Dr. Song Cui

Associate Professor - Digital Agriculture

Dr. Song Cui
615-898-5833
+1 (615) 898-51
Room 102, Horticulture Facility (HC)
MTSU Box 5, Murfreesboro, TN 37132
Office Hours

 TR 9:00-10:00

Degree Information

  • PHD, Texas Tech University (2011)
  • BS, Lanzhou University (2006)

Areas of Expertise

Digital Agriculture

Biography

Dr. Song Cui is an Associate Professor in the School of Agriculture at Middle Tennessee State University. He received his B.S. in Agricultural Science from Lanzhou University (China) in 2006, and his Ph.D. in Crop Science from Texas Tech University in 2011. He is a broadly trained agronomist with expertise in big data algorithms, crop physiology, forage production, simulation modeling, soil and boundary layer flux (CO2, water vapor, and greenhouse gases) measurements, and large-scale agroecos...

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Dr. Song Cui is an Associate Professor in the School of Agriculture at Middle Tennessee State University. He received his B.S. in Agricultural Science from Lanzhou University (China) in 2006, and his Ph.D. in Crop Science from Texas Tech University in 2011. He is a broadly trained agronomist with expertise in big data algorithms, crop physiology, forage production, simulation modeling, soil and boundary layer flux (CO2, water vapor, and greenhouse gases) measurements, and large-scale agroecosystem studies addressing issues such as water sustainability and climate change.  Particularly, Dr. Cui is interested in integrating machine learning techniques into advanced agronomic research domains, including remote sensing, bioinformatics, meta-analysis, and plant growth/ecological modeling. Furthermore, as a field agronomist by training, Dr. Cui enjoys conducting variety trials and cropping system research on various row, forage, and specialty crops in different agroecosystems.

Dr. Cui has authored and co-authored more than 30 high-impact journal articles since 2013. He has served as reviewers for more than ten peer-reviewed journals and had reviewed more than 20 journal articles in the past five years. Furthermore, Dr. Cui has served as the Principal Investigator for more than $1.9M externally funded research projects (mainly from USDA-NIFA) since 2013. Collectively, He has involved in more than $7.5M collaborative projects nationwide. Dr. Cui is currently serving as the Chair of the Airborne and Satellite Remote Sensing Community of the American Society of Agronomy. Meanwhile, he is also serving as the Vice President of Southern Branch of the American Society of Agronomy. Dr. Cui has served as an external grant reviewer panelist on six external funding programs since 2013, including four USDA-NIFA sponsored competitive funding programs. Dr. Cui frequently mentors undergraduate and graduate student research projects and teaches courses including Forage Crop Production, Field Crop Production, Crop Ecophysiology, Precision Agriculture, and Agricultural Statistics and Data Analysis.

