An Integrated Precision Production and Environmental Management Analysis of a Kentucky Dairy Farm

This study compares and contrasts the profitability of different dairy management practices through precision livestock farming. Feed analysis and crop yields were simulated. The proposed alternative feeding program demonstrated less manure and nutrient excretions. When mathematical programming model was employed, uniform rate application manifested the highest selected economic values.Management practices, environmental, Environmental Economics and Policy,

Menu Planning Model for Malaysian Boarding School Using Self-Adaptive Hybrid Genetic Algorithms

Malnutrition problem is the gravest single threat to the world's public health today. Statistics have showed that the number of under-nourished and over-nourished children and adolescents is increasing day by day. Thus, proper menu planning process among menu planners or caterers is important to avoid some diet-related diseases in the hture. Manual calculation of menu planning is unable to consider macronutrients and micronutrients simultaneously due to complexities of data and length of time. In this study, self-adaptive hybrid genetic algorithm (SHGA) approach has been proposed to solve the menu planning problem for Malaysian boarding school students aged 13 to 18 years old. The objectives of our menu planning model are to optimize the budget allocation for each student, to take into consideration the caterer's ability, to llfill the standard recommended nutrient intake (RNI) and maximize the variety of daily meals. New local search was adopted in this study, the insertion search with delete-and-create (ISDC) method, which combined the insertion search (IS) and delete-and-create (DC) local search method. The implementation of IS itself could not guarantee the production of feasible solutions as it only explores a small neighborhood area. Thus, the ISDC was utilized to enhance the search towards a large neighborhood area and the results indicated that the proposed algorithm is able to produce 100% feasible solutions with the best fitness value. Besides that, implementation of self-adaptive probability for mutation has significantly minimized computational time taken to generate the good solutions in just few minutes. Hybridization technique of local search method and self-adaptive strategy have improved the performance of traditional genetic algorithm through balanced exploitation and exploration scheme. Finally, the present study has developed a menu planning prototype for caterers to provide healthy and nutritious daily meals using simple and fhendly user interface

Evolutionary algorithms with average crossover and power heuristics for aquaculture diet formulation

The aquaculture farming industry is one of the most important industries in Malaysia since it generates income to economic growth and produces main source of food for the nation. One of the pillars in aquaculture farming industries is formulation of food for the animal, which is also known as feed mix or diet formulation. However, the feed component in the aquaculture industry incurs the most expensive operational cost, and has drawn many studies regarding diet formulation. The lack of studies involving modelling approaches had motivated to embark on diet formulation, which searches for the best combination of feed ingredients while satisfying nutritional requirements at a minimum cost. Hence, this thesis investigates a potential approach of Evolutionary Algorithm (EA) to propose a diet formulation solution for aquaculture farming, specifically the shrimp. In order to obtain a good combination of ingredients in the feed, a filtering heuristics known as Power Heuristics was introduced in the initialization stage of the EA methodology. This methodology was capableof filtering certain unwanted ingredients which could lead to potential poor solutions. The success of the proposed EA also relies on a new selection and crossover operators that have improved the overall performance of the solutions. Hence, three main EA model variants were constructed with new initialization mechanism, diverse selection and crossover operators, whereby the proposed EAPH-RWS-Avg Model emerged as the most effective in producing a good solution with the minimum penalty value. The newly proposed model is efficient and able to adapt to changes in the parameters, thus assists relevant users in managing the shrimp diet formulation issues, especially using local ingredients. Moreover, this diet formulation strategy provides user preference elements to choose from a range of preferred ingredients and the preferred total ingredient weights

Multiobjective Optimization to Optimal Moroccan Diet Using Genetic Algorithm

Proper glucose control is designed to prevent or delay the complications of diabetes. Various contexts can lead to a fluctuation of the blood sugar level to a greater or lesser extent. It can be, for example, eating habits, treatment, intense physical activity, etc. The feeding problem interpolated by a minimum cost function is well-known in the literature. The main goal of this paper is to introduce a multiobjective programming model with constraints for the diet problem with two objective functions, the first of which is the total glycemic load of the diet while the second objective function is the cost of the diet. the MOGA (multiobjective Genetic Algorithm) algorithm was used to resolve the proposed model. The experimental results show that our system ([proposed model – MOGA]) is able to produce adequate diets that can settle glycemic load and cost while respecting the patient\u27s requirements

Risk management on application of minimum-cost feed ration for nitrogen and phosphorus reduction on dairy farm

The traditional mathematical programming model with the objective function of feed ration cost minimization is used to accommodate risk management responses to price variability associated with feeding a particular feed ration over time. The model incorporated biophysical simulation data using Cornell Net Carbohydrate and Protein System (CNCPS) software in addressing nutrient requirements and excretions. In addition, it used historic feedstuff prices in a mean-variance (E-V) framework analysis. The optimized seasonal feeding indicated to have a lower mean ration cost and lowest nutrient loading followed by optimized uniform feeding program. The feed cost optimization proved to be a better strategy in minimizing ration cost and reducing excretions both manure and nutrients. The results in this study can be used as guidelines for making nutrient. The information in this study can be used by a producer facing feed price risk to select optimal ration while reducing environmental pollution.Risk and Uncertainty,

Selection of Food Items for Diet Problem Using a Multi-objective Approach under Uncertainty

It is a problem that concerns us all: what should we eat on a day-to-day basis to meet our health goals? Scientists have been utilizing mathematical programming to answer this question. Through the use of operations research techniques, it is possible to find a list of foods that, in a certain quantity, can provide all nutrient recommendations in a day. In this research, a multi-objective programming model is provided to determine the selected food items for a diet problem. Two solution approaches are developed to solve this problem including weighted-sums and ε-constraint methods. Two sources of uncertainty have been considered in the model. To handle these sources, a scenario-based approach is utilized. The application of this model is shown using a case study in Canada. Using the proposed model and the solution approaches, the best food items can be selected and purchased to minimize the total cost and maximize health

Mathematical modelling in animal nutrition: a centenary review

A centenary review presents an opportunity to ponder over the processes of concept development and give thought to future directions. The current review aims to ascertain the ontogeny of current concepts, underline the connection between ideas and people and pay tribute to those pioneers who have contributed significantly to modelling in animal nutrition. Firstly, the paper draws a brief portrait of the use of mathematics in agriculture and animal nutrition prior to 1925. Thereafter, attention turns towards the historical development of growth modelling, feed evaluation systems and animal response models. Introduction of the factorial and compartmental approaches into animal nutrition is noted along with the particular branches of mathematics encountered in various models. Furthermore, certain concepts, especially bioenergetics or the heat doctrine, are challenged and alternatives are reviewed. The current state of knowledge of animal nutrition modelling results mostly from the discernment and unceasing efforts of our predecessors rather than serendipitous discoveries. The current review may stimulate those who wish for greater understanding and appreciation

Teaching Linear Programing in Mathematics Education to Improve Human Health

The 1984 World Health Organization (WHO) defines health as "the extent to which an individual or group is able to realize aspirations and satisfy needs, and to change or cope with the environment.” Health is a resource for everyday life. It is a positive concept emphasizing social and personal resources, as well as physical capacities. To maintain human health is a complex matter, thus the need to apply mathematics education, and in particular, linear programming knowledge and skills, in order to bring up a healthy community. Linear Programming is about making maximum benefit or minimum loss out of limited resources in daily life. Applications of linear programming date back to 1930 and were first attempted by the Soviet mathematician Leonid Kantorovich and by the American economist, Wasilly Leontief. Linear programming is applied in many health programs. These include; application of linear programming in health care, in the most affordable heath diet, in surgery, menu planning, food production and in feeding. Linear programming is used by farmers to determine how much space to be used for each crop especially when practicing mixed farming and for optimal health care resource allocation. Linear programming is also used in home health care and medical services. It is used in radiation therapy treatment, for menu planning in restaurants and in nurses scheduling. It is therefore recommended that the topic linear programming be taught to all Kenyan students irrespective of what career they hope to pursue. This will go a long way in enabling the Kenyan society to maintain good health at minimum cost

Optimising economic, environmental, and social objectives: a goal-programming approach in the food sector

The business-decision environment is increasingly complicated by the emergence of competing economic, environmental, and social goals, a notion typified by the current pressures of global economic instability and climate-change targets. Trade-offs are often unclear and contributions by different actors and stakeholders in the supply chain may be unequal but, due to the interdependencies between businesses and stakeholders in relation to total environmental or social impact, a whole chain, simultaneous, and strategic approach is required. After a review of relevant literature and the identification of knowledge gaps, the author introduces and illustrates the use of goal programming as a technique that could facilitate this approach and uses real case evidence for alternative food supply chain strategies, at local, regional, and national levels. It is shown that the method can simplify a complex simultaneous decision situation into a useful and constructive decision and planning framework. Results show how a priori beliefs may be challenged and how operational and resource efficiency could be improved through the use of such a model, which enables a broad stakeholder appreciation and the opportunity to explore and test new environmental or social challenges

Developing the Spatial Dimension of Farm Business Models

A non-linear mathematical farm business optimisation model, that is set within a spatial economic framework, has been developed. The model incorporates factors such as location, spatial market orientation and technology use, and identifies the business strategy that is optimal in different market and policy environments. Farm household time-use is incorporated centrally within the model, enabling it to examine how on-farm and off-farm activities compete for limited farm household human resources. The model is applied to a beef and sheep farm that can choose between selling livestock to meat processors or processing on-farm and selling direct to consumers. Model simulations reveal when it is optimal for the farm business to innovate in this way and how this decision is affected by changes in key parameters. The farm business model is solved using the GAMS/LINDOGlobal mathematical programming software package. While traditional nonlinear programming and mixed-integer nonlinear programming algorithms are guaranteed to converge only under certain convexity assumptions, GAMS/LINDOGlobal finds guaranteed globally optimal solutions to general nonlinear problems. The model and model results are discussed within the context of theoretical underpinnings, model tractability, and potential applications.Farm Management,