Nov 11, 2022
Version 1
Methodology for Frontier Model of the Environmental Inefficiency effects on Livestock Bioeconomy V.1
- 1National Autonomous University of Nicaragua, Leon. Bioeconomy and Climate Change Research Center
Protocol Citation: C A Zuniga-Gonzalez 2022. Methodology for Frontier Model of the Environmental Inefficiency effects on Livestock Bioeconomy. protocols.io https://dx.doi.org/10.17504/protocols.io.5jyl8jpn9g2w/v1
Manuscript citation:
Zuniga-Gonzalez, C. A. J. L. Jaramillo-Villanueva. Frontier Model of the Environmental Inefficiency effects on Livestock Bioeconomy. [version 1; peer review: awaiting peer review]. F1000Research 2022, 11:
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: November 11, 2022
Last Modified: November 11, 2022
Protocol Integer ID: 72604
Keywords: SFA, Bieoconomic model, Efficiency
Abstract
This protocol considers the methodology used for Stochastic Frontier Analysis on the Livestocks Bioeconomic model. The applied methodology measured the eco-efficiency of Mexican dairy farms using the empirical application of a stochastic frontier bioeconomy model. The productive sector of the bioeconomy studied was the Eco-intensification of the livestock production system (dairies).
Guidelines
For this protocol is very important to consider the theoretical bioeconomic model when the methane variable is included. software used in this process is very important for organizing the data and collecting information.
Materials
Frontier version 4.1 software
Questionary
Transport
PC and Laptop
Team of researcher
Before start
If necessary define the bioeconomy model. For this is very important the design of the questionary according to the file data required in the software Frontier 4.1.
Stochatics Frontier Analysis
Stochatics Frontier Analysis
Collecting Data. The study was carried out in 102 cattle farms for milk production in six municipalities of the state of Tlaxcala Mexico, in 2020. The production units were randomly selected, and dividing them among the four regions of importance in dairy production in the state of Tlaxcala. The questionnaires contained technical information, owner information, and economic data. In addition, 102 dairy cattle farms were monitored. For the collection of data, the following procedure was followed:
an a) identification of the areas of the state with the highest volume of milk production.
b) identification of the production units present in the study areas, which is located in the Mexican Altiplano and at the geographic coordinate’s 98°43'' west longitude and 19°44' north latitude, and 97°38' east longitude and 19° north latitude. 06 south latitude. The prevailing climate in the state is sub-humid temperate with summer rains. The average altitude of the study region is 2,200 meters above sea level.
c) design and application of a questionnaire to collect information, which considers the variables that will be included in the model of efficiency and inefficiency.
d) analysis of the data obtained. Before running the software is necessary to consider 3 files. The first is the data, the second is the instruction and the third is the results or outputs. The FRONTIER Version 4.1 computer program (Battese & Coelli, 1988, 1,992 and 1995) was used to obtain a maximum likelihood estimate (MLE) of the selected data in the study period; this is raised in the literature review section. The first model is the Stochastic Frontier Model for a Livestock Bioeconomy System.
The second is the Model of Technical Production Inefficiency for a Livestock Bioeconomy System: Environmental Condition and variability in Climate Change.
Hypothesis to be tested: If the inefficiency model is stochastic, then the technical efficiency of the dairy farm system can be explained by the Stochastic Frontier model for a Livestock Bioeconomy system influenced by climate change variability (Greenhouse Gas Emissions).
Stochastic Frontier Analysis running Frontier 4.1, implies organizing the information in figures and tables according to the purpose of the research.