Spatiotemporal land use change and environmental degradation surrounding CAFOs in Michigan and North Carolina

Landscape-scale predictions of future grassland conversion to cropland or development

Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R2 = 0.89 [range: 0.83-0.96], root mean squared error [RMSE] = 9.29 ha [range: 2.83-22.77 ha]; proportional loss model: weighted group mean R2 = 0.74 [range: 0.64-0.87], RMSE = 0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.

Machine learning-based identification of animal feeding operations in the United States on a parcel-scale

The increasing global demand for meat and dairy products, fueled by rapid industrialization, has led to the expansion of Animal Feeding Operations (AFOs) in the United States (US). These operations, often found in clusters, generate large amounts of manure, posing a considerable risk to water quality due to the concentrated waste streams they produce. Accurately mapping AFOs is essential for effective environmental and disease management, yet many facilities remain undocumented due to variations in federal and state regulations. Current techniques for mapping AFOs in the US rely on a mix of manual digitization, aerial imaging, and image processing. By applying a machine learning-based random forest (RF) classification method to a socio-environmental dataset that excluded aerial images in this work, we overcame some of the limitations associated with aerial image-based approaches, enhancing mapping accuracy to 87 %. We used publicly available environmental, nutrient-focused, and socioeconomic data downscaled to the parcel level, which more accurately reflects farm boundaries and operations than previous methods. Our study incorporates 58 variables, with canopy cover, surrounding vegetation, day and nighttime land surface temperatures, and phosphorus from animals identified as key predictors of AFO presence. The relevance of these variables varies across states, influenced by whether the dominant land covers are human-induced, like croplands, or natural, such as savannas and grasslands. Thus, our public-data based approach, easily replicable, not only improves the precision of AFO detection, but also facilitates the monitoring of nutrient flows at the parcel level-critical for nutrient budgeting and recovery, water quality management, and disease risk assessment and tracing.

The role of geospatial technologies for sustainable livestock manure management: A systematic review

Optimal livestock production is a key contributor to the achievement of sustainable development goals. The management and disposal of livestock manure is one of the main issues facing the sector in terms of soil, water and air pollution. Proper and sustainable management of livestock manure also requires a systemic approach to the problem, considering it at different territorial levels. In order to identify existing strategies to support this issue, this review investigated the use of Geographic Information System (GIS) analysis as a support for livestock manure management, highlighting the several GIS methodologies used to provide insight into the complexity, power, and potential offered by these approaches in study areas with different economic, social, and environmental variables, and to provide insights for future research. The study was performed on 139 papers chosen from a literature screening. Three study themes were identified by co-word analysis: Bioenergy, Environmental pollution and Landscape management/development, with a percentage division of research articles of 38 %, 47 % and 15 %, respectively. This study provides a theoretical and prospective framework for the long-term expansion of the livestock sector, which is critical to promoting a balance between sector development and environmental impact. The use of spatial analysis, along with additional tools and methods such as modelling, multivariate and spatial statistics, life cycle assessment, machine learning and multi-criteria analysis, has proven to be widely applied.

Missing phosphorus legacy of the Anthropocene: Quantifying residual phosphorus in the biosphere

A defining feature of the Anthropocene is the distortion of the biosphere phosphorus (P) cycle. A relatively sudden acceleration of input fluxes without a concomitant increase in output fluxes has led to net accumulation of P in the terrestrial-aquatic continuum. Over the past century, P has been mined from geological deposits to produce crop fertilizers. When P inputs are not fully removed with harvest of crop biomass, the remaining P accumulates in soils. This residual P is a uniquely anthropogenic pool of P, and its management is critical for agronomic and environmental sustainability. Managing residual P first requires its quantification-but measuring residual P is challenging. In this review, we synthesize approaches to quantifying residual P, with emphasis on advantages, disadvantages, and complementarity. Common approaches to estimate residual P are mass balances, long-term experiments, soil test P trends and chronosequences, with varying suitability or even limitations to distinct spatiotemporal scales. We demonstrate that individual quantification approaches are (i) constrained, (ii) often complementary, and (iii) may be feasible at only certain time-space scales. While some of these challenges are inherent to the quantification approach, in many cases there are surmountable challenges that can be addressed by unifying existing P pool and flux datasets, standardizing and synchronizing data collection on pools and fluxes, and quantifying uncertainty. Though defined as a magnitude, the distribution and speciation of residual P is relatively less understood but shapes its utilization and environmental impacts. The form of residual P will vary by agroecosystem context due to edaphoclimatic-specific transformation of the accumulated P, which has implications for management (e.g., crop usage) and future policies (e.g., lag times in P loading from non-point sources). Quantifying the uncertainty in measuring residual P holds value beyond scientific understanding, as it supports prioritization of monitoring and management resources and inform policy.

Concentrated animal feeding operations (CAFOs) in relation to environmental justice related variables in Wisconsin, United States

BACKGROUND

The growth of concentrated animal feeding operations (CAFOs) has caused significant environmental detriments and raised concerns regarding environmental justice with CAFOs exposure.

OBJECTIVE

This study examined environmental disparities in exposure to CAFOs with several environmental justice related variables and considered exposure intensity.

