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Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model

Year 2023, , 53 - 59, 01.04.2023

Ali Kaan Yetik , Tefide Kızıldeniz , Zeynep Ünal

https://doi.org/10.34248/bsengineering.1181263
Cited By: 1

Abstract

Sugar beet is an essential crop for the sugar industry that have a very crucial role in agro-industry of Türkiye and Konya ranks first in terms of total sugar beet production and harvested area. The predictions, that the world's human population will reach 9 billion by the end of the current century and that demand for food will increase, are forcing farmers for the decision to search for new areas for agriculture or choose the crops that will be most productive in already cultivated lands. The aim of this study was to apply the LINTUL-MULTICROP Model for investigating the adaptation of sugar beet for the current climatic conditions and for climate change scenarios to show the response of sugar beet to an increase level of carbon dioxide and temperature. Four different scenarios were compared to check the effects of the climate change on sugar beet farming in the semi-arid Konya Region as followings: i) scenario (a) is the current climate conditions; ii) scenario (b) is the average temperatures increased 2 °C, iii) scenario (c) is 200 ppm increasing atmospheric CO2; iv) scenario (d) new optimum sowing and harvest dates in sugar beet farming and increased temperatures and atmospheric CO2 amount were simulated together. The optimum sowing and harvesting dates of sugar beet were moved 13 days back for sowing, and 8 days forward for harvesting. The highest yield was estimated under conditions of 2 °C and 200 ppm increased atmosphere temperature and CO2 levels with new sowing and harvest dates. The yields under irrigated conditions varied between 74.4 t ha-1 and 111.2 t ha-1. The irrigation water requirements of sugar beet were ranged from 618.8 mm to 688.5 mm for different scenarios. In conclusion, the cultivation of sugar beet tends to alter in semi-arid Konya environment.

