decision support

NIRS: Near infrared reflectance spectroscopy

gabe.saldana · May 5, 2022 ·

NIRS: Near-infrared reflectance spectroscopy

Technology for rapidly assessing diet nutritional value of free-ranging livestock.

What is NIRS?

Near-infrared reflectance spectroscopy (NIRS) is the technology that the GAN Lab uses to analyze animal fecal samples.

The NIRS process involves exposing a dried, ground fecal sample to light energy. The intensity of reflectance is measured across several hundred wavelengths in the near-infrared band. Reflectance is influenced by the number and type of chemical bonds in the feces. Primary wavelengths in prediction equations appear to be associated with the fiber, alkane, and microbial fractions of the feces.

Advancing research with NIRS

A team of scientists with Texas A&M AgriLife began researching NIRS technology in the early 1990s. They succeeded at predicting dietary crude protein (CP) and digestible organic matter (DOM), of cattle (Lyons and Stuth 1992) and later with Spanish goats (Leite and Stuth 1995) via fecal profiling. This research was conducted in collaboration with experiment stations at various sites in mid-South Texas, Central Texas, Central Oklahoma, and Central Missouri. Laboratory values were regressed against matched fecal spectra using a Perstorp Scientific 6500 machine equipped with ISI software. Known diet samples from esophageal fistulate were matched with feces of intact cows grazing a wide variety of forages. These diet – fecal pairs were used to develop a reference data set to build prediction or calibration equations. Fecal equation diet quality predictions were then validated against herds with known diet qualities. Equations developed to date have been evaluated in a wide variety of forage types. Currently, the lab can predict dietary CP (%) and DOM (%) as well as fecal nitrogen (FN%) and fecal phosphorus (FP%).

Expanding NIRS

Since the initial NIRS studies concluded, over 30 research projects have been conducted using NIRS technology to analyze the diet quality of cattle, deer, elk, bison, and giant pandas among others. In 1995, the original nutritional balance software, or NUTBAL app, was delveloped to help create a least-cost nutritional management plan by using results from NIRS fecal profiling. NUTBAL through the years has been augmented with further research and is now an online application designed to provide site-specific nutrition recommendations.

Client decision support through the NIRS/NUTBAL system

NIRS results provide forage quality data needed by the NUTBAL decision support software. This software combines the NIRS results with information about animal descriptions (kind, class, breed), body condition, forage conditions, supplemental feed information, environmental conditions and performance targets. Scientists of the GAN Lab use this “NIRS/NUTBAL system” to produce nutritional balance reports for protein and net energy and a report for least-cost feeding solutions. If a deficiency exists, NUTBAL can determine the amount of least-cost feedstuff needed to correct the problem.

These reports can be used by producers, by GAN Lab staff, by trained consultants, and extension personnel to provide advisory reports to clients. Customers of the GAN Lab may purchase a NUTBAL Advisory in addition to the NIRS/NUTBAL System Report. This Advisory is an interpretation of the results and recommendations for nutritional management.

NUTBAL: Livestock nutrition balance decision support system

gabe.saldana · April 25, 2022 ·

NUTBAL: Livestock nutrition balance decision support system

A decision support system that monitors animal diet nutrient concentrations for diet optimization toward producer goals

Animal nutrition-based decision making

NUTBAL’s primary purpose is to provide the livestock industry with the means to monitor the nutrient concentration in the animal’s diet and determine if the current diet is sufficient to meet performance goals set by the producer. NUTBAL is a decision support system that models the crude protein and net energy status of cattle. This computerized decision aid lets the user their herd, environmental conditions, and establish weight performance targets. This information is then coupled with results from a near-infrared reflectance spectroscopy, or NIRS fecal analysis, by scientists at the Grazingland Animal Nutrition Laboratory (GAN Lab). From this, the lab produces an animal performance report and the least-cost nutrition management plan.

