HYDROLOGIC CHARACTERIZATION OF A RAIN GARDEN MITIGATING STORMWATER RUNOFF FROM A COMMERCIAL AREA

Impervious surfaces such as roads, sidewalks, and roofs increase the volume of runoff generated in a watershed. Traditional stormwater management techniques emphasize conveyance of runoff away from impervious surfaces in order to reduce flooding. Rain gardens are becoming popular as a different means to manage stormwater in such a way that runoff is captured and infiltrated onsite rather than conveyed offsite. A stormwater management system consisting of a rainwater harvest system, rain garden, and infiltration chamber was built at the Coca-Cola Refreshments USA, Inc. distribution center in Lexington, Kentucky during the fall of 2011. Precipitation, inflow, and water level were measured from May, 2012 to April, 2013 to evaluate the hydrologic performance of the rain garden. The rain garden had a high infiltrative capability and was able to capture and infiltrate 100% of the runoff generated during the study period. The results of the study were used to formulate recommendations for rain garden design and construction in central Kentucky

Water in South Dakota Stakeholder Guided Strategies for Moving Forward

The 2017 Eastern South Dakota Water Conference included a stakeholder working session that resulted in over 350 comments. This paper reflects the challenges, goals and action items pertaining to South Dakota’s water resources as identified by the state’s diverse stakeholders

Development and evaluation of selected low impact development practices for runoff management at the nursery and watershed scales

Agricultural and urban runoff can threaten water resources. Land disturbances related to urbanization and agriculture can change hydrologic characteristics such as volume of runoff and peak flow. In addition, activities related to urbanization and agriculture can increase pollutants such as pesticides and nutrients present in runoff. The goal of this research was to evaluate and develop selected low impact development practices for management of runoff in two different settings, plant nurseries and urban areas. The specific objectives were: (1) Evaluate pesticide, nutrient, and sediment removal performance of two different types of constructed wetlands (one subsurface-flow and one free-surface) at two nurseries in Oklahoma; (2) Examine the effects of saturation conditions and irrigation patterns on pesticide removal using a lab-scale column study; and, (3) Develop a simple tool that enables practitioners with limited technical expertise to quickly and easily determine optimal combinations of LID practices that optimizes runoff reduction and cost. The pollutant removal performance evaluation of the two constructed wetlands demonstrated that both systems effectively reduced nutrients in runoff, but pesticide reduction was variable. The subsurface-flow constructed wetland significantly reduced most of the commonly seen pesticides however, pesticide removal was variable in the free-surface constructed wetland and no pesticide compound exhibited mass reduction that was statistically significant. While the lab-scale column study was exploratory in nature, results indicated higher pesticide removal under certain hydrologic patterns. There was a general trend indicating that holding water within the column system for a longer time increased removal efficiency. There was no indication that saturation conditions (fully saturated vs variably saturated) impacted pesticide removal. Finally, the optimization procedure addressed a need for developers and smaller municipalities that want to implement low impact development practices to reduce runoff while minimizing cost. The procedure used available software that did not require significant expertise in programming or hydrology, Microsoft Excel and the EPA Stormwater Calculator. Users could determine combinations that met different hydrologic or cost goals by modifying the objective function and/or constraints. Overall, meeting each research objective contributed to the overarching goal of reducing the impact of agricultural and urban runoff on water resources

Utility of Daily 3 m Planet Fusion Surface Reflectance Data for Tillage Practice Mapping with Deep Learning

Tillage practices alter soil surface structure that can be potentially captured by satellite images with both high spatial and temporal resolution. This study explored tillage practice mapping using the daily Planet Fusion surface reflectance (PF-SR) gap-free 3 m data generated by fusing PlanetScope with Landsat-8, Sentinel-2 and MODIS surface reflectance data. The study area is a 220 × 220 km2 agricultural area in South Dakota, USA, and the study used 3285 PF-SR images from September 1, 2020 to August 31, 2021. The PF-SR images for the surveyed 433 fields were sliced into 10,747 training (70%) and evaluation (30%) non-overlapping time series patches. The training and evaluation patches were from different fields for evaluation data independence. The performance of four deep learning models (i.e., 2D convolutional neural networks (CNN), 3D CNN, CNN-Long short-term memory (LSTM), and attention CNN-LSTM) in tillage practice mapping, as well as their sensitivity to different spatial (i.e., 3 m, 24 m, and 96 m) and temporal resolutions (16-day, 8-day, 4-day, 2-day and 1-day) were examined. Classification accuracy continuously increased with increases in both temporal and spatial resolutions. The optimal models (3D CNN and attention CNN-LSTM) achieved ~77% accuracy using 2-day or daily 3 m resolution data as opposed to ~72% accuracy using 16-day 3 m resolution data or daily 24 m resolution data. This study also analyzed the feature importance of different acquisition dates for the two optimal models. The 3D CNN model feature importances were found to agree well with the tillage practice time. High feature importance was associated with observations during the fall and spring tillage period (i.e., fresh tillage signals) whereas the crop peak growing period (i.e., tillage signals weathered and confounded by dense canopy) was characterized by a relatively low feature importance. The work provides valuable insights into the utility of deep learning for tillage mapping and change event time identification based on high resolution imagery

Rethinking ‘Responsibility’ in Precision Agriculture Innovation: Lessons from an Interdisciplinary Research Team

We examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility\u27 in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations

Effectiveness of Denitrifying Bioreactors on Water Pollutant Reduction from Agricultural Areas

