Research Article
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Year 2023, Volume: 4 Issue: 1, 104 - 124, 30.06.2023

Nura Jafar Shanono , Lawal Ahmad , Nuraddeen Mukhtar Nasidi , Abdul'aziz Nuhu Jibril , Mukhtar Nuhu Yahya

https://doi.org/10.46592/turkager.1283793
Cited By: 1

Abstract

References

  • Abdollah S, Ali A, Ritzema H, Dam J Van and Hellegers P (2022). A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Agricultural Water Management, 271 (December 2021), 107741. https://doi.org/10.1016/j.agwat.2022.107741
  • Ahmad MT and Haie N (2018). Assessing the impacts of population growth and climate change on performance of water use systems and water allocation in Kano River basin, Nigeria. Water (Switzerland), 10(12). https://doi.org/10.3390/w10121766
  • Akbari M, Gheysari M, Mostafazadeh-Fard B and Shayannejad M (2018). Surface irrigation simulation-optimization model based on meta-heuristic algorithms. Agricultural Water Management, 201 (January), 46-57. https://doi.org/10.1016/j.agwat.2018.01.015
  • Ara I, Turner L, Harrison MT, Monjardino M, deVoil P and Rodriguez D (2021). Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agricultural Water Management, 257 (June), 107161. https://doi.org/10.1016/j.agwat.2021.107161
  • Bitri M, Grazhdani S and Ahmeti A (2014). Validation of the aquacrop model for full and deficit ırrigated potato production in environmental condition of Korça Zone, South-Eastern Albania. International Journal of Innovative Research in Science, Engineering and Technology, 3(4): 1-8.
  • Cheng M, Wang H, Fan J, Xiang Y, Liu X, Liao Z, Elsayed A, Zhang F and Li Z (2022). Evaluation of AquaCrop model for greenhouse cherry tomato with plastic film mulch under various water and nitrogen supplies. Agricultural Water Management, 274 (July), 107949. https://doi.org/10.1016/j.agwat.2022.107949
  • Ebrahimipak NA, Egdernezhad A, Tafteh A and Ansari MA (2022). The effect of ırrigation water management and fertilizer amount on aquacrop accuracy and efficiency for tomato yield and water use efficiency simulation. Iranian Journal of Irrigation and Water Engineering, 47(3): 121-136. https://doi.org/10.22125/IWE.2020.243948.1405
  • Eiben AE and Smith JE (2015). Introduction to evolutionary computing. In Kybernetes (Second, Vol. 33). Springer Berlin Heidelberg. https://doi.org/10.1108/03684920410699216
  • Farrokhi E, Mahallati MN, Koocheki A and Beheshti SA (2021). Simulation of growth and development of tomato (Lycopersicon esculentum Mill.) under drought stress: 2- Simulation of water productivity, Above ground biomass and yield. Journal of Water and Soil, 35(5): 627-643. https://doi.org/10.22067/JSW.2021.15035.0
  • Gaelen HV, Tsegay A, Delbecque N, Shrestha N, Garcia M, Fajardo H, Miranda R, Vanuytrecht E, Abrha B, Diels J and Raes D (2015). A semi-quantitative approach for modelling crop response to soil fertility: Evaluation of the AquaCrop procedure. Journal of Agricultural Science, 153(7): 1218-1233. https://doi.org/10.1017/S0021859614000872
  • Hadebe ST, Modi AT and Mabhaudhi T (2017). Calibration and testing of AquaCrop for selected sorghum genotypes. Water SA, 43(2): 209-221. https://doi.org/10.4314/wsa.v43i2.05
  • Hanan L, Qiushi L and Shaobin L (2016). An integrated optimization design method based on surrogate modeling applied to diverging duct design. International Journal of Turbo and Jet Engines, 33(4): 395-405. https://doi.org/10.1515/tjj-2015-0042
  • Hendy ZM, Attaher SM, Abdelhady SA, Abdel-aziz AA and El-Gindy AEGM (2019). Simulation of the effect of deficit irrigation schemes on tomato crop production using aquacrop model. Misr Journal of Agricultural Engineering, 36(1): 175-194.
  • Heng LK, Hsiao T, Evett S, Howell T and Steduto P (2009). Validating the FAO aquacrop model for irrigated and water defi cient field maize. Agronomy Journal, 101(3): 488-498. https://doi.org/10.2134/agronj2008.0029xs
  • Ikudayisi A and Adeyemo J (2015). Irrigation water optimization using evolutionary algorithms. Environmental Economics, 6(1): 200–205.
  • Isah AS, Amans EB, Odion EC and Yusuf AA (2014). Growth rate and yield of two tomato varieties (Lycopersicon esculentum mill) under green manure and NPK fertilizer rate Samaru northern guinea savanna. International Journal of Agronomy, 2014(1): 1-8. https://doi.org/10.1155/2014/932759
  • Jadhav R, Jadhav SB, Awari HW, Ingle VK and Khodke UM (2022). Assessment of AquaCrop Model for irrigated cotton under deficit ırrigation in semi-arid tropics of Maharashtra. International Journal of Current Microbiology and Applied Sciences, 11(01): 123-135. https://doi.org/https://doi.org/10.20546/ijcmas.2022.1101.015
  • Jame YW and Cutforth HW (1996). Crop growth models for decision support systems. Plant Science, 76, 9-19. https://doi.org/197.210.70.172 on 11/16/21
  • Jiang Y, Xu X, Huang Q, Huo Z and Huang G (2016). Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model. Agricultural Water Management, 178: 76-88. https://doi.org/10.1016/j.agwat.2016.08.035
  • Katerji N, Pasquale C and Mastrolli M (2013). Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agricural Water Management, 130: 14-26. https://doi.org/https://doi.org/10.1016/j.agwat.2013.08.005
  • Kephe PN, Ayisi KK and Petja BM (2021). Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa. Agriculture and Food Security, 10(1): 1-24. https://doi.org/10.1186/s40066-020-00283-5
  • Kloss S, Schütze N and Schmidhalter U (2014). Evaluation of very high soil-water tension threshold values in sensor-based deficit ırrigation systems. Journal of Irrigation and Drainage Engineering, 140(9). https://doi.org/10.1061/(asce)ir.1943-4774.0000722
  • Ko J, Piccinni G, Guo W and Steglich E (2009). Parameterization of EPIC crop model for simulation of cotton growth in South Texas. Journal of Agricultural Science, 147: 169-178. https://doi.org/10.1017/S0021859608008356
  • Lawal A and Shanono NJ (2022). Development and testing of sensor-based drip irrigation to improve tomato production in semi-arid Nigeria. Proceedings of the 22nd International Conference and 42nd Annual General Meetings of the Nigerian Institution of Agricultural Engineers (A Division of Nigerian Society of Engineers) Niae Conference September 20th -24th, 2022 | ASABA, NIGERIA, September, 103–111.
