Personal Profile:

Jinglun Peng, Ph.D., Associate Professor of Editorship.

Research field: Forage crop suitability evaluation and yield modeling, Ruminant nutrition and forage utilization, International Academic Publishing.

Email: pengjl@lzu.edu.cn

Education and Work Experiences:

May 2021-present, Lanzhou University, Associate Professor of Editorship, Associate Managing Editor of Grassland Research.

April 2018-December 2020, Institute of Animal Resources, Kangwon National University, Senior Researcher.

January 2017-March 2018, University-Industry Cooperation Foundation, Kangwon National University, Full-time Researcher.

August 2012-February 2017, Kangwon National University, Ph.D.

September 2008-July 2012, Linyi University, Bachelor.

Research Projects:

1. March 201-February 2021, Young Scientist Grants, National Research Foundation of Korea, PI.

2. June 2017-May 2018, Basic Science Research Program, National Research Foundation of Korea, PI.

International Collaboration:

1. January 2019, College of Animal Life Sciences, Kangwon National University—College of Pastoral Agriculture Science and Technology, ChunCheon, South Korea.

2. November 2019, College of Animal Life Sciences, Kangwon National University—The Institute of Dairy Science, Zhejiang University, Hangzhou, China.

3. December 2019-January 2020, College of Animal Life Sciences, Kangwon National University—College of Pastoral Agriculture Science and Technology, Lanzhou University, Chuncheon, South Korea.

4. November 2023, Grassland Research—Natural Resources Institute Finland (Luke), Kuopio, Finland.

Main Publications:

1. Peng, J. L.*, Kim, J. Y., Lee, B., Kim, B., & Sung, K. (2023). Whole crop maize yield modeling based on regional climatic data considering cultivar maturity grouping. Grassland Science, 69(4), 268-276. https://doi.org/10.1111/grs.12412

2. Peng, J. L.*, Kim, M., Kim, K., & Sung, K. (2020). Climatic suitability mapping and driving factors detection for whole crop maize and sorghum–sudangrass hybrid production in the south area of the Korean Peninsula and Jeju Island. Grassland Science, 66(4), 207-214. https://doi.org/10.1111/grs.12270

3. Peng, J. L., Kim, M., & Sung, K. (2020). Yield prediction modeling for sorghum–sudangrass hybrid based on climatic, soil, and cultivar data in the Republic of Korea. Agriculture, 10(4), 137. https://doi.org/10.3390/agriculture10040137

4. Guan, L., Peng, J. L.*, Han, K., & Sung, K. (2019). Yield modeling for prediction of regional whole‐crop barley productivity. Grassland Science, 65(3), 179-188. https://doi.org/10.1111/grs.12233

5. Peng, J. L., Kim, K. D., Jo, M. H., Kim, M. J., Lee, B. H., Kim, J. Y., ... & Sung, K. I. (2018). Climatic suitability mapping of whole-crop rye cultivation in the Republic of Korea. Journal of The Korean Society of Grassland and Forage Science, 38(4), 337-342. https://doi.org/10.5333/KGFS.2018.38.4.337

6. Peng J. L., Wang J., Kim M., Jo M., Kim B., Sung K.. Construction of a yield prediction model for whole crop maize on the basis of climatic data in South Korea. Pratacultural Science, 2018,35(4): 857-866. https://doi.org/10.11829/j.issn.1001-0629.2017-0359

7. Peng, J. L., Kim, M., Kim, Y., Jo, M., Kim, B., Sung, K., & Lv, S. (2017). Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea. Grassland Science, 63(3), 184-195. https://doi.org/10.1111/grs.12163

8. Peng, J. L., Kim, M. J., Jo, M. H., Min, D. H., Kim, K. D., Lee, B. H., ... & Sung, K. I. (2017). Accuracy evaluation of the crop-weather yield predictive models of Italian ryegrass and forage rye using cross-validation. Journal of Crop Science and Biotechnology, 20(4), 327-334. https://doi.org/10.1007/s12892-017-0090-0

9. Peng, J. L., Kim, B. W., Lee, B. H., Nejad, J. G., & Sung, K. I. (2017). Effects of feeding high- and low-forage diets containing different forage sources on rumen fermentation characteristics and blood parameters in non-pregnant dry Holstein cows. Journal of The Korean Society of Grassland and Forage Science, 37(1), 1-9. https://doi.org/10.5333/KGFS.2017.37.1.1

10. Peng, J. L., Kim, M. J., Kim, B. W., & Sung, K. I. (2016). A yield estimation model of forage rye based on climate data by locations in South Korea using general linear model. Journal of the Korean Society of Grassland and Forage Science, 36(3), 205-214. https://doi.org/10.5333/KGFS.2016.36.3.205

11. Peng, J. L., Kim, M. J., Kim, B. W., & Sung, K. I. (2016). Models for estimating yield of Italian ryegrass in south areas of Korean Peninsula and Jeju Island. Journal of the Korean Society of Grassland and Forage Science, 36(3), 223-236. https://doi.org/10.5333/KGFS.2016.36.3.223

