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In the current study the hypothesis that 1.0% dietary inclusion of glutamine (Gln), a conditionally essential amino acid that influences protein synthesis and shows anti-osteoporotic effect, can influence eggshell quality was tested on laying hens in the post-peak laying period. Thirty-week-old Bovans Brown hens were randomly assigned to control group or group supplemented with Gln in the form of alphaketoglutarate (10 g/kg) with 12 replicate cages (2 hens/cage) in each group. The experimental period lasted for 30 weeks, from the 31st to the 60th week of age of hens, when eggs were collected and selected eggshell quality indices were determined. While Gln supplementation had no effect on egg geometry and eggshell cracking force, the analysis of eggshell quality showed an increase of the eggshell thickness (P<0.01), porosity (P<0.01), eggshell specific surface (P<0.001) and positive changes in other mechanical parameters (decrease of eggshell stiffness and Young’s modulus, P<0.05 and P<0.01, respectively; increase of work needed to crack the eggshell, P<0.05). Changes in eggshell mineral composition, including an increase of Ca content (P<0.001), were also observed. In conclusion, the current study showed a beneficial effect of Gln on the eggshell thickness, calcification, and some mechanical parameters.

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well‐known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models. The main wetting branch of the soil water retention curve is estimated based on its main drying branchThe machine learning method is used for analysisResults are compared with classical methods of estimating the main wetting branch

The splash phenomenon is the first stage of water erosion on the soil. It occurs when the water drops of the rain strike the surface. The impact of the water drop is fast. But if one has a fast enough measuring system, it can be considered as a temporally extended phenomenon. The aim of this paper is to describe a system for the measurement of the dynamic of the changing forces when a water drop interacts with the surface during impact. The constructive assumptions and validation of the measurement system, which has been based on piezoelectric sensors, are also presented. The system allows one to measure the changes of the force with the frequency of 150 kHz. The maximum force is 200 N, which is quite sufficient for all sizes of water drops which occur in nature and their maximum speeds. The results of test measurements, that is, the changes of the force obtained from water drops falling on the sensors, are also shown.