Milk Quality Week 6 Week 12 Treatment Fat % Protein SCC MUN Fat % Protein SCC MUN % x1000 % x1000 3.68 ± 3.09 ± 75.2 ± 12.9 ± 3.68 ± 3.01 ± 76.1 ± 13.0 ± Shredlage 0.67 0.33 127.8 1.99 0.83 0.46 277.9 2.34 Conventional 3.73 ± 3.10 ± 88.8 ± 13.2 ± 3.71 ± 3.06 ± 53.6 ± 12.9 ± 0.67 0.33 277.3 2.08 0.72 0.39 87.2 2.09 No differences
Fecal Starch • Fecal starch less than 2 % indicates complete use of starch Treatment 6 Week 6 Week 12 Week 12 Fecal ± Fecal Week in the diet Starch Starch ± • Fecal NDF was measured Shredlage 2.18 1.16 1.46 0.64 • Shredlage Week 6 – 48.0 % • Conventional Week 6 – 49.8 % Conventional 1.95 0.78 1.66 0.86 • Shredlage Week 12 – 49.7 % • Conventional Week 12 – 49.7 %
Shredlage Results - Summary • UW Trial 1 – 50% Shredlage or 50% Conventional as a % of DM • No sorting • 0.80 kg/day milk increase (NS) • Shredlage cows consumed 0.70 kg DM/day more • No difference in milk quality • Total Tract Starch Digestibility was higher with shredlage – Fecal starch not reported • UW Trial 2 – 45% Shredlage or 45% Conventional as a % of DM • No sorting • No difference in DMI • Varied milk response over 14 weeks • No difference in milk quality
Shredlage Results - Summary • Cornell Trial - 45% Shredlage or 45% Conventional as a % of DM • No difference in milk • No difference in DMI • No difference in milk quality • Allenwaite Project • No sorting • No Milk quality differences • Lower CS inclusion rate (38% of DM) • Similar DMI • Milk response of 0.45 to 1.6 kg/cow/day • No Fecal Starch Difference
Characterizing Corn Silage • Chemical Analysis • Penn State Shaker • Corn Silage Processing Score (CSPS) • Others?
Corn Silage Penn State Box Sample % Upper % Middle % Lower % Bottom Shredlage 36.8 39.1 22.9 1.2 Conv CS 13.9 64.8 20.2 1.0 Shredlage Top Penn State Screen Conventional CS Top Penn State Screen
Corn Silage Processing Score (CSPS) • Coarse Fraction - material on sieves > 4.75 mm • Stimulates chewing activity • Starch in the particles will be poorly digested • Rate of digestion will be slow and may escape the rumen as unchewed particles • Medium Fraction – material on sieves between 4.75 and 1.18 mm • Fine Fraction - materials that pass through the < 1.18 mm • May not contribute to chewing activity or physical effectiveness • Starch in the fine particles may ferment very rapidly in the rumen and cause problems when rations low in effective fiber • Knowing what in in this fraction may be a useful tool for trouble shooting some feeding problems.