Keywords

Crop modelling Yield estimation Climate change Sugar beet

References

  • Acar M. 2015. Şeker pancarının seyreltmeli ve blok ekim uygulamalarının sıra üzeri bitki dağılımı ile kalite özelliklerine etkisi. PhD thesis, Selçuk University, Institute of Science, Konya, Türkiye, pp: 54.
  • Adiele JG, Schut AGT, Beuken RPM, Ezui KS, Pypers P, Ano AO, Giller KE. 2021. A recalibrated and tested LINTUL-Cassava simulation model provides insight into the high yield potential of cassava under rainfed conditions. Eur J Agron, 124: 126242.
  • Akhavizadegan FJ, Ansarifar LW, Huber I, Archontoulis SV. 2021. A time-dependent parameter estimation framework for crop modeling. Sci Rep, 11(1): 1-15.
  • Alexandrov VA, Hoogenboom G. 2000. The impact of climate variability and change on crop yield in Bulgaria. Agric For Meteorol, 104(4): 315-327.
  • Asseng S, Martre P, Maiorano A, Rötter RP, O’Leary GJ, Fitzgerald GJ, Ewert F. 2019. Climate change impact and adaptation for wheat protein. Glob Chang Biol, 25(1): 155-173.
  • Battisti R, Sentelhas PC, Parker PS, Nendel C, Gil MDS, Farias JR, Basso CJ. 2018. Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop Pasture Sci, 69(2): 154-162.
  • Boote KJ, Jones JW, White JW, Asseng S, Lizaso JI. 2013. Putting mechanisms into crop production models. Plant Cell Environ, 36(9): 1658-1672.
  • Cabral JS, Valente L, Hartig F. 2017. Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects. Ecography, 40(2): 267-280.
  • Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N. 2014. A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang, 4(4): 287-291.
  • Durr C, Boiffin J. 1995. Sugarbeet seedling growth from germination to first leaf stage. J Agric Sci, 124(3): 427-435.
  • Faberio C, Santa Olalla M, Lopez R, Dominguez A. 2003. Production and quality of sugar beet (Beta vulgaris L.) cultivated under controlled deficit irrigation condition in semiarid-climate. Agric Water Manag, 62: 215-227.
  • Franke AC, Haverkort AJ, Steyn JM. 2013. Climate change and potato production in contrasting South African agro-ecosystems 2. Assessing risks and opportunities of adaptation strategies. Potato Res, 56(1): 51-66.
  • Freckleton RP, Watkinson AR, Webb DJ, Thomas TH. 1999. Yield of sugar beet in relation to weather and nutrients. Agric For Meteorol, 93(1): 39-51.
  • García-León D, López-Lozano R, Toreti A, Zampieri M. 2020. Local-scale cereal yield forecasting in Italy: Lessons from different statistical models and spatial aggregations. Agron J, 10(6): 806.
  • Garcia-Vila M, Morillo-Velarde R, Fereres E. 2019. Modeling sugar beet responses to irrigation with AquaCrop for optimizing water allocation. Water, 11(9): 1918.
  • Gezgin S, Hamurcu M, Apaydin M. 2001. Effect of boron application on the yield and quality of sugar beet. Turk J Agric Forest, 25(2): 89-95.
  • Gimplinger DM, Kaul HP. 2012. Calibration and validation of the crop growth model LINTUL for grain amaranth (Amaranthus sp.). J Appl Bot Food Qual, 82(2): 183-192.
  • Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Toulmin C. 2010. Food security: the challenge of feeding 9 billion people. Science, 327(5967): 812-818.
  • Haverkort AJ, Franke AC, Engelbrecht FA, Steyn JM. 2013. Climate change and potato production in contrasting South African agro-ecosystems 1. Effects on land and water use efficiencies. Potato Res, 56(1): 31-50.
  • Haverkort AJ, Kooman PL. 1997. The use of modeling growth and development in ideotyping of potato under conditions defining, limiting and reducing yields. Euphytica, 94: 191-200.
  • Hoffmann CM, Kenter C. 2018. Yield potential of sugar beet–have we hit the ceiling? Front Plant Sci, 9: 289.
  • Howden SM, Soussana JF, Tubiello FN, Chhetri N, Dunlop M, Meinke H. 2007. Adapting agriculture to climate change. Proc Natl Acad Sci, 104(50): 19691-19696.
  • Iizumi T, Luo JJ, Challinor AJ, Sakurai G, Yokozawa M, Sakuma H, Yamagata T. 2014. Impacts of El Niño Southern Oscillation on the global yields of major crops. Nat Commun, 5(1): 3712.
  • Jégo G, Martínez M, Antigüedad I, Launay M, Sanchez-Pérez JM, Justes E. 2008. Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil–crop model. Sci Total Environ, 394(2-3): 207-221.
  • Jones PD, Lister DH, Jaggard KW, Pidgeon JD. 2003. Future climate impact on the productivity of sugar beet (Beta vulgaris L.) in Europe. Clim Change, 58(1): 93-108.
  • Kamali HR, Zand-Parsa S, Zare M, Sapaskhah AR, Kamgar-Haghighi AA. 2022. Development of a simulation model for sugar beet growth under water and nitrogen deficiency. Irrig Sci, 40(3): 337-358.
  • Kenter C, Hoffmann CM, Märländer B. 2006. Effects of weather variables on sugar beet yield development (Beta vulgaris L.). Eur J Agron, 24(1): 62-69.
  • Köksal ES, Güngör Y, Yildirim YE. 2011. Spectral reflectance characteristics of sugar beet under different levels of irrigation water and relationships between growth parameters and spectral indexes. Irrig Drain, 60: 187-195.
