Prediction of Manure and Nutrient Excretion from Dairy Cattle

Predicting nitrogen excretion of cattle kept under tropical and subtropical conditions using semimechanistic models

The present study aims at evaluating whether current semimechanistic models developed for temperate cattle systems can be adopted for cattle under (sub-) tropical husbandry systems to adequately (accurately and precisely) predict total nitrogen (TN), urine nitrogen (UN), faecal nitrogen (FN) excretion and its partition into different FN fractions. Selected models were built based on the feeding recommendations for ruminants of the British (Model A), German (Model G) and French (INRA; Model I) system. Model evaluation was conducted using eight nitrogen balance studies performed in El Salvador, Kenya and Peru (n = 392 individual observations including lactating cows, heifers and steers). Concordance correlation coefficient, root mean square errors (RMSE), and mean biases were estimated to evaluate the models' adequacy in predicting nitrogen excretion. Input variables causing greatest variation in nitrogen excretion prediction were identified by a sensitivity analysis and adjusted. Model G was able to adequately (i.e., RMSE of <25% of observed mean, systematic error of <5% of the mean square error) predict TN excretion through a compensation between overestimation of UN excretion and underestimation of FN excretion. None of the models were able to adequately predict UN, FN, and different FN fractions. Model I adequately predicted FN (RMSE = 18%) when duodenal microbial crude protein flow was increased, and the intercept used to predict FN excretion was reduced from 4.30 to 3.82 g of nitrogen per kilogram of dry matter intake. These adjustments, however, were not sufficient to predict adequately UN excretion (RMSE = 38%), individual FN fractions (RMSE > 56%), and TN (RMSE = 22%) excretion, by Model I.

Modeling ammonia emissions from manure in conventional, organic, and grazing dairy systems and practices to mitigate emissions

Almost 60% of all ammonia (NH3) emissions are from livestock manure. Understanding the sources and magnitude of NH3 emissions from manure systems is critical to implement mitigation strategies. This study models 13 archetypical conventional (5 farms), organic (5 farms), and grazing (3 farms) dairy farms to estimate NH3 emissions from manure at the barn, storage, and after land application. Mitigation practices related to management of the herd, crop production, and manure are subsequently modeled to quantify the change in NH3 emissions from manure by comparing archetypical practices with these alternative practices. A mass balance of nutrients is also conducted. Emissions per tonne of excreted manure for the manure system (barn, storage, and land application) range from 3.0 to 4.4 g of NH3 for conventional farms, 3.5 to 4.4 g of NH3 for organic farms, and 3.4 to 3.9 g of NH3 for grazing farms. For all farm types, storage and land application are the main sources of NH3 emissions from manure. In general, solid manures have higher emission intensities due to higher pH during storage (pH = 7.4 for liquid, 7.8 for slurry, and 8.5 for solid manure) and lower infiltration rates after land application when compared with slurry and liquid manures. The most effective management practices to reduce NH3 emissions from manure systems are combining solid-liquid separation with manure injection (up to 49% reduction in NH3 emissions), followed by injection alone, and reducing crude protein in the dairy ration, especially in organic and grazing farms that have grazing and forages as the main component of the dairy ration. This study also shows that the intensity of NH3 emissions from manure depends significantly on the functional unit and presents results per manure excreted, total solids in excreted manure, animal units, and fat- and protein-corrected milk.

Methane Emission, Carbon Footprint and Productivity of Specialized Dairy Cows Supplemented with Bitter Cassava (Manihot esculenta Crantz)

The objective of this research was to determine the effect of cassava (Manihot esculenta Crantz) supplementation on enteric methane (CH4) emissions, carbon footprint, and production parameters in dairy cows. Daily concentrate supply for Jersey and Jersey * Holstein breeds was evaluated in four treatments (T): T1: 100% commercial concentrate; T2: 70% concentrate + 30% cassava leaves; T3: 70% concentrate + 30% cassava roots; and T4: 70% concentrate + 15% cassava leaves + 15% cassava root chips. Measurements of CH4 emissions were performed using the polytunnel technique. Average daily dry matter intake ranged from 7.8 to 8.5 kg dry matter (DM). Cassava leaves were characterized by a high crude protein (CP) content (171 g CP/kg DM), with 5 times more neutral detergent fiber (NDF) content than cassava root (587 vs. 108 g NDF/kg DM). Average enteric CH4 emissions per animal ranged from 194 to 234 g/d (p > 0.05). The carbon footprint was reduced by replacing 30% of the concentrate with cassava leaves and/or roots. Energy-corrected milk production was 1.15 times higher in Jersey * Holstein animals than Jersey cows (47 vs. 55 kg). Therefore, supplementation with cassava leaves and/or roots is a nutritionally and environmentally sustainable strategy.

Increasing farm size is an effective way to decrease the carbon footprint in dairy cattle production

The purpose of this study was to investigate the effects of the farm size on the carbon footprint of dairy cattle farms in Isparta province in Türkiye. For this purpose, face-to-face interviews were conducted with 159 farms which represent 1866 dairy cattle farms in Isparta province. The number of animals on the farm was converted into animal unit (AU) and the farms were divided into three groups. Accordingly, farms were classified as small, medium, and large farms. The carbon footprint produced per AU in the farm was the sum of feed, enteric fermentation, CH4 from manure, CO2 from manure, N2O from manure, and anthropogenic emissions. The milk produced in the farms was standardized according to 4% fat and 3.3% protein (FPCM) and the ratio of meat to milk was found by dividing the total live weight gain produced except for cows by FPCM. Accordingly, 65% of the greenhouse gas emissions of dairy farms were allocated to milk and 35% to meat. Of the total emissions, enteric fermentation and emission on feed contributed the highest proportion. Results showed that when using the IPCC (2021) global warming potential (GWP) values, the carbon footprint for 1 kg of FPCM milk was 1.26 kg CO2-eq on average, whereas the carbon footprint for 1 kg of meat was 11.78 kg CO2-eq on average. Results showed that as farm size increased carbon footprint for a kilogram of FPCM and meat decreased and this showed the effectiveness of farm size on decreasing carbon footprint per unit of product.

Estimating Excreted Nutrients to Improve Nutrient Management for Grazing System Dairy Farms

Improving nutrient management in grazing system dairy farms requires determining nutrient flows through animals, the placement of cows within farms and potential for collection, and the re-use and loss of nutrients. We applied a model incorporating data collected at a range of temporal and spatial scales to quantify nutrient excretion in all locations that lactating herds visited on five days over a year on 43 conventional and organic grazing system dairy farms. The calculated nutrient loads excreted by cows in different places were highly skewed; while N, P and K deposited loads were consistent across the year, S, Ca and Mg loads varied between sampling times and seasons. The greatest mean and range in nutrient loads were deposited in paddocks, with the smallest amounts deposited in dairy sheds. All excreted nutrient loads increased with farm and herd sizes and milk production. Mean daily loads of 112, 15, 85, 11, 22 and 13 kg of N, P, K, S, Ca and Mg were deposited by the herds which, when standardised to a 305-day lactation, amounted to 24, 4, 20, 3, 5 and 3 t excreted annually, respectively. In addition to routine manure collection in dairy sheds, ensuring collection and recycling of nutrients excreted on feed pads and holding areas would decrease potential nutrient losses by 29% on average. Non-collected, recycled nutrients were disproportionately returned to paddocks in which cows spent time overnight, and except for S and Ca, nutrient loading rates were greater than rates applied as fertilisers. These data demonstrate the extent of excreted nutrients in grazing dairy systems and indicate the need to account for these nutrients in nutrient management plans for Australian dairy farms. We propose incorporating excretion data in current budgeting tools using data currently collected on most Australian grazing system dairy farms.

Effects of hindgut acidosis on production, metabolism, and inflammatory biomarkers in previously immune-activated lactating dairy cows

Previous stressors and systemic inflammation may increase the intestine's susceptibility to hindgut acidosis (HGA). Therefore, our experimental objectives were to evaluate the effects of isolated HGA on metabolism, production, and inflammation in simultaneously immune-activated lactating cows. Twelve rumen-cannulated Holstein cows (118 ± 41 d in milk; 1.7 ± 0.8 parity) were enrolled in a study with 3 experimental periods (P). Baseline data were collected during P1 (5 d). On d 1 of P2 (2 d), all cows received an i.v. lipopolysaccharide (LPS) bolus (0.2 µg/kg of body weight; BW). During P3 (4 d), cows were randomly assigned to 1 of 2 abomasal infusion treatments: (1) control (LPS-CON; 6 L of H₂O/d; n = 6) or (2) starch infused (LPS-ST; 4 kg of corn starch + 6 L of H₂O/d; n = 6). Treatments were allocated into 4 equal doses (1.5 L of H₂O or 1 kg of starch and 1.5 L of H₂O, respectively) and administered at 0000, 0600, 1200, and 1800 h daily. Additionally, both treatments received i.v. LPS on d 1 and 3 of P3 (0.8 and 1.6 µg/kg of BW, respectively) to maintain an inflamed state. Effects of treatment, time, and their interaction were assessed. Repeated LPS administration initiated and maintained an immune-activated state, as indicated by increased circulating white blood cells (WBC), serum amyloid A (SAA), and LPS-binding protein (LBP) during P2 and P3 (29%, 3-fold, and 50% relative to P1, respectively) for both abomasal infusion treatments. Regardless of abomasal treatment, milk yield and dry matter intake were decreased throughout P2 and P3 but with lesser severity following each LPS challenge (54, 44, and 37%, and 49, 42, and 40% relative to baseline on d 1 of P2, d 1 and d 3 of P3, respectively). As expected, starch infusions markedly decreased fecal pH (5.56 at nadir vs. 6.57 during P1) and increased P3 fecal starch relative to LPS-CON (23.7 vs. 2.4% of dry matter). Neither LPS nor starch infusions altered circulating glucose, insulin, nonesterified fatty acids, or β-hydroxybutyrate, although LPS-ST cows had decreased blood urea nitrogen throughout P3 (16% relative to LPS-CON). Despite the striking reduction in fecal pH, HGA had no additional effect on circulating WBC, SAA, or LBP. Thus, in previously immune-activated dairy cows, HGA did not augment the inflammatory state, as indicated by a lack of perturbations in production, metabolism, and inflammatory biomarkers.

Assessing the environmental impact of resource recovery from dairy manure

Manure management is a major contributor to environmental impacts from large-scale dairy production. In this study, technologies for recovering energy, nutrients, and water from dairy manure were evaluated using life cycle assessment (LCA) and compared to conventional practices on California dairy farms. Six scenarios were evaluated: conventional manure management practices, anaerobic digestion (AD) for biogas recovery, and four scenarios for nutrients, energy, and water integrated recovery, called NEWIR. The NEWIR system consists of hydrothermal carbonization (HTC) for energy recovery via hydrochar, algae cultivation in the HTC aqueous product for nutrient recovery and production of protein-rich cattle feed, and water recovery from algae pond effluent via membrane distillation. Four NEWIR scenarios were evaluated, each with a different species of algae. Based on the results of the LCA, AD improves GHG emissions relative to conventional practices by 82%, but has similar eutrophication impacts, posing similar concerns for nutrient management as current practices. Results for the NEWIR system are highly dependent on the algae species used. Three of the four species evaluated (Chlamydomonas reinhardtii, Chlorella vulgaris, and Scenedesmus obliquus) improve GHG emissions by 420-500 kg CO₂-eq. per functional unit, while net water consumption is increased by approximately 75% over AD and conventional practices Spirulina maxima requires more water and chemical inputs for cultivation than the other species, resulting in higher water use (21 times higher than baseline), though GHG emissions are still reduced by 85 kg CO₂-eq. per functional unit relative to conventional practices. All NEWIR scenarios improve eutrophication impacts relative to AD and conventional practices by 16-46% for marine eutrophication and 18-99% for freshwater eutrophication, depending on the algae species used. The results suggest integrated resource recovery through NEWIR is a promising treatment method for manure to mitigate GHG emissions and improve nutrient management on large-scale farms. In addition, carbon and nutrient trading policies are discussed in relation to resource recovery technologies and their potential to incentivize producers to recover products from dairy manure.

Novel epidemiological model of gastrointestinal nematode infection to assess grazing cattle resilience by integrating host growth, parasite, grass and environmental dynamics

Gastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised for Ostertagia ostertagi, the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection by O. ostertagi and Cooperia oncophora, a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first known mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies.

Effects of hindgut acidosis on inflammation, metabolism, and productivity in lactating dairy cows fed a high-fiber diet

Hindgut acidosis (HGA) may cause or contribute to the inflammatory state of transition dairy cows by compromising the intestinal barrier. Previous experiments isolating the effects of HGA on inflammatory metrics have generated inconsistent results, which may be explained by acclimation to low- versus high-starch diets. Thus, study objectives were to evaluate the effects of HGA in cows acclimated to a high-fiber diet. Ten rumen-cannulated Holstein cows (38 ± 5 kg/d milk yield; 243 ± 62 d in milk; 1.6 ± 1.1 parity; 663 ± 57 kg of body weight) were enrolled in a study with 2 experimental periods (P). Before P1, all cows were acclimated to a high-fiber, low-starch diet (50% neutral detergent fiber, 15% starch) for 17 d. During P1 (4 d), baseline data were collected for use as covariates. During P2 (7 d), cows were assigned to 1 of 2 abomasal infusion treatments: (1) control (CON; 1.5 L of H₂O/infusion; n = 4) or (2) starch infused (ST; 1 kg of corn starch + 1.5 L of H₂O/infusion; n = 6). All cows were infused with their respective treatments every 6 h daily at 0000, 0600, 1200, and 1800 h, such that ST cows received a total of 4 kg of corn starch/d. Starch infusions successfully induced HGA, as indicated by a marked decrease in fecal pH (1.2 units) relative to CON. However, in contrast to our assumptions, infusing starch had no deleterious effects on milk yield, energy-corrected milk, or voluntary dry matter intake during P2. Milk protein, lactose, their yields, fat yield, and somatic cell score remained unaffected by starch infusions, whereas milk fat content and urea nitrogen were decreased in ST relative to CON (8 and 17%, respectively). Overall, circulating glucose and β-hydroxybutyrate concentrations remained similar between treatments, but starch infusions decreased nonesterified fatty acids on d 3 relative to CON. Blood urea nitrogen decreased throughout P2 in ST (38%) relative to CON. In contrast to our hypothesis, HGA did not alter circulating serum amyloid A or lipopolysaccharide binding protein, nor did it affect rectal temperature. In summary, HGA moderately altered metabolism but did not affect production or elicit an inflammatory response in lactating dairy cows previously acclimated to a high-fiber diet.

Effects of hindgut acidosis on production, metabolism, and inflammatory biomarkers in feed-restricted lactating dairy cows

Study objectives were to evaluate the effects of hindgut acidosis (HGA) on production, metabolism, and inflammation in feed-restricted (FR) dairy cows. Twelve rumen-cannulated cows were enrolled in a study with 3 experimental periods (P). During P1 (5 d), baseline data were collected. During P2 (2 d), all cows were FR to 40% of their baseline feed intake. During P3 (4 d), cows remained FR and were assigned to 1 of 2 abomasal infusion treatments: (1) control (FR-CON; 6 L of H₂O/d; n = 6) or (2) starch (FR-ST; 4 kg of corn starch + 6 L of H₂O/d; n = 6). Respective treatments were partitioned into 4 equal doses (1 kg of corn starch/infusion) and were abomasally infused daily at 0000, 0600, 1200, and 1800 h. All 3 P were analyzed independently and the effects of treatment, time, and treatment × time were assessed using PROC MIXED, and P1 and P2 data were analyzed using the treatments cows were destined to be assigned to during P3. Hallmark production and metabolic responses to feed restriction were observed in both treatments, including decreased milk yield (39%) and energy-corrected milk (32%), circulating glucose (12%), insulin (71%), and increased circulating nonesterified fatty acids (3.2-fold) throughout both P2 and P3, relative to P1. However, despite a marked reduction in fecal pH (0.96 units), the aforementioned metrics were unaltered by HGA. During P3, starch infusions increased circulating β-hydroxybutyrate, with the most pronounced increase occurring on d 2 (81% relative to FR-CON). Further, feed restriction decreased blood urea nitrogen during P2 (17% relative to P1) in both treatments, and this was exacerbated by starch infusions during P3 (31% decrease relative to FR-CON). In contrast to our hypothesis, neither feed restriction nor HGA increased circulating acute-phase proteins (serum amyloid A and lipopolysaccharide binding protein) relative to P1 or FR-CON, respectively. Thus, despite marked reductions in fecal pH, prior feed restriction did not appear to increase the susceptibility to HGA.

Effects of hindgut acidosis on metabolism, inflammation, and production in dairy cows consuming a standard lactation diet

Postruminal intestinal barrier dysfunction caused by excessive hindgut fermentation may be a source of peripheral inflammation in dairy cattle. Therefore, the study objectives were to evaluate the effects of isolated hindgut acidosis on metabolism, inflammation, and production in lactating dairy cows. Five rumen-cannulated lactating Holstein cows (32.6 ± 7.2 kg/d of milk yield, 242 ± 108 d in milk; 642 ± 99 kg of body weight; 1.8 ± 1.0 parity) were enrolled in a study with 2 experimental periods (P). During P1 (4 d), cows were fed ad libitum a standard lactating cow diet (26% starch dry matter) and baseline data were collected. During P2 (7 d), all cows were fed the same diet ad libitum and abomasally infused with 4 kg/d of pure corn starch (1 kg of corn starch + 1.25 L of H₂O/infusion at 0600, 1200, 1800, and 0000 h). Effects of time (hour relative to the first infusion or day) relative to P1 were evaluated using PROC MIXED in SAS (version 9.4; SAS Institute Inc.). Infusing starch markedly reduced fecal pH (5.84 vs. 6.76) and increased fecal starch (2.2 to 9.6% of dry matter) relative to baseline. During P2, milk yield, milk components, energy-corrected milk yield, and voluntary dry matter intake remained unchanged. At 14 h, plasma insulin and β-hydroxybutyrate increased (2.4-fold and 53%, respectively), whereas circulating glucose concentrations remained unaltered. Furthermore, blood urea nitrogen increased at 2 h (23%) before promptly decreasing below baseline at 14 h (13%). Nonesterified fatty acids tended to decrease from 2 to 26 h (40%). Circulating white blood cells and neutrophils increased on d 4 (36 and 73%, respectively) and somatic cell count increased on d 5 (4.8-fold). However, circulating serum amyloid A and lipopolysaccharide-binding protein concentrations were unaffected by starch infusions. Despite minor changes in postabsorptive energetics and leukocyte dynamics, abomasal starch infusions and the subsequent hindgut acidosis had little or no meaningful effects on biomarkers of immune activation or production variables.