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Publications

  1. Li, Z., S. Cui, R. Q. Zhang, G. Xu, Q. Feng, C. Chen, and Y. Li. 2022. Optimizing Wheat Yield, Water, and Nitrogen Use Efficiency With Water and Nitrogen Inputs in China: A Synthesis and Life Cycle Assessment. Front. Plant Sci. 13:930484 (IF = 4.41)
  2. Kubesch, J.O.C. *, R.L.G. Nave, S. Cui, G.E. Bates, D.M. Butler, and V. Pantalone. 2022. Transitional organic forage systems in the U.S. Southeast: Production and nutritive value. Agr...
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  1. Li, Z., S. Cui, R. Q. Zhang, G. Xu, Q. Feng, C. Chen, and Y. Li. 2022. Optimizing Wheat Yield, Water, and Nitrogen Use Efficiency With Water and Nitrogen Inputs in China: A Synthesis and Life Cycle Assessment. Front. Plant Sci. 13:930484 (IF = 4.41)
  2. Kubesch, J.O.C. *, R.L.G. Nave, S. Cui, G.E. Bates, D.M. Butler, and V. Pantalone. 2022. Transitional organic forage systems in the U.S. Southeast: Production and nutritive value. Agron. J. 114 (2): 1269-1283 (IF = 1.68)
  3. Yang, X.,* D. Menefee, S. Cui, and N. Rajan. 2021. Assessing the impacts of projected climate changes on maize (Zea mays) productivity using crop models and climate scenario simulation. Crop Pasture Sci. 72:969-984 (IF = 2.2)
  4. Li, Z. C. Chen, A. Nevins, T. Pirtle,* and S. Cui. 2021. Assessing and Modeling Ecosystem Carbon Exchange and Water Vapor Flux of a Pasture Ecosystem in the Temperate Climate-Transition Zone. Agronomy. 11:2071 (IF = 3.64)
  5. Adkins, J.B., J.P. Gulizia, K.M. Downs, and S. Cui. 2021. SXI-11 Assessing in situ rumen degradability of late season kudzu (Pueraria montana var. lobata). J. Anim. Sci. 99: 350 (IF = 1.714)
  6. Menefee, D., N. Rajan, S. Cui, M. Bagavathiannan, R. Schnell, and J. West. 2021. Simulation of Dryland Maize Growth and Evapotranspiration using DSSAT-CERES-Maize Model. Agron. J. 113:1317-1332. (IF = 1.68)
  7. Xu, S., S. Jagadamma, R. Nave, S. Cui, E. Byers, and Z. Li. 2021. Potential and Challenges of Growing Cover Crops in Organic Production Systems. In: Cover Crops and Sustainable Agriculture. R. Islam, and B. Sherman (eds.). CRC Press. Landon. Chapter 3.
  8. Cui, X., T. Goff, S. Cui, D. Menefee, Q. Wu, N. Rajan, S. Nair, N. Phillips, and F. Walker. 2021. Predicting Carbon and Water Vapor Fluxes using Machine Learning and Novel Feature Ranking Algorithms. Sci. Total Environ. 775:145130 (IF = 6.55)
  9. Li, Y., Z. Li, S. Cui, G. Liang, and Q. Zhang. 2021. Microbial-derived carbon components are critical for enhancing soil organic carbon in no-tillage croplands: A global perspective. Soil Tillage Res. 205:104758. (IF = 4.60)
  10. Haruna, S., S.H. Anderson, R.P. Udawatta, C.J. Gantzer, N.C. Phillips, S. Cui, and Y. Gao. 2020. Improving soil physical properties through the use of cover crops: A review. Agrosyst. Geosci. Environ. 3:e20105.
  11. Menefee, D., N. Rajan, S.Cui, M. Bagavathiannan, R. Schnell, and J. West. 2020. Carbon exchange of a dryland cotton field and its relationship with PlanetScope remote sensing data. Agric. Forest Meteorol. 294:108130. (IF = 4.60)
  12. Li, Z., Q. Zhang, W. Wei, S. Cui, W. Tang, and Y. Li. 2020. Determining effects of water and nitrogen inputs on wheat yield and water productivity and nitrogen use efficiency in China: A quantitative synthesis. Agric. Water Manage. 242:106397. (IF = 4.02)
