Evaluation of woodchips-amended biosand filter for nitrate and MS2 bacteriophage reduction.

In this study, two types of woodchip-amended biosand filters (Filter A sand: woodchip = 33%: 67% versus Filter B sand: woodchip = 50%: 50%, by volume) were constructed, and their abilities to remove MS2 bacteriophage and nitrate were investigated. The results indicated that Filter A and Filter B could reduce nitrate up to 40 and 36%, respectively, indicating that the nitrate reduction increased with the increase in woodchip proportion. The study underscores a positive correlation between nitrate reduction and proportional increase in woodchip content, implying the potential for fine-tuning nitrate removal by varying sand-woodchip compositions. W-BSFs could remove MS2 bacteriophage to 1.91-log10 (98.8%) by Filter A and 1.88-log10 (98.7%) by Filter B over 39 weeks. The difference in sand-woodchip proportion did not significantly impact the MS2 reduction, demonstrating that a single W-BSF can maintain its virus removal performance fairly well over a long-term period. These results indicated that the nitrate reduction could be adjusted by varying sand-woodchip contents without impacting virus removal performance. Microbial community analysis indicated that the nitrate removal by the W-BSFs could be attributed to the denitrifying bacteria, such as the family Streptomycetaceae, the genera Pseudomonas, and Bacillus, and relative abundances of the phylum Nitrospirae.

Integrating plant morphological traits with remote-sensed multispectral imageries for accurate corn grain yield prediction.

Sustainable crop production requires adequate and efficient management practices to reduce the negative environmental impacts of excessive nitrogen (N) fertilization. Remote sensing has gained traction as a low-cost and time-efficient tool for monitoring and managing cropping systems. In this study, vegetation indices (VIs) obtained from an unmanned aerial vehicle (UAV) were used to detect corn (Zea mays L.) response to varying N rates (ranging from 0 to 208 kg N ha-1) and fertilizer application methods (liquid urea ammonium nitrate (UAN), urea side-dressing and slow-release fertilizer). Four VIs were evaluated at three different growth stages of corn (V6, R3, and physiological maturity) along with morphological traits including plant height and leaf chlorophyll content (SPAD) to determine their predictive capability for corn yield. Our results show no differences in grain yield (average 13.2 Mg ha-1) between furrow-applied slow-release fertilizer at ≥156 kg N ha-1 and 208 kg N ha-1 side-dressed urea. Early season remote-sensed VIs and morphological data collected at V6 were least effective for grain yield prediction. Moreover, multivariate grain yield prediction was more accurate than univariate. Late-season measurements at the R3 and mature growth stages using a combination of normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) in a multilinear regression model showed effective prediction for corn yield. Additionally, a combination of NDVI and normalized difference red edge index (NDRE) in a multi-exponential regression model also demonstrated good prediction capabilities.