Evaluation of feed restriction and abomasal infusion of resistant starch as models to induce intestinal barrier dysfunction in healthy lactating cows

Intestinal hyperpermeability and subsequent immune activation alters nutrient partitioning and thus, decreases productivity. Developing experimental models of intestinal barrier dysfunction in heathy cows is a prerequisite in identifying nutritional strategies to mitigate it. Six cannulated Holstein cows (mean ± standard deviation, 37 ± 10 kg/d milk yield; 219 ± 97 d in milk; 691 ± 70 kg body weight) were used in a replicated 3 × 3 Latin square design experiment with 21-d periods (16-d wash-out and 5-d challenge) to evaluate either feed restriction or hindgut acidosis as potential models for inducing intestinal hyperpermeability. Cows were randomly assigned to treatment sequence within square and treatment sequences were balanced for carryover effects. Treatments during the challenge were (1) control (CTR; ad libitum feeding); (2) feed restriction (FR; total mixed ration fed at 50% of ad libitum feed intake); and (3) resistant starch (RS; 500 g of resistant starch infused in abomasum once a day as a pulse-dose 30 min before morning feeding). The RS (ActiStar RT 75330, Cargill Inc.) was tapioca starch that was expected to be resistant to enzymatic digestion in the small intestine and highly fermentable in the hindgut. Blood samples were collected 4 h after feeding on d 13 and 14 of the wash-out periods (baseline data used as covariate), and on d 1, 3, and 5 of the challenge periods. Fecal samples were collected 4 and 8 h after the morning feeding on d 14 of the wash-out periods and d 5 of the challenge periods. By design, FR decreased dry matter intake (48%) relative to CTR and RS, and this resulted in marked reductions in milk and 3.5% FCM yields over time, with the most pronounced decrease occurring on d 5 of the challenge (34 and 27%, respectively). Further, FR increased somatic cell count by 115% on d 5 of the challenge relative to CTR and RS. Overall, FR increased nonesterified fatty acids (159 vs. 79 mEq/L) and decreased BHB (8.5 vs. 11.2 mg/dL), but did not change circulating glucose relative to CTR. However, RS had no effect on production or metabolism metrics. Resistant starch decreased fecal pH 8 h after the morning feeding (6.26 vs. 6.81) relative to CTR and FR. Further, RS increased circulating lipopolysaccharide binding protein (4.26 vs. 2.74 µg/mL) compared with FR only on d 1 of the challenge. Resistant starch also increased Hp (1.52 vs. 0.48 µg/mL) compared with CTR, but only on d 5 of the challenge. However, neither RS or FR affected concentrations of serum amyloid A, IL1β, or circulating endotoxin compared with CTR. The lack of consistent responses in inflammatory biomarkers suggests that FR and RS did not meaningfully affect intestinal barrier function. Thus, future research evaluating the effects of hindgut acidosis and FR using more intense insults and direct metrics of intestinal barrier function is warranted.

Resource recovery for environmental management of dilute livestock manure using a solid-liquid separation approach

Mechanical solid-liquid separation is an emerging closed-loop technology to recover and recycle carbon, nutrients and water from dilute livestock manure. This closed-loop concept is tested using a modular separation technology (Z-Filter) applied at full-scale for the first time to treat effluent from a pasture-based dairy. Effluent flow rates were 200-400 L min^-1 at a total solids (TS) content of 0.52% (pH 7.2). Separation efficiency and composition of the separated solid fraction were determined, and chemically-assisted separation with cationic polymer flocculant with/without hydrated lime was also tested. Without flocculant and lime, 25.9% of TS and 33.4% of volatile solids (VS) ended up in the solid fraction, but total Kjeldahl nitrogen (TKN), phosphorus (P) and potassium recovery was not significant, likely being in poorly separable fine particle or soluble fractions. With a 5% flow-based dosage of flocculant, most of the TS (69%) and VS (85%), and notable amounts of TKN (52-56%) and P (40%) ended up in the solid fraction. Phosphorus recovery was further increased to 91% when both flocculant and hydrated lime was added up to pH 9.2. The solid fraction was stackable with 16-20% TS, making transport more economical to enable further processing and beneficial reuse of nutrients and organic matter. Removal of VS also reduces fugitive methane emissions from uncovered anaerobic effluent ponds. Overall, the results indicated that solid-liquid separation could provide improved environmental management options for dairy farmers with dilute manure effluent to beneficially utilise organic matter and nutrients.

Potential of ESBL-producing Escherichia coli selection in bovine feces after intramammary administration of first generation cephalosporins using in vitro experiments

Selection and spread of Extended Spectrum Beta-Lactamase (ESBL) -producing Enterobacteriaceae within animal production systems and potential spillover to humans is a major concern. Intramammary treatment of dairy cows with first-generation cephalosporins is a common practice and potentially selects for ESBL-producing Enterobacteriaceae, although it is unknown whether this really occurs in the bovine fecal environment. We aimed to study the potential effects of intramammary application of cephapirin (CP) and cefalonium (CL) to select for ESBL-producing Escherichia coli in the intestinal content of treated dairy cows and in manure slurry, using in vitro competition experiments with ESBL and non-ESBL E. coli isolates. No selection of ESBL-producing E. coli was observed at or below concentrations of 0.8 µg/ml and 4.0 µg/ml in bovine feces for CP and CL, respectively, and at or below 8.0 µg/ml and 4.0 µg/ml, respectively, in manure slurry. We calculated that the maximum concentration of CP and CL after intramammary treatment with commercial products will not exceed 0.29 µg/ml in feces and 0.03 µg/ml in manure slurry. Therefore, the results of this study did not find evidence supporting the selection of ESBL-producing E. coli in bovine feces or in manure slurry after intramammary use of commercial CP or CL-containing products.

Tracking gastrointestinal nematode risk on cattle farms through pasture contamination mapping

Gastrointestinal nematode (GIN) parasites in grazing cattle are a major cause of production loss and their control is increasingly difficult due to anthelmintic resistance and climate change. Rotational grazing can support control and decrease reliance on chemical intervention, but is often complex due to the need to track grazing periods and infection levels, and the effect of weather on larval availability. In this paper, a simulation model was developed to predict the availability of infective larvae of the bovine GIN, Ostertagia ostertagi, at the level of individual pastures. The model was applied within a complex rotational grazing system and successfully reproduced observed variation in larval density between fields and over time. Four groups of cattle in their second grazing season (n = 44) were followed throughout the temperate grazing season with regular assessment of GIN faecal egg counts, which were dominated by O. ostertagi, animal weight and recording of field rotations. Each group of cattle was rotationally grazed on six group-specific fields throughout the 2019 grazing season. Maps and calendars were produced to illustrate the change in pasture infectivity (density of L3 on herbage) across the 24 separate grazing fields. Simulations predicted differences in pasture contamination levels in relation to the timing of grazing and the return period. A proportion of L3 was predicted to persist on herbage over winter, declining to similar intensities across fields before the start of the following grazing season, irrespective of contamination levels in the previous year. Model predictions showed good agreement with pasture larval counts. The model also simulated differences in seasonal pasture infectivity under rotational grazing in systems that differed in temperature and rainfall profiles. Further application could support individual farm decisions on evasive grazing and refugia management, and improved regional evaluation of optimal grazing strategies for parasite control. The integration of weather and livestock movement is inherent to the model, and facilitates consideration of climate change adaptation through improved disease control.

