2015 AQ Summit: The Role of Research in AQ Development

Looking at the problems and opportunities associated with research and development in the State of Maine

2016 AQ Summit: Shellfish Sub-sector Update by Carter Newell

This is an update on the current status and R&D needs of the shellfish aquaculture sub-sector in Maine, USA

2015 AQ Summit: Mussels Sub-sector Update

A brief overview of the Mussel industry in Maine

Modeling the impact of floating oyster (Crassostrea virginica) aquaculture on sediment−water nutrient and oxygen fluxes

Bivalve aquaculture relies on naturally occurring phytoplankton, zooplankton, and detritus as food sources, thereby avoiding external nutrient inputs that are commonly associated with finfish aquaculture. High filtration rates and concentrated bivalve biomass within aquacul- ture operations, however, result in intense biodeposition of particulate organic matter (POM) on surrounding sediments, with potential adverse environmental impacts. Estimating the net deposi- tional flux is difficult in shallow waters due to methodological constraints and dynamic processes such as resuspension and advection. In this study, we combined sediment trap deployments with simulations from a mechanistic sediment flux model to estimate seasonal POM deposition, resus- pension, and processing within sediments in the vicinity of an eastern oyster Crassostrea virginica farm in the Choptank River, Maryland, USA. The model is the stand-alone version of a 2-layer sediment flux model currently implemented within larger models for understanding ecosystem responses to nutrient management. Modeled sediment−water fluxes were compared to observed denitrification rates and nitrite + nitrate (NO2 −+NO3 −), phosphate (PO4 3−) and dissolved O2 fluxes. Model-derived estimates of POM deposition, which represent POM incorporated and processed within the sediment, comprised a small fraction of the material collected in sediment traps. These results highlight the roles of biodeposit resuspension and transport in effectively removing oyster biodeposits away from this particular farm, resulting in a highly diminished local environmental impact. This study highlights the value of sediment models as a practical tool for computing inte- grated measures of nitrogen cycling as a function of seasonal dynamics in the vicinity of aquaculture operations

Changes in wave climate over the northwest European shelf seas during the last 12,000 years

Because of the depth attenuation of wave orbital velocity, wave-induced bed shear stress is much more sensitive to changes in total water depth than tidal-induced bed shear stress. The ratio between wave- and tidal-induced bed shear stress in many shelf sea regions has varied considerably over the recent geological past because of combined eustatic changes in sea level and isostatic adjustment. In order to capture the high-frequency nature of wind events, a two-dimensional spectral wave model is here applied at high temporal resolution to time slices from 12 ka BP to present using paleobathymetries of the NW European shelf seas. By contrasting paleowave climates and bed shear stress distributions with present-day conditions, the model results demonstrate that, in regions of the shelf seas that remained wet continuously over the last 12,000 years, annual root-mean-square (rms) and peak wave heights increased from 12 ka BP to present. This increase in wave height was accompanied by a large reduction in the annual rms wave- induced bed shear stress, primarily caused by a reduction in the magnitude of wave orbital velocity penetrating to the bed for increasing relative sea level. In regions of the shelf seas which remained wet over the last 12,000 years, the annual mean ratio of wave- to (M-2) tidal-induced bed shear stress decreased from 1 (at 12 ka BP) to its present-day value of 0.5. Therefore compared to present- day conditions, waves had a more important contribution to large-scale sediment transport processes in the Celtic Sea and the northwestern North Sea at 12 ka BP

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions