Sean A. Woznicki’s research while affiliated with Valley City State University and other places

Information on crop harvest events has become valuable input for models related to food security and agricultural management and optimization. Precise large scale harvest detection depends on temporal resolution and satellite images availability. Synthetic Aperture Radar (SAR) data are more suitable than optical, since the images are not affected by clouds. This study compares two methods for harvest detection of soybean in Vojvodina province (Serbia), using the C-band of Sentinel-1. The first method represents a maximum difference of ascending VH polarization backscatter (σVH) between consecutive dates of observation. The second method uses a Radar Vegetation Index (RVI) threshold value of 0.39, optimized to minimize Mean Absolute Error (MAE). The training data consisted of 50 m point buffers’ mean value with ground-truth harvest dates (n=100) from the 2018 and 2019 growing seasons. The first method showed better performance with Pearson correlation coefficient r=0.85 and MAE=5 days, whereas the calculated metrics for the RVI threshold method were r=0.69 and MAE=8 days. Therefore, validation was performed only for the method of maximum VH backscatter difference where mean values of parcels with ground-truth harvest dates for 2020 had generated the validation dataset (n=67). Performance metrics (r=0.83 and MAE=3 days) confirmed the suitability for accurate harvest detection. Ultimately, a soybean harvest map was generated on a parcel level for Vojvodina province.

Nitellopsis obtusa was first documented in the St. Lawrence River in 1974 and likely spread via human-assisted activity to at-least seven states in the U.S.A. This invasive macroalga is a nuisance for native plants, animals, and recreational activities. Because eradication of invasive species is more difficult after establishment, early detection plans are an important tool in preventing and slowing their spread. Macro-scale data analyses have improved our ability to predict changes in freshwater ecosystems and are important to assess and control invasive species. We developed species distribution models (random forest, boosted regression trees, and Maxent) using presence records of N. obtusa coupled with publicly available in-lake temperature and chemistry (bicarbonate and chloride) model data and landscape scale lake watershed characteristics for over 48,000 individual lakes in the Midwest and northeast U.S.A. January and July–August–September growing degrees days, bicarbonate concentrations, and chloride concentrations correlate with high relative likelihood of occurrence. Our analyses found N. obtusa likelihood of occurrence is high in developed, lake-dense regions with ~ 2000 July–August–September growing degree days, 1–3 mMol bicarbonate, and > 10 mg L⁻¹ chloride. Based on relative likelihood of occurrence predictions, N. obtusa has the potential to spread to new lakes within Midwest and northeast USA states that currently do not have known populations of N. obtusa, including inland lakes in Illinois, Iowa, Ohio, and Pennsylvania.

Climate change has significant implications for irrigated agriculture and global food security. Understanding how altered precipitation patterns and magnitudes, coupled with rising growing season temperatures, affect irrigation demand and crop production is a prerequisite for formulating effective water resources management strategies. This study evaluated the effects of near-term climate change (centered on 2035) on irrigation demand, green water scarcity, and row crop yields in a major agricultural watershed in southern New Jersey, USA. Downscaled precipitation and temperature from six General Circulation Models (GCMs) for two representative concentration pathways (RCP-4.5 and 8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were used to drive the Soil and Water Assessment Tool hydrological model. Temperature and precipitation increases resulted in greater surface runoff, lateral flow, groundwater recharge, and total streamflow. Seasonal ET for corn is projected to alter between −3.0 % to 0.5 %, with irrigation demand between −17 % to −1 %, and yield ranges between −4 % to +9 % depending on the GCMs in the RCP-4.5 scenario, with similar patterns projected by RCP-8.5 scenario. For soybean, the simulation also indicates a declining trend of ET and irrigation demand while increasing yield. Increasing yield for both crops is attributed to changes in agronomic management practices combined with genetically improved cultivars and higher soil fertility due to CO2 fertilization. Green water scarcity analysis under future climate change for corn and soybean display a decreased soil moisture stress due to increased water use efficiency resulting from reduced stomatal conductance under elevated CO2 concentration.