Animal Feed Formulation Using Dynamic Nitrogen-Energy Modeling
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Solution Overview
Problem
Existing feed formulations often provide too much or too little of nutritional elements, leading to waste, delayed growth, and inefficient tissue growth outcomes due to the interplay of physiological factors affecting nutrient metabolism in animals.
Innovation Solution
A dynamic model that predicts energy provided by animal feed diets, separating nitrogen into essential and non-essential components, and dynamically adjusts feed formulations based on real-world data inputs, including animal measurements, geographical, and environmental conditions, to optimize tissue growth and reduce waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feed formulations are used based on feed conversion ratio and estimated energy needs, then feed can be provided to meet basic nutritional requirements, but the formulations provide too much or too little of nutritional elements leading to waste and inefficient tissue growth
Solution Approach 1:
The patent implements dynamic adjustment of feed formulations based on real-time monitoring of animal physiological factors including body composition, growth rate, and metabolic status. The system continuously updates nutrient requirements and adjusts feed composition accordingly, transitioning from static traditional formulations to dynamic personalized feeding strategies that optimize tissue growth efficiency while minimizing nutritional waste.
Solution Approach 2:
The system changes multiple parameters simultaneously including protein content, energy density, amino acid profiles, and feeding frequency based on measured animal responses. By adjusting these nutritional parameters dynamically according to actual growth outcomes and physiological measurements, the system optimizes the balance between growth efficiency and waste reduction.
2Quantity of substance
If increasing the amount of protein in food supply is provided, then more nutritional ingredients are available, but it does not necessarily lead to increased metabolizing of protein by growing animals due to interrelated physiological factors
Solution Approach 1:
The system employs feedback mechanisms where animal performance data including growth rate, body composition changes, and protein deposition efficiency are continuously monitored and fed back to adjust future feed formulations. This closed-loop control ensures that protein quantity in feed is optimized based on actual protein metabolism efficiency and tissue deposition rates, preventing both deficiency and excessive supplementation.
Solution Approach 2:
The patent applies local quality by customizing feed composition for specific anatomical regions and tissue types being targeted for growth. Different protein sources and amino acid profiles are provided based on the specific metabolic needs of different tissue types, ensuring that protein is metabolized efficiently for the intended growth outcomes rather than being uniformly distributed without regard to physiological requirements.
3Measurement precision
If detailed nutritional analysis is performed, then nutritional requirements can be evaluated, but interrelated physiological factors still affect how fast and how many nutritional ingredients are metabolized and converted into energy
Solution Approach 1:
The system enables animals to effectively 'self-regulate' their nutrient intake by using real-time monitoring of their own physiological parameters to dynamically adjust their feed allocations. The automated system continuously measures body composition, growth rate, and metabolic indicators, then automatically adjusts feed formulations without manual intervention, ensuring that nutritional analysis accuracy translates into reliable growth predictions through continuous adaptive optimization.
Data Source
AI summary
Disclosed are various approaches for determining an animal feed formulation. In some implementations, a system may obtain data that provides characteristics of an animal population located at a site. The system may identify a target growth schedule for the animal population. The system may use a data processing model to predict nutrition requirements for tissue growth in the animal population, using the target growth schedule and the characteristics of the animal population, as the model predicts nutrition requirements based on projected nitrogenous energy requirements for the tissue growth. The system may identify a feed formulation producible from a combination of animal feed ingredients to satisfy the nutrition requirements for tissue growth. The system may generate a data output for use of the feed formulation with the animal population, the data output indicating the identified feed formulation producible from the combination of animal feed ingredients.