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Title: APPLICATION OF MULTISPECTRAL AERIAL IMAGERY IN ESTIMATING GROWTH OF COTTON FERTILIZED WITH POULTRY LITTER AND INORGANIC NITROGEN

Author
item Tewolde, Haile
item IGBAL, J - MISSISSIPPI STATE UNIV
item Rowe, Dennis
item Sistani, Karamat
item Read, John

Submitted to: Agronomy Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2003
Publication Date: 10/1/2003
Citation: Igbal, J., Tewolde, H., Rowe, D.E., Sistani, K.R., Read, J.J. 2003. Application of multispectral aerial imagery in estimating growth of cotton fertilized with poultry litter and inorganic nitrogen [abstract]. Agronomy Abstracts. CD-ROM.

Interpretive Summary:

Technical Abstract: Mississippi annually produces 730 million broiler chickens. Environmentally safe disposal of poultry litter as a plant nutrient and soil amendment without affecting the water quality is a concern. Remote sensing of crop canopy reflectance may assist growers in nitrogen management decisions. A poultry litter (PL) study was conducted during 2002 at Paul Good Farm, Macon, Mississippi, to determine the potential of aerial imagery to estimate and discriminate growth of cotton crops. Four rates of PL and N as UAN were applied in a RCB design. Aerial images were collected with a digital camera system that acquired four bands with 2-m spatial resolution. Data set collected was analyzed in SAS using general linear model for the overall main effects of PL and inorganic N,PL rate differences within each inorganic N rate and vice versa. Results indicate overall significant effects of PL and inorganic N treatments in plant growth variables, yield, and NDVI. Inorganic N treatment showed significant differences in each rate of PL in all the plant growth variables, yield and NDVI. Leaf N sampled on July 7 was significantly related to NIR-July 8 (r=0.47), NIR-July 18 (r=0.61); Red-July 8 (r=0.44); Green-June 18 (r=0.36); Blue-July 18 (r=0.43); NDVI-July (r=0.59) and NDVI-July 18 (r=0.71). Lint yield was significantly correlated with: NDVI (r=0.45-0.73); NIR (r=0.42-0.62); and Red (r=-0.49). Results suggest that remotely sensed data could be a useful tool for N management.