Review
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Zeytin Verim Tahmininde Kullanılan Sayısal Modellere İlişkin Bir Literatür Araştırması

Year 2021, Issue: 24, 351 - 358, 15.04.2021
https://doi.org/10.31590/ejosat.900368

Abstract

Verim tahmini, tarımsal ürünlerin pazarlanmasında üreticiler ve diğer paydaşlar için önemli girdi sağlamaktadır. Zeytin verim tahmini çalışmalarında çeşitli yöntemler ile meteorolojik ve fenolojik ölçümlere dayalı verilerin incelendiği görülmektedir. Bu çalışmada, zeytin verim tahminleme çalışmalarına ilişkin bir derleme sunularak, bu kapsamda kullanılmış yaygın yöntemler ele alınmaktadır. İncelenen çalışmalar, polen endeksi ve zeytin verimi tahmini odaklı modeller biçiminde iki ana grupta toplanmıştır. Bununla birlikte, sunulan modellerde yaygın olarak kullanılan yöntemler ile incelenen nitelikler ele alınmış, İzmir’de başlayan zeytin verim tahmini projesi kapsamında elde edilen öncü veriler sunulmuştur. Son olarak, zeytin verim tahmini modellerinde incelenen nitelikler ve kullanılan yöntemlere ilişkin değerlendirmelere yer verilmiştir.

Supporting Institution

T.C. Tarım ve Orman Bakanlığı

Project Number

TAGEM/TSKA D/B/18/A9/P6/1246

Thanks

Bu çalışma, Türkiye Cumhuriyeti Tarım ve Orman Bakanlığı tarafından desteklenen ve Bornova Zeytincilik Araştırma Enstitüsü tarafından yürütülen 'Zeytin Verim Tahmininde Polen Konsantrasyonu ve Bazı İklim Verileri Arasındaki İlişkilerin Belirlenmesi' projesi kapsamında gerçekleştirilmiştir.

