Publications

1.    Noa-Yarasca, E.*, J.M. Osorio Leyton, K. Adhikari, C. B. Hajda, and D.R. Smith. (2025) Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? AI (6) 58. https://doi.org/10.3390/ai6030058

The article contributes to the understanding of spatial ecology within agricultural systems by highlighting the critical role of spatial neighborhood information in enhancing corn yield prediction models. This research underscores the importance of integrating advanced geospatial techniques and consideration of spatial dependencies, promoting effective land management and sustainable agricultural practices in the face of environmental challenges. (Impact Factor: 3.1 – Citations: 0)

2.    Noa-Yarasca, E.*, J.M. Osorio Leyton, M. White, J. Gao, and J. Arnold. (2025) Enhancing Hydrological Modeling with the Soil and Water Assessment Tool (SWAT+) Using the High-Resolution POLARIS Soil Dataset. Water (17) 670. https://doi.org/10.3390/w17050670

The study provides an advanced, reliable soil data alternative that enhances hydrological and water quality modeling. This integration of improved soil data supports effective land management and resilience in agricultural landscapes, aligning with the program's goals of promoting sustainability and addressing environmental challenges across various scales and systems. (Impact Factor: 3.0 – Citations: 0)

3.    Peters, K., J. Kiese, I. Oswald, B. Guse, E. Noa-Yarasca, J. Arnold, J. M. Osorio Leyton, K. Bieger, and N Fohrer. (2024) The integration of hydrological and heat exchange processes improves stream temperature simulations in an ecohydrological model. Hydrological Processes.

The study provides insights into the complex interplay of hydrological and thermal processes in agricultural landscapes, particularly in relation to stream temperature dynamics. Its findings on improved modeling can inform sustainable land management practices in agricultural systems, enhancing ecological resilience and promoting effective decision-making amid environmental challenges like climate change. (Impact Factor: 2.8 – Citations: 0)

4.    Rengifo, H.*, Haro, R., Espinosa Marín, J., Bastidas, W., Ramirez Avila, J., Osorio Leyton. (2024). Validación del método de Número de Curva en tres microcuencas aportantes del Río Pisque en Ecuador. GeoFocus, Revista Internacional de Ciencia y Tecnología de la Información Geográfica (Artigos), 34, 61-85. http://dx.doi.org/10.21138/GF.848

The article contributes to the understanding of how localized validation of hydrological models, such as the Curve Number method, can enhance effective land management and promote sustainability in agricultural systems by aligning hydrological assessments with local ecological conditions. This alignment supports the overarching goal of improving resilience to environmental challenges while facilitating informed decision-making for sustainable resource management at both local and regional scales. (Impact Factor: 1.8 – Citations: 0)

5.    Fiusa de Morais, L., J.M. Osorio Leyton, A.C. Rodrigues Cavalcante, C.A. Gomes Costa, R.G. da Silva; V.H. Macedo, S. Rocha. (2024) Modeling techniques for simulating total forage biomass in rangelands of the Caatinga Biome, Brazil, Scientia Agricola (81): 1-12

The article contributes to the understanding of spatial ecology within agricultural systems by demonstrating the effectiveness of the PHYGROW model as a reliable tool for simulating total forage biomass in the Caatinga rangelands, which supports sustainable land management practices and enhances resilience to environmental challenges in semi-arid regions. This research highlights the significance of integrating advanced modeling techniques with ecological processes to optimize biomass estimation and promote effective resource management strategies. (Impact Factor: 1.8 – Citations: 0)

6.    Sarkar, S.*, J.M. Osorio Leyton, E. Noa-Yarasca, K. Adhikari, C. B. Hajda, and D.R. Smith. (2024) Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US. Sensors 25, 543. https: doi.org/10.3390/s25020543.

The article demonstrates how integrating advanced remote sensing data, soil features, and machine learning techniques can enhance yield predictions in agricultural systems, thus informing effective land management and promoting sustainable agricultural practices in the face of environmental challenges. This aligns with the program's goals of understanding ecological processes and improving decision-making for sustainable resource management across various scales and landscapes. (Impact Factor: 3.4 – Citations: 2)

7.    Vigabriel Navarro, L. M.*, J. M., Osorio Leyton, C. E., Quezada Lambertin, J. P. Benavides Lopez. (2024) Estimación de la biomasa del cultivo de cebada (Hordeum vulgare L.) mediante teledetección de imágenes multiespectrales. Revista de Investigación e Innovación Agropecuaria y de Recursos Naturales, 11(2), 18–29. https://doi.org/10.53287/iguo9951ru99j

The article contributes to understanding spatial ecology within agricultural systems by demonstrating the effectiveness of UAV technology and NDVI analysis in accurately estimating biomass, thereby providing actionable data that can inform land management practices and enhance the sustainability and resilience of agricultural systems in the face of environmental challenges. This integration of advanced geospatial methods supports broader research objectives on optimizing agricultural productivity and resource management. (Impact Factor: x – Citations: 0)

8.    Noa-Yarasca, E.*, J.M. Osorio Leyton, and J.P. Angerer. (2024) Extending Multi-Output Methods for Long-Term Aboveground Biomass Time Series Forecasting Using Convolutional Neural Networks. Mach. Learn. Knowl. Extr.: 6, 1633–1652. https://doi.org/10.3390/make6030079

The article presents innovative multi-output forecasting methods that enhance the accuracy of long-term aboveground biomass predictions, thereby supporting effective land management and sustainable agricultural practices. By integrating these advanced forecasting techniques, the study provides valuable insights for decision-making in rangeland and pastureland ecology, aiding in resilience to climate change and promoting sustainable resource management. (Impact Factor: 4.0 – Citations: 2)

9.    Schantz, M.C., D.R. Smith, D. Harmel, D.J. Goodwin, D.R. Tolleson, J. M. Osorio Leyton, K.C. Flynn, J. Yost, K.R. Thorp, J.G. Arnold, M.J. White, K. Adhikari, C. Hajda. (2024) The LTAR-integrated grazing land common experiment at the Texas Gulf. J Environ Qual: 26. doi: 10.1002/jeq2.20573.

The article provides data and insights on how alternative grazing management strategies can enhance ecological resilience and productivity in agricultural systems, particularly in the face of extreme weather events. It exemplifies the integration of ecological principles with data-driven approaches to inform effective land management practices that promote sustainability and adaptability in rangeland and pastureland ecosystems. (Impact Factor: 2.2 – Citations: 2)

10. Schmidt, H.E.*, J.M. Osorio Leyton, S.C. Popescu, E. Noa Yarasca, S. Sarkar, B.P. Wilcox. (2024) Connecting the Dots: How Ecohydrological Connectivity Can Support Remote Sensing and Modeling to Inform Management of Woody Plant Encroachment. Rangeland Ecology & Management. /doi.org/10.1016/j.rama.2024.05.001.

The article provides a robust framework for understanding the ecohydrological impacts of woody plant encroachment in rangelands, utilizing advanced remote sensing and modeling techniques to analyze spatial patterns and inform sustainable land management strategies. By integrating insights from ecohydrology and interdisciplinary approaches, the findings can enhance decision-making processes, promoting resilience to environmental challenges within agricultural systems. (Impact Factor: 2.4 – Citations: 2)

11. Noa-Yarasca, E.*; J.M. Osorio Leyton, J.P. Angerer. (2024) Deep Learning Model Effectiveness in Forecasting Limited - Size Aboveground Vegetation Biomass Time Series: Kenyan Grasslands Case Study. Agronomy 14, 349.  https://doi.org/10.3390/agronomy14020349.

