measuring-stolons-rhizomes-turfgrasses-using-digital-image-analysis

Measuring Stolons and Rhizomes of Turfgrasses Using a Digital Image Analysis System

Length and diameter of stolons or rhizomes are usually measured using simple rulers and calipers. This procedure is slow and laborious, so it is often ...

University of Padova

Pastures are strongly affected by local environmental variables in terms of their species richness, plant composition and herbage production. A multi-site monitoring study was conducted over three years to investigate the influence of morphoclimatic factors on the seasonal variations in dry matter (DM) yield from Karst pastures. Seven sites located on the Italian and Slovenian Karst regions were investigated that differed in terms of their geological and geomorphological features, as well as their soil types. At each site, the daily DM yield (kg ha-1 d-1) was determined using Corral-Fenlon method which permits to simulate herbage utilization from grazing herds. The morphoclimatic features were also analysed, with the aim to evaluate the link between seasonal DM yield and geomorphological and environmental factors. Generalized non-linear mixed models were built to study the observed seasonal variations in DM yield, using day of the year (DOY), growing degree days (GDD), and cumulative rainfall. Furthermore, environmental descriptors were included in the model in order to evaluate their effects on DM yield. The seasonal variations in yield showed two growing periods (spring and late summer), which were described by Gaussian curves. For the spring growing period, the model improved when the interaction between soil granulometry and growing degree days corresponding to the curve peak was taken into account. This confirms the influence of soil type and air temperature on pasture yield. For the late summer growing period, the interaction between the sand classes and the number of rainy days from the beginning of the period to the peak of the curve improved the model. The curve parameters of our models are correlated with environmental descriptors depending on the lithology and particle size of soils. The results are essential for the optimization of pasture management and avoiding degradation due to over- or under-grazing.

Stefano Macolino

Biography

JoVE Journal Publications

Other Publications