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# Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method

D. Andueza, William Martin-Rosset

► To cite this version:

D. Andueza, William Martin-Rosset. Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method. Animal, 2025, 19 (8), pp.101597. 10.1016/j.animal.2025.101597. hal-05242479

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# Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method

<figure><img src="image_3.png" /></figure>

D. Andueza*, W. Martin-Rosset

Université Clermont Auvergne, INRAE VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France

## ARTICLE INFO

**Article history:**

Received 30 December 2024

Revised 30 June 2025

Accepted 1 July 2025

Available online 9 July 2025

**Keywords:**

Cellulase

Equine nutrition

Forage

Nutritive value

Regression models

## ABSTRACT

The nutritive value of forage for horses is closely dependent on the digestibility of organic matter (OMD), which is typically measured in vivo. Researchers have developed a variety of prediction methods to estimate the digestibility value of hay. In ruminants, pepsin-cellulase degradability, expressed as dry matter enzymatic degradability (dCS) and organic matter enzymatic degradability (dCO), is a commonly employed method for predicting OMD in forage types. The objective of this study was to evaluate the feasibility of predicting the dry matter digestibility (DMD), OMD, energy digestibility (ED) and digestible energy (DE) content of hay through enzymatic degradability or a combination of enzymatic methods with other chemical composition parameters. For that purpose, a database of 43 hays harvested in a temperate zone (32 natural grassland hays, five grass hays and six lucerne hays) was constructed from studies carried out at the French National Institute of Agricultural Research. The dCS and dCO of each hay type were determined, as well as their in vivo digestibility in horses. The range of OMD across the entire dataset was between 0.42 and 0.63, and the range of dCS cellulase digestibility was between 41.0 and 62%. The results demonstrated that there were no significant differences (P > 0.05) between the slopes of the models obtained for the various forage types. The models used for the prediction of DMD, OMD, ED and DE from dCS and dCO were characterised by R² values between 0.87 and 0.91. The incorporation of chemical composition variables as independent variables into the prediction models did not result in a meaningful improvement in the model results obtained from dCS and dCO. The proposed models represent an alternative approach to the existing models for predicting the nutritive value and DE content of hay from their chemical composition.

© 2025 The Authors. Published by Elsevier B.V. on behalf of The animal Consortium. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)).

## Implications

End users either consult the feed tables or ask commercial laboratories to predict the digestible energy value of hay in order to formulate balanced rations. Laboratories predict the digestible energy value of hays based on the determination of relevant chemical content. This process is time-consuming and costly, and does not differentiate between botanical families. A new method using an enzyme (cellulase), which simulates the digestion of forage in horses, has been tested to predict the energy content of hay. This study proposes new, botanical family-specific models for hay that are accurate, very easy and inexpensive for laboratories to implement.

## Introduction

The energy content of forages fed to equines depends primarily on the organic matter digestibility (OMD), which is usually

measured *in vivo* using the French National Institute of Agricultural Research System (INRA, 1984; INRA, 2015) as for ruminants (INRA, 2018; Jarrige 1980). It is important for end-users to be aware of the OMD of forage to ensure that animals receive the appropriate amount of feed to meet their nutritional needs and to prevent the occurrence of overfeeding that can potentially lead to disease. However, the measurement of the OMD of forage is a costly process that requires the use of animals, large quantities of forage, and specialised staff. Furthermore, it can only be conducted in research centres for a few forages. Therefore, methods for predicting the energy value of forages on the basis of more easily obtainable criteria have been developed for end-users. Consequently, a variety of methods for estimating the *in vivo* digestibility of feeds have been developed. In the *in situ* bag technique, the bags are placed directly into the caecum of fistulated equines for a period of 24 h. The rates at which the contents are digested are closely correlated with the OMD. (Applegate and Hershberger, 1969; Koller et al., 1978; Uden and Van Soest, 1984). An alternative *in vitro* technique has been developed that utilises rumen fluid (Lindberg and Ragnarsson, 2010; Smolders et al., 1990) or caecum

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: donato.andueza@inrae.fr (D. Andueza).</footnote>
fluid (Macheboeuf et al., 1997) as a source of inoculum. Unfortunately, all of these techniques can be used only for research to determine degradability, mainly in the hindgut of equines, to estimate the relative extent and rate of digestion but not to predict the energy content of forages (Rymer et al., 2005).