Prediction of nitrogen excretion from data on dairy cows fed a wide range of diets compiled in an intercontinental database: A meta-analysis

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

Can machine learning algorithms perform better than multiple linear regression in predicting nitrogen excretion from lactating dairy cows

This study aims to compare the performance of multiple linear regression and machine learning algorithms for predicting manure nitrogen excretion in lactating dairy cows, and to develop new machine learning prediction models for MN excretion. Dataset used were collated from 43 total diet digestibility studies with 951 lactating dairy cows. Prediction models for MN were developed and evaluated using MLR technique and three machine learning algorithms, artificial neural networks, random forest regression and support vector regression. The ANN model produced a lower RMSE and a higher CCC, compared to the MLR, RFR and SVR model, in the tenfold cross validation. Meanwhile, a hybrid knowledge-based and data-driven approach was developed and implemented to selecting features in this study. Results showed that the performance of ANN models were greatly improved by the turning process of selection of features and learning algorithms. The proposed new ANN models for prediction of MN were developed using nitrogen intake as the primary predictor. Alternative models were also developed based on live weight and milk yield for use in the condition where nitrogen intake data are not available (e.g., in some commercial farms). These new models provide benchmark information for prediction and mitigation of nitrogen excretion under typical dairy production conditions managed within grassland-based dairy systems.

Nitrogen excretion from beef cattle fed a wide range of diets compiled in an intercontinental dataset: a meta-analysis

Manure N from cattle contributes to nitrate leaching, nitrous oxide and ammonia emissions. Measurement of manure N outputs on commercial beef cattle operations is laborious, expensive, and impractical; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were to (1) collate an international dataset of N excretion in feces and urine based on individual observations from beef cattle; (2) determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; (3) develop robust and reliable N excretion prediction models based on individual observation from beef cattle consuming various diets. A meta-analysis based on individual beef data from different experiments was carried from a raw dataset including 1,004 observations from 33 experiments collected from 5 research institutes in Europe (n = 3), North America (n = 1), and South America (n = 1). A sequential approach was taken in developing models of increasing complexity by incrementally adding significant variables that effected fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models with experiment as a random effect. Simple models including dry matter intake (DMI) were better at predicting fecal N excretion, than those using only dietary nutrient composition or BW. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI. A model including DMI and dietary component concentrations led to the most robust prediction of fecal and urinary N excretion, generating root mean square prediction errors as a percentage of the observed mean values of 25.0% for feces and 25.6% for urine. Complex total manure N excretion models based on BW and dietary component concentrations led to the lowest prediction errors of about 14.6%. In conclusion, several models to predict N excretion already exist, but the ones developed in this study are based on individual observations encompassing larger variability than the previous developed models. In addition, models that include information on DMI or N intake are required for accurate prediction of fecal, urinary and total manure N excretion. In the absence of intake data, equations have poor performance as compared to equations based on intake and dietary component concentrations.

The Dairy Cow Slurry Composition Used as Organic Fertilizer Is Influenced by the Level and Origin of the Dietary Protein

Simple Summary

Dairy cattle is a source of ammonia because only 25–35% of the dietary nitrogen is used for the synthesis of milk, and the remainder is excreted through feces and urine. A reduction in dietary nitrogen is an effective way to decrease nitrogen excretions and subsequent ammonia emissions. However, this reduction should not induce a decrease in the potential yield of the cows. On the other hand, legumes are more susceptible than grasses to undergo proteolysis in the silage process due to their higher protein content. However, not all legumes have the same rate of proteolysis rate. With the main objective of improving the quality of the slurry to be used as organic fertilizer, two sequential experiments were carried out. In the first, it was intended to determine the optimal level of dietary nitrogen intake necessary for high-production dairy cows. Once this level was established, two legume silages with different proteolysis rates were evaluated. In conclusion, dairy cows producing more than 30 kg of milk per day can meet their needs with diets with 13% of protein, reducing nitrogen losses through urine. The main pathway for the excretion of dietary nitrogen provided by legume silage is the urine, and the protein of field pea silage is metabolized towards ammonia production to a larger extent than the protein of faba bean silage.

Abstract

Less than 30% of dairy cattle’s nitrogen ingested is retained in milk. Therefore, large amounts of nitrogen can be excreted in manure and urine with a potential environmental impact. In addition, some legume forages can be more susceptible to proteolysis during the silage process than grasses, and dairy cows fed these legume silages would excrete a larger quantity of nitrogen in slurry. The objectives of this work were to evaluate the amount of nitrogen excretion in dairy cows fed different protein levels and legume silages with a view to improve the slurry quality as a co-product that can be used as fertilizer. Two double 3 × 3 Latin square trials were carried out in order to study three different protein levels (high, medium, and low) and three different silages (grass, faba bean, and field pea). Dry matter intake, milk production, and composition were not affected by treatments. The excretion of ammonia-N in the urine was almost four times lower in the diet with the lowest protein level. The ammonia-N in the urine was twice as high with the pea silage than faba bean and grass silages. In conclusion, the diet containing 13% of protein meets the protein requirement for lactating cows producing 31 kg daily, with low nitrogen excretion in the urine, and the main pathway for the excretion of surplus nitrogen from legume silages is through urine and the metabolization of pea silage protein goes toward ammonia-N.

Organic Nutrient Source Allocation and Use in Smallholder Farming Communities: What Are We Missing?

Organic nutrient sources (ONS) are managed as a key resource by smallholder farmers to maintain the productivity of soils. Recycling of ONS by applying them to soils is a globally dominant strategy of ecological nutrient management. Understanding how ONS produced on-farm are allocated and what drives farmer decision making around their use is critical for sustainable nutrient management in smallholder agroecosystems. Using focus group discussions and a survey of 184 farming households, we studied socio-economic, socio-cultural, and environmental drivers of ONS allocation and use at the farm scale in three contrasting agroecological zones of western Kenya. Farm typologies of ONS management were also developed using cluster analysis based on resource endowment and the connectedness of farmers, management norms, and interaction with extension. Our findings suggest that the more resource endowed a farmer is, the more ONS are allocated to the main plot within the farm. We also observed that farmers preferred allocating more resources to plots that were considered more fertile. Land tenure had an important influence, in that main plots not owned by farmers were more likely to retain ONS such as crop residues. Management of residues is dependent on farmer gender, for instance, female farmers tended to burn legume residues in particular, which is notable since these higher quality residues are often considered key to sustainable soil nutrient management. Farm typologies featured different allocation patterns of ONS and were associated with resource endowment and farmer networks, including external ties to extension agents and internal ties to other farmers. Finally, there was a strong overarching influence of agroecological zone that often escapes characterization on the allocation of ONS. As research and development organizations continue to engage with smallholder farmers to reduce the burden of global food insecurity, the insights gained by this research will allow better anticipation of drivers and obstacles to improved nutrient management in these farming landscapes and communities.

Evaluation of nitrogen excretion equations for ryegrass pasture-fed dairy cows

Accurate and precise estimates of nitrogen (N) excretion in faeces and urine of dairy cattle may provide direct tools to improve N management and thus, to mitigate environmental pollution from dairy production. Empirical equations of N excretion have been evaluated for indoor dairy cattle but there is no evaluation for cows fed high proportions of fresh forage. Therefore, the objective of the current study was to evaluate N excretion equations with a unique data set of zero-grazing experiments. Through literature searches, 89 predictive equations were identified from 13 studies. An independent data set was developed from seven zero-grazing experiments with, in total, 55 dairy Holstein-Friesian cows. Models' performance was evaluated with statistics derived from a mixed-effect model and a simple regression analysis model. Squared sample correlation coefficients were used as indicators of precision and based on either the best linear unbiased predictions (R²_BLUP) or model-predicted estimates (R²_MDP) derived from the mixed model and simple regression analysis, respectively. The slope (β₀), the intercept (β₁) and the root mean square prediction error (RMSPE_m%) were calculated with the mixed-effect model and used to assess accuracy. The root mean square prediction error (RMSPE_sr%) and the decomposition of the mean square prediction error were calculated with the simple regression analysis and were used to estimate the error due to central tendency (mean bias), regression (systematic bias), and random variation. Concordance correlation coefficient (CCC) were also calculated with the simple regression analysis model and were used to simultaneously assess accuracy and precision. Considering both analysis models, results suggested that urinary N excretion (UN; R²_MDP = 0.76, R²_BLUP = 0.89, RMSPE_m% = 17.2, CCC = 0.82), total manure N excretion (ManN; R²_MDP = 0.83, R²_BLUP = 0.90, RMSPE_m% = 11.0, CCC = 0.84) and N apparently digested (NAD; R²_MDP = 0.97, R²_BLUP = 0.97, RMSPE_m% = 5.3, CCC = 0.95) were closely related to N intake. Milk N secretion was better predicted using milk yield as a single independent variable (MilkN; R²_MDP = 0.77, R²_BLUP = 0.97, RMSPE_m% = 6.0, CCC = 0.74). Additionally, DM intake was a good predictor of UN and ManN and dietary CP concentration of UN and ManN. Consequently, results suggest that several evaluated empirical equations can be used to make accurate and precise predictions concerning N excretion from dairy cows being fed on fresh forage.

Sources and Risk Factors for Nitrate and Microbial Contamination of Private Household Wells in the Fractured Dolomite Aquifer of Northeastern Wisconsin

BACKGROUND

Groundwater quality in the Silurian dolomite aquifer in northeastern Wisconsin, USA, has become contentious as dairy farms and exurban development expand.

OBJECTIVES

We investigated private household wells in the region, determining the extent, sources, and risk factors of nitrate and microbial contamination.

