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Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.
Grazing and difficult circumstances: Economic benefits depend on milk price and grazing efficiency.
in: van den Pol-van Dasselaar A. van den Aarts H.F.M. De Vliegher A. Elgersma A. Reheul D. Reijneveld J.A. Verloop J. Hopkins A. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2015: 236-238
) benefits. However, recent estimates have shown that only 49 to 52% of lactating dairy cows in European countries have access to pasture, and the numbers have been declining in recent years (
), the decline in grazing may be the result of larger herds, fragmentation or lack of land, development of automatic milking systems, and farmer expectations regarding productivity in a pasture-based system, as mentioned by
The use of grazing in intensive dairy production and assessment of farmers' attitude towards grazing.
in: Schnyder H. Isselstein J. Taube F. Auerswald K. Schellberg J. Wachendorf M. Herrmann A. Gierus M. Wrage N. Hopkins A. Proc. EGF, Grassland Science in Europe, Kiel, Germany. Mecke Druck und Verlag,
Duderstadt, Germany2010: 964-966
. Information about individual herbage DMI (HDMI) allows estimating the nutrient supply from pastures and determining an adapted optional supplementation in the barn that may improve efficiency and lead to higher acceptance in practice. Furthermore, information about intake compared with production can be used to assess the nutrient and energy efficiency of individual grazing dairy cows and may enable selection for this trait. Several methods for HDMI estimation exist. These include measuring herbage mass or sward surface height before and after grazing, back calculation from the energy requirements of the cows and the energy density of the offered herbage, near-infrared reflectance spectroscopy, and marker techniques (
). However, these techniques are expensive, time consuming, and impracticable at the farm level and yield only group- or herd-mean HDMI estimations. Behavioral characteristics may also be considered in estimating the individual animal HDMI (
calculated grass intake using regressions based on grazing time and animal individual bite frequency. Various methods based on jaw switches, pressure sensors, microphones, accelerometers, and electromyography have been developed to record behavioral characteristics automatically. Among these, pressure sensors and microphones can detect jaw movements with high accuracy (
). Because the RumiWatch system (RWS; Itin and Hoch GmbH, Liestal, Switzerland), based on a pressure sensor and a triaxial accelerometer, showed reliable accuracy in detecting eating and rumination behavior (
), it was chosen for the present investigation. However, eating chews or prehension bites alone seem insufficient to estimate HDMI, and they explain only a minor part of HDMI variation. Therefore, in addition to behavioral characteristics,
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
showed a reliable estimation of HDMI using behavioral as well as animal- and herbage-related variables in the HDMI estimation model. In our study, we built models for HDMI estimation based on 2 approaches using the RWS, which can differentiate between mastication chews and prehension bites. Data from 4 experiments were used. The data contained differences in farm management, pasture size, grazing duration, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. The main objective of our study was to develop models for HDMI estimation based on the merged data set of the 4 experiments mentioned above. First, a global approach (GA) was explored, where all measured variables were used for the model development. Then, a second approach was investigated without the information about the supplemented amounts of forage or concentrate in the barn (referred to as WSB). Finally, we studied the effect of behavioral characteristics recorded with the RWS on the accuracy of the HDMI estimation under both approaches.
MATERIALS AND METHODS
Experimental Design, Animals, and Housing
Three grazing experiments were conducted at the Agroscope experimental farm in Posieux, Switzerland, and 1 was conducted at the Ferme-Ecole in Sorens, Switzerland, from 2014 to 2016 (Table 1). All experimental procedures were in accordance with Swiss guidelines for animal welfare and were approved by the Animal Care Committee of the Canton Fribourg, Switzerland (no. 2014_38_FR, 2014_51_FR, 2015_11_FR, and 2015_22_FR). Before selection of the experimental cows, all cows passed a medical check. In general, each period consisted of 2 wk of adaptation to adjust pasture-accustomed cows to the various feeding treatments and measuring devices followed by 1 wk of data collection. Consequently, the experiments lasted 21 to 63 d. Between 18 and 28 Holstein and Red Holstein cows were used in the experiments. The experimental cows were distributed equally across the experimental groups based on BW, milk yield, DIM, and lactation number. At the beginning of the experiments, the cows had an average BW of 601 ± 58.7 kg, were 155 ± 64.5 DIM, were in lactation 2.5 ± 1.75 on average, and produced 24.2 ± 5.18 kg of milk/d (±SD).