The OMD is affected by the chemical composition of the forages and mainly by their cell wall content and degree of lignification. Several biochemical criteria have been identified as representative of the cell wall content of the forages, e. g., crude fibre (CF), ADF, and NDF (INRA 1984 – 2015; NRC 1989 – 2007; DLG 1995 – GFE 2014). Recently, the chemical composition of forages in equine diets has been demonstrated to be a significant predictor of their OMD (Martin-Rosset et al., 2024). Nevertheless, the process of predicting digestibility remains too time-consuming, and sometimes, the associated costs are considerable, rendering it unsuitable for practical applications.

The challenge of alternative methods of forage energy content assessment is to compare the *in vivo* determination of forage digestibility in equines with their *in vitro* degradability, which can be measured in a short time. This comparison must be made with respect to accuracy, repeatability, reproducibility and cost. In this context, a biological method currently employed in the estimation of the nutritive value of forages for ruminants is based on the utilisation of fungal enzymes, such as cellulase, which facilitate the solubilisation of the digestible fraction of dry and/or organic matter of the forages. The proportion of the plant material that has been solubilised is indicative of its digestibility. A correlation is calculated between *in vivo* and cellulase degradability to develop a predictive equation for digestibility using the cellulase method. This method has been successfully initiated for equines (Miraglia and Tisserand, 1985) using the method developed in ruminants by Aufrère (1982).

The aims of this study were to evaluate (1) the relevance of the enzymatic method to predict the dry matter digestibility (DMD), OMD, energy digestibility (ED) and digestible energy (DE) content of equine forages for which the *in vivo* digestibility was previously determined at the French National Institute of Agricultural Research by the reference method; and (2) whether the combination of the enzymatic method with other parameters of chemical composition improves the predictive accuracy of DMD, OMD, ED and DE models. We hypothesised that enzymatic degradability could be an interesting method for predicting the energy content of forages and that its association with chemical composition parameters could improve the accuracy of models in relation to those based on enzymatic degradability alone.

## Material and methods

### Database

A dataset of chemical composition, cellulase degradability, and *in vivo* measured values, such as DMD, OMD, and ED, was used. Data were obtained from studies carried out previously at the French National Institute of Agricultural Research, and they were extracted from database of the French National Institute of Agricultural Research (Martin-Rosset et al., 2015; Martin-Rosset et al., 2024). This database was composed of 43 hays: 32 natural grassland hays, five grass hays, and six lucerne hays harvested in the same temperate area. The natural grassland hays consisted mainly of grasses. They were included in the grassland hay types as determined by Andueza et al. (2016). Among grass hays, the species represented were bromegrass, fescue, cocksfoot and timothy. The natural grassland plots were harvested under good climate conditions in early June or mid of July (66%) and during regrowth in September (34%). The grass plots were harvested in June or July

(50%) or during regrowth in September (50%). The Lucerne plots were obtained from harvest in June (15%), mid of July (54%) or in September (31%).

### Determination of *in vivo* digestibility and digestible energy value of hay

The measurement of digestibility was carried out using the total faeces collection method in horses of light breeds according to the same methodology implemented in a previous study (Martin-Rosset et al., 2020). Briefly, all data were obtained with 5–6 horses of warm blood breeds (500–550 kg of BW; Standardbred) that were fed hay-only diets and with total faeces collection over at least 5 days after 14 or 7 days of adaptation when a sequential or a Latin square design was used, respectively. Hays were supplied between 1.0 and 1.3 times the maintenance energy requirement used by each system and offered twice a day. Hays offered were sampled daily for each horse. The individual horse samples were pooled and mixed to be representative of the whole individual horse period. Refusal, if any, was determined and sampled accordingly. Faeces were collected daily from each horse and then pooled accordingly. The hay samples were stored at a normal temperature (15–25 °C), and the faeces samples were stored at –20 °C.

According to Martin-Rosset et al. (2020), hays and faeces were assayed on oven-dried samples after milling through a 1–mm screen on average according to the same official or published and accepted methods: DM in an oven during full day at 80 °C for hay and at 60 °C for faeces; ash using oven incineration for calculating organic matter; CF with the Weende method; NDF, ADF and ADL using Van Soest's method (Van Soest et al., 1991); nitrogen with the macro-Kjeldahl method and multiplying by 6.25 for calculating the CP; and total free reducing sugars by Somogyi (1952). The gross energy was determined using adiabatic calorimetry. The individual digestibility coefficients (DMD, OMD, ED) and DE content of the hay samples were calculated.

