For ranchers and conservationists alike, the secret to managing the vast rangelands of the West may lie not in a lab, but in the light reflecting off a blade of grass.
Settling the semiarid and arid Great Plains and Intermountain regions of North America in the late nineteenth century came at a heavy environmental cost. The combination of crop production and domestic livestock grazing led to severe rangeland disturbance, which culminated during the Dust Bowl era of the 1930s. The result was widespread loss of desirable native vegetation, increased soil erosion, and invasion by non-native annual weeds, threatening the ecological function of millions of hectares 1 .
First introduction of crested wheatgrass to North America
Hectares revegetated in the United States
Hectares revegetated in Canada
In response, researchers turned to a resilient plant from Eurasia: crested wheatgrass (Agropyron cristatum and A. desertorum). First introduced to North America in 1898, this hardy grass demonstrated remarkable drought resistance and winter hardiness. It proved exceptionally capable of establishing rapidly from seed, stabilizing soil, suppressing invasive weeds, and providing a reliable, high-quality feed source for livestock and wildlife 1 .
Today, crested wheatgrass has been instrumental in revegetating 6 to 11 million hectares in the United States and another million in Canada. Despite criticisms over its non-native status, its ability to thrive on severely disturbed sites has made it a cornerstone of rangeland restoration and agriculture 1 . Its management, however, hinges on a critical question: How do you quickly and accurately measure its nutritional value?
To understand how scientists unlock the secrets of forage, we must first understand Near-Infrared Reflectance Spectroscopy (NIRS). This powerful, non-destructive analytical technique is revolutionizing agricultural analysis.
NIRS operates on a simple premise: when a sample of dried, ground plant material is exposed to near-infrared light (wavelengths between 780 and 2500 nm), the chemical bonds within the sample absorb specific amounts of this energy. Crucially, different chemical compounds have unique "spectral fingerprints." The organic components that make up forage quality—such as the covalent bonds in C-H (carbon-hydrogen), O-H (oxygen-hydrogen), and N-H (nitrogen-hydrogen) groups—vibrate and absorb light in characteristic ways 3 .
The raw spectral data is meaningless without a translation key. This key is a calibration model 3 .
Assemble diverse crested wheatgrass samples
Analyze samples with wet-chemistry methods
Use NIRS and statistical methods like PLSR
Use model for new samples in minutes
Developing this model is a multi-step process:
Once the calibration is established and validated, it can be used to predict the nutrient content of new, unknown samples in a matter of minutes—without any chemicals or waste.
To illustrate the power of this technique, let's examine a foundational study that paved the way for its use in predicting a critical nutrient: plant-available nitrogen.
A study published in Plant and Soil aimed to evaluate the ability of NIRS to predict nitrogen uptake by winter wheat—a process directly related to the nitrogen mineralization capacity of the soil. This is analogous to predicting the soluble nitrogen content in forage, as both rely on the technology's ability to assess nitrogen-related properties .
Researchers collected soil samples from unfertilized plots in winter wheat fields over multiple years.
They measured the total nitrogen content in the above-ground plant material at harvest, which represented the actual nitrogen uptake from the soil.
The soil samples were scanned using an NIRS spectrometer to obtain their spectral signatures.
Calibration models were built to predict N-uptake based on the soil spectra. The models were rigorously tested through cross-validation and by applying them to data from different years and fields .
The study yielded compelling evidence for NIRS. The cross-validated calibrations predicted nitrogen uptake with an error of only 12.1 to 15.4 kg N per hectare .
A key metric for evaluating NIRS models is the Ratio of Performance to Deviation (RPD), which compares the standard deviation of the reference data to the prediction error. Generally, an RPD above 2.0 is considered good for screening purposes. The NIRS-based calibrations in this study achieved RPD values between 1.9 and 2.5, indicating robust predictive ability .
| Calibration Method | Prediction Error (kg N/ha) | RPD Value | Key Insight |
|---|---|---|---|
| NIRS-based | 12.1 - 15.4 | 1.9 - 2.5 | Integrates multiple soil properties for a more robust prediction. |
| Organic Carbon-based | 11.7 - 28.2 | 1.3 - 2.5 | Relies on a single parameter, leading to variable performance. |
| Data adapted from Stenberg et al., 2005 | |||
Perhaps most importantly, the researchers concluded that "NIR-spectroscopy integrates information about organic C with other relevant soil components and therefore has a good potential to predict complex functions of soils such as N-mineralization" . This finding is directly transferable to forage analysis, suggesting that NIRS can integrate multiple subtle spectral features to predict complex nutritional traits like soluble nitrogen in crested wheatgrass.
Moving from concept to practical application requires a suite of specialized tools and reagents. The following table details the key components of the NIRS workflow for analyzing forage quality.
| Item | Function in NIRS Analysis | Brief Explanation |
|---|---|---|
| NIRS Spectrometer | The core instrument that emits NIR light and measures the spectrum reflected by the sample. | Can be a benchtop unit for lab use or a portable, hand-held device for analysis directly in the field. |
| Calibration Set | A library of 50-200 forage samples with known chemical values, used to build the prediction model. | The foundation of the entire system; the model's accuracy is limited by the quality and diversity of this set. |
| Reference Chemistry Data | The "ground truth" measurements for parameters like soluble nitrogen and fibrous fractions. | Obtained via traditional lab methods (e.g., Kjeldahl for protein, Van Soest for fiber) to train the NIRS model. |
| Chemometric Software | Software that uses algorithms like PLSR to find correlations between spectral data and reference chemistry. | This is the "brain" that decodes the spectral language into meaningful nutritional information. |
| Grinding Mill | Creates a uniform particle size from dried plant samples. | Critical for reducing light scatter, a physical phenomenon that can introduce noise into the spectral data. |
| Sample Cups | Hold the ground sample during scanning in a consistent and reproducible manner. | Typically made of a non-reflective material to ensure all measured light has interacted with the sample. |
Time-consuming, chemical-intensive methods that provide reference data for NIRS calibration.
Fast, non-destructive method that uses light reflection to determine nutritional content.
Statistical models correlate spectral data with chemical analysis for accurate predictions.
The integration of NIRS technology into rangeland management represents a paradigm shift. What was once a slow, costly, and chemical-intensive process is now rapid, environmentally friendly, and cost-effective. For the managers of vast rangelands seeded with crested wheatgrass, this means they can now make data-driven decisions about grazing schedules and supplemental feeding with unprecedented speed 3 .
| Advantage | Impact on Forage Analysis |
|---|---|
| Speed | Analysis takes 1-2 minutes per sample, enabling real-time management decisions. |
| Non-Destructive | The sample is preserved and can be re-scanned or used for other tests. |
| Environmentally Friendly | Eliminates the use of hazardous chemicals and produces no toxic waste. |
| Multi-Parameter Analysis | A single scan can predict protein, fiber, moisture, and more simultaneously. |
| Cost-Effective | After the initial investment, the cost per analysis is extremely low. |
The implications are profound. Ranchers can optimize the use of their land, ensuring livestock receive the best possible nutrition while preventing overgrazing. Conservationists can more accurately monitor the health of restored ecosystems. Furthermore, plant breeders, who have been working on crested wheatgrass for over a century to develop 18 distinct cultivars, can use NIRS to rapidly screen thousands of plants for superior nutritional traits, accelerating the development of even better varieties 1 .
As this technology continues to evolve, becoming more accessible and integrated with other tools like remote sensing, our ability to sustainably steward these critical landscapes will only grow stronger. The humble crested wheatgrass, a plant that helped heal the land after the Dust Bowl, now, through the prism of light, offers up the secrets to its own nutritional power.