Assistant Professor - Climate & agriculture, remote sensing for agriculture & water mgmt, vadose zone hydrology, agri-modeling, data assimmilation.
Climate & agriculture, remote sensing for agriculture & water mgmt, vadose zone hydrology, agri-modeling, data assimmilation.
Ph.D., Water Engineering & Management, Asian Institute of Technology, Thailand
M.Sc., Water Engineering & Management, Asian Institute of Technology, Thailand
B.Sc., Agricultural Engineering (magna cum laude), Mariano Marcos State University, Philippines
Climate and agriculture, remote sensing, agricultural modeling, vadose zone hydrology, data assimilation, optimization, inverse modeling, agro-information service
Ines' group works on the integrations of advanced modeling techniques, in-situ/proximal/remote sensing and advanced climate information for decision supports in agriculture and water management. Using agricultural models, data science, simulation and optimization techniques, we seek to understand the complex processes of the agricultural production system (at different scales) and its interactions with climate, soil, plants/livestock and management to: elucidate adaptation and mitigation strategies to climate variability and change; assess adoption potential and impact of new technologies on smallholder farming system performance and food security; study potentials of social-ecological agricultural systems; elucidate drivers of sustainable intensification and improved soil and water management. We are interested to explore the whole value-chain of data generation-integration-modeling-value-added information-dissemination for a smarter decision support in agriculture.
Mishra, A., Ines, A.V.M., Das, N.N., Khedun, C.P., Singh, V.P., Sivakumar, B. and J.W. Hansen. 2015. Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. Journal of Hydrology. DOI: 10.1016/j.jhydrol.2014.10.038.
Greene, A.M., Goddard, L. Gonzalez, P.L. Ines, A.V.M. and J. Chryssanthacopoulos. 2015. A climate generator for agricultural planning in southeastern South America. Agricultural and Forest Meteorology. 203: 217?228. doi: 10.1016/j.agrformet.2015.01.008
Honda, K., A.V.M. Ines, A. Yui, A. Witayangkurn, R. Chinnachodteeranun and K. Teeravech. 2014. Agriculture information service built on geospatial data infrastructure and crop modeling. International Workshop on Web Intelligence and Smart Sensing, IWWISS '14, Sep 01-02 2014, Saint Etienne, France. ACM 978-1-4503-2747-3/14/09 http://dx.doi.org/10.1145/2637064.2637094 (peer-reviewed).
Honda Kiyoshi, Akihiro Yui, Amor V.M. Ines, Rassarin Chinnachodteeranun, Apichon Witayangkurn, Yuka Matsubara, Hirotomo Nagai and Jun Miyamoto. 2014. FieldTouch: An Innovative Agriculture Decision Support Service Based on Multi-scale Sensor Platform. 2014 Annual SRII Global Conference (SRII pp. 228-229, doi:10.1109/SRII.2014.39 (peer-reviewed)
Garcia, K., Lasco, R.D., Ines, A.V.M., Lyon, B. and F. Pulhin. 2013. Predicting geographic distribution and habitat suitability due to climate change of selected threatened forest tree species in the Philippines. Applied Geography. 44: 12-22 doi: 10.1016/j.apgeog.2013.07.005.
Ines, A.V.M., Das, N.N., Hansen, J.W. and E.G. Njoku. 2013. Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction. Remote Sensing of Environment. 138: 149?164. doi: 10.1016/j.rse.2013.07.018. (Top 4 Most Downloaded Article)
Shin, Y., Mohanty, B.P. and A.V.M. Ines. 2013. Estimating effective soil hydraulic properties using spatially distributed soil moisture and evapotranspiration products at multiple scales. Vadose Zone Journal. 12: 1-16. doi: 10.2136/vzj2012.0094.
Ines, A.V.M., Mohanty, B.P. and Y. Shin. 2013. An unmixing algorithm for remotely sensed soil moisture. Water Resources Research. 49: 408-425, doi:10.1029/2012WR012379.
Koide, N., Robertson, A.W., Ines, A.V.M., Qian, J., DeWitt, D. and A. Lucero. 2013. Predictability of rice production in the Philippines with seasonal climate forecasts. Journal of Applied Meteorology and Climatology. 52: 552-569. doi:10.1175/JAMC-D-11-0254.1
Mishra, A.K., Ines, A.V.M., Singh, V.P. and J.W. Hansen. 2013. Extraction of information contents from downscaled precipitation variables for crop simulations. Stochastic Environmental Research and Risk Assessment. 27: 449-457. doi: 10.1007/s00477-012-0667-9.
Shin, Y., Mohanty, B.P. and A.V.M. Ines. 2012. Soil hydraulic properties in layered soil profile using layer-specific soil moisture assimilation scheme. Water Resources Research. 48, W06529, doi:10.1029/2010WR009581.