  • Li M, Fu Q, Singh V, Liu D and Gong X (2020). Risk-based agricultural water allocation under multiple uncertainties. Agricultural Water Management, 233(4): 106105.
  • McCarthy AC, Hancock NH and Raine SR (2013). Advanced process control of irrigation: the current state and an analysis to aid future development. Irrigation Science, 31: 183-192. https://doi.org/dx.doi.org/10.1007/s00271-011-0313-1
  • Muroyiwa GATM, Mashonjowa E and Muchuweti M (2022). Evaluation of FAO AquaCrop Model for ability to simulate attainable yields and water use for field tomatoes grown under deficit ırrigation in Harare, Zimbabwe. African Crop Science Journal by African Crop Science Society, 30(2), 245-269. https://doi.org/https://dx.doi.org/10.4314/acsj.v30i2.10
  • Perea RG, Daccache A, Di´az JAR, Poyato EC and Knox JW (2017). Modelling impacts of precision irrigation on crop yield and in-field water management. Precision Agriculture, 19: 497-512. https://doi.org/https://doi.org/10.1007/s11119-017-9535-4
  • Raes D, Steduto P, Hsiao TC and Feres E (2022). AquaCrop Version 7.0 Reference manual; Chapter 1 FAO crop-water productivity model to simulate yield response to water (Issue August). Food and Agriculture Organization of the United Nations. https://www.fao.org/3/br246e/br246e.pdf
  • Reynolds M, Kropff M, Crossa J, Koo J, Kruseman G, Molero Milan A, Rutkoski J, Schulthess U, Singh B, Sonder K, Tonnang H and Vadez V (2018). Role of modelling in international crop research: Overview and some case studies. Agronomy 8(12): 291.
  • Rinaldi M, Garofalo P, Rubino P and Steduto P (2011). Processing tomatoes under different irrigation regimes in Southern Italy: agronomic and economic assessments in a simulation case study. Italian Journal of Agrometeorology 3(3): 39-56.
  • Saberi E, Khashei Siuki A, Pourreza-Bilondi M and Shahidi A (2020). Development of a simulation–optimization model with a multi-objective framework for automatic design of a furrow irrigation system. Irrigation and Drainage, 69(4): 603-617. https://doi.org/10.1002/ird.2460
  • Salemi H, Amin M, Soom M, Mousavi S and Ganji A (2011). Irrigated silage maize yield and water productivity response to deficit ırrigation in an arid region. Polish Journal of Environmental Studies, 20(5). https://doi.org/: https://www.researchgate.net/publication/275953655
  • Sang HJ (2020). Optimisation of tomato water productivty under deficit sub-surface drip irrigation and mulching systems. Egerton University.
  • Seidel S (2012). Optimal simulation based design Dresdner Schriften zur Hydrologie.
  • Shanono NJ (2019). Assessing the impact of human behaviour on reservoir system performance using dynamic co-evolution. A PhD Thesis Submitted to University of the Witwatersrand, Johannesburg. https://doi.org/http://wiredspace.wits.ac.za/handle/10539/29043
  • Shanono NJ, Nasidi NM, Zakari MD and Bello MM (2014). Assessment of field channels performance at watari ırrigation project Kano, Nigeria. 1st International Conference on Dryland, Center for Dryland Agriculture, Bayero University Kano, Nigeria. 8th-12th December, 2014, 144-150.
  • Shanono NJ, Othman MK, Nasidi NM and Isma’il H (2012). Evaluation of Soil and water quality of watari ırrigation project in semi-arid region, Kano, Nigeria. Proceedings of the 33rd National Conference and Annual General Meeting of the Nigerian Institute of Agricultural Engineers (NIAE) Bauchi., 181-186.
  • Shanono NJ, Abba BS and Nasidi NM (2022). Evaluation of Aqua-Crop Model using onion crop under deficit ırrigation and mulch in semi-arid Nigeria. Turkish Journal of Agricultural Engineering Research (TURKAGER), 3(1): 131-145. https://doi.org/10.46592/turkager.1078082
  • Singels A, Annandale JG, Jager JM De, Schulze RE, Durand W, Rensburg LD Van, Heerden PS Van, Crosby CT, Green GC and Steyn JM (2013). Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future. 1862. https://doi.org/10.1080/02571862.2010.10639970
  • Singh A (2012). An overview of the optimization modelling applications. Journal of Hydrology, 466-467(August), 167-182. https://doi.org/10.1016/j.jhydrol.2012.08.004
  • Steduto P, Hsiao TC and Fereres E (2012). Crop yield response to water. Food and Agricultural Organisation. www.fao.org
  • Takács S, Csengeri E, Pék Z, Bíró T, Szuvandzsiev P, Palotás G and LH (2021). Performance evaluation of aquacrop model in processing tomato biomass, fruit yield and water stress ındicator modelling. Water, 13(3587). https://doi.org/https://doi.org/w13243587
  • Takács S, Rácz I, Csengeri E and Bíró T (2019). Biomass production estimation of processing tomato using AquaCrop under different irrigation treatments. Acta Agraria Debreceniensis, 2: 131-136. https://doi.org/10.34101/actaagrar/2/3691
  • Thangaraju NKA (2020). Predicting crop water requirements and yield for tomato under a humid climate (Issue April) [McGill University, Montreal]. https://escholarship.mcgill.ca/theses
  • Thompson RB and Gallardo M (2005). Use of Soil moisture sensors for ırrigation scheduling. “ımprovement of water use efficiency in protected crops, January, 1–6. https://doi.org/https://www.researchgate.net/publication/285422793_Use_of_soil_sensors_for_irrigation_scheduling/link/566481dc08ae418a786d6a93/download
  • Vegu G, Geethalakshmi V and Bhuvaneswari K (2018). Evaluation of the AquaCrop Model for simulating yield response of tomato crop over Thiruchirapalli District Of Tamilnadu. Journal in Science, Agriculture & Engineering, 7(Special issue).
  • Walser S, Schütze N, Marcus G, Susanne L and Schmidhalter U (2011). Evaluation of the transferability of a SVAT model--results from field and greenhouse applications. Irrigation and Drainage, 60(Suppl. 1): 59-70. https://doi.org/10.1002/ird.669
  • Whitley D (2001). An overview of evolutionary algorithms: Practical issues and common pitfalls. Information and Software Technology, 43(14): 817-831. https://doi.org/10.1016/S0950-5849(01)00188-4

