Transpiration reduction functions are often used in hydrological modeling to estimate actual transpiration as a function of soil water status. Empirical reduction functions are most frequently used due to the higher data needs and computational requirements of mechanistic models. Empirical models, however, lack a description of physical mechanisms and their parameters require extensive calibration. We derive a process‐based reduction function predicting system potentials, resistances, and water flows. An analytical solution for a special case of Brooks and Corey soils is presented. A numerical version of the reduction function for van Genuchten soils was implemented in the Soil–Water–Atmosphere–Plant (SWAP) hydrological model, allowing predictions for layered soil profiles and root length density variations over depth. The analytical and numerical versions of the model allow an increasingly quantitative insight into the mechanism of root water uptake, such as the existence of a maximum root water uptake rate as a function of soil water status, soil hydraulic properties, root length density, and root radius, in addition to the fact that sensitivity of simulated root water uptake to the radial root conductivity and axial conductance decrease when root length density increases. The approach can be used for the estimation of threshold values for empirical reduction functions.

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SUMMARYUnder field conditions, thermal diffusivity can be estimated from soil temperature data but also from the properties of soil components together with their spatial organization. We aimed to determine soil thermal diffusivity from half-hourly temperature measurements in a Rhodic Kanhapludalf, using three calculation procedures (the amplitude ratio, phase lag and Seemann procedures), as well as from soil component properties, for a comparison of procedures and methods. To determine thermal conductivity for short wave periods (one day), the phase lag method was more reliable than the amplitude ratio or the

The effects of water stress on crop yield through modifications of plant architecture are vital to crop performance such as common bean plants. To assess the extent of this effect, an outdoor experiment was conducted in which common bean plants received five treatments: fully irrigated, and irrigation deficits of 30% and 50% applied in flowering or pod formation stages onwards. Evapotranspiration, number and length of pods, shoot biomass, grain yield and harvest index were assessed, and architectural traits (length and thickness of internodes, length of petioles and petiolules, length and width of leaflet blades and angles) were recorded and analyzed using regression models. The highest irrigation deficit in the flowering stage had the most pronounced effect on plant architecture. Stressed plants were shorter, leaves were smaller and pointing downward, indicating that plants permanently altered their exposure to sunlight. The combined effect of irrigation deficit and less exposure to light lead to shorter pods, less shoot biomass and lower grain yield. Fitted empirical models between water deficit and plant architecture can be included in architectural simulation models to quantify plant light interception under water stress, which, in turn, can supply crop models adding a second order of water stress effects on crop yield simulation.

The polymer tensiometer is a novel instrument to measure soil water pressure heads from saturation to permanent wilting conditions. We used tensiometers of this type in an experiment to determine the hydraulic properties of evaporating soil samples in the laboratory. Relative errors in the hydraulic conductivity function in the wet part were high due to the relatively low accuracy of the pressure transducers, resulting in a large uncertainty in the hydraulic gradient and therefore in the calculated hydraulic conductivity. In the dry part, the error related to this accuracy was on the same order of magnitude as the error related to balance accuracy. Therefore, the method can be assumed adequate for measuring soil hydraulic properties except under very wet conditions. In our experiments, relative error and bias increased significantly at pressure heads less negative than −1 m.

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The objective of this study was to comparatively determine the characteristics of growth rate, leaf area index, shoot dry mass partitioning and grain yield of chia plants (Salvia hispanica L.) on different sowing dates. A field experiment was conducted in the crop year of 2016/2017 in five sowing dates (09/22/16, 10/28/16, 01/03/17, 02/08/17 and 03/24/17) with a randomized complete block design and four replicates. Plant growth was determined through field samplings to determine the dry matter mass and leaf area performed every 15 days. The following physiological indexes were calculated: relative growth rate, absolute growth rate, net assimilation rate, leaf area ratio, specific leaf area and leaf mass ratio. To weekly evaluate plant height, ten plants per plot were marked after emergence, and the final height was considered when plants reached physiological maturity. A useful area of 2.10 m² per plot was collected for evaluating grain yield. The physiological indexes indicated that at earlier sowing dates there is a greater plant growth, either in shoot dry matter mass, height and leaf area index. The leaf area index of branches is progressively increased with plant development and contributes significantly to total leaf area index of chia plants in all studied sowing dates. The main stem represents between 60 and 70% of the shoot dry matter accumulated in the early sowing dates, and between 40 and 50% in late sowing. The best sowing date in terms of grain yield is 01/03/17.

In modeling, actual crop transpiration as a function of soil hydraulic conditions is usually estimated from a water content or pressure head dependent reduction function. We compared the performance of the empirical pressure head based reduction function of Feddes (FRF) and a more physically based reduction function using matric flux potential as the main parameter (DRF), both available in the SWAP ecohydrological model. Model performance was evaluated by comparison of SWAP predictions and observed water contents and pressure head values in a field experiment with a common bean (Phaseolus vulgaris L.) crop. For >50 d, no rain occurred and the soil reached very dry conditions with pressure heads in the range −100 to −150 m. The SWAP–DRF‐predicted pressure head and water content values were less sensitive to root length density distribution than those predicted by SWAP–FRF. Varying wilting pressure head did not improve predictive performance. Root water uptake distribution with time and depth simulated by SWAP showed very different patterns depending on the reduction function used. Root water uptake estimated by SWAP–FRF showed smooth transitions with time and between layers, whereas SWAP–DRF, highly sensitive to hydraulic conditions, generally predicted uptake to be concentrated at a few depths. The order of magnitude of the pressure head difference between root xylem and root surface based on SWAP–DRF‐predicted uptake rates, root length density, and reported values of root conductance was the same as the order of magnitude of the limiting root water pressure head, implying the necessity to include root hydraulic resistance in the DRF.