### Enzymatic degradability

The technique used was that generally employed for research or for applications in large laboratories (Aufrère, 1982 modified by Aufrère and Michalet-Doreau, 1988) to predict the digestibility of by-products, concentrates and compound feeds. This technique involves two steps: (1) pretreatment with 0.2% pepsin (pepsin Merck no 7190; 1/10 000) in hydrochloric acid (0.1 N HCL) in a water bath at 40 °C for 24 h; and (2) attack by cellulase (1 g/L of sodium acetate buffer; cellulase Onozuka R10 extracted from Tri-choderma viride, Yakult Honsha Co. Ltd, Japan) after filtration and rinsing for 24 h in a water bath at 40 °C and ashing samples in a furnace at 550 °C for 5 h. The dry matter enzymatic degradability (dCS) and organic matter enzymatic degradability (dCO) were calculated (Aufrère et al., 2007).

### Statistical analysis

The dataset was characterised by the mean, SD, and minimum and maximum values.

In vivo DMD, OMD, ED and DE content were subjected to covariance analysis (ANCOVA) with the fixed effects of the type of forage (natural grassland hay, grass hay or lucerne hay) and dCS or dCO and chemical composition parameters as covariates. The models used were as follows:

$$Y = TF + dC + TF * dC + \varepsilon$$

and

$$Y = TF + dC + cc + TF * dC + TF * cc + \varepsilon$$
where Y = DMD or OMD or ED or DE; dC = dCS or dCO and cc = CP or CF or NDF or ADF or ADL and TF = type of forage (natural grassland hay, grass hay or lucerne hay).

In the ANCOVA, first, the test of the homogeneity of slopes for each type of forage was carried out at the 0.05 probability level. If the null hypothesis (the slopes were homogeneous) was rejected, a regression model by type of forage was built. If the null hypothesis was not rejected, the test of the homogeneity of intercepts of the regression models developed for each type of forage was carried out at the 0.05 probability level. When the null hypothesis (intercept homogeneity) was not rejected, a common regression model was developed for all types of forage. If the null hypothesis of intercept homogeneity was rejected, a regression model by type of forage was built, but all the models had the same slope. The retained models were characterised by the R² statistic and the RMSE. The r between chemical composition variables, dCS and dCO, was calculated, and the multiple linear regression models were tested for the multicollinearity of the coefficients from the calculation of the variation inflation factors of the estimated coefficients of chemical parameters and the coefficients of dCS and dCO. All the statistical analyses were performed using the Statistical Analysis System statistics package (SAS, 1998).

## Results

### Chemical composition, enzymatic degradability, digestibility coefficients and energy content of hay

The mean and SD values of the chemical composition, enzymatic degradability, digestion coefficients and energy contents of the natural grassland, grass and lucerne hays are given in Table 1. The minimum and maximum organic matter, CP, CF, NDF, ADF, ADL and total free reducing sugars contents of the natural grassland hay ranged from 885 to 933, 55–214, 235–386, 527–687, 264–403, 25–71 and 30–136 g/kg DM, respectively; the dCS and dCO contents ranged from 41.0–69.1 and 39.3–65.0%; the DMD, OMD and ED contents ranged from 0.40 to 0.62, from 0.42 to 0.63 and from 0.39 to 0.60, respectively. The gross energy and DE contents ranged

from 17.57 to 19.95 and from 6.90 to 11.25 MJ/kg DM, respectively. The minimum and maximum organic matter, CP, CF, NDF, ADF, ADL and total free reducing sugars contents of grass hay ranged from 901 to 937, 97–192, 278–372, 587–718, 297–403, 37–72 and to 33–74 g/kg DM, respectively; the dCS and dCO ranged from 43.5 to 63.3 and 41.6 to 61.6%, respectively; the DMD and OMD ranged from 0.40 to 0.58 and 0.42–0.59, respectively; the ED ranged from 0.381 to 0.542; and the gross energy and DE ranged from 18.18 to 18.71 and 7.02–10.14 MJ/kg DM, respectively. The minimum and maximum organic matter, CP, CF, NDF, ADF, ADL and total free reducing sugars contents of the lucerne hay ranged from 896 to 919, 140–182, 281–394, 479–581, 304–401, 74–97 and 18–50 g/kg DM, respectively; dCS and dCO ranged from 53.5–67.0% and 50.0–63.8%; DMD, OMD and ED ranged from 0.51 to 0.62, 0.51–0.62 and 0.47–0.58, respectively; and gross energy and DE ranged from 18.52 to 18.88 and 8.72–10.79 MJ/kg DM, respectively. Hay from natural grasslands and grass had similar values for chemical composition, dCS, dCO and in vivo digestibility coefficients (Table 1). However, lucerne hays were characterised by higher values for ash, CP, dCS, dCO and in vivo digestibility coefficients than grass and natural grassland hays. Finally, the NDF and total free reducing sugars contents of the lucerne hay samples were lower than those of the grass and natural grassland hay samples.