  13. Li, Y., Z. Li, S.X. Chang, S. Cui, S. Jagadamma, Q. Zhang, and Y. Cai. 2020. Residue retention promotes soil carbon accumulation in minimum tillage systems: Implication for conservation agriculture. Sci. Total Environ. 740:140147. (IF = 6.55)
  14. Li, Y., S. Cui, Z. Zhang, K. Zhuang, Z. Wang, and Q, Zhang. 2020. Determine effects of water and nitrogen input on maize (Zea mays) yield, water- and nitrogen-use efficiency: A global synthesis. Sci. Rep. 10:9699. (IF = 3.99)
  15. Li, Y., Z. Li, S. Cui, and Q. Zhang. 2020. Trade-off between soil pH, bulk density and other soil physical properties under global no-tillage agriculture. Geoderma 361:114099. (IF = 4.84)
  16. Yang, X., Z. Li., S. Cui, Q. Cao, J. Deng, X. Lai, and Y. Shen. 2020. Cropping system productivity and evapotranspiration in the semiarid Loess Plateau of China under future temperature and precipitation changes: An APSIM-based analysis of rotational vs. continuous systems. Agric. Water Manage. 229:105959. (IF = 4.02)
  17. Cui, S., Q. Wu, J. West, and J. Bai. 2019. Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease. PLoS Comput. Biol. 15(8):e1007264. (IF = 4.70)
  18. Gulizia, J.P., K.M. Downs, and S. Cui. 2019. Kudzu (Pueraria montana var. lobata) age variability effects on total and nutrient-specific in situ rumen degradation. J. Appl. Anim. Res. 47:433-439. (IF = 1.24)
  19. Li, Y., Z. Li, S. Cui, S. Jagadamma, and Q. Zhang. 2019. Residue retention plus minimum tillage improve physical environment of the soil in cropland: A global meta-analysis. Soil Tillage Res. 194:104292. (IF = 4.60)
  20. Miao, F., Y. Li, S. Cui, S. Jagadamma, G. Yang, and Q. Zhang. 2019. Soil extracellular enzyme activities under long-term fertilization management in the croplands of China: A meta-analysis. Nutr. Cycling Agroecosyst. 114:125-138. (IF = 2.45)
  21. Sharma, S., N. Rajan, S. Cui, S. Maas, K.D. Casey, S. Ale, and R. Jessup. 2019. Carbon and evapotranspiration dynamics of a non-native perennial grass with biofuel potential in the Southern U.S. Great Plains. Agric. Forest Meteorol. 269-270:285-293. (IF = 4.60)
  22. Pirtle, T., L. Rumble, M. Klug, F. Walker, S. Cui, and N. Phillips. 2019. Impact of biochar and different nitrogen sources on forage radish production in Middle Tennessee. J. Adv. Agric. 10:1594-1610.
  23. Li, Y., Z. Li, S. Cui, S.X. Chang, C. Jia, and Q. Zhang. 2019. A global synthesis of the effect of water and nitrogen input on maize (Zea mays) yield, water productivity and nitrogen use efficiency. Agric. Forest Metereol. 268:136-145. (IF = 4.60)
  24. Li, Y., S. Cui, S.X. Chang, and Q. Zhang. 2019. Liming affects soil pH and crop yield depending on lime material type, application method and rate, and crop species: a global meta-analysis. J. Soils Sediment 19:1393-1406. (IF = 2.76)
  25. Yang, X., L. Zheng, Q. Yang, Z. Wang, S. Cui, and Y. Shen. 2018. Modeling the effects of conservation tillage on crop water productivity, soil water dynamics and evapotranspiration of a maize-winter wheat-soybean rotation system on the Loess Plateau of China using APSIM. Agric. Syst. 166:111-123. (IF = 4.21)
  26. Li, Z., X. Yang, S.Cui, Q. Yang, X. Yang, J. Li, Y. Shen. 2018. Developing sustainable crop rotation with conservation tillage practices on the Loess Plateau, a long-term imperative. Field Crops Res. 222:164-179. (IF = 4.30)