Charoenhirunyingyos, S., Honda, K., Kamthonkiat, D. and A.V.M. Ines. 2011. Soil hydraulic parameters estimated by satellite information through data assimilation. International Journal of Remote Sensing. 32 (23): 8033-8051.
Ines, A.V.M., Hansen, J.W. and A.W. Robertson. 2011. Enhancing the utility of daily GCM rainfall for crop yield prediction. International Journal of Climatology. 31 (14): 2168-2182
Joshi, C., Mohanty, B. P., Jacobs, J. and A.V.M. Ines. 2011. Spatio-temporal analysis of soil moisture at different hydro-climatic regions. Water Resources Research. 47, W01508, doi:10.1029/2009WR009002.
Charoenhirunyingyos, S., Honda, K., Kamthonkiat, D. and A.V.M. Ines. 2011. Soil moisture estimation from inverse modeling using multiple criteria functions. Computers and Electronics in Agriculture. 75(2): 278-287.
Sahoo, D., Smith, P. H. and A.V.M. Ines. 2010. Autocalibration of HSPF for simulation of streamflow using genetic algorithm. Transactions of the American Society of Agricultural and Biological Engineers 53: 75-86.
Ines, A.V.M. and B.P. Mohanty. 2009. Near-surface soil moisture assimilation for quantifying effective soil hydraulic properties using genetic algorithm. II. with air-borne remote sensing during SGP97 and SMEX02. Water Resources Research. doi:10.1029/2008WR007022.
Ines, A.V.M. and B.P. Mohanty. 2008. Near-surface soil moisture assimilation to quantify effective soil hydraulic properties under different hydro-climatic conditions. Vadose Zone Journal. 7:39-52. doi:10.2136/vzj2007.0048.
Ines, A.V.M. and B. P. Mohanty. 2008. Parameter conditioning with a noisy Monte Carlo genetic algorithm to estimate effective soil hydraulic properties from space. Water Resources Research. Vol. 44, W08441, doi:10.1029/2007WR006125.
Ines, A.V.M. and B.P. Mohanty. 2008. Near-surface soil moisture assimilation to quantify effective soil hydraulic properties using genetic algorithm. I. Conceptual modeling. Water Resources Research. Vol. 44, W06422, doi:10.1029/2007WR005990.
Robertson, A.W., Ines, A.V.M. and J.W. Hansen. 2007. Downscaling of seasonal precipitation for crop simulation. Journal of Applied Meteorology and Climatology. 46: 677-693.
Ines, A.V.M., Honda, K., Gupta, A.D., Droogers, P. and R.S. Clemente. 2006. Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture. Agricultural Water Management. 83: 221-232.
Ines, A.V.M. and J.W. Hansen. 2006. Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology. 138: 44-53. (Highly Cited Paper)
Hansen, J.W., Challinor, A., Ines, A.V.M., Wheeler, T. and V. Moron. 2006. Translating climate forecasts into agricultural terms: Advances and challenges. Climate Research. 33(1): 27-41.
Ines, A.V.M. and K. Honda. 2005. On quantifying agricultural and water management practices from low spatial resolution RS data using genetic algorithms: a numerical study for mixed pixel environment. Advances in Water Resources. 28: 856-870.
Hansen, J.W. and A.V.M. Ines. 2005. Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agricultural and Forest Meteorology. 131: 233-246.
Chemin, Y., Honda, K. and A.V.M. Ines. 2005. Genetic algorithm for assimilating remotely sensed evapotranspiration data using a soil-water-atmosphere-plant model - implementation issues. Technical letter. International Journal of Geoinformatics. 1(1):87-90.
Ines, A.V.M. and P. Droogers. 2002. Inverse modelling in estimating soil hydraulic functions: a genetic algorithm approach. Hydrology and Earth System Sciences. 6 (1): 49-65.
Ines, A.V.M. and P. Droogers. 2002. Inverse modeling to quantify irrigation system characteristics and operational management. Irrigation and Drainage Systems. 16 (3): 233-252.
Ines, A.V.M., Gupta, A. D. and R. Loof. 2002. Application of GIS and crop growth models in estimating water productivity. Agricultural Water Management. 54 (3): 205-225.
Developing a graduate course on: Applied Agricultural Systems Modeling
Multiple Regression Tool for Crop Predictability Analysis (Fortran) (http://iri.columbia.edu/~ines/My_Softwares/MLRegression/)
GCM Bias Correction Tool for Cropping System Modeling (Fortran) (http://iri.columbia.edu/~ines/My_Softwares/GCM_Bias_Corr2_Ver0.4a/)
Quasi-analytic Tool for Modeling Solute Transport in the Soil Profile (Fortran) (http://iri.columbia.edu/%7Eines/My_Softwares/QuasiAnalytic/)