Physical Sciences and Mathematics Commons^™

Map Based Www Tool For Recommending Forage Species, M. H. Hall, D. A. Miller, J. H. Cherney

IGC Proceedings (1993-2023)

Selecting the “best” forage species or species mixture requires a knowledge and understanding of the consuming animal, soil characteristics, and forage species. The multivariate nature of the problem presents challenges to those interested in solving the practical problem of maximizing available resources. Earlier work at Cornell University resulted in a WWW-based interface that allowed the user to provide a soil type or zip code and additional management factors to initiate the species selection. This approach relies on either user knowledge of soil series or an implied relationship between zip code and soil series that may often be wrong. To improve …

Remote Sensing Application To Grassland Monitoring, Z. B. Nan

IGC Proceedings (1993-2023)

Application of remote sensing to the management of grassland resources, the role this plays in developing sustainable grassland farming systems and opportunities for further development are outlined. Use of remote sensing technologies in grassland monitoring has a history of more than 30 years. Both fine- and coarse-grained remote sensing techniques are used to monitor and study grasslands. Fine-grained techniques are used to study landscape scale processes through the use of sensors providing spatial resolution of a few meters, whereas coarse-grained techniques are used to study catchment scale areas, and even entire biomes, using satellite-based sensors with a spatial resolution of …

Modelling Grassland Ecosystems, J. H. M. Thornley

IGC Proceedings (1993-2023)

In this contribution a view of the promise and difficulties of modelling grassland is given. This is largely centred around work with a grassland ecosystem simulator known as the Hurley Pasture Model.

A brief introduction sets forth possible reasons for building a large ecosystem model, and stresses the importance of modelling objectives. It is suggested that a model is de rigeur for any research programme which aims to take a firm grasp of the complex responses of grassland. Mechanistic models are required to provide the understanding needed for intelligent and flexible management of grassland, whatever the prevailing environmental or economic …

Estimating Herbage Mass In Stargrass (Cynodon Nlenfuensis Var Nlenfuensis) Using Sward Surface Height And The Rising Plate Meter, D. E. Oliveira, S. R. Medeiros, L. J. M. Aroeira, L. G. Barioni, D. P. D. Lanna

IGC Proceedings (1993-2023)

Estimation of herbage mass is necessary both in research and farm management. Methods for such estimations should be inexpensive, rapid and reliable. Since estimations through hand clipping are time consuming, a range of indirect methods have been developed and tested in the literature. This study had the aim of comparing precision of two indirect methods, sward surface height (SSH) and the rising plate meter (RPM), to estimate herbage mass in stargrass pastures (Cynodon nlenfuensis var. nlenfuensis). Pre-grazing measurements of SSH and RPM readings were taken and compared with the paddock herbage mass. RPM readings were converted into centimeters. Average …

Tropical Cyclones Negatively Impact The Tourism Economy In Coastal Regions, Sergio Alvarez, Arthur Huang

Rosen Research Review

Tropical cyclones can have a devastating effect on communities, causing loss of property, businesses, and life. In areas that rely on tourism, these effects can be long-lasting. In their recent work, Dr. Sergio Alvarez and Dr. Arthur Huang from UCF Rosen College of Hospitality Management have examined the impact that tropical cyclones have on the tourism economy in Florida. The results suggest that these cyclones negatively impact the economy, especially in coastal regions, and that policy changes are needed to help the tourism sector recover

Mgrre_Thinsections_12-A_4, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_1, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_3, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_6, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_7, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_12, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_14, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_16, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_17, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_19, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_20, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_24, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_21, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_28, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_29, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_31, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_32, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_36, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_34, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_33, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_38, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_37, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_43, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_45, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_12-A_46, Mgrre

Thin Sections

No abstract provided.