Prediction of nitrogen excretion in dairy farms located in north Florida: a comparison of three models

Short-Term Aerial Pollutant Concentrations in a Southwestern China Pig-Fattening House

Concentrations of critical aerial pollutants within animal farms are important to the health of animals and farm staff and can be reduced via manure management, ventilation control, and barn design. This study characterized measurements of ammonia (NH3), total suspended particle (TSP), and airborne microbial communities of a large-scale pig-fattening house, as well as their correlations with environmental variables in Southwestern China. Monitoring was conducted for 15 consecutive days during both August and January, at various locations inside the pig house. The concentrations of NH3 and TSP averaged 3.22 and 0.55 mg m−3, respectively, while the average number of airborne microbial colonies was 3.91 log cfu m−3. The aerial pollutant concentrations displayed significant seasonal differences (p < 0.05). Specifically, concentrations in winter were significantly higher than those in summer (p < 0.05), and the 07:00 measurements were the highest among the three measurement times. The concentrations were significantly correlated with indoor temperature and relative humidity. In summer, TSP concentration was negatively correlated with temperature (correlation coefficient = −0.732), while NH3 concentration was positively correlated with temperature (correlation coefficient = 0.58). In winter, TSP and NH3 concentrations were negatively correlated with relative humidity (correlation coefficients = −0.739 and −0.713, respectively), while the airborne microbial colonies were not correlated with either humidity or temperature in summer or winter. These findings confirm that the aerial pollutant concentrations in a Southwestern China pig-fattening house exhibited significant seasonal and diurnal variations. Air quality can be improved by more precise ventilation control as observed by the correlation of concentrations with ventilation control, indoor temperature, and humidity.

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.

A simple formulation and solution to the replacement problem: a practical tool to assess the economic cow value, the value of a new pregnancy, and the cost of a pregnancy loss

This study contributes to the research literature by providing a new formulation for the cow replacement problem, and it also contributes to the Extension deliverables by providing a user-friendly decision support system tool that would more likely be adopted and applied for practical decision making. The cow value, its related values of a new pregnancy and a pregnancy loss, and their associated replacement policies determine profitability in dairy farming. One objective of this study was to present a simple, interactive, dynamic, and robust formulation of the cow value and the replacement problem, including expectancy of the future production of the cow and the genetic gain of the replacement. The proven hypothesis of this study was that all the above requirements could be achieved by using a Markov chain algorithm. The Markov chain model allowed (1) calculation of a forward expected value of a studied cow and its replacement; (2) use of a single model (the Markov chain) to calculate both the replacement policies and the herd statistics; (3) use of a predefined, preestablished farm reproductive replacement policy; (4) inclusion of a farmer's assessment of the expected future performance of a cow; (5) inclusion of a farmer's assessment of genetic gain with a replacement; and (6) use of a simple spreadsheet or an online system to implement the decision support system. Results clearly demonstrated that the decision policies found with the Markov chain model were consistent with more complex dynamic programming models. The final user-friendly decision support tool is available at http://dairymgt.info/ → Tools → The Economic Value of a Dairy Cow. This tool calculates the cow value instantaneously and is highly interactive, dynamic, and robust. When a Wisconsin dairy farm was studied using the model, the solution policy called for replacing nonpregnant cows 11 mo after calving or months in milk (MIM) if in the first lactation and 9 MIM if in later lactations. The cow value for an average second-lactation cow was as follows: (1) when nonpregnant, (a) $897 in MIM = 1 and (b) $68 in MIM = 8; (2) when the cow just became pregnant,(a) $889 for a pregnancy in MIM = 3 and (b) $298 for a pregnancy in MIM = 8; and (3) the value of a pregnancy loss when a cow became pregnant in MIM = 5 was (a) $221 when the loss was in the first month of pregnancy and (b) $897 when the loss was in the ninth month of pregnancy. The cow value indicated pregnant cows should be kept. The expected future production of a cow with respect to a similar average cow was an important determinant in the cow replacement decision. The expected production in the rest of the lactation was more important for nonpregnant cows, and the expected production in successive lactations was more important for pregnant cows. A 120% expected milk production for a cow with MIM = 16 and 6 mo pregnant in the present lactation or in successive lactations determined between 1.52 and 6.48 times the cow value, respectively, of an average production cow. The cow value decreased by $211 for every 1 percentage point of expected genetic gain of the replacement. A break-even analysis of the cow value with respect to expected milk production of an average second-parity cow indicated that (1) nonpregnant cows in MIM = 1 and 8 could still remain in the herd if they produced at least 84 and 98% in the present lactation or if they produced at least 78 and 97% in future lactations, respectively; and (2) cows becoming pregnant in MIM = 5 would require at least 64% of milk production in the rest of the lactation or 93% in successive lactations to remain in the herd.

Evaluation of solids, nitrogen, and phosphorus excretion models for lactating dairy cows

Monitoring or accurately predicting manure quantities and nutrient concentrations is important for dairy farms facing strict environmental regulations. The objectives of this project were to determine the daily out-flow of manure nutrients from a free-stall barn using mass balance and to compare results with published excretion models. The project was conducted at the free-stall facility housing the lactating cow herd of the Virginia Tech Dairy Center in 2005. The herd consisted of 142 (+/-8.9) Holstein and Jersey cows with a mean body weight of 568 (+/-6.2) kg and average milk yield of 29.8 (+/-1.7) kg/d with 3.18% (+/-0.07) true protein and 3.81% (+/-0.13) milk fat on 18 sampling days. The intakes of dry matter (DM), N, and P were estimated from the formulated ration. Daily consumption averaged 21.7 (+/-0.27) kg of DM with 17.7% (+/-0.26) crude protein and 0.46% (+/-0.03) P. Approximately 110 (+/- 27.9) kg/d of sawdust was used as bedding; its contribution to manure flow was subtracted. The alleys in the free-stall barn were flushed every 6 h with recycled wastewater, and the slurry was collected. On 18 sampling days the volumes and constituents of the flushwater and the flushed manure were determined for a 6-h flush cycle and extrapolated to daily values. Net daily flow of solids and nutrients in manure were calculated as the differences between masses in flushed slurry and flushwater. Nitrogen and P excretion were also calculated from dietary inputs and milk output. The flow was compared with the American Society of Agricultural Engineers' (ASAE) standards. Each cow produced 5.80 kg/d of total solids (remainder after drying at 105 degrees C). The ASAE standard predicted DM (remainder after drying at 60 degrees C) excretion of 8.02 to 8.53 kg/d per cow. Recovery of P amounted to 74.8 g/d per cow. Overall, 102% of intake P was recovered; 75.1% in the manure outflow and 26.9% in milk. About 285 g/d and 148 g/d of N per cow were recaptured in manure and milk, respectively; 182 g/d was presumably volatilized. All models of N excretion appeared to underestimate N excretion. Volatilization rate of N amounted to 18.1%/h for the 6-h flush interval. Measured outflow of manure-P from the facility was similar to excretion predictions. Presentation of excreted solids as both total solids and DM is warranted. We conclude that using excretion prediction equations is useful for predicting excretion and outflow of P in a lactating cow facility, but N excretion predictions exhibited bias and have to be used prudently for predicting N outflow and N volatilization.