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In-field assessment: Current tools and future directions

Throughout the alfalfa production season, careful and informed harvest decisions increase the chances of meeting production goals. A stand’s growth from one cut to the next will always vary according to stand health and a range of environmental factors.

Accurately assessing an alfalfa crop in the field is critical for maximizing profitability, in terms of both quality and yield.

In the upper Midwest, where the dairy industry drives forage demands, the crop’s value is especially dependent on forage quality. Higher quality means higher milk per ton, which means greater profitability per ton of forage.

Alfalfa maturity as a quality indicator

Alfalfa maturity currently is the most accurate and consistent indicator of quality. As maturity increases, forage quality decreases (Figure 1).

Generally speaking, good quality means higher crude protein and lower fiber fractions. Quality is highest when the leaf-stem ratio is highest (more leaves, fewer stems). As alfalfa develops and growth shifts from vegetative to reproductive, quality quickly begins to decrease.

A graph showing several points obtained from random sampling of a stand. This graph shows reduced forage quality (RFQ) with increased maturity (MSC)
Fig. 1: Relative forage quality (RFQ) compared to alfalfa maturity (MSC) from periodic sampling of a stand in Rosemount in 2014.

Technology tools to increase profitability

Various new precision agriculture tools and applications are enabling the most efficient use of resources and maximum profitability in other major crops. For example, producers are accounting for in-field variability with variable rate fertilizer application and variable rate planting.

Unmanned aerial vehicles (UAVs) or drones are being equipped with GPS technology and a wide array of sensors/cameras to assess crop health, progress, disease/insect pressure, nutrient deficiencies, etc., and are informing management decisions.

Crop remote sensing

One of most widely used technologies in crop remote sensing is the measurement of canopy reflectance. Broadband spectral indices such as NDVI (Normalized difference vegetative index) are valuable indicators of greenness, crop health or percent ground cover.

More specific indices such as MTCI (MERIS terrestrial chlorophyll index) are designed for more precise applications. Indices designed for specific purposes use the spectral reflectance of particular wavebands (ranges of nanometers in the visible and near-infrared spectrum). The wavebands of importance can vary depending on the crop and target application.

Drones or ground vehicles equipped with these sensors can travel through the field, collecting and mapping data that correlates to the crop’s current status across the whole field.


Reagan L. Noland, graduate student, College of Food, Agricultural and Natural Resource Sciences (CFANS); Craig Sheaffer, agronomist, CFANS and M. Scott Wells, Extension agronomist

Reviewed in 2018

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