Assessing disease stress and modeling yield losses in alfalfa
Alfalfa is the most important forage crop in the U.S. and worldwide. Fungal foliar diseases are believed to cause significant yield losses in alfalfa, yet, little quantitative information exists regarding the amount of crop loss;Different fungicides and application frequencies were used as tools to generate a range of foliar disease intensities in Ames and Nashua, IA. Visual disease assessments (disease incidence, disease severity, and percentage defoliation) were obtained weekly for each alfalfa growth cycle (two to three growing cycles per season). Remote sensing assessments were performed using a hand-held, multispectral radiometer to measure the amount and quality of sunlight reflected from alfalfa canopies. Factors such as incident radiation, sun angle, sensor height, and leaf wetness were all found to significantly affect the percentage reflectance of sunlight reflected from alfalfa canopies;The precision of visual and remote sensing assessment methods was quantified. Precision was defined as the intra-rater repeatability and inter-rater reliability of assessment methods. F-tests, slopes, intercepts, and coefficients of determination (R2) were used to compare assessment methods for precision. Results showed that among the three visual disease assessment methods (disease incidence, disease severity, and percentage defoliation), percentage defoliation had the highest intra-rater repeatability and inter-rater reliability. Remote sensing assessment method had better precision than the percentage defoliation assessment method based upon higher intra-rater repeatability and inter-rater reliability;Significant linear relationships between canopy reflectance (810 nm), percentage defoliation and yield were detected using linear regression and percentage reflectance (810 nm) assessments were found to have a stronger relationship with yield than percentage defoliation assessments;There were also significant linear relationships between percentage defoliation, dry weight, percentage reflectance (810 nm), and green leaf area index (GLAI). Percentage reflectance (810 nm) assessments had a stronger relationship with dry weight and green leaf area index than percentage defoliation assessments. Our research conclusively demonstrates that percentage reflectance measurements can be used to nondestructively assess green leaf area index which is a direct measure of plant health and an indirect measure of productivity;This research conclusively demonstrates that remote sensing is superior to visual assessment method to assess alfalfa stress and to model yield and GLAI in the alfalfa foliar disease pathosystem.