  27. Li, Z., X. Lai, Q. Yang, X. Yang, S. Cui and Y. Shen. 2018. In search of long-term sustainable tillage and straw mulching practices for a maize-winter wheat-soybean rotation system in the Loess Plateau of China. Field Crops Res. 217:199-210. (IF = 4.30)
  28. Li, Z., Q. Zhang, Q. Yang, X, Yang, J. Li, S. Cui, and Y. Shen. 2017. Yield, Water Productivity and Economic Return of Dryland Wheat in the Loess Plateau in Response to Conservation Tillage Practices. J. Agric. Sci. 155:1272-1286.  
  29. Sharma, S., N. Rajan, S. Cui, K. Casey, S. Ale, R. Jessup, C. West and S. Maas. 2017. Seasonal variability of evapotranspiration and carbon exchanges over a biomass sorghum field in the Southern US Great Plains. Biomass Bioenergy 105:392-401. (IF = 3.55)
  30. Guo, W., S. Cui, J. Torrion, and N. Rajan. 2015. Data-Driven Precision Agriculture: Opportunities and Challenges. In: Soil-Specific Farming: Precision Architecture. R. Lal, and B. Stewart (eds.). CRC Press. Landon. Chapter 14. p. 353-371.
  31. Attia, A, N. Rajan, G. Ritchie, E. Barnes, S.Cui, A. Ibrahim, D. Hays, Q. Xue, and J. Wilborn. 2015. Yield, quality, and spectral reflectance responses of cotton under subsurface drip irrigation. Agron. J. 107:1355-1364. (IF = 1.68)
  32. Rajan, N., S.J. Maas R. Kellison, M. Dollar, S. Cui, S. Sharma, and A. Attia. 2015. Emitter uniformity and application efficiency for center pivot irrigation systems. Irrig. Drain. 64:353-361. (IF = 1.20)
  33. Rajan, N, S.J. Maas, and S. Cui. 2015. Extreme drought effects on summer evapotranspiration and energy balance of a grassland in the Southern Great Plains. Ecohydrol. 8:1194-1204. (IF = 2.76)
  34. Mirik, M., Y. Emendack, A. Attia, S. Chaudhuri, M. Roy, G.F. Backoulou, and S. Cui. 2014. Detecting musk thistle (Carduus nutans) infection using a target recognition algorithm. Adv. Remote Sens. 3:95–105.
  35. Cui, S., C.J. Zilverberg, V.G. Allen, C.P. Brown, J. Moore-Kucera, D.B. Wester, M. Mirik, S. Chaudhuri, and N. Phillips. 2014. Carbon and nitrogen responses of three old world bluestems to nitrogen fertilization or inclusion of a legume. Field Crops Res. 164:45–53. (IF = 4.30)
  36. Cui, S., E. Youn, J. Lee, and S.J. Maas. 2014. An improved systematic approach to predicting transcription factor target genes using support vector machine. PLoS ONE 9(4): e94519. (IF = 2.74)
  37. Mirik, M., R.J. Ansley, K. Steddom, C.M. Rush, G.J. Michels, Jr., F. Workneh, S. Cui, and N.C. Elliott. 2014. High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier. J. Appl. Remote Sens. 8(1):083661. (IF = 1.36)
  38. Cui, S., N. Rajan, S.J. Maas, and E. Youn. 2014. An automated soil line identification method using relevance vector machine. Remote Sens. Lett. 5:175–184. (IF = 2.29)
  39. Rajan, N, S.J. Maas, and S. Cui. 2013. Extreme drought effects on carbon dynamics of a pasture in the semi-arid Southern High Plains of Texas. Agron. J. 105:1749–1760. (IF = 1.68)
  40. Cui, S., V.G. Allen, C.P. Brown, and D.B. Wester. 2013. Growth and nutritive value of three old world bluestems and three legumes in semiarid Texas High Plains. Crop Sci. 53:329–340. (IF = 1.87)
  41. Li, C., Z. Nan, S. Cui, Y. Hu, D. Li, Q. Li, Y. Li, and Z. Li. 2003. Pathogenicity of ten fungi affecting three common cool-season turfgrasses of China. Pratacultural Sci. 20:75–77.

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Courses

PLSO 3700 Fundemental Precision Agric.

PLSO 3330 Field Crop Prod.

PLSO 4310 Forage Crop Prod.

PLSO 4690 Crop Ecophysiology

AGRI 4710/5710 Agric. Stats. Data Anal.