METHODS

We obtained data on permitted CAFOs (July 2021) from the Wisconsin Department of Natural Resources. We used Census tract level variables from the 2010 Census to evaluate environmental disparities by environmental justice related variables (i.e., percentages of Non-Hispanic White, Non-Hispanic Black, or Hispanic; percentage living below the poverty level; median annual household income; income inequality (Gini index); percentage with education less than high school diploma; racial isolation (RI) for Non-Hispanic Black; and educational isolation (EI) for population without a college degree). We assessed exposure to CAFOs as the sum of animal units (AUs) within each Census tract and investigated exposure disparities by comparing distributions of environmental justice related variables based on CAFO status (i.e., never, expired, or current) and Census tract-level CAFOs exposure intensity categories (i.e., from low exposure (quartile 1) to high exposure (quartile 4)).

RESULTS

CAFOs in Wisconsin were generally located in areas with lower percentages of racial minority persons and high SES communities; however, within the areas with current CAFO exposure, areas with high CAFOs exposure intensity had higher percentages of non-Hispanic Black and Hispanic, and lower percentages of non-Hispanic White populations compared to areas with low CAFOs exposure.

IMPACT STATEMENT

This study compared distributions of CAFO exposure and multiple environmental justice related variables and considered exposure intensity based on animal units for CAFOs exposure metric. Although CAFOs in Wisconsin were generally located in areas with lower percentages of racial/ethnic minority subpopulations and high SES communities, we found complex disparities with higher exposure for disadvantaged communities within areas with CAFOs. This work adds to the existing evidence that some populations such as racial/ethnic minority populations may face disproportionate burdens from CAFOs.

Do animal husbandry operations contaminate groundwater sources with antimicrobial resistance: systematic review

Antimicrobial resistance (AMR) is a critical global health concern. Animal husbandry operations are AMR hotspots due to heavy antibiotic use and dissemination of animal waste into the environment. In this systematic review, we examined the impact of swine, poultry, and cattle operations on AMR in groundwater. We searched PubMed, Web of Science, CAB Direct, and the North Carolina State University Agricultural and Environmental Science databases in June 2022. The search returned 2487 studies. Of the 23 eligible studies, 17 were conducted in high-income countries (primarily the USA, also Canada, Saudi Arabia, Cyprus), and 6 were conducted in a single upper-middle-income country (China). Studies investigated facilities for swine (13), poultry (4), cattle (3), and multiple types of animals (3). The sampling distance ranged from onsite to > 20 km from facilities; the majority of studies (19) sampled onsite. Most studies collected samples from monitoring wells; only 5 studies investigated private drinking water wells. AMR in groundwater was associated with animal husbandry operations in 74% (17/23) of all studies, 65% (11/17) of studies in high-income countries, and 100% (6/6) of studies in China. Contamination was mostly found in onsite wells, especially downgradient of waste lagoons, but also in offsite private wells up to 2-3 km away. Few studies reported weather data, but AMR contamination appeared to increase with rainy conditions. Future studies should sample private wells at varying distances from animal husbandry operations under different weather conditions and include low- and middle-income countries where food animal production is intensifying.

Development of a national antibiotic multimetric index for identifying watersheds vulnerable to antibiotic pollution

Improved surveillance of antibiotics and antibiotic resistance (AR) throughout the environment is an important aspect of the prevention and control of threats posed to human and ecological health. In response to field investigations often limited by resources and time, this study aims to develop a systematic approach to assess watershed vulnerability to antibiotic pollution and AR by integrating modeling and field studies. The national antibiotic pollution vulnerability index was developed to identify watersheds most impacted by antibiotic sources. The index incorporates multiple metrics representing antibiotic pollution driven by both agricultural activities and municipal wastewater (i.e. outpatient antibiotic prescriptions, wastewater treatment plant effluent flow, stream order and dilution factor of effluent-receiving streams, manure application, and animal facilities), alongside climate change indicators (i.e., temperature, precipitation, and runoff). The pollution index was applied at a state level in North Carolina to identify the most-impacted watersheds and inform site selection for targeted field study quantifying azithromycin, ciprofloxacin, sulfamethoxazole, and trimethoprim concentrations. Modeled-informed sites in NC demonstrated the highest reported concentrations of azithromycin, trimethoprim, and sulfamethoxazole compared to previous NC studies, confirming the index effectiveness in identifying watersheds with higher antibiotic concentrations. At the national scale, watersheds relatively more vulnerable to antibiotic pollution are predominantly located in the Midwest, South, and Northeast regions of the U.S., with Iowa and Indiana being the most impacted states. Climate change is expected to exacerbate watershed vulnerability to agriculture-driven AR in the Midwest and Northeast due to an increase in precipitation and mean temperature coupled with intense agricultural activities. In addition, due to climate change-induced reductions in precipitation and runoff, watersheds in the Midwest, Mid-Atlantic, and South Central are dominantly at higher risk of effluent-driven AR occurrences. We have disseminated the developed indices as open-source online tools to aid in prioritizing strategies to mitigate AR occurrence across the U.S.

