To accurately visualize the spatial distribution of heavy metal pollution and provide basic information on soil remediation in dairy farm, Geographic Information System (GIS) is used for optimization of sample collection and data analysis. Based on GIS technology, dairy manure, 10 cm-depth surface soil, 50 cm-depth sub soil, and surface water samples were collected from dairy farm in Dulbert Mongolian Autonomous County, Daqing City, Heilongjiang Province in China. The spatial distribution and assessment of heavy metals were performed by using GIS inverse distance weighted interpolation and pollution index method. The single factor pollution index value of As element in the soil was found to indicate the class of extreme contamination, whereas Ni in both surface water inside and outside the farm, and Sb in the cow drinking water were assigned to the level of moderate contamination. The comprehensive pollution index implied serious contamination for soil samples, slight contamination for water samples and safety for manure samples, respectively. Comprehensive score for heavy metal elements followed the orders of As>Zn>Cr>Ni>Cu>Pb>Cd>Hg. The horizontal pollution that mainly occurred in the middle and east regions was increased from north to south, and west to east district. Historically, the dairy farm belonged to heavily polluted saline-alkali soil, where the heavy metals might enter the food chain through transportation from soil to water, the cows, and eventually to the milk and human body. Visualizing spatial distribution of heavy metal contamination by using GIS technology will be of significance to provide useful information for soil remediation of dairy farm.

Manure treatment technologies are rapidly developing to minimize eutrophication of surrounding environments and potentially decrease the introduction of antibiotics and antibiotic resistant genes (ARGs) into the environment. While laboratory and pilot-scale manure treatment systems boast promising results, antibiotic and ARG removals in full-scale systems receiving continuous manure input have not been evaluated. The effect of treatment on ARGs is similarly lacking. This study examines the occurrence and transformation of sulfonamides, tetracyclines, tetracycline degradation products, and related ARGs throughout a full-scale advanced anaerobic digester (AAD) receiving continuous manure and antibiotic input. Manure samples were collected throughout the AAD system to evaluate baseline antibiotic and ARG input (raw manure), the effect of hygenization (post-pasteurized manure) and anaerobic digestion (post-digestion manure) on antibiotic and ARG levels. Antibiotics were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the ARGs tet(O), tet(W), sul1 and sul2 were analyzed by quantitative polymerase chain reaction (Q-PCR). Significant reductions in the concentrations of chlortetracycline, oxytetracycline, tetracycline and their degradation products were observed in manure liquids following treatment (p < 0.001), concomitant to significant increases in manure solids (p < 0.001). These results suggest sorption is the major removal route for tetracyclines during AAD. Significant decreases in the epimer-to-total residue ratios for chlortetracycline and tetracycline in manure solids further indicate degradation is desorption-limited. Moreover, sul1 and sul2 copies decreased significantly (p < 0.001) following AAD in the absence of sulfonamide antibiotics, while tetracyclines-resistant genes remained unchanged. A cross-sectional study of dairy farms utilizing natural aeration and liquid-solid separation treatments was additionally performed to compare levels of antibiotics and ARGs found in AAD with the levels in common manure management systems. The concentration of antibiotics in raw manure varied greatly between farms while minimal differences in ARGs were observed. However, significant (p < 0.01) differences in the levels of antibiotics and ARGs (except tet(W)) were observed in the effluents from the three different manure management systems.

Agricultural wastes are a source of steroid estrogens and, if present, conjugated estrogens may add to the estrogen load released to soil and aquatic environments. Dairy shed effluent samples were collected from 18 farms for analysis of steroid estrogens by GC–MS, conjugated estrogens by LC–MS–MS, and estrogenic activity by E-screen in vitro bioassay. 17α-estradiol was found at highest concentrations (median 730 ng l−1), followed by estrone (100 ng l−1) and 17β-estradiol (24 ng l−1). Conjugated estrogens (estrone-3-sulfate, 17α-estradiol-3-sulfate and 17β-estradiol-3,17-disulfate) were measured in most samples (12–320 ng l−1). Median estrogenic activity was 46 ng l−1 17β-estradiol equivalents. Conjugated estrogens contributed up to 22% of the total estrogen load from dairy farming, demonstrating their significance. Steroid estrogens dominated overall estrogenic activity measured in the samples. Significantly, 17α-estradiol contributed 25% of overall activity, despite potency 2% that of 17β-estradiol, highlighting the importance in environmental risk assessments of this previously neglected compound. In rural ecosystems, 17α-estradiol and conjugated estrogens are significant sources of environmental estrogens from agricultural wastes.

Dairy cattle of different ages experience different living conditions and varied frequency of antibiotic administration that likely influence the distribution of microbiome and resistome in ways that reflect different risks of microbial transmission. To assess the degree of variance in these distributions, fecal and soil samples were collected from six distinct housing areas on commercial dairy farms (n = 7) in Washington State. 16S rRNA gene sequencing indicated that the microbiota differed between different on-farm locations in feces and soil, and in both cases, the microbiota of dairy calves was often distinct from others (P < 0.05). Thirty-two specific antibiotic resistance genes (ARGs) were widely distributed on dairies, of which several clinically relevant ARGs (including cfr, cfrB, and optrA) were identified for the first time at U.S. dairies. Overall, ARGs were observed more frequently in feces and soil from dairy calves and heifers than from hospital, fresh, lactation and dry pens. Droplet-digital PCR demonstrated that the absolute abundance of floR varied greatly across housing areas and this gene was enriched the most in calves and heifers. Furthermore, in an extended analysis with 14 dairies, environmental soils in calf pens had the most antibiotic-resistant Escherichia coli followed by heifer and hospital pens. All soil E. coli isolates (n = 1,905) are resistant to at least 4 different antibiotics, and the PFGE analysis indicated that florfenicol-resistant E. coli is probably shared across geographically-separated farms. This study identified a discrete but predictable distribution of antibiotic resistance genes and organisms, which is important for designing mitigation for higher risk areas on dairy farms.

