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* Department of Soil Quality,
Department of Animal Sciences,
Alterra, Wageningen University and Research Center, and
Wageningen Institute of Animal Sciences, Animal Nutrition Group, Wageningen University, 6700 Wageningen, the Netherlands
1 Corresponding author: Petra.vanvliet{at}wur.nl
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: DNA fingerprinting • manure composition • bacterial diversity • dairy farming
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Integrated and quantitative information on the effect of dietary changes on excreta composition might therefore be helpful to improve the utilization of manure N. However, current feed evaluation systems are still largely focused on the output of milk, and the prediction of manure composition is not of major concern. Furthermore, energy and protein evaluation systems are empirically based and have been developed independently, despite vast evidence of the interactions between energy- and protein-yielding nutrients in the gastrointestinal tract of ruminants. Therefore, these systems are unlikely to predict effects of dietary changes upon manure composition accurately (Dijkstra et al., 2007). Recently, Reijs (2007) developed an integrated model that predicts the composition of excreta as a function of diet composition. Excreta composition comprises OM and C and N output with different fecal and urinary components.
Besides chemical changes, changes in the fecal microbial community also may be expected from dietary adjustments as the diet affects nutrient flow into the hindgut and hence likely affects the microbial community there. Information on differences in fecal microbial community as a result of dietary changes might also contribute to the understanding of differences in the fertilization potential of different manures. Microorganisms in ruminants are primarily involved in decomposition of the feed and the N metabolism in the animal. Feeds with abundant foliage contain a large amount of protein, which is degraded in the rumen, and when not enough rumen-fermentable energy is present, it is excreted in urine. In the case of low-energy contents, microbial growth in the rumen, in the large intestine, and in the feces will be C- rather than N-limited. When sufficient degradable carbohydrate is present, microorganisms can utilize a large fraction of this protein and keep the N in organic form. Moreover, recycling of N by urea entering the rumen with saliva or through the rumen or hindgut wall further increases the dietary situations of C-limited growth in both rumen and hindgut (Dijkstra et al., 2002).
Numerous literature references on pathogenic organisms in manure can be found (Pell, 1997; Stoddard et al., 1998), but information considering abundances of naturally occurring bacteria in slurry manures is limited. In swine manure, 2 x 1011 bacteria/g of fresh weight (Cotta et al., 2003) or 1 x 1010 cells/mL (Leung and Topp, 2001) were counted. Until now, no detailed analyses of the microbial community structure, biomass, and activity in cow feces have been published. Molecular techniques for detecting and identifying microorganisms by certain molecular markers have become available and are frequently used to explore the microbial diversity. Using molecular techniques, including sequencing, Ouwerkerk and Klieve (2001) found that 16.3% of the total number of species were predominating culturable species in cattle manure. Also, the frequency of detection of novel bacteria in manure was much higher using molecular techniques. Fingerprints created by the denaturating gradient gel electrophoresis (DGGE) technique can be used for monitoring shifts in community structure (Muyzer and Smalla, 1998; Zoetendal et al., 2004).
We have examined the consequences of changes in cow rations for the chemical characteristics and the biomass, activity, and structure of the microbial community in the feces. For this experiment, 8 different diets with high and low levels of dietary protein and net energy were created with different forage types (Reijs et al., 2007). We also related our results to predictions using a mechanistic model of fermentation and digestion in the gastrointestinal tract of ruminants (Reijs, 2007). These research questions have led to the following hypotheses:
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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). Cows were fed ad libitum twice daily (morning and evening); food not consumed was removed and weighed before fresh food was supplied. No bedding material was used. A 3-wk adaptation period was applied before the start of the experiment. The diets differed in forage and concentrate type and contained 2 levels of CP and 2 levels of NEL. The 2 CP levels (high: 
182 to 202 g/kg of DM; low 100 to 117 g/kg of DM) were combined with 2 NEL levels (high: 6.6 to 6.7 MJ/kg of DM; low: 5.3 to 5.5 MJ/kg of DM). The 2 regimens at the high NEL level differed slightly in the proportion of NDF, ADF, and starch level (Table 1). After the adaptation period, feces of the 16 cows were collected for chemical and microbiological analyses during 1 d. In case any diurnal pattern in the fecal composition was present, the quantitative sampling ensured a proper average sample of a full day. Part of the feces was frozen at –20°C for DNA analysis; the remaining feces were stored at 4°C for other microbiological and chemical analyses for a short period.
