Forages are plants or parts of plants eaten by both livestock and wildlife. Forages provide bulk, help with weight maintenance, and combat several issues [1]. Alfalfa forage is high in protein—well-suited to promote muscle mass in beef cattle or to increase the production of dairy cows [2]. Alfalfa is also commonly used for horse feed. These animals require forages with good palatability along with high digestibility, intake potential, and protein levels, thus increasing the demand for alfalfa and other high-quality feeds. Farmers have responded by producing even higher-quality alfalfa in recent years. Since forage quality depends on chemical, biological, and dynamic properties, both measured and calculated methods must be used. Standard alfalfa assays measure neutral detergent fiber (NDF), acid detergent fiber (ADF), crude protein (CP), ash, moisture, and protein. For alfalfa pellets, the mean particle size is also important. Near-infrared spectroscopy (NIRS) offers rapid and reliable prediction of fat, moisture, crude protein, fiber, ash and starch in a few seconds and without sample preparation.
Samples of alfalfa (including fresh alfalfa, alfalfa pellets, and alfalfa cubes) were analyzed on a Metrohm NIR Analyzer. All measurements were performed in reflection mode using the large cup. The samples were measured in rotation to collect spectral data from several areas. Spectral averaging of signals from several spots helped to reduce sample inhomogeneity.
The reference values were measured according to ISO norms described at the end of this Application Note. Metrohm software was used for all data acquisition and prediction model development.
The obtained NIR spectra (Figure 1) were used to create a prediction model for quantification of fat, moisture, crude protein, fiber, ash, and starch in alfalfa. The quality of the prediction models was evaluated using correlation diagrams (Figures 2–5) which display a very high correlation between the NIR prediction and the reference values. The respective figures of merit (FOM) display the expected precision of a prediction during routine analysis of different variations of alfalfa feed (Tables 1–3).
Result protein content
| R2 | SEC (%) | SECV (%) | SEP (%) |
| 0.922 | 0.5 | 0.52 | 0.52 |
Result moisture content
| R2 | SEC (%) | SECV (%) | SEP (%) |
| 0.893 | 0.50 | 0.50 | 0.60 |
Result ADF content
| R2 | SEC (%) | SECV (%) | SEP (%) |
| 0.906 | 1.27 | 1.32 | 1.22 |
Result NDF content
| R2 | SEC (%) | SECV (%) | SEP (%) |
| 0.935 | 2.02 | 2.24 | 2.16 |
Figures of merit
The following tables display the figures of merit for the prediction models of alfalfa pellets (Table 1), alfalfa cubes (Table 2), and fresh alfalfa (Table 3).
| Parameter (Range) | No. Spectra | SEC (%) | SECV (%) | SEP (%) | R2 |
|---|---|---|---|---|---|
| Fiber (18–35%) | 385 | 1.24 | 1.35 | 1.34 | 0.714 |
| Moisture (16–34%) | 976 | 0.50 | 0.50 | 0.60 | 0.893 |
| Crude protein (10–21%) | 1577 | 0.51 | 0.52 | 0.52 | 0.922 |
| ADF (23–43%) | 633 | 1.27 | 1.32 | 1.22 | 0.906 |
| NDF (33–73%) | 336 | 2.02 | 2.24 | 2.16 | 0.935 |
| Ash (7–17%) | 216 | 0.78 | 0.86 | 0.81 | 0.723 |
| MPS (14–23 mm) | 43 | 0.40 | 0.47 | N/A | 0.888 |
| Parameter (Range) | No. Spectra | SEC (%) | SECV (%) | SEP (%) | R2 |
|---|---|---|---|---|---|
| Ash (8–14%) | 72/23 | 0.33 | 0.36 | 0.30 | 0.887 |
| Fiber (20–37%) | 86/27 | 1.38 | 1.63 | 1.48 | 0.758 |
| Protein (10–21%) | 101/34 | 0.58 | 0.63 | 0.65 | 0.857 |
| Moisture (10–20%) | 87/28 | 0.23 | 0.30 | 0.29 | 0.974 |
| NDF (34–56%) | 96/22 | 1.73 | 2.11 | 1.44 | 0.918 |
| ADF (25–43%) | 102/35 | 1.38 | 1.65 | 1.44 | 0.837 |
| Parameter (Range) | No. Spectra | SEC (%) | SECV (%) | SEP (%) | R2 |
|---|---|---|---|---|---|
| Fiber (18–35%) | 385 | 1.24 | 1.35 | 1.34 | 0.714 |
| Moisture (16–34%) | 976 | 0.50 | 0.50 | 0.60 | 0.893 |
| Crude protein (10–21%) | 1577 | 0.51 | 0.52 | 0.52 | 0.922 |
This Application Note demonstrates the feasibility to determine multiple key parameters of alfalfa forage with NIR spectroscopy. Several analytical methods are usually required to measure key quality parameters for forage (Table 4). NIRS forage analysis enables a highly accurate, cost-effective, and fast alternative.
| Parameter | Method |
|---|---|
| Starch | ISO 6493:2000 Animal feeding stuffs — Determination of starch content — Polarimetric method |
| Crude ash | ISO 5984:2022 Animal feeding stuffs — Determination of crude ash |
| Crude fiber | ISO 6865:2000 Animal feeding stuffs — Determination of crude fibre content — Method with intermediate filtration |
| Crude protein | ISO 5983-1:2005 Animal feeding stuffs — Determination of nitrogen content and calculation of crude protein content — Part 1: Kjeldahl method |
| Moisture | ISO 6496:1999 Animal Feeding Stuffs — Determination of moisture and other volatile matter content |
| Fat | ISO 6492:1999 Animal feeding stuffs — Determination of fat content |
- Saracen Horse Feeds. The Importance of Forage. Saracen Horse Feeds. https://saracenhorsefeeds.com/sports/feeding-the-ex-racehorse/the-importance-of-forage (accessed 2025-07-09).
- Douliere Hay France. Alfalfa Hay - Lucerne hay for beef and dairy cow, sheep, goat, broadmare and chicken. https://doulierehayfrance.com/en/produit/alfalfa-hay/ (accessed 2025-07-09).