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Palouse Region of the Pacific Northwest. My PhD research focuses on agricultural relationships to climate for this study area.

Research Area 1: Climatic analysis, data mining, and agriculture

As part of my PhD research, I’m exploring how variations in climatic processes can be understood thru the use of data mining and machine learning approaches for predictive threshold analysis (Maimon and Rokach, 2014, King et al 2015). I’m specifically focusing on agricultural commodity systems and insurance losses.  The USDA has an extensive archive of insurance claim information, aggregated at a county and monthly level, with cause of damage (drought, heat, failure of irrigation supply, etc).  My research is exploring how this extensive dataset, in combination with a variety of other climatic factors – can be used to construct a usable predictive agricultural commodity loss model.

As an integrated effort using the above concepts, I’m also working to develop a modular architecture API for data science integration, with a focus on climatic processes and machine learning algorithm use.

 

Example of agricultural commodity loss animation for Washington state wheat, from 1989-2015, for only drought-related claims
 

Climatic Analysis  Research Abstract

Agricultural systems are an essential and growing aspect of our society. Not only is agriculture going through a data-centric revolution (Smith and Katz, 2013), the methods and analysis approaches for such efforts are also becoming much more complex (Gray, 2006, Bell, 2009, Clarke, 2009, Maimon and Rokach, 2010). Precision agriculture systems, cloud-based data assembly for farmers, and machine learning algorithms for predictive analytics, are all example areas of scientific discovery that are pushing efficient agricultural systems forward. This research builds upon this data growth, through the development of a modular data mining and machine learning methodology, initially focused on agricultural systems. The proposed methodology will be applied to 1) irrigated and 2) dryland agricultural systems in the Pacific Northwest region, stepping thru the processes of data assembly and geographic characterization, feature transformation and engineering, classifier/regressor selection, optimization, tuning, and finally, incorporation into a custom application programming interface (API). Each model and API will use climate outcomes to predict agricultural crop loss, estimating the influence of these changing conditional relationships over time. (e.g. how influential is drought on crop loss for a particular county, and does that influence change into the future?).  Finally, the API, models, and analytics are integrated into a technology platform for access by land managers, farmers, or scientists, with the added capability of extending the methodology to other climate impact areas, such as health or land subsidence.

The research question focuses on agricultural systems and their impact given a changing climate. In this methodology example, we explore crop insurance claim losses that are submitted to the USDA , and how these data might be associated with climate data for a related time period.

 

Related Publications

Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131.

Abatzoglou, J. T., & Brown, T. J. (2012). A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology, 32(5), 772–780. http://doi.org/10.1002/joc.2312

Abatzoglou, J. T., Rupp, D. E., & Mote, P. W. (2014). Seasonal climate variability and change in the pacific northwest of the united states. Journal of Climate, 27(5), 2125–2142. http://doi.org/10.1175/JCLI-D-13-00218.1

Alston, J. M., Beddow, J. M., & Pardey, P. G. (2009). Agricultural Research, Productivity, and Food Prices in the Long Run. Science, 325(September), 4–5.

Beguería, S., Vicente-Serrano, S. M., Reig, F., & Latorre, B. (2014). Standardized precipitation evapotranspiration index (SPEI) revisited: Parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. International Journal of Climatology, 34(10), 3001–3023. http://doi.org/10.1002/joc.3887

Christensen, L. R. (1975). Concepts and Measurement of Agricultural Productivity. American Journal of Agricultural Economics, 57(5), 910. http://doi.org/10.2307/1239102

Diskin, P. (1997). Agricultural Productivity Indicators Measurement Guide. Food and Nutrition Technical Assistance Project (FANTA), (December).

Fan, M., Pena, A. A., & Perloff, J. M. (2016). Effects of the Great Recession on the U.S. Agricultural Labor Market. American Journal of Agricultural Economics, 98(4), 1146–1157. http://doi.org/10.1093/ajae/aaw023

Gornall, J., Betts, R., Burke, E., Clark, R., Camp, J., Willett, K., & Wiltshire, A. (2010). Implications of climate change for agricultural productivity in the early twenty-first century. Philosophic

Gundersen, C., Kreider, B., & Pepper, J. (2011). The economics of food insecurity in the United States.Applied Economic Perspectives and Policy, 33(3), 281–303. http://doi.org/10.1093/aepp/ppr022