The article provides insights into how advanced modeling techniques can be applied to predict ecological outcomes in agricultural landscapes, specifically focusing on aboveground vegetation biomass. By evaluating the performance of deep learning models against traditional statistical methods, this study supports effective land management practices and enhances resilience to environmental changes, aligning with my goals of promoting sustainable agricultural systems. (Impact Factor: 3.3 – Citations: 6)

12. Meki, M.N., J. Osorio Leyton, E.M. Steglich, J.R. Kiniry, M. Propato, M. Winchell, H. Rathjens, J.P. Angerer, and L.M. Norfleet. (2023) Plant parameterization and APEXgraze model calibration and validation for US land resource region H grazing lands. Ag. Sys. (207) 16p. https://doi.org/10.1016/j.agsy.2023.103631

The article contributes to the broader research program on spatial ecology within agricultural systems by providing a validated modeling framework through the APEXgraze model, which allows for the analysis of ecological processes and management practices specifically related to grazing lands. This research underscores the importance of data-driven approaches in understanding and enhancing sustainability, productivity, and resilience in agricultural landscapes, particularly in the face of environmental challenges such as soil erosion and water quality degradation. (Impact Factor: 6.1 – Citations: 7)

13. Kiniry, J.R.; J.G. Fernandez, F. Aziz, J. Jacot, A.S. Williams, M.N. Meki, J. Osorio Leyton, A.D. Baez-Gonzalez, M.-V. V. Johnson. (2023) Tropical Tree Crop Simulation with a Process-Based, Daily Timestep Simulation Model (ALMANAC): Description of Model Adaptation and Examples with Coffee and Cocoa Simulations. Agronomy, 13, 580. https://doi.org/10.3390/agronomy13020580

This article explores how agricultural systems interact with ecological processes. It simulates species competition and yield responses under different environmental conditions. The study also examines how agroforestry practices affect crop productivity and resilience, providing insights for sustainable land management and climate adaptation. (Impact Factor: 3.3 – Citations: 1)

14. White, M., J. Arnold, K. Bieger, P. Allen, J. Gao, N. Čerkasova, M. Gambone, S. Park, D.D. Bosch, H. Yen, J.M. Osorio. (2022) Development of a Field Scale SWAT+ Modeling Framework for the Contiguous U.S. JAWRA 58(5): 1–16. https://doi.org/10.1111/1752-1688.13056.

The article contributes to the field of spatial ecology within agricultural systems by providing a robust, data-driven framework that enables comprehensive analysis of hydrologic impacts on water quality and quantity across diverse landscapes. By integrating advanced modeling techniques and publicly available data, NAM supports sustainable land management and informs decision-making processes to enhance agricultural productivity and resilience against environmental challenges, aligning with the broader goals of ecological research in agriculture. (Impact Factor: 2.6 – Citations: 37)

15. Meki, M.N., J.M. Osorio, E.M. Steglich, J.R. Kiniry. (2022) Drought-Induced Nitrogen and Phosphorus Carryover Nutrients in Corn/Soybean Rotations in the Upper Mississippi River Basin. Sustainability 2022, 14(22), 15108; https://doi.org/10.3390/su142215108

The article contributes to advancing spatial ecology in agricultural systems by providing data-driven insights into the impacts of drought on nitrogen and phosphorus management, thereby providing effective land management strategies that enhance resource efficiency, sustainability, and resilience against environmental challenges. By emphasizing optimal nutrient applications and the use of cover crops, the findings align with the broader goals of integrating ecological processes into agricultural decision-making for improved outcomes in productivity and environmental stewardship. (Impact Factor: 3.3 – Citations: 2)

16. Quezada Lambertin, C.*, J.P. Benavides Lopez, and J.M. Osorio Leyton. (2021) Modelos de simulación de cultivos como herramienta para mejorar la producción de papa en Bolivia: Calibración y validación del modelo APEX para tres cultivares de papa producidas en la región andina boliviana. LAJED No-34 7-33, ISSN: 2074-4706. (Impact Factor: x – Citations: 1)