### Enzymatic degradability of hay and its relationships with in vivo digestibility and digestible energy content

When dCS or dCO was used as a single explanatory variable to predict in vivo digestibility coefficients and DE, each variable was always significant (P < 0.05). However, the interaction between forage type and dCS or between forage type and dCO was always non-significant, meaning that the regression models for each determination were characterised by the same slope, whereas the intercept varied among forage types (Tables 2 and 3). When the variable dCS or dCO was combined with other variables of chemical composition for predicting in vivo digestibility coefficients and the DE content of hay, the interaction between forage type and dCS or dCO and between forage type and cc was not significant

<caption>**Table 1**</caption>

Mean chemical composition (g/kg DM), energy content (MJ/kg DM), enzymatic degradability assayed (%) and mean digestion coefficients determined in vivo in horses for the natural grassland, grass and lucerne hays in the dataset.

<table><thead><tr><th rowspan="3">Chemical composition</th><th colspan="4">Natural grasslands</th><th colspan="4">Grasses</th><th colspan="4">Lucernes</th></tr><tr><th colspan="4">N = 32</th><th colspan="4">N = 5</th><th colspan="4">N = 6</th></tr><tr><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th><th>Mean</th><th>SD</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>OM</td><td>913</td><td>13.5</td><td>885</td><td>933</td><td>918</td><td>15.9</td><td>901</td><td>937</td><td>907</td><td>9.8</td><td>896</td><td>919</td></tr><tr><td>CP</td><td>117</td><td>43.1</td><td>55</td><td>214</td><td>133</td><td>38.5</td><td>97</td><td>192</td><td>161</td><td>17.6</td><td>140</td><td>182</td></tr><tr><td>CF</td><td>318</td><td>43.8</td><td>235</td><td>386</td><td>323</td><td>40.5</td><td>278</td><td>372</td><td>334</td><td>39.4</td><td>281</td><td>394</td></tr><tr><td>NDF</td><td>618</td><td>52.3</td><td>527</td><td>687</td><td>656</td><td>60.6</td><td>587</td><td>718</td><td>513</td><td>37.9</td><td>479</td><td>581</td></tr><tr><td>ADF</td><td>344</td><td>43.6</td><td>264</td><td>403</td><td>352</td><td>50.9</td><td>297</td><td>403</td><td>346</td><td>32.0</td><td>304</td><td>401</td></tr><tr><td>ADL</td><td>48</td><td>12.3</td><td>25</td><td>71</td><td>54</td><td>14.6</td><td>37</td><td>72</td><td>83</td><td>8.4</td><td>74</td><td>97</td></tr><tr><td>TFRS</td><td>78</td><td>27.8</td><td>30</td><td>136</td><td>48</td><td>16.5</td><td>33</td><td>74</td><td>31</td><td>11.0</td><td>18</td><td>50</td></tr><tr><td>Enzymatic degradability</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>dCS</td><td>51.7</td><td>7.58</td><td>41.0</td><td>69.1</td><td>51.6</td><td>9.40</td><td>43.5</td><td>63.3</td><td>61.7</td><td>5.15</td><td>53.5</td><td>67.0</td></tr><tr><td>dCO</td><td>50.0</td><td>7.54</td><td>39.3</td><td>65.0</td><td>49.5</td><td>9.44</td><td>41.6</td><td>61.6</td><td>58.2</td><td>5.49</td><td>50.0</td><td>63.8</td></tr><tr><td>Digestibility coefficients</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DMD</td><td>0.51</td><td>0.064</td><td>0.40</td><td>0.62</td><td>0.49</td><td>0.077</td><td>0.40</td><td>0.58</td><td>0.57</td><td>0.040</td><td>0.51</td><td>0.62</td></tr><tr><td>OMD</td><td>0.52</td><td>0.066</td><td>0.42</td><td>0.63</td><td>0.51</td><td>0.077</td><td>0.42</td><td>0.59</td><td>0.58</td><td>0.039</td><td>0.51</td><td>0.62</td></tr><tr><td>ED</td><td>0.49</td><td>0.082</td><td>0.39</td><td>0.60</td><td>0.46</td><td>0.064</td><td>0.38</td><td>0.54</td><td>0.53</td><td>0.040</td><td>0.47</td><td>0.58</td></tr><tr><td>Energy content</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GE</td><td>18.3</td><td>0.42</td><td>17.6</td><td>20.0</td><td>18.5</td><td>0.20</td><td>18.2</td><td>18.7</td><td>18.7</td><td>0.14</td><td>18.5</td><td>18.9</td></tr><tr><td>DE</td><td>8.8</td><td>1.20</td><td>6.9</td><td>11.2</td><td>8.5</td><td>1.21</td><td>7.0</td><td>10.1</td><td>9.9</td><td>0.75</td><td>8.7</td><td>10.8</td></tr></tbody></table>

N: Number of samples, Min: minimum value, Max: Maximum value, OM: organic matter, CF: crude fibre, TFRS: Total free reducing sugars, dCS: DM enzymatic degradability, dCO: organic matter enzymatic degradability, DMD: DM digestibility, OMD: organic matter digestibility, ED: energy digestibility, GE: gross energy, DE: digestible energy.
<caption>**Table 2**</caption>

Prediction models, coefficients of determination and RMSE values for DM digestibility (DMD), organic matter digestibility (OMD), energy digestibility (ED) and digestible energy (DE) content determined in horses from pepsin-cellulase degradability expressed in relation to DM (dCS).

<table><thead><tr><th>dCS</th><th>Δ value</th><th>RMSE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Natural grasslands<br>DMD = 0.103 + 0.0078*dCS + Δ</td><td>Δ = -0.014 for grasses and lucerne</td><td>0.022</td><td>0.90</td></tr><tr><td>Natural grasslands<br>OMD = 0.102 + 0.0082*dCS + Δ</td><td>Δ = -0.014 for grasses<br>Δ = -0.028 for lucerne</td><td>0.020</td><td>0.91</td></tr><tr><td>Natural grasslands ED = 0.0973 + 0.0074*dCS + Δ</td><td>Δ = 0.097 for grasses<br>Δ = -0.076 for lucerne</td><td>0.020</td><td>0.90</td></tr><tr><td>Natural grasslands<br>DE = 1.348 + 0.145*dCS + Δ</td><td>Δ = -0.301 for grasses<br>Δ = -0.353 for lucerne</td><td>0.456</td><td>0.87</td></tr><tr><td>Natural grasslands<br>DE = 2.107 + 0.115*dCS + 0.007*CP + Δ</td><td>Δ = 0.348 for lucerne<br>Δ Not available for grasses</td><td>0.431</td><td>0.89</td></tr></tbody></table>

Δ = Difference in relation to the intercept for grasses or lucernes compared with that for natural grassland. This value should be subtracted or added to the shown intercept for natural grassland forages.

<caption>**Table 3**</caption>

Prediction models, coefficients of determination and RMSE values for DM digestibility (DMD), organic matter digestibility (OMD), energy digestibility (ED) and digestible energy (DE) content determined in horses from pepsin-cellulase degradability expressed in relation to organic matter (dCO).

<table><thead><tr><th>dCO</th><th>Δ value</th><th>RMSE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Natural grasslands<br>DMD = 0.117 + 0.0078*dCO + Δ</td><td>Δ = -0.010 for grasses<br>Δ = 0.001 for lucerne</td><td>0.022</td><td>0.89</td></tr><tr><td>Natural grasslands<br>OMD = 0.113 + 0.0082*dCO + Δ</td><td>Δ = -0.011 for grasses<br>Δ = -0.012 for lucerne</td><td>0.020</td><td>0.91</td></tr><tr><td>Natural grasslands<br>ED = 0.108 + 0.0075*dCO + Δ</td><td>Δ = -0.018 for grasses<br>Δ = -0.012 for lucerne</td><td>0.020</td><td>0.90</td></tr><tr><td>Natural grasslands<br>DE = 1.549 + 0.145*dCO + Δ</td><td>Δ = -0.233 for grasses<br>Δ = -0.084 for lucerne</td><td>0.446</td><td>0.87</td></tr><tr><td>Natural grasslands<br>DE = 2.282 + 0.114*dCO-<br>0.007CP + Δ</td><td>Δ = -0.145 for lucerne<br>Δ Not available for grasses</td><td>0.413</td><td>0.89</td></tr></tbody></table>

Δ = Difference in relation to the intercept for grasses or lucernes compared with that for natural grassland. This value should be subtracted or added to the shown intercept for natural grassland forages.

(P > 0.05). For CP, ADL and total free reducing sugars, the intercept was also non-significant (P > 0.05) with the exception of CP for the prediction of DE. The relationships between the digestibility coefficients and the DE content and enzymatic degradability are presented in Tables 2 and 3. The models for predicting digestibility coefficients from dCS and dCO are characterised by R² values greater than or equal to 0.89 and RMSE values of approximately 0.02 (Tables 2 and 3). The R² values associated with the models for predicting the DE content were both 0.87 for dCS and dCO, and the RMSE values were 0.46 MJ/kg DM and 0.45 MJ/kg DM for dCS and dCO, respectively.

Multiple linear regression statistics associated with models for predicting digestibility coefficients and DE from dCS and dCO and chemical composition parameters were greater than or equal to 0.91 for R² and between 0.017 and 0.021 for RMSE. The R² values for predicting the DE content ranged from 0.89 to 0.90, and the RMSE values were lower than 0.44 MJ/kg DM and higher than 0.40 MJ/kg DM (Tables 4 and 5). The r between the chemical composition parameters and the dCS and dCO values for natural grasslands are presented in Table 6. The r values between the cell wall

content and lignocellulose, lignin and CF contents were greater than or equal to 0.89. The r values between the different components of structural carbohydrates partitioned by the detergent system, the CF and the enzymatic degradability determinations resulted in variation inflation factor values of 5.64, 4.86 and 5.79, respectively, for multiple linear regression models when dCS and CF or NDF or ADF were combined. The variation inflation factors values for the regression models of natural grasslands when dCO and CF or NDF or ADF determinations were combined were 5.66, 4.88 and 5.85, respectively.

## Discussion

The aim of this study was to evaluate the relevance and feasibility of using enzymatic degradability as a single variable or in combination with other chemical composition parameters for predicting the nutritional value of hay for use in feed for equines. The range of hays included in the database used in the present study was considered to be representative of the variability observed in the tables of the feed value of hays for horses in tem-

<caption>**Table 4**</caption>

Coefficient of determination and RMSE values of the models developed for DM digestibility (DMD), organic matter digestibility (OMD), energy digestibility (ED) and digestible energy content (DE, MJ/kg DM) determined in horses from pepsin-cellulase degradability expressed in relation to DM (dCS) and crude fibre (CF), NDF, ADF and CP.

<table><thead><tr><th rowspan="2">Item</th><th colspan="2">DMD</th><th colspan="2">OMD</th><th colspan="2">ED</th><th colspan="2">DE</th></tr><tr><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>CF</td><td>0.019</td><td>0.92</td><td>0.018</td><td>0.93</td><td>0.017</td><td>0.93</td><td>0.411</td><td>0.90</td></tr><tr><td>NDF</td><td>0.020</td><td>0.92</td><td>0.020</td><td>0.92</td><td>0.018</td><td>0.92</td><td>0.431</td><td>0.89</td></tr><tr><td>ADF</td><td>0.021</td><td>0.91</td><td>0.019</td><td>0.92</td><td>0.019</td><td>0.91</td><td></td><td></td></tr><tr><td>CP</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.431</td><td>0.89</td></tr></tbody></table>
<caption>**Table 5**</caption>

Coefficient of determination and RMSE values of the models developed for DM digestibility (DMD), organic matter digestibility (OMD), energy digestibility (ED) and digestible energy content (DE, MJ/kg DM) determined in horses from pepsin-cellulase degradability expressed in relation to organic matter (dCO) and crude fibre (CF), NDF, ADF and CP.