Simulation-Optimization Modelling of Yield and Yield Components of Tomato Crop

Year 2023, Volume: 4 Issue: 1, 104 - 124, 30.06.2023

Nura Jafar Shanono , Lawal Ahmad , Nuraddeen Mukhtar Nasidi , Abdul'aziz Nuhu Jibril , Mukhtar Nuhu Yahya

https://doi.org/10.46592/turkager.1283793
Cited By: 1

Abstract

This study simulate and optimize the yield and yield parameters of tomato using AquaCrop model and genetic algorthm (GA) respectively. The AquaCrop model was firstly calibrated using the data obtained from the field and was later used to simulate the observed yield, water productivity and biomass of tomato. The Root Mean Square Error (RMSE), Coefficient of Residual Mass (CRM) Normalized Root Mean Square Error (NRMSE) and Modelling efficiency (EF) were used to compare the observed and simulated values. The governing equation of AquaCrop simulation software was then optimized using the evolutionary optimization method of GA with MATLAB programming software. All the statistical indices except CRM used in comparing the simulated and observed values indicated good agreement. The CRM values of -0.11, -0.06 and -0.20 were obtained for the yield, biomass and water productivity of tomato which indicated a very slight over-estimation of the observed results by the AquaCrop model. The optimization algorithm terminated when the optimal values of yield and biomass were 4.496 〖ton ha〗^(-1) and 4.90 〖ton ha〗^(-1) respectively. The GA revealed that the yield and biomass of tomato can be increased by 57% and 23% respectively if the optimized parameters were either attained on the field experiment or used during simulation. Thus, the study ascertained that crop simulation models such as AquaCrop and optimization algorithms can be used to identify optimal parameters that if maintained on the field could improve the yield of crops such as tomato.

Keywords

AquaCrop model Drip irrigation Genetic algorithm Simulation-Optimization Tomato yield

References

  • Abdollah S, Ali A, Ritzema H, Dam J Van and Hellegers P (2022). A combined model approach to optimize surface irrigation practice: SWAP and WinSRFR. Agricultural Water Management, 271 (December 2021), 107741. https://doi.org/10.1016/j.agwat.2022.107741
  • Ahmad MT and Haie N (2018). Assessing the impacts of population growth and climate change on performance of water use systems and water allocation in Kano River basin, Nigeria. Water (Switzerland), 10(12). https://doi.org/10.3390/w10121766
  • Akbari M, Gheysari M, Mostafazadeh-Fard B and Shayannejad M (2018). Surface irrigation simulation-optimization model based on meta-heuristic algorithms. Agricultural Water Management, 201 (January), 46-57. https://doi.org/10.1016/j.agwat.2018.01.015
  • Ara I, Turner L, Harrison MT, Monjardino M, deVoil P and Rodriguez D (2021). Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review. Agricultural Water Management, 257 (June), 107161. https://doi.org/10.1016/j.agwat.2021.107161
  • Bitri M, Grazhdani S and Ahmeti A (2014). Validation of the aquacrop model for full and deficit ırrigated potato production in environmental condition of Korça Zone, South-Eastern Albania. International Journal of Innovative Research in Science, Engineering and Technology, 3(4): 1-8.
  • Cheng M, Wang H, Fan J, Xiang Y, Liu X, Liao Z, Elsayed A, Zhang F and Li Z (2022). Evaluation of AquaCrop model for greenhouse cherry tomato with plastic film mulch under various water and nitrogen supplies. Agricultural Water Management, 274 (July), 107949. https://doi.org/10.1016/j.agwat.2022.107949
  • Ebrahimipak NA, Egdernezhad A, Tafteh A and Ansari MA (2022). The effect of ırrigation water management and fertilizer amount on aquacrop accuracy and efficiency for tomato yield and water use efficiency simulation. Iranian Journal of Irrigation and Water Engineering, 47(3): 121-136. https://doi.org/10.22125/IWE.2020.243948.1405
  • Eiben AE and Smith JE (2015). Introduction to evolutionary computing. In Kybernetes (Second, Vol. 33). Springer Berlin Heidelberg. https://doi.org/10.1108/03684920410699216
  • Farrokhi E, Mahallati MN, Koocheki A and Beheshti SA (2021). Simulation of growth and development of tomato (Lycopersicon esculentum Mill.) under drought stress: 2- Simulation of water productivity, Above ground biomass and yield. Journal of Water and Soil, 35(5): 627-643. https://doi.org/10.22067/JSW.2021.15035.0
  • Gaelen HV, Tsegay A, Delbecque N, Shrestha N, Garcia M, Fajardo H, Miranda R, Vanuytrecht E, Abrha B, Diels J and Raes D (2015). A semi-quantitative approach for modelling crop response to soil fertility: Evaluation of the AquaCrop procedure. Journal of Agricultural Science, 153(7): 1218-1233. https://doi.org/10.1017/S0021859614000872
  • Hadebe ST, Modi AT and Mabhaudhi T (2017). Calibration and testing of AquaCrop for selected sorghum genotypes. Water SA, 43(2): 209-221. https://doi.org/10.4314/wsa.v43i2.05
  • Hanan L, Qiushi L and Shaobin L (2016). An integrated optimization design method based on surrogate modeling applied to diverging duct design. International Journal of Turbo and Jet Engines, 33(4): 395-405. https://doi.org/10.1515/tjj-2015-0042
  • Hendy ZM, Attaher SM, Abdelhady SA, Abdel-aziz AA and El-Gindy AEGM (2019). Simulation of the effect of deficit irrigation schemes on tomato crop production using aquacrop model. Misr Journal of Agricultural Engineering, 36(1): 175-194.
  • Heng LK, Hsiao T, Evett S, Howell T and Steduto P (2009). Validating the FAO aquacrop model for irrigated and water defi cient field maize. Agronomy Journal, 101(3): 488-498. https://doi.org/10.2134/agronj2008.0029xs
  • Ikudayisi A and Adeyemo J (2015). Irrigation water optimization using evolutionary algorithms. Environmental Economics, 6(1): 200–205.
  • Isah AS, Amans EB, Odion EC and Yusuf AA (2014). Growth rate and yield of two tomato varieties (Lycopersicon esculentum mill) under green manure and NPK fertilizer rate Samaru northern guinea savanna. International Journal of Agronomy, 2014(1): 1-8. https://doi.org/10.1155/2014/932759
  • Jadhav R, Jadhav SB, Awari HW, Ingle VK and Khodke UM (2022). Assessment of AquaCrop Model for irrigated cotton under deficit ırrigation in semi-arid tropics of Maharashtra. International Journal of Current Microbiology and Applied Sciences, 11(01): 123-135. https://doi.org/https://doi.org/10.20546/ijcmas.2022.1101.015
  • Jame YW and Cutforth HW (1996). Crop growth models for decision support systems. Plant Science, 76, 9-19. https://doi.org/197.210.70.172 on 11/16/21
  • Jiang Y, Xu X, Huang Q, Huo Z and Huang G (2016). Optimizing regional irrigation water use by integrating a two-level optimization model and an agro-hydrological model. Agricultural Water Management, 178: 76-88. https://doi.org/10.1016/j.agwat.2016.08.035
  • Katerji N, Pasquale C and Mastrolli M (2013). Productivity, evapotranspiration, and water use efficiency of corn and tomato crops simulated by AquaCrop under contrasting water stress conditions in the Mediterranean region. Agricural Water Management, 130: 14-26. https://doi.org/https://doi.org/10.1016/j.agwat.2013.08.005
  • Kephe PN, Ayisi KK and Petja BM (2021). Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa. Agriculture and Food Security, 10(1): 1-24. https://doi.org/10.1186/s40066-020-00283-5
  • Kloss S, Schütze N and Schmidhalter U (2014). Evaluation of very high soil-water tension threshold values in sensor-based deficit ırrigation systems. Journal of Irrigation and Drainage Engineering, 140(9). https://doi.org/10.1061/(asce)ir.1943-4774.0000722
  • Ko J, Piccinni G, Guo W and Steglich E (2009). Parameterization of EPIC crop model for simulation of cotton growth in South Texas. Journal of Agricultural Science, 147: 169-178. https://doi.org/10.1017/S0021859608008356
  • Lawal A and Shanono NJ (2022). Development and testing of sensor-based drip irrigation to improve tomato production in semi-arid Nigeria. Proceedings of the 22nd International Conference and 42nd Annual General Meetings of the Nigerian Institution of Agricultural Engineers (A Division of Nigerian Society of Engineers) Niae Conference September 20th -24th, 2022 | ASABA, NIGERIA, September, 103–111.
  • Li M, Fu Q, Singh V, Liu D and Gong X (2020). Risk-based agricultural water allocation under multiple uncertainties. Agricultural Water Management, 233(4): 106105.
  • McCarthy AC, Hancock NH and Raine SR (2013). Advanced process control of irrigation: the current state and an analysis to aid future development. Irrigation Science, 31: 183-192. https://doi.org/dx.doi.org/10.1007/s00271-011-0313-1
  • Muroyiwa GATM, Mashonjowa E and Muchuweti M (2022). Evaluation of FAO AquaCrop Model for ability to simulate attainable yields and water use for field tomatoes grown under deficit ırrigation in Harare, Zimbabwe. African Crop Science Journal by African Crop Science Society, 30(2), 245-269. https://doi.org/https://dx.doi.org/10.4314/acsj.v30i2.10
  • Perea RG, Daccache A, Di´az JAR, Poyato EC and Knox JW (2017). Modelling impacts of precision irrigation on crop yield and in-field water management. Precision Agriculture, 19: 497-512. https://doi.org/https://doi.org/10.1007/s11119-017-9535-4
  • Raes D, Steduto P, Hsiao TC and Feres E (2022). AquaCrop Version 7.0 Reference manual; Chapter 1 FAO crop-water productivity model to simulate yield response to water (Issue August). Food and Agriculture Organization of the United Nations. https://www.fao.org/3/br246e/br246e.pdf
  • Reynolds M, Kropff M, Crossa J, Koo J, Kruseman G, Molero Milan A, Rutkoski J, Schulthess U, Singh B, Sonder K, Tonnang H and Vadez V (2018). Role of modelling in international crop research: Overview and some case studies. Agronomy 8(12): 291.
  • Rinaldi M, Garofalo P, Rubino P and Steduto P (2011). Processing tomatoes under different irrigation regimes in Southern Italy: agronomic and economic assessments in a simulation case study. Italian Journal of Agrometeorology 3(3): 39-56.
  • Saberi E, Khashei Siuki A, Pourreza-Bilondi M and Shahidi A (2020). Development of a simulation–optimization model with a multi-objective framework for automatic design of a furrow irrigation system. Irrigation and Drainage, 69(4): 603-617. https://doi.org/10.1002/ird.2460
