Juan Landivar Bowles, Ph.D.

Juan Landivar Bowles, Ph.D.

Juan Landivar Bowles, Ph.D.

Professor and Center Director, Research
Texas A&M AgriLife Research and Extension Center
10345 Hwy 44, Corpus Christi, TX 78406
361-265-9201
Jalandivar@ag.tamu.edu

 

Main Links

Presentations
Cotton Management
UAS hub
UAS Program
Corpus AgriLife page
Weslaco Agrilife page
AgriLife web page

Brief Profile:

Dr. Juan Landivar Bowles earned a bachelor’s degree in crop science in 1976, a master’s degree in plant genetics in 1979 and doctorate in crop physiology in 1987, from Mississippi State University.   In 1988, joined the Texas A&M System as a cropping systems expert at the Corpus Christi center.   Areas of expertise include development of management strategies for the use of growth regulators in cotton and for helping to develop a Crop Weather Station Network with simulation models and management tools for cotton and sorghum farmers.   In 1998, Landivar joined Delta and Pine Land Co. as director of research and technical services for Latin America.  Before returning to Corpus Christi, Dr. Landivar served as vice-president of the company’s board of directors’ joint ventures in Brazil.  Currently (2008 to date) serves as Center Director at Texas A&M AgriLife Research and Extension Centers at Corpus Christi and Weslaco Texas, where he directs programs in the development of cropping systems for Cotton, Sorghum, Wheat, Citrus, Sugar Cane and Vegetables for South Texas, the development of mariculture technology, Beef Cattle reproductive physiology and nutrition and in the development of sustainable biofuel production systems.

Areas of Research interest;

Main objective is the development of crop management tools for the design of economic and environmentally sustainable cropping systems for Texas and beyond.

  1. Physiology of crop growth and yield
  2. Development and application of process level crop simulation models
  3. Mode of action and uses of plant growth regulators
  4. Development of remote sensing systems for research and precision management
  5. Development of UAS based platforms for high throughput phenotyping

SELECTED PUBLICATIONS:

