Dairyproductionsystems Farming management practices
a b s t r a c t
Dairyproductionsystems are hot spots of ammonia (NH 3 ) emission. However, there remains large un- certainty in quantifying and mitigating NH 3 emissionsfromdairy farms due to the lack of both long-term ﬁeld measurements and reliable methods for extrapolating these measurements. In this study, a process- based biogeochemical model, Manure-DNDC, was tested against measurements of NH 3 ﬂuxes from ﬁve barns and one lagoon in four dairy farms over a range of environmental conditions and management practices in the UnitedStates. Results from the validation tests indicate that the magnitudes and seasonal patterns of NH 3 ﬂuxes simulated by Manure-DNDC were in agreement with the observations across the sites. The model was then applied to assess impacts of alternative management practices on NH 3 emissions at the farm scale. The alternatives included reduction of crude protein content in feed, replacement of scraping with ﬂushing for removal of manure from barn, lagoon coverage, increase in frequency for removal of slurry from lagoon, and replacement of surface spreading with incorporation for manure land application. The simulations demonstrate that: (a) all the tested alternative management practices decreased the NH 3 emissions although the efﬁciency of mitigation varied; (b) a change of management in an upstream facility affected the NH 3 emissionsfrom all downstream facilities; and (c) an optimized strategy by combining the alternative practices on feed, manure removal, manure storage, and land application could reduce the farm-scale NH 3 emission by up to 50%. The results from this study may provide useful information for mitigating NH 3 emissionsfromdairyproductionsystems and emphasize the necessity of whole-farm perspectives on the assessment of potential technical options for NH 3 mitigation. This study also demonstrates the potential of utilizing process-based models, such as Manure-DNDC, to quantify and mitigate NH 3 emissionsfromdairy farms.
Agricultural activities, livestock production in particular, have been reported to be the largest contributor of ammoniaemissions into the atmosphere (Arogo et al., 2002). Over the last ten years, concentrated animal feeding operations (AFOs) in the UnitedStates have expanded greatly. These operations produce large amounts of waste on relatively small areas, and ammonia emission from those AFOs is a growing concern due to potential effects on animal health, human health, and environmental pollution. The importance of ammoniaemissionsfrom AFOs has been well recognized (Van der Hoek, 1991; Zhao et al., 1994; Sutton et al., 1995; Aneja et al., 2000; Arogo et al., 2001; Hutchings et al., 2001; Lee and Park, 2002; Battye et al., 2003; Hyde et al., 2003; Xin et al., 2003; Wheeler et al., 2003; Liang et al., 2003; Gates et al., 2004). With increasingly stringent federal, state and local air pollution regulations and the emerging pressure to regulate agricultural enterprises, emissions of ammonia and other pollutants from AFOs have become an increasing concern to
Brito and Broderick (2003) found that an equal mix of forage from alfalfa silage with corn silage in lactating dairy cows’ diet gave the greatest improvement in N efﬁciency, without loss of yield of milk, fat, and protein, compared with diets dominated by either one of these forages. Beyond the improvements seen with proper mixes of alfalfa and corn silage, the feeding value of perennial forages is enhanced by condensed tannins (CT) and polyphenols, which are lacking in most feeds used in the UnitedStates. Modest amounts of CT (2 to 4% of DM), as is found in birdsfoot trefoil (Lotus corniculatus), reduce protein breakdown during ensil- ing and rumen fermentation by up to 50% (Albrecht and Muck, 1991; Broderick and Albrecht, 1997). Studies with sheep indicate that modest concentrations of tan- nin permit extensive protein digestion in the abomasum and small intestine, and greater subsequent absorption of amino acids, without adversely affecting feed con- sumption or digestion (Min et al., 2003). In a New Zealand study, tannins in birdsfoot trefoil increased milk production of nonsupplemented Holstein cows by 2.7 kg/d (Woodward et al., 1999). In addition to enhanc- ing protein use by ruminants, experiments with forage and browse in Africa suggest that tannins and pol- yphenols shift N excretion from urine to feces and from soluble to insoluble N forms in feces (Powell et al., 1994).