NIRS/NUTBAL Reports include the following information:

plane of nutrition
weight gain or loss
the nutrient most limiting animal performance
least cost feeding solution
amount of feed and forage consumed

Go To NUTBAL Online

Fecal sample submission and services

Learn more about the GANLAB

Learn more about NIRS

Unlike many nutritional balance packages geared toward pen-fed animals, NUTBAL calculates what animals will consume ad libitum under grazing conditions. In many cases, voluntary intake of ruminants in free-ranging conditions differs from that predicted by published equations.

Applications

As part of the NIRS/NUTBAL system, NUTBAL is an effective nutritional monitoring tool designed for ranchers and other free-grazing managers. The system generates valuable information that enables the user to make informed and timely decisions regarding animal nutrition and grazing management. Private enterprise applications of the NIRS/NUTBAL system are varied and can be custom designed to meet the goals of the individual user. Most private users employ NUTBAL to assist in one or a combination of:

Improving body condition more economically
Managing weight loss during drought or dormant forage periods
Maintaining desired body condition during critical periods to enhance productivity
Enhancing effectiveness of supplement feeding by identifying when forage is inadequate, when forage quality recovers and how much feed is needed to meet goals

Various agencies and groups also use the NIRS/NUTBAL system as a research tool in developing guidelines for the public, collecting data, and facilitating environmental conservation, to name a few. Such programs include EQIP, CSP, and the Forage Quality & Animal Well-being Program.

Availability

Access to NUTBAL is available via the interactive website, NUTBAL online. The online application allows users to submit their information for NIRS analysis on a livestock fecal sample which is then mailed to the GAN Lab. Once the GAN Lab completes and records the sample’s NIR analysis, the interactive website automatically generates NUTBAL reports for the sample based on data entered by a user. The NUTBAL software is available in a metric unit of measure version as well as the English unit version.

Model Systems

The NUTBAL model uses a combination of published systems including the NRC’s 1984, 1987, 1996 basic nutrient requirements formulas, Fox et al. (1988) adjustments to the NRC equations, McCollum’s rumen degradable protein thresholds and DOM/CP ratio concepts and Moore and Kunkel’s concept of intake change rate and deviation of metabolizable energy due to associative effects in growing animals. Where NUTBAL deviates from other systems is in the application of a quasi-metabolic fill system to predict dry matter intake of the animal. This approach allows modeling of fecal output processes, which consider more than just the digestion process. Other factors are derived from literature review, expert opinion and unpublished data extrapolated from prior studies. Impacts of forage availability, appetite drive, and associative effects can be characterized in both fecal output as a proportion of fat-corrected body weight and metabolizability of ingested forage.

CNRIT Projects

NUTBAL and the NIRS/NUTBAL monitoring system are an important part of several CNRIT projects that include, East Africa LEWS, FRAMS, Mali Livestock, and Pastoralist Initiative, and Mongolia LEWS. While US-based projects tend to focus towards conservation or efficiency issues, international projects benefit from the ability to evaluate and project changes in animal well-being which can be closely followed by changes in the people’s well-being in a region.

Browse CNRIT Projects

FRAMS: Forage Risk Assessment Management System

gabe.saldana · April 25, 2022 ·

FRAMS: Forage Risk Assessment Management System

A dynamic, 24/7, web-based forage risk assessment and management system for the ranching industry

Addressing the threat of drought

Drought represents one of the greatest risks facing ranchers, unlike other livestock industries such as poultry and pork. Because of long-term investment in breeding stocks, unfavorable market prices or demands for land payments, ranchers face tough choices when confronted with decisions to retain livestock and feed, partially destock and feed or sell animals. Currently, the ranching industry has been underserved given the limited tools and techniques made available to the industry to cope with a fluctuation in weather and market conditions. Livestock producers desire ways to explore trade-offs of rotating, selling, replacing or buying animals in response to forage, animal, and market conditions.

Objective

Forage Risk Assessment Management System (FRAMS) is a dynamic risk management decision tool currently in the BETA test phase of development. Its objective is to offer the ranching industry a web-based risk management tool for a forage risk assessment and management system that is available 24/7. The system provides the means to monitor and assess the performance of free-grazing animals, the forage conditions in response to site-specific weather, and the potential least-cost feeding or destocking decisions relative to market and weather risk.

