Scholarly Works, School of Plant and Environmental Sciences
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- Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine LearningVahidi, Milad; Shafian, Sanaz; Frame, William Hunter (MDPI, 2025-01-28)Accurately estimating soil moisture at multiple depths is essential for sustainable farming practices, as it supports efficient irrigation management, optimizes crop yields, and conserves water resources. This study integrates a drone-mounted hyperspectral sensor with machine learning techniques to enhance soil moisture estimation at 10 cm and 30 cm depths in a cornfield. The primary aim was to understand the relationship between root zone water content and canopy reflectance, pinpoint the depths where this relationship is most significant, identify the most informative wavelengths, and train a machine learning model using those wavelengths to estimate soil moisture. Our results demonstrate that PCA effectively detected critical variables for soil moisture estimation, with the ANN model outperforming other machine learning algorithms, including Random Forest (RF), Support Vector Regression (SVR), and Gradient Boosting (XGBoost). Model comparisons between irrigated and non-irrigated treatments showed that soil moisture in non-irrigated plots could be estimated with greater accuracy across various dates. This finding indicates that plants experiencing high water stress exhibit more significant spectral variability in their canopy, enhancing the correlation with soil moisture in the root zone. Moreover, over the growing season, when corn exhibits high chlorophyll content and increased resilience to environmental stressors, the correlation between canopy spectrum and root zone soil moisture weakens. Error analysis revealed the lowest relative estimation errors in non-irrigated plots at a 30 cm depth, aligning with periods of elevated water stress at shallower levels, which drove deeper root growth and strengthened the canopy reflectance relationship. This correlation corresponded to lower RMSE values, highlighting improved model accuracy.
- Effects of Sericea Lespedeza Supplementation on Steers Grazing Wild-Type Endophyte-Infected Tall FescuePoudel, Sanjok; Pent, Gabriel J.; Fike, John H.; Zeller, Wayne E.; Davis, Brittany E. (MDPI, 2025-01-28)Condensed tannins (CTs) in certain leguminous forages can mitigate toxic alkaloid absorption linked to fescue toxicosis due to their high affinity towards various steroidal and protein-like alkaloids. However, their use as feed supplements remains underexplored. This study evaluated the impact of CT-rich sericea lespedeza (Lespedeza cuneata) pellets on the post-ingestive effects of fescue toxicosis. Twelve steers on wild-type endophyte-infected tall fescue pastures received either sericea lespedeza pellets (LES) or LES with polyethylene glycol (LPEG; negative control) for 12 weeks over three consecutive summers. Body weight, hair coat scores, temperatures (rectal and extremity), cortisol levels, and caudal artery lumen area were measured every four weeks. Steers fed LES showed trends toward higher ADG (p = 0.0999) and reduced hair retention (p = 0.0547) compared to those fed LPEG. Steers on LES also showed hotter tail skin temperatures (p = 0.0053) and cooler rectal temperatures (p < 0.0001) compared to those fed LPEG. LES-fed steers had a 21% larger caudal artery lumen area (p < 0.01), suggesting reduced vasoconstriction. Additionally, LES-fed steers tended to have lower hair cortisol (p = 0.0746), indicating reduced chronic stress. These results suggest that supplementation with CTs may alleviate the post-ingestive effects of fescue toxicosis, potentially by improving blood flow and reducing stress. However, further research is needed to determine whether CTs directly reduce alkaloid absorption, as well as to validate the long-term efficacy of CT supplementation.