T he environmental impact of livestock production is of concern because it generates greenhouse gases and NH 3 emissions, which contribute to air, water, and soil pollution (FAO, 2002). Ammonia emitted from animal opera- tions is of particular concern because it can cause animal health hazards when concentrations reach critical levels in confined spaces (National Research Council, 2003) and contributes to the formation of fine particulate matter that is linked to human respiratory problems (Fu et al., 1999). Ammoniaemissions can also cause regional degradation of terrestrial and aquatic ecosys- tems through acid deposition and eutrophication, and it repre- sents a net loss of manure fertilizer value (Leytem and Dungan, 2014). In the UnitedStates, NH 3 emission is regulated by the USEPA in response to the Clean Air Act (USEPA, 1990), whereas in the European Union, capping of NH 3 emission is part of the National Emission Ceilings Directive 2001/81/EC (European Commission, 2001) currently being reviewed as part of the EU Clean Air Policy Package. Approximately 3.9 Tg of NH 3 were emitted in the UnitedStates in 2011, with 82% of emissions attributed to agriculture (USEPA, 2011). Similarly, in Europe 3.4 Tg of NH 3 were emitted in 2012, with 93% coming from agriculture (European Commission, 2013).
One area that has gained attention in the past several years is the link between GHG emissions and climate change. The gases of greatest concern, relative to animal production, are methane (CH 4 ) and nitrous oxide (N 2 O), whereas NH 3 is considered a secondary source of GHG because its redeposition in the landscape can lead to emissions of N 2 O (IPCC, 2006). Additionally, in the atmosphere, NH 3 primarily reacts to form ammonium sulfate and ammonium nitrate aerosols, which contribute to PM 2.5 (particulates with an aerodynamic diameter of 2.5 mm) formation. The emissions of PM 2.5 are regulated as part of the USEPA National Ambient Air Quality Standards because they are considered to be a human health concern. Because NH 3 is highly correlated with PM 2.5 formation, it is anticipated that NH 3 emissionsfrom confined animal feeding operations in the UnitedStates may be regulated in the near future. It is estimated that >70% of the total NH 3 emissions in the UnitedStates are from the livestock sector (USEPA, 2004), whereas 3.3% of total CO 2 e is from enteric CH 4 production and manure management (combined CH 4 and N 2 O emissions) (USEPA, 2011). Enteric CH 4 production and manure management account for 32% of the total agricultural sources of GHG emissions (USDA, 2008), making cattle production a target for emissions reductions. The implementation of air quality regulations in livestock-producing states increases the need for accurate on-farm determination
until 2023. The partial equilibrium model covers 14 major crops and three major livestock categories (cattle, swine and poultry) as well as the biofuel and dairy industries. The model is non-spatial in the sense that trade relations between specific countries are not analyzed but there exists a price that clears the import and export markets globally, i.e., total imports equal total exports. The model solves for a market clearing world price and includes macroeconomic variables such as population growth and policy parameters such as price supports and/or import tariffs. Long-term changes in technology and preferences such as yield increases and dietary shifts in developing countries are included in the model. The long-run equilibrium is characterized by a zero economic profit condition for the crop, livestock, and biofuel sectors. Although the model’s output includes consumption by sector, prices, and quantities of processed crop and livestock commodities such as cheese, butter and biofuels, we focus on crop area allocation and livestock herd size because those values are pertinent for our GHG calculations. The model has been used to analyze the land-use change impacts of biofuels [ 10 , 20 , 21 ], the effect of trade barriers on ethanol markets [ 22 ], and the effect of high crude oil prices on commodity prices and biofuels [ 23 ]. The structure of the model is described in more detail in the referenced publications.
Initially average abatement costs have serves as a determinative factor for scenarios’ effi- ciency. However, implementation of several emission mitigation options resulting in financial surplus for farmers may come up with low emission abatement, e.g., CP-limited pigs feeding, but major costly abatement measures lead to a relatively high emission reduction. Moreover, if average abatement costs are a sufficient criterion for efficiency assessment from farmer per- spective, this approach seems to be limited at the regional level. Nevertheless, to work out the efficient emission abatement strategy it is important to combine emission reduction options meeting expectations of both farmers and policy makers. For this sake, avoided damage costs are calculated with a special model elaborated at IER, University of Stuttgart, and provided for this study in the framework of the DFG-project. It is done to estimate pollutants’ harmful effect for the overall economy. Computed for each farm type avoided damage costs in com- parison with mitigation costs and resulting net benefits are presented in Table 50. Net benefits or monetarised externalities, in turn, result from the distraction of absolute change in gross margin (abatement costs) from avoided costs of damage. Net benefits give a better under- standing of emission mitigation option’s effect on health and terrestrial biodiversities (W AGN- ER et al.).
3.3. Evaluation and analysis of model
To make a prediction of the watermelon yield and GHG emis- sions several networks, were trained using the Matlab 7.2 (R2012a) software package. In this study, a Levenberg–Mar- quardt learning algorithm was selected and trained for build- ing the prediction models using the training sets formed by including 75 percent of data. To aim to test the developed net- work testing datasets including 90 samples were applied using the Levenberg–Marquardt learning algorithm. Eleven inputs and two outputs were presented in the experimental tests. Also, the farm size was selected as one of the input parameters. The results revealed that the best model consisted of an input layer with eleven input variables, one hidden layer with ten neurons and an output layer with two output variables (11–10–2 struc- ture). Fig. 1 displays schematic diagram of the best structure. The results of training and testing are given in Table 6 . Accordingly, the best topology had the highest R 2 and the low- est values of RMSE and MAPE for watermelon yield and GHG emissions in both training and testing which indicate the ANN predicted watermelon yield by this model tends to follow the corresponding actual ones quite closely.
Based on the results shown in Table 5, a regression equation was found, where the predicted monthly energy generation can be found from the equation: Predicted Energy generation = 912.584 + 124.743 (System Capac- ity – 5.63) – 29.127 (Orientation D1) – 9.027 (Latitude – 38˚50'N) + 2.743 (Longitude – 95˚40'W) – 50.845 (Shad- ing D1) – 67.054 (Season D1) – 261.658(Season D2) – 394.221 (Season D3). As shown by the t values and the corresponding significance values, all of the slopes are statistically significant (i.e., we can conclude at the 0.05 level that all slopes are not zero). Hence, all the inde- pendent variables contribute in predicting the amount of energy generated monthly by the solar panels included in the sample. Since none of the independent variables has a variance inflation factor (VIF) greater than five, there are no apparent multi-collinearity problems ; in other words, there is no variable in the model that measures the same relationship/quantity as is measured by another variable. Moreover, the fitted model was found to not violate other basic assumptions required in a valid re- gression model. Because the coefficients of the inde-
The Dairy Greenhouse Gas Model (DairyGHG) is a software tool for estimating the greenhouse gas emissions and carbon footprint of dairyproductionsystems (USDA-ARS, 2011b). A dairyproduction system generally represents the processes used on a given farm, but the full system extends beyond the farm boundaries. A production system is defined to include emissions during the production of all feeds whether produced on the given farm or elsewhere. It also includes emissions that occur during the production of resources used on the farm such as machinery, fuel, electricity, and fertilizer. Manure is assumed to be applied to cropland producing feed, but any portion of the manure produced can be exported to other uses external to the system. DairyGHG uses process-based relationships and emission factors to predict the primary GHG emissionsfrom the production system. Primary sources include the net emission of carbon dioxide plus all emissions of methane and nitrous oxide occurring from the production system. Emissions are predicted through a daily simulation of feed use and manure handling. Daily emission values of each gas are summed to obtain annual values. Total greenhouse gas emission is determined as the sum of the net emissions of all three gases where methane and nitrous oxide are converted to carbon dioxide equivalent units (CO 2 e).
Germany. In Graphs III, IV, V, and VI, similar results persist. While Graphs III and IV are similar, Graphs V and VI are less similar, but still primarily show the same result. Germany has “Opera” as the highest search rating, while the UnitedStates has “Opera” as the lowest in almost every comparison. Surprisingly in Graph V, the term “Opera” in the UnitedStates has a higher search rating than the number one song in the UnitedStates. Although the top song was released very recently in July of 2015, the search rating came close to being higher than the term “Opera” during that month. If Google Trends showed recent data, it would be interesting to see if the top song has surpassed the opera term. Even so, Graphs I and V show a significant decline in search rating in the UnitedStates, whereas in Graphs II and VI, the Germany “Opera” search rating has remained steady. To reinforce this point, Graph VII clearly shows the continuous decline in the search rating of the word “Opera” in the UnitedStates, compared to Graph VIII which shows a relatively stagnant search rate in Germany from 2009 to today. Although the actual search ratings are not comparable between the UnitedStates and Germany, the graphic representation alone shows the trends through each country’s music history. As not all four of the regression analyses support my broad hypothesis of the operas decline, I believe that each of the individual graphs do. The graphs clearly show that there has been a cultural shift (declining trend) away from the opera in the UnitedStates over the last twelve years, as well as a positive or
EFMA would also advocate a further development for the authorisation of fertilizer plants. The plants can be complex, with the integration of several production processes and they can be located close to other industries. Thus there should be a shift away from authorisation gov- erned by concentration values of single point emission sources. It would be better to define maximum allowable load values from an entire operation, eg from a total site area. However, this implies that emissionsfrom single units should be allowed to exceed the values in the BAT Booklets, provided that the total load from the whole complex is comparable with that which can be deduced from the BAT Booklets. This approach will enable plant management to find the most cost-effective environmental solutions and would be to the benefit of our common environment.