METHODS

Total coliforms, Escherichia coli, and nitrate were evaluated by synoptic sampling during groundwater recharge and no-recharge periods. Additional seasonal sampling measured genetic markers of human and bovine fecal-associated microbes and enteric zoonotic pathogens. We constructed multivariable regression models of detection probability (log-binomial) and concentration (gamma) for each contaminant to identify risk factors related to land use, precipitation, hydrogeology, and well construction.

RESULTS

Total coliforms and nitrate were strongly associated with depth-to-bedrock at well sites and nearby agricultural land use, but not septic systems. Both human wastewater and cattle manure contributed to well contamination. Rotavirus group A, Cryptosporidium, and Salmonella were the most frequently detected pathogens. Wells positive for human fecal markers were associated with depth-to-groundwater and number of septic system drainfield within 229m. Manure-contaminated wells were associated with groundwater recharge and the area size of nearby agricultural land. Wells positive for any fecal-associated microbe, regardless of source, were associated with septic system density and manure storage proximity modified by bedrock depth. Well construction was generally not related to contamination, indicating land use, groundwater recharge, and bedrock depth were the most important risk factors.

DISCUSSION

These findings may inform policies to minimize contamination of the Silurian dolomite aquifer, a major water supply for the U.S. and Canadian Great Lakes region. https://doi.org/10.1289/EHP7813.

Toward Specialized or Integrated Systems in Northwest Europe: On-Farm Eco-Efficiency of Dairy Farming in Germany

Intensive confinement (IC) systems for dairying have become widespread during the last decades. However, potential advantages of alternative systems such as full-grazing (FG) or integrated dairy/cash-crop (IFG) systems with regards to better provision of ecosystem services are widely discussed. To investigate performance and environmental impacts, we compared four prevailing dairy systems using an on-farm research study. The farm types differed in their share of pasture access and quantity of resource inputs: (i) an IC with a high import of supplements and mineral fertilizers; (ii) a semi-confinement (SC) with daytime pasture access during summer and moderate import of supplementary feeds representing the base-line scenario; (iii) a FG based on grazed seeded grass-clover swards with no purchased N-fertilizers and low quantities of supplementary feeds; and (iv) an IFG comparable to FG but based on grass-clover leys integrated in a cash-crop rotation. Results revealed highest milk productivity (16 t energy-corrected-milk (ECM) ha−1) and farm-N-balance (230 kg N ha−1) in IC; however, the highest product carbon footprint (PCF; 1.2 CO2eq kg ECM−1) and highest N-footprint (13 g N kg ECM−1) were found in the baseline system SC. The FG and IFG revealed on average similar forage dry matter yields (10 – 11 t DM ha−1) at similar crude protein and net-energy-lactation ratios per kg DM-intake compared to the IC and SC. The PCF in FG were comparable to IC (0.9 vs. 1.1 kg CO2eq kg ECM−1) but at a lower N-footprint (9 vs. 12 g N kg ECM−1). However, despite low measured N-losses in the FG system, the farm-N-surplus was exceeded by 90 kg N ha−1. A further reduction was only possible in the IFG (50 kg N ha−1) by accounting for a potential N-carry-over from N-rich plant residues to the cash-crop unit, leading to the lowest PCF (0.6 kg CO2eq kg ECM−1) for the IFG, with still moderate milk yield levels (~10,500 kg ECM ha−1). According to this bottom-up approach based on field data, improved integrated grazing systems could provide an important opportunity to increase the ecosystem services from dairy farming, operating with land use efficiencies similar to IC.

Environmental Impact of Rotationally Grazed Pastures at Different Management Intensities in South Africa

Simple Summary

Nitrogen fertilization, irrigation and concentrate feeding are key management factors in grazed dairy-pasture systems. However, the extent to which these management factors affect environmental efficiency is a current debate among scientists. We designed a study to investigate dairy system environmental efficiency through the evaluation of the carbon footprint of milk and the nitrogen-balance as a result of different fertilization rates applied on irrigated dairy-pastures in South Africa. The lowest carbon footprint was observed when low rates of fertilizer were applied. Methane from ruminal digestion and nitrous oxide emissions from grazed pastures were the main contributors affecting the carbon footprint. The application of fertilizer resulted in only small herbage yield differences between treatments. The nitrogen-balance was negative when no nitrogen was applied. This indicates that such an approach will not be sustainable without adapting sward species composition (e.g., introduction of forage legumes), even though nitrogen circulates through animal manure to the pasture. The environmental impact of milk produced from pasture-based dairy farms can be reduced through increased farm nitrogen use-efficiency and improved irrigation systems in South Africa.

Abstract

Nitrogen fertilization, irrigation and concentrate feeding are important factors in rotational pasture management for dairy farms in South Africa. The extent to which these factors affect environmental efficiency is subject to current and intense debate among scientists. A three-year field study was conducted to investigate the yield response of different N-fertilizer treatments (0 (N0), 220 (N20), 440 (N40), 660 (N60) and 880 (N80) kg N ha⁻¹ year⁻¹) on grazed pastures and to calculate the carbon footprint (CF) of milk produced. Excessive N-fertilization (N60 and N80) did not increase herbage dry matter and energy yields from pastures. However, N80 indicated the highest N-yield but at the same time also the highest N surpluses at field level. A maximum fertilizer rate of 220 kg ha⁻¹ year⁻¹ (in addition to excreted N from grazing animals) appears sufficient to ensure adequate herbage yields (~20 t DM ha⁻¹ year⁻¹) with a slightly positive field-N-balance. This amount will prevent the depletion of soil C and N, with low N losses to the environment, where adequate milk yields of ~17 t ECM ha⁻¹ with a low CF (~1.3 kg CO₂ kg ECM⁻¹) are reached. Methane from enteric fermentation (~49% ± 3.3) and N₂O (~16% ± 3.2) emissions from irrigated pastures were the main contributors to the CF. A further CF reduction can be achieved by improved N-fertilization planning, low emission irrigation techniques and strategies to limit N₂O emissions from pasture soils in South Africa.

Dairy Farms and Life Cycle Assessment (LCA): The Allocation Criterion Useful to Estimate Undesirable Products

In this study, the life cycle assessment (LCA) principle was performed to estimate the environmental impact of three dairy farms that operate using different farming systems, namely, conventional (CON), organic (ORG), and high-quality (HQ) modes. In Italy, the typical style of high-quality (HQ) farming is commonly included in the conventional system but is more strictly regulated by the Decree of the Italian Ministry of Health N° 185/1991. Although the farms are not fully representative of each conduct, they showed intrinsic peculiarities, e.g., the cow-culling rate of each system. This rate requires a quantification as it may be related to loss of income. Allocation criteria were applied to attribute the quantities of pollutants to the co-products: wheat, involved in the congruence and number of cows culled, the latter being undesirable and therefore necessary to quantify. Analysis of variance (ANOVA) highlighted that the no-dairy products significantly mitigated (p < 0.05) some of the impacts’ categories. The allocation of culled cows decreased the impacts of the CON and particularly those of the ORG farms when the mass mode was adopted. HQ showed values similar to the results without allocation. Overall, the objective of identifying a “marker” of undesirable products, estimated by the culling rate, was partially achieved.

Updating analysis of nitrogen balance experiments in dairy cows

Nitrogen balance (NB) experiments allow calculation of N retention in the body by subtracting N excreted in feces (NF), urine (NU) and milk (NM) from N intake (NI). In a previous study, we found that NB data from experiments with lactating dairy cows were generally high and, in the current meta-analysis, we update our earlier study with experiments from the last 2 decades and investigate probable causes of error. A total of 83 publications, with 86 experiments and 307 dietary treatments, were selected from top-ranked scientific journals that reported all NB components. The NB and NB components were analyzed by linear regression with a model that used NI as an independent variable and experiment as a random effect. The NF, NU and NM each represented 27 to 34% of NI, and the remaining N accumulated in the body was equal to 38.5 g/d (overall SD = 43.2 g/d). Retained N (as g/d or % of NI) increased linearly with NI, and this led to unlikely high N retentions, especially at high NI. Both NF and NU (g/d) increased with increasing NI, and we assume that some N in feces and urine were unaccounted. Only ~22% of experiments measured N in wet feces samples and, when analysis used dry samples, no mention of corrections due to potential volatile N losses during drying were reported. No experimentalists preserved feces immediately to prevent volatilization during collection. Moreover, ~27% of experiments estimated urine volumes by concentration of creatinine in spot samples, and in these experiments, NU was ~12% lower than those where total urine was collected (168 vs. 191 g/d). Only 40 experiments reported the volume and concentration of acids used for urine preservation, 33 furnished incomplete information, and the remainder did not describe the urine preservation method. In conclusion, the results of NB experiments using lactating dairy cows overestimate N retention, and the losses of N from feces and urine are the most probable reason.

