Performing Department
(N/A)
Non Technical Summary
Beef cattle production is the most important agricultural industry in the US, consistently accounting for the largest share of total cash receipts for agricultural commodities. In 2021, cattle production represented about 17% of agricultural commodities' $391 billion cash receipts.The cattle feeding industry in Texas is a significant contributor to the agricultural commodity receipts of the US, contributing approximately $10.5 billion annually (Texas Department of Agriculture; https://www.texasagriculture.gov). Texas has the largest cattle-feeding region in the US, with about 3 million cattle on feed distributed among its feedlots. In addition, the state has approximately 10 million cows, heifers, steers, bulls, and calves on feed every year. An ever-increasing body of literature shows that erratic feed intake, particularly by feedlot animals,can cause harmful problems to the animals' healththat undeniably reduce production, profitability, and also welfare in the short term,but become unsustainable in the long run due to the excessive use of resources and animals' underperformance. There are management strategies that can mitigate and, for the most part, eliminate these deleterious problems, but feed intake amount and pattern must be known. Feed efficiency (i.e., the ratio between animal weight gain and feed intake) is a prerequisite to achieving sustainable livestock intensification to satisfy the demand for beef.Thus, knowing the feed intake of feedlot animals will facilitate the 1.) enhancement of animals' health and production, 2.) selection for more efficient animals and the reduction of resource use, and 3.) development of a fairer system for allocating (and charging) feed share of group-fed animals of different ownerships.The overarching goals of the state-of-the-art PLF facility at TAMU are: i.) to support research based on artificial intelligence (AI) and smart agriculture technologies in collaboration with USDA-NRCS-funded Texas Climate-Smart Initiative Plan, ii.) to provide means for teaching undergraduate and graduate students interested in cutting-edge technologies (e.g., sensor, AI) in animal agriculture, and iii.) to demonstrate the potential of PLF-based cattle management for all stakeholders of the beef cattle industry by mimicking their production limitations and conditions (e.g., internet connectivity, data storage, workforce capacitation).
Animal Health Component
0%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Our long-term research goal is to integrate sensor-based data streams related to precision feeding and health monitoring sensors into existing and newly developed decision models to implement real-time management decisions.
Project Methods
Most of the facility's renovation, including planning and design, compliance with requirements, and installations, will be conducted in the first year, as most of the buildings can be done without requiring modifications in the foundation or structure of the facility. The research facility is ready for these additions, and we already have contract estimates of the expenses and changes needed for ethernet cables and power wires, as shown in the Budget Narrative. Equipment installation will be performed in Year 1, and data collection, analysis, and publications will be conducted in Years 2 and 3. We plan to collect data as the equipment becomes available and operational.