Work out a parasite control plan with advice from the vet or animal health adviser. Grazing cattle will be exposed to gut worms, lungworm and liver fluke. Faecal samples can be used to assess the worm egg and larva burden in groups of stock. Control is aimed at limiting production loss from parasite burdens and not overusing anthelmintics. Resistance to anthelmintics can occur if products are not used correctly. So make sure cattle are not under-dosed and apply pour-on products carefully.
2Al 2 O 3 þ 3C ¼ 4Al þ 3CO 2 ð12Þ
3. Data sources
The activity production data for alumina, plate glass, soda ash, ammonia and calcium carbide are all openly available from the National Statistics Yearbook for 2014 (Table 13-12, Output of Industrial Products)  , which is compiled by China’s National Bureau of Statistics (NBS). The NBS is also the official source of the industrial, social and economic data that used for creating international datasets such as the datasets provided by the IEA [21,22] , World Bank  and the IMF. The data on energy con- sumption and industry production provided by the NBS is also reported by the China National Greenhouse Gas Inventory  and has been used for National Communication of Climate Change  . The emission factors used in this study are from the IPCC guidelines for national greenhouse gas inventories and the NDRC reports for China’s national greenhouse gas inventories  , which are also consistent with National Communication of Climate Change  (see Fig. 1 ).
The soils in Jokioinen were tentatively classified as a Vertic, Stagnic Cambi- sol (Table 4). The locations in Ruukki included a Sapric Histosol with a layer of Carex peat (thickness about 70 cm) overlying a subsoil consisting of fine sand (dominated by the fraction of 0.06–0.2 mm), and a Haplic Regosol where texture, up to the soil surface, also consisted of fine sand. Until some 50 years ago, the Sapric Histosol had received mineral soil as an amendment, and in the late 1980s, digging of open drains had brought some fine sand to the Ap horizon. In Vihti, the experiments took place on a Vertic, Stagnic Cambisol and a Haplic Gleysol reclaimed from a drained lake several dec- ades ago. All the soils are artificially drained with subsurface tile lines in- stalled to the depth of 1–1.2 m, which is considered normal practice in Finland. In principle, this suggests that the soils have aquic moisture regimes. However, the fine sand of Ruukki has also natively been better drained than the other experimental soils, which exhibit naturally poor or somewhat poor drainage.
Past and Projected Trends: 1990 - 2020
Retrospective calculations based on the 2009 inventory methodology were made for the years 1990 to 2009 and projections for the years 2010, 2015 and 2020 (Table 4). Projected changes in livestock numbers, N fertiliser use and management practices are detailed below. There has been a steady decline in emissions (27%) from UK agriculture over the period 1990 – 2009, largely due to declining livestock numbers (Fig. 1) and fertiliser N use (Fig. 2). The decline is projected to level off, with an estimated 23% reduction from 1990-2020. These projections are subject to much uncertainty and further work is required to both generate more robust projections in agricultural activity data and an estimate of uncertainties relating to the assumptions made in deriving the projections.
