OMER
VANLI
Cell Number: +905054539286
Email: ovanli@gmail.com / vanli@itu.edu.tr
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PROFESSIONAL
PREPARATION:
2011-2019 Ph.D. Geographical
Information Technology program, Dept. of Applied Informatics, Istanbul
Technical University, Istanbul- Turkey.
Thesis Title: Wheat Yield
Estimation Using DSSAT Crop Simulation Model and Indices Obtained from Remote
Sensing: Islahiye and Nurdagi Case.
2002-2007 M.Sc.
Environmental Science, Engineering and Management Program, Dept. of
Environmental Engineering, Istanbul Technical University, Istanbul- Turkey.
Thesis Title: Removal of Pb, Cd, B
Elements from Soil By Chelate Assisted Phytoremediation Method.
1993-1997 B.Sc. Field Crops
dept. Faculty of Agriculture Engineering, Harran University, Sanliurfa-Turkey
EDUCATIONAL / PROFESSIONAL BACKGROUND AND AWARDS:
·
1999-2011,
Union Of Chambers Of Turkish Engineers and Architects (TMMOB), The
Chamber of Agricultural Engineers, member in Gaziantep and Istanbul,
Turkey.
·
2004-2007,
ITU Ecology Club Presidency, President, Istanbul, Turkey.
·
2006,
3. Organic Agriculture Symposium held in Yalova Turkey "most original
poster" Yalova, Turkey.
·
2006,
Increase in Yield and Quality in Red Pepper Production and Reduction of Alpha
toxin Amount ”European Union Project, Specialist Islahiye, Gaziantep, Turkey.
· 2013, ArcGIS advanced training organized by Islem GIS, Istanbul, Turkey.
·
2015, “Art of Modeling” course organized by Wageningen University, Netherlands.
· 2015, “DSSAT usage” course organized by Bahri Dağdaş International Agricultural Research Institute, Konya, Turkey.
· 2016, "Tell me about your thesis in 3 minutes" competition organized by Gebze Technical University, Gebze, Kocaeli, Turkey.
· 2016, "AgMIP Mid-Term (TOA-MD)" workshop organized by Faisalabad Agricultural University, Faisalabad, Pakistan.
· 2017, One-year doctorate research visit at the University of Florida with a scholarship provided by TÜBİTAK BİDEB 2214-A, Gainesville, Florida, USA.
RESEARCH PUBLICATIONS:
National and International Proceedings:
·
Vanlı
Ö., Genetically Modified
Agricultural Products and Risk Management, Symposium on Modern Methods in
Science, Kocaeli, Turkey 2005. (participant)
·
Yazgan
M.S., Vanli O., Chelate Assisted
Phytoremediation of Boron from Soils, International Phytotechnology Society
Meeting, 24-26 September 2006, Denver, USA.
·
Vanlı
Ö., Phytoremediation
Technique for Cleaning of Heavy Metals Contaminated Soil, 3th Organic
Agriculture Symposium 1 - 4 November, 2006, Yalova, Turkey (participant)
·
Vanli O., Yazgan M.S., Chelate Assisted
Phytoremediation of Cadmium and Lead from Soils, 14th International Symposium
on Environmental Pollution and its Impact on life in the Mediterranean Region.
MESEAP Symposium, Sevilla, Spain, 2007. (participant)
·
Vanlı
Ö., Şahin İlknur K., Removal of the Boron Element by Chelate-supported Phytoremediation Method,
7th International Participation of Turkish Toxicology Association Congress, 30
May - 01 June 2009, METU Culture and Congress Center, Ankara, Turkey
(participant)
·
Vanlı Ö., Yıldız V., Şahin
İlknur K., The Role of Ecology Parks in the Integration of Universities
with Society, IX. National Ecology and Environment Congress, 7 - 10 October
2009, Nevsehir, Turkey (participant)
· Şahin İlknur K., Vanlı Ö., Possibilities to Clean the Environmental Pollution by Phytoremediation Method and Plants with Gene Transfer IX. National Ecology and Environment Congress, 7 - 10 October 2009, Nevsehir, Turkey (participant)
· Vanli O, Ustundag B.B., Sertel E., Determination of Leaf Area Index Value in the Pepper Fields with Different Method Approaches Using the Remote Sensing and LAI Analyzer Sensor, Poster Presentation, AgMIP 6. Workshop, Montpellier, France, 2016. (participant)
·
Vanli O.,
Sabuncu A., Uça Avcı Z. D., Regional Classification of Winter Wheat Using
Remote Sensing Data in Southeastern Turkey, International Conference on
Agro-Geoinformatics, 16-19 July 2019, Istanbul, Turkey. (participant)
National and International Papers:
·
Vanli O., Yazgan M.S., Chelate Assisted
Phytoremediation of Pb, Cd and B by Sunflower, Maize and Canola, Fresenius
Environmental Bulletin Journal, 2015, Volume 24, Issue 9.
·
Vanli O., Ustundag B.B., Ahmad I., M.
Hernandez-Ochoad I., Hoogenboom G., 2019: Using Crop Modeling to Evaluate the
Impacts of Climate Change on Wheat in Southeastern Turkey. Environmental Science and Pollution Research doi:
10.1007/s11356-019-06061-6
·
Demirel,
M.C.; Özen, A.; Orta, S.; Toker, E.; Demir, H.K.; Ekmekcio, Ö.; Tay, H.; Eruçar,
S.; Erdem, H.; Melih, M.; Vanlı, Ö.;
et al. Additional Value of Using Satellite-Based Soil Moisture and Two Sources
of Groundwater Data for Hydrological Model Calibration. Water (Switzerland)
2019, 11, 2083.
·
Vanlı Ö., Ustundag BB., Machine Learning Framework for Yield forecasting of Winter
Wheat at Southeastern , Turkey. pg.1–12. Journal of Indian Social Remote Sensing
(submitted manuscript, October 2019)
TECHNICAL
SKILLS:
·
Use of Crop Models (DSSAT) in agriculture
·
Climate Change Impact assessment and
development of Adaptations strategies using crop models for different cropping
system
·
Use of Remote sensing for regional yield
forecasting and seasonal and interannual variability assessment
·
Developed Machine Learning Algorithm for satellite
Image Classification
·
R programing for plotting, image
classification, data analysis and for crop models run at regional scale
·
QGIS and ArcGIS for Mapping and data
interpolation
VISITED
COUNTRIES:
·
Florida-USA
·
Wageningen-Netherlands
·
Montpellier-France
·
Seville-Spain
·
Faisalabad-Pakistan
PROFICIENCY IN LANGUAGES:
·
Turkish,
Kurdish (Basic)
·
English, Arabic (Second Language)
RESEARCH AND
TEACHING INTERESTS:
I am broadly
interested in agricultural sciences with particular emphasis on decision
support systems such as crop modeling, remote sensing and geographical
information systems. In particular, my future education interests are adopt the
use of technological facilities to researchers and especially students. As a
graduated in interdisciplinary fields namely undergraduate agricultural
engineering, master environmental engineering and doctorate in geographic
information systems, The use of technological decision support systems and
early warning systems in environmental problems and safe food production has
become very important in the world (Shelia et al., 2019). My research
agenda focuses on the question of how to use these decision support systems
effectively to teaching relevant students.
Dissertation
Research – Using Crop models and Remote sensing systems
With the developed computer technology, it is
also possible to use technological tools such as Crop Simulation Models (CSM),
Remote Sensing (RS) and Geographic Information Systems (GIS). This provides
important data for healthy monitoring and evaluation of production on a point
and area basis. Thus, it is provided to make more accurate decisions with
reliable results for the management of agricultural areas at local, regional,
national and even global scale. Crop Simulation Models, also referred to as crop
growth, crop development, crop simulation and crop climate, are allow analysis
practices such as yield and risk estimates of the plant's growth and
development as a function of soil and climate, plant characteristics and
management practices (Jones et al.,
2003). In addition, there may be healthy
assessment and solution methods for the agricultural condition of the region
related to obtaining indices from different reflection rates to determine
information about plants in larger agricultural areas by remote sensing (Alganci,
Ozdogan, Sertel, & Ormeci, 2014) . Finally, GIS are specified as the
information system that collects stores and analyzes information and obtained
from all these regional location-based processes, and finally provides them
with mapping (Yomralıoğlu,
2009). In agricultural applications,
various methods are used to understand the relationship between input data such
as fertilization and irrigation and yield output information such as product
and biomass. Models can simulate the environmental components that the plant
interacts with at all stages of the growing process based on mathematical
background.
In my dissertation research named “Wheat yield
estimation using DSSAT crop simulation model and indices obtained from remote
sensing: islahiye and nurdagi case” I obtained important findings about my
agricultural research region.
The study area is İslahiye and Nurdagi
agricultural plains in a fertile valley, the region has favorable climate, and
first class agricultural soil for agricultural production and it is possible to
grow high quality economic products with planned agricultural production. Among
the eighteen wheat fields in the 2016-2017 growing season, high yielding fields
such as Yelliburun well location village field, in front of Yelliburun village
field, Mali Akınyolu village field and Selver village field were used for
model calibration. Moreover, medium yielding fields such as Sakçagözü village
field, Bizim Akınyolu village field, Çetin Akınyolu village field and
Gözlühöyük village field were used for model performance testing.
Among the data, minimum and maximum
temperature, average rainfall, relative humidity and solar radiation were used
as climate data. As soil data, that is one of the most important components,
general soil information, soil surface information and soil layer parameters
such as structure, texture, pH, organic matter and nitrogen content were used.
As maintenance data, another data group, included information such as planting
date, planting method, planting depth, number of plants per m2,
fertilization/irrigation/harvest amounts and dates. Finally, as the observed
and measured data in the actual field collected for comparison with the model
estimation results, the above ground crop weight, stem and leaf weight, sibling
number, yield, biomass, anthesis and physiological maturity time, leaf area
index were collected. The plant phenology and development (P1V and P1D), then growth
(P5, PHINT) and finally yield (G1, G2, G3) parameters were calibrated. The
performance of the model was evaluated using RMSE and % error between observed
and simulated values. In order to determine the effects of climate change in
the region, they were examined in the RCP 4.5 and 8.5 scenarios of three global
climate models for mid-century (2036-2065) and end-century (2066-2095).
According to previous climate change forecasts, global temperature will
increase by 2.5 ° C in 2050. The increase in temperatures predicts that it may
reduce future agricultural productivity, especially in semi-arid regions in
Turkey.
Apart from all these, another technological
tool used in agricultural field is remote sensing systems. It plays a role as
an important data source in the production of different spatial - temporal
resolution information with the images obtained from agricultural fields. By
using radiation reflected from the canopy of plants, regional yield values can
also be determined by calculating the vegetation indices such as land
classification and NDVI. The fields obtained from the Farmer Registration
System in the Nurdagi and İslahiye plains were used for yield estimation.
In addition, data such as parcel area, crop species, planting and harvest dates
and yield values were also used. Moreover, 13 Landsat-8 images from 17 November
2016 to 29 June 2017 were used. Eight machine-learning algorithms were used for
spatial distribution of wheat. NDVI values were calculated at 16-day intervals
for each field throughout the season, and the yield prediction model was
developed with the Bootstrapping method. The LASSO regression model was also
successfully used for regional yield estimates. Significant values were
obtained in all analysis results.
Regarding the genetic coefficients for the
Golia cultivar, the days for the optimum vernalization (P1V) were slightly
higher, while the photoperiodic requirement (PID) and the thermal time (P5)
causing the grain filling were slightly higher. While G1 and G2 were found to be
balancing each other, G3 coefficient was found to normal value as a parameter
related to biomass production and plant height. At the end of the calibration,
the measured and simulated values of the maximum LAI were close
to each other with -5.26 % error and 0.21 root mean square error, while yield
was below the measured value with -11.32 % error and 586 kg / ha RMSE. It
showed a close agreement with -9.56 % error and 896 kg/ha RMSE in above ground
plant weight. According to the results of climate change projection in
Turkey's southeast, in the mid-century (2065), maximum temperature will
increase from 1.6 °C (RCP 4.5) to 2.3 °C (8.5 RCP); minimum temperature will
increase from 0.6 °C (RCP 4.5) to 1.9 °C (RCP 8.5). In the end-century (2095),
maximum temperature will increase from 2 °C (RCP 4.5) to 4 °C (RCP 8.5),
minimum temperature will increase from 1 °C (RCP 4.5) to 3.4 °C (RCP 8.5). In
the future temperature increase, wheat yield will decrease in İslahiye
with 16.3 % by the mid-century and with16.8 % by the end- century. In Nurdagi,
the model showed that it will decrease with 13.4 % in the mid-century and 14.4
% at the end-century.
Another regional yield analysis study, the
results of NDVI indices obtained from satellite images showed a close agreement
between the observed and predicted yields for both regions. In Nurdagi
(2013-2017), it was recorded as the root mean square error value which is
higher with 145 kg/ha for 5 years, while in Islahiye it was recorded with a
root mean square error of approximately 70 kg/ha. In the Nurdagi region, the
error between observed and estimated yield ranged from 1.96 % to 10.61 % for 5
years. However, the error in the Islahiye region ranged from 0.81 % to 7.65 %.
As a result, the calibrated DSSAT model
CERES-Wheat module and also NDVI values estimated the regional yield are useful
methods. This method can also be used easily for other regions and crops of
Turkey.
Future Research
Directions- New Projects
In parallel with the developing technology, I
would like to work to adopt the importance of decision support systems such as
GIS and Remote Sensing to students and relevant stakeholders. Moreover, I think
there are many projects to be done in understanding, using, and expanding the
studies in this field.
Selected References
Alganci, U., Ozdogan, M., Sertel, E., & Ormeci, C.
(2014). Estimating maize and cotton yield in southeastern Turkey with
integrated use of satellite images, meteorological data and digital photographs.
Field Crops Research, 157, 8–19. doi:10.1016/j.fcr.2013.12.006
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K.
J., Batchelor, W. D., Hunt, L. A., … Ritchie, J. T. (2003). The DSSAT
cropping system model. European Journal of Agronomy (Vol. 18).
Elsevier. doi:10.1016/S1161-0301(02)00107-7
Shelia, V., Hansen, J., Sharda, V., Porter, C.,
Aggarwal, P., Wilkerson, C. J., & Hoogenboom, G. (2019). A Multi-scale and
Multi-model Gridded Framework for Forecasting Crop Production, Risk Analysis,
and Climate Change Impact Studies. Environmental Modelling & Software,
115(February), 144–154. doi:10.1016/J.ENVSOFT.2019.02.006
Yomralıoğlu, T. (2009). Coğrafi
Bilgi Sistemleri, temel kavramlar ve uygulamalar (5.). Trabzon.