Summary

FRAMS is supported by several automated monitoring procedures. NOAA weather data needed for forage modeling and animal nutrient requirement models are utilized within the system. To assess livestock nutritional status and performance, ranchers would collect fecal samples, enter the animal-related information online and mail them by 2-day priority mail to the Grazingland Animal Nutrition lab (GANLAB) at Texas A&M University for analysis. Forage quality will be determined by NIRS analysis of the fecal sample. The Nutritional Balance Analyzer software (NUTBAL) will use crude protein and digestible organic matter estimates (based on the NIRS scans) to predict animal performance. NUTBAL reports will be provided online to the rancher. FRAMS will also allow ranchers to establish geo-referenced rain gauges online and assign a pre-parameterized reference plant community to them. Each site will be automatically linked to the NOAA 12×12 or 4×4 mile weather grid system where the recorded rainfall input by the rancher is integrated with the solar radiation and temperature data to drive a site-specific forage growth simulation model (PHYGROW) that computes forage deviation from normal and percentile ranking.

Rancher Benefits

Ranchers participating in the FRAMS project will have almost immediate access to the FRAMS system and be able to monitor the status of their ranch after forage surveys of ranch sites are completed and PHYGROW is parameterized for the ranch. Ranchers will enjoy the benefits of using both the NIRS/NUTBAL services and the FRAMS system at no charge (not including postage for mailing in fecal samples). Use of the NIRS/NUTBAL services alone can be valued at approximately $600+ per year. Employing services from other sources that offer similar outputs or results would be several times that amount.

Members of the ALPHA test team found participating in the development of FRAMS to be educational and a benefit to their operation.

History

The first 2-phase, 3-year pilot study for FRAMS has been completed. The first RMA funded study included a group of ranchers (ALPHA testers) in four states representing a diverse set of environmental and production decision environments covering parts of New Mexico, Texas, West Virginia, and Wyoming. Funding for the expansion of FRAMS brought in ranchers from Oklahoma and Louisiana as well as additional ranchers in the original 4 states in 2007 and 2008. Members of the FRAMS design team include ranchers, Texas AgriLife Research’s Ranching Systems Group, extension agents, Natural Resource Conservation Service (NRCS) grazingland conservationists, USDA Risk Management Agency specialists, Grazingland Animal Systems, Inc. and AgriLogic, Inc., to help design and test the system.

Contact Information

For more information, contact Jay Angerer at the Center for Natural Resource Information Technology (CNRIT) at Texas AgriLife Research.

BRASS: Burning Risk Assessment Support System

gabe.saldana · April 25, 2022 ·

BRASS: Burning Risk Assessment Support System

A continuous means for helping land managers assess vegetation, weather and wildfire risk in decision-making related to prescribed burns

Assessing, mitigating wildfire risk

Wildfire risk on rangeland is directly related to the state and condition of the vegetation and weather variables. These communities pose a high potential for wildfires during dry, windy weather conditions. Prescribed burning regimes are increasingly being utilized to mitigate future wildfire risks; however, concerns regarding unpredictable fire behavior have hampered the efforts of land managers and fire professionals. Effective vegetation monitoring and the influence of dynamic weather on potential fire behavior are of key importance in the management of rangelands. The BRASS (Burning Risk Assessment Support System) decision support tool provides a continuous means for land managers to assess vegetation and weather to support decisions related to prescribed burning and/or the risk of wildfire by utilizing near real-time weather conditions and fuel loads.

Illustration showing BRASS spatial layers

General System Overview

The key management objective of BRASS is to provide a decision support tool to aid in the management of rangelands, prescribed fires and wildfires, and livestock grazing. The system is comprised of three parts, 1) a near-real-time plant growth model, PHYGROW, that is updated daily utilizing current and historical weather conditions from the National Oceanic and Atmospheric Administration (NOAA), 2) a fire simulation model called PHYRESIM, that uses the same core functionality that drives the highly respected BEHAVE Plus burning application, 3) a fire growth model called PHYREFLY that is a derivative of the FARSITE fire growth simulator.