10 values described above will vary within and between years and across species’ ranges. For example, breeding success in a given population shows considerable inter-annual variation, and the number of days spent at a colony often varies among populations depending on latitude and/or breeding conditions. Habitat will vary in relation to fine-scale heterogeneity across a colony and through the season, in particular, amongst precocial species, where chicks move away from the nest soon after hatching. Whilst we fully recognise the importance of this variation across multiple temporal and spatial scales, our approach was to use a representative estimate of each parameter for each species, suitable for input to the global NH 3 emission model. Bird population data are subject to large uncertainties, and these have a large impact on the estimates of NH 3 emissions. It is difficult to derive a global seabird population uncertainty from colony data because there is no standard method for reporting uncertainty. An attempt has been made at estimating the uncertainty in counts of seabird populations, based on the penguin population uncertainty estimates, since these are responsible for 80 % of the total NH 3 emission from seabirds. The uncertainty in the penguin population is ± 36 % (Woehler, 1993). An uncertainty analysis was conducted on non-breeder attendance, the values of nitrogen content of the food, the energy content of the food, assimilation efficiency of ingested food and thermodynamic effects. Variation in non-breeder attendance corresponds to an uncertainty in NH 3 emission of ± 13 %. The uncertainty in NH 3 emission caused by E:N ratio of food is ± 5 % and A Eff is ± 15 %. Table 1 shows the uncertainty in NH 3 emission associated with thermodynamic effects is ± 49 %. Combining these (using Scenario 3 as the best estimate) suggests that global seabird NH 3 emissions are 270 Gg NH 3 year -1 within the range of 97 - 442 Gg NH 3 year -1 .
The key legislation regarding ammoniaemissions and their leakage from livestock stables is the Act No. 86/2002 on atmosphere protection and Act No. 76/2002 on integrated pollution prevention and con- trol (IPPC). These acts signiﬁcantly change the view of emissions of load gases as ammonia, methane, carbon dioxide, nitrogen oxides, hydrogen sulphide and other, e.g. odour gases. The Acts No. 76/2002 and 86/2002 specify categories of farm animals un- der their competence (JELÍNEK et al. 2004). Good agricultural practice resulting from the Czech legis- lation concerning atmosphere protection is based on the principle of ammonia abatement technologies. These veriﬁed technologies reduce ammonia emis- sions by certain percentage in comparison with re- ference technologies commonly used in the housing, storage and spreading systems.
Several attempts have been made to characterize and homog- enize the emission inventories and their compilation and calcula- tion procedures. The Convention on Long-Range Transboundary Air Pollution, CLRTAP, in 1979 laid the foundation for the 1984 Co- operative Programme for Monitoring and Evaluation of the Long- Range Transmission of Air Pollutants in Europe, EMEP ( CEIP, 2007 ). Among the major objectives of current EMEP programme are the compilation and analysis of emission data and the regular supply of truthful and veri ﬁed information about emissions to the scienti ﬁc and politic communities. Usually following a bottom-up approach, these emissions are aggregated and reported for main pollutants, aerosols, heavy metals and persistent organic pollut- ants, by sector and geographically referenced over a grid with a spatial resolution of 50 50 km 2 .
2. Key Laboratory of Agricultural Equipment, Ministry of Agriculture and Rural Affairs of China, Wuhan 430070, China; 3. College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China) Abstract: Experiments were conducted to investigate the influences of type of litter, initial moisture content (IMC) of litter, and dry weight ratio of manure to litter (DWRML) on ammoniaemissionsfrom chicken manure and the effects of pH values of tea leaves and the mixtures of tea leaves and other litter on the ammoniaemissionsfrom chicken manure. For the experiments, four kinds of litter, Northeast pine sawdust (sawdust), rice husk, tea leaves, and wheat straw, were selected. The IMCs of the litter were (20±2)%, (30±2)%, and (40±2)%; and the DWRML values were 1:4, 1:6 and 1:8, respectively. The different litters adjusted at different moisture contents were mixed with chicken manure in different DWRML and then placed in different static test chambers, which were real-time monitored the ammonia concentrations. Pure chicken manure without any litter was used as a control group. The four kinds of litter had obvious inhibitory effects on the ammoniaemissionsfrom chicken manure under various conditions. There were significant differences among four kinds of litter (p < 0.01). Under the same conditions, the best inhibitory effect was achieved by using tea leaves, followed by straw, rice husk, and sawdust. The IMC of litter had no significant effects on the ammonia inhibition (p > 0.05). The DWRML had no significant effects on ammonia emission inhibition for tea leaves (p > 0.05), but had a significant effects on the ammonia emission inhibition for the other three kinds of litter (p < 0.05). The pH value of tea leaves had no significant effects on the inhibition of ammoniaemissions (p > 0.05). The mixed litter made of tea leaves and sawdust, rice husk, or straw were significantly better than the tea leaves and other single litter (p < 0.01). It indicated that adding appropriate amount of tea leaves in the litter can effectively inhibit ammoniaemissionsfrom chicken manure.