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Feed samples were analyzed for total N (Ntotal), using the CE Instruments EA 1110 CHN analyzer (CE Instruments, Milan, Italy) after freeze-drying. Crude protein in the feed was calculated as Ntotal x 6.25. The NDF and ADF were analyzed with amylase after freeze-drying according to Van Soest et al. (1991) and expressed on an ash-free basis. Feed starch was determined using a hot ethanol extraction, 
-amylase, and amyloglucosidase and measured on a HPLC. The degraded protein balance in the rumen (g/d) was calculated according to Tamminga et al. (1994). The digested OM percentage was calculated as the ingested OM (in kg/d) minus the OM in the slurry at the end of the collection period (in kg/d) divided by ingested OM (in kg/d) multiplied by 100%.
Microbial Analyses
Microbiological analyses were started the day after collection of the feces. The total number of bacteria and several morphological characteristics of the microbial community were determined in a feces smear using confocal laser scanning microscopy and image analysis (Bloem et al., 1995b). Bacterial biomass was calculated using the total number and the average cell volume of the fluorescently stained bacteria combined with a specific C content of 3.1 x 10–13 g of C/µm3 (Fry, 1990). The preparation of the feces smear was similar to the preparation of soil smears as described in Bloem et al. (1995a). In summary, for each fecal sample, 20 g of fresh fecal material was homogenized with 190 mL of deionized water in a blender for 2 min. A 9-mL sample of the fecal suspension was fixed by adding 1 mL of 37% formaldehyde. The suspensions were diluted 10 times, after which 10 µL was applied (after shaking and a settling time of 2 min) to printed microscopy slides with a 12-mm well. After a drying time of 1 h at 50°C, staining, destaining, and mounting the smears were done as described by Bloem et al. (1995a). Each fecal sample was analyzed in duplicate.
Bacterial growth rates were determined using the incorporation of 14C-Leu (Michel and Bloem, 1993; Bloem and Bolhuis, 2006). Leucine is an AA that is incorporated in proteins. The bacterial growth rate is reflected by the incorporation rate of 14C-Leu into proteins during a short incubation of 1 h. If the incubation is short enough, growth rate is not affected by the incubation. Briefly, 20 g of fecal material was homogenized in 95 mL of mineral medium. From this diluted fecal suspension, 100 µL was added to 20 µL of labeled 14C-Leu (2 µM final concentration) and incubated for 1 h. Proteins were extracted overnight in a warm base solution. Particles were removed by centrifugation, and proteins were precipitated by cold acid and collected on a filter. Radioactivity was measured, and Leu incorporation was calculated.
The DNA was extracted from the feces using a Fast-DNA SPIN Kit (BIO 101 Qbiogene, Carlsbad, CA) normally used to extract DNA from soil. In the extraction, 0.10 to 0.17 g of fecal material was used. If necessary, samples were purified using the Wizard DNA Clean Up System (Promega, Madison, WI). Of the DNA extract, 1 µL was used in the PCR (Dilly et al., 2004). The variable V3 region of 16S rRNA gene sequences from nucleotide 341 to nucleotide 534 (Escherichia coli numbering) was amplified by PCR using eubacterial primers 2 and 3 and the hot start-touchdown protocol with a total of 30 cycles as described by Muyzer et al. (1993). A 2% agarose gel was used to check the result of the PCR and to determine the amount of DNA that should be used for the DGGE. Equal amounts of DNA were loaded onto an 8% polyacrylamide gel with a 40 to 75% denaturant (urea-formamide) gradient (Dilly et al., 2004).