Environmental risk assessment of the anthelmintic albendazole in Eastern Africa, based on a systematic review

This study performs an environmental risk assessment (ERA) of the anthelmintic medicine albendazole (ABZ) in the eastern African region. A systematic literature search strategy was applied to obtain quantitative information on the physicochemical characteristics, the metabolization-fate, the ecotoxicity and the environmental occurrence in different countries worldwide serving as model regions. In addition, insilico tools were employed to obtain data on physicochemical characteristics and toxic hazards of ABZ and its metabolites. Moreover, ERA models were used to predict environmental concentrations in different compartments and compare them with the measured environmental concentrations. Finally, the environmental risk of ABZ in the eastern Africa was estimated by calculating the risk quotient (RQ), and its uncertainty estimated by Monte Carlo simulation. The predicted environmental concentrations of ABZ in surface water in the model region based on consumption (1.6-267 ng/L) were within the range of values obtained from the measured environmental concentrations of the same region (0.05-101,000 ng/L). Using these models with adapted input variables for eastern Africa, the predicted surface water concentration in that region was 19,600 ± 150 ng/L (95% CI). The calculated soil concentrations of ABZ in the model regions and the eastern Africa were found to be 0.057 ± 0.0 μg/kg and 0.022 ± 0.0 μg/kg, respectively. The environmental risk expressed as risk quotient of ABZ in eastern Africa estimated for the aquatic compartment (146 ± 1) indicated a significant environmental risk calling on appropriate actions from the competent authorities to reduce this risk in this region.

Valuing economic impact reductions of nutrient pollution from livestock waste

Nutrient pollution from livestock waste impacts both fresh and marine coastal waters. Harmful algae blooms (HABs) are a common ecosystem-level response to such pollution that is detrimental to both aquatic life and human health and that generates economic losses (e.g., property values and lost tourism). Waste treatment and management technologies are not well established practices due, in part, to the difficulty to attribute economic value to associated social and environmental impacts of nutrient pollution. In this work, we propose a computational framework to quantify the economic impacts of HABs. We demonstrate the advantage of quantifying these impacts through a case study on livestock waste management in the Upper Yahara watershed region (in the state of Wisconsin, USA). Our analysis reveals that every excess kilogram of phosphorus runoff from livestock waste results in total economic losses of 74.5 USD. Furthermore, we use a coordinated market analysis to demonstrate that this economic impact provides a strong enough incentive to activate a nutrient management and valorization market that can help balance phosphorus within the study area. The proposed framework can help state, tribes, and federal regulatory agencies develop regulatory and non-regulatory policies to mitigate the impacts of nutrient pollution.

Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed

Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (British Columbia, Canada and Washington, US) using locally-derived data, national statistics and standard parameters. Feed imports for dairy (mainly in the US) and poultry (mainly in Canada) accounted for 30 and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, lower than atmospheric deposition (10%). N imported by returning salmon representing marine derived nutrients accounted for <0.06 % of total N input. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the US and poultry products in Canada. Riverine export of N was estimated at 28% of total N export. The commonly used crop nitrogen use efficiency (NUE) metric alone did not provide sufficient information on farming activities but in combination with other criteria such as farm-gate NUE may better represent management efficiency. Agriculture was the primary driver of N inputs to the environment as a result of its regional importance; the N budget information can inform management to minimize N losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.

Predicting nutrient excretion from dairy cows on smallholder farms in Indonesia using readily available farm data

Objective

This study was conducted to provide models to accurately predict nitrogen (N) and phosphorus (P) excretion of dairy cows on smallholder farms in Indonesia based on readily available farm data.

Methods

The generic model in this study is based on the principles of the Lucas equation, describing the relation between dry matter intake (DMI) and faecal N excretion to predict the quantity of faecal N (Q_FN). Excretion of urinary N and faecal P were calculated based on National Research Council recommendations for dairy cows. A farm survey was conducted to collect input parameters for the models. The data set was used to calibrate the model to predict Q_FN for the specific case. The model was validated by comparing the predicted quantity of faecal N with the actual quantity of faecal N (Q_FNACT) based on measurements, and the calibrated model was compared to the Lucas equation. The models were used to predict N and P excretion of all 144 dairy cows in the data set.

Results

Our estimate of true N digestibility equalled the standard value of 92% in the original Lucas equation, whereas our estimate of metabolic faecal N was −0.60 g/100 g DMI, with the standard value being −0.61 g/100 g DMI. Results of the model validation showed that the R² was 0.63, the MAE was 15 g/animal/d (17% from Q_FNACT), and the RMSE was 20 g/animal/d (22% from Q_FNACT). We predicted that the total N excretion of dairy cows in Indonesia was on average 197 g/animal/d, whereas P excretion was on average 56 g/animal/d.

Conclusion

The proposed models can be used with reasonable accuracy to predict N and P excretion of dairy cattle on smallholder farms in Indonesia, which can contribute to improving manure management and reduce environmental issues related to nutrient losses.

Nitrous oxide emissions from the urine of beef cattle as regulated by dietary crude protein and gallic acid1

Two consecutive trials were carried out to study the effects of dietary CP and adding gallic acid (GA) in basal rations on nitrogen (N) metabolism and nitrous oxide (N2O) emissions from the urine of beef cattle. In Trial I, eight Simmental castrated male cattle with initial liveweight of 310.5 ± 21.5 kg were used as experimental animals. Two levels of dietary CP (113.5 and 150.8 g/kg DM) and two levels of GA (0.0 and 15.2 g/kg DM) were used as experimental treatments in a 2 × 2 reversal design. Two cattle received each treatment in each of two experimental periods. Each experimental period lasted 19 d, of which the first 14 d were for adaptation and the last 5 d were for sampling. In Trial II, the urine samples collected from Trial I were used for measuring N2O-N emissions using static incubation technique. Glass jars containing soil were used as the incubation vessels. Three jars were used for each of the urine samples as replicates and two jars without urine samples were used as blanks. The incubation lasted 15 d, and the daily N2O-N emission from each jar was determined using gas chromatography. The results showed that no effects of interactions were found between dietary CP and GA on the N metabolism of beef cattle and the estimated cattle N2O-N emissions (P > 0.05). Increasing dietary CP from 113.5 to 150.8 g/kg DM increased the excretions of total N, urinary N, and urea (P < 0.001), whereas adding GA at 15.2 g/kg DM in ration did not affect these parameters (P > 0.05). Increasing dietary CP from 113.5 to 150.8 g/kg DM increased the estimated cattle urine N2O-N emissions by 36.8% (without adding GA) and 32.3% (adding GA at 15.2 g/kg DM) (P < 0.01), whereas adding GA at 15.2 g/kg DM in ration decreased the estimated cattle urine N2O-N emissions by 28.5% (dietary CP 113.5 g/kg DM) and 30.9% (dietary CP 150.8 g/kg DM) (P < 0.01). The inhibiting effects of GA on decreasing the N2O-N emissions of urine could have been resulted from the effects of GA metabolites including pyrogallol and resorcinol excreted in urine. Feeding cattle with relatively low dietary CP or adding GA in ration is effective to decrease the N2O-N emissions from the urine patches of beef cattle applied to soil.

GLOWORM-PARA: a flexible framework to simulate the population dynamics of the parasitic phase of gastrointestinal nematodes infecting grazing livestock

Gastrointestinal nematodes are a significant threat to the economic and environmental sustainability of keeping livestock, as adequate control becomes increasingly difficult due to the development of anthelmintic resistance in some systems and climate-driven changes to infection dynamics. To mitigate any negative impacts of climate on gastrointestinal nematode epidemiology and slow anthelmintic resistance development, there is a need to develop effective, targeted control strategies that minimise the unnecessary use of anthelmintic drugs and incorporate alternative strategies such as vaccination and evasive grazing. However, the impacts climate and gastrointestinal nematode epidemiology may have on the optimal control strategy are generally not considered, due to lack of available evidence to drive recommendations. Parasite transmission models can support control strategy evaluation to target field trials, thus reducing the resources and lead-time required to develop evidence-based control recommendations incorporating climate stochasticity. Gastrointestinal nematode population dynamics arising from natural infections have been difficult to replicate and model applications have often focussed on the free-living stages. A flexible framework is presented for the parasitic phase of gastrointestinal nematodes, GLOWORM-PARA, which complements an existing model of the free-living stages, GLOWORM-FL. Longitudinal parasitological data for two species that are of major economic importance in cattle, Ostertagia ostertagi and Cooperia oncophora, were obtained from seven cattle farms in Belgium for model validation. The framework replicated the observed seasonal dynamics of infection in cattle on these farms and overall, there was no evidence of systematic under- or over-prediction of faecal egg counts. However, the model under-predicted the faecal egg counts observed on one farm with very young calves, highlighting potential areas of uncertainty that may need further investigation if the model is to be applied to young livestock. The model could be used to drive further research into alternative parasite control strategies such as vaccine development and novel treatment approaches, and to understand gastrointestinal nematode epidemiology under changing climate and host management.

Coordinated Management of Organic Waste and Derived Products

We propose a coordination framework for managing urban and rural organic waste in a scalable manner by orchestrating waste exchange, transportation, and transformation into value-added products. The framework is inspired by coordinated management systems that are currently used to operate power grids across the world and that have been instrumental in achieving high levels of efficiency and technological innovation. In the proposed framework, suppliers and consumers of waste and derived products as well as transportation and technology providers bid into a coordination system that is operated by an independent system operator. Allocations and prices for waste and derived products are obtained by the operator by solving a dispatch problem that maximizes the social welfare and that balances supply and demand across a given geographical region. Coordination enables handling of complex constraints and interdependencies that arise from transportation and bio-physico-chemical transformations of waste into products. We prove that the coordination system delivers prices and product allocations that satisfy economic and efficiency properties of a competitive market. The framework is scalable in that it can provide open access that fosters transactions between small and large players in urban and rural areas and over wide geographical regions. Moreover, the framework provides a systematic approach to enable coordinated responses to externalities such as droughts and extreme weather events, to monetize environmental impacts and remediation, to achieve complex social goals such as geographical nutrient balancing, and to justify technology investment and development efforts. Furthermore, the framework can facilitate coordination with electrical, natural gas, water, and transportation, and food distribution infrastructures.

Linking Soil Health to Sustainable Crop Production: Dairy Compost Effects on Soil Properties and Sorghum Biomass

Dairy compost is utilized in agricultural fields to supplement nutrients, yet its role in optimizing nutrient supply and health of semiarid soils is not clear. A greenhouse study was conducted over two months to evaluate soil properties and forage sorghum production under various compost rates. The study had six treatments and four replications. Treatments included compost application rates at 6.7 (C1), 13.5 (C2), 20.2 (C3), 26.9 (C4), and 33.6 Mg ha−1 (C5) and a control (C0). Soil samples were analyzed for soil organic carbon (SOC), potentially mineralizable carbon (PMC), total nitrogen (N), inorganic N, potentially mineralizable N (PMN), available phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and sulfur (S). Plant biomass production and biomass C, N, and lignin contents were also estimated. High compost rates improved soil properties significantly (p < 0.05) indicated by increased SOC, N, P, K, Ca, and cation exchange capacity (CEC). Sorghum biomass production did not increase significantly with compost rate, while shoot N content increased at higher rates of compost. A nutrient management plan that integrates dairy compost application has potential to improve soil health and support sustainable forage production in semiarid regions.

Salt Content of Dairy Farm Effluents as an Indicator of Salinization Risk to Soils

Water used for irrigation is a leading source of induced salinity in semiarid areas. Within the Irrigation District 005 in northern Mexico, there are more than 100 dairy farms housing over 72,000 dairy cows, 74% of which are concentrated in approximately 30 intensive-operation farms. Dairy farm effluents (DFE) and manure are collected and stored temporarily until they are applied to the land to fertilize pasture and other crops. DFE vary in salt content, depending on specific farm operations. The risk of soil salinization by DFE was estimated by measuring electrical conductivity (EC) of both well water and DFE, and comparing these values with 2.0 mS cm−1, a Mexican guideline for wastewater used in agriculture. Half of the effluents exceeded the EC limit, with values as high as 12.4 mS cm−1, whereas a few exceeded the EC limit in both well and effluent water. The generation of salt and its passing into soils expose a potential for soil salinization, if preventive measures are not taken. A salt load map was created that depicted the areas at higher risk of salinization. The simple technique utilized here can be applied in estimating salinization potential in areas where monitoring of soils, irrigation drains, and shallow groundwater is infrequent.

Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model

The causes of seasonal variability in pathogen transmission are not well understood, and have not been comprehensively investigated. In an example for enteric pathogens, incidence of Escherichia coli O157 (STEC) colonization in cattle is consistently higher during warmer months compared to cooler months in various cattle production systems. However, actual mechanisms for this seasonality remain elusive. In addition, the influence of host (cattle) behavior on this pattern has not been thoroughly considered. To that end, we constructed a spatially explicit agent-based model that accounted for the effect of temperature fluctuations on cattle behavior (direct contact among cattle and indirect between cattle and environment), as well as its effect on pathogen survival in the environment. We then simulated the model in a factorial approach to evaluate the hypothesis that temperature fluctuations can lead to seasonal STEC transmission dynamics by influencing cattle aggregation, grazing, and drinking behaviors. Simulation results showed that higher temperatures increased the frequency at which cattle aggregated under shade in pasture, resulting in increased direct contact and transmission of STEC between individual cattle, and hence higher incidence over model simulations in the warm season. In contrast, increased drinking behavior during warm season was not an important transmission pathway. Although sensitivity analyses suggested that the relative importance of direct vs. indirect (environmental) pathways depend to upon model parameterization, model simulations indicated that factors influencing cattle aggregation, such as temperature, were likely strong drivers of transmission dynamics of enteric pathogens.

Carbon dioxide assisted thermal decomposition of cattle excreta

To develop the environmentally benign thermo-chemical process, this study placed great emphasis on the influence of CO₂ on pyrolysis of cattle excreta for energy recovery. To this end, this study evaluates the possible enhanced energy recovery from cattle excreta using CO₂ as reaction medium/feedstock in the thermal degradation of cattle excreta. The enhanced generation of CO in the presence of CO₂ reached up to 15.15mol% (reference value: 0.369mol%) at 690°C, which was equivalent to ~4000 times more generation of CO. In addition to the enhanced generation of CO, the enhanced generation of H₂ and CH₄ in the thermal degradation of cattle excreta in CO₂. Thus, the findings of this study revealed two genuine roles of CO₂: 1) enhanced thermal cracking of volatile organic carbons (VOCs) evolved from the thermal degradation of cattle excreta and 2) direct reaction between VOCs and CO₂ via gas phase reaction.

Quantification of volatile fatty acids from cattle manure via non-catalytic esterification for odour indication

This report proposes a new approach to evaluate the odour nuisance of cattle manure samples from three different cattle breeds (i.e., native cattle, beef cattle, and milk cow) by means of quantification and speciation of volatile fatty acids (VFAs). To this end, non-catalytic esterification thermally induced in the presence of a porous material (silica) was undertaken, and the optimal operational parameters such as the derivatizing temperature (330°C) for the maximum yield (≥99±0.4%) of volatile fatty acid methyl esters (VFAMEs) were established. Among the VFA species in cattle manure based on quantification of VFAs, the major species were acetic, butyric and valeric acid. Considering the odour threshold of each VFA, our experimental results suggested that the major contributors to odour nuisance were C_4-5 VFA species (i.e., butyric and valeric acid). Hydrothermal treatment was performed at 150°C for 0-40min to correlate the formation of VFAs with different types of cattle feed formulations. Our experimental data demonstrated that the formation of total VFAs is linearly proportional to the hydrothermal treatment duration and the total content of VFAs in native cattle, beef cattle, and milk cow manure samples reached up to ~1000, ~3200, and ~2800ppm, respectively. Thus, this study demonstrated that the degree of VFA formation is highly dependent on cattle feed formulations, which rely significantly on the protein content. Furthermore, the hydrothermal treatment provides a favourable condition for generating more VFAs. In this context, producing cattle manure into refused derived fuel (RDF) via a hydrothermal treatment is not a viable option to control odour.

Prediction of fecal nitrogen and phosphorus excretion for Chinese Holstein lactating dairy cows

Mathematical models for predicting nitrogen and phosphorus excretion play a key role in manure application and environment monitoring. An analysis for prediction of fecal nitrogen (FN, g/d) and fecal phosphorus (FP, g/d) excretion for Chinese Holstein lactating dairy cows was conducted using a data set from 15 dairy farms in northern China. The whole independent-variable data set, obtained with questionnaires, consisted of 110 sets of average diet nutrient compositions, including DMI (kg/d), CP content (% DM), OM intake (OMI, kg/d), nitrogen intake (NI, g/d), and phosphorus intake (PI, g/d), and animal characteristics, including average days in milk (DIM, d), average milk yield (MY, kg/d), and average BW (kg). In addition, 110 fecal samples in total were collected to analyze FN and FP excretions, which were considered dependent variables of prediction equations. Correlations between diet and animal variables were examined, and several variable subpools were derived that were used to develop equations to predict FN and FP excretions by stepwise regression analysis. The results showed that among all variables, OMI was the best predictor for FN excretion (root-mean-square prediction error [RMSPE] = 9.58%, = 0.70), followed by NI (RMSPE = 10.19%, = 0.67). However, when both DMI and CP were used as independent variables, the equation showed more accurate prediction for FN excretion (RMSPE = 8.55%, = 0.77) in comparison with univariate prediction equations. Simultaneously, PI was the best predictor of FP excretion (RMSPE = 10.28%, = 0.67). Evaluation results using 3-fold cross validation and comparison with extant equations indicated that the proposed equations were accurate with low prediction errors, which could be recommended for use to estimate FN and FP excretions from Chinese Holstein lactating dairy cows.

