No abstract

Biostimulants are preparations that favorably impact the growth, development, and yield of plants. The research objective was to examine the effect of the frequency of use of Kelpak, Terra Sorb Complex and Fylloton biostimulants on improving the yield and nutritional properties of beans. Been seeds (variety Oczko) were sown in the first week of May in 2015, 2016, and 2017. During the growing season, Fylloton (1%), Terra Sorb Complex (0.5%), and Kelpak (1%) biostimulants were applied by single (BBCH 12-13) and double spraying of plants (BBCH 12-13, BBCH 61). All variants of treatment with biostimulants were compared with the control. Single application of Kelpak had a positive effect on increasing the number of pods. The double application of Kelpak increased the number and yield of seeds and protein contents. Double application of Fylloton increased the number of seeds, and application of Terra Sorb Complex increased the protein content in the beans. Application of all biostimulants increased the flavonoid content. Biostimulants containing seaweed (Kelpak–Ecklonia maxima extract) or amino-acid extracts (Fylloton–Ascophyllum nodosum extract and amino acids or Terra Sorb Complex–amino acids) increased the seed yield, while improving its quality by increasing the content of protein, polyphenols, and flavonoids. It was found that the double application of Kelpak biostimulant stimulated the yield and quality of beans to a greater extent.

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.