The fingerprints generated with the DGGE were analyzed for similarity using GelCompar II (Applied Maths, Kortrijk, Belgium). The unweighted paired group method using arithmetic averages in combination with Jaccard coefficient and the Pearson coefficient was used for determination of similarity matrices of the different fingerprints. Jaccard’s coefficient is based on the presence of bands in pairs of fingerprints; absence of a band in both fingerprints is not accounted for. Pearson’s coefficient is based on the presence and height of bands in pairs of fingerprints. The intensity of the DNA band is an indication of the amount of DNA present: a thicker band implies more DNA and a greater number of cells per bacterial species.
The fingerprint data were used to calculate the Shannon-Wiener diversity index (H') which was calculated as:
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where S = the number of DNA bands; ni = the mass of the ith DNA band; and N = the total mass of all bands (Atlas and Bartha, 1987). The mass of a DNA band was calculated by multiplying the width of the band with the signal intensity (gray level) of the band. The Shannon-Wiener diversity index is a general diversity index that is sensitive to the number of species and the relative species abundance (Atlas and Bartha, 1987). For further analysis, we have assumed that the number of bands present on a DGGE gel represents the number of bacterial species present. Dilly et al. (2004) and Dolfing et al. (2004) have shown that, in general, the number of DNA bands present on a fingerprint reflects the number of bacterial species, whereas the intensity of the band strongly correlates with relative amounts of the different species.
Statistical Analyses
For statistical analysis of the chemical and biological data, SAS/STAT version 9.1 (SAS Institute Inc., Cary, NC) was used (ANOVA, correlations). Before analysis, all variables were checked for normality and transformed if necessary. The Tukey posthoc test was used to determine significant differences between the different fecal materials.
The observed chemical and microbial data of the feces were compared with predictions using the model described by Reijs (2007). This model comprises a mechanistic part describing the rumen fermentation processes, extended with equations to describe the digestion and fermentation processes in the small and large intestine. The model predicts OM, C, and N (organic and inorganic) output in feces and urine. The mean square prediction error (MSPE) was used to assess the error of predicted relative to the observed values:
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where i = 1, 2, ... n; n = the number of experimental observations (n = 16); and Oi and Pi = the observed and predicted value of the ith observation, respectively. The square root of the MSPE is expressed in the same units as the observed values. Comparing the root MSPE as a percentage of the observed mean provides an indication of the overall error of prediction.
Residual plots showing the predicted value vs. the difference between observed and predicted values were used to evaluate the simulation model. For prediction bias analyses, the regressor used in the equations was shifted and centralized to its mean value. In this method, the intercept and slope estimates are independent of each other and t-tests can be used to determine the bias of the model. The intercept term measures the overall prediction bias, whereas the slope of the regression is an estimate of the linear prediction bias (St-Pierre, 2003).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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). Numerically, the diets based on old grass silage (GO; PHEL-GO and PLEL-GO) had a higher average DM content.
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). A higher Ntotal concentration coincided with a higher Norg concentration (Pearson = 0.999, P < 0.001). An increase in the Ntotal concentration also coincided with a higher concentration of Ninorg in the feces (Pearson = 0.751, P = 0.001). For all 8 feces types, Ninorg was about 5 to 8% of the Ntotal content (Table 2
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) and the C:Norg ratio (Table 2) differed significantly for the different feces. With an increasing CP and NEL, the C:Ntotal and C:Norg ratios decreased significantly (interactions were not significant.). The feces from the low-protein diets combined with straw, maize (Zea mays), or mature grass silage, or the high-protein with straw feeding regimen had a high C:Ntotal ratio. The diet high in protein combined with young grass silage (PHEH-GY) had the lowest C:Ntotal and C:Norg ratio.