17. Mckenna, O., D. Mushet, K.D. Behrman, L. Doro, and J. Osorio. (2020) Development of a framework for modeling field scale conservation effects of depressional wetlands in agricultural landscapes. J. Soil Water Conserv. 75(5): 1-9. (Impact Factor: 2.2 – Citations: 7)

18. Meki, N.M., J. Kiniry, A. Williams, S. Kim, A. Worqlul, J. Osorio, J. Relly, R. Greeson. (2020) Field and Simulation-Based Assessment of Vetivergrass Bioenergy Feedstock Production Potential in Texas. Agron. J. 112(4): 2602-2707. (Impact Factor: 2.0 – Citations: 3)

19. Osorio Leyton, J.M. (2019). APEXeditor: A spreadsheet-based tool for editing APEX model input and output files. J. Software Engineering and Applications (JSEA) 12 (10): 432-446. (Impact Factor: 2.0 – Citations: 11)

20. Jeong, J., K. Wagner, J. Flores, T. Cawthon, Y. Her, J. Osorio, H. Yen. (2018) Linking watershed modeling and bacterial source tracking to better assess E. coli sources. Science of the Total Environment 648: 164-175. (Impact Factor: 8.2 – Citations: 27)

21. Zhang, L.*, T.E. Juenger, J.M. Osorio, and K.D. Behrman. 2018. Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas. Bioenerg. 11: 69-85. (Impact Factor: 3.1 – Citations: 3)

22. Worqlul, A.W.*, J. Jeong, Y.T. Dile, J. Osorio, P. Schmitter,T. Gerik, R. Srinivasan, N. Clark. (2017) Assessing potential land suitable for surface irrigation using groundwater in Ethiopia. Applied Geography: 85: 1-13. (Impact Factor: 8.0 – Citations: 199)

23. Worqlul, A.W.*, E.K. Ayana, B.H.P. Maathuis, C. MacAlister, W.D. Philpot, J.M. Osorio Leyton, T.S. Steenhuis. (2017) Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia. J. Hydrol. 556: 1182-1191. (Impact Factor: 5.9 – Citations: 73)

24. N. Clarke, J.C. Bizimana, Y. Dile, A. Worqlul, J. Osorio, B. Herbst, J.W. Richardson, R. Srinivasan, T.J. Gerik, J. Williams, C.A. Jones, J. Jeong. (2016) Evaluation of new farming technologies in Ethiopia using the Integrated Decision Support System (IDSS). Ag. Water Mng. 180-B: 267-279. (Impact Factor: 5.9 – Citations: 65)

25. M.N. Meki, J.R. Kiniry, A.H. Youkhana, S.E. Crow, R.M. Ogoshi, M.H. Nakahata, R. Tirado-Corbalá, R.G. Anderson, J. Osorio and J. Jeong. (2015) Two-Year Growth Cycle Sugarcane Crop Parameter Attributes and their Application in Modeling. Agron. J. 107(4): 1310-1320. (Impact Factor: 2.0 – Citations: 23)

26. Osorio, J., J. Jeong, J. Arnold, and K. Bieger. (2014) Influence of potential evapotranspiration on the water balance of sugarcane fields in Maui, Hawaii - Special Issue: Evapotranspiration. J. Wat. Res.Prot. 6: 852-868. (Impact Factor: 1.7 – Citations: 22)

27. Bosch, D., J. Pease, M.L. Wolfe, C. Zobel, J. Osorio, T. Denckla Cobb, and G. Evanylo. (2012) Community DECISIONS: Stakeholder focused watershed planning. J. Environ. Manage. 112: 226-232. (Impact Factor: 8.0 – Citations: 28)

28. Canas, R., C. Leon-Velarde, R. Quiroz, and J. Osorio. 2003. Quantifying energy dissipation by grazing animals in harsh environments. J. Theo. Bio. 225: 351-359. (Impact Factor: 1.9 – Citations: 34)