The possible effect of heat stress (HS), measured with the temperature–humidity index (THI) across seasons of the year (SY) upon milk production (MP), feed-to-milk efficiency (FME), and cow comfort (CC) was assessed in Holstein–Friesian cows in northern-arid Mexico. Data from 2467 cows (2146 milking and 321 dry) were recorded across SY [spring (SP), summer (SM), autumn (AT), and winter (WN)] between 2016 and 2019 in an intensive dairy farm located in the Comarca Lagunera (25° NL) with large fluctuations regarding ambient temperature and solar radiation. The THI was stratified into four classes: non-HS, <68; light HS, 68–71; moderate HS, 72–76; and intense HS, ≥77. The considered response variables were Milk production: both on a farm basis (totMP) and on a cow basis (cowMP); Nutritional efficiency: dry matter intake (DMI, kg); Feed conversion efficiency (FCE, kg) and energy-corrected milk (ECM, kg); Percentage of milking cows: (MC%); and Cow comfort: lying time (LT, h). Analyses of variance for unbalanced data were performed through “R”. Both totMP and cowMP differed (p < 0.05) as HS increased; the largest values (i.e., 77,886 L and 35.9 L) occurred at lower THIs (i.e., <68 and 68–71) while the milk production fell (i.e., 66,584 L and 31.7 L) with the highest THIs (i.e., ≥77). Not only feed-to-milk efficiency (i.e., DMI, FCE, and ECM) but also the MC% exhibited a similar trend; a visible drop (p < 0.05) occurred from a THI of 68–71 onwards. Furthermore, the LT declined as the THI augmented, from 10.6 h at <68 to 8.5 h at ≥77. Moreover, differences (p < 0.05) also arose across seasons; TotMP, cowMP, DMI, FCE, and ECM revealed their largest (p < 0.05) values in WN and SP, halfway ones in AT, with the lowermost figures in SM. In the same way, cow comfort differed (p < 0.05) among seasons, with diverse lying times (h); WT, 10.5; AT, 10.20; SP, 9.3 h; and 8.8 in SM. Finally, the potential economic burden that HS caused at the producer (USD 233.2 million) and industry-market levels (USD 311.1 M), as well as its impact upon nutrient and alimentary security at the society level (i.e., 311 M milk liters and 195,415.82 Gcal), were also quantified.

La economía de Tamaulipas presenta un marcado proceso de formación de clusters. El objetivo del trabajo es identificar los principales encadenamientos y clusters del estado, a partir de la matriz insumo producto (MIP) regionalizada. Se realizó un análisis de componentes principales con la metodología de Feser y Bergman, así como un estudio de los encadenamientos con los índices de Chenery y Watanabe y los de Rasmussen de poder y sensibilidad de dispersión. Se identificaron once clusters de la economía tamaulipeca. Destacan principalmente ocho por su contribución a la demanda intermedia de consumo y su relación con la estructura económica estatal.

Se espera que el sector más afectado económicamente por el cambio climático sea la agricultura, viéndose afectado por la disminución del ingreso económico. El objetivo de la investigación fue conocer el impacto del cambio climático en la agricultura en el Distrito de Riego 005 ubicado en el estado de Chihuahua, a través de la aplicación de encuestas en 2020. Se realizaron pruebas de Chi-cuadrada, de los encuestados, el 40% [X2(1,38.32) p<.001] posee más de 20 hectáreas, el 65% cultiva principalmente nogal pecanero (Carya illinoinensis), el 97.89% [X2(1,87.17) p<.001)] ha escuchado hablar del cambio climático y el 44.09% [X2(1,55.33) p<.001)] lo define como variación climática pronunciada entre estaciones. El análisis de correspondencia múltiple estimó que las variables que presentaron mayor inercia para la primera dimensión fueron: labranza cero (M1), cambio de tipo de cultivo (M3), técnicas de captación de agua (M4), otras (M5) y porcentaje de contribución (Cmedidas); para la segunda dimensión, las que presentaron mayor inercia fueron: ha sufrido pérdidas económicas (Peconomicas), periodo más afectado (Periodo), causa de la afectación (Causa), porcentaje de pérdidas económicas (Pp). Se corrieron dos modelos de regresión logística multinomial, las medidas aplicadas para reducir los efectos del cambio climático fueron labranza cero y el uso de productos orgánicos. Los resultados obtenidos mostraron que conforme la edad de los productores aumenta, la posibilidad de que conozcan el cambio climático sus causas y opten por contratar un seguro disminuye. Finalmente, es necesario aplicar más de una medida de mitigación/adaptación para contrarrestar el cambio climático.

