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Keywords = Waseca

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16 pages, 523 KiB  
Article
Soil Enzyme Activity Behavior after Urea Nitrogen Application
by Benjamin Davies, Jeffrey A. Coulter and Paulo H. Pagliari
Plants 2022, 11(17), 2247; https://doi.org/10.3390/plants11172247 - 29 Aug 2022
Cited by 19 | Viewed by 3438
Abstract
Understanding how fertilizer application (particularly N, the most used chemical fertilizer worldwide) interacts with soil microbes is important for the development of best management practices that target improved microbial activity to enhance sustainable food production. This study was conducted to determine whether urea [...] Read more.
Understanding how fertilizer application (particularly N, the most used chemical fertilizer worldwide) interacts with soil microbes is important for the development of best management practices that target improved microbial activity to enhance sustainable food production. This study was conducted to determine whether urea N rate and time of application to maize (Zea mays) influenced soil enzyme activity. Enzyme activity was determined by monitoring fluorescein diacetate (FDA) hydrolysis, ß-glucosidase, acid-phosphomonoesterase, and arylsulfatase activities. Experiments were conducted from 2014 through 2016 to compare single (fall or spring applications) and split applications of N at varying N rates under irrigation (Becker) and rainfed conditions (Lamberton and Waseca) in MN, USA. Nitrogen rates varied by location and were based on University of Minnesota guidelines. Soil samples were collected seven times each season. Nitrogen application split into two applications increased FDA activity by 10% compared with fall and spring applied N at Waseca. Fall or spring N application decreased arylsulfatase activity by 19% at Becker and by between 13% and 16% at Lamberton. ß-Glucosidase and acid-phosphomonoesterase activities were unaffected by N application. Sampling time and year had the greatest impact on enzyme activity, but the results varied by location. A negative linear relationship occurred between FDA and ß-glucosidase activity at all three sites. In summary, urea N application had small effects on enzyme activity at the sites studied, suggesting that some form of organic N could be more important than the ammonium provided by urea. Full article
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17 pages, 4845 KiB  
Article
Drainage Ditch Berm Delineation Using Lidar Data: A Case Study of Waseca County, Minnesota
by Jonathan Graves, Rama Mohapatra and Nicholas Flatgard
Sustainability 2020, 12(22), 9600; https://doi.org/10.3390/su12229600 - 18 Nov 2020
Cited by 3 | Viewed by 2940
Abstract
Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the [...] Read more.
Within a drainage system, drainage ditches are designed to improve existing natural drainage. Although drainage ditches are mostly engineered, they can also be part of natural watercourses. For environmental sustainability, in many places there are guidelines to establish vegetative buffer strips along the boundary of drainage ditches. In this landscape planning study, a geospatial modeling framework was established to identify these drainage system landforms and the boundary that separates these landforms from their surrounding areas across Waseca County in south-central Minnesota. By employing almost 2000 GPS spot elevation measurements from five ditch systems and one-meter Light Detection and Ranging (LiDAR) derived digital elevation model (DEM) data, the drainage ditch berm polygons were delineated. Eight low light angle hillshade rasters at 45-degree azimuth intervals were used to construct the model. These hillshade rasters were combined to form a composite raster so that the effect of multiple azimuths can be captured during ditch berm delineation. The GPS points identified as the top of the berm were used to extract cell values from the combined hillshade. These cell values were modeled further using statistical distribution graphs. The statistical model derived +0.5 and +1 standard deviation values (cell values 812 and 827, respectively) of the combined hillshade raster were utilized to obtain complete berm polygons. In this semi-automated method, between 67.30% to 79.80% of ditch berm lengths were mapped with an average error that is less than the resolution of the DEM. Demarcation of these boundaries are important for local governments in Minnesota and throughout the world, as it could help guide land–water management and aid sustainable agriculture. Full article
(This article belongs to the Special Issue Landscape Planning for Sustainability)
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18 pages, 4591 KiB  
Article
Improving Site-Specific Maize Yield Estimation by Integrating Satellite Multispectral Data into a Crop Model
by Vijaya R. Joshi, Kelly R. Thorp, Jeffrey A. Coulter, Gregg A. Johnson, Paul M. Porter, Jeffrey S. Strock and Axel Garcia y Garcia
Agronomy 2019, 9(11), 719; https://doi.org/10.3390/agronomy9110719 - 6 Nov 2019
Cited by 20 | Viewed by 4631
Abstract
Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a [...] Read more.
Integrating remote sensing data into crop models offers opportunities for improved crop yield estimation. To compare site-specific yield estimation accuracy of a stand-alone crop model with a data-integration approach, a study was conducted in 2016–2017 with nitrogen (N)-fertilized and unfertilized treatments across a heterogeneous 7-ha maize field. For each treatment, yield data were grouped into five classes resulting in 109 spatial zones. In each zone, the Crop Environment Resource Synthesis (CERES)-Maize model was run using the GeoSim plugin within Quantum GIS. In the data integration approach, maize biomass values estimated using satellite imagery at the five (V5) and ten (V10) leaf collar stages were used to optimize the total soil nitrogen concentration (SLNI) and soil fertility factor (SLPF) in CERES-Maize. Without integration, maize yield was simulated with root mean square error (RMSE) of 1264 kg ha−1. Optimization of SLNI improved yield simulations at both V5 and V10. However, better simulations were obtained from optimization at V10 (RMSE 1026 kg ha−1) as compared to V5 (RMSE 1158 kg ha−1). Optimization of SLPF together with SLNI did not further improve the yield simulations. This study shows that integrating remote sensing data into a crop model can improve site-specific maize yield estimations as compared to the stand-alone crop modeling approach. Full article
(This article belongs to the Special Issue Precision Agriculture)
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