Examining the Impact of Patch Burning on Livestock Grazing Patterns in Edwards Plateau, Texas

Production of sheep, goat, and cattle are major agricultural enterprises on West Texas rangelands, especially in the Edwards Plateau. In this region, the use of fire as a management tool was suppressed until recently. Although previous studies have been conducted to evaluate cattle use of burned versus unburned patches, there has been a lack of studies where combinations of livestock species grazing together on patch burned areas have been evaluated. The objective of this study is to examine grazing patterns of cattle, sheep and goats, both spatially and temporally, on areas where patch burning has been implemented. The study site is the Texas A&M AgriLife Research Ranch, Martin Ranch, located in Mesquite-Oak-Savanna ecosystem in Menard County of Texas, USA. In February 2019 and September 2020, patch burns were implemented on the ranch and represented about 29% of the total ranch area (480 out of 1655 ha). After the burns, animals from the resident herd were randomly selected and GPS collars were placed on 34 goats, 33 sheep, and 8 cows to reflect the proportion of animals in the resident herd. The GPS collars were set to collect movement data every 10 minutes during a fourteen-month period. Gates and fences for the entire ranch were opened and livestock were free to choose areas to graze. Data from the GPS’s were evaluated to determine locations where the animals grazed and their preferences for different areas of the landscape. Initial observations indicate that cattle and sheep were more attracted by recent burned patches compared to goats. Livestock forage use patterns and time spent by species in the burned and unburned areas and among different vegetation land cover classes are presented. Information from this study will assist in providing information to producers on how implementation of patch burning would influence their management of these grazing lands

Forecasting Forage Yields Using the ARIMA Model in Pastoral Areas of East Africa

Predicting forage supply is an age old quest for pastoralists, particularly in fragile and drought- prone areas of Africa. Traditional methods of forecasting forage used by many communities have become less effective due to climate change, frequent droughts and decline of grazing areas. Conflicts relating to available forage and water resources are increasing, because more marginal lands are put to crop production. A new forage forecasting technology has been developed that provides a comprehensive view of current forage condition (Stuth et al., 2004). A multiple species grazing land plant growth hydrology based model (PHYGROW) was parameterised with site-specific soil, plant community, grazer data that was spatially linked with satellite weather and predicted daily available forage (Rowan, 1995). The objective of this study was to explore use of the Auto-Regressive Integrated Moving-Average (ARIMA) procedure in forecasting a 30, 60 and 90-day available forage

Emerging Near-Real Time Forage Monitoring Technology with Application to Large Herbivore Management in Mongolia

Large herbivore livestock and wildlife in Mongolia depend almost entirely for substance on forage standing crop produced each year on natural pastureland. Consequently, both livestock and wildlife are continuously subject to environmental risk, especially drought and severe winter storms, while livestock are also subject to financial risk. As consumption-based livestock production changes to commercialized livestock production, steps taken by the livestock herder to avert both environmental and financial risk to livestock can increase environmental risk to large wild herbivores. A realistic and workable pastureland and risk management system will be critical for conservation of large herbivore habitat. New technologies are becoming available to facilitate understanding of risk and resource allocation. Texas A&M University has developed a suite of innovative technologies that facilitate resolving risk and resource allocation issues. A pre-parameterized rangeland model (i.e., PHYGROW) provides daily estimates of forage available to a mixed population of herbivores. Near Infra-Red Spectroscopy (NIRS) allows prediction of diet quality of free-ranging large herbivores via fecal scans which, when coupled with advanced nutritional management software (i.e., NUTBAL), can predict performance of animals. Oregon State University has developed a computerized multi-criteria decision-making tool (i.e., KRESS) that can take landscape parameters and determine the suitability of each cell or unit of the landscape for use by large herbivores. Emerging near real-time technologies can help clarify habitat needs, identify habitat improvements, and enable better management of large herbivore wildlife and livestock

Emerging Near-Real Time Forage Monitoring Technology with Application to Large Herbivore Management in Mongolia