HighlightsDenitrifying woodchip bioreactors treat nitrate-N in a variety of applications and geographies.This review focuses on subsurface drainage bioreactors and bed-style designs (including in-ditch).Monitoring and reporting recommendations are provided to advance bioreactor science and engineering. Denitrifying bioreactors enhance the natural process of denitrification in a practical way to treat nitrate-nitrogen (N) in a variety of N-laden water matrices. The design and construction of bioreactors for treatment of subsurface drainage in the U.S. is guided by USDA-NRCS Conservation Practice Standard 605. This review consolidates the state of the science for denitrifying bioreactors using case studies from across the globe with an emphasis on full-size bioreactor nitrate-N removal and cost-effectiveness. The focus is on bed-style bioreactors (including in-ditch modifications), although there is mention of denitrifying walls, which broaden the applicability of bioreactor technology in some areas. Subsurface drainage denitrifying bioreactors have been assessed as removing 20% to 40% of annual nitrate-N loss in the Midwest, and an evaluation across the peer-reviewed literature published over the past three years showed that bioreactors around the world have been generally consistent with that (N load reduction median: 46%; mean ±SD: 40% ±26%; n = 15). Reported N removal rates were on the order of 5.1 g N m-3 d-1 (median; mean ±SD: 7.2 ±9.6 g N m-3 d-1; n = 27). Subsurface drainage bioreactor installation costs have ranged from less than $5,000 to$27,000, with estimated cost efficiencies ranging from less than $2.50 kg-1 N year-1 to roughly$20 kg-1 N year-1 (although they can be as high as $48 kg-1 N year-1). A suggested monitoring setup is described primarily for the context of conservation practitioners and watershed groups for assessing annual nitrate-N load removal performance of subsurface drainage denitrifying bioreactors. Recommended minimum reporting measures for assessing and comparing annual N removal performance include: bioreactor dimensions and installation date; fill media size, porosity, and type; nitrate-N concentrations and water temperatures; bioreactor flow treatment details; basic drainage system and bioreactor design characteristics; and N removal rate and efficiency

10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product

The 30 m resolution U.S. Department of Agriculture (USDA) crop data layer (CDL) is a widely used crop type map for agricultural management and assessment, environmental impact assessment, and food security. A finer resolution crop type map can potentially reduce errors related to crop area estimation, field size characterization, and precision agriculture activities that requires crop growth information at scales finer than crop field. This study is to develop a method for crop type mapping using Sentinel-2 10 m bands (i.e., red, green, blue, and near-infrared) and to examine the benefit of the derived 10 m crop type map. The crop type mapping was conducted for two study areas with significantly different field sizes and crop types in South Dakota and California, respectively. The Sentinel-2 10 m surface reflectance and the derived normalized difference vegetation index (NDVI) acquired in the 2019 growing season were used to generate monthly median composites as classification input. The training and evaluation samples were derived from CDL by (i) finding good quality 30 m CDL pixels and (ii) identifying a single representative Sentinel-2 10 m pixel time series for each 30 m good quality CDL pixel. The random forest algorithm was trained using 80% of the samples and evaluated using the 20% remaining samples, and the results showed high overall accuracies of 94% and 83% for South Dakota and California study areas, respectively. The major crops in both study areas obtained high user’s and producer’s accuracies (>87%). There is a good agreement between the class proportions in the 10 m crop type map and 30 m CDL for both study areas with R2 ≥ 0.94 and root mean square error (RMSE) ≤ 3%. More importantly, compared to the 30 m CDL, the 10 m crop type map has much less salt-pepper and crop boundary-aliasing effects and defines better the small surface features (e.g., small fields, roads, and rivers). The potential of the method for large area 10 m crop type mapping is discussed

Utility of daily 3 m Planet Fusion Surface Reflectance data for tillage practice mapping with deep learning

Tillage practices alter soil surface structure that can be potentially captured by satellite images with both high spatial and temporal resolution. This study explored tillage practice mapping using the daily Planet Fusion surface reflectance (PF-SR) gap-free 3 m data generated by fusing PlanetScope with Landsat-8, Sentinel-2 and MODIS surface reflectance data. The study area is a 220 × 220 km2 agricultural area in South Dakota, USA, and the study used 3285 PF-SR images from September 1, 2020 to August 31, 2021. The PF-SR images for the surveyed 433 fields were sliced into 10,747 training (70%) and evaluation (30%) non-overlapping time series patches. The training and evaluation patches were from different fields for evaluation data independence. The performance of four deep learning models (i.e., 2D convolutional neural networks (CNN), 3D CNN, CNN-Long short-term memory (LSTM), and attention CNN-LSTM) in tillage practice mapping, as well as their sensitivity to different spatial (i.e., 3 m, 24 m, and 96 m) and temporal resolutions (16-day, 8-day, 4-day, 2-day and 1-day) were examined. Classification accuracy continuously increased with increases in both temporal and spatial resolutions. The optimal models (3D CNN and attention CNN-LSTM) achieved ∼77% accuracy using 2-day or daily 3 m resolution data as opposed to ∼72% accuracy using 16-day 3 m resolution data or daily 24 m resolution data. This study also analyzed the feature importance of different acquisition dates for the two optimal models. The 3D CNN model feature importances were found to agree well with the tillage practice time. High feature importance was associated with observations during the fall and spring tillage period (i.e., fresh tillage signals) whereas the crop peak growing period (i.e., tillage signals weathered and confounded by dense canopy) was characterized by a relatively low feature importance. The work provides valuable insights into the utility of deep learning for tillage mapping and change event time identification based on high resolution imagery

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

ABSTRACTWe examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility' in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations