Frontier model of the environmental inefficiency effects on livestock bioeconomy

Abstract

Background: This work was focused on measuring environmental inefficiency in Mexican dairy farms, considering climate change variables related to the emission of greenhouse gases (GHG) and planetary geomagnetic activity. Methods: The applied methodology measures the eco-efficiency of Mexican dairy farms using the empirical application of a stochastic frontier model of the bioeconomy. The productive sector of the bioeconomy studied was the eco-intensification of the livestock production system (dairies). The environmental inefficiency effect was assumed to be a distribution-independent truncation of a normal distribution with constant variance, while the mean was a linear environmental function of the observable variable. Results: The results showed that the coefficients of the frontier model were highly significant, highlighting the investment in livestock (50%). The inefficiency model had an impact on climate variation with greenhouse gas emissions CH4 (1.96%). The results of the environmental technical efficiency in geometric average were 81.28%. The producers that reached the border with a technical efficiency equal to 1 are the references for the rest, marking the relative technical efficiency. Conclusions: It was concluded that the coefficients in the model were very significant, showing the level of investment in livestock (50%). The low-performance model estimates the impact of climate change on GHG emissions CH4 (1.96%) explaining the trend of increasing GHG emissions, keeping in view that the management of food and cattle during the study period were affected by summer feeding, which allowed considering the activity of GHG emissions. According to the results, the geometric mean environmental performance of engineering is 81.28%.

Inputs-Oriented VRS DEA in dairy farms

“…This enabled us to plot u 0 y i /v 0 x i represents an M-byM-by-1 vector of output weights, v represents a K-by-1 vector of input weights or proportions. 7 The outcome of this calculation, u 0 y i /v 0 x i represents the efficiency (ϕ) measured as a percentage. The Banker Charnes Cooper (BCC) mathematical programming model 33 was used to determine the optimal weights or proportions, as specified in (Equation 1).…”

“…The unit of measure is in USD USA. 7 Input 1 (CIG ij ): This variable represents the annual value of the cattle investment quantified in USD USA.…”

“…Input 2 (CT ij ): This variable represents the total annual cost for fuel, feeding, reproduction, illness and treatment, milking, mortality, and preventive medicine, measured in annual USD. 7 Input 3 (MO ij ): This variable represents the annual cost of family and hired labor, measured in USD.…”

“…Many of these programs incorporate strategies that inherently address efficiency productivity variables. 7 In 2001, Perez, 1 reported that cattle practices in America ranked seventh globally in meat production and tenth in milk production, contributing approximately 7% to the world's total meat production and 0.17% to milk. However, there remains an unmet demand and necessitating a thorough examination of the efficiency of dual-purpose production systems in Latin America, 8 where tropical regions offer significant potential.…”

“…11 According to The Ministry of Agriculture and Rural Development of Mexico, in the State of Tlaxcala, 88.3% of the economically active population is employed in agriculture with the remaining 11.7% engaged in the livestock industry. 7 According to the analysis of the 2013-2018 Sectoral Program for Agrarian, Fisheries and Nutritional Progress of Mexico, it's projected that the global population will reach 9.3 billion by 2050. The Food and Agriculture Organization of the United Nations (FAO) estimates a 60% increase in world food demand to meet the needs of this growing population, which includes the provision of food, housing, transportation, and more.…”