Large herbivore livestock and wildlife in Mongolia depend almost entirely for substance on forage standing crop produced each year on natural pastureland. Consequently, both livestock and wildlife are continuously subject to environmental risk, especially drought and severe winter storms, while livestock are also subject to financial risk. As consumption-based livestock production changes to commercialized livestock production, steps taken by the livestock herder to avert both environmental and financial risk to livestock can increase environmental risk to large wild herbivores. A realistic and workable pastureland and risk management system will be critical for conservation of large herbivore habitat. New technologies are becoming available to facilitate understanding of risk and resource allocation. Texas A&M University has developed a suite of innovative technologies that facilitate resolving risk and resource allocation issues. A pre-parameterized rangeland model (i.e., PHYGROW) provides daily estimates of forage available to a mixed population of herbivores. Near Infra-Red Spectroscopy (NIRS) allows prediction of diet quality of free-ranging large herbivores via fecal scans which, when coupled with advanced nutritional management software (i.e., NUTBAL), can predict performance of animals. Oregon State University has developed a computerized multi-criteria decision-making tool (i.e., KRESS) that can take landscape parameters and determine the suitability of each cell or unit of the landscape for use by large herbivores. Emerging near real-time technologies can help clarify habitat needs, identify habitat improvements, and enable better management of large herbivore wildlife and livestock

Forage Monitoring Technology to Improve Risk Management Decision Making by Herders in the Gobi Region of Mongolia

In the period from 1999 to 2002, Mongolia experienced a series of droughts and severe winters that lowered livestock numbers by approximately 30% countrywide. In the Gobi region, livestock mortality reached 50% with many households losing entire herds (Siurua & Swift 2002). In March 2004, a program was initiated by the United States Agency for International Development (USAID) through the Global Livestock Collaborative Research and Support Program (GLCRSP). The goal of this program is to develop forage monitoring technologies that provide early warning of drought and winter disaster to improve livestock herder decision making in the Gobi region. The program has two major objectives: (1) to develop a regional forage monitoring system that provides near-real time spatial and temporal assessment of current and forecasted forage conditions, and (2) to develop a communication infrastructure that provides herders with data on forage conditions to assist them in making timely and specific management decisions

Mackintosh lecture: Association and cognition: two processes, one system

This is the author accepted manuscript. The final version is available from SAGE Publications via the DOI in this record.There is another ORE record for this item: http://hdl.handle.net/10871/33264This paper argues that the dual-process position can be a useful first approximation when studying human mental life, but it cannot be the whole truth. Instead, we argue that cognition is built on association, in that associative processes provide the fundamental building blocks that enable propositional thought. One consequence of this position is to suggest that humans are able to learn associatively in a similar fashion to a rat or a pigeon, but another is that we must typically suppress the expression of basic associative learning in favour of rule-based computation. This stance conceptualizes us as capable of symbolic computation, but acknowledges that, given certain circumstances, we will learn associatively and, more importantly, be seen to do so. We present three types of evidence that support this position: The first is data on human Pavlovian conditioning that directly supports this view. The second is data taken from task switching experiments that provides convergent evidence for at least two modes of processing, one of which is automatic and carried out “in the background”. And the last suggests that when the output of propositional processes is uncertain, then the influence of associative processes on behaviour can manifest

Fast Photon Detection for Particle Identification with COMPASS RICH-1

Particle identification at high rates is an important challenge for many current and future high-energy physics experiments. The upgrade of the COMPASS RICH-1 detector requires a new technique for Cherenkov photon detection at count rates of several $10^6$ per channel in the central detector region, and a read-out system allowing for trigger rates of up to 100 kHz. To cope with these requirements, the photon detectors in the central region have been replaced with the detection system described in this paper. In the peripheral regions, the existing multi-wire proportional chambers with CsI photocathode are now read out via a new system employing APV pre-amplifiers and flash ADC chips. The new detection system consists of multi-anode photomultiplier tubes (MAPMT) and fast read-out electronics based on the MAD4 discriminator and the F1-TDC chip. The RICH-1 is in operation in its upgraded version for the 2006 CERN SPS run. We present the photon detection design, constructive aspects and the first Cherenkov light in the detector.Comment: Proceedings of the Imaging 2006 conference, Stockholm, Sweden, 27-30 June 2006, 5 pages, 6 figures, to appear in NIM A; corrected typo in caption of Fig.

Fast photon detection for the COMPASS RICH detector

The COMPASS experiment at the SPS accelerator at CERN uses a large scale Ring Imaging CHerenkov detector (RICH) to identify pions, kaons and protons in a wide momentum range. For the data taking in 2006, the COMPASS RICH has been upgraded in the central photon detection area (25% of the surface) with a new technology to detect Cherenkov photons at very high count rates of several 10^6 per second and channel and a new dead-time free read-out system, which allows trigger rates up to 100 kHz. The Cherenkov photons are detected by an array of 576 visible and ultra-violet sensitive multi-anode photomultipliers with 16 channels each. The upgraded detector showed an excellent performance during the 2006 data taking.Comment: Proceeding of the IPRD06 conference (Siena, Okt. 06