<table><caption>**Table 6**</caption><thead><tr><th rowspan="2">Item</th><th colspan="2">DMD</th><th colspan="2">OMD</th><th colspan="2">ED</th><th colspan="2">DE</th></tr><tr><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th><th>RMSE</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>CF</td><td>0.019</td><td>0.92</td><td>0.018</td><td>0.93</td><td>0.017</td><td>0.93</td><td>0.402</td><td>0.90</td></tr><tr><td>NDF</td><td>0.020</td><td>0.92</td><td>0.017</td><td>0.94</td><td>0.017</td><td>0.93</td><td>0.423</td><td>0.89</td></tr><tr><td>ADF</td><td>0.021</td><td>0.91</td><td>0.019</td><td>0.93</td><td>0.019</td><td>0.92</td><td></td><td></td></tr><tr><td>CP</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.413</td><td>0.89</td></tr></tbody></table>

Correlation coefficients between different parameters of the chemical composition and the enzymatic degradability of natural grassland hays used in the feeding of horses.

<table><thead><tr><th>Item</th><th>OM</th><th>CP</th><th>TFRS</th><th>CF</th><th>NDF</th><th>ADF</th><th>ADL</th><th>dCS</th><th>dCO</th></tr></thead><tbody><tr><td>OM</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CP</td><td>-0.28</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TFRS</td><td>0.12</td><td>-0.57</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CF</td><td>0.41</td><td>-0.80</td><td>0.08</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NDF</td><td>0.42</td><td>-0.75</td><td>0.01</td><td>0.98</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>ADF</td><td>0.41</td><td>-0.77</td><td>0.04</td><td>0.98</td><td>0.99</td><td>1</td><td></td><td></td><td></td></tr><tr><td>ADL</td><td>0.48</td><td>-0.63</td><td>-0.02</td><td>0.89</td><td>0.87</td><td>0.87</td><td>1</td><td></td><td></td></tr><tr><td>dCS</td><td>-0.57</td><td>0.80</td><td>-0.22</td><td>-0.91</td><td>-0.89</td><td>-0.91</td><td>-0.77</td><td>1</td><td></td></tr><tr><td>dCO</td><td>-0.51</td><td>0.78</td><td>-0.20</td><td>-0.91</td><td>-0.89</td><td>-0.91</td><td>-0.77</td><td>0.98</td><td>1</td></tr></tbody></table>

OM = organic matter; TFRS = Total free reducing sugars; CF = crude fibre; dCS = enzymatic degradability expressed in DM; dCO = enzymatic degradation expressed in organic matter.

operate areas (INRA, 2015), with OMD values varying between 0.41 and 0.63. Only a few barn-dried hay samples harvested at early stages fell outside the validity range of the proposed models. The database accurately represents the variations in chemical composition and nutritive value of hay across different forage types. The bromatological compositions, digestibility coefficients, and DE contents of grass hay and natural grassland hay are comparable. Conversely, lucerne hay presented higher CP and ADL values than did grass hay and natural grassland hay. Additionally, their mean NDF values were lower than those of grass and natural grassland hay. In contrast to those of ruminants, in horses, lucerne hays displayed higher digestibility values than did grass hays or hays from natural grasslands with equivalent ADF and CF contents (Martin-Rosset et al., 1984). The lower amount of lucerne cell wall content compared to grasses with equivalent ADF contents may be a contributing factor to these findings. Furthermore, it is probable that the digestive content of lucerne diets reaching the large intestine of horses contains a greater quantity of CP and a lower amount of NDF than does the digestive content of grasses. Consequently, the efficiency of digestion of lucerne residues in the large intestine could be greater in lucerne diets than in grass diets. This fact could contribute to the higher digestibility values observed for lucerne hays than for grass hays (Martin-Rosset et al., 1984).

This study is among the first to propose models for predicting the nutritive value of hay for horses on the basis of the enzymatic degradability of forages in a laboratory context. The enzymatic degradability method proposed by Aufrère et al. (2007) was used in the present study. One of the main advantages of this technique is that it can be applied to forage mixtures (multispecific sown pastures) and forage issues from permanent grasslands, which are important in temperate areas worldwide. Miraglia and Tisserand (1985) employed 13 samples of straw, meadow hay, and lucerne hay and reported a significant r between enzymatic degradability and DMD and OMD using an enzymatic degradability method (Aufrère, 1982) similar to that used in the current study. However, they did not propose a prediction model.

As observed by Aufrère et al. (2007) in their research on the prediction of forage digestibility in ruminants on the basis of enzymatic degradability, the slopes of the models generated in this study were consistent across grasses and lucerne. The results of

the current study demonstrate the efficacy of enzymatic degradability as a predictor of DMD, OMD, ED and DE contents of hay for horses. The variance explained by the models obtained is approximately 0.90, whereas the RMSE values, expressed as a percentage of the mean value, were 3.8 and 3.4 for grass + natural grassland and lucerne, respectively. In the study reported by Aufrère et al. (2007), lower values of R² and slightly higher values of RMSE were reported for the prediction of OMD in ruminants from dCS and dCO. The R² values were 0.77 for grass + natural grassland and 0.77 for legumes, while the RMSE values were 4.2 and 4.5%, respectively.

The results of the prediction of the nutritive value of hay from enzymatic degradability were similar to or slightly better than those obtained for the prediction of the nutritive value of hay for horses from the chemical composition of hay. Thus, the R² values of the models developed in the present study for the prediction of OMD (R² = 0.91) were greater than those reported by Martin-Rosset et al. (2024), who used a database similar to that of the present study (0.87, 0.71 and 0.72 for natural grassland, grass and lucerne hay, respectively), whereas the RMSE values, expressed as a percentage of the mean value were slightly lower than those reported by these authors (3.84, 3.84 and 3.44 vs 4.56, 6.46 and 3.67 for permanent grassland, grass and lucerne hay, respectively). There were no significant discrepancies between the dCS and dCO methods with respect to their efficacy in predicting digestibility and DE content. The former is preferable because of its reduced reliance on laboratory procedures and potential cost savings. However, the determination of dCO may be of interest in scenarios where the mineral composition of forages is a primary concern (Aufrère et al., 2007), such as lucerne hay, or when hay contamination by soil is a concern.

If the variables dCS and dCO were combined with chemical composition variables to predict the nutritive value and DE content of hay, the prediction outcomes exhibited marginal improvements when the variables CF, NDF and ADF were used. However, the variation inflation factors values for these variables indicated that the variance of the estimated coefficient for these variables was inflated by factors approaching 5 and exceeding 10 for grasses. As the proposed models are intended to be used in practical conditions for the prediction of the nutritive value of hay for horses, it
has been decided not to present these models to avoid the problem of reducing the precision of the estimated values. With respect to the prediction of the DE content of hay, the CP variable was significant, and its variation inflation factor value was lower than 2.5 for natural grasslands and lucerne; therefore, the models used are included in Tables 2 and 3. According to SAS (1998), there are no formal criteria for deciding whether a variation inflation factor is large enough to affect the predicted values. However, some authors consider that variation inflation factor values of 5 or 10 may indicate collinearity. The results of the present study were obtained from a limited amount of data, which makes it challenging to find in vivo digestibility and DE content data comparable to those used in this study. Therefore, to increase the applicability of the proposed models, the species, stage and vegetation cycle were not incorporated as variables in the model, which may have resulted in slightly greater residual variability.

## Conclusion

This study presents models for predicting the nutritive value and DE content of hay harvested in temperate zones using an inexpensive easy-to-implement, repeatable and reproducible laboratory method on the basis of enzymatic degradability. The models derived from cellulase degradability represent an alternative to the current equations for predicting nutritive value and the DE content from chemical composition because of their performance. The combination of dCS and dCO with other variables in relation to the chemical composition of hay was not relevant, with the exception of the inclusion of the variable CP for the prediction of the DE content. The results of this study contribute to the existing body of knowledge on the prediction of the nutritive value of horse feed with satisfactory accuracy for practical use. Further research is needed to extend the range of models to other forage types, such as green and fermented forages, and to increase the number of hay samples, particularly for grasses and leguminous hay.

## Ethics approval

Not applicable.

## Data and model availability statement

None of the data were deposited in an official repository. The data that support the study findings are available from the authors upon request.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any AI and AI-assisted technologies.

## Authors ORCIDs

Donato Andueza: https://orcid.org/0000-0002-1839-4461.

## CRediT authorship contribution statement

D. Andueza: Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Investigation, Formal analysis, Conceptualisation. W. Martin-Rosset: Writing – review & editing, Writing – original draft, Validation, Supervision,

Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualisation.

## Declaration of interest

None.

## Acknowledgments

The authors would like to thank H. Dubroeuq who managed the measurements of in vivo digestion with horses, M. Jestin who carried out the determination of the enzymatic degradability of the hays, M. Dudilieu who assayed the chemical composition of hays and M. Vermorel for reviewing the manuscript.

## Financial support statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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