References

  • Allen, P.G. (1994). Economic Forecasting in Agriculture. International Journal of Forecasting, 10, 81-135.
  • Basso, B., Cammarano, D., Carfagna, E. (2013). Review of crop yield forecasting methods and early warning systems. Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy, 18-19.
  • Bishan, C., Bing, L., Chixin, C., Junxia, S., Shulin, Z., Cailang, L., Siqiao, Y., Chuanxiu, L. (2020). Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China. Plos one, 15(10), 1-17. https://doi.org/10.1371/journal.pone.0240160
  • Buters, J., Schmidt‐Weber, C., Oteros, J. (2018). Next generation pollen monitoring and dissemination. Allergy, 73, 1944-1945. https://doi.org/10.1111/all.13585
  • Camacho, I., Caeiro, E., Nunes, C., Morais-Almeida, M. (2020). Airborne pollen calendar of Portugal: a 15-year survey (2002–2017). Allergologia et immunopathologia, 48(2), 194-201. https://doi.org/10.1016/j.aller.2019.06.012
  • Celenk, S., Bicakci, A., Tamay, Z., Guler, N., Altunoglu, M. K., Canitez, Y., Malyer, H., Sapan, N., Ones, U. (2010). Airborne pollen in European and Asian parts of Istanbul. Environmental monitoring and assessment, 164(1), 391-402. https://doi.org/10.1007/s10661-009-0901-1
  • Corrales, D. C., Corrales, J. C., Figueroa-Casas, A. (2015). Towards detecting crop diseases and pest by supervised learning, Ingeniería y Universidad, 19(1), 207-228. https://doi.org/10.11144/Javeriana.iyu19-1.tdcd
  • Crossa-Raynaud, P. (1955). Effets des hivers doux sur le comportement des arbres fruitiers à feuilles caduques: Observations faites en Tunisie à la suite de l'hiver 1954-1955. Impr. La Rapide, 1-22.
  • Cunha, M., Ribeiro, H., Abreu, I. (2016). Pollen-based predictive modelling of wine production: application to an arid region. European Journal of Agranomy, 73, 42-54. https://doi.org/10.1016/j.eja.2015.10.088
  • Çolakoğlu, C.A., Tunalıoğlu, R. (2010). Determination of Relationship between Climate Data and Olive Production Data in Aydın Province. Journal of Adnan Menderes University Agricultural Faculty, 7(1), 71-77.
  • De Clercq, M., Vats, A., Biel, A. (2009). Agriculture 4.0: The Future of Farming Technology. 2018 World Government Summit Report. Source: https://www.worldgovernmentsummit.org/api/publications/document?id=95df8ac4-e97c-6578-b2f8-ff0000a7ddb6
  • Dhiab, A. B., Mimoun, M. B., Oteros, J., Garcia-Mozo, H., Domínguez-Vilches, E., Galán, C., Abichou, M., Msallem, M. (2017). Modeling olive-crop forecasting in Tunisia. Theoretical and applied climatology, 128(3-4), 541-549. https://doi.org/10.1007/s00704-015-1726-1
  • European Commission (2020). “Agri-Food Data Portal”, Available: https://agridata.ec.europa.eu/extensions/DataPortal/home.html, Data Accessed: 07.02.2021.
  • Fornaciari, M., Orlandi, F., Romano, B. (2005). Yield forecasting for olive trees: a new approach in a historical series (Umbria, Central Italy). Agronomy Journal, 97(6), 1537-1542. https://doi.org/10.2134/agronj2005.0067
  • Galán, C., Vázquez, L., Garcia-Mozo, H., Dominguez, E. (2004). Forecasting Olive (Olea Europaea) Crop Yield Based on Pollen Emission. Field Crops Research, 86(1), 43-51.
  • Galán, C., Alcázar, P., Oteros, J., García-Mozo, H., Aira, M. J., Belmonte, J., de la Guardia, C.D., Fernández-González, D., Gutierrez-Bustillo, M., Moreno-Grau, S., Pérez-Badia, R., Rodríguez-Rajo, J., Ruiz-Valenzuela, L., Tormo, R., Trigo, M.M., Domínguez-Vilches, E. (2016). Airborne pollen trends in the Iberian Peninsula. Science of the Total Environment, 550, 53-59. https://doi.org/10.1016/j.scitotenv.2016.01.069
  • García-Mozo, H. (2011). The use of aerobiological data on agronomical studies. Annals of Agricultural and Environmental Medicine, 18(1), 1-6.
  • García-Mozo, H., Yaezel, L., Oteros, J., Galán, C. (2014). Statistical approach to the analysis of olive long-term pollen season trends in southern Spain. Science of the Total Environment, 473, 103-109. http://dx.doi.org/10.1016/j.scitotenv.2013.11.142
  • García-Mozo, H., Oteros, J. A., Galán, C. (2016). Impact of land cover changes and climate on the main airborne pollen types in Southern Spain. Science of the Total Environment, 548, 221-228. https://doi.org/10.1016/j.scitotenv.2016.01.005
  • Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.), O’Reilly, Canada.
