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Extension > Agriculture > Forage Production > Utilization > In–field assessment of alfalfa quality

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In–field assessment of alfalfa quality: Current tools and future directions

Reagan Noland, Craig Sheaffer, and M. Scott Wells, University of Minnesota
2015

Throughout the alfalfa production season, careful and informed harvest decisions increase the chances of meeting production goals. The growth of a stand, from one cut to the next, will always vary according to stand health as well as a range of environmental factors. Accurate in–field assessment of an alfalfa crop is critical to maximize profitability, in terms of both quality and yield. In the upper Midwest, where forage demands are driven by the dairy industry, the value of a crop is especially dependent on forage quality. Higher quality means higher milk/ton, which means greater profitability per ton of forage.

Alfalfa maturity as a quality indicator

forage-quality-vs-maturity

Figure 1. Relative Forage Quality (RFQ) compared to alfalfa maturity (MSC) from periodic sampling of a stand in Rosemount, MN, 2014.

Alfalfa maturity is currently 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, less stems). As alfalfa develops and growth shifts from vegetative to reproductive, quality begins to decrease quickly.

Growth staging alfalfa on the 0–9 maturity scale (established by Kalu and Fick, 1981) enables the calculation of maturity indices, such as mean stage by count (MSC) and mean stage by weight (MSW). The MSC or MSW values can then be interpreted as indicators of forage quality parameters. A sample of alfalfa stems can be individually categorized into the appropriate stages described in Table 1 and illustrated in Figures 2 and 3. To calculate MSC, the stems in each stage need to be counted and the resulting values entered into the index equation. To calculate MSW, each maturity group needs to be dried and weighed, then the corresponding values plugged into the equation.

alfalfa-vegetative-stages

Figure 2. Growth staging of alfalfa: 1) Vegetative alfalfa stages 0, 1, & 2 (left to right); 2) Alfalfa buds – indicating stages 3 & 4; 3) Open flowers, stages 5 & 6; and 4) Alfalfa seed pod, stages 7, 8, & 9.

alfalfa-reproductive-morphology

Figure 3. Alfalfa reproductive morphology: 1) Closed flower, 2) Open flower, and 3) Seed pod.

Table 1. Alfalfa growth staging guide adapted from Kalu and Fick, 1981.

Stage number Stage name Stage definition
0 Early vegetative Stem length < 15 cm; no buds, flowers or seed pods
1 Mid–vegetative Stem length 16 – 30 cm; no buds, flowers or seed pods
2 Late vegetative Stem length > 31 cm; no buds, flowers or seed pods
3 Early bud 1 to 2 nodes with buds; no flowers or seed pods
4 Late bud 3 or more nodes with buds; no flowers or seed pods
5 Early flower One node with one open flower (standard open); no seed pods
6 Late flower Two or more nodes with open flowers; no seed pods
7 Early seed pod 1 to 3 nodes with green seed pods
8 Late seed pod 4 or more nodes with green seed pods
9 Ripe seed pod Nodes with mostly brown mature seed pods

The efficacy of these maturity indices may change as alfalfa is harvested earlier for higher quality and as new, novel varieties of alfalfa are being developed with lower lignin (i.e. higher digestible fiber). The introduction of these lines will introduce new flexibility into alfalfa harvest management and limit the applications of traditional assessment tools. Although alfalfa maturity will still correlate with quality in these new lines, higher quality will be maintained with greater maturity. Therefore, equal quality can be achieved with higher yields, or higher quality can be achieved with equal (conventional) yields. Precise and intensive management will be critical to optimize the use of these resources and maximize profit margins.

Technology tools to increase profitability

Various new tools and applications in the area of precision agriculture are enabling maximum resource use efficiency and profitability in other major crops (i.e. 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 utilize the spectral reflectance of particular wavebands (ranges of nanometers in the visible and near–infrared spectrum), and 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 and collecting and mapping data that correlates to the current status of the crop across the whole field.

Predicting alfalfa quality

vegetative-index-vs-alfalfa-maturity

Figure 4. A spectral vegetative index called REIP (Red edge inflection point) compared to alfalfa maturity. Spectral measurements were taken prior to periodic destructive sampling throughout the growth of a stand in Rosemount, MN, 2014.

In 2014, a pilot study was conducted to determine whether spectral indices could be used to predict alfalfa maturity. A full range spectrophotometer, measuring reflectance values 350–2500 nm was used to periodically scan alfalfa plots throughout the growth of a stand, followed by destructive harvest, sampling and analysis for yield, quality, and maturity. Preliminary analysis indicate that there is potential for known spectral indices to predict alfalfa maturity (Figure 4). However, alfalfa–specific indices have not yet been developed.

A follow–up study with similar principles is being conducted in 2015. Treatments within a replicated experiment are being mowed periodically to set up a maturity gradient in the field (Figure 5). The resulting stand represents a range of maturity, from early vegetative to full flower. Then, all plots are scanned with multiple forms of remote sensing instrumentation prior to harvest and analysis. An added technology this year is the use of LiDAR (Light detection and ranging) to remotely measure crop height. Preliminary results indicate potential for this estimated crop height to predict biomass as long as the stand is still erect (prior to lodging) (Figure 6). As this project continues, researchers aim to develop an alfalfa specific remote sensing platform as a practical tool for optimized decision making in alfalfa management.

alfalfa-maturity-plots

Figure 5. Alfalfa plots representing a wide range of maturity stages, Rosemount, MN, 2015.

alfalfa-height-vs-biomass

Figure 6. Estimated stand height as it correlates to actual alfalfa biomass.


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