Components of Dairy Manure Management Systems

Horn, H.H. Van; Wilkie, Ann C.; Powers, Wendy; Nordstedt, R. A.

doi:10.3168/jds.s0022-0302(94)77147-2

Cited by 198 publications

(105 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore it is important to find relevant methods to detect overfeeding. The amount of P excreted with manure depends on P intake and the level of milk production, but under practical conditions more than two-thirds of the P consumed by cows is excreted with manure (van Horn et al, 1994). In contrast to many other species, for example pigs, adult ruminants normally excrete excess P in faeces, while only trace amounts of P are excreted in urine (Valk et al, 2000).…”

Section: Introductionmentioning

confidence: 99%

Methods for assessing phosphorus overfeeding on organic and conventional dairy farms

Nordqvist

Holtenius

Spörndly

2014

Animal

View full text Add to dashboard Cite

Phosphorus (P) losses from dairy farms can severely damage aquatic ecosystems, so it is important to have tools to assess overfeeding of P. This study screened P intake and faecal excretion of different P fractions in dairy cows on conventional and organic farms, compared the P feeding level of the herds against the recommendations and analysed different sampling and analysis methods for assessing the general status of P feeding on the farms. The organic (n = 14) and conventional farms (n = 15) were of comparable size and were located in southern Sweden. On each farm, feed intake was registered for 10 cows representing four different lactation stages and their P intake was calculated and related to current recommendations. Faecal samples taken from the same cows were analysed for total P (TP) and soluble P. Milk production data for the cows were obtained from the Swedish official milk recording scheme. TP was determined in one slurry sample per farm. More than 70% of the cows studied, representing both conventional and organic herds, consumed P in excess of the recommendations. Conventional herds had higher P content in the ration than organic herds, and lactating cows in conventional herds had higher faecal concentrations of total and soluble P than those in organic herds. However in dry cows, the P content of the ration and soluble P and TP in faeces did not differ between the two management systems. Soluble P was well correlated to TP in faeces, and both were good indicators of P overfeeding.Keywords: dairy cow, phosphorus, faeces, organic management ImplicationsThis field study on Swedish commercial dairy farms showed the differences in P feeding between organic and conventional systems (CS) and examined different sampling methods for assessing P overfeeding of dairy cows. The results showed an over supplementation of P in both systems, more significant in the CS. The methods that were used were analysis of the soluble P in faeces as well as total P in faeces.

show abstract

Section: Introductionmentioning

confidence: 99%

Methods for assessing phosphorus overfeeding on organic and conventional dairy farms

Nordqvist

Holtenius

Spörndly

2014

Animal

View full text Add to dashboard Cite

show abstract

“…New models developed for predicting P output (g/d) in feces (P f ), urine (P u ), manure (P Ma ), and milk (P m ), and factors affecting P concentration in milk (P MilkC , g/kg of milk) Table 5. Performance of models challenged with independent data including 354 measurements of fecal P output (P f ) and 177 measurements of manure P output (P Ma ) 19.6 7.1 0.6 99.2 Extant models P f (g/ d) = −2.3 + 0.63 × Pi (Weiss and Wyatt, 2004a) 19.8 4.0 1.3 94.7 = 27.1 + 0.99 × DMI (Klop et al, 2013) 26.5 16.1 3.2 80.7 = −31.3 + 0.73 × DMI + 16.78 × P (Klop et al, 2013) 22.6 13.4 6.6 80.0 = −3.8 + 0.64 × P i (Klop et al, 2013) 20.0 6.4 1.0 92.5 = −15.6 + 0.82 × DMI + 16.47 × P − 0.096 × CP (Klop et al, 2013) 22.5 13.2 8.7 78.1 = −48.9 + 0.84 × DMI + 16.96 × P + 0.043 × NDF (Klop et al, 2013) 22.4 22.8 11.8 65.4 = −34.7 + 0.90 × DMI + 17.28 × P − 0.093 × CP + 0.041 × NDF (Klop et al, 2013) 22.4 20.7 9.6 67.7 = 19.9 + 0.79 × P i − 1.04 × milk (Klop et al, 2013) 19.3 4.8 0.2 95.00 = −58.3 + 1.85 × DMI + 16.90 × P + 0.056 × NDF -0.48 × milk (Klop et al, 2013) 20.7 23.7 6.9 69.4 = −43.9 +1.72 × DMI+ 17.15 × P -0.082 × CP + 0.052 × NDF -0.39 × milk (Klop et al, 2013) 20.9 20.9 6.6 72.5 P Ma (g/ d) = (P i × 560.7) + 21.1 24.7 55.1 0.1 44.7 = 7.5 + 0.78 × P i − 0.702 × milk (Weiss and Wyatt, 2004a) 26.7 53.6 4.0 42.3 = −2.5 + 0.64 × P i (Weiss and Wyatt, 2004a) 21.2 11.2 0.5 88.7 = P i − (Milk × 0.9) (Van Horn et al, 1994) 20.9 0.8 8.5 90.6 = 14.67 + 0.6786 × P i + 0.00196 × P i 2 -0.317 × milk (Morse et al, 1992) 35.4 73.8 38.6 2.23 = (milk × 0.781) + 50.4 46.7 73.4 3.0 23.6 1 DMI in kg/d; P i = P intake (g/d); milk = milk yield (kg/d); ash% and P% = dietary total ash and P, respectively (% of DM); CP% = dietary CP (% of DM); NDF, CP, and P = dietary NDF, CP, and P, respectively (g/kg of DM); MProt = milk protein (%); RMSPE% = root mean square prediction error as a percentage of observed mean; MB = mean bias; SB = slope bias; RB = random bias (all as percentage of total bias). this study performed well when evaluated using an independent data set.…”