12. Peng, J. L., Kim, M. J., Kim, Y. J., Jo, M. H., Nejad, J. G., Lee, B. H., ... & Sung, K. I. (2015). Detecting the climate factors related to dry matter yield of whole crop maize. Korean Journal of Agricultural and Forest Meteorology, 17(3), 261-269. https://doi.org/10.5532/KJAFM.2015.17.3.261

13. Zhang Y., Huang Y., Liu Y., Fan Y., Peng J. L., Tang Z., Xia C., Nan Z.. (2023). Strategic thinking on developing grassland agriculture to ensure China’s food security under the New Situation. Strategic Study of Chinese Academy of Engineering, 25(4), 73-80. https://doi.org/10.15302/J-SSCAE-2023.04.007

14. Kim M., Peng, J. L., Sung, K. (2020). Causality of climate and soil factors affecting whole crop rye (Secale cereale L.) yield as part of natural ecosystem structure via longitudinal structural equation model in the Republic of Korea. Grassland Science, 66(2), 110-115. https://doi.org/10.1111/grs.12253

15. Kim M., Peng, J. L., Sung, K. (2019). Causality between climatic and soil factors on Italian ryegrass yield in paddy field via climate and soil big data. Journal of Animal Science and Technology, 61(6), 324-332. https://doi.org/10.5187/jast.2019.61.6.324

Conference Presentation:

1. Italian ryegrass yield prediction for forage supply to ruminant livestock farming in South Korea. In Proceedings of the 70th Annual Meeting of the European Federation of Animal Science. August 28, 2019. Ghent, Belgium.

2. Application of climatic algorithm for prediction of regional whole-crop barley productivity. In Proceedings of the 12th World Conference on Animal Production. July 07, 2018. Vancouver, Canada. / Journal of Animal Science, 96(suppl_3):510-511, DOI:10.1093/jas/sky404.1117.

3. A forage rye dry matter yield estimation model based on climate data by locations in South Korea using general linear model. In Proceedings of the 6th Korea-China-Japan Grassland Conference. August 17, 2016. Jeju, South Korea.

4. Effect of feeding whole crop barley mixed with Italian ryegrass silage versus tall fescue hay on performance, hair cortisol concentration and blood hematology profile in Holstein growing cattle. In Proceedings of the 11th World Conference on Animal Production. October 15, 2013. Beijing, China.

5. Construction of Italian ryegrass yield prediction model based on meteorological, soil and crop variety data. In Proceedings of the 2018 Annual Conference of Chinese Grassland Society. November 07, 2018. Chengdu, China.

6. Cultivation suitability evaluation and yield prediction modeling of forage crops in South Korea based on meteorological and geographic information. In Proceedings of the 2017 Annual Conference of Chinese Grassland Society. November 05, 2017. Guangzhou, China.

7. Climatic suitability mapping and driving factors detection for whole crop maize and sorghum–sudangrass hybrid production in Korea. In Proceedings of 2019 Annual Congress of Korean Society of Animal Science and Technology. June 26, 2019. Jinju, Gyeongsangnam-do, South Korea.

8. Detecting the reason for the negative effects of accumulated precipitation on the yield of whole crop maize and sorghum-sudangrass hybrid based on field experimental data in Korea. In Proceedings of 2017 Annual Congress of Korean Society of Grassland and Forage Science. September 14, 2017. Cheonan, Chungcheongnam-do, South Korea.

9. Detection on yield model construction of sorghum-sudangrass hybrid based on climatic data by locations in the Republic of Korea. In Proceedings of 2017 Annual Congress of Korean Society of Animal Science and Technology. June 29, 2017. Gwangju, South Korea.

10. A comparison of rumen microbial community in Holstein and Hanwoo cows fed with whole crop barely mixed with Italian ryegrass silage versus tall fescue hay based diet by 16S rRNA sequencing. In Proceedings of 2016 Annual Congress of Korean Society of Animal Science and Technology. June 23, 2016. Seoul, South Korea.

11. A comparison of rumen fluid characteristics and blood parameters of Holstein cows fed with whole crop barely mixed with Italian ryegrass silage based diet versus tall fescue hay based diet. In Proceedings of 2014 Annual Congress of Korean Society of Animal Science and Technology. June 26, 2014. Hongcheon, Gangwon-do, South Korea.

12. Comparison of annual dry matter yields of whole crop corn. In Proceedings of 2013 Annual Congress of Korean Society of Grassland and Forage Science. September 04, 2013. Naju, Jeollanam-do, South Korea.

13. Prediction of forage maize dry matter production using soil and climate data. In Proceedings of 2013 Annual Congress of Korean Society of Animal Science and Technology. June 27, 2013. Jeju, South Korea.

Awards:

2017: Outstanding Academic Award, Kangwon National University.