- Treatment of cattle with ivermectin and its effect on dung degradation and larval abundance in a tropical savanna settingRuhinda, Miriam; Xia, Kang; Rist, Cassidy; Shija, Gerald; Lyimo, Issa N.; Meza, Felician; Brewster, Carlyle; Chaccour, Carlos; Rabinovich, N. Regina; Schuerch, Roger (Elsevier, 2024-12-12)When ingested as part of a blood meal, the antiparasitic drug ivermectin kills mosquitoes, making it a candidate for mass drug administration (MDA) in humans and livestock to reduce malaria transmission. When administered to livestock, most ivermectin is excreted unmetabolized in the dung within 5 days post administration. Presence of ivermectin, has been shown to adversely affect dung colonizers and dung degradation in temperate settings; however, those findings may not apply to, tropical environment, where ivermectin MDA against malaria would occur. Here we report results of a randomized field experiment conducted with dung from ivermectin-treated and control cattle to determine the effect of ivermectin on dung degradation in tropical Tanzania. For intact pats, we measured termite colonization, larval numbers and pat wet and dry weights. Pat organic matter was interpolated from a subsample of the pat (10 g wet weight). Additionally, we counted larvae growing in the treated and untreated pats in a semi-field setting. We found that termites colonized ivermectin pats more readily than controls. Despite this, wet weight decreased significantly slower in the ivermectin-treated pats in the first two weeks. As water was lost, sub-sample dry weight increased, and organic matter decreased similarly over time for the treatment and control. Interpolated for whole pats, total organic matter was higher, and larval counts were lower in the ivermectin-treated pats after the first month. Our results demonstrate an effect of ivermectin and its metabolites on dung degradation and fauna in a tropical savanna setting. Because slow dung degradation and low insect abundance negatively impact pastureland, these non-target, environmental effects must be further investigated within the context of real-world implementation of ivermectin MDA in cattle and weighed against the potential benefits for malaria control.
- Evaluation of Insecticides to Control Stink Bug and Broad Headed Bug in Edamame, 2021Sutton, Kemper L.; Wilczek, Daniel; Kuhar, Thomas P.; McIntyre, Kelly; Rideout, Steven L.; Zhang, Bo (Oxford University Press, 2022-06-23)
- UAV‐Based Prediction of Sugarbeet Yield and QualityWalsh, Olga S. (Wiley, 2023-03-07)In Idaho, nitrogen (N) and water management are two major factors affecting sugarbeet yield and quality. If sugarbeet crop yield potential can be accurately predicted early in the growing season, then the crop can be topdressed based on crop nutrient requirements. This approach has the potential to improve the economic returns to sugarbeet growers and processors. The objective of this study was to assess the feasibility of predicting root yield and estimated recoverable sugar using UAV‐based normalized difference vegetation index (NDVI) in sugarbeets grown under varied N and water rates.
- Teff: Food for the FutureLamichhane, Ritika; Walsh, Olga S. (Wiley, 2023-03-06)Teff (Eragrostis tef) is a native Ethiopian grain crop that is gaining popularity in the United States as a nutritious grain and a high quality forage crop. It is a reliable low‐risk crop that is relatively resistant to biotic and abiotic stresses and can be grown under moisture stress or waterlogged conditions. While it has a lot of potential, more research and education on teff is needed, particularly in the area of breeding and in the cropʼs nutrient and water requirements.
- Drones for Fruit ProducersNambi, Eva; Walsh, Olga S.; Ansah, Emmanuella Owusu; Lamichhane, Ritika (Wiley, 2022-07-12)Fruit orchards require site‐specific or even individual‐tree‐specific management throughout the growing season. Remote sensing via drones, or unmanned aerial vehicles (UAVs), is becoming more common among growers and is useful for real‐time crop monitoring, weed detection, tree classification, water stress assessment, disease detection, yield and fruit quality estimation, and various pest and nutrient management strategies.
- Impact of 2021 Drought in the Pacific NorthwestAnsah, Emmanuella Owusu; Walsh, Olga S. (Wiley, 2021-11-03)Drought causes tremendous losses to agriculture and impacts water supply, energy production, and public health. This year’s drought in Idaho is extreme due to a very dry spring followed by an extreme, prolonged summer heat wave. This article will provide an overview of the current drought in the Pacific Northwest, how crops have been impacted, and look at ways farmers can adapt.
- Varietal Response of Wheat and Barley to NitrogenWalsh, Olga S.; Spackman, J. A.; Adjesiwor, A. T.; Lamichhane, R.; Ansah, E. Owusu (Wiley, 2021-09-03)
- Recap of the 2021 Western Society of Crop Science Virtual MeetingWalsh, Olga S.; Islam, Anowarul; Kesoju, Sandya; Marsalis, Mark; Ghimire, Rajan (Wiley, 2021-08)
- Monitoring Wind and Particle Concentrations Near Freshwater and Marine Harmful Algal Blooms (HABs)Bilyeu, Landon; Gonzalez-Rocha, Javier; Hanlon, Regina; AlAmiri, Noora; Foroutan, Hosein; Alading, Kun; Ross, Shane D.; Schmale, David G. III (Royal Society of Chemistry, 2023-10-05)Harmful algal blooms (HABs) are a threat to aquatic ecosystems worldwide. New information is needed about the environmental conditions associated with the aerosolization and transport of HAB cells and their associated toxins. This information is critical to help inform our understanding of potential exposures. We used a ground-based sensor package to monitor weather, measure airborne particles, and collect air samples on the shore of a freshwater HAB (bloom of predominantly Rhaphidiopsis, Lake Anna, Virginia) and a marine HAB (bloom of Karenia brevis, Gulf Coast, Florida). Each sensor package contained a sonic anemometer, impinger, and optical particle counter. A drone was used to measure vertical profiles of windspeed and wind direction at the shore and above the freshwater HAB. At the Florida sites, airborne particle number concentrations (cm−3) increased throughout the day and the wind direction (offshore versus onshore) was strongly associated with these particle number concentrations (cm−3). Offshore wind sources had particle number concentrations (cm−3) 3 to 4 times higher than those of onshore wind sources. A predictive model, trained on a random set of weather and particle number concentrations (cm−3) collected over the same time period, was able to predict airborne particle number concentrations (cm−3) with an R squared value of 0.581 for the freshwater HAB in Virginia and an R squared value of 0.804 for the marine HAB in Florida. The drone-based vertical profiles of the wind velocity showed differences in wind speed and direction at different altitudes, highlighting the need for wind measurements at multiple heights to capture environmental conditions driving the atmospheric transport of aerosolized HAB toxins. A surface flux equation was used to determine the rate of aerosol production at the beach sites based on the measured particle number concentrations (cm−3) and weather conditions. Additional work is needed to better understand the short-term fate and transport of aerosolized cyanobacterial cells and toxins and how this is influenced by local weather conditions.
- Wheat Yield and Protein Estimation with Handheld and Unmanned Aerial Vehicle-Mounted SensorsWalsh, Olga S.; Marshall, Juliet M.; Nambi, Eva; Jackson, Chad A.; Ansah, Emmanuella Owusu; Lamichhane, Ritika; McClintick-Chess, Jordan; Bautista, Francisco (MDPI, 2023-01-10)Accurate sensor-based prediction of crop yield and grain quality in-season would enable growers to adjust nitrogen (N) fertilizer management for optimized production. This study assessed the feasibility (and compared the accuracy) of wheat (Triticum aestivum L.) yield, grain N uptake, and protein content prediction with in-season crop spectral reflectance measurements (Normalized Difference Vegetative Index, NDVI) obtained with a handheld GreenSeeker (GS) sensor and an Unmanned Aerial Vehicle (UAV)-mounted sensor. A strong positive correlation was observed between GS NDVI and UAV NDVI at Feekes 5 (R2 = 0.78) and Feekes 10 (R2 = 0.70). At Feekes 5, GS NDVI and UAV NDVI explained 42% and 43% of wheat yield, respectively. The correlation was weaker at Feekes 10 (R2 of 0.34 and 0.25 for GS NDVI and UAV NDVI, respectively). The accuracy of wheat grain N uptake prediction was comparable to that of yield: the R2 values for GS NDVI and UAV NDVI were 0.53 and 0.37 at Feekes 5 and 0.13 and 0.20 at Feekes 10. We found that neither GS NDVI nor UAV NDVI in-season data were useful in prediction of wheat grain protein content. In conclusion, wheat yield and grain N uptake can be estimated at Feekes 5 using either handheld or aerial based NDVI with comparable accuracy.