  • Güvensen, A., Uğuz, U., Altun, T., Eşiz-Dereboylu, A., Şengonca-Tort, N. (2020). Aeropalynological survey in the city center of Aydın (Turkey). Turkish Journal of Botany, 44(5), 539-551. https://doi.org/10.3906/bot-1909-38
  • Hill, M. G., Connolly, P. G., Reutemann, P., Fletcher, D. (2014). The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Computers and electronics in agriculture, 108, 250-257. https://doi.org/10.1016/j.compag.2014.08.011
  • Kabasakal, İ., Özaltaş, M. (2018). Quantitative Methods for Olive Harvest Prediction: A Classification Based on Prior Research. Proceedings of the 29th International Scientific - Expert Conference of Agriculture and Food Industry. September 26-28, Çeşme, İzmir, Turkey, 164-171.
  • Mancuso, S., Pasquali, G., & Fiorino, P. (2002). Phenology modelling and forecasting in olive (Olea europaea L.) using artificial neural networks. Advances in Horticultural Science, 16(3/4), 155-164.
  • Merkoci, A. L., Hasimi, A., Dvorani, M. (2019). Yield forecasting for olive tree by meteorological factors and pollen emission. Micro Macro & Mezzo Geo Information, 12, 7-16.
  • Ojha, T., Misra, S., Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges, Computers and Electronics in Agriculture, 118, 66-84. https://doi.org/10.1016/j.compag.2015.08.011
  • Orlandi, F., Aguilera, F., Galán, C., Msallem, M., Fornaciari, M. (2016). Olive Yields Forecasts and Oil Price Trends in Mediterranean Areas: A Comprehensive Analysis of the last two Decades. Experimental Agriculture, 53(1), 71-83. https://doi.org/10.1017/S0014479716000077
  • Osborne, C. P., Chuine, I., Viner, D., & Woodward, F. I. (2000). Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean. Plant, Cell & Environment, 23(7), 701-710. https://doi.org/10.1046/j.1365-3040.2000.00584.x
  • Oteros, J., García-Mozo, H., Hervás, C., Galán, C. (2013). Biometeorological and autoregressive indices for predicting olive pollen intensity. International Journal of Biometeorology, 57(2), 307-316. https://doi.org/10.1007/s00484-012-0555-5
  • Oteros, J., Orlandi, F., García-Mozo, H., Aguilera, F., Dhiab, A. B., Bonofiglio, T., Abichou, M., Ruiz-Valenzuela, L., Mar del Trigo, M., Díaz de la Guardia, C., Domínguez-Vilches, E., Msallem, M., Fornaciari, M. (2014). Better Prediction of Mediterranean Olive Production Using Pollen-Based Models. Agronomy for Sustainable Development, 34(3), 685-694. https://doi.org/10.1007/s13593-013-0198-x
  • Oteros, J., Pusch, G., Weichenmeier, I., Heimann, U., Möller, R., Röseler, S., Traidl-Hoffmann, C., Schmidt-Weber, C., Buters, J.T.M. (2015). Automatic and online pollen monitoring. International Archives of Allergy and Immunology, 167(3), 158-166. https://doi.org/10.1159/000436968
  • Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., Athanasiadis, I. N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, 103016. https://doi.org/10.1016/j.agsy.2020.103016
  • Sabu, K. M., Kumar, T. M. (2020). Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699-708. https://doi.org/10.1016/j.procs.2020.04.076
  • Şimşek, O., Mermer, A., Yıldız, H., Özaydın, K. A., Çakmak, B. (2007). AgroMetShell modeli kullanılarak Türkiye’de buğdayın verim tahmini. Journal of Agricultural Sciences, 13(03), 299-308.
  • Tosunoglu, A., Altunoglu, M. K., Bicakci, A., Kilic, O., Gonca, T., Yilmazer, I., Saatcioglu, G., Akkaya, A., Celenk, S., Canitez, Y., Malyer, H., Sapan, N. (2015). Atmospheric pollen concentrations in Antalya, South Turkey. Aerobiologia, 31(1), 99-109. https://doi.org/10.1007/s10453-014-9350-6
  • Tseng, Y. T., Kawashima, S., Kobayashi, S., Takeuchi, S., & Nakamura, K. (2020). Forecasting the seasonal pollen index by using a hidden Markov model combining meteorological and biological factors. Science of the Total Environment, 698, 1-10. https://doi.org/10.1016/j.scitotenv.2019.134246
  • Ulaş, U., Güvensen, A. (2019). Olea europaea L. polenlerinin Aydın, Manisa ve Muğla atmosferindeki dağılımları. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(3), 936-942. https://doi.org/10.19113/sdufenbed.565330
  • Yildirak, K., Kalaylıoglu, Z., & Mermer, A. (2015). Bayesian estimation of crop yield function: drought based wheat prediction model for tigem farms. Environmental and ecological statistics, 22(4), 693-704. https://doi.org/10.1007/s10651-015-0327-6

A Review of Literature on the Quantitative Methods for Olive Yield Forecasting

Year 2021, Issue: 24, 351 - 358, 15.04.2021
https://doi.org/10.31590/ejosat.900368

Abstract

Yield forecasting is a task that provides critical inputs for producers and other stakeholders in agricultural marketing. Various methods have been applied to forecast olive yield in prior studies that primarily analyze datasets involving meteorological and phenological measurements. Our study reviews the prior literature on olive yield forecasting and explores the prominent methods employed in this context. Accordingly, we categorize prior models into two broad groups: pollen index forecasting and olive yield forecasting models. Moreover, our study highlights the popular methods and attributes involved in previous research, and reports the initial findings of the ongoing olive-yield forecasting project held in İzmir, Turkey. Finally, a discussion is presented regarding the techniques utilized and the attributes analyzed in forecasting models.

Project Number

TAGEM/TSKA D/B/18/A9/P6/1246

References

  • Allen, P.G. (1994). Economic Forecasting in Agriculture. International Journal of Forecasting, 10, 81-135.
  • Basso, B., Cammarano, D., Carfagna, E. (2013). Review of crop yield forecasting methods and early warning systems. Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy, 18-19.
  • Bishan, C., Bing, L., Chixin, C., Junxia, S., Shulin, Z., Cailang, L., Siqiao, Y., Chuanxiu, L. (2020). Relationship between airborne pollen assemblages and major meteorological parameters in Zhanjiang, South China. Plos one, 15(10), 1-17. https://doi.org/10.1371/journal.pone.0240160
  • Buters, J., Schmidt‐Weber, C., Oteros, J. (2018). Next generation pollen monitoring and dissemination. Allergy, 73, 1944-1945. https://doi.org/10.1111/all.13585
  • Camacho, I., Caeiro, E., Nunes, C., Morais-Almeida, M. (2020). Airborne pollen calendar of Portugal: a 15-year survey (2002–2017). Allergologia et immunopathologia, 48(2), 194-201. https://doi.org/10.1016/j.aller.2019.06.012
  • Celenk, S., Bicakci, A., Tamay, Z., Guler, N., Altunoglu, M. K., Canitez, Y., Malyer, H., Sapan, N., Ones, U. (2010). Airborne pollen in European and Asian parts of Istanbul. Environmental monitoring and assessment, 164(1), 391-402. https://doi.org/10.1007/s10661-009-0901-1
  • Corrales, D. C., Corrales, J. C., Figueroa-Casas, A. (2015). Towards detecting crop diseases and pest by supervised learning, Ingeniería y Universidad, 19(1), 207-228. https://doi.org/10.11144/Javeriana.iyu19-1.tdcd
  • Crossa-Raynaud, P. (1955). Effets des hivers doux sur le comportement des arbres fruitiers à feuilles caduques: Observations faites en Tunisie à la suite de l'hiver 1954-1955. Impr. La Rapide, 1-22.
  • Cunha, M., Ribeiro, H., Abreu, I. (2016). Pollen-based predictive modelling of wine production: application to an arid region. European Journal of Agranomy, 73, 42-54. https://doi.org/10.1016/j.eja.2015.10.088
  • Çolakoğlu, C.A., Tunalıoğlu, R. (2010). Determination of Relationship between Climate Data and Olive Production Data in Aydın Province. Journal of Adnan Menderes University Agricultural Faculty, 7(1), 71-77.
  • De Clercq, M., Vats, A., Biel, A. (2009). Agriculture 4.0: The Future of Farming Technology. 2018 World Government Summit Report. Source: https://www.worldgovernmentsummit.org/api/publications/document?id=95df8ac4-e97c-6578-b2f8-ff0000a7ddb6
  • Dhiab, A. B., Mimoun, M. B., Oteros, J., Garcia-Mozo, H., Domínguez-Vilches, E., Galán, C., Abichou, M., Msallem, M. (2017). Modeling olive-crop forecasting in Tunisia. Theoretical and applied climatology, 128(3-4), 541-549. https://doi.org/10.1007/s00704-015-1726-1
  • European Commission (2020). “Agri-Food Data Portal”, Available: https://agridata.ec.europa.eu/extensions/DataPortal/home.html, Data Accessed: 07.02.2021.
  • Fornaciari, M., Orlandi, F., Romano, B. (2005). Yield forecasting for olive trees: a new approach in a historical series (Umbria, Central Italy). Agronomy Journal, 97(6), 1537-1542. https://doi.org/10.2134/agronj2005.0067
  • Galán, C., Vázquez, L., Garcia-Mozo, H., Dominguez, E. (2004). Forecasting Olive (Olea Europaea) Crop Yield Based on Pollen Emission. Field Crops Research, 86(1), 43-51.
  • Galán, C., Alcázar, P., Oteros, J., García-Mozo, H., Aira, M. J., Belmonte, J., de la Guardia, C.D., Fernández-González, D., Gutierrez-Bustillo, M., Moreno-Grau, S., Pérez-Badia, R., Rodríguez-Rajo, J., Ruiz-Valenzuela, L., Tormo, R., Trigo, M.M., Domínguez-Vilches, E. (2016). Airborne pollen trends in the Iberian Peninsula. Science of the Total Environment, 550, 53-59. https://doi.org/10.1016/j.scitotenv.2016.01.069
  • García-Mozo, H. (2011). The use of aerobiological data on agronomical studies. Annals of Agricultural and Environmental Medicine, 18(1), 1-6.
  • García-Mozo, H., Yaezel, L., Oteros, J., Galán, C. (2014). Statistical approach to the analysis of olive long-term pollen season trends in southern Spain. Science of the Total Environment, 473, 103-109. http://dx.doi.org/10.1016/j.scitotenv.2013.11.142
  • García-Mozo, H., Oteros, J. A., Galán, C. (2016). Impact of land cover changes and climate on the main airborne pollen types in Southern Spain. Science of the Total Environment, 548, 221-228. https://doi.org/10.1016/j.scitotenv.2016.01.005
  • Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.), O’Reilly, Canada.
  • Güvensen, A., Uğuz, U., Altun, T., Eşiz-Dereboylu, A., Şengonca-Tort, N. (2020). Aeropalynological survey in the city center of Aydın (Turkey). Turkish Journal of Botany, 44(5), 539-551. https://doi.org/10.3906/bot-1909-38
  • Hill, M. G., Connolly, P. G., Reutemann, P., Fletcher, D. (2014). The use of data mining to assist crop protection decisions on kiwifruit in New Zealand. Computers and electronics in agriculture, 108, 250-257. https://doi.org/10.1016/j.compag.2014.08.011
  • Kabasakal, İ., Özaltaş, M. (2018). Quantitative Methods for Olive Harvest Prediction: A Classification Based on Prior Research. Proceedings of the 29th International Scientific - Expert Conference of Agriculture and Food Industry. September 26-28, Çeşme, İzmir, Turkey, 164-171.
  • Mancuso, S., Pasquali, G., & Fiorino, P. (2002). Phenology modelling and forecasting in olive (Olea europaea L.) using artificial neural networks. Advances in Horticultural Science, 16(3/4), 155-164.
  • Merkoci, A. L., Hasimi, A., Dvorani, M. (2019). Yield forecasting for olive tree by meteorological factors and pollen emission. Micro Macro & Mezzo Geo Information, 12, 7-16.
  • Ojha, T., Misra, S., Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges, Computers and Electronics in Agriculture, 118, 66-84. https://doi.org/10.1016/j.compag.2015.08.011
  • Orlandi, F., Aguilera, F., Galán, C., Msallem, M., Fornaciari, M. (2016). Olive Yields Forecasts and Oil Price Trends in Mediterranean Areas: A Comprehensive Analysis of the last two Decades. Experimental Agriculture, 53(1), 71-83. https://doi.org/10.1017/S0014479716000077
  • Osborne, C. P., Chuine, I., Viner, D., & Woodward, F. I. (2000). Olive phenology as a sensitive indicator of future climatic warming in the Mediterranean. Plant, Cell & Environment, 23(7), 701-710. https://doi.org/10.1046/j.1365-3040.2000.00584.x
  • Oteros, J., García-Mozo, H., Hervás, C., Galán, C. (2013). Biometeorological and autoregressive indices for predicting olive pollen intensity. International Journal of Biometeorology, 57(2), 307-316. https://doi.org/10.1007/s00484-012-0555-5
  • Oteros, J., Orlandi, F., García-Mozo, H., Aguilera, F., Dhiab, A. B., Bonofiglio, T., Abichou, M., Ruiz-Valenzuela, L., Mar del Trigo, M., Díaz de la Guardia, C., Domínguez-Vilches, E., Msallem, M., Fornaciari, M. (2014). Better Prediction of Mediterranean Olive Production Using Pollen-Based Models. Agronomy for Sustainable Development, 34(3), 685-694. https://doi.org/10.1007/s13593-013-0198-x
  • Oteros, J., Pusch, G., Weichenmeier, I., Heimann, U., Möller, R., Röseler, S., Traidl-Hoffmann, C., Schmidt-Weber, C., Buters, J.T.M. (2015). Automatic and online pollen monitoring. International Archives of Allergy and Immunology, 167(3), 158-166. https://doi.org/10.1159/000436968
  • Paudel, D., Boogaard, H., de Wit, A., Janssen, S., Osinga, S., Pylianidis, C., Athanasiadis, I. N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, 103016. https://doi.org/10.1016/j.agsy.2020.103016
  • Sabu, K. M., Kumar, T. M. (2020). Predictive analytics in Agriculture: Forecasting prices of Arecanuts in Kerala. Procedia Computer Science, 171, 699-708. https://doi.org/10.1016/j.procs.2020.04.076
  • Şimşek, O., Mermer, A., Yıldız, H., Özaydın, K. A., Çakmak, B. (2007). AgroMetShell modeli kullanılarak Türkiye’de buğdayın verim tahmini. Journal of Agricultural Sciences, 13(03), 299-308.
  • Tosunoglu, A., Altunoglu, M. K., Bicakci, A., Kilic, O., Gonca, T., Yilmazer, I., Saatcioglu, G., Akkaya, A., Celenk, S., Canitez, Y., Malyer, H., Sapan, N. (2015). Atmospheric pollen concentrations in Antalya, South Turkey. Aerobiologia, 31(1), 99-109. https://doi.org/10.1007/s10453-014-9350-6
  • Tseng, Y. T., Kawashima, S., Kobayashi, S., Takeuchi, S., & Nakamura, K. (2020). Forecasting the seasonal pollen index by using a hidden Markov model combining meteorological and biological factors. Science of the Total Environment, 698, 1-10. https://doi.org/10.1016/j.scitotenv.2019.134246
  • Ulaş, U., Güvensen, A. (2019). Olea europaea L. polenlerinin Aydın, Manisa ve Muğla atmosferindeki dağılımları. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(3), 936-942. https://doi.org/10.19113/sdufenbed.565330
  • Yildirak, K., Kalaylıoglu, Z., & Mermer, A. (2015). Bayesian estimation of crop yield function: drought based wheat prediction model for tigem farms. Environmental and ecological statistics, 22(4), 693-704. https://doi.org/10.1007/s10651-015-0327-6
There are 38 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

İnanç Kabasakal 0000-0003-0098-0144

Murat Özaltaş 0000-0003-0021-9443

Project Number TAGEM/TSKA D/B/18/A9/P6/1246
Publication Date April 15, 2021
Published in Issue Year 2021 Issue: 24

Cite

APA Kabasakal, İ., & Özaltaş, M. (2021). A Review of Literature on the Quantitative Methods for Olive Yield Forecasting. Avrupa Bilim Ve Teknoloji Dergisi(24), 351-358. https://doi.org/10.31590/ejosat.900368