Using a urine sensor to estimate nitrogen excretion by lactating dairy cows in Australian grazing systems

A.Ahmed1, S.R Aarons2

1 School of life sciences, Latrobe University, Bundoora, VIC, 3083,

2 Agriculture, Research and Development, Department of Economic Development, Jobs, Transport and Resources, Ellinbank, Vic, 3821,


Ruminants excrete most of their N intake, with most of the N excreted in urine. As a result, urine is the greatest contributor of N losses to the environment in dairy systems worldwide, due to the high use of N. While quantifying N excretion will assist in the development of improved N management practices, measurement of urinary N is difficult in grazing dairy systems. A urine sensor developed by AgResearch, New Zealand which allows the determination of N concentration (%) and volume of each individual excretion event performed by grazing cows was tested in Australian conditions. Twenty Friesian-cross lactating dairy cows were fitted with urine sensors and data were recorded over a 48 hour period in spring 2014 and late winter 2015. A total of 420 urination events were recorded in this study. Urine volume excreted by these grazing lactating cows ranged from 8.2 to 43 L/day while urinary N concentration varied between 1.2 and 15.7 g N/L, similar to previously reported results. The average urination frequency was 18 times per day and average volume per event was 8.2 L/event. These data also showed that N concentration varies between time of the day and showed higher concentration in the early morning and afternoon.

Using metabolomic methods in the Victorian Dairy industry to understand the importance of organic nitrogen: from factory to farm

Michael W. Heaven1, Sharon R. Aarons1, Lori Phillips2, Brunda Nijagal3, Komal Kanojia3, T. Vincent Verheyen4, Alicia J. Reynolds4, Murray Hannah1, David Nash1, Pauline Mele2, Dedreia Tull3, Amsha Nahid2

1Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Ellinbank, Victoria, Australia.
2DEDJTR, AgriBio, La Trobe University, Bundoora, Victoria, Australia.
3Metabolomics Australia, Bio21 Institute, The University of Melbourne, Parkville, Victoria, Australia.
4School of Applied and Biomedical Sciences, Federation University Gippsland Campus, Churchill, Victoria, Australia.


Metabolomic techniques were used to identify metabolites that are produced and used in the dairy industry. Organic nitrogen (OrgN) compounds were identified in dairy factory wastewater streams. OrgN metabolites were predominantly found in the effluent stream and successfully segregated within the factory from recycled water streams used for irrigation and replenishment of a nearby waterway. Tolytriazoles were identified in the effluent waste water. Due to their recalcitrant nature, they have the potential to act as a marker of downstream pollution. A time based study of the dairy factory bioreactor waste waters identified another potential marker of factory productivity. The OrgN metabolite, 4-nitrophenol was found to be correlated with increasing anaerobicity of the bioreactor. The methodologies optimised from this research were used to identify OrgN metabolites in soil samples from farms in the main dairy regions of Victoria. Amino acids were the largest component of all metabolites identified. Several metabolites (e.g. cytidine) were found to be significantly changes in concentration in response to increasing potassium fertiliser application rates. These metabolites may be related to microbial or plant biochemical metabolic pathways. Microbial community analyses showed similar trends in regards to microbes (archaea, bacteria) associated with N metabolite production.

Building a Bayesian network to identify key intervention points for improving nitrogen efficiency in New Zealand dairy farm systems

Gina Lucci1, Cecile DeKlein2, Vicki Burggraaf1, Diana Selbie1 and David Pacheco3

1 AgResearch, Private Bag 3123, Hamilton 3240, New Zealand
2 AgResearch, Puddle Alley, Private Bag 50034, Mosgiel 9053, New Zealand
3 AgResearch, Private Bag 11008, Palmerston North, 4442, New Zealand


Nitrogen (N) losses from New Zealand dairy farms are, in part, due to inefficiencies in N use within the system. Nitrogen cycling in pastoral dairy farming systems is complex, and understanding the interactions and interdependencies of N sources, N use and processes that control N losses will enable a more targeted approach to improving the overall N efficiency of the system. Bayesian Network (BN) modelling is an alternative to conventional modelling as it can evaluate complex multifactor problems using both forward and backward reasoning (cause-to-effect, and effect-to-cause), as well as assign probabilities to different outcomes. We developed a BN to identify the relative contribution of different components within a NZ dairy system to N leaching losses. An initial analysis revealed that the BN model can be a valuable tool for understanding how elements of the dairy N system fit together and their relative importance to overall N loss. Preliminary results also show that N leaching was most affected by feed N content and DM intake as opposed to the breed and weight of the cow. After further validation of the model it will be used to assess how current systems can be changed to meet N leaching targets, and to identify future strategies for improving N efficiency that target the key intervention points.

Predicting N excretion in commercial grazing system dairy farms

Sharon R Aarons1, Cameron JP Gourley1, Mark Powell2,

1 Agriculture Research and Development, Department of Economic Development, Jobs, Transport and Resources, Ellinbank Dairy Centre, 1301 Hazeldean Road, Ellinbank, Victoria 3821, Australia website,

2 US Dairy Forage Research Center, USDA Agricultural Research Service, 1925 Linden Drive West, University of Wisconsin, Madison, WI 53706, USA


Improving nitrogen (N) management on dairy farms is best facilitated through management of dairy cow dietary N intakes, due to strong associations between intakes, nutrient use efficiencies and N excretion.  Milk urea N (MUN) has also been used as an indicator of excess N in dairy systems.  While a number of predictive relationships between these parameters have been developed for confinement based systems, less information is available for grazing dairy systems.  Feed intake, N excretion and MUN data were determined from samples collected at five quarterly visits over a year on 43 commercial grazing-based dairy farms representing a range of production systems (n=227).  Relationships were developed between feed N intake, excreted N, feed N use efficiency (NUE) and MUN using these data.  The regression relationships were generally similar to the prediction equations reported in the literature for confinement-based dairy systems.  The coefficient of determination for the relationship between excreted N and N intake (ExcrN = 0.84NIn – 23.6; R2=0.97) was greater than the literature, probably due to the method of estimating excreted N.  Lactating cow N use efficiency declined with N intake (NUE = -0.009NIn + 25.9; R2=0.08), but the relationship to crude protein concentration was stronger (NUE = -0.79CP + 35.9; R2=0.50).  Mean MUN for these grazing system dairy cows (12.7 mg/dL) was similar to levels reported for commercial herd and significant relationships were observed between MUN and crude protein (R2=0.19), N use efficiency (R2=0.10) and excreted N (R2=0.17).  The weaker relationships observed were most likely due to the range of breeds, milk production and feeding systems used by these farmers, in contrast to the experimental herds and confinement systems reported elsewhere.  Despite the lower R2, these relationships suggest that prediction of N intake and excretion could improve nutrient management in grazing systems.

Improving nitrogen and phosphorus response of corn (Zea mays L.) to dairy slurry by precision injection: benefits and risks

Derek Hunt1, Shabtai Bittman1, Coby Hoogendoorn2 and Hongjie Zhang1

Agriculture and Agri-Food Canada, Box 1000, Agassiz, BC, Canada, V0M 1A0,

2 Independent Researcher, 15 Beattie St, Feilding, New Zealand, 4772


We evaluated the benefits and risks of precision planting corn near dairy slurry injection furrows (DS-I) in terms of crop performance and environmental impact relative to mineral fertilizer (MF) and broadcast/ incorporated (DS-B) slurry. The study was conducted in 2010-2014 on silty loam in a cool maritime climate in south coastal BC, Canada. Injected manure improved N and P uptake and yield relative to broadcast manure at all application rates. Phosphorous (P) uptake was comparable or better than fertilizer but nitrogen (N) uptake was lower. Apparent N uptake (% of applied N adjusted for control), depending on application rates, was 53-79% for MF, 41-53% for DS-I, 36-42% for DS-B.  Crop response to DS-I plus starter (i.e. DS-I+S) approached MF for most variables and for P uptake it was higher. DS-I had about two times higher emissions of nitrous oxide (N2O) than DS-B due to greater emission peaks within a month of nutrient application; N2O emissions were slightly higher than IPCC factors for DS-I but substantially lower for DS-B. Movement of nitrate below the root zone had modest peaks after application and after summer drought but unlike N2O continued through the cool rainy season. The study showed that precision injection improves corn performance compared to conventional methods but to reach maximum yield either starter fertilizer or relatively high N manure rates are required.

The effect of defoliation severity during late autumn on herbage production, regrowth and nitrogen uptake of diverse pastures in Canterbury, New Zealand

Grace S. Cun*, Grant R. Edwards, Racheal H. Bryant

Faculty of Agriculture and Life Sciences, Lincoln University, Lincoln 7647, Christchurch

*Corresponding author email:


Pasture management strategies are sought to reduce nitrate leaching by enhancing nitrogen (N) uptake over the winter period. The objective of this study was to determine the effect of five post grazing heights on herbage production, and N uptake of a diverse pasture mixture containing perennial ryegrass, white clover, chicory, plantain, and lucerne during the late autumn/winter season. In late autumn, pastures were defoliated to five residual heights (20, 30, 40, 50, 60 mm), and herbage dry matter (DM) and N accumulated over a 112 d regrowth period was measured. Swards defoliated to 20, 30 and 40 mm accumulated more herbage above ground level (1884, 1508, 1322 kg DM/ha, respectively) than those defoliated to 60 mm (1289 kg DM/ha) over 112 days. Repeated measures analysis on herbage N concentration showed a significant interaction (P=0.012) of defoliation treatment with time. For the 20 mm defoliation, N concentration increased over time from 18.8 to 29.7 g N/kg while for the 60 mm defoliation, decreased from 26.1 to 24.9 g N/kg during the regrowth period. During this 112 d regrowth period, pastures defoliated to 20 mm accumulated more DM and more N than plots defoliated to 60 mm (56 vs. 32 kg N/ha, respectively). The results indicate grazing severely to post grazing heights <40 mm may improve growth and N uptake in the late autumn/winter.

Improving nitrogen use efficiency in subtropical dairy systems – A modelling approach using POAMA and DayCent

Martin Labadz1, Clemens Scheer1, David W. Rowlings1, Beverly Henry1, William Parton2, Peter Hayman3, Oscar Alves4, Griffith Young4 and Peter Grace1

1 Institute for Future Environments, Queensland University of Technology, 2 George Street, Brisbane QLD 4000, Australia

2 Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, United States

3 Government of South Australia, SARDI, 2b Hartley Grove, Urrbrae SA 5064, Australia

4 Centre for Australian Weather and Climate Research, Melbourne, Victoria, Australia


The DayCent biogeochemical model was used to assess the applicability of POAMA-2 weather forecasts to assist dairy farmers in future nitrogen fertiliser decisions. Simulated soil mineral nitrogen, water-filled pore space, and biomass was calibrated and validated against field measurements from a dairy farm in subtropical Queensland, Australia, for the season 2012/2013 with a ryegrass/kikuyu rotation. DayCent was able to predict water movement in the soil profile, soil nitrogen dynamics and biomass production; however, there were some discrepancies between simulated and measured mineral nitrogen content in the soil and biomass production. This study showed that combining weather forecasts with biogeochemical models as a decision support tool for farmers to estimate mineralisation and assess N fertiliser demand is a promising approach to avoid excessive nitrogen application for dairy cropping systems. However, there are still shortcomings in an accurate simulation of soil nitrogen turnover and plant nitrogen uptake, in particular in highly fertilised systems such as the one presented here. More confidence in the accurate representation of the complex nitrogen transformation processes on dairy farms in biogeochemical models is necessary to use weather forecasts as fertiliser nitrogen decision support tool.

Substitutions of corn silage, alfalfa silage and corn grain in cow rations impact N use and N loss from dairy farms

