No abstract

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.

Accurate estimation of land use/land cover (LULC) areas is critical, especially over the semi-arid environments of the southwestern United States where water shortage and loss of rangelands and croplands are affecting the food production systems. This study was conducted within the context of providing an improved understanding of New Mexico’s (NM’s) Food–Energy–Water Systems (FEWS) at the county level. The main goal of this analysis was to evaluate the most important LULC classes for NM’s FEWS by implementing standardized protocols of accuracy assessment and providing bias-corrected area estimates of these classes. The LULC data used in the study was based on National Land Cover Database (NLCD) legacy maps of 1992, 2001, 2006, 2011, and 2016. The analysis was conducted using the cloud-based geospatial processing and modeling tools available from System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring (SEPAL) of the Food and Agricultural Organization. Accuracy assessment, uncertainty analysis, and bias-adjusted area estimates were evaluated by collecting a total of 11,428 reference samples using the Open Foris Collect Earth tool that provided access to high spatial and temporal resolution images available in Google Earth. The reference samples were allocated using a stratified random sampling approach. The results showed an overall accuracy that ranged from 71%–100% in all six study counties. The user’s and producer’s accuracy of most LULC classes were about or above 80%. The obtained bias-adjusted area estimates were higher than those based on pixel counting. The bias-adjusted area estimates simultaneously showed decreasing and increasing trends in grassland and shrubland, respectively in four counties that include Curry, Roosevelt, Lea, and Eddy during the 1992–2016 period. Doña Ana county experienced increasing and decreasing trends in grassland and shrubland areas, respectively. San Juan county experienced decreasing trends in both grassland and shrubland areas. Cultivated cropland areas showed decreasing trends in three counties in southeast NM that rely on groundwater resources including Curry, Roosevelt, and Lea. Similarly, cultivated cropland areas showed increasing trends in the other three counties that rely on surface water or conjunctive use of surface and groundwater resources including San Juan, Doña Ana, and Eddy. The use of SEPAL allowed for efficient assessment and production of more accurate bias-adjusted area estimates compared to using pixel counting. Providing such information can help in understanding the behavior of NM’s food production systems including rangelands and croplands, better monitoring and characterizing NM’s FEWS, and evaluating their behavior under changing environmental and climatic conditions. More effort is needed to evaluate the ability of the NLCD data and other similar products to provide more accurate LULC area estimates at local scales.

Interconnected food, energy, and water (FEW) nexus systems face many challenges to support human well-being (HWB) and maintain resilience, especially in arid and semiarid regions like New Mexico (NM), United States (US). Insufficient FEW resources, unstable economic growth due to fluctuations in prices of crude oil and natural gas, inequitable education and employment, and climate change are some of these challenges. Enhancing the resilience of such coupled socio-environmental systems depends on the efficient use of resources, improved understanding of the interlinkages across FEW system components, and adopting adaptable alternative management strategies. The goal of this study was to develop a framework that can be used to enhance the resilience of these systems. An integrated food, energy, water, well-being, and resilience (FEW-WISE) framework was developed and introduced in this study. This framework consists mainly of five steps to qualitatively and quantitatively assess FEW system relationships, identify important external drivers, integrate FEW systems using system dynamics models, develop FEW and HWB performance indices, and develop a resilience monitoring criterion using a threshold-based approach that integrates these indices. The FEW-WISE framework can be used to evaluate and predict the dynamic behavior of FEW systems in response to environmental and socioeconomic changes using resilience indicators. In conclusion, the derived resilience index can be used to inform the decision-making processes to guide the development of alternative scenario-based management strategies to enhance the resilience of ecological and socioeconomic well-being of vulnerable regions like NM.

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 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 (R 2 = 0.83) and beef cattle population (R 2 = 0.85) were negatively correlated with hay production. A moderate declining trend in mean annual hay prices was also observed. Mean annual range conditions (R 2 = 0.60) was negatively correlated with mean annual hay prices, whereas mean annual crude oil prices (R 2 = 0.48) showed a positive relationship. Grain sorghum production showed a consistent declining trend since 1971 and in 2017, it dropped bỹ 91% compared to that of 1971. Mean annual temperature (R 2 = 0.58) was negatively correlated with grain sorghum production, while beef cattle population (R 2 = 0.61) and range conditions (R 2 = 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 (R 2 = 0.72) and beef cattle population (R 2 = 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 (R 2 = 0.85) and beef cattle population (R 2 = 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 (R 2 = 0.83) and negatively correlated with crude oil production (R 2 = 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.