Corn Silage Processing Score 80,0 Optimum CSPS 70,0 Adequate CSPS 60,0 50,0 Inadequate CSPS Score 40,0 Shredlage CSPS Conventional CSPS 30,0 20,0 10,0 0,0 0 1 2 3 4 5 6 7 8 9 10 11 12 Week
Is CSPS Enough to Explain Milk Response? CSPS vs. Milk Production 101,00 99,00 Milk Production/day 97,00 95,00 CSPS vs. Milk Response 93,00 Shredlage 4,00 91,00 Conventional Milk Production Reponse 3,50 89,00 3,00 87,00 2,50 85,00 2,00 48,0 53,0 58,0 63,0 68,0 73,0 CCSPS Score 1,50 1,00 0,50 0,00 57,0 59,0 61,0 63,0 65,0 67,0 69,0 Maybe, but can we do better? CSPS Score
Coarse % Starch Medium % Starch 50,0 40,0 45,0 35,0 40,0 30,0 35,0 % Starch 30,0 25,0 % Starch 25,0 20,0 20,0 15,0 15,0 10,0 10,0 5,0 5,0 0,0 0,0 0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12 Week Week Shredlage Conventional Shredlage Conventional Fine % Starch 60,0 50,0 More detailed measures 40,0 % Starch of CSPS Fractions - Starch 30,0 20,0 10,0 0,0 0 1 2 3 4 5 6 7 8 9 10 11 12 Week Shredlage Conventional
Medium Starch vs. Milk Production Coarse Starch vs. Milk Production 101,00 101,00 99,00 99,00 Milk Production/Day Milk Productio/Day 97,00 97,00 95,00 95,00 93,00 93,00 Shredlage Shredlage 91,00 91,00 Conventional Conventional 89,00 89,00 87,00 87,00 85,00 85,00 30,0 35,0 40,0 45,0 25,0 30,0 35,0 40,0 % Starch % Starch Fine Starch vs. Milk Production 101,00 99,00 Milk Production/Day More detailed measures 97,00 95,00 of CSPS Fractions - Starch 93,00 Shredlage 91,00 Conventional 89,00 87,00 85,00 40,0 45,0 50,0 55,0 60,0 % Starch
Coarse % aNDF Medium % aNDF 50,0 60,0 45,0 40,0 50,0 35,0 40,0 30,0 % aNDF % aNDF 30,0 25,0 20,0 20,0 15,0 10,0 10,0 5,0 0,0 0,0 0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12 Week Week Shredlage Conventional Shredlage Conventional Fine % aNDF More detailed measures of 40,0 35,0 CSPS Fractions - aNDF 30,0 25,0 % aNDF 20,0 15,0 10,0 5,0 0,0 0 1 2 3 4 5 6 7 8 9 10 11 12 Week Shredlage Conventional
Coarse aNDF vs. Milk Production Medium aNDF vs. Milk Production 101,00 101,00 99,00 99,00 Milk Production lbs/day Milk Production lbs/day 97,00 97,00 95,00 95,00 93,00 93,00 Shredlage Shredlage 91,00 91,00 Conventional Conventional 89,00 89,00 87,00 87,00 85,00 85,00 40,0 42,0 44,0 46,0 48,0 50,0 52,0 34,0 36,0 38,0 40,0 42,0 44,0 46,0 % aNDF % aNDF Fine aNDF vs. Milk Production More detailed measures of 101,00 99,00 Milk Production lbs/Day CSPS Fractions - aNDF 97,00 95,00 93,00 Shredlage 91,00 Conventional 89,00 87,00 85,00 25,0 27,0 29,0 31,0 33,0 35,0 37,0 % aNDF
Corn Silage Measures • CSPS does not look like the best measure for cow performance • Fine Fraction measures do not appear to be related to cow performance • Medium % Starch and % aNDF may be related to cow performance • More samples and production information to build data set
Where to go next? • More samples with milk response for aNDF and Starch in Medium CSPS Fraction • Follow cows that were in 12 week study into early lactation for any carryover
Thank You • Allenwaite Farm and Staff • Sue Greth and Russ Seville from Cargill • Dairy One Lab Staff
Percent Grass NIR
Predicted vs Actual Grass Percentage in Samples 100,0 90,0 80,0 70,0 Predicted 60,0 50,0 40,0 R² = 0,9914 30,0 20,0 10,0 0,0 0 10 20 30 40 50 60 70 80 90 100 Actual
Percent Grass and Percent Alfalfa • Why is it important to know the alfalfa-grass ratio both pre- and post- harvest? • Help to identify the optimum quality harvest date. • Allow ranking of fields for harvest, based on alfalfa %. • Help to decide when to start treating a stand like grass, from a fertility standpoint. • Provide information for deciding when to rotate a field. • Assess stand deterioration due to alfalfa insect/disease problems, such as alfalfa- snout beetle in northern NY. • Some nutrient record keeping software requires input of alfalfa %. • Required information for some forage quality software, such as MILK2006, alfalfa- grass version. • May help with ration balancing. • Quality control: serves as a check on just how representative the forage sampling is. Highly variable alfalfa % over time indicates unrepresentative sampling.
The Nutritionist Forage Lab Forum Matt Michonski — Cumberland Valley Analytical Services Fatty Acids and NIR for Intestinal Protein Digestibility
The Nutritionist Webinar Series Current Focus Concepts at CVAS: Fatty Acid Evaluations by NIR Intestinal Protein Digestibility Assay Matt Michonski Cumberland Valley Analytical Services www.foragelab.com
Why consider fatty acids? • Crude fat is the traditional method for evaluation fat in feedstuffs – “ether extract”. • Ether extract is not a uniform entity – may include waxes, cutin, fermentation acids and chemical entities that are not fatty acids. • For many feed ingredients there is little difference between crude fat and total fatty acids.
Why consider fatty acids? • However, for fermented feeds and some byproducts there may be significant differences between crude fat and total fatty acids.
Total Fatty Acids as a Percent of Fat in Hay Crop Silage 30% N=11,883 Ave. = 51.4 St. Dev. = 7.68 25% Percent of Samples 20% 15% 10% 5% 0% <25 30 35 40 45 50 55 60 65 70 75 80 85 >85 Total Fatty Acids as Percent of Fat
Fatty Acid Determination • Fatty acid determination is generally an involved extraction followed by analysis by gas chromatography. This is expensive and time consuming. • NIR can be an applicable technology for routine analysis of total fatty acids and even individual fatty acids.
Fatty Acids by NIR Successful NIR calibrations are based on the following characteristics: • Organic bonding and chemical uniformity • Range in the nutrient being analyzed • Precision in the analysis being performed by chemistry analysis
Fatty Acids by NIR Fatty Acid evaluation of corn silage, corn grain, and TMR by NIR meet the criteria for generating quality NIR calibrations: • They are well defined organic compounds; • There is significant range in composition; • Chemistry evaluation by gas chromatography provides significant precision of analysis.
Fatty Acids in Corn Silage NIR Equation Statistics (CVAS, 2016) Fatty Acid Mean SEC RSQ C18_1 .521 .046 .86 C18_2 1.22 .057 .94 C18_3 .150 .019 .88 RUFAL 1.89 .075 .96 Total Fatty Acids 2.50 .092 .94
Fatty Acids in Corn Grain NIR Equation Statistics (CVAS, 2016) Fatty Acid Mean SEC RSQ C18_1 .895 .069 .84 C18_2 2.05 .101 .93 C18_3 .059 .006 .51 RUFAL 3.03 .109 .96 Total Fatty Acids 3.72 .135 .95
Distribution of Total Fatty Acids (%DM) in Corn Silage CVAS 2016 20% 18% N=2481 Ave. = 16% % of Samples 14% 12% 10% 8% 6% 4% 2% 0% <1.25 1.55 1.85 2.15 2.45 2.75 3.05 3.35 Total Fatty Acids, %DM
Distribution of Rumen Unsaturated Fatty Acids (RUFAL, %DM) in Corn Silage , CVAS 2016 20% 18% N=2481 16% Ave. = % of Samples 14% 12% 10% 8% 6% 4% 2% 0% <0.80 0.95 1.10 1.25 1.40 1.55 1.70 1.85 2.00 2.15 2.30 2.45 2.60 >2.6 RUFAL, %DM
Distribution of Total Fatty Acids (%DM) in Corn Grain CVAS 2016 25% N=1534 Ave. = 3.73 20% % of Samples 15% 10% 5% 0% <2.25 >5.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 4.25 4.50 4.75 5.00 5.25 Total Fatty Acids, %DM
Distribution of Rumen Unsaturated Fatty Acids (RUFAL, %DM) in Corn Grain , CVAS 2016 30% N=1534 25% Ave. = % of Samples 20% 15% 10% 5% 0% RUFAL, %DM
Distribution of Total Fatty Acids (%DM) In Production Dairy TMR CVAS 2015 14% N=6262 12% Ave. = 10% % of Samples 8% 6% 4% 2% 0% Total Fatty Acids, %DM
Distribution of Rumen Unsaturated Fatty Acids (RUFAL, %DM) in Production Dairy TMR , CVAS 2015 18% 16% N=6262 Ave. = 14% % of Samples 12% 10% 8% 6% 4% 2% 0% RUFAL, %DM
In-vitro N Indigestibility Assay (Ross et al., 2013) • We refer to it as the “Multi -Step Protein Evaluation” (MSPE) Assay; • Multiple labs have adopted this assay in the last several years; • Provides a tool for evaluating protein sources and byproduct materials allowing for characterization of indigestible nitrogen (protein).
