Abstract #227
Section: ADSA-SAD (Student Affiliate Division) Undergraduate Competition
Session: ADSA-SAD (Student Affiliate Division) Undergraduate Competition: Original Research
Format: Oral
Day/Time: Monday 2:00 PM–2:15 PM
Location: St. John's 26/27
Session: ADSA-SAD (Student Affiliate Division) Undergraduate Competition: Original Research
Format: Oral
Day/Time: Monday 2:00 PM–2:15 PM
Location: St. John's 26/27
# 227
Use of green vegetative index maps to predict nutritional quality variation of corn silage.
Eleonor L. Cayford*1, Leyang Feng2, Shao Yang2, Gonzalo Ferreira1, 1Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, 2Geography Department, Virginia Polytechnic Institute and State University, Blacksburg, VA.
Key Words: corn silage, nutritional variation, normalized difference vegetation index
Use of green vegetative index maps to predict nutritional quality variation of corn silage.
Eleonor L. Cayford*1, Leyang Feng2, Shao Yang2, Gonzalo Ferreira1, 1Department of Dairy Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, 2Geography Department, Virginia Polytechnic Institute and State University, Blacksburg, VA.
The objective of this study was to evaluate the use of remote sensing techniques to anticipate the nutritional variation of corn silage. Cornfields (i.e., fields) from 3 commercial dairy farms located in Pittsylvania, Montgomery, and Washington counties in Virginia were used. Fields had an approximate surface of 25, 10, and 54 ha, respectively. Landsat images were obtained from the US Geological Survey online EarthExplorer system for the months Apr to Sep from 2000 to 2014. The spatial resolution of the images is 30 m. Visual assessment of cloud contamination in and around the 3 fields was made using red band (Rred) and near infrared band (Rnir) imagery. Images were then made into subsets using the field boundaries to calculate the normalized difference vegetation index (NDVI). Normalized difference vegetation index maps were derived for all images as follows: NDVI = (Rnir − Rred)/(Rnir + Rred). This NDVI equation produces values in the range from −1 to 1, where positive values indicate vegetated areas and negative values represent non-vegetated areas, such as water, clouds, or snow. The NDVI values for each pixel of all images were calculated under ArcGIS/Arcpy environment. Low, mid, and high NDVI values within a field were represented by red, yellow, and green pixels, respectively. Coordinates for a single red, yellow, and green area within each field were obtained. At harvesting time, each of the 3 selected areas within each field was reached using a GPS, and 3 samples composed of 8 plants were cut 15 cm above ground (3 areas × 3 samples = 9 samples per field). Whole-plants were weighed, chopped, mixed, and ensiled in bags for 60 d. Nutritional composition of corn silage was performed by wet chemistry. Data was analyzed as a randomized complete block design, where field and NDVI were blocks and treatments, respectively. Dry plant biomass was similar among NDVI areas (270 g/plant; P > 0.39). Concentrations of DM (28.3%; P > 0.25), ash (4.55; P > 0.38), CP (10.5%; P > 0.29), NDF (41.3%; P > 0.49), and ADF (25.3%; P > 0.89) did not differ among NDVI areas. In conclusion, differences in NDVI in cornfields did not correspond with differences in nutritional composition of corn silage.
Key Words: corn silage, nutritional variation, normalized difference vegetation index