The PHYGROW model provides the fuel loads and live moisture contents on a daily basis for each vegetation polygon in the project area. The PHYRESIM model is then run for each vegetation polygon to simulate dead fuel moisture, spread rate, flame height, and fire intensity. A fire danger advisory map is produced from these values for a forecasted time period of 6 days. The PHYREFLY fire growth model is run on-demand when a fire is presently using the latest available data to predict the area covered by a fire at 30-minute intervals. The PHYREFLY model can be used to simulate fires for a period of up to 6 days.

Interface

Two interfaces for the models were developed to allow maximum flexibility in model usage. The primary interface is a web-based application that can be accessed through a web browser from any computer. The application can be customized to address the needs of the project through the Common Web Interface management software. The software allows for the customized presentation of data, map layers, and user interactivity controls. The second interface is an ArcGIS toolbar application that can be integrated with ESRI products and third-party add-ons to support a multi-tiered customized desktop environment.

LMIS: Livestock Marketing Information System

gabe.saldana · April 25, 2022 ·

LMIS: Livestock Marketing Information System

Developing reliable, timely livestock market information for the development of East African countries

A need for reliable, timely market information

The livelihood of a vast majority of people in eastern Africa is highly dependent on income from livestock and livestock products. Therefore, the development of reliable and timely livestock market information is vital for the development of the countries in the region and provides a basis for livestock producers and traders to make marketing decisions.

The Problem

In the past few years, the urgency to address the needs of livestock-keeping communities in eastern Africa has risen dramatically, prompting national governments, NGOs and international donors to explore high impact interventions. Given the high dependency of livestock keepers’ family livelihoods on cash income from the sale of livestock and livestock products, the institutional focus has been directed toward improving livestock market information, infrastructure, and efficiency.

An extensive review of the wide array of livestock market development activities in eastern Africa revealed a lack of viable livestock market information system to support decision making of traders, producers and policymakers. A reliable market information system creates transparency and a basis for the livestock keepers to make marketing decisions.

Steps Toward A Solution

With funding support from USAID, the Livestock Information Network and Knowledge System (LINKS) of the Global Livestock Collaborative Research Support Program (GL-CRSP) has developed an Information Communication and Technology (ICT) system to extend the technical and human capacity to meet livestock information needs to support decision-making for livestock producers, traders, and policymakers in East Africa.

Using a partnership approach with existing livestock marketing institutions in the eastern Africa region, LINKS has designed and is delivering a livestock information and communication technology that provides monitoring and analysis technology to foster strategic partnerships between livestock keepers, markets and policy. Autonomous systems with near real-time databases have been established in Kenya (http://www.lmiske.net) Tanzania (http://www.lmistz.net) and Ethiopia (http://www.lmiset.net). Additionally, given the cross-border nature of livestock trade, the project offers a regional framework where countries involved can collaborate, network and share experiences.

How it Works

One of the major aims of the LINKS project is to determine the application of and usefulness of integrated spatial, information, and communications technologies in improving the livestock market information infrastructure in eastern Africa.

The LINKS project is built around emerging information technology coupled with spatial models of livestock movement and expected prices and volumes at secondary and terminal markets to add value to the market information system.

The spatial information and communication toolkit includes Global Positioning System (GPS), mobile phones, Worldspace radios, computing analysis, and web-based platforms. Integration of these tools makes it possible for the system to carry out market chain analysis indicating the source of animals, the time taken to truck them and the associated costs of getting them to designated markets.

Obtaining Market Data

Market monitors are trained in the use of livestock market data collection formats and are given instructions and guidance on the proper ways of approaching sellers, brokers, and traders to collect reliable data in an effective way. The monitors are provided with mobile phones and scratch cards to enable them to send the collected data to the database system.

Livestock prices and volumes are collected through interviews during the peak of a market day. A trained livestock market monitor interviews five cases of each of the dominant breed, class and grade combination of animal species on that market day. Average prices by animal kind, breed, class, and grade is then calculated along with the total volumes of livestock by animal kind and the information is coded and sent into the database system using SMS, e-mail or posted directly on the web into the database system.