- Virginia Small Grain Official Variety Trials, 2024Walsh, Olga S.; Santantonio, Nicholas; Bishop, Caleb; Khulal, Aarati; Kumari, Sheetal; Rathore, Jitender; Wright, Matthew (2024-12-11)
- Virginia Corn Hybrid Trials in 2023Walsh, Olga S.; Bishop, Caleb; Santantonio, Nicholas; Khulal, Aarati; Kumari, Sheetal; Rathore, Jitender (2024-10-09)
- Understanding the Mineral Nutrient Value of Wheat ResidueAdams, Curtis B.; Rogers, Christopher W.; Marshall, Juliet M.; Hatzenbuehler, Patrick; Walsh, Olga S.; Thurgood, Garrett; Dari, Biswanath; Loomis, Grant; Tarkalson, David (Wiley, 2024-10-25)There is a substantial pool of mineral nutrients contained in wheat residue, concentrated in K, which has substantial economic value. Given this value, it is important for wheat producers to weigh the relative benefits of residue harvest, which gives immediate but marginal revenue gains, and residue retention, which has multifaceted benefits that include substantial savings on future nutrient costs. Persistent removal of nutrients from agronomic systems through residue harvest affects soil nutrient availability in the short‐ and long‐term, and the timing and magnitude of these changes will depend on the cropping system and soil. Earn 1 CEU in Nutrient Management by reading the article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.
- Characterization of key aroma compounds in microgreens and mature plants of hydroponic fennel (Foeniculum vulgare Mill.).Liu, Jingsi; Li, Song; O'Keefe, Sean F.; Hurley, Ken; Rutto, Laban; Eriksen, Renee; Yin, Yun (Elsevier, 2024-10-23)Fennel is a popular culinary herb known for its unique flavor. In this study, we identified key aroma-active compounds in fresh fennel (Foeniculum vulgare Mill.) and its microgreens. Fennel was cultivated hydroponically with soilless substrates. Upon harvesting, samples were ground with liquid nitrogen, and headspace solid phase microextraction (SPME) gas chromatography-mass-spectrometry-olfactometry (GC-MS-O) was used for volatile analysis. Thirty-two and 28 key aroma-active compounds were identified in fennel microgreens and mature leaves, respectively. Phenylpropenes, especially (E)-anethole, were the predominant aroma-active compound in all samples (36.3–83.4 %). Quantitation results showed that fennel microgreens contained a significantly higher amount of monoterpenes, showing an 81.4–98.1 % increase when compared to mature fennel. Principal component analysis (PCA) of identified volatiles indicated a distinctive difference in the overall aroma profile between microgreens and mature leaf. The changes in aroma contents over different growth stages revealed the underlying volatile biosynthesis discrepancy. This study provided baseline information for understanding the aroma evolution from microgreen to mature fennel herbs.
- Depth-specific soil moisture estimation in vegetated corn fields using a canopy-informed model: A fusion of RGB-thermal drone data and machine learningVahidi, Milad; Shafian, Sanaz; Frame, William Hunter (Elsevier, 2025-02-01)Accurate soil moisture estimation is fundamental for optimizing irrigation strategies, enhancing crop yields, and managing water resources efficiently. This study harnesses time-series RGB-thermal imagery to assess soil moisture throughout various growth stages of corn, emphasizing depth-specific soil moisture estimation and time-series analysis of canopy information such as canopy structure and canopy spectral across growth stages. By integrating a comprehensive dataset that covers the full spectrum of the growing season from early to late stages. we evaluated soil moisture at multiple depths including 10, 20, 30, and 40 cm. Sophisticated regression models such as Gradient Boosting Machines (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), and Support Vector Machines (SVM) were employed to analyze the effects of spectral indices, land surface temperature (LST), and structural canopy variables on soil moisture estimation accuracy. Our results reveal that thermal variables, particularly LST, exhibit significant correlations with soil moisture at shallower depths, especially in non-irrigated plots where moisture variability tends to be greater. The GBM model performed exceptionally well, achieving a coefficient of determination (R²) of 0.79 and a root mean square error (RMSE) of 1.86 % at a depth of 10 cm, showcasing its precision in moisture prediction. At a depth of 30 cm, the GBM model still demonstrated robust performance with an R² of 0.69 and an RMSE of 3.38 %, adapting effectively to different canopy densities and soil conditions. As canopy density increased, the effectiveness of LST in predicting soil moisture decreased, underscoring the dynamic interaction between plant growth stages and moisture estimation accuracy.