  • Salemi H, Amin M, Soom M, Mousavi S and Ganji A (2011). Irrigated silage maize yield and water productivity response to deficit ırrigation in an arid region. Polish Journal of Environmental Studies, 20(5). https://doi.org/: https://www.researchgate.net/publication/275953655
  • Sang HJ (2020). Optimisation of tomato water productivty under deficit sub-surface drip irrigation and mulching systems. Egerton University.
  • Seidel S (2012). Optimal simulation based design Dresdner Schriften zur Hydrologie.
  • Shanono NJ (2019). Assessing the impact of human behaviour on reservoir system performance using dynamic co-evolution. A PhD Thesis Submitted to University of the Witwatersrand, Johannesburg. https://doi.org/http://wiredspace.wits.ac.za/handle/10539/29043
  • Shanono NJ, Nasidi NM, Zakari MD and Bello MM (2014). Assessment of field channels performance at watari ırrigation project Kano, Nigeria. 1st International Conference on Dryland, Center for Dryland Agriculture, Bayero University Kano, Nigeria. 8th-12th December, 2014, 144-150.
  • Shanono NJ, Othman MK, Nasidi NM and Isma’il H (2012). Evaluation of Soil and water quality of watari ırrigation project in semi-arid region, Kano, Nigeria. Proceedings of the 33rd National Conference and Annual General Meeting of the Nigerian Institute of Agricultural Engineers (NIAE) Bauchi., 181-186.
  • Shanono NJ, Abba BS and Nasidi NM (2022). Evaluation of Aqua-Crop Model using onion crop under deficit ırrigation and mulch in semi-arid Nigeria. Turkish Journal of Agricultural Engineering Research (TURKAGER), 3(1): 131-145. https://doi.org/10.46592/turkager.1078082
  • Singels A, Annandale JG, Jager JM De, Schulze RE, Durand W, Rensburg LD Van, Heerden PS Van, Crosby CT, Green GC and Steyn JM (2013). Modelling crop growth and crop water relations in South Africa: Past achievements and lessons for the future. 1862. https://doi.org/10.1080/02571862.2010.10639970
  • Singh A (2012). An overview of the optimization modelling applications. Journal of Hydrology, 466-467(August), 167-182. https://doi.org/10.1016/j.jhydrol.2012.08.004
  • Steduto P, Hsiao TC and Fereres E (2012). Crop yield response to water. Food and Agricultural Organisation. www.fao.org
  • Takács S, Csengeri E, Pék Z, Bíró T, Szuvandzsiev P, Palotás G and LH (2021). Performance evaluation of aquacrop model in processing tomato biomass, fruit yield and water stress ındicator modelling. Water, 13(3587). https://doi.org/https://doi.org/w13243587
  • Takács S, Rácz I, Csengeri E and Bíró T (2019). Biomass production estimation of processing tomato using AquaCrop under different irrigation treatments. Acta Agraria Debreceniensis, 2: 131-136. https://doi.org/10.34101/actaagrar/2/3691
  • Thangaraju NKA (2020). Predicting crop water requirements and yield for tomato under a humid climate (Issue April) [McGill University, Montreal]. https://escholarship.mcgill.ca/theses
  • Thompson RB and Gallardo M (2005). Use of Soil moisture sensors for ırrigation scheduling. “ımprovement of water use efficiency in protected crops, January, 1–6. https://doi.org/https://www.researchgate.net/publication/285422793_Use_of_soil_sensors_for_irrigation_scheduling/link/566481dc08ae418a786d6a93/download
  • Vegu G, Geethalakshmi V and Bhuvaneswari K (2018). Evaluation of the AquaCrop Model for simulating yield response of tomato crop over Thiruchirapalli District Of Tamilnadu. Journal in Science, Agriculture & Engineering, 7(Special issue).
  • Walser S, Schütze N, Marcus G, Susanne L and Schmidhalter U (2011). Evaluation of the transferability of a SVAT model--results from field and greenhouse applications. Irrigation and Drainage, 60(Suppl. 1): 59-70. https://doi.org/10.1002/ird.669
  • Whitley D (2001). An overview of evolutionary algorithms: Practical issues and common pitfalls. Information and Software Technology, 43(14): 817-831. https://doi.org/10.1016/S0950-5849(01)00188-4
There are 49 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering
Journal Section Research Articles
Authors

