彭京伦，男，汉族，中共党员，博士。

研究方向：1）饲料作物栽培适宜性评价与产量预测；2）反刍动物粗饲料利用；3）国际科技期刊。

邮箱：pengjl@lzu.edu.cn

二、 教育和工作经历

2021.05-现在，兰州大学，副编审，Grassland Research助理执行编辑；

2018.04-2020.12, Institute of Animal Resources, Kangwon National University, Senior Researcher；

2017.01-2018.03, University-Industry Cooperation Foundation, Kangwon National University, Full-time Researcher；

2012.08-2017.02，Kangwon National University，动物营养与饲料科学，博士；

2008.09-2012.07，临沂大学，生物技术，学士。

三、科研项目

1. 主持韩国国家研究基金会新晋研究者项目1项，起止时间：2018.03-2021.02，资助额度：16209万韩币（约93万人民币）。

2. 主持韩国国家研究基金会基础科学研究项目，起止时间：2017.06-2018.05，资助额度：4982万韩币（约29万人民币）。

四、国际交流与合作

1. College of Animal Life Sciences, Kangwon National University—兰州大学草地农业科技学院，2019年1月21-25日，春川，韩国；

2. College of Animal Life Sciences, Kangwon National University—浙江大学奶业科学研究所，2019年11月7日至11月10日，杭州，中国；

3. College of Animal Life Sciences, Kangwon National University—兰州大学草地农业科技学院，2019年12月29日至2020年1月2日，春川，韩国；

4. Grassland Research—芬兰自然资源研究所，2023年11月7日-11日，库奥皮奥，芬兰。

五、主要科研成果

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. 彭京伦, 王娟, 金抆主, 曹武焕, 金炳完, 成庆一. 基于生长度日和降水量的韩国饲用玉米产量预测模型构建[J]. 草业科学, 2018, 12(4): 857-866. doi: 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. 张岩, 黄毅, 刘颖, 范玉兵, 彭京伦, 唐增, 夏超, 南志标. 新形势下发展草地农业保障食物安全的战略思考[J]. 中国工程科学, 2023, 25(4), 73-80.

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

六、会议报告

（一）国际会议

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. 2019.08.28. 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. 2018.07.07. 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. 2016.08.17. 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. 2013.10.15. Beijing, China.

（二）国内会议

1. 基于气象、土壤和作物品种数据的一年生黑麦草产量预测模型构建. 中国草学会. 2018中国草学会年会论文集[C]. 2018年11月07日, 中国四川成都.

2. 基于气象与地理信息的韩国饲用作物栽培适宜性评价和产量预测模型构建研究. 中国草学会.2017中国草学会年会论文集[C] . 2017年11月05日, 中国广东广州.

（三）韩国会议

1. 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. 2019.06.26. Jinju, Gyeongsangnam-do, South Korea.

2. 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. 2017.09.14. Cheonan, Chungcheongnam-do, South Korea.

3. 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. 2017.06.29. Gwangju, South Korea.

4. 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. 2016.06.23. Seoul, South Korea.

5. 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. 2014.06.26. Hongcheon, Gangwon-do, South Korea.

6. Comparison of annual dry matter yields of whole crop corn. In Proceedings of 2013 Annual Congress of Korean Society of Grassland and Forage Science. 2013.09.04. Naju, Jeollanam-do, South Korea.

7. 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. 2013.06.27. Jeju, South Korea.

七、获奖情况

2017年：韩国江原大学“Outstanding Academic Award”

2024年1月更新