Bacterial Diversity in the Feces
The bacterial diversity in the feces was similar (P > 0.05) for all diets. The number of DNA bands ranged from 35.5 (PLEL-GO) to 41 (PLEH-M and PHEH-GYM), and the Shannon-Wiener index was around 3.4 (Table 2). The similarity in species composition of the 16 analyzed feces based on the presence and absence of DNA bands (Jaccard coefficient) varied from 64 to 92%. A Jaccard coefficient of 100% implies complete similarity, whereas a coefficient of 0 implies no similarity at all between the different samples. Cluster analysis with the Jaccard coefficient showed no clear effects of the different diets on the species composition in the produced feces (data not shown). If the relative abundance of the present bacterial species (equal intensity of the bands) was taken into account (in the Pearson coefficient), a clear separation in protein level was visible (Figure 2).
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, bacterial biomass (in g of C/kg of OM) in the 8 different treatments is shown. A higher biomass in the feces was found with the feeding regimens containing a high energy level (P = 0.008). However, the interaction between protein and energy was significant (P = 0.04). This interaction occurs because feces of high-energy diets have higher microbial biomass at high dietary protein levels but not at low dietary protein levels. The feces produced on diets that combine a high energy and high protein level (PHEH-GYM and PHEH-GY) contained a large bacterial biomass (respectively, 8 and 5 g of C/kg of OM). However, the activity of the bacterial community in this material, expressed as incorporation of 14C-Leu, was low (Figure 3B). Feces produced on diets with a mature grass silage (PLEL-GO and PHEL-GO) showed the opposite and combined a relatively low microbial biomass with a high activity.
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. Strong positive correlations between the bacterial biomass present and the concentration of Ninorg, Ntotal, and Norg were found (Table 3). Bacterial biomass decreased as the C:N ratio (C:Ntotal and C:Norg) of the feces increased. Microbial biomass was not correlated (P > 0.05) with the DM and Ctotal content of the feces. Bacterial activity was strongly positively correlated (P = 0.004) with the DM content of the feces and strongly negatively correlated (P 
0.001) with the Ctotal content of the feces.
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and 4). The root MSPE was 5.9 g of N/kg of OM, which was 23% of the observed mean fecal N concentration. The overestimation of the N concentrations is also seen in the predicted C:Ntotal ratios, which were all lower than the observed values. The root MSPE was 4.4, which was 24% of the observed mean C:Ntotal ratio for the feces.
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and 4). Model estimates were higher than the observed values for all measurements. The root MSPE was 6.2 g of C/kg of OM, which is about 157% of the observed mean microbial biomass C. The model for microbial biomass showed a linear bias (slope = –0.60 ± 0.27, P = 0.04). This implies a bias of less than 4.3 g of C/kg of OM at the minimum (7.3 g of C/kg of OM) and of less than 7.6 g of C/kg of OM at the maximum (12.7 g of C/kg of OM) predicted microbial biomass C. These biases are much larger than the standard error (0.55). In this model, the overall prediction bias or mean bias was also significant (intercept = –5.79 ± 0.53, P < 0.001).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Similar to other studies (Kyvsgaard et al., 2000; Sørensen et al., 2003), we find that the concentration of Norg in the feces increases as the digestibility of the diet increases. The model provides insight into the mechanisms responsible for this increase. The higher DM intake level on the high-energy diets will increase fractional passage rates and consequently result in lower fractions of DM fermented in the rumen and a higher efficiency of microbial protein synthesis (Dijkstra et al., 2002). The higher simulated flow of OM (largely fiber) into the large intestine on high-energy diets will increase the endogenous protein losses, because these losses are related to the flow of indigested matter through the gastrointestinal tract (Tamminga et al., 1994). Moreover, a relatively higher fraction of the large intestinal flow of OM on the high-energy diets consists of potentially fermentable OM that has bypassed rumen fermentation and thus will increase microbial biomass production in the large intestine (Figure 3A).