  • Kothari K, Battisti R, Boote KJ, Archontoulis SV, Confalone A, Constantin J, Salmerón M. 2022. Are soybean models ready for climate change food impact assessments? Eur J Agron, 135: 126482.
  • Licker R, Johnston M, Foley JA, Barford C, Kucharik CJ, Monfreda C, Ramankutty N. 2010. Mind the gap: how do climate and agricultural management explain the ‘yield gap’of croplands around the world? Glob Ecol Biogeogr, 19(6): 769-782.
  • Mamyandi MM, Pirzad A, Zardoshti MR. 2012. Effect of Nano-iron spraying at varying growth stage of sugar beet (Beta vulgaris L.) on the size of different plant parts. Intern J Agric Crop Sci, 4(12): 740-745.
  • Manschadi AM, Eitzinger J, Breisch M, Fuchs W, Neubauer T, Soltani A. 2021. Full parameterisation matters for the best performance of crop models: inter-comparison of a simple and a detailed maize model. Int J Plant Prod, 15(1): 61-78.
  • Mendelsohn R, Nordhaus WD, Shaw D. 1994. The impact of global warming on agriculture: A Ricardian analysis. American Econ Rev, 1: 753-771.
  • Noor A, Khan MR. 2015. Efficacy and safety of mixing azoxystrobin and starter fertilizers for controlling Rhizoctonia solani in sugar beet. Phytoparasit, 43(1): 51-55.
  • Oteng-Darko P, Yeboah S, Addy SNT, Amponsah S, Danquah ETO. 2013. Crop modeling: A tool for agricultural research–A review. J Agric Res Dev, 2(1): 001-006
  • Petkeviciene B. 2009. The effects of climate factors on sugar beet early sowing timing. Agron Res, 7: 436-443.
  • Prequeno DN, Hernandez-Ochoa IM, Reynolds M, Sonder K, MoleroMilan A, Robertson RD, Asseng S. 2021. Climate impact and adaptation to heat and drought stress of regional and global wheat production. Environ Res, 16(5): 054070.
  • Rahman MH, Ahmad A, Wang X, Wajid A, Nasim W, Hussain M, Hoogenboom G. 2018. Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agric For Meteorol, 253: 94-113.
  • Richter GM, Qi A, Semenov MA, Jaggard KW. 2006. Modelling the variability of UK sugar beet yields under climate change and husbandry adaptations. Soil Use Manag, 22(1): 39-47.
  • Rosenzweig C, Hillel D. 1998. Climate change and the global harvest: potential impacts of the greenhouse effect on agriculture. Oxford University Press, Oxford, UK, 1st ed., pp: 336.
  • Spitters CJ, Schapendonk AH. 1990. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Gen Aspects of Plant Min Nut, 2: 151-161.
  • Spitters CJT. 1989. Crop Growth Models: Their usefulness and limitations. Acta Hortic, 267: 349-368.
  • Starke P, Hoffmann C. 2014. Yield parameters of Beta beets as a basis to estimate the biogas yield. Sug Ind, 2: 169-176.
  • Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L. 2011. Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agric Water Manag, 98(10): 1615-1621.
  • Süheri S, Topak R, Yavuz D. 2007. Farkli sulama programlarinin şeker pancari verimine ve su kullanim randimanina etkisi. Selcuk J Agric Food Sci, 21(43): 37-45.
  • Terry N. 1968. Developmental physiology of sugar beet: i. the influence of light and temperature on growth. J Exp Bot, 19(4): 795-811.
  • Tingem M, Rivington M. 2009. Adaptation for crop agriculture to climate change in Cameroon: turning on the heat. Mitig Adapt Strateg Glob Chang, 14(2): 153-168.
  • Tognetti R, Palladino M, Minnocci A, Delfine S, Alvino A. 2003. The response of sugar beet to drip and low-pressure sprinkler irrigation in southern Italy. Agri Water Manage, 60(2): 135-155.
  • Topak R, Süheri S, Acar B. 2011. Effect of different drip irrigation regimes on sugar beet (Beta vulgaris L.) yield, quality and water use efficiency in Middle Anatolian, Turkey. Irrig Sci, 29(1): 79-89.
  • TSMS. 2021. Turkish State Meteorological Service, climate data of long term period. URL: https://mgm.gov.tr (access date: August 2, 2022).
  • TURKSTAT. 2022. Turkish Statistical Institute. URL: https://tuikweb.tuik.gov.tr/ (access date: August 2, 2022).
  • Viver IA. 2022. Translation and evaluation of the LINTUL2-Banana crop growth model in R. MSc Thesis, Wagenigen University and Research, Wagenigen, Nederland, pp: 78.
  • Werker AR, Jaggard KW. 1998. Dependence of sugar beet yield on light interception and evapotranspiration. Agric For Meteorol, 89(3-4): 229-240.
  • Yagiz AK, Cakici M, Aydogan N, Omezli S, Yerlikaya BA, Ayten S, Haverkort AJ. 2020. Exploration of climate change effects on shifting potato seasons, yields and water use employing NASA and national long-term weather data. Potato Res, 63(4): 565-577.
  • Yetik AK, Candoğan BN. 2022. Optimisation of irrigation strategy in sugar beet farming based on yield, quality and water productivity. Plant Soil Environ, 68: 358-365.
  • Zengin M, Gökmen F, Yazici MA, Gezgin S. 2009. Effects of potassium, magnesium, and sulphur containing fertilizers on yield and quality of sugar beets (Beta vulgaris L.). Turkish J Agric Forest, 33(5): 495-502.
  • Zengin M, Uyanöz R, Çetin Ü. 2003. A study on the soil tare of sugar beet in Konya. Selcuk J Agric Sci, 17: 53-55.

Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model

Year 2023, , 53 - 59, 01.04.2023

Ali Kaan Yetik , Tefide Kızıldeniz , Zeynep Ünal

https://doi.org/10.34248/bsengineering.1181263
Cited By: 1

Abstract

Sugar beet is an essential crop for the sugar industry that have a very crucial role in agro-industry of Türkiye and Konya ranks first in terms of total sugar beet production and harvested area. The predictions, that the world's human population will reach 9 billion by the end of the current century and that demand for food will increase, are forcing farmers for the decision to search for new areas for agriculture or choose the crops that will be most productive in already cultivated lands. The aim of this study was to apply the LINTUL-MULTICROP Model for investigating the adaptation of sugar beet for the current climatic conditions and for climate change scenarios to show the response of sugar beet to an increase level of carbon dioxide and temperature. Four different scenarios were compared to check the effects of the climate change on sugar beet farming in the semi-arid Konya Region as followings: i) scenario (a) is the current climate conditions; ii) scenario (b) is the average temperatures increased 2 °C, iii) scenario (c) is 200 ppm increasing atmospheric CO2; iv) scenario (d) new optimum sowing and harvest dates in sugar beet farming and increased temperatures and atmospheric CO2 amount were simulated together. The optimum sowing and harvesting dates of sugar beet were moved 13 days back for sowing, and 8 days forward for harvesting. The highest yield was estimated under conditions of 2 °C and 200 ppm increased atmosphere temperature and CO2 levels with new sowing and harvest dates. The yields under irrigated conditions varied between 74.4 t ha-1 and 111.2 t ha-1. The irrigation water requirements of sugar beet were ranged from 618.8 mm to 688.5 mm for different scenarios. In conclusion, the cultivation of sugar beet tends to alter in semi-arid Konya environment.

Keywords

Crop modelling Yield estimation Climate change Sugar beet

References

  • Acar M. 2015. Şeker pancarının seyreltmeli ve blok ekim uygulamalarının sıra üzeri bitki dağılımı ile kalite özelliklerine etkisi. PhD thesis, Selçuk University, Institute of Science, Konya, Türkiye, pp: 54.
  • Adiele JG, Schut AGT, Beuken RPM, Ezui KS, Pypers P, Ano AO, Giller KE. 2021. A recalibrated and tested LINTUL-Cassava simulation model provides insight into the high yield potential of cassava under rainfed conditions. Eur J Agron, 124: 126242.
  • Akhavizadegan FJ, Ansarifar LW, Huber I, Archontoulis SV. 2021. A time-dependent parameter estimation framework for crop modeling. Sci Rep, 11(1): 1-15.
  • Alexandrov VA, Hoogenboom G. 2000. The impact of climate variability and change on crop yield in Bulgaria. Agric For Meteorol, 104(4): 315-327.
  • Asseng S, Martre P, Maiorano A, Rötter RP, O’Leary GJ, Fitzgerald GJ, Ewert F. 2019. Climate change impact and adaptation for wheat protein. Glob Chang Biol, 25(1): 155-173.
  • Battisti R, Sentelhas PC, Parker PS, Nendel C, Gil MDS, Farias JR, Basso CJ. 2018. Assessment of crop-management strategies to improve soybean resilience to climate change in Southern Brazil. Crop Pasture Sci, 69(2): 154-162.
  • Boote KJ, Jones JW, White JW, Asseng S, Lizaso JI. 2013. Putting mechanisms into crop production models. Plant Cell Environ, 36(9): 1658-1672.
  • Cabral JS, Valente L, Hartig F. 2017. Mechanistic simulation models in macroecology and biogeography: state‐of‐art and prospects. Ecography, 40(2): 267-280.
  • Challinor AJ, Watson J, Lobell DB, Howden SM, Smith DR, Chhetri N. 2014. A meta-analysis of crop yield under climate change and adaptation. Nat Clim Chang, 4(4): 287-291.
  • Durr C, Boiffin J. 1995. Sugarbeet seedling growth from germination to first leaf stage. J Agric Sci, 124(3): 427-435.
  • Faberio C, Santa Olalla M, Lopez R, Dominguez A. 2003. Production and quality of sugar beet (Beta vulgaris L.) cultivated under controlled deficit irrigation condition in semiarid-climate. Agric Water Manag, 62: 215-227.
  • Franke AC, Haverkort AJ, Steyn JM. 2013. Climate change and potato production in contrasting South African agro-ecosystems 2. Assessing risks and opportunities of adaptation strategies. Potato Res, 56(1): 51-66.
  • Freckleton RP, Watkinson AR, Webb DJ, Thomas TH. 1999. Yield of sugar beet in relation to weather and nutrients. Agric For Meteorol, 93(1): 39-51.
  • García-León D, López-Lozano R, Toreti A, Zampieri M. 2020. Local-scale cereal yield forecasting in Italy: Lessons from different statistical models and spatial aggregations. Agron J, 10(6): 806.
  • Garcia-Vila M, Morillo-Velarde R, Fereres E. 2019. Modeling sugar beet responses to irrigation with AquaCrop for optimizing water allocation. Water, 11(9): 1918.
  • Gezgin S, Hamurcu M, Apaydin M. 2001. Effect of boron application on the yield and quality of sugar beet. Turk J Agric Forest, 25(2): 89-95.
  • Gimplinger DM, Kaul HP. 2012. Calibration and validation of the crop growth model LINTUL for grain amaranth (Amaranthus sp.). J Appl Bot Food Qual, 82(2): 183-192.
  • Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Toulmin C. 2010. Food security: the challenge of feeding 9 billion people. Science, 327(5967): 812-818.
  • Haverkort AJ, Franke AC, Engelbrecht FA, Steyn JM. 2013. Climate change and potato production in contrasting South African agro-ecosystems 1. Effects on land and water use efficiencies. Potato Res, 56(1): 31-50.
  • Haverkort AJ, Kooman PL. 1997. The use of modeling growth and development in ideotyping of potato under conditions defining, limiting and reducing yields. Euphytica, 94: 191-200.
  • Hoffmann CM, Kenter C. 2018. Yield potential of sugar beet–have we hit the ceiling? Front Plant Sci, 9: 289.
  • Howden SM, Soussana JF, Tubiello FN, Chhetri N, Dunlop M, Meinke H. 2007. Adapting agriculture to climate change. Proc Natl Acad Sci, 104(50): 19691-19696.
  • Iizumi T, Luo JJ, Challinor AJ, Sakurai G, Yokozawa M, Sakuma H, Yamagata T. 2014. Impacts of El Niño Southern Oscillation on the global yields of major crops. Nat Commun, 5(1): 3712.
  • Jégo G, Martínez M, Antigüedad I, Launay M, Sanchez-Pérez JM, Justes E. 2008. Evaluation of the impact of various agricultural practices on nitrate leaching under the root zone of potato and sugar beet using the STICS soil–crop model. Sci Total Environ, 394(2-3): 207-221.
  • Jones PD, Lister DH, Jaggard KW, Pidgeon JD. 2003. Future climate impact on the productivity of sugar beet (Beta vulgaris L.) in Europe. Clim Change, 58(1): 93-108.
  • Kamali HR, Zand-Parsa S, Zare M, Sapaskhah AR, Kamgar-Haghighi AA. 2022. Development of a simulation model for sugar beet growth under water and nitrogen deficiency. Irrig Sci, 40(3): 337-358.
  • Kenter C, Hoffmann CM, Märländer B. 2006. Effects of weather variables on sugar beet yield development (Beta vulgaris L.). Eur J Agron, 24(1): 62-69.
  • Köksal ES, Güngör Y, Yildirim YE. 2011. Spectral reflectance characteristics of sugar beet under different levels of irrigation water and relationships between growth parameters and spectral indexes. Irrig Drain, 60: 187-195.