As the effects of global climate change intensify, it is increasingly important to implement more effective water management practices, particularly in arid and semi-arid regions such as Meoqui, Chihuahua, situated in the arid northern center of Mexico. The objective of this study was to identify the optimal time-series model for analyzing the pattern of water extraction volumes and predicting a one-year forecast. It was hypothesized that the volume of water extracted over time could be explained by a statistical time-series model, with the objective of predicting future trends. To achieve this objective, three time-series models were evaluated. To assess the pattern of groundwater extraction, three time-series models were employed: the seasonal autoregressive integrated moving average (SARIMA), Prophet, and Prophet with extreme gradient boosting (XGBoost). The mean extraction volume for the entire period was 50,935 ± 47,540 m³, with a total of 67,233,578 m³ extracted from all wells. The greatest volume of water extracted has historically been from urban wells, with an average extraction of 55,720 ± 48,865 m³ and a total of 63,520,284 m³. The mean extraction volume for raw water wells was determined to be 20,629 ± 19,767 m³, with a total extraction volume of 3,713,294 m³. The SARIMA(1,1,1)(1,0,0)₁₂ model was identified as the optimal time-series model for general extraction, while a “white noise” model, an ARIMA(0,1,0) for raw water, and an SARIMA(2,1,1)(2,0,0)₁₂ model were identified as optimal for urban wells. These findings serve to reinforce the efficacy of the SARIMA model in forecasting and provide a basis for water resource managers in the region to develop policies that promote sustainable water management.

Water is a vital resource for sustaining life and for numerous processes within the transformation industry. It is a finite resource, albeit one that can be renewed, and thus sustainable management is imperative. To achieve this objective, it is necessary to have the appropriate tools to assist with the planning policies for its management. This paper presents a time series analysis approach to measure and predict the pattern of water consumption by humans throughout subsectors (domestic, commercial, public sector, education, industry, and raw water) and total water consumption in Meoqui, Chihuahua, Mexico with data from 2011 to 2023, applying calibration model techniques to measure uncertainty in the forecasting. The municipality of Meoqui encompasses an area of 342 km². The climate is semi-arid, with an average annual rainfall of 272 mm and average temperatures of 26.4 °C in summer and 9.7 °C in winter. The municipal seat, which has a population of 23,140, is supplied with water from ten wells, with an average consumption of 20 ± 579 m³ per user. The consumption of the general population indicates the existence of a seasonal autoregressive integrated moving average (SARIMA) (0,1,2)(0,0,2)₁₂ model. (Sen’s Slope = 682.7, p < 0.001). The domestic sector exhibited the highest overall consumption, with a total volume of 17,169,009 m³ (13 ± 93). A SARIMA (2,1,0)(2,0,0)₁₂ model was estimated, with a Sen’s slope of 221.65 and a p-value of less than 0.001. The second-largest consumer of total water was the “raw water” sector, which consumed 5,124,795 (30,146 ± 35,841) m³ and exhibited an SARIMA (0,1,1)(2,0,0)₁₂ model with no statistically significant trend. The resulting models will facilitate the company’s ability to define water resource management strategies in a sustainable manner, in alignment with projected consumption trends.

The environment in which an animal is situated can have a profound impact on its health, welfare, and productivity. This phenomenon is particularly evident in the case of dairy cattle, then, in order to quantify the impact of ambient temperature (°C) and the relative humidity (%) on dairy cattle, the temperature-humidity index (THI) is employed as a metric. This indicator enables the practical estimation of the stress imposed on cattle by ambient temperature and humidity. A seasonal autoregressive integrated moving average (SARIMA) (4,1,0)(0,1,0)₃₆₅ model was estimated using daily data from the maximum daily THI of 4 years (2016–2019) of the Comarca Lagunera, an arid region of central-northern Mexico. The resulting model indicated that the THI of any given day in the area can be estimated based on the THI values of the previous four days. Furthermore, the data demonstrate an annual increase in the number of days the THI indicates a risk of heat stress. It is essential to continue building predictive models to develop effective strategies to mitigate the adverse effects of heat stress in dairy cattle (and other species) in the region.