Simulated and estimated bacterial biomass were qualitatively similar for the different diets; the high-energy (EH) rations always had a higher biomass than the low-energy (EL) rations. Bacterial biomass in the low-protein (PL) rations was high, because a reduced protein concentration in the feed coincides with a higher concentration of carbohydrates. Some of these carbohydrates will enter the large intestine and will be fermented in there. The low-protein rations also contained higher concentrations of starch. Starch that has bypassed the rumen and the small intestine is also a good substrate for bacteria in the large intestine.
Predictions from the model for Ntotal and the C:Ntotal ratio in the feces differed maximal 24% of the mean, whereas Ntotal was overestimated and C:Ntotal underestimated. The overestimation of the N content of the feces is caused by an underestimation of the apparent N digestion or an overestimation of the OM digestion. This might be caused by errors in the input of the model or by (individual) deviations from the intestinal digestion coefficients used in the model. The latter effect might be amplified in this experiment, because extreme feeding regimens were used, whereas the intestinal digestion coefficients in the model are estimated as the average of a large range of feeding regimens.
The PLEL-GO and the PLEL-S diets had a high NDF and ADF content and had smaller amounts of N in the feces than the PLEH-M and PLEH-GYM diets. This is mainly due to the protein source in the different diets. In the PLEL-GO and the PLEL-S diets, the protein is derived from soy, which is highly degradable, resulting in a small concentration of undigested feed N in the feces. Protein in the PLEH-M and PLEH-GYM diets is for a large part derived from roughage; the protein is therefore of a lower quality than when derived from soy and is arduous to decompose.
Due to high plant cell wall levels (high NDF), high levels of lignin within the cell walls, and the low N content in the PLEL-S feeding regimen, the nutritive value of this feeding regimen was low and resulted in feces with the highest C:Ntotal. The reduction in energy level in the high-protein feeding regimens (PHEH-GYM + PHEH-GY to PHEL-S + PHEL-GO) resulted in feces with a lower concentration of total and Ninorg. The lower rumen fermentable OM with the low-energy diets may have reduced microbial protein synthesis in the rumen. Because microbial protein digestion in the gut is not 100%, this reduction therefore results in decreased fecal N of microbial origin.
Microbiological Aspects
The number of bacteria found in the feces [on average 39 x 109 cells (g of DM) 6 x 109 cells/mL] was comparable to the number found by direct counts for liquid swine manure [1 x 1010 cells/mL by Leung and Topp (2001)] but lower than the number found by Cotta et al. (2003), who recorded 2 x 1011 bacteria/g in fresh swine feces. The amount of microbial C in the feces is determined by microbial synthesis in the rumen and large intestine; the simulations indicated that the major part of microbial C in the feces is of rumen origin (undigested rumen microbial biomass). In the rumen, synthesis is mainly dependent on the availability of degradable energy and protein. Because the rumen fermentation of starch is usually higher than that of fiber, a lower starch content in the diet will provoke a lower amount of fermentable ME for microbial synthesis. In the large intestine, the synthesis is probably dependent on the amount of available carbohydrates (fiber and starch) not fermented in the rumen or digested in the small intestine. The amount of microbial C is also very much dependent on this flow. In our study, the percentage of microbial biomass C in the fresh feces varied from 0.3 (PHEL-GO) to 1.9% (PHEH-GYM). In the model used in our study, microbially derived C in the feces ranged from 12 to 24% of the Ctotal present. However, statistical tests showed that the model is biased. The linear bias translates to values at the minimum and maximum level that are much larger than the standard error, implicating that the model does not simulate microbial biomass well. The differences between measured and predicted values are large and are probably due to a difference in definition of microbial C. The microbial biomass C data in this study were calculated from the numbers of bacteria found in the feces. As stated earlier, the bacterial numbers recorded are comparable to other studies. For the conversion of numbers to microbial C, we have used the average cell volume based on the actual measured sizes of all individual cells and a specific C content of 310 fg of C/µm3 as proposed by Fry (1990). A potential error can be caused by the conversion of volume to mass. Therefore, we have calibrated our image analysis system using fluorescent microspheres of known sizes and by comparing bacterial bio-volume with directly measured bacterial C (Bloem et al., 1995b). We assume that potential errors are similar for all fecal materials and that comparisons between diet treatments are valid. The prediction that 12 to 24% of the fecal C is microbially derived also includes damaged microbial material, whereas we have only recorded complete bacteria. Intact bacteria washed out from the rumen probably lyse in the acid abomasal environment, and subsequently, partial digestion of contents in damaged bacterial cells in the small intestine occurs. An indication of this partial digestion is the much lower digestion of diaminopimelic acid (a component of the bacterial cell wall) compared with that of AA and nucleic acids (Storm et al., 1983). These processes cause undigested material in the feces originating from rumen microbial matter from damaged microbial cells. This might explain the large difference between the 2 estimates. More measurements of microbial C in slurry manure, feces, or both, are needed to determine which estimate is closest to reality.