NSP = not supplemented in the barn; corn = supplemented in the barn with chopped whole-plant corn silage; corn protein = supplemented in the barn with chopped whole-plant corn silage and with protein concentrate; concentrate = supplemented in the barn with concentrate (UFA 275; UFA AG, Herzogenbuchsee, Switzerland).
1 Experiments 1 and 2: all experimental cows grazed together in the same paddocks; experiments 3 and 4: the 2 experimental groups grazed on 2 paddocks.
2 NSP = not supplemented in the barn; corn = supplemented in the barn with chopped whole-plant corn silage; corn protein = supplemented in the barn with chopped whole-plant corn silage and with protein concentrate; concentrate = supplemented in the barn with concentrate (UFA 275; UFA AG, Herzogenbuchsee, Switzerland).
In all 4 experiments, the paddocks were grazed rotationally, and the stocking periods lasted 1 to 3 d. All cows grazed day and night between 16 and 19 h/d depending on the experiment. In the meantime, the cows were kept in freestall barns and were supplemented if intended as part of the treatment structure. In addition, they were milked, milk was sampled, and alkane marker capsules were administered. On average, the pastures (permanent and sown) comprised grasses (mainly Lolium perenne, Poa pratensis, and Lolium multiflorum; 58–84% of the fresh herbage biomass), legumes (Trifolium repens and Trifolium pratense; 6–26% of the fresh herbage biomass), and forbs (mainly Taraxacum officinale, Plantago lanceolata, and Rumex acetosa; 2–29% of the fresh herbage biomass).
The first experiment (EX1) was organized as a crossover block design with 3 treatments and 3 periods (Table 1). All experimental cows in EX1 grazed as a single group in the same paddocks. Two out of 3 experimental groups were supplemented in the barn at weighing troughs (Insentec B.V., Marknesse, the Netherlands) with either 10 kg DM/d of chopped whole-plant corn silage or with 8.2 kg DM/d of chopped whole-plant corn silage mixed with 1.8 kg DM/d of protein concentrate (60% expeller soybean meal, 25% corn gluten, 10% potato protein, and 5% dried sugar beet pulp). The third group was not supplemented. Each cow served as its own control and underwent all 3 treatments. The second (EX2), third (EX3), and fourth (EX4) experiments were performed as balanced block designs including 2 treatments and 1 period. In EX2, 1 group was supplemented with an average of 3 kg DM/d of concentrate (UFA 275; UFA AG, Herzogenbuchsee, Switzerland) through an automatic concentrate feeder in the barn. The other group received no concentrate. All experimental cows in EX2 grazed as a single group in the same paddocks. In EX3, one group of cows grazed on swards with a high herbage mass of 2,288 kg of DM/ha, and the other group grazed on swards with a low herbage mass of 589 kg of DM/ha. The cows in both groups had a similar herbage allowance of approximately 22 kg of DM/d per cow and were not supplemented in the barn. In EX3 and EX4, the cows from each group grazed on different paddocks. One experimental group in EX4 was supplemented in the barn at the weighing troughs (Insentec B.V.) with 4 kg DM/d of chopped whole-plant corn silage. The other group received no supplementation. The chemical composition of the herbage and the supplements fed during the experimental periods are shown in Table 2, Table 3. The cows had free access to drinking water during all experiments. The ambient outdoor temperature and the amount of rainfall during the experiments were recorded daily at the meteorological station in Grangeneuve (Meteo-Schweiz, Station Posieux, Switzerland).
Table 2Chemical composition of the herbage grazed during the 4 experiments
Low = group that grazed on swards with a sward height of 10 click units and an herbage mass of 589 kg of DM/ha; High = group that grazed on swards with a sward height of 18 click units and an herbage mass of 2,288 kg of DM/ha.
1 Low = group that grazed on swards with a sward height of 10 click units and an herbage mass of 589 kg of DM/ha; High = group that grazed on swards with a sward height of 18 click units and an herbage mass of 2,288 kg of DM/ha.
2 NSP = not supplemented in the barn; SP = supplemented in the barn with 4 kg of DM/d of chopped whole-plant corn silage.
3 ADF corrected for residual ash.
4 NDF corrected for residual ash.
5 Absorbable protein in the small intestine when rumen-fermentable energy is limiting microbial protein synthesis in the rumen.