Nura Jafar Shanono 0000-0002-1731-145X

Lawal Ahmad 0009-0004-2956-2522

Nuraddeen Mukhtar Nasidi 0000-0002-7933-8906

Abdul'aziz Nuhu Jibril 0000-0003-3391-6421

Mukhtar Nuhu Yahya 0000-0002-7804-7277

Early Pub Date June 25, 2023
Publication Date June 30, 2023
Submission Date April 16, 2023
Acceptance Date June 14, 2023
Published in Issue Year 2023 Volume: 4 Issue: 1

Cite

APA Shanono, N. J., Ahmad, L., Nasidi, N. M., Jibril, A. N., et al. (2023). Simulation-Optimization Modelling of Yield and Yield Components of Tomato Crop. Turkish Journal of Agricultural Engineering Research, 4(1), 104-124. https://doi.org/10.46592/turkager.1283793

Cited By

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia
Advances in Agriculture
https://doi.org/10.1155/2024/7201523
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Turkish Journal of Agricultural Engineering Research (TURKAGER) is indexed/abstracted in Information Matrix for the Analysis of Journals (MIAR), EBSCO, CABI, Food Science & Technology Abstracts (FSTA), CAS Source Index (CASSI).

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Publisher: Ebubekir ALTUNTAŞ

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