The bacterial diversity was higher than the diversity measured in manure slurry (van Vliet et al., 2006) and similar for all fecal materials. We assume that the number of bands on a DGGE-gel reflects the number of bacterial species. According to Hill et al. (2000), separation of DNA on the denaturant gradient will resolve individual bands, each corresponding to a unique sequence (genotype, presumably species). However, the relationship between bands on a fingerprint and the number of species present is not a simple 1-to-1 relationship. In a gram of manure, billions of bacteria are found, whereas the number of DNA bands on a gel is less than 100. These are the abundant species. According to Zoetendal et al. (1998), DGGE is sensitive enough to detect bacteria that constitute as little as 1% of the total bacterial community present. Dilly et al. (2004) found an even lower detection limit of about 1
. Differences in diet composition resulted in changes in the relative abundances of bacterial DNA bands (genotypes or species). These changes were induced mainly by the protein level of the diets. It is possible that a lowering of the protein level of the feed resulted in a shift within the bacterial community toward bacteria with a relatively high N assimilation efficiency. Because we have not identified the different bands on the DGGE gel, we cannot draw any conclusions about this possible shift. Kocherginskaya et al. (2001) determined bacterial diversity in rumen fluid of steers kept on a hay or corn diet with DGGE and a random sequencing approach. The species diversity was higher in the corn diet compared with the hay diet. On the contrary, we conclude that differences in diet composition did not affect the species composition of the feces but changed the relative abundances of the different species. Hypothesis 2 is rejected, because the species diversity of feces produced with low digestible feed (PHEL-GO, PLEL-GO, PHEL-S, and PLEL-S) was not significantly statistically different from the species diversity of feces produced with other diets.
In the digestive tract of a cow, the fecal material endures anaerobic conditions. As soon as the material is voided from the body, feces are exposed to oxygen-rich conditions. The bacteria active in the digestive tract are obligately or facultatively anaerobic. The activity of obligately anaerobic bacteria in the feces will cease as soon as aerobic conditions occur, whereas facultative anaerobes will switch to aerobic metabolism (Paul and Clark, 1989). After exposure to lower temperatures outside the body and oxygen-enriched conditions, a large fraction of the bacterial biomass present in the feces will succumb. Dead cells may be incorporated in our total bacterial counts. The measured microbial biomass is therefore probably an overestimation of the living biomass present in the voided feces. No other measurements of microbial biomass in the feces are known, and for further evaluation of the model, more measurements need to be done.
The microbial activity measurements were not performed under anaerobic conditions. According to Michel and Bloem (1993), all bacteria can incorporate Leu. Feces of the PHEL-GO and PLEL-GO feeding regimens had the highest DM content and thus the lowest moisture content. This may have resulted in a fast aeration of the feces, resulting in a high activity of aerobic bacteria and therefore a high incorporation of Leu.