The sward height was measured daily in all experiments using an electronic rising plate meter (Jenquip, Feilding, New Zealand; 1 click unit = 0.5 cm; EX1, EX3, and EX4) or a C-Dax Pasture Meter (C-Dax Ltd., Palmerston North, New Zealand; EX2). Herbage mass was measured before and after grazing each paddock by cutting 2 strips (1 m wide × 9.4 ± 3.02 (SD) m long; >6.7 click units or 3.35-cm stubble height) with a sickle-bar mower (Rekord 38, Bucher Landtechnik AG, Niederweningen, Switzerland). The herbage mass of the cut strips and the analyzed herbage DM content were used to calculate the herbage mass.
The RWS, consisting of the RumiWatch Halter (RWH; Itin and Hoch GmbH) and the RumiWatch Converter 0.7.3.31 (Itin and Hoch GmbH), was used in all experiments to record and evaluate the behavioral characteristics of the experimental cows. Further details about the RWS, the accuracy and definition of the recorded behavioral characteristics, and the setup and handling of the RWH have been described previously (
). To accustom the cows to the measuring system, the RWH were attached to the cows 4 d before the start of each measuring week and left on throughout the week. In EX1, 15 cows were equipped with version 3.0 of the RWH and 3 cows were equipped with version 6.0. These differ in materials, adjustability, and wearing comfort. In EX2, EX3, and EX4, all cows were equipped with RWH version 6.0. The recorded raw data were read through the interface software RumiWatch Manager (version 2.1.0.0; Itin and Hoch GmbH) and processed using the evaluation software RumiWatch Converter (version 0.7.3.31). Compared with the converters used in
, RumiWatch Converter 0.7.3.31 allows a differentiation of other bites, mastication chews with the head up, mastication chews with the head down, prehension bites, and time spent masticating and eating.
Milk yield was measured twice daily during the milkings in the milking parlor (EX1, EX3, EX4: Fullwood, Arnold Bertschy AG, Guschelmuth, Switzerland; EX2: MidiLine, DeLaval AG, Sursee, Switzerland) with a Pulsameter (EX1, EX3, EX4: LMS GmbH Stützerbach, Ilmenau, Germany) or an MM15 (EX2: DeLaval AG). Milk composition was measured for EX1, EX2, and EX4 on d 2 and 5 and for EX3 on d 2, 4, and 6 during the measuring week. Aliquots of subsamples from the morning and evening milkings were pooled and preserved in 1 sample tube containing a Broad-Spectrum Microtab II (Gerber Instruments AG, Effretikon, Switzerland) and stored at 8°C for subsequent analysis of milk fat, protein, lactose, and casein content. After each milking, BW was measured with an animal weighing system (EX1, EX3, EX4: Ga5010, Insentec B.V.; EX2: W-2000, DeLaval AG).
Individual HDMI was estimated using the n-alkane double indicator method (
). Six days before each measuring week until the next-to-last day of the measuring weeks, cows were dosed twice daily with 1 gelatine capsule (HGK-17-60 sl; Capsula GmbH, Ratingen, Germany) containing 0.5 g of dotriacontane (C32H66, HC32; Minakem Beuvry Production S.A.S., Beuvry la Forêt, France) as the external alkane marker on a carrier of 4.5 g of dried fruit pomace. During 7 d, once per day after the morning milking, feces of each cow were spot-sampled indoors to determine the content of alkanes in the feces. Samples were taken from spontaneous defecations or with mild stimuli, pooled for each cow and measuring week, and stored at −20°C for further analysis. To determine the content of alkanes in the herbage eaten by the cows, herbage collection was carried out 7 d, in the morning and afternoon. By following the experimental cows one after another and observing their grazing selection, small samples of the most likely grazed herbage were cut with a battery grass shearer (Gardena; Husqvarna Schweiz AG, Mägenwil, Switzerland). The herbage sampling started 24 h before the feces sampling and ended 24 h earlier. These samples were chopped and stored at −20°C for further analysis.
Laboratory Analysis
Milk samples were analyzed using Fourier-transform mid-infrared spectrometry (Combi-Foss FT +; Foss, Hillerød, Denmark) to determine fat, protein, casein, and lactose content. Fluorescence flow cytometry (Fossomatic FC200; Foss) was used to count the number of somatic cells in the milk samples.