Kucharik, C. J., & Serbin, S. P. (2008). Impacts of recent climate change on Wisconsin corn and soybean yield trends. Environmental Research Letters, 3(3). http://doi.org/10.1088/1748-9326/3/3/034003

Li, Y., Ye, W., Wang, M., & Yan, X. (2009). Climate change and drought: a risk assessment of crop-yield impacts. Climate Research, 39(June), 31–46. http://doi.org/10.3354/cr00797

Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon, W. P., & Naylor, R. L. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science,319(5863), 607–610. http://doi.org/10.1126/science.1152339

Lobell, D. B., Schlenker, W., & Costa-Robert, J. (2011). Climate trends and global crop production since 1980. Science, 333(2011), 616–620. http://doi.org/10.1126/science.1204531

Lobell, D. B., & Burke, M. B. (2010). On the use of statistical models to predict crop yield responses to climate change. Agricultural and Forest Meteorology, 150(11), 1443–1452. http://doi.org/10.1016/j.agrformet.2010.07.008

McCarl, B. A., Villavicencio, X., & Wu, X. (2008). Climate change and future analysis: Is stationarity dying? American Journal of Agricultural Economics, 90(5), 1241–1247. http://doi.org/10.1111/j.1467-8276.2008.01211.x

Milly, P. C. D., & Dunne, K. A. (2016). Potential evapotranspiration and continental drying. Nature Climate Change, 6(10), 946–949. http://doi.org/10.1038/nclimate3046

Miranda, M. J., & Glauber, J. W. (1997). Systemic Risk, Reinsurance, and the Failure of Crop Insurance Markets. American Journal of Agricultural Economics, 79(1), 206–215. http://doi.org/10.2307/1243954

Peel, M. C., Finlayson, B. L., & McMahon, T. a. (2006). Updated world map of the K ̈oppen-Geiger climate classification. Meteorologische Zeitschrift, 15, 259–263. http://doi.org/10.1127/0941-2948/2006/0130

Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature Communications, 6, 5989. http://doi.org/10.103

Sandison, D. I. (2017). 2015 Drought and Agriculture, 495(February). Retrieved from https://agr.wa.gov/FP/Pubs/docs/495-2015DroughtReport.pdf

Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598. http://doi.org/10.1073/pnas.0906865106

Smith, A. B., & Katz, R. W. (2013). US billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Natural Hazards, 67(2), 387–410. http://doi.org/10.1007/s1106

Sorte, B., & Rahe, M. (2015). Oregon Agriculture , Food and Fiber : An Economic Analysis, (December).

Thornton, P. K., Jones, P. G., Alagarswamy, G., & Andresen, J. (2009). Spatial variation of crop yield response to climate change in East Africa. Global Environmental Change, 19(1), 54–65. http://doi.org/10.1016/j.gloenvcha.2008.08.005

Vargas, M., Glaz, B., Alvarado, G., Pietragalla, J., Morgounov, A., Zelenskiy, Y., & Crossa, J. (2015). Analysis and interpretation of interactions in agricultural research. Agronomy Journal, 107(2), 748–762. http://doi.org/10.2134/agronj13.0405

Walker, K., & Rahe, M. (2015). Oregon Agriculture , Food and Fiber : An Economic Analysis, (December).

Wallander, S., Aillery, M., Hellerstein, D., & Hand, M. (2013). The Role of Conservation Programs in Drought Risk Adaptation. Economic Research Service, April(148), 1–68. Retrieved from http://ers.usda.gov/media/1094684/err-148-summary.pdf

Yorgey, G., & Kruger, C. E. (2017). Advances in Dryland Farming in the Inland Pacific Northwest. Washington State University Extension Publications. Retrieved from https://books.google.com/books?id=vZnEswEACAAJ

 


 

Research Area 2: Child development and GIS

 

Im also working on a line of research with several other researchers in the areas of child psychology, to explore inter-relationships between location and how children are effected.   We have several studies examining the relationship of crime to child temperament and biophyiscal markers:

Gartstein, M.A., E. Seamon, T. Dishion.  GEOSPATIAL ECOLOGY OF ADOLESCENT PROBLEM BEHAVIOR: CONTRIBUTIONS OF COMMUNITY FACTORS AND PARENTAL MONITORING – Journal of Community Psychology,  March 2014

Gartstein, MA, E. Seamon, L. Lengua. Community Crime Exposure and Risk for Obesity in Preschool Children: Moderation by the Hypothalamic–Pituitary–Adrenal (HPA)-Axis.  Journal of Pediatric Psychology 2017