Web of Science Journals:

12. Yamaç, S.S., Kurtuluş, B., Memon, A.M., Alomair, G., Todorovic, M., 2024. Are supervised learning methods suitable for estimating crop water consumption under optimal and deficit Irrigation?. Agronomy, 14, 532. DOI: 10.3390/agronomy14030532

11. Sezen, S.M., Yamaç, S.S., Bozdoğan Konuşkan, D., Yilmaz, I., Yıldız, M., Kara, O., Maambo, C.M., 2023. Comparison of the partial root drying and conventional drip irrigation regimes on seed, oil yield quality, and economic return for peanut crop. Irrigation Science, 41, 603-628. DOI: 10.1007/s00271-023-00854-x

10. Eyshi, E., Gohar, G., Yamaç, S.S., 2023. Crop production in Türkiye: trends and driving variables. Environmental Research Communications, 5, 031001. DOI: 10.1088/2515-7620/acbd1e

9. Memon, A.M., AlHems, L.M., Yamaç, S.S., Barry, M.S., Alam, A., AlMuhanna, A., 2022. Aquaponics in Saudi Arabia: initial steps towards addressing food security in the arid region. Agriculture, 12(12), 2094. DOI: 10.3390/agriculture12122094

8. Yamaç, S.S., Negiş, H., Şeker C., Memon, A.M., Kurtuluş, B., Todorovic, M., Alomair, G., 2022. Saturated hydraulic conductivity estimation using artificial intelligence techniques: a case study for calcareous alluvial soils in a semiarid region. Water, 14(23), 3875. DOI: 10.3390/w14233875

7. Yamaç, S.S., 2021. Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area. Agricultural Water Management, 254, 106968. DOI: 10.1016/j.agwat.2021.106968

6. Yamaç, S.S., 2021. Reference evapotranspiration estimation with kNN and ANN models using different climate input combinations in the semi-arid environment. Journal of Agricultural Sciences, 27(02), 129-137. DOI: 10.15832/ankutbd.630303

5. Yamaç, S.S., Şeker C., Negiş, H., 2020. Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area. Agricultural Water Management, 234, 106121. DOI: 10.1016/j.agwat.2020.106121

4. Yamaç, S.S., Todorovic, M., 2020. Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data. Agricultural Water Management, 228, 105875. DOI: 10.1016/j.agwat.2019.105875

3. Capelli, G., Yamaç, S.S., Stella, T., Francone, C., Paleari, L., Negri, M., Confalonieri, R., 2015. Are advantages from partial replacement of corn with second generation energy crops undermined by climate change? A case study for giant reed in Northern Italy. Biomass and Bioenergy, 80, 85-93. DOI: 10.1016/j.biombioe.2015.04.038

2. Stella, T., Francone, C., Yamaç, S.S., Ceotto, E., Pagani, V., Pilu, R., Confalonieri, R., 2015. Reimplementation and reuse of the Canegro model: From sugarcane to giant reed. Computers and Electronics in Agriculture, 113, 193-202. DOI: 10.1016/j.compag.2015.02.009

1. Cantore, V., Wassar, F., Yamaç, S.S., Sellami, M.H., Albrizio, R., Stellacci, A.M., Todorovic, M., 2014. Yield and water use efficiency of early potato grown under different irrigation regimes. International Journal of Plant Production, 8, 409-428. DOI: 10.22069/IJPP.2014.1617


1. Özgür A., Yamaç S.S., 2020. Modelling of daily reference evapotranspiration using deep neural network in different climates.

Other Peer-Reviewed Journals:

4. Yamaç, S. S., 2023. Ereğli/Konya Bölgesindeki Tarım Arazilerinin Evapotranspirasyon Verilerinin Uydu Görüntüleriyle İncelenmesi. Ereğli Tarım Bilimleri Dergisi, 3(1), 8-15. DOI : 10.54498/ETBD.2023.17

3. Yamaç S.S., 2020. Forecasting of long-tern sugar beet water requirement in the Middle Anatolia, Turkey. Alınteri Journal of Agriculture Science, 35(2), 7-13. DOI: 10.47059/alinteri/V35I2/AJAS20068

2. Yamaç S.S., 2018. Estimation of long-term reference evapotranspiration using limited weather data in sugar beet plantation area from Middle Anatolian, Turkey. Süleyman Demirel Üniversitesi Ziraat Fakültesi Dergisi, 1. Uluslararası Tarımsal Yapılar ve Sulama Kongresi Özel Sayısı, 160-165.

1. Kumar N., Yamaç S.S., Velmurugan A., 2015. Applications of remote sensing and GIS in natural resource management. Journal of the Andaman Science Association, 20, 1-6.

International Books:

1. Eyshi, E.E., Yamaç, S.S., 2023. Modelling the impacts of climate change on agriculture in North Africa and Southwest Asia. In: Nendel, C. (Eds.), Modelling climate change impacts on agricultural systems. Burleigh dodds science publishing, Cambridge, UK.