The herbage and supplement samples, except the protein supplement in EX1 and concentrate supplement in EX2, were stored at −20°C until they were lyophilized (Delta 1-24 LSC; Christ, Osterode, Germany). Subsequently, all samples, including protein and concentrate supplements, were milled through a 1.0-mm screen (Brabender mill with titanium blades; Brabender GmbH & Co. KG, Duisburg, Germany), dried for 3 h at 105°C to determine DM, and finally incinerated at 550°C until a stable mass was reached to determine the ash content (
; method 942.05). Mineral residues in the ash were dissolved by nitric acid and analyzed for Ca, P, Na, Mg, and K with inductively coupled plasma optical emission spectrometry (ICP-OES Optima 7300 DV; PerkinElmer, Waltham, MA) based on
ISO (International Organization for Standardization)
Food products—Determination of the total nitrogen content by combustion according to the Dumas principle and calculation of the crude protein content. Method 16634-1.
; method 16634-1) on a C/N analyzer (Trumac CNS; Leco Instruments, St. Joseph, MI); the results were multiplied by 6.25 to obtain the CP content. The contents of ADF (
; method 978.10) for the herbage and supplement samples were analyzed with Gerhardt Fibertherm (Gerhardt GmbH & Co. KG, Königswinter, Germany). The NDF and ADF contents were separately determined (parallel). For NDF analysis, heat-stable amylase and sodium sulfite were added. A correction for the residual ash obtained after 2 h of incineration at 550°C was made for ADF corrected for residual ash and NDF corrected for residual ash.
Calculations and Data Analysis
The NEL content of herbage was calculated from chemical composition according to
. Equation 1 was used to calculate the daily HDMI of every single experimental cow in the 4 experiments:
[1]
where HDMI represents the daily HDMI (kg); F33, H33, P33, CN33, and CR33 are the concentrations of tritriacontane (mg/kg of DM) in feces, herbage, protein supplement, concentrate, and chopped whole-plant corn silage, respectively; F32, H32, P32, CN32, and CR32 are the concentrations of HC32 (mg/kg of DM) in feces, herbage, protein supplement, concentrate, and chopped whole-plant corn silage consumed, respectively; P, CN, and CR are the amounts (kg of DM/d) of consumed protein supplement, concentrate, and chopped whole-plant corn silage, respectively; and A32 is the daily dose of HC32 (mg/d) administered via the alkane capsules.
The weekly averages of pasture, herbage, intake, animal, milk, and behavioral data used for the development of the different HDMI estimation models are shown in Table 4. The HDMI estimated by the n-alkane double indicator method was used as the reference herbage intake on pasture for the development and validation of 4 approaches. First, a GA was explored, where all variables in the data set were used for the model development. Afterward, a WSB approach was investigated without information about the amount of the supplements fed in the barn; the variables whole-plant corn silage, protein supplement, and concentrate intake were removed from the data set for the model development. Finally, to study the benefits of behavioral characteristics as predictors for HDMI estimation, the behavioral characteristics recorded with the RWS were removed from the data set of the GA and the WSB. These approaches without RWS variables are hereafter referred to as GAwRW and WSBwRW, respectively.
Table 4Mean (n = 109) and range of the pasture, intake, animal, milk, and behavioral characteristics used for the herbage DMI estimation models
Univariate and bivariate graphics and descriptive statistics were used to provide an overview of the data set. Linear dependencies between regression variables were detected by Pearson correlation coefficients near or equal to 1 and prevented by the exclusion of redundant variables.