  1. Yeom, J., J. Jung, A. Chang, A. Ashapure, M. Maeda, A. Maeda, Landivar. 2019. Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture. Remote Sensing, 11(13).
  2. Ashapure, A., J. Jung, J. Yeom, A. Chang, M. Maeda, Landivar. 2019. A Novel Framework to Detect Conventional tillage and No-tillage Cropping System Effect on Cotton Growth and Development Using Multi-temporal UAS Data. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 49-64
  3. Enciso, J., C. Avila, J. Jung, S. Elsayed-Farag, A. Chang, J. Yeom, Landivar, M. Maeda, J. Chavez. 2019. Validation of agronomic UAV and field measurements for tomato varieties. Computers and Electronics in Agriculture, 158, 278-283.
  4. Yeom, J., J. Jung, A. Chang, M. Maeda, Landivar. 2018. Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data. Remote Sensing.
  5. Jung, J., M. Maeda, A. Chang, A. Landivar, J. Yeom, J. McGinty. 2018. “Unmanned Aerial System assisted framework for the selection of high yielding cotton genotypes,” Computers and Electronics in Agriculture, 152, pp. 74-82.
  6. Yeom, J., J. Jung, A. Chang, M. Maeda, A. Landivar. 2018. “Automated Open Cotton Boll Detection for Yield Estimation Using Unmanned Aircraft Vehicle (UAV) Data,” Remote Sensing. 10(12), 1895. doi:10.3390/rs10121895
  7. Yang, Y., L.T. Wilson, J. Jifon, A. Landivar, J. da Silva, M.M. Maeda, J. Wang, E. Christensen, 2018. Energycane growth dynamics and potential early harvest penalties along the Texas Gulf Coast. Biomass and Bioenergy 113, 1-14.
  8. Chen, R., T. Chu, J.A. Landivar, C. Yang and M.M. Maeda. 2017. Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images. Precis Agric: 1-17.
  9. Enciso, J., M. Maeda, A. Landivar, J. Jung, A. Chang. 2017. A ground-based platform for high throughput phenotyping. Computers and Electronics in Agriculture: 141: 286-291.
  10. Chang, A., J. Jung, M. Maeda, and A. Landivar. 2017. Crop height monitoring with digital imagery from Unmanned Aerial System (UAS). Computers and Electronics in Agriculture 141: 232-237.
  11. Goolsby, J., J. Jung, Landivar. 2016. Evaluation of Unmanned Aerial Vehicles (UAVs) for detection of cattle in the Cattle Fever Tick Permanent Quarantine Zone. Subtropical Agriculture and Environments.
  12. Tianxing Chu, Ruizhi Chen, Juan A. Landivar, Murilo Maeda, Chenghai Yang, Michael Starek. 2016. Cotton growth modeling and assessment using unmanned aircraft system visual-band imagery. Journal of Applied Remote Sensing. 10(3). http://dx.doi.org/10.1117/1.JRS.10.036018
  13. Landivar, J. A., R Sui, C. J. Fernandez and C. W. Livingston. Evaluation of Cotton Genotypes Performance Using Ground-Based Remote Sensing. Beltwide Cotton Research Conference.  San Antonio, Texas, Jan. 2105.  National Cotton Council of America
  14. Yang, C., G.N. Odvody, C.J. Fernandez, A. Landivar, R.R. Minzenmayer, R. L. Nichols. 2014. Monitoring cotton root rot progression within a growing season using airborne multispectral imagery. Journal of Cotton Science. 18(1):85-93.
  15. Yang, C., G.N. Odvody, C.J. Fernandez, A. Landivar, R.R. Minzenmayer, R. L. Nichols. 2014. Evaluating unsupervised and supervised image classification methods for mapping cotton root rot. Precision Agriculture (Springer Science). DOI 10.1007/s11119-014-9370-9. Sep. 2114.
  16. Landivar, J.A., K. Raja Reddy and H. F. Hodges 2010. Physiological Simulation of Cotton Growth and Yield. Chapter 28. In Physiology of Cotton. J. Steward, D. Oosterhuis, J. Heithold and J. Mauney (eds.). Pp. 318-331. Springer, New York.
  17. Kiniry, J.R., A. Landivar, M.Witt, T.Gerik, J. Cavero and LJ. Wade.1998. Radiation use efficiency response to vapor pressure deficit for maize and sorghum. Field Crop Res. 56:265-270.
  18. Davidonis, G.H., A. Johnson and A. Landivar. 1997. Cotton Mote Frequency Under Rainfed and Irrigated Conditions. Crop Science Research Journal.
  19. Landivar, J.A. 1993. PMAP. A Plant Map Analysis Program for Cotton. Texas Agricultural Experiment Station. MP 1740. Texas Agricultural Experiment Station. College Station, TX.
  20. Ring, D.R., J.H. Benedict, A. Landivar, and B.R. Eddleman. 1993. Economic Injury Levels and Development and Application of Response Surfaces Relating Insect Injury. Environ. Entomol. 22:273-282.
  21. Baker, D. N. and A. Landivar, 1991. The Simulation of Plant Development in GOSSYM, Chapter 14. _In Predicting Crop Phenology, Tom Hodges (ed). CRC Press, Boca Raton, FL. 233 Pages.
  22. Landivar, J.A., D.N. Baker and J.N. Jenkins. Application of GOSSYM to Genetic Feasibility Studies. II. Analysis of Increasing Photosynthesis, Specific Leaf Weight, and Longevity of Leaves in Cotton. Crop Sci. 23:504-510.
  23. Landivar, J.A., D.N. Baker and J.N. Jenkins. 1983. Applications of GOSSYM to Genetic Feasibility Studies. I. Analysis of Fruit Abscission and Yield in Okra-Leaf Cottons. Crop Sci. 23:487-50

 

Comments are closed.