National Books:

2. Yamaç S.S., 2021. Pandemi sonrası yeni nesil tarım, Bölüm adı: Tarımsal sistemlerin analizi ve modellenmesi, Bilimsel Kitap - Yayın Evi: Sonçağ Akademi - Basım Sayısı:1 - Sayfa Sayısı:298 - ISBN: 978-625-7333-72-6- (Bölüm Sayfaları:149 - 170) (in Turkish)

1. Yamaç S.S., 2020. Patates, Bölüm adı: Patates ve Sulama, Bilimsel Kitap - Yayın Evi:Bey Medya San.Tic.Ltd.Şti. - Basım Sayısı:1 - Sayfa Sayısı:174 - ISBN:978-625-400-971-6 - (Bölüm Sayfaları:79 - 85) (in Turkish)

Conference Proceedings:

15. Tokluoğlu, E., Kurtuluş B., Yamaç, S.S., Pekkan, E., Sağır, Ç., 2024. Advancements in GIS Through High-Resolution Earth Observation: The Role of PlanetScope Satellites in Sustainable Development and Precision Agriculture, Ahi Evran 4th International Conference on Scientific Research, 26-28 April, Kırşehir, Türkiye.

14. Wong, J., Butsic, V., Müller, D., Yamaç, S.S., Adrah, E., Yin, H., 2024. The effects of armed conflicts on cropland in Syria. American Association of Geographers (AAG), 16 - 20 April, Honolulu, USA.

13. Alomair, G., Yamaç, S.S., Memon A.M., Kurtuluş B., 2023. Evaluation of random forest method for crop estimation in Al-Ahsa Oasis - Saudi Arabia. The Joint Statistical Meetings, 5- 10 August, Toronto, Canada.

12. Yin, H., Müller, D., Butsic, V., Yamaç, S.S., 2022. Effects of the Syrian Civil War on Cropland in Syria. American Geophysical Union (AGU) Meeting, 12-16 December, Chicago, USA.

11. Rezaei, E.E., Yamaç, S.S., Ghazaryan, G., Moradi, R., Dubovyk, O., Siebert, S., 2022. Cropping systems in the Middle East: Production is primarily controlled by harvested area, not yield. 17th European Society for Agronomy, 29 August- 2 September, Potsdam, Germany.

10. Yamaç S. S., 2019. Comparing Artifical Neural Network and Hargreaves-Samani Method for Estimating Sugar Beet Crop Evapotranspiration in Çumra. 3rd International Eurasian Agriculture and Natural Sciences Congress, 17-20 October, Antalya, Türkiye.

9. Yamaç S. S., 2019. Artifical Neural Network Algorithm for estimating sugar beet irrigation water requirement in Çumra. 1st International Congress on Biosystems Engineering, 24-27 September, Hatay, Türkiye.

8. Yamaç S. S., 2019. Prediction of reference evapotranspiration by k-nearest neighbor method in Çumra. International Conference on Computer Technologies and Applications in Food and Agriculture, 11-12 July, Konya, Türkiye.

7. Yamaç S. S., 2019. Potential of machine learning techniques for estimating soil water capacity. International Soil Congress, 17-19 June, Ankara, Türkiye.

6. Yamaç S. S., Yılmaz, B., 2018. Agricultural system modelling and its applications in precision agriculture. 1st International Congress on Agricultural Structures and Irrigation, 26-28 September, Antalya, Türkiye.

5. Yamaç S. S., 2018. Estimation of long-term reference evapotranspiration using limited weather data in sugar beet plantation area from Middle Anatolian, Turkey. 1st International Congress on Agricultural Structures and Irrigation, 26-28 September, Antalya, Türkiye. 

4. Aksoy L., Yamaç S. S., 2018. Management guidelines for slow onset disasters: The case of climate change and Central Anatolian Drought. EconWorld, 23-25 January, Lisbon, Portugal.

3. Kumar N., Voigt H., Yin H., Yamaç S. S., 2015. Nexus of multi-disciplines for sustainable water resource management: concepts, interlinkages and models. Global Water System Project (GWSP) - International Conference of Sustainable Development Goals: A Water Perspective, 17-18 August, Bonn, Germany.

2. Capelli, G., Stella, T., Yamaç, S. S., Francone, C., Paleari, L., Negri., M., Confalonieri, R. 2014. In silico evaluation of giant reed productivity in a changing climate: the case of Lombardy plain northern Italy. 13th European Society for Agronomy Congress, 25-29 August, Debrecen, Hungary.

1. Stella, T., Pagani, V., Francone C., Yamaç S. S., Finotto, G., Bregaglio, S., Ceotto E., Confalonieri R., 2014. “Reimplementation and reuse of Canegro model”, 7th International Congress on Environmental Modelling and Software (iEMSs), 15-19 June, San Diego, USA.