A preliminary set of predictive linear models was based on the combined results of various statistical approaches (principal component analysis; partial least squares; forward, backward, and sequential selection; and best subset regression) using Systat 13 (version 13.0; Systat Software, Chicago, IL) and R (
Spearman correlation coefficients facilitated the identification of monotonous relations between the reference variable (HDMI) and the continuous or ordinal regressors. Principal component analysis using the Pearson and Spearman correlation matrices, partial least squares regression (including dummy variables for categorical factors), linear models (forward, backward, and sequential selection), canonical correlation, and finally best subset regression modeling was applied to identify variables that (from a statistical point of view) could be deleted from the set of regressors, leaving a list of potentially important predictor variables. The complete set of variables as well as the statistical findings were assessed, and variables were included or excluded based on the statistical findings, pertinent knowledge, and experience. The resulting combined set of 25 variables was still too large. Therefore, model reduction by the best subset regression approach was applied, and models with fewer than 14 variables were kept for the final validation. As the sample size was too small to keep an independent validation set, the bootstrap validation method proposed by
) was used. This function combines modeling, model reduction, and bootstrap validation. The root mean squared prediction error (RMSPE) calculation was based on the results of 5,000 bootstrap samples (
) to identify the optimal predictive model while still preventing overfitting. Moreover, the bootstrap validation method was enhanced by including the optimism bias to prevent an underestimation of the RMSPE. Equation 2 shows the multiple linear regression structure of the HDMI estimation models:
[2]
where y represents the average daily HDMI (kg/cow) over 1 wk; µ is the model mean; and V1, V2, …, Vn are the explanatory variables with the corresponding coefficients C1, C2, …, Cn.
RESULTS
A data set of 130 measurements taken over 7 d consisting of 52 variables for GA and 50 variables for WSB (without protein supplement or concentrate and chopped whole-plant corn silage intake) was the database for the development and validation of the HDMI estimation models. For the development and validation of the WSBwRW and GAwRW HDMI estimation models, the data sets consisted of 42 variables for GAwRW (without 10 behavioral characteristics) and 40 variables for WSBwRW (without 10 behavioral characteristics and without protein supplement or concentrate and chopped whole-plant corn silage intake), and 130 measurements taken over 7 d were used. Due to technical difficulties with the RWH, in particular liquid leakage in the pressure sensor tubes, 21 (16%) of the records could not be correctly evaluated over the whole measuring week and were therefore not used for further model development and validation if behavioral characteristics were used in the HDMI estimation models. Thus, the final data set for GA and WSB model development consisted of 109 measurements.
HDMI Estimation Under a GA
The model with the lowest RMSPE for HDMI estimation under GA without overfitting (eliminating the nonsignificant variables) was model GA7 (Table 5). In this model, according to the β value (used to classify the variables and their effect on the HDMI estimation models), intake of chopped whole-plant corn silage [P < 0.001; standardized coefficient (β) = −0.646] showed the greatest effect on the target variable (HDMI), followed by milk yield (P < 0.001, β = 0.570), milk protein content (P < 0.001, β = 0.410), intake of protein supplement or concentrate (P < 0.001, β = −0.393), herbage CP (P < 0.001, β = −0.265), lactation number (P = 0.002, β = −0.191), milk lactose content (P < 0.001, β = −0.187), postgrazing herbage mass (P < 0.001, β = −0.180), BW (P = 0.021, β = 0.170), and bite rate performed at pasture (P = 0.046, β = −0.101).
Table 5Evaluated linear model GA7 for herbage DMI estimation for use under a global approach
HDMI Estimation Model Under an Approach Without Knowledge of the Supplements Fed
The model with the lowest RMSPE for HDMI estimation under WSB without overfitting (eliminating the nonsignificant variables) was model WSB8 (Table 6). In this model, according to the β value, number of prehension bites performed at pasture (P < 0.001, β = 2.475) showed the greatest effect on HDMI, followed by number of prehension bites (P = 0.002, β = −1.994), total time spent eating on pasture (P < 0.001, β = 0.636), number of total eating chews (P < 0.001, β = −0.488), BW (P = 0.001, β = 0.265), milk lactose content (P < 0.001, β = −0.236), herbage ash (P = 0.004, β = 0.236), milk yield (P = 0.004, β = 0.216), herbage CP (P = 0.003, β = −0.209), lactation number (P = 0.011, β = −0.199), and milk protein content (P = 0.014, β = 0.172).
Table 6Evaluated linear model WSB8 for herbage DMI estimation for use under the approach without knowledge of the supplements fed
The models suggested for GA (GA1–GA7) showed an estimation accuracy [100% − relative prediction error (RPE)] of 86.9% (GA1), increasing by an average of 0.3% for every variable added to 88.9% for model GA7. Model GA7 explained 79% of the HDMI variation, with an RMSPE of 1.38 kg of DM/animal per day and an RPE of 11.1% (Table 7).
