação por rega. O delineamento experimental utilizado foi o inteiramente casualizado, com quatro repetições, totalizando 20 parcelas experimentais. Verificou-se que os porta-enxertos limão Cravo Santa Cruz e o limão Volkameriano foram os mais resistentes em condições iniciais de restrição hídrica, avaliado pelo método de BRIGGS & SHANTZ (1912), com registro das umidades de 0,0488 e 0,0489, respectivamente. Em condições mais severas de restrição hídrica, determinado pelo método de rega, o limão Volkameriano foi o que apresentou maior resistência, com a umidade de 0,0371.

Climate change is expected to exacerbate drought for many plants, making drought tolerance a key driver of species and ecosystem responses. Plant drought tolerance is determined by multiple traits, but the relationships among traits, either within individual plants or across species, have not been evaluated for general patterns across plant diversity. We synthesized the published data for stomatal closure, wilting, declines in hydraulic conductivity in the leaves, stems, and roots, and plant mortality for 262 woody angiosperm and 48 gymnosperm species. We evaluated the correlations among the drought tolerance traits across species, and the general sequence of water potential thresholds for these traits within individual plants. The trait correlations across species provide a framework for predicting plant responses to a wide range of water stress from one or two sampled traits, increasing the ability to rapidly characterize drought tolerance across diverse species. Analyzing these correlations also identified correlations among the leaf and stem hydraulic traits and the wilting point, or turgor loss point, beyond those expected from shared ancestry or independent associations with water stress alone. Further, on average, the angiosperm species generally exhibited a sequence of drought tolerance traits that is expected to limit severe tissue damage during drought, such as wilting and substantial stem embolism. This synthesis of the relationships among the drought tolerance traits provides crucial, empirically supported insight into representing variation in multiple traits in models of plant and ecosystem responses to drought.

• A growing number of field studies report isotopic offsets between stem water and its potential sources that prevent the unambiguous identification of plant water origin using water isotopes. We explored the causes of this isotopic offset by conducting a controlled experiment on the temperate tree species Fagus sylvatica. • We measured δ²H and δ18O of soil and stem water from potted saplings growing on three soil substrates and subjected to two watering regimes. • Regardless of substrate, soil and stem water δ²H were similar only near permanent wilting point. Under moister conditions, stem water δ²H was 11 ± 3‰ more negative than soil water δ²H, coherent with field studies. Under drier conditions, stem water δ²H became progressively more enriched than soil water δ²H. Although stem water δ18O broadly reflected that of soil water, soil–stem δ²H and δ18O differences were correlated (r = 0.76) and increased with transpiration rates indicated by proxies. • Soil–stem isotopic offsets are more likely to be caused by water isotope heterogeneities within the soil pore and stem tissues, which would be masked under drier conditions as a result of evaporative enrichment, than by fractionation under root water uptake. Our results challenge our current understanding of isotopic signals in the soil–plant continuum.