Statistical indices of soil water content between measurement and simulation.

<p>Statistical indices of soil water content between measurement and simulation.</p

Comparison of soil water content at the depths of 15 cm (a), 45 cm (b) and 75 cm (c) in the 2006–2007 experiment.

<p>Comparison of soil water content at the depths of 15 cm (a), 45 cm (b) and 75 cm (c) in the 2006–2007 experiment.</p

Model parameter values used in the simulations.

<p>Model parameter values used in the simulations.</p

Simulated seasonal ET (a) and the contributions of precipitation and soil water to the simulated seasonal ET (b) from 2000–2010.

<p>Simulated seasonal ET (a) and the contributions of precipitation and soil water to the simulated seasonal ET (b) from 2000–2010.</p

Schematic diagram of calculation procedures in the model used for soil-crop water dynamics in this study.

<p>Schematic diagram of calculation procedures in the model used for soil-crop water dynamics in this study.</p

Overall comparison of soil water content (SWC) between simulation and measurement.

<p>Overall comparison of soil water content (SWC) between simulation and measurement.</p

Summary of experiments from 2000–2010.

<p>Summary of experiments from 2000–2010.</p

Investigation of Water Dynamics and the Effect of Evapotranspiration on Grain Yield of Rainfed Wheat and Barley under a Mediterranean Environment: A Modelling Approach

<div><p>Agro-hydrological models have increasingly become useful and powerful tools in optimizing water and fertilizer application, and in studying the environmental consequences. Accurate prediction of water dynamics in such models is essential for models to produce reasonable results. In this study, detailed simulations were performed for water dynamics of rainfed winter wheat and barley grown under a Mediterranean climate over a 10-year period. The model employed (Yang et al., 2009. J. Hydrol., 370, 177-190) uses easily available agronomic data, and takes into consideration of all key soil and plant processes in controlling water dynamics in the soil-crop system, including the dynamics of root growth. The water requirement for crop growth was calculated according to the FAO56, and the soil hydraulic properties were estimated using peto-transfer functions (PTFs) based on soil physical properties and soil organic matter content. Results show that the simulated values of soil water content at the depths of 15, 45 and 75 cm agreed with the measurements well with the root of the mean squared errors of 0.027 cm³ cm^-3 and the model agreement index of 0.875. The simulated seasonal evapotranspiration (ET) ranged from 208 to 388 mm, and grain yield was found to correlate with the simulated seasonal ET in a linear manner within the studied ET range. The simulated rates of grain yield increase were 17.3 and 23.7 kg ha^-l for every mm of water evapotranspired for wheat and barley, respectively. The good agreement of soil water content between measurement and simulation and the simulated relationships between grain yield and seasonal ET supported by the data in the literature indicates that the model performed well in modelling water dynamics for the studied soil-crop system, and therefore has the potential to be applied reliably and widely in precision agriculture. Finally, a two-staged approach using inverse modelling techniques to further improve model performance was discussed.</p></div

Measured daily mean air temperature (a), daily and cumulative precipitation (b) and calculated daily reference evapotranspiration (ET_o) (c) during 2000–2010.

<p>Measured daily mean air temperature (a), daily and cumulative precipitation (b) and calculated daily reference evapotranspiration (ET_o) (c) during 2000–2010.</p

Kinetic measurements on surfaces with different ligand densities.

<p>Six concentrations of scFv (2-fold dilutions from 24 to 0.75 nM) were injected at 25°C. The responses from the in-line reference flow cell and blank injections were subtracted from all curves. The curves were globally fitted to a 1:1 Langmuir binding model. For each surface density, the experimental sensorgrams (coloured lines) were overlain with the theoretical fitted curves (black lines). In each case, the relative residual plots show the difference between the experimental and the theoretical curves expressed as a percentage of the observed response for each analyte concentration. The spikes observed at the beginning and the end of analyte injections are the result of in-line reference subtraction with the slight sensorgran misalignment introduced with sequential analyte flow in the multichannel mode. Nevertheless, residuals are mostly within 1% of R_obs. The surface density, expressed as the maximum analyte binding capacity (Rmax) was determined experimentally: Rmax ≈ 49 RU (a); 465 RU (b); 3100 RU (c).</p