Table 7Structure and accuracy of the evaluated herbage DMI estimation models
The models suggested for WSB (WSB1–WSB8) showed an estimation accuracy of 85.1% (WSB1), increasing by an average of 0.2% for every variable added to 86.8% for model WSB8. Furthermore, model WSB8 explained 70% of the HDMI variation, with an RMSPE of 1.64 kg of DM/animal per day and an RPE of 13.2% (Table 7).
The models suggested for GAwRW (GAwRW1–GAwRW7; detailed data not shown) showed an estimation accuracy of 86.9% (GAwRW1), increasing by an average of 0.2% for every variable added to 88.3% for model GAwRW7. Furthermore, the model for GAwRW7 explained 77% of the HDMI variation, with an RMSPE of 1.5 kg of DM/animal per day and an RPE of 11.7%.
The models suggested for the approaches without behavioral characteristics (WSBwRW1–WSBwRW8; detailed data not shown) exhibited an estimation accuracy of 81.9% (WSBwRW1), decreasing by an average of 0.03% for every variable added to 81.7% for model WSBwRW8. Furthermore, the model WSBwRW8 explained 45% of the HDMI variation, with an RMSPE of 2.3 kg of DM/animal per day and an RPE of 18.3%.
Figure 1 presents the RMSPE of the HDMI estimation models with and without behavioral characteristics (RWS variables). Models without information about supplementation and behavioral characteristics exhibit an RMSPE between 2.2 and 2.3 kg of DM/animal per day depending on the number of predictors in the model. Inclusion of behavioral characteristics reduced the error term by about 0.5 kg of DM/animal per day. If amounts of supplements fed in the barn were available for HDMI estimation, the error term decreased again about 0.3 kg of DM/animal per day to end at 1.3 to 1.6 kg of DM/animal per day. In this case, inclusion of behavioral information did not additionally reduce the RMSPE.
Figure 1Comparison between the root mean squared prediction error (RMSPE) of the herbage DMI estimation models with or without behavioral characteristics in the data set for model development. GA = global approach; WSB = approach without the information about the supplemented amounts of forage or concentrate in the barn; GAwRW = GA without RumiWatch (Itin and Hoch GmbH, Liestal, Switzerland) system variables; WSBwRW = WSB without RumiWatch system variables.
The range of the measured variables in the 4 experiments that constituted the data set of our study allowed for the development and validation of different models for HDMI estimation under GA (GA1–GA10), WSB (WSB1–WSB10), GAwRW, and WSBwRW.
HDMI Estimation Using the n-Alkane Method
Herbage DMI estimated with the n-alkane method was used as the reference for model development and validation. In earlier investigations with barn-fed dairy cows, compared with the weighed intake, DMI estimation using the n-alkane method showed a low mean deviation of 0.05 kg, and the estimated and weighed DMI were highly correlated (R2 = 0.93) for pooled samples (
Energy expenditure of grazing cows and cows fed grass indoors as determined by the 13C bicarbonate dilution technique using an automatic blood sampling system.
found a mean deviation between estimated and weighed DMI of 0.2 kg for cows fed in the barn with fresh herbage. No exact reference for the daily HDMI for individual animals exists under grazing conditions; thus, a genuine validation of the n-alkane method is not possible. Nevertheless, the n-alkane method is commonly suggested as one of the best available techniques for individual HDMI estimation of grazing animals (
N-alkanes v. ytterbium/faecal index as two methods for estimating herbage intake of dairy cows fed on diets differing in the herbage:maize silage ratio and feeding level.
mentioned the diurnal variation of the n-alkane concentration in feces as one possible source of variation. To prevent variations, we administered HC32 on an apple pomace carrier twice daily. A further challenge was the collection of a representative sample of the herbage eaten by the grazing dairy cows during the day, especially in multispecies swards. Grazing cows can select for certain plant groups and graze on layers that may contain different alkane concentrations (
). To minimize this risk, we took herbage samples twice daily by imitating the feed selection of almost each experimental cow on pasture and pooled these. To prevent varying alkane dosing, as mentioned by
, HC32 was not applied on feeds or concentrates; instead, capsules containing the exact intended amount of HC32 were administered twice daily with a bolus gun.
Comparing our HDMI results generated with the n-alkane method [11.9 ± 3.31 (SD) kg of HDMI], the reference method, with those obtained from estimation equations for grazing dairy cows proposed by
using our data set [13.3 ± 3.72 (SD) kg of HDMI], an R2 of 0.77 (y = 1.178 + 0.962x; standard error of estimate = 1.76) was obtained (Figure 2). This suggests an acceptable correlation and general accordance of our reference method for HDMI, as no genuine or much better method exists.
