Jonghan Ko

227 Indiana Ave. C107

Lubbock, TX 79415

(806) 771-5195 (home), (806) 749-5560 (office)

jko@lbk.ars.usda.gov

EDUCATION

Texas Tech University, Lubbock, Texas

Ph. D. in Agronomy

Graduation Date: August 7, 2004

Kangwon National University, Chuncheon, Republic of Korea

Master of Agriculture in Agronomy

Graduation Date: February, 1998

Kangwon National University, Chuncheon, Republic of Korea

Bachelor of Agriculture in Agronomy

Graduation Date: February, 1993

THESIS AND DISSERTATION

Ph. D. Dissertation: Development of a cotton crop model that uses remote sensing data. TTU.

Master’s Thesis: Bioassay and analysis of allelopathic substances in rice (Oryza sativa L.) husks. KNU.

WORK EXPERIENCE

Texas Tech University, Lubbock, Texas

September 2004 – present

Post Doctoral Research Associate

Crop Modeling for the YieldTracker project funded by USDA-Cooperative State Research, Extension, and Education Service under the direction of Dr. Stephan J. Maas, Texas Tech University Plant and Soil Science Department.

Texas Tech University, Lubbock, Texas

August 2001 – August 2004

Research Assistant

Field study and development of a cotton crop model for the YieldTracker project under the direction of Dr. Stephan J. Maas, Texas Tech University Plant and Soil Science Department.

Kangwon Agricultural Research and Extension Services (KARES),Chuncheon, Republic of Korea

July 1992 - July 2001

Agricultural Researcher

Field studies on lowland rice: yield forecasting, photosynthesis, seedling media, and weed studies.

COMPUTER SKILLS

Programming languages: Basic, Fortran, JAVA

Remote Sensing and GIS software: ERDAS IMAGINE, ENVI, ArcGIS, SST Toolbox

Statistics/Mathematics software: SAS, MATLAB

Database: Microsoft Access

HONORS OR AWARDS

A. W. Young Graduate Student Endowment Scholarship. Texas Tech University. May 28, 2003.

Plant and Soil Science Scholarship. Texas Tech University. June 28, 2002.

Good Public Servant of the Year. Awarded by provincial governor. Kangwon Province, Korea. December 30, 2000.

Good Research Team of the Year. Awarded by National Institute of Korean Agricultural Development, Korea. December 22, 1999.

MEMBERSHIP IN ACADEMIC SOCIETIES

American Society of Agronomy

Crop Science Society of America

Soil Science Society of America

The Korean Society of Agricultural and Forest Meteorology

The Korean Society of Crop Science

RESEARCH INTERESTS

Crop modeling: mathematical model that uses remote sensing data.

Remote sensing: vegetation studies to monitor crop growth and/or to provide model input.

Geospatial analysis: geospatial models to describe and predict the temporal and spatial distribution of vegetation and soil properties.

PUBLICATIONS

Jonghan Ko and Stephan J. Maas. Modification of the GRAMI model for cotton. Agro. Journal. (submitted)

Jonghan Ko, Jin-kwan Ham, Yong-bok Kim, Youn-su Lee, and Byun-woo Lee. Expanded rice husk (ERH) as a rice seedling bed medium – I. chemical and physical characteristics of ERH. Kor. J. Crop Sci. (submitted)

Jonghan Ko, Doo-yeol Kim, Jong-gu Sa, Youn-su Lee, and Byun-woo Lee. Expanded rice husk (ERH) as a rice seedling bed medium – II. Seedling cultivation methods using the ERH contained bed medium. Kor. J. Crop Sci. (submitted)

Jonghan Ko, Byunwoo Lee, and Dong-ha Cho. Comparison of meteorological factors and rice yield components between inland and coastal regions in Kangwon province. Kor. J. Crop Sci. (submitted)

Stephan J. Maas, Sepalika Rajapakse, Robert Lascano, Wenxuan Guo, Jill Booker, and Jonghan Ko. 2005. Relation between RADARSAT imagery and cotton field characteristics. Proc., Beltwide cotton conference, New Orleans, LA. (In press)

Stephan Maas, Robert Lascano, Daniel Cooke, Clarence Richardson, Daniel Upchurch, Donald Wanjura, Daniel Krieg, Susan Mengel, Jonghan Ko, Jerry Brightbill, Kevin Bronson, William Payne, Charles Rush, Sepalika Rajapakse, Wenxuan Guo, and Elizabeth Morris. 2004. Within-season Estimation of Evapotranspiration and Soil Moisture in the High Plains Using YieldTracker. Proc., Conf. on High Plains groundwater resources: challenges and opportunities, Lubbock, TX. CD-ROM

Jonghan Ko, Stephan J. Maas, and Yeong-Sang Jung. 2004. A cotton crop model able to use remotely sensed data. Proc., Kor. J. Env. Agr., Special Ed. 115-125.

Stephan J. Maas and Jonghan Ko. 2004. Canopy architecture model for cotton. Proc., Beltwide cotton conference. San Antonio, Texas. 2039-2142.