Co-contaminated soils by organic pollutants (OPs), antibiotics and antibiotic resistance genes (ARGs) have been becoming an emerging problem. However, it is unclear if an interaction exists between mixed pollutants and ARG abundance. Therefore, the potential relationship between OP contents and ARG and class 1 integron-integrase gene (intI1) abundance was investigated from seven dairy farms in Nanjing, Eastern China. Phenanthrene, pentachlorophenol, sulfadiazine, roxithromycin, associated ARG genes, and intI1 had the highest detection frequencies. Correlation analysis suggested a stronger positive relationship between the ARG abundance and the bioaccessible OP content than the total OP content. Additionally, the significant correlation between the bioaccessible mixed pollutant contents and ARG/intI1 abundance suggested a direct/indirect impact of the bioaccessible mixed pollutants on soil ARG dissemination. This study provided a preliminary understanding of the interaction between mixed pollutants and ARGs in co-contaminated soils.

We assessed the compliance of a Dutch landscape, dominated by dairy farming, with environmental quality standards using a combination of model calculations and measurements. The total ammonia emission of 2.4 kton NH₃ yr⁻¹ does not exceed the environmental quality standard (2.6 kton NH₃ yr⁻¹). Nevertheless, the total N deposition (on average 24.4 kg N ha⁻¹ yr⁻¹) is such that critical N loads are exceeded at 53% of the nature areas. The deposited N mainly results from non-agricultural sources and agricultural sources outside the area (72%). The calculated average NO₃ ⁻ concentration in the upper groundwater does not exceed the 50 mg l⁻¹ threshold. Calculated annual average N-total and P-total concentrations in discharge water are relatively high but these cannot be directly compared with thresholds for surface water. The results suggest that compliance monitoring at the landscape scale needs to include source indicators and cannot be based on state indicators alone.

Modelling complex systems such as farms often requires quantification of a large number of input factors. Sensitivity analyses are useful to reduce the number of input factors that are required to be measured or estimated accurately. Three methods of sensitivity analysis (the Morris method, the rank regression and correlation method and the Extended Fourier Amplitude Sensitivity Test method) were compared in the case of the CERES-EGC model applied to crops of a dairy farm. The qualitative Morris method provided a screening of the input factors. The two other quantitative methods were used to investigate more thoroughly the effects of input factors on output variables. Despite differences in terms of concepts and assumptions, the three methods provided similar results. Among the 44 factors under study, N₂O emissions were mainly sensitive to the fraction of N₂O emitted during denitrification, the maximum rate of nitrification, the soil bulk density and the cropland area.

For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l−1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.

Nonpoint source phosphorus (P) and nitrogen (N) pollution from agriculture is a global concern. Planting a cover crop after harvesting annual crops such as maize may help mitigate nutrient transport risk to surface and groundwater. Few studies have focused on the impact of a winter rye cover crop on both surface runoff (SR) and tile drainage (TD) water quality. Here, we measured N and P losses in SR and TD from maize plots grown with and without a winter rye cover crop. Four plots (46 × 23 m) in northern New York, USA, equipped with automated SR and TD flow monitoring were planted with winter rye (Secale cereal) in 2016 and 2017 after maize silage harvest. Plots were managed as typical silage fields for dairy farms in the region and received fertilizer and manure applications. Dissolved reactive P (DRP), total P (TP), nitrate-N, total N (TN), and total suspended solids (TSS) loads were monitored from 4/7/16 to 6/29/17. Cumulative SR (volumetric depth equivalent) was 1.8-fold lower for rye compared to control plots. Although runoff and loading were variable, cumulative TSS, TP, and DRP losses were approximately 3-fold lower for rye plots compared to control. Cumulative TN and nitrate-N loads for TD were similar; however, cumulative TN loss for SR was lower for rye plots. Surface runoff was the main pathway of P loss (> 90% of DRP and TP loss) with > 90% of cumulative P exported from 2017 snowmelt events. Results suggest winter rye mitigated N and P transport risk in SR compared to the common practice of leaving maize silage fields bare after harvest.

In dairy farming, recirculation and continuous use of nutrients are necessary for sustainable nutrient management in both the short- and long-term. When managed effectively, nutrient recycling can improve soil fertility. On the other hand, if applied nutrients are in excess of the soil’s nutrient holding capacity, nutrients can enter surface and ground water bodies resulting in eutrophication. Phosphorus (P) characterisation in manure, pond sludge and crust, in terms of plant availability, is the first step to sustainably manage nutrients within dairy farms. In this study, pond sludge and crust were characterised for P using a sequential fractionation method. Pond sludge and crust contained significant amounts of labile P. Water extractable P (H₂O-P) in primary anaerobic pond and secondary facultative pond sludges was found to be about 8 to 13 times higher than the amount found in the surface soil (0–10 cm) of a grazing paddock. Similarly, sodium bicarbonate extractable P (NaHCO₃-P) in the two types of sludges was found to be about 6 times higher than in the soil. The relatively higher labile P (H₂O-P and NaHCO₃-P) in sludge compared to soil indicates that the sludge can be utilised as a P fertilizer on grazing pasture. In contrast, lower H₂O-P compared to that of raw manure indicates that the use of sludge and crust instead of raw manure can reduce the possibility of P loss by surface runoff and leaching. Hence, it is preferable to use pond sludge and crust as fertiliser instead of raw manure.