Figure 2Comparison of our herbage DMI results generated with the n-alkane method and those obtained from estimation equations proposed by
Behavioral characteristics are useful for HDMI estimation in many cases, although the comparison of RMSPE of the GA and GAwRW models showed no improvement in accuracy of the HDMI estimation. The importance of behavioral characteristics for HDMI estimation is shown under an approach without knowledge of the supplements fed because total eating chews, total eating time performed at pasture, prehension bites performed on pasture, and total prehension bites represent the most important variables in the WSB models. Compared with the WSBwRW models, WSB models had a lower RMSPE on average by 0.58 kg/cow per day. Clearly, behavioral characteristics seem to improve the accuracy of HDMI estimation if information about the amount of supplements fed in the barn to pasture is lacking.
already concluded that herbage intake of grazing cattle can be estimated with reasonable precision using eating bites and bite size based on esophageal fistula samples. The challenge would be to know the bite size with intact animals. Later,
investigated the effect of behavioral characteristics in DMI models for intact, TMR-fed cows housed in open, no-stall cowsheds. They found an improvement of 1.3 kg/cow per day on RMSPE, even greater than that in our study, when behavioral characteristics were incorporated in a DMI estimation model. Additional benefits of including behavioral characteristics in an intake estimation model might be the detection of sick or injured cows as well as cows in heat, as short-term feeding behavior is modified in characteristic ways in such cases (
advocated the potential use of behavioral characteristics for HDMI estimation, as the bite is the elementary and indivisible unit of the whole grazing process. This is also seen in the present study, as according to β the variables prehension bites performed at pasture and total prehension bites were the 2 most important variables for the WSB models. An explanation for opposite effects of prehension bites performed at pasture and total prehension bites on HDMI might be the effect of supplements fed in the barn on HDMI (substitution of herbage); this also applies to the contrasting effects of total eating chews and total eating time at pasture.
Although behavioral characteristics represent the most important variables in the WSB model, a large variation in bite mass exists among animals, grazing, pasture, and feeding managements. Stated values range between 0.33 and 0.74 g of DM/bite (
, grazing activity displays a close correlation with the predicted intake, yet further characteristics may have an influence on herbage intake. Therefore, in addition to behavioral data, animal, feed, grazing, and environmental variables must be included to reduce the estimation error of HDMI estimation models.
In our study, postgrazing herbage mass (222 kg of DM/ha; minimum: 63 kg of DM/ha; maximum: 554 kg of DM/ha) had a negative effect (−0.004 kg of DMI/d per kg of DM per ha) on HDMI. Contradicting results were found by
with no or a positive effect of postgrazing sward height up to 0.99 kg of DMI/cm per day. As postgrazing sward height or postgrazing herbage mass are a function of pregrazing herbage mass and daily herbage allowance, differences in these factors as well as their interaction might have been partly the source of the contrasting results. Also, herbage CP content showed a negative effect on HDMI. This contradicts the findings of
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
, who found a positive effect of milk urea content, which is associated with the intake of either CP or ruminally degraded CP. Results similar to those of
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
likewise found an effect of 3.79 kg of DMI/% of milk protein per day. The partially contradicting and partially similar results of our models and those of
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
Amounts of concentrate and protein supplement were merged into a single variable because the substitution rates are similar and lower than those for a forage supplement (chopped whole-plant corn silage;
observed a substitution rate of 0.58 to 0.71 kg of HDMI/kg of concentrate compared with our results (0.63 kg of HDMI/kg of concentrate). An even smaller reduction of 0.36 kg of HDMI/kg of concentrate was found for early-lactation dairy cows (
) in most of our studies. Thus, previous studies showed a substitution rate of zero for a high grazing pressure up to 0.6 to 0.8 for a low grazing pressure (
) or lactation stage could have had an influence; thus, cows in early lactation showed a lower substitution rate compared with cows in mid or late lactation (
Other reasons for the high substitution rate might be that the interaction between variables used in the models affected the coefficients, as discussed by
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
, and maybe some bias due to different conditions between experiments. For example, the average of the calculated substitution rate between supplemented and not-supplemented pairs within experiments [0.63 ± 0.015 (SD) kg of HDMI/kg of concentrate or protein supplement] was lower compared with the coefficient of the explanatory variable protein and concentrate supplements (1.09 kg of DM/d) in GA7. The calculated substitution rate is similar to the findings of
. The decrease in HDMI caused by the supplementation of chopped whole-plant corn silage was smaller compared with that found for concentrate and protein supplementation. However, the coefficients (0.64 kg of DM/d) in this case are comparable with the calculated substitution rate [0.47 ± 0.015 (SD) kg of HDMI/kg of chopped whole-plant corn silage supplement]. Usually, substitution rates resulting from supplementing forage to pasture are greater than those from supplementing concentrates due to the greater forage fill (unité d'encombrement;
). Different experiments were used to calculate substitution rates. Varying environmental, feed, and animal characteristics may have therefore caused inconsistent substitution rates between concentrates and forage even though the individual substitution rates were in an expected range.