Dongha Cho, Chaegon Lee, Jonghan Ko, and Maurice Ku. 2000. Photosynthetic performance of transgenic rice plants over-expressing maize C4 photosynthesis enzymes. Joint meeting of three different scientific societies – The Korean Society of Crop Science, The Korean Society of Agricultural and Forest Meteorology, and The Korean Society of Agro-Informatics. Suweon, Korea. 206-207. (In Korean)

Jonghan Ko, SeokHyun Eom, Myong-jo Kim, Chang-yeon Yu, Youn-su Lee, Hyeon-so Ji, Jin-kwan Ham, Doo-youl Kim, and E-Hun Kim. 1998. Bioassay and analysis of allelopathic substances in rice husks (Oryza sativa L.). Proceedings of Annual Meeting of the Korean Society of Crop Science – Korean J. Crop Science 42(2). Jinju, Korea. 106-107. (In Korean)

 Jonghan Ko, Jin-An Jung, Doo-Youl Kim, Dong-Ha Cho, E-Hun Kim. 1997. Studies of the difference in dry matter production and photosynthetic rate of rice cultivars in the eastern and the western regions of Kangwon province. Proceedings of Annual Meeting of Korean Breeding Society -Korean J. Breeding 30-2. Suweon, Korea. 66-67. (In Korean)

K. S. Kim, J. R. Kim, H. S. Ji, Jonghan Ko, M. A. Lee, D. Y. Kim, Y. B. Shin, and S. U. Park. 1995. Survey of cool weather injury to rice in alpine areas of Kangwon province in 1993, RDA J. Agricultural Sci. 37(2): 74-89. (In Korean)

Kim, K. S., Jonghan Ko, J. G. Sa, J. S. Chang, and D. Y. Kim. 1994. Weed flora changes in lowland rice fields in Kangwon province. Kor. J. Weed Sci. 14(4): 258-264. (In Korean)

ANNUAL REPORTS

Jonghan Ko. Rice yield forecast test in the Kangwon region. KARES. 1998, 1999, and 2000. (in Korean)

Jonghan Ko, D. Y. Kim, J. K. Ham, and J. G. Sa. A study of using expanded rice husks for rice seedling growth media, KARES. 2000 and 1999. (in Korean)

Jonghan Ko. A Study of yield differences in some rice varieties growing in the inland plain and in the eastern coastal area of Kangwon region. KARES. 1998 and 1997. (in Korean)

Jonghan Ko. A study of chilling injury decrease during the early growing periods in the eastern coastal area of the Kangwon region. KARES. 1998. (in Korean)

Jonghan Ko, T. S. Jeong, and J. K. Ham. Investigation of rice yield increase in the eastern coastal area of the Kangwon region, KARES. 1997. (in Korean)

Jonghan Ko, H. S. Ji, D.Y. Kim, and J. U. Park. Weed flora ecosystem and followed-by control with direct seeding in lowland rice fields, KARES. 1996. (in Korean)

Jonghan Ko, H. S. Ji, D. Y. Kim, Y. H. Kim, and S. U. Park. Weed control test with direct seeding in lowland rice fields. KARES. 1995. (in Korean)

Jonghan Ko, J. G. Sa, J. R. Kim, and K. S. Kim. Estimating appropriate transplanting time of young rice seedlings in an alpine area in Kangwon province. KARES. 1994. (in Korean)

Jonghan Ko, J. G. Sa, D. Y. Kim, K. S. Kim, and B. R. Heo. A survey of weed flora in rice paddy fields at different elevations, KARES. 1992. (in Korean)

ABSTRACT OF DISSERTATION

A new cotton crop model able to use remote sensing data as input was developed and tested using field data sets based on GRAMI. Based on analysis of a 2002 field data set obtained at Halfway, TX, the primary parameters that affect the cotton simulation model were estimated. These included radiation use efficiency (RUE: 2.3 g MJ^-1), light extinction coefficient (K: 0.9), specific leaf area (SLA: 0.01 m² g^-1), and a coefficient of boll production (γ: 0.57 GDD^-1). The model also included boll growth and lint yield estimation functions. The model was first verified using cotton field data obtained at Halfway, TX, in 2002, and then validated using data sets obtained in Lamesa County, TX, in 1999 and 2001. Model verification was performed using both leaf area index (LAI) and remotely sensed ground cover (GC) data. Simulated results using LAI as input are as follows: simulated values matched with measured values with R² 0.91-0.97 and RMSE 0.16-0.35 for LAI; with R² 0.92-0.96 and RMSE 95.7-142.7 for AGDM; with R² 0.83-0.9 and RMSE 13.9-36.0 for boll number; with R² 0.37 and RMSE 15.9 for harvestable boll number; and with R² 0.5 and RMSE 153.3 for lint yield in three fields. Simulated results using remotely sensed GC as input were as follows: simulated values agreed with measured values with R² 0.95-0.98 and RMSE 0.06-0.07 for GC; with R² 0.91-0.95 and RMSE 94.7-131.6 for AGDM; with R² 0.82-0.91 and RMSE 17.4-21.9 for boll number; with R² 0.3 and RMSE 14.5 for simulated harvestable bolls; and with R² 0.3 and RMSE 121.8 for lint yield. The validated results were as follows. Simulated values matched with measured values with R² 0.94 and RMSE 0.12 for LAI, and with R² 0.97 and RMSE 48.5 for AGDM in 1999 data; with R² 0.93 and RMSE 0.39 for LAI, and with R² 0.98 and RMSE 131.1 for AGDM in 2001 data. In both years, simulated boll number, harvestable boll number, and lint yield matched with measurements with R² 0.88 and RMSE 19.3 for boll number; with R² 0.73 and RMSE 12.6 for harvestable boll number; and with R² 0.75 and RMSE 181.8 for lint yield. In general, simulated values obtained with the new cotton model showed reasonable agreements with their corresponding measurements. The new model not only has simple input requirements but is also easy to use, and applicable to regional cotton growth monitoring and lint yield mapping projects.