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In support of Food-Energy-Water Systems (FEWS) analysis to enhance its sustainability for New Mexico (NM), this study evaluated observed trends in beef cattle population in response to environmental and economic changes. The specific goal was to provide an improved understanding of the behavior of NM’s beef cattle production systems relative to precipitation, temperature, rangeland conditions, production of hay and crude oil, and prices of hay and crude oil. Historical data of all variables were available for the 1973–2017 period. The analysis was conducted using generalized autoregressive conditional heteroscedasticity models. The results indicated declining trends in beef cattle population and prices. The most important predictors of beef cattle population variation were hay production, mean annual hay prices, and mean annual temperature, whereas mean annual temperature, cattle feed sold, and crude oil production were the most important predictors for calf population that weigh under 500 lb. Prices of beef cattle showed a strong positive relationship with crude oil production, mean annual hay prices, rangeland conditions, and mean annual precipitation. However, mean annual temperature had a negative relationship with mean annual beef prices. Variation in mean annual calf prices was explained by hay production, mean annual temperature, and crude oil production. This analysis suggested that NM’s beef cattle production systems were affected mainly and directly by mean annual temperature and crude oil production, and to a lesser extent by other factors studied in this research.
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Historical Trends in New Mexico Forage Crop Production in Relation to Climate, Energy, and Rangelands
This study was conducted within the context of providing an improved understanding of New Mexico’s food, energy, water systems (FEWS) and their behavior under variable climate and socioeconomic conditions. The goal of this paper was to characterize the relationships between production and prices of some forage crops (hay, grain sorghum, and corn) that can be used as feed supplements for beef cattle production and the potential impacts from a changing climate (precipitation, temperature) and energy inputs (crude oil production and prices). The analysis was based on 60 years of data (1958–2017) using generalized autoregressive conditional heteroscedasticity models. Hay production showed a declining trend since 2000 and in 2017, it dropped by ~33% compared to that of 2000. Crude oil production (R2 = 0.83) and beef cattle population (R2 = 0.85) were negatively correlated with hay production. A moderate declining trend in mean annual hay prices was also observed. Mean annual range conditions (R2 = 0.60) was negatively correlated with mean annual hay prices, whereas mean annual crude oil prices (R2 = 0.48) showed a positive relationship. Grain sorghum production showed a consistent declining trend since 1971 and in 2017, it dropped by ~91% compared to that of 1971. Mean annual temperature (R2 = 0.58) was negatively correlated with grain sorghum production, while beef cattle population (R2 = 0.61) and range conditions (R2 = 0.51) showed positive linear relationships. Mean annual grain sorghum prices decreased since the peak of 1974 and in 2017, they dropped by ~77% compared to those of 1974. Crude oil prices (R2 = 0.72) and beef cattle population (R2 = 0.73) were positively correlated with mean annual grain sorghum prices. Corn production in 2017 dropped by ~61% compared to the peak that occurred in 1999. Crude oil production (R2 = 0.85) and beef cattle population (R2 = 0.86) were negatively correlated with corn production. Mean annual corn prices showed a declining trend since 1974 and in 2017, they dropped by ~75% compared to those of 1974. Mean annual corn prices were positively correlated with mean annual precipitation (R2 = 0.83) and negatively correlated with crude oil production (R2 = 0.84). These finding can particularly help in developing a more holistic model that integrates FEWS components to explain their response to internal (i.e., management practices) and external (i.e., environmental) stressors. Such holistic modeling can further inform the development and adoption of more sustainable production and resource use practices.
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- Award ID(s):
- 1739835
- PAR ID:
- 10185361
- Date Published:
- Journal Name:
- Sustainability
- Volume:
- 12
- Issue:
- 5
- ISSN:
- 2071-1050
- Page Range / eLocation ID:
- 2051
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/su12052051