Local and Environmental Reservoirs of Salmonella enterica After Hurricane Florence Flooding

In many regions of the world, including the United States, human and animal fecal genetic markers have been found in flood waters. In this study, we use high-resolution whole genomic sequencing to examine the origin and distribution of Salmonella enterica after the 2018 Hurricane Florence flooding. We specifically asked whether S. enterica isolated from water samples collected near swine farms in North Carolina shortly after Hurricane Florence had evidence of swine origin. To investigate this, we isolated and fully sequenced 18 independent S. enterica strains from 10 locations (five flooded and five unflooded). We found that all strains have extremely similar chromosomes with only five single nucleotide polymorphisms (SNPs) and possessed two plasmids assigned bioinformatically to the incompatibility groups IncFIB and IncFII. The chromosomal core genome and the IncFIB plasmid are most closely related to environmental Salmonella strains isolated previously from the southeastern US. In contrast, the IncFII plasmid was found in environmental S. enterica strains whose genomes were more divergent, suggesting the IncFII plasmid is more promiscuous than the IncFIB type. We identified 65 antibiotic resistance genes (ARGs) in each of our 18 S. enterica isolates. All ARGs were located on the Salmonella chromosome, similar to other previously characterized environmental isolates. All isolates with different SNPs were resistant to a panel of commonly used antibiotics. These results highlight the importance of environmental sources of antibiotic-resistant S. enterica after extreme flood events.

Association of distance to swine concentrated animal feeding operations with immune-mediated diseases: An exploratory gene-environment study

BACKGROUND

Concentrated animal feeding operations (CAFOs) are a source of environmental pollution and have been associated with a variety of health outcomes. Immune-mediated diseases (IMD) are characterized by dysregulation of the normal immune response and, while they may be affected by gene and environmental factors, their association with living in proximity to a CAFO is unknown.

OBJECTIVES

We explored gene, environment, and gene-environment (GxE) relationships between IMD, CAFOs, and single nucleotide polymorphisms (SNPs) of prototypical xenobiotic response genes AHR, ARNT, and AHRR and prototypical immune response gene PTPN22.

METHODS

The exposure analysis cohort consisted of 6,464 participants who completed the Personalized Environment and Genes Study Health and Exposure Survey and a subset of 1,541 participants who were genotyped. We assessed the association between participants' residential proximity to a CAFO in gene, environment, and GxE models. We recombined individual associations in a transethnic model using METAL meta-analysis.

RESULTS

In White participants, ARNT SNP rs11204735 was associated with autoimmune diseases and rheumatoid arthritis (RA), and ARNT SNP rs1889740 was associated with RA. In a transethnic genetic analysis, ARNT SNPs rs11204735 and rs1889740 and PTPN22 SNP rs2476601 were associated with autoimmune diseases and RA. In participants living closer than one mile to a CAFO, the log-distance to a CAFO was associated with autoimmune diseases and RA. In a GxE interaction model, White participants with ARNT SNPs rs11204735 and rs1889740 living closer than eight miles to a CAFO had increased odds of RA and autoimmune diseases, respectively. The transethnic model revealed similar GxE interactions.

CONCLUSIONS

Our results suggest increased risk of autoimmune diseases and RA in those living in proximity to a CAFO and a potential role of the AHR-ARNT pathway in conferring risk. We also report the first association of ARNT SNPs rs11204735 and rs1889740 with RA. Our findings, if confirmed, could allow for novel genetically-targeted or other preventive approaches for certain IMD.

Using remote sensing to identify liquid manure applications in eastern North Carolina

Nutrient pollution from farm fertilizers and manure is a global concern. Excess nitrogen and phosphorous has been linked to algal blooms and a host of other water quality issues. In the U.S., most animal production occurs in concentrated animal feeding operations (CAFOs) housing a significant number of animals in a confined space. CAFOs tend to cluster in space and thus generate large quantities of manures within a small area. Liquid manure from CAFOs is often stored in open-air lagoons and then applied via irrigation to crops on nearby 'sprayfields'. The full scope and extent of CAFO impacts remain unclear because of the paucity of public information regarding animal numbers, barn and lagoon locations, and manure management practices. Where and when manure is applied on the landscape is key missing data that is needed to better understand and mitigate consequences of CAFO management practices. The aim of this study was to detect land applications of liquid manure using a remote sensing approach. We used random forest models incorporating C-Band synthetic-aperture radar, multispectral imagery, and other predictors to examine soil moisture conditions indicating probable liquid manure applications across known sprayfields in eastern North Carolina. Our models successfully distinguished saturated and unsaturated soils within corn, soybean, grassland, and 'other' crops, with 93-98% accuracy against validation for clear weather periods during the dormant, early, and late growing seasons. A Kruskal-Wallis test revealed that the mean soil saturation frequency was significantly higher on sprayfields than non-sprayfields of the same crop type (p < 2.2e-16). We also found that manure applications were concentrated within ∼1 km from the point of generation. This is the first application of satellite-based radar for identifying the location and timing of manure applications over broad areas. Future work can build on these methods to further understand manure management at CAFOs, as well as to improve pollution source tracking and modeling.