Microbial biomass in the feces was primarily affected by the composition of the feed supplied to the cow. The feces of the treatments PHEH-GY and PHEH-GYM had a low C:Norg ratio, a high microbial biomass, and a high Ninorg concentration. The PHEH-GY diet (low C:N ratio, low NDF, see Table 1) contained a large amount of N and many easily decomposable C sources. A C:N ratio of 5 and an efficiency of 30% are generally assumed for bacteria (de Ruiter et al., 1993). In that case, when the C:N ratio of the organic material (feed) is lower than 15, net mineralization of N occurs, whereas at a higher C:N ratio, net immobilization of N by the microorganisms occurs (Bloem et al., 1997). This N is fixed in microbial biomass. After passage through the rumen, C limitation has probably occurred in the large intestine, and the microbial community present in the intestine became less active. When bacterial production rates are limited, for instance, due to a limited C supply, and a relatively large amount of N is present, part of the protein will not be used for production of bacterial biomass but will be mineralized. This can result in higher Ninorg concentrations in the fecal material. The PHEH-GYM regimen had an even lower NDF value, which resulted in a similar scenario as the PHEH-GY treatment: low C:Norg ratio, high bacterial numbers, and high Ninorg concentration in the feces. The highest Ninorg content was measured in the feces of this diet. In the feeding regimens with a high CP and high energy level, a potentially large amount of fermentable material will arrive in the hindgut, resulting in Norg concentrations in the feces that were higher than any of the other feeding regimens. However, these concentrations remain small when compared with the N concentrations excreted in urine.
Overall, bacterial biomass in the feces reflected the feeding regimen. Diets with low protein and high fiber content resulted in less bacterial biomass and less excretion of N in the feces. Although the absolute amount of excreted N was clearly reduced in these diets (less in, less out), the fraction of Ninorg in the feces and thus immobilization into bacterial biomass was not significantly different compared with the other feeding regimens.
Consequences
In practical farming situations, feces are combined with urine into slurry manure. Urine contains a high concentration of N. The C:N ratio of the slurry manure will therefore be much lower than that of the feces. In this experiment, the C:N ratio of the slurry manure ranged from 5 (PHEH-GY) to 11 (PLEL-GO). Reijs et al. (2007) used these slurries in a field experiment and found a negative relationship between the C:N ratio of the slurries and the N availability in the soil. A reduction of the dietary protein content of the feed resulted in a significant decrease of the first-year N availability. However, the addition of manures with a high C:N ratio may contribute to the soil N supply in the longer run, as was shown in a long-term experiment by Silgram and Chambers (2002). In their experiment, 10 yr of addition of straw increased the size of the labile soil Norg fraction, which contributed to the soil N supply. Simulations using the PLEL-GO and PHEH-GY slurry manures showed that the usage of PLEL-GO slurry manure due to its high C:N ratio led to a reduced availability of Ninorg (Reijs et al., 2007). This has to be compensated by soil N mineralization, which in turn is driven by OM input. The chemical composition, in particular the DM and C content of the feces, which is affected by diet composition, is therefore important.
The model used in this experiment was developed on a wide variety of diets, including a wide range of DMI levels and fractional passage rates. Extensive model evaluation did not indicate any relation between DMI level and prediction error. The model can therefore be used at low DMI levels (e.g., dry cattle) as well as high DMI levels (e.g., high-producing cattle). The manure composition of dry cows may differ from that of high-producing cattle, because fractional passage rates and digestibility will differ. However, this is no reason for model simulations to deviate from observations. Non-lactating dairy cows have very low requirements for energy and protein due to the absence of milk production. The low energy requirement made it possible to use forages with a very low digestibility (straw and low digestible grass silage) in this experiment. The low protein requirements implied a higher excretion of N in feces and urine compared with lactating cows with the same protein intake. Furthermore, it should be noted that only 2 cows per diet were used. This implies that differences between diets are heavily affected by feed intake, water intake, and digestion efficiency of the individual animals. Therefore, the relations between diet and feces characteristics observed in this experiment cannot be directly extrapolated to any other situation, especially not for lactating cows.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication January 30, 2007. Accepted for publication July 24, 2007.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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