Vianey González-Hernández

Objective: to analyze the value chain of cheese production at the municipality scale, through a mixed approach combining the general guide, for evaluating value chains development, with the specific approach from a business perspective. Design/ Methodology/ Approach: the integration of both approaches allowed for a systemic analysis incorporating social and business dimensions. Main social actors, relationships and bottlenecks —congested points such as operations, specialized work force, or transport— along the chain were identified. Those processes that generate value and determine competitiveness in the local context were highlighted. Results: the value chain of cheese production in Aculco shows highly artisanal specialization, strong cultural roots, and significant contribution to local development. However, through the SWOT analysis, we found this production chain faces limitations in the articulation among producers, scarce innovation, and vulnerability to external economic and political factors. The mixed approach in this study conformed a methodological reference which can be applied to other agrifood systems to identify and strengthen organizational and business management capacities. Limitations/ Implications of the study: the analysis was limited to a specific municipality (Aculco, State of Mexico, Mexico) and results must be interpreted within that territorial scope. However, as a methodological reference it is applicable to other production chains, in localities with similar characteristics. Findings/ Conclusions: the value chain of cheese production in Aculco has both economic and cultural values, which make it a suitable option for the development of the local population. It is necessary to promote territorial integration and encourage sustainable practices that allow producers to foresee strategic scenarios in order to be adapted to changes in the environment.

To identify market strategies for Mexico’s apiculture sector, this study projects national honey production and the leading producing states through 2032. The states were considered strategic market units, with their positioning and growth stochastically projected over a 10-year period based on their honey production value. From 2023 to 2032, honey production volume is projected to decline. However, in the prospective scenario, the value of honey production is expected to rise, with Yucatán maintaining its position as the leading producer, followed by Chiapas, Jalisco, Campeche, and Oaxaca. Veracruz experienced a significant decline, while Yucatán positioned itself in the cash cow quadrant. Guerrero and Oaxaca showed growth and joined the question mark quadrant, where Quintana Roo remained. Meanwhile, Campeche and Veracruz were placed in the dog quadrant alongside Puebla, Jalisco, and Chiapas, even though Chiapas showed production growth. After analyzing their future performance, distinct market strategies were established for each state.

In the present study, the price of milk was estimated deterministically and stochastically for the period 2023-2028, with the objective of analyzing the economic situation of cow's milk production in Mexico, through the univariate series statistical method of time (ARIMA). A base average price was established, as well as its confidence intervals reflected in the lower limit price and upper limit price. The statistical distribution that best explained the behavior of base prices was the geometric distribution, which was used to estimate the stochastic model. The price of milk is expected to have a deterministic average behavior of 9.38 ± 0.76 pesos per liter for the next six years. The above suggests a methodological approach through which the estimation of the price of agricultural products can be explored and improved to obtain a differentiated price based on quality effect.

Objective: This study aims to assess the productive and economic performance of the Mexican milk sector, particularly focusing on small and medium-scale dairy farms, and examining pessimistic, base, and optimistic scenarios. Design/methodology/approach: Employing a statistical univariate method with time series analysis (ARIMA), we analyzed productive efficiency and price behavior in Mexican dairy systems. Deterministic and stochastic estimations for production volume, milk price, and cattle inventory from 2021 to 2030 were established using confidence intervals to construct pessimistic (lower interval), base (mean), and optimistic (upper interval) scenarios. Results: The evaluated period witnessed an estimated 10.27% increase in production, equivalent to 576 million liters of milk, with an average annual growth rate of 1.0922%. Milk prices displayed an upward trend, with average prices of $0.66, $0.69, and $0.72 under pessimistic, base, and optimistic scenarios, respectively. In 2030, a 22% price increase compared to 2021 was observed. Considering a base price of $0.45 USD per liter in 2030, costs under pessimistic, base, and optimistic scenarios were $1,658.21, $1,756.43, and $1,855.31, resulting in profits of $1,160.75, $1,229.50, and $1,022.45 from milk sales. Cattle inventory exhibited an upward trend, paralleling milk volumes and prices. Limitations on study/implications: The study's use of a univariate method may incompletely capture market dynamics complexity, potentially underestimating the impact of external market forces and global economic conditions on milk prices. Findings/conclusions: To secure forecasted milk volumes in base and optimistic scenarios, maintaining and enhancing good management practices is crucial. Additionally, addressing the imperative to augment production efficiency and improve environmental sustainability and animal welfare is essential.

Background: Small-scale dairy systems are vital in rural communities and strategic for livelihoods an economic development. Objective: To estimate the economic and financial viability of small-scale dairy Representative Production Units (RPU) in the high plateau of the State of Mexico using a Monte Carlo simulation. Methodology: Net yields were estimated for a 5-year planning horizon via simulated values for the distribution of input and product processes, establishing 2022 as the base scenario. A stochastic modelling approach was used to determine the economic and financial outlook considering the cost of two sources of labor for the simulations: (1) Cost of family labor (FL); and (2) Cost of external hired labor (EL). Results: A panorama of economic viability was evidenced. However, a lack of financial wellbeing was also determined. When FL was factored in, net income was estimated in $17.99 thousand USD in 2023 and $26.35 thousand USD in 2027. On the other hand, considering EL resulted in a net income of $9.88 thousand USD in 2023 and $16.31 USD in 2027. The Net Present Value in 2027 was calculated at $-161.84 thousand USD and $-191.27 thousand USD, with FL and EL, respectively. While the RPUs were determined to be competitive, the level of risk was significant due to the high monetary value, limited degree of specialization of the RPUs, and the prevailing volatile scenario at the time. Implications: This study can enable decision-makers, on one hand, to analyze and design policies and differentiated strategies for small-scale dairy production systems. On the other hand, it can empower dairy farmers to assess scenarios of technical and productive intervention. Conclusions: The financial and economic weakening of the studied RPU could have negative implications in terms of public policy, labor, marketing, value chains, and the persistence of producers in such activity.

El objetivo de este estudio fue evaluar el desempeño ambiental de la producción de leche de vaca en sistema de pequeña y mediana escala en México, mediante análisis de ciclo de vida con un enfoque de la cuna a la puerta de la granja, para el periodo 2021-2030. Se estableció como unidad funcional 1 kg de leche corregida por grasa y proteína. La evaluación de impacto se realizó con el software OpenLCA 1.11.0, mediante el método ReCiPe, se consideraron siete categorías de impacto: ocupación de suelo agrícola (ALO), ecotoxicidad marina (ME), toxicidad humana (TH), cambio climático (CC), agotamiento fósil (FD), acidificación terrestre (TA) y agotamiento de agua (WD). Como principales resultados de la investigación se identificó que la producción de alimento para el ganado es el principal contribuyente a las cargas ambientales en la mayoría de las categorías con porcentajes superiores al 71 %, mientras que las emisiones generadas en la granja contribuyen a las cargas ambientales para las categorías CC (28 %), FD (26 %) y TA (59 %). Se realizó una comparación entre escenarios pesimista, base y optimista para los años 2021 y 2030, lo que confirmó una mejora en la eficiencia ambiental en el escenario optimista prospectado, el incremento en el volumen de producción representó una disminución del 6 % y 5 % respectivamente, en las categorías de impacto evaluadas.