Mark Powell1, C. Alan Rotz2, Peter A. Vadas1 and Kristan F. Reed1

1 USDA, Agricultural Research Service, US Dairy Forage Research Center, 1925 Linden Drive, Madison, Wisconsin 53711 USA,

2 USDA-Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, University Park, Pennsylvania 16802 USA


Many dairy farms in the USA are growing and feeding more corn silage (CS) and less alfalfa silage (AS) to reduce feed costs. More corn grain (CG)-based concentrates are also being promoted to reduce enteric methane, a potent greenhouse gas. Whole farm simulations illustrate that growing more CS and less AS reduces the land requirement for feed production by approximately 27%, maintains milk production, increases animal N use efficiency (from 20 to 25%), and decreases manure N excretion (from 26.5 to 20.8 g N/kg milk). Growing more CS however, requires more fertilizer N (80 kg N/ha) and increases N losses (by 35 kg N/ha). Feeding more CG does not greatly impact milk production or animal N use efficiency, but requires about 40% more CG land area, more fertilizer N (23 kg N/ha), and increases nitrate leaching (by 10 kg N/ha). CS, AS and CG were labeled with stable isotope 15N and fed to mid-lactation dairy cows. Consumed 15N from AS and CS were distributed similarly into milk N and faecal N. Relatively more of the 15N contained in CG was transformed into milk N compared to 15N contained in AS and CS. After land application, more of the manure 15N from AS and CG was taken up by corn silage than manure 15N from CS. Trade-offs in N use and N loss need to be more fully considered when recommending more CS and CG in dairy cow rations.

Nitrogen budgets for lowland temperate beef and sheep grazing systems: The North Wyke Farm Platform

Tom Misselbrook1, Alison Carswell1, Graham McAuliffe1, Taro Takahashi1,2, Laura Cardenas1, Michael Lee1,2

1 Rothamsted Resesarch, North Wyke, Okehampton, Devon EX20 2SB, UK,;

2 University of Bristol, School of Veterinary Sciences, Langford, Somerset BS40 5DU, UK


The North Wyke Farm Platform comprises three 22 ha beef and sheep grazing ‘farmlets’ which are highly instrumented to monitor hydrology, weather, nutrient flows and productivity. Typical management for lowland UK grazing systems, based on permanent pasture, was applied to all three systems for an initial two-year baseline period. Following that, the platform has been progressively modified with the underlying principle being to improve the sustainability (economic, social and environmental) of two of the three farmlets, by reseeding of one with a grass-clover sward to drive production through nitrogen (N) fixation and one with a grass monoculture utilising the latest grass breeding advancements and higher yield potential. The third farmlet continued under permanent grassland. This paper presents the framework to be used in assessing the impacts of the reseeding period and to estimate system scale N budgets for the three systems. Data are being compiled with no results yet available, but will consist of a combination of measurements and modelled N pools and flows based on detailed management and production data. N use efficiency will be evaluated at the partial (forage production) and full (livestock product output) system level. A range of metrics expressing N flows on a product and land area basis will be derived. For a more complete understanding of the impacts of the system interventions and potential for future interventions to further improve NUE, further measurement data are required including N losses though denitrification, ammonia volatilisation and total N losses to water and N inputs through fixation.

Assessing three nitrogen use performance indicators for pig supply chains in East and Southeast Asia

Aimable Uwizeye1,2,3*, Pierre J. Gerber1,2, Rogier P.O. Schulte3, Imke J.M. de Boer1

1 Animal Production Systems group, Wageningen University, PO Box 338, 6700 AH, Wageningen, the Netherlands

2 Food and Agriculture Organization of the United Nations, Animal Production and Health Division, Viale delle Terme di Caracalla, 00153 Rome, Italy

3 Teagasc – Crops, Environment and Land Use Programme, Johnstown Castle, Wexford, Ireland


Pig supply chains are developing rapidly in East and Southeast Asia (ESEA), fuelled by population growth, growing incomes and urbanization that lead to increased demand for animal produce. Pig supply chains, however, are associated with losses of reactive nitrogen (Nr) to the environment at various stages of the chain. To benchmark livestock supply chains and identify improvement options, we previously developed a framework to assess Nr use efficiency at chain level. This framework compromises three indicators: life-cycle nitrogen use efficiency (life-cycle-NUEN), life-cycle net nitrogen balance (life-cycle-NNBN), and nitrogen hotspot index (NHIN). The aim of this study is to apply these three indicators to pig supply chains in ESEA. Preliminary results showed that the computed Life-cycle Nr use efficiency indicators vary greatly between backyard, intermediate and industrial supply chains. Industrial supply chains had relatively higher estimates of life-cycle-NUEN than intermediate and backyard supply chains. Our data showed a negative relationship between life-cycle-NNBN and NHIN demonstrating the presence of hotspots of Nr losses in backyard and intermediate supply chains, as compared to industrial supply chains. These differences between supply chains result from differences in the origin of feed material, feed conversion, manure management system and animal health status. This study demonstrates that there is a scope to improve the Nr use efficiency in pig supply chains in ESEA, especially by focusing on the optimization of fertilization of local feed crops and manure management systems. Further research is required to assess the potential effectiveness of each of these interventions.