Section: Discussionmentioning

confidence: 99%

“…Measuring daily P f can be laborious and expensive; therefore, several mathematical models have been developed for predicting P f or P excreted in both feces and urine (P Ma ). The majority of the extant models (e.g., Van Horn et al, 1994;Wu et al, 2001;Weiss and Wyatt, 2004a;Nennich et al, 2005) require DMI of individual cows as an input, which may not be routinely available in dairy farms. Phosphorus secretion in milk is generally predicted assuming P concentration in milk (P MilkC ) is constant at 0.90 g/kg (NRC, 2001).…”

Section: Introductionmentioning

confidence: 99%

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

Álvarez-Fuentes

Appuhamy

Kebreab

2016

Journal of Dairy Science

View full text Add to dashboard Cite

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 (P f ), manure (P Ma ), and milk (P m , 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 P f , 97 P Ma , and 118 P m or milk P concentration (P MilkC ) 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 (P i ) was the major determinant of P f and P Ma . Milk yield negatively affected P i partitioning to P f or P Ma . In the absence of DMI, milk yield, body weight, and dietary P content became the major determinants of P f and P Ma . Milk P concentration (P MilkC ) 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 P MilkC and explained 42% of the heterogeneity. The new models predicted P f and P Ma 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 P f and P Ma well (RMSPE% = 19.3 to 20.0%) using DMI. The new models without DMI as a variable predicted P f and P Ma 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 P MilkC 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.

show abstract

“…A robust manure nutrient management plan is an essential first step to reducing nutrient releases, and simultaneously reducing GHG emissions (Steed and Hashimoto, 1994;Van Horn et al, 1994;Rico et al, 2007;AgCC, 2016). In addition, manure management for intensive livestock systems will need to adapt to climate change in several ways.…”

Section: Priorities For Mitigation and Adaptation In Livestock Systemsmentioning

confidence: 99%

Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Yorgey

Hall

Allen

et al. 2017

Front. Environ. Sci.

View full text Add to dashboard Cite

In order for agricultural systems to successfully mitigate and adapt to climate change there is a need to coordinate and prioritize next steps for research and extension. This includes focusing on "win-win" management practices that simultaneously provide short-term benefits to farmers and improve the sustainability and resiliency of agricultural systems with respect to climate change. In the Northwest U.S., a collaborative process has been used to engage individuals spanning the research-practice continuum. This collaborative approach was utilized at a 2016 workshop titled "Agriculture in a Changing Climate," that included a broad range of participants including university faculty and students, crop and livestock producers, and individuals representing state, tribal and federal government agencies, industry, nonprofit organizations, and conservation districts. The Northwest U.S. encompasses a range of agro-ecological systems and diverse geographic and climatic contexts. Regional research and science communication efforts for climate change and agriculture have a strong history of engaging diverse stakeholders. These features of the Northwest U.S. provide a foundation for the collaborative research and extension prioritization presented here. We focus on identifying research and extension actions that can be taken over the next 5 years in four areas identified as important areas by conference organizers and participants: (1) cropping systems, (2) livestock systems, (3) decision support systems to support consideration of climate change in agricultural management decisions; and (4) partnerships among researchers and stakeholders. We couple insights from the workshop and a review of current literature to articulate current scientific understanding, and priorities recommended by workshop participants that target existing knowledge Yorgey et al.Priorities for U.S. Northwest Agriculture in a Changing Climate gaps, challenges, and opportunities. Priorities defined at the Agriculture in a Changing Climate workshop highlight the need for ongoing investment in interdisciplinary research integrating social, economic, and biophysical sciences, strategic collaborations, and knowledge sharing to develop actionable science that can support informed decision-making in the agriculture sector as the climate changes.

show abstract

Components of Dairy Manure Management Systems

Cited by 198 publications

References 21 publications

Methods for assessing phosphorus overfeeding on organic and conventional dairy farms

Methods for assessing phosphorus overfeeding on organic and conventional dairy farms

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

Northwest U.S. Agriculture in a Changing Climate: Collaboratively Defined Research and Extension Priorities

Contact Info

Product

Resources

About