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Decision support through data and computer modeling

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decision support

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, sheep, and goats.  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 Lab, GANLAB. 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:

  • Monitoring a reproductive herd’s nutrition year-round winter feed management.
  • Monitoring nutrition of grazing stockers.

Developing and managing replacement breeding stock with grazing protocols that correspond to the goals of NUTBAL users might include:

  • 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
  • Meeting weight gain goals
  • Others

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

Farmer taking manure sample

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 GANLAB.  Once the GANLAB 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.

Go to NUTBAL Online
Fecal sample submission and services

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.

Go to NUTBAL Online

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

Mali Livestock and Pastoralist Initiative

gabe.saldana · April 25, 2022 ·

Mali Livestock and Pastoralist Initiative

Access to technology, capacity building for a stronger livestock system in Mali

Mali livestock and pastoralist initiative logo
Mali livestock and pastoralist initiative logo

A mission to improve the Mali livestock system

USAID-Mali has identified an overall goal to “improve the productivity and income of the producers in Mali by enabling them to access technologies and build the capacity of all actors involved in the development of an extensive livestock system”.  To meet this goal, USAID-Mali has outlined these specific objectives:

  1. Promote the development of the extensive livestock sector,
  2. Empower pastoralists and improve their capacity for risk management,
  3. Create equitable livestock information and communication systems that provide monitoring and analysis technology to foster strategic partnerships between pastoral communities, markets, and policy,
  4. Markets development and integration,
  5. Build capacity of Mali to sustain the new techniques and technologies.

To meet this overall goal and the specific objectives, the Mali Livestock and Pastoralist Initiative project was initiated in 2008.  The project is led by Texas AgriLife Research with US partners that include Syracuse University, University of Arizona, University of Wisconsin, and South Dakota State University.  Government and educational partner organizations in Mali include Observatoire du Marche Agricole (OMA), Direction Nationale des Productions et des Industries Animales (DNPIA), Institut d’Economie Rurale (IER), and l’Institut Polytechnique Rural (IPR).

Phygrow: Phytomass growth model

gabe.saldana · April 25, 2022 ·

PHYGROW

A decision support system for above-ground herb and shrub growth, forage consumption and hydrologic processes

PHYGROW overview

graph illustration

PHYGROW, short for phytomass growth model, is a daily time step computation engine that models above ground herb and shrub growth, forage consumption, and hydrologic processes.  It is capable of modeling the growth dynamics of many plant species competing for limited resources while modeling grazing by herbivores in competition for forage resources.

Phygrow is a point-based, daily time step, algorithmic or computation engine that models above-ground plant growth, forage consumption and hydrological processes.  The model was first coded in 1990 and has undergone many enhancements since that time. The model’s original computation algorithms are a mixture of formulas adapted from other plant growth models — CREAMS, GLEAMS, EPIC, WEPP, SPUR, CENTURY, ERHYM-II — as well as the biological relationship from grass tiller level research and dietary selection conducted at Texas A&M University.

The model is capable of stimulating the growth of multiple species of plants subject to selective grazing by multiple animals on soil with multiple layers for indefinite periods of time.  Phygrow is designed to be integrated with a wide variety of weather databases, vegetation databases, and stocking rule databases. It provides output for a wide variety of data sources and formats including all relational databases, NetCDF file formats, commas-separated and tab-delimited file formats, and linkages to other models and the internet using Python, Perl, and Java web applications.

Data Requirements

Phygrow requires 4 primary data sources to run: soil, weather, grazers, and plant data. Consequently, due to the lack of available sources for plant parameters, the largest repository for such data is the database link to PHYGROW. For soils, the system was primarily designed to work with data from SSURGO or STATSGO, however, it can use soil data from any source, provided the attributes needed to run the model are supplied.  Grazing information is usually collected in the field by survey crews and entered directly into the model’s parameter database.  Weather data can be pulled from numerous sources.  We primarily use products from NOAA, however, we can use data collected from weather stations and other various sources.

Final data input to the PHYGROW model is required to be in the form of a comma-separated values (CSV) ascii file with a defined data format.  However, many data import interfaces have been written for the PHYGROW computational engine that allows for source data to be input via an interactive web page (eg. PhyWeb 2.0) or imported from existing database systems or spreadsheets.

Technology

The model has a unique capability to be started and stopped at any point in the computational process to allow full integration with data acquisition systems, automation systems, and/or other models. The PHYGROW model engine is written in the C++ programming language and uses an object-oriented design, thus allowing high efficiency in the incorporation of new scientific relationships when necessary. Because of the start-restart features of the model, simulations can be integrated at various spatial scales in terms of explicit areas across a landscape, or in a virtual landscape representing multiple plant communities and soil combinations via a spatially explicit multiple run mode.  The PHYGROW model can be run in an automated environment across multiple platforms most Linux environments and as a standalone application in Windows 10 and later.

The primary system located at CNRIT utilizes a distributive computing environment.  This allows streaming of data from many weather and soil database sources thus allowing near real-time computation of plant growth.  At the other end of the equation, other entities around the world can write applications that access the output from PHYGROW, or the output can be sent to other distributed computing systems around the world.

PHYGROW does not require the use of commercial or customized data storage systems. However, the model can use both commercial and customized data storage systems.  All the tools and middleware for the automated systems are developed with non-proprietary software.  The way the data is acquired, stored and output is dictated by the needs of the user. That is, what kind of weather data, the number of plant community/soils/grazer/weather combinations to be modeled, the frequency of reporting, the required linkages to other applications (e.g. actuarial, insurance companies, RMA) and the nature of the output (graphical, text or both on the web, ftp or other) will determine the design and functionality of the automation process.

How to Get and Use Phygrow

For information about the software, please contact Javier Osorio Leyton, Ph.D.  A user guide is also available for download.

PLEASE NOTE: if you will be running Phygrow on a Microsoft Windows computer to test your model runs and then you also run the same model on a UNIX machine, you will see slight variations in the model output. This is due to the different ways in which floating point arithmetic is handled in Windows C++ compilers versus UNIX C++ compilers. It amounts to several decimals places deep rounding error, but the errors can compound to show visibly differing results for the same model. Phygrow has been developed and tuned against the UNIX results because this version does not truncate the floating point numbers arbitrarily and therefore produces the most accurate numbers.

View Phygrow User Guide
Go to PHYWEB
Reach out for Phygrow Info

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