- Integrating RS data with fuzzy decision systems for innovative crop water needs assessmentSadat Hashemi, F.; Javad Valadan Zoej, M.; Youssefi, F.; Li, H.; Shafian, Sanaz; Farnaghi, M.; Pirasteh, S. (Elsevier, 2025-02-01)Irrigation is a critical component of global water usage, accounting for approximately 70 % of water consumption. As the world's population continues to grow, the demand for food will increase, making it essential to improve irrigation management by reducing water waste and increasing efficiency. This study aims to develop and validate a fuzzy decision-making system that determines crop irrigation needs based on parameters that affect plant water requirements. These parameters can be monitored using Remote sensing (RS) satellites, enabling large-scale agricultural irrigation monitoring. The study utilized Landsat-8 satellite data and meteorological data. It also employed a fuzzy decision system with inputs of estimated evapotranspiration, Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Land Surface Temperature (LST), Crop Water Stress Index (CWSI), Stress Index (SI), and Soil Moisture (SM). The output of the fuzzy model is a map that effectively determines the irrigation requirements for agricultural land relatively. The system was tested on six Landsat images of winter wheat crops in Tehran University's agricultural fields. The estimated evapotranspiration was compared to Reference Evapotranspiration (ETr) obtained from the FAO-Penman-Monteith equation, resulting in a root mean square error of 0.33 mm. The fuzzy decision system was evaluated by comparing its results with Vegetation Water Content (VWC) measurements during satellite overpass time. The NDVI, CWSI, SI, and SM variables had the highest R2 with VWC data (0.71––0.92) on all six dates. This approach has significant implications for improving irrigation management practices, reducing water waste, and increasing crop yields, which can contribute to global food security. The study highlights the potential of RS technology and fuzzy decision-making systems in promoting sustainable agriculture.
- Implications of germination tolerances on invasion potential of Arthraxon hispidusBeall, Michael C.; Barney, Jacob N.; Welbaum, Gregory E.; Reid, J. Leighton (Public Library of Science, 2024-06-04)Arthraxon hispidus is an introduced, rapidly spreading, and newly invasive grass in the eastern United States, yet little is known about the foundational biology of this aggressive invader. Germination responses to environmental factors including salinity, pH, osmotic potential, temperature, and burial depth were investigated to better understand its germination niche. Seeds from six populations in the Mid-Atlantic US germinated 95% with an average mean time to germination of 3.42 days of imbibition in the dark at 23°C. Germination occurred across a temperature range of 8-37°C and a pH range of 5-10 (≥83%), suggesting that neither pH nor temperature will limit germination in many environments. Arthraxon hispidus germination occurred in high salinity (342 mM NaCl) and osmotic potentials as low as -0.83MPa. The NaCl concentration required to reduce germination by 50% exceeded salinity concentrations found in soil and some brackish water saltmarsh systems. While drought adversely affects A. hispidus, 50% germination occurred at osmotic potentials ranging from -0.25 to -0.67 MPa. Given the climatic conditions of North America, drought stress is unlikely to restrict germination in large regions. Finally, emergence greatly decreased with burial depth. Emergence was reduced to 45% at 1-2 cm burial depths, and 0% at 8 cm. Emergence depths in concert with adequate moisture, germination across a range of temperatures, and rapid germination suggests A. hispidus' seed bank may be short-lived in moist environments, but further investigation is warranted. Given the broad abiotic tolerances of A. hispidus and a widespread native range, A. hispidus has the potential to germinate in novel territories beyond its currently observed invaded range.