Why the need for the MSPE? • Availability of metabolizable protein (MP) is a function of intestinal digestibility (ID) and ID is a static library value • Most model (NRC, CNCPS) feed libraries have static values for ID • We know this is not true and monogastric species rely on ID to balance for protein and amino acids Source: Van Amburgh
Unavailable Nitrogen as calculated within the CNCPS uN = [PB2 * (kd / (kd + kp) * (1- 0.8)] + ADIN where, • PB2 = (NDIN – ADIN), • Kd in the rate of degradation for each ingredient, • Kp is the passage rate for solids (0.05/h), • 0.80 is the intestinal digestibility constant of PB2 (NDIN) (NRC, 1989) Source: Van Amburgh
INTESTINAL DIGESTIBILITY Potentially rumen un-degradable protein 100% ID 0% ID A2 B1 B2 C 80% ID 100% ID Bound fiber Source: Van Amburgh 79
New/Updated In Vitro ID assay • Modification of existing methods to better estimate N unavailable fraction – flasks instead of bags (sample loss, lag time) – physiological enzyme mix • reduce variation in proteolytic activity • filtering residue on 1.5 μ m, 90 mm glass filter paper (Whatman AH 934 or equivalent) instead of TCA precipitation Source: Van Amburgh
New/Updated In Vitro ID assay • Filtration may not always be appropriate for recovery of treated fractions however. • If nitrogen source is soluble or significantly micronized it may pass through the filter and will lead to a perception of lower rumen ungradable protein.
New/Updated In Vitro ID assay • In order to overcome the limitations of filtration, the use of freeze drying for recovery of materials in the assay is critical for RUP definition. • Blood meal or feed mixes containing blood meal are key examples of materials where freeze drying is necessary. • It is important to characterize feed materials submitted to the lab so that the correct procedural approach may be applied. • Why not always use freeze drying? Cost and time involved.
Blood meal filtered through 1.5 μ m glass fiber filter – may be significantly soluble
Comparison of Filtration vs Freeze Drying in Three Blood Meals (CVAS, 2015) Soluble Filter Freeze Dry Total Tract CP Protein RUP RUP Undig. CP %DM % CP % CP % CP % CP Blood 98.3 48.8 28.0 74.2 7.9 Meal 1 Blood 98.8 2.0 96.3 97 23.9 Meal 2 Blood 99.1 2.2 94.4 95.8 18.7 Meal 3
New/Updated In Vitro ID assay • Why not always use freeze drying? Cost and time involved: – Basic freeze drying units cost $25K to $30K; – Operational costs: operating a compressor and vacuum pump for multiple days per run; – Run time can be 3 to 5 days.
Sample Rumen buffer pH 6.8 Rumen fluid Fermentation anaerobic 16-h, 39 ° C Procedure in a single flask kp = 6.25 %/h Acidify 3 M HCl (pH 1.8 - 2) Gastric Digestion (pH 2 HCl) + Pepsin Neutralize 2 M NaOH Source: Van Amburgh Enzyme Mix trypsin, chymotrypsin, amylase, lipase and bile acids N Incubation Filter determination 39 ° C, 24-h Shaking bath Kjeldahl or Leco
What the Ross intestinal digestibility assay was not designed to do… According to Van Amburgh: • “It was not designed to provide a robust RUP value”; • “We provided the single time point estimate of RUP because no one would believe the uN value unless we provided the RUP”; • “A more robust RUP determination requires multiple time points and is not part of this assay”.
Comparison of ADIN and Ross in-vitro indigestible N Ross In-vitro Feed N (% ADIN (%N) indigestible N DM) (% N) Regular blood 16.2 4.7 16 meal Heat damaged 16.1 1.8 93 blood meal Soybean meal 7.6 6.7 8 solvent extracted Soybean meal 7.3 7.9 11 heat treated Slide Source: Van Amburgh 89 Source: Ross, 2013
Example MSPE Data CVAS, 2015 RUP, % CP Total tract uCP, % CP Blood 1 94.1 65.7 Blood 2 90.0 11.5 Canola 1 31.3 20.6 Canola 2 43.8 11.3 Distillers 1 53.3 16.3 Distillers 2 81.2 8.7 Untreated SBM 32.8 4.1 Treated SBM 1 51.2 7.9 Treated SBM 2 73.4 12.9 Treated SBM 3 86.7 10.7
Does The Cow Care? ? Source: Van Amburgh
Research at Cornell Objective: • Test the accuracy and precision of the in-vitro N indigestibility assay (Ross et al., 2013) in lactating dairy cattle • Evaluate the use of the uN values in the CNCPS to predict cattle performance Source: Van Amburgh
Experimental Design • 128 cows – 96 multiparous (1,587 lb (720 kg) BW; 147 DIM) – 32 primiparous (1,338 lb (610 kg) BW; 97 DIM) • Cattle distributed by BW and DIM • 8 pens of 16 cows (12 multiparous and 4 primiparous) • Pens stratified into four levels by milk production and each stratum randomly allocated to treatments • Random allocation of pens to treatments Source: Van Amburgh
Treatment Diets • Diets designed to iso-energetic and iso- nitrogenous • Treatment difference was created by using two different blood meals • One blood meal was 9% uN, the other was 34% uN • The calculated difference in N digestibility between the two treatments was 38 g N – cattle were consuming ~667 g N (5.8% of intake) Source: Van Amburgh
Nitrogen Intake (LS means) LOW uN HIGH uN 1000 (P<0.77) N Intake (g/d) 750 500 250 0 0 1 2 3 4 5 6 7 8 9 Week of experiment Source: Van Amburgh
Energy Corrected Milk (LS Means) LOW uN HIGH uN 47 (P<0.01) 45 ECMY (kg/d) 43 41 39 37 35 0 1 2 3 4 5 6 7 8 9 Week of experiment Source: Van Amburgh
Summary • Total Fatty Acids is a more significant nutritional entity than Crude Fat; • NIR is able to predict Total Fatty Acids and Unsaturated Fatty Acids with significant accuracy and precision.
Summary • The Intestinal Digestibility Assay of Ross and Van Amburgh (MSPE) is a significant improvement in a laboratory approach to evaluate the indigestible fraction in feed materials. • The use of freeze drying in place of filtration is necessary for proper characterization of products that contain significant soluble or micronized sources of nitrogen. • The assay was meant to evaluate the indigestible protein fraction in feeds and not rumen ungradable protein. While RUP values are provided in this assay and have some value, they are not meant to be formally defining.
The Nutritionist Webinar Series Thank you for your attention! Matt Michonski mmichonski@foragelab.com Cumberland Valley Analytical Services www.foragelab.com
13 de abril 19:00 (toda segunda quarta feira do mês) Dr. Jim Drackley, PhD, Professor, University of Illinois Alimentação de bezerras — estratégias para casinha e pós casinha
Sua empresa pode ser parceira no próximo Webinar. Ajude-nos a trazer aos nutricionistas Brasileiros o que existe de mais novo em nutrição de vacas leiteiras no mundo. eventos@3rlab.com.br
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