  • Kothari K, Battisti R, Boote KJ, Archontoulis SV, Confalone A, Constantin J, Salmerón M. 2022. Are soybean models ready for climate change food impact assessments? Eur J Agron, 135: 126482.
  • Licker R, Johnston M, Foley JA, Barford C, Kucharik CJ, Monfreda C, Ramankutty N. 2010. Mind the gap: how do climate and agricultural management explain the ‘yield gap’of croplands around the world? Glob Ecol Biogeogr, 19(6): 769-782.
  • Mamyandi MM, Pirzad A, Zardoshti MR. 2012. Effect of Nano-iron spraying at varying growth stage of sugar beet (Beta vulgaris L.) on the size of different plant parts. Intern J Agric Crop Sci, 4(12): 740-745.
  • Manschadi AM, Eitzinger J, Breisch M, Fuchs W, Neubauer T, Soltani A. 2021. Full parameterisation matters for the best performance of crop models: inter-comparison of a simple and a detailed maize model. Int J Plant Prod, 15(1): 61-78.
  • Mendelsohn R, Nordhaus WD, Shaw D. 1994. The impact of global warming on agriculture: A Ricardian analysis. American Econ Rev, 1: 753-771.
  • Noor A, Khan MR. 2015. Efficacy and safety of mixing azoxystrobin and starter fertilizers for controlling Rhizoctonia solani in sugar beet. Phytoparasit, 43(1): 51-55.
  • Oteng-Darko P, Yeboah S, Addy SNT, Amponsah S, Danquah ETO. 2013. Crop modeling: A tool for agricultural research–A review. J Agric Res Dev, 2(1): 001-006
  • Petkeviciene B. 2009. The effects of climate factors on sugar beet early sowing timing. Agron Res, 7: 436-443.
  • Prequeno DN, Hernandez-Ochoa IM, Reynolds M, Sonder K, MoleroMilan A, Robertson RD, Asseng S. 2021. Climate impact and adaptation to heat and drought stress of regional and global wheat production. Environ Res, 16(5): 054070.
  • Rahman MH, Ahmad A, Wang X, Wajid A, Nasim W, Hussain M, Hoogenboom G. 2018. Multi-model projections of future climate and climate change impacts uncertainty assessment for cotton production in Pakistan. Agric For Meteorol, 253: 94-113.
  • Richter GM, Qi A, Semenov MA, Jaggard KW. 2006. Modelling the variability of UK sugar beet yields under climate change and husbandry adaptations. Soil Use Manag, 22(1): 39-47.
  • Rosenzweig C, Hillel D. 1998. Climate change and the global harvest: potential impacts of the greenhouse effect on agriculture. Oxford University Press, Oxford, UK, 1st ed., pp: 336.
  • Spitters CJ, Schapendonk AH. 1990. Evaluation of breeding strategies for drought tolerance in potato by means of crop growth simulation. Gen Aspects of Plant Min Nut, 2: 151-161.
  • Spitters CJT. 1989. Crop Growth Models: Their usefulness and limitations. Acta Hortic, 267: 349-368.
  • Starke P, Hoffmann C. 2014. Yield parameters of Beta beets as a basis to estimate the biogas yield. Sug Ind, 2: 169-176.
  • Stricevic R, Cosic M, Djurovic N, Pejic B, Maksimovic L. 2011. Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agric Water Manag, 98(10): 1615-1621.
  • Süheri S, Topak R, Yavuz D. 2007. Farkli sulama programlarinin şeker pancari verimine ve su kullanim randimanina etkisi. Selcuk J Agric Food Sci, 21(43): 37-45.
  • Terry N. 1968. Developmental physiology of sugar beet: i. the influence of light and temperature on growth. J Exp Bot, 19(4): 795-811.
  • Tingem M, Rivington M. 2009. Adaptation for crop agriculture to climate change in Cameroon: turning on the heat. Mitig Adapt Strateg Glob Chang, 14(2): 153-168.
  • Tognetti R, Palladino M, Minnocci A, Delfine S, Alvino A. 2003. The response of sugar beet to drip and low-pressure sprinkler irrigation in southern Italy. Agri Water Manage, 60(2): 135-155.
  • Topak R, Süheri S, Acar B. 2011. Effect of different drip irrigation regimes on sugar beet (Beta vulgaris L.) yield, quality and water use efficiency in Middle Anatolian, Turkey. Irrig Sci, 29(1): 79-89.
  • TSMS. 2021. Turkish State Meteorological Service, climate data of long term period. URL: https://mgm.gov.tr (access date: August 2, 2022).
  • TURKSTAT. 2022. Turkish Statistical Institute. URL: https://tuikweb.tuik.gov.tr/ (access date: August 2, 2022).
  • Viver IA. 2022. Translation and evaluation of the LINTUL2-Banana crop growth model in R. MSc Thesis, Wagenigen University and Research, Wagenigen, Nederland, pp: 78.
  • Werker AR, Jaggard KW. 1998. Dependence of sugar beet yield on light interception and evapotranspiration. Agric For Meteorol, 89(3-4): 229-240.
  • Yagiz AK, Cakici M, Aydogan N, Omezli S, Yerlikaya BA, Ayten S, Haverkort AJ. 2020. Exploration of climate change effects on shifting potato seasons, yields and water use employing NASA and national long-term weather data. Potato Res, 63(4): 565-577.
  • Yetik AK, Candoğan BN. 2022. Optimisation of irrigation strategy in sugar beet farming based on yield, quality and water productivity. Plant Soil Environ, 68: 358-365.
  • Zengin M, Gökmen F, Yazici MA, Gezgin S. 2009. Effects of potassium, magnesium, and sulphur containing fertilizers on yield and quality of sugar beets (Beta vulgaris L.). Turkish J Agric Forest, 33(5): 495-502.
  • Zengin M, Uyanöz R, Çetin Ü. 2003. A study on the soil tare of sugar beet in Konya. Selcuk J Agric Sci, 17: 53-55.
There are 57 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ali Kaan Yetik 0000-0003-1372-8407