Grazing intensity affects the environmental impact of dairy systems

Dairy products are major components of the human diet but are also important contributors to global environmental impacts. This study evaluated greenhouse gas (GHG) emissions, net energy intensity (NEI), and land use of confined dairy systems with increasing levels of pasture in the diet. A Wisconsin farm was modeled to represent practices adopted by dairy operations in a humid continental climate typical in the Great Lakes region and other climates that have large differences in seasonal temperatures. Five grazing scenarios (all of which contained some portion of confinement) were modeled based on different concentrations of dry matter intake from pasture and feed supplementation from corn grain, corn silage, and soybean meal. Scenarios that incorporate grazing consisted of 5 mo of pasture feeding from May to September and 7 mo of confined feeding from October to April. Environmental impacts were compared within the 5 scenarios that incorporate grazing and across 2 entirely confined scenarios with and without on-farm electricity production through anaerobic digestion (AD). To conduct a fair comparison, all scenarios were evaluated based on the same total amount of milk produced per day where resource inputs were adjusted according to the characteristics of each scenario. A cradle-to-farm gate life cycle assessment evaluated the environmental burdens that were partitioned by allocation between milk and meat and by system expansion when biogas-based electricity was produced. Overall, results for all scenarios were comparable. Enteric methane was the greatest contributor to GHG emissions, and the production of crops was the most energy-intense process. For the confined scenario without AD, GHG emissions were 0.87 kg of CO₂ equivalents, NEI was 1.59 MJ, and land use was 1.59 m²/kg of fat- and protein-corrected milk (FPCM). Anaerobic digestion significantly reduced emissions to 0.28 kg of CO₂ equivalents/kg of FPCM and reduced NEI to -1.26 MJ/kg of FPCM, indicating a net energy producing system and highlighting the potential of AD to improve the sustainability of confined systems. For scenarios that combined confinement and grazing, GHG emissions ranged from 0.84 to 0.92 kg of CO₂ equivalents, NEI ranged from 1.42 to 1.59 MJ, and land use ranged from 1.19 to 1.26 m²/kg of FPCM. All environmental impacts were minimized in scenarios that supplemented enough feed to increase milk yield but maintained dry matter intake from pasture at a level high enough to reduce material and energy use.

Mass balance analyses of nutrients on California dairies to evaluate data quality for regulatory review

Effective regulations may help reduce nitrate contamination of groundwater from agriculture. Dairy farmers in California must maintain a ratio below 1.4 of total nitrogen (N) applied to total N-removed (N-Ratio) on cropland receiving manure application. In annual reports to the regulatory agency, farmers detail nutrients applied to cropland, removed in harvests, and exported off farm. Data were extracted from all available annual reports for 62 dairies from 2011, 2012, and 2013. Excretions of N, phosphorus (P), and potassium (K) were calculated using reported herd demographics and standard excretion equations from the American Society of Agricultural and Biological Engineers. Calculated nutrient excretion values were compared to the reported values of manure nutrients applied to cropland and exported off farm. Reported N-Ratios were compared to mass balance simulations exploring variable crop yields and alfalfa management. In the nutrient excretion balance, the distribution of the percent of N and P recovered in manures applied or exported peaked at 24% (median=31%) and 26% (median=53%) of excreted, respectively. The distribution of recovered K was fairly uniform from 0% to 300% (median=146%) of excreted K. In N-ratio simulations, 62% and 66% of all reported N-ratios were lower than their respective simulated N-ratio, assuming alfalfa crops received no N fertilization and minimal fertilization (26% of N-removed in harvest) respectively. When simulated crop yields were normally (sd=0.25) or Student's t distributed (df=154) around expected crop yields, 28% and 57% of all reported ratios fell within the 95% confidence interval of the simulations, respectively. Low and erratic recovery rates of excreted P and K existed. Additionally, reported N-Ratios were generally lower and more varied than necessary for farmers to maintain crop yields while complying with regulations. Greater understanding of low recovery rates is needed before data are used to assess the impact of regulations.

Short communication: Evaluation of nitrogen excretion equations from cattle

Nitrogen excretion in dairy manure is a precursor for N2O and NH3 formation in livestock housing, manure storage facilities, and after manure is applied to land. Nitrous oxide is a major contributor to greenhouse gas emissions, and reducing N output from dairy production facilities can reduce the amount of anthropogenic N2O entering the atmosphere. The objective of the study was to conduct a comprehensive evaluation of extant prediction models for N excretion in feces and urine using extensive literature data. A total of 45 N excretion equations were evaluated for lactating cows, heifers, and nonlactating cows and steers. These equations were evaluated with 215 treatment means from 69 published studies collected over 20 yr from 1995 to 2015. Two evaluation methods were used: the root mean square prediction error and the concordance correlation coefficient. Equations constructed using a more rigorous development process fared better than older extant equations. Equations for heifers and nonlactating cows had greater error of prediction compared with equations used for lactating cows. This could be due to limited amount of data available for construction and evaluation of the equations. Urinary N equations had greater prediction errors than other forms of excretion, possibly due to high variability in urinary N excretion and challenges in urine collection. Fecal N equations had low error bias and reached an acceptable level of precision and accuracy.

Long-term impacts of manure amendments on carbon and greenhouse gas dynamics of rangelands

Livestock manure is applied to rangelands as an organic fertilizer to stimulate forage production, but the long-term impacts of this practice on soil carbon (C) and greenhouse gas (GHG) dynamics are poorly known. We collected soil samples from manured and nonmanured fields on commercial dairies and found that manure amendments increased soil C stocks by 19.0 ± 7.3 Mg C ha(-1) and N stocks by 1.94 ± 0.63 Mg N ha(-1) compared to nonmanured fields (0-20 cm depth). Long-term historical (1700-present) and future (present-2100) impacts of management on soil C and N dynamics, net primary productivity (NPP), and GHG emissions were modeled with DayCent. Modeled total soil C and N stocks increased with the onset of dairying. Nitrous oxide (N2 O) emissions also increased by ~2 kg N2 O-N ha(-1)  yr(-1) . These emissions were proportional to total N additions and offset 75-100% of soil C sequestration. All fields were small net methane (CH4 ) sinks, averaging -4.7 ± 1.2 kg CH4 -C ha(-1)  yr(-1) . Overall, manured fields were net GHG sinks between 1954 and 2011 (-0.74 ± 0.73 Mg CO2 e ha(-1)  yr(-1) , CO2 e are carbon dioxide equivalents), whereas nonmanured fields varied around zero. Future soil C pools stabilized 40-60 years faster in manured fields than nonmanured fields, at which point manured fields were significantly larger sources than nonmanured fields (1.45 ± 0.52 Mg CO2 e ha(-1)  yr(-1) and 0.51 ± 0.60 Mg CO2 e ha(-1)  yr(-1) , respectively). Modeling also revealed a large background loss of soil C from the passive soil pool associated with the shift from perennial to annual grasses, equivalent to 29.4 ± 1.47 Tg CO2 e in California between 1820 and 2011. Manure applications increased NPP and soil C storage, but plant community changes and GHG emissions decreased, and eventually eliminated, the net climate benefit of this practice.

Prediction of phosphorus output in manure and milk by lactating dairy cows

Mathematical models for predicting P excretions play a key role in evaluating P use efficiency and monitoring the environmental impact of dairy cows. However, the majority of extant models require feed intake as predictor variable, which is not routinely available at farm level. The objectives of the study were to (1) explore factors explaining heterogeneity in P output; (2) develop a set of empirical models for predicting P output in feces (Pf), manure (PMa), and milk (Pm, all in g/cow per day) with and without dry matter intake (DMI) using literature data; and (3) evaluate new and extant P models using an independent data set. Random effect meta-regression analyses were conducted using 190 Pf, 97 PMa, and 118 Pm or milk P concentration (PMilkC) treatment means from 38 studies. Dietary nutrient composition, milk yield and composition, and days in milk were used as potential covariates to the models with and without DMI. Dietary phosphorus intake (Pi) was the major determinant of Pf and PMa. Milk yield negatively affected Pi partitioning to Pf or PMa. In the absence of DMI, milk yield, body weight, and dietary P content became the major determinants of Pf and PMa. Milk P concentration (PMilkC) was heterogeneous across the treatment groups, with a mean of 0.92 g/kg of milk. Milk yield, days in milk, and dietary Ca-to-ash ratio were negatively correlated with PMilkC and explained 42% of the heterogeneity. The new models predicted Pf and PMa with root mean square prediction error as a percentage of observed mean (RMSPE%) of 18.3 and 19.2%, respectively, using DMI when evaluated with an independent data set. Some of the extant models also predicted Pf and PMa well (RMSPE%=19.3 to 20.0%) using DMI. The new models without DMI as a variable predicted Pf and PMa with RMSPE% of 22.3 and 19.6%, respectively, which can be used in monitoring P excretions at farm level. When evaluated with an independent data set, the new model and extant models based on milk protein content predicted PMilkC with RMSPE% of 12.7 to 19.6%. Although models using P intake information gave better predictions, P output from lactating dairy cows can also be predicted well without intake using milk yield, milk protein content, body weight, and dietary P, Ca, and total ash contents.