The following paper about the neglected spatiotemporal variations of model biases in ensemble-based climate projections has been recently accepted for publication by Geophysical Research Letters.
Song, T., G. Huang, and X. Wang. Neglected Spatiotemporal Variations of Model Biases in Ensemble-Based Climate Projections. Geophysical Research Letters, accepted on August 4, 2022.
More details will come soon once the paper is published.
Title: Field evaluation of a deep learning-based smart variable-rate sprayer for targeted application of agrochemicals
Journal: Smart Agricultural Technology
Abstract: The field performance of a newly developed novel smart variable-rate sprayer was evaluated. The sprayer uses convolutional neural networks (CNNs) for target detection and spot-applications of agrochemicals within potato (Solanum tuberosum L.) fields attacked by lamb’s quarters (Chenopodium album L.) and corn spurry (Spergula arvensis L.) weeds and the early blight potato disease caused by Alternaria solani Sorauer. There was a non-significant effect of treatment conditions (i.e., cloudy, partly cloudy, and sunny) on spray volume during weed and diseased plant detection experiments (p-value = 0.93 and 0.75, respectively) showing that the smart sprayer performed well during all treatment conditions. There was a significant effect of spraying application techniques on the use of spray volume (p-value ≤ 0.05) reflecting a significant saving of spraying liquid during variable-rate application (VA). On average, the sprayer reduced spray volume by 47 and 51% for weeds and diseased plant detection experiments as compared to the values of chemicals applied at constant-rate application (CA), respectively, under all treatment conditions. The analysis of water-sensitive papers (WSP) data resulted in non-significant differences between CA and VA under all field conditions. These results suggest that this sprayer has a great potential to get a suitable spot application of agrochemicals and reduce the use of plant protection products thereby ensuring farm profits and environmental stewardship.
Title: Mitigation of Greenhouse Gas Emissions from Agricultural Fields through Bioresource Management
Abstract: Efficient bioresource management can alter soil biochemistry and soil physical properties, leading to reduced greenhouse gas (GHG) emissions from agricultural fields. The objective of this study was to evaluate the role of organic amendments including biodigestate (BD), biochar (BC), and their combinations with inorganic fertilizer (IF) in increasing carbon sequestration potential and mitigation of GHG emissions from potato (Solanum tuberosum) fields. Six soil amendments including BD, BC, IF, and their combinations BDIF and BCIF, and control (C) were replicated four times under a completely randomized block design during the 2021 growing season of potatoes in Prince Edward Island, Canada. An LI-COR gas analyzer was used to monitor emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from treatment plots. Analysis of variance (ANOVA) results depicted higher soil moisture-holding capacities in plots at relatively lower elevations and comparatively lesser volumetric moisture content in plots at higher elevations. Soil moisture was also impacted by soil temperature and rainfall events. There was a significant effect of events of data collection, i.e., the length of the growing season (p-value ≤ 0.05) on soil surface temperature, leading to increased GHG emissions during the summer months. ANOVA results also revealed that BD, BC, and BCIF significantly (p-value ≤ 0.05) sequestered more soil organic carbon than other treatments. The six experimental treatments and twelve data collection events had significant effects (p-value ≤ 0.05) on the emission of CO2. However, the BD plots had the least emissions of CO2 followed by BC plots, and the emissions increased with an increase in atmospheric/soil temperature. Results concluded that organic fertilizers and their combinations with inorganic fertilizers help to reduce the emissions from the agricultural soils and enhance environmental sustainability.
Title: Flood Management, Characterization and Vulnerability Analysis Using an Integrated RS-GIS and 2D Hydrodynamic Modelling Approach: The Case of Deg Nullah, Pakistan
Journal: Remote Sensing
Abstract: One-dimensional (1D) hydraulic models have been extensively used to conduct flood simulations for investigating flood depth and extent maps. However, the 1D models cannot simulate many other flood characteristics, such as flood velocity, duration, arrival time and recession time when the flow is not restricted within the channel. These flood characteristics cannot be disregarded as they play an important role in developing flood mitigation and evacuation strategies. This study formulates a two-dimensional (2D) hydrodynamic model combined with remote sensing (RS) and geographic information system (GIS) approach to generate additional flood characteristic maps that cannot be produced with 1D models. The model was applied to a transboundary river of Deg Nullah in Pakistan to simulate an extreme flood event experience in 2014. The flood extent images from the moderate resolution imaging spectroradiometer (MODIS) and observed flood extents were used to evaluate the model performance. Moreover, an entropy distance-based approach was proposed to facilitate the integrated multivariate flood vulnerability classification. The simulated 2D flood modeling results showed a good agreement with the flood extents registered by MODIS and the observed ones. The northwest parts of Deg Nullah near Seowal, Dullam Kahalwan and Zafarwal were the most vulnerable areas due to high flood depths and prolonged flooding duration. Whereas high flood velocities, short flood arrival time, prolonged flood duration and recession times were observed in the upper reach of Deg Nullah thereby making it the most susceptible, critical and vulnerable region to flooding events.
The following paper about an integrated flood modeling approach has been recently accepted for publication by Remote Sensing.
Ahmad, I., X. Wang, M. Waseem, M. Zaman, F. Aziz, R. Z. N. Khan, and M. Ashraf. Flood Management, Characterization and Vulnerability Analysis using an
Integrated RS-GIS and 2D Hydrodynamic Modelling Approach. Remote Sensing, accepted on April 26, 2022.
More details will come soon once the paper is published.