Tefide Kızıldeniz 0000-0002-5627-1307

Zeynep Ünal 0000-0002-9954-1151

Publication Date April 1, 2023
Submission Date September 28, 2022
Acceptance Date November 29, 2022
Published in Issue Year 2023

Cite

APA Yetik, A. K., Kızıldeniz, T., & Ünal, Z. (2023). Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model. Black Sea Journal of Engineering and Science, 6(2), 53-59. https://doi.org/10.34248/bsengineering.1181263
AMA Yetik AK, Kızıldeniz T, Ünal Z. Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model. BSJ Eng. Sci. April 2023;6(2):53-59. doi:10.34248/bsengineering.1181263
Chicago Yetik, Ali Kaan, Tefide Kızıldeniz, and Zeynep Ünal. “Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model”. Black Sea Journal of Engineering and Science 6, no. 2 (April 2023): 53-59. https://doi.org/10.34248/bsengineering.1181263.
EndNote Yetik AK, Kızıldeniz T, Ünal Z (April 1, 2023) Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model. Black Sea Journal of Engineering and Science 6 2 53–59.
IEEE A. K. Yetik, T. Kızıldeniz, and Z. Ünal, “Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model”, BSJ Eng. Sci., vol. 6, no. 2, pp. 53–59, 2023, doi: 10.34248/bsengineering.1181263.
ISNAD Yetik, Ali Kaan et al. “Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model”. Black Sea Journal of Engineering and Science 6/2 (April 2023), 53-59. https://doi.org/10.34248/bsengineering.1181263.
JAMA Yetik AK, Kızıldeniz T, Ünal Z. Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model. BSJ Eng. Sci. 2023;6:53–59.
MLA Yetik, Ali Kaan et al. “Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model”. Black Sea Journal of Engineering and Science, vol. 6, no. 2, 2023, pp. 53-59, doi:10.34248/bsengineering.1181263.
Vancouver Yetik AK, Kızıldeniz T, Ünal Z. Simulating the Yield Responses of Sugar Beet to Different Climate Change Scenarios by LINTUL-MULTICROP Model. BSJ Eng. Sci. 2023;6(2):53-9.

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