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An Integrated Model of Forage, Meat and Emission in a Beef Cattle Production System

Muhammad Shahinur Alam
Dept. of Agricultural Engineering, Sher-e-Bangla Agricultural University (SAU), Dhaka-1207

Pablo Andres Segovia
Managing Director at Legro America, Legro Potgrondbedrijf BV, Netherlands

Tanjina Islam
Dept. of Horticulture, Sher-e-Bangla Agricultural University (SAU), Dhaka-1207

Kowshik Kumar Saha
Farm Machinery and Postharvest Process Engineering Division, Bangladesh Agricultural Research Institute (BARI), Gazipur-1701, Bangladesh

Abstract/ Executive Summary:

The beef cattle production system is a complex system as it includes biological, economic and social factors which are also individually intricate in nature. In this approach a simplified relationship and interactions between various factors that control the system have been presumed to develop the integrated model of meat production, use and storage of forage, manure excretion and emission. Three major subsystems namely meat production, forage management and emissions were described separately and finally combined into a single system to simulate the system output. A dynamic Monte Carlo simulation software (GoldSim) has been used to simulate the system. Various management scenarios are considered to see the model performance and uncertainty.

Keywords: Beef Cattle, Forage, Simulation, Biological systems and GoldSim

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End Date:

Program Area: Socio-economic and Policy

Commodity: Beef, Cattle

Objectives:

The specific objectives of this study are to introduce the GoldSim modelling software for the purpose of simulating a complex system like beef cow production and to see the usability of the methods for further precise modelling of the system.

Methodology:

Model conceptualization For the beef cows we considered a standard farm (400 ha) with a fixed ratio between young and mature cows. The feed uptake and weight increment for both groups are almost constant over the year with a slight seasonal variation. Similar assumptions are made for the manure production and the composition of manure. Besides the accumulation of animal weight, the model has also accumulation functions for manure and the amount of grass and corn in stock. The model was developed to be able to predict the feed stock, the increase of weight, the slaughtering moments and also the manure production. An imaginary structure of a standard beef cow production system comprising three subsystems of meat production, feed stock and emission were constructed. The basic constrained variable was considered as the available land resources. The initial input into the system as young cows was then determined considering the capacity and profitability of the system with a fixed ratio between young and matured cows. In this study following assumptions are made to construct the model:1. Only beef cows are in the system and there is no variation of growth and feed uptake of same weight cows. 2. Cows are only fed with grass and corn. 3. Similar assumptions for production and composition of manure. 4. Off-farm activities including Nitrogen fertilizer production, transport and application are not included in the model. 5. No loss of animal or crop due to unexpected reason. A mathematical model let us demonstrate the interactions between the elements of the system. With this information an optimal productivity of the system would be guaranteed. Simulations of the system with different quantity variables are going to be presented in order to let the decision makers, choose the best option for the system that now we are analyzing.
Energy requirement for beef cattle In animal production, the energy for maintenance is higher than the energy for growth. For that reason net energy values must be separated on two kinds, energy for physiological functions and energy for gaining weight.According to Lofgreen and Garrett (1968), for measuring the energy for maintenance, it is important to know the amount of energy required for heating a fasting animal. Heat production in animals depends on the basal metabolism, the heat increment and the heat produced by the activity. On the other hand, the net energy required for growth is considered as the energy used to gain weight. There are different values to be considered on heifers and steers because of the body and metabolism conditions. The net energy requirement for maintenance and the net energy requirement for gaining weight can be calculated with the following equations (Lofgreen and Garrett, 1968). Feed uptake The determination of the requirements of the animals is the most important task for establishing a diet. Cow population We had many options to select the number of cows and their age of entering into the system. We did some scenario study taking into account the number of cows and their ages and periodic input of different ages of cows throughout the year. As we have a limitation of area, we assumed that the maximum number of total cow population never exceed 350 (Items). The number of young cows and matured cows can be adjusted for maximizing the profit in different situations and according to the availability of grass and corn and the requirements of the market. Feed storage In the model, we assumed only grass and corn produced in the farm are used as feed. The grass is harvested twice in a year and the corn just once. “Grass and Corn” is a different subsystem and we incorporated the result of that subsystem into our model. The necessary amounts of grass and corn are stored so that cows can be continuously feed up until the next harvesting time. We assumed 50% of the area is used to produce grass and the other 50% is to produce corn. The grass and corn are used in bio refinery and for feeding the cows. There is no fixed ratio but depends on our model prediction that how much grass and corn we need to store to feed the cows. After harvesting, the grass and corn are needed to be stored to feed the cows throughout the season. The percentage of total production of grass and corn needed to be stored at the time of harvesting depends on the number of cows and their stage of growth. The model simulates the amount of grass and corn to be stored during the first and second harvest. Initially we need such an amount of grass and corn in the storage so that it becomes to zero at the next harvesting time. We stored the amount of grass and corn in order that after the season we can have the same amount of storage as it was during the beginning of the season. Manure production The annual manure production by grazing animals is difficult to determine, therefore the amounts are based entirely on estimations and consulting with experts. The quantities of manure excretions for the cattle subsystem are proportional to the number of cows and its weights. For that reason we considered the two different stages of animals that are part of the subsystem (young and matured animals) as two Integrated model in a beef cattle farming system parameters for manure. In the case of young cows we assumed the amount of 6 kg of manure per day, for a range of animals between 100kg and 250kg. Also, it is considered 21kg of manure per day for animals between 300kg and 450kg. Steps for the modelling GoldSim modelling software has been used to construct the model. To reach the objective, we took into account values we obtained from the calculations using equations. In some cases we used conversion factors, used some parameter values from literature study and from the consultation with the experts. After constructing the main model we studied the scenario analysis by changing important variables and in some cases we calibrated the model by trial and error method. Figure 02 shows the combined model with the relationship of each major subsystem.

Source of Information: J. Biosci. Agric. Res. 16(02): 1337-1347

Article Link (Online Journal): https://doi.org/10.18801/jbar.160218.166

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Achievement/Findings:

To make a complete model it is very important to find the accurate data and equations. In this study we conquer our objective that was to learn the way to analyze a complex system using the Goldsim modelling tool. Only the most important variables were included in the model, therefore, the results obtained may not match perfectly with real life situation but according to the information described, we can analyze data results that demonstrate the development of a field. The objective has been obtained according to the tools and parameters established. Nevertheless, for a deeper exercise, is possible to go more in detail. Modelling with complete information allows the analyst to show reasonable results for the decision maker. This hypothetic case gives the experience to approach a system and optimize it with the correct use of mathematical equation. Recollecting the best information and determining correctly the assumptions would guarantee the best results to the process.

Knowledge Management: Journal