As a measure for body size and an indicator for rumen size, BW was positively correlated with HDMI in our study (i.e., an increase of 0.8 to 1 kg/100 kg of BW for our models). Similar increases of 0.95 and 1.3 kg/100 kg of BW were measured by
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
showed a greater increase in DMI per 100 kg of BW for cows in early lactation compared with cows in mid or late lactation.
Besides the physical feed intake capacity of the cow, proxied by BW, total DMI seems to be one driving force for milk production and vice versa, a conclusion that has been drawn in several other studies (
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
). The coefficient of the explanatory variable milk yield was between 0.13 (WSB8) and 0.35 (GA7) kg of HDMI/kg of milk per day. A reason for differences in the coefficients for milk yield between GA7 and WSB8 might be the aforementioned interaction between the variables used in the models.
Precision of the HDMI Estimation Models
The models GA7 and WSB8 showed the highest accuracy in estimating HDMI, with an R2 of 0.81 and 0.73 and an RPE of 11.1 and 13.2%, respectively. The explained proportion of variation of the HDMI and the accuracy of these models are similar to those of other investigations. The models of
The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
found an RPE for grass silage DMI between 10 and 20% for dairy cows fed indoors. Overall, the RPE of all of our models, developed for use under GA (RPE = 10.8–13.1%) as well as under WSB (RPE = 13.3–14.9%), showed an acceptable prediction of HDMI using detectable variables.
suggested that an RPE value lower than 10% indicates a satisfactory prediction of the DMI, whereas an RPE between 10 and 20% indicates a relatively good or acceptable prediction, and an RPE greater than 20% indicates an unsatisfactory prediction. According to
, a threshold for good intake estimation models for practical use should be a mean prediction error of 10%. Nevertheless, this threshold is hard to achieve, particularly for grazing dairy cows, as the available reference methods for development and validation of the HDMI estimation models are indirect methods. Furthermore,
Gruber, L., F. J. Schwarz, D. Erdin, B. Fischer, H. Spiekers, H. Steingass, U. Meyer, A. Chassot, T. Jilg, A. Omermaier, and T. Gruggenberg. 2005. Vorhersage der Futteraufnahme von Milchkühen Datenbasis von 10 Forschungs- und Universitätsinstituten Deutschlands, Österreichs and der Schweiz. 116. VDLUFA-Kongress, Rostock, Germany.
stated that a biologically determined natural scattering of about 10% might also impede the development of reliable intake estimation models.
CONCLUSIONS
With the HDMI estimation models developed in this study, HDMI could be estimated with an RPE between 11.1% (GA7) and 13.2% (WSB8) at best. When the amount of supplements fed was unknown, the inclusion of behavioral characteristics in the model showed a clear added value regarding the accuracy of the individual HDMI estimation. On the other hand, the inclusion of behavioral characteristics in the HDMI estimation model when amount of supplements fed was known did not significantly reduce the RMSPE. Finally, a larger data set (in terms of number of animals and herds and covering more breeds, different grazing systems, and management patterns, including, for example, varying supplementation) will be needed for further development and validation of a robust HDMI estimation model.
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The application of behavior sensors and sward measurement to support grazing management.
in: Höglind M. Bakken A.K. Hovstad K.A. Kallioniemi E. Riley H. Steinshamn H. Østrem L. Proc. EGF, Grassland Science in Europe, Trondheim, Norway. Wageningen Academic Publishers,
Wageningen, the Netherlands2016: 80-82
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