Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst

Taofeek Samuel Wahab; Alkhan Sarıyev; Mert Acar; Celaleddin Barutçular; Ahmet Çilek

doi:10.31015/2025.3.7

Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst

Abstract

Optimizing agricultural output through modeling can aid in planning, reduce the environmental impacts of agricultural activities, and enhance yields under deficit agricultural inputs. Climatic and agronomic data were collected from three subsurface irrigation regimes (I100 irrigation to field capacity (FC), I75 irrigation to 75% FC and I50 irrigation to 50% FC) over three years (2017–2019) at Cukurova University Agricultural Experimental Station. The treatments were applied in a completely randomized block design with three replications. 2019 data was used for model calibration, and 2017 and 2018 data were used for validation. Optimization involved adjusting the sowing date backward and manipulating temperature levels. The Cropping Systems Simulation Model (CropSyst) was successfully calibrated, except for the I50 Leaf area index (LAI), having a low regression coefficient and high root mean square error, an indication that CropSyst may not accurately simulate LAI at low deficit irrigation (DI). Promising results were obtained through sowing date optimization, particularly with I50 exhibiting the greatest yield improvement. Altering the average atmospheric temperature by a few degrees did not negatively affect I100, while DI applications resulted in yield loss at temperatures higher than 30 °C. CropSyst could not estimate maize yield at extreme temperatures for all treatments (above 40 °C) during the anthesis stage, indicating that the model may not be sensitive to certain maize growth stages. The optimization results indicate that for DI regimes to be competitively adopted as an alternative strategy for irrigation water conservation, the appropriate sowing date or temperature range must be carefully considered.

Keywords

Calibration, Crop growth, Crop modeling, Simulation, Soil water management

Supporting Institution

The Research Foundation of Cukurova University

Project Number

This study was supported by the Scientific Research Projects Unit of Çukurova University. Project number: FYL-2019-12298

Thanks

Thank you for taking your time to examine this manuscript

References

Abraha, M. G., Savage, M. J. (2006). Potential impacts of climate change on the grain yield of maize for the midlands of KwaZulu-Natal, South Africa. Agriculture, ecosystems & environment, 115(1-4), 150-160. https://doi.org/10/c62r8h
Ale, S., Omani, N., Himanshu, S. K., Bordovsky, J. P., Thorp, K. R., Barnes, E. M. (2020). Determining optimum irrigation termination periods for cotton production in the Texas High Plains. Transactions of the ASABE, 63(1), 105-115. https://doi.org/10.13031/trans.13483
Allen, R. G., Pereira, L. S., Raes, D., Smith, M. (1998). Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome 300.
Ambrona, C. G. H. D., Gigena, R., Mendoza, C. O. (2013). Climate change impacts on maize and dry bean yields of smallholder farmers in Honduras. Revista iberoamericana de estudios de desarrollo= Iberoamerican journal of development studies, 2(1), 4-22. http://doi.org/10.26754/ojs_ried/ijds.43
Araya, A., Kisekka, I., Holman, J. (2016). Evaluating deficit irrigation management strategies for grain sorghum using AquaCrop. Irrigation science, 34, 465-481. https://doi.org/10.1007/s00271-016-0515-7
Asseng, S., Ewert, F., Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., ... Wolf, J. (2013). Uncertainty in simulating wheat yields under climate change. Nature climate change, 3(9), 827-832. https://doi.org/10.1038/nclimate1916
Ayars, J. E., Fulton, A., Taylor, B. (2015). Subsurface drip irrigation in California—Here to stay?. Agricultural water management, 157, 39-47. https://doi.org/10.1016/j.agwat.2015.01.001
Bai, J., Chen, X., Dobermann, A., Yang, H., Cassman, K. G., Zhang, F. (2010). Evaluation of NASA satellite‐and model‐derived weather data for simulation of maize yield potential in China. Agronomy Journal, 102(1), 9-16. https://doi.org/10.2134/agronj2009.0085
Barker, J. B., Bhatti, S., Heeren, D. M., Neale, C. M., Rudnick, D. R. (2019). Variable rate irrigation of maize and soybean in West-Central Nebraska under full and deficit irrigation. Frontiers in big Data, 2, 34. https://doi.org/10.3389/fdata.2019.00034
Baum, M. E., Archontoulis, S. V., Licht, M. A. (2019). Planting date, hybrid maturity, and weather effects on maize yield and crop stage. Agronomy Journal, 111(1), 303-313. https://doi.org/10/gjhfkg

Bellocchi, G., Silvestri, N., Mazzoncini, M., Meini, S. (2002). Using the CropSyst model in continuous rainfed maize (Zea mais L.) under alternative management options. Italian Journal of Agronomy, 6(1), 43-56.
Bhattacharya, A. (2019). Global Climate Change and Its Impact on Agriculture. In: Changing Climate and Resource Use Efficiency in Plants. Elsevier, pp 1–50
Bocchiola, D., Nana, E., Soncini, A. (2013). Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy. Agricultural water management, 116, 50-61. https://doi.org/10/f4j5tq
Boonwichai, S., Shrestha, S., Babel, M. S., Weesakul, S., Datta, A. (2018). Climate change impacts on irrigation water requirement, crop water productivity and rice yield in the Songkhram River Basin, Thailand. Journal of Cleaner Production, 198, 1157-1164. https://doi.org/10.1016/j.jclepro.2018.07.146
Bouniols, A., Cabelguenne, M., Jones, C. A., Chalamet, A., Charpenteau, J. L., Marty, J. R. (1991). Simulation of soybean nitrogen nutrition for a silty clay soil in southern France. Field Crops Research, 26(1), 19-34.
Brouwer, C., Heibloem, M. (1986). Irrigation water management: irrigation water needs. Training manual, 3, 1-5.
Cilek, A., Berberoglu, S. (2019). Biotope conservation in a Mediterranean agricultural land by incorporating crop modelling. Ecological Modelling, 392, 52-66. https://doi.org/10.1016/j.ecolmodel.2018.11.008
Cilek, A., Berberoglu, S., Donnez, C. (2019). Simulating Second Crop Maize Growth under Different Irrigation Regimes in Lower Seyhan Plain using CropSyst Model. In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) (pp. 1-4). IEEE., https://doi.org/10.1109/Agro-Geoinformatics.2019.8820658.
Comas, L. H., Trout, T. J., DeJonge, K. C., Zhang, H., Gleason, S. M. (2019). Water productivity under strategic growth stage-based deficit irrigation in maize. Agricultural water management, 212, 433-440. https://doi.org/10.1016/j.agwat.2018.07.015
Crafts-Brandner, S. J., Salvucci, M. E. (2002). Sensitivity of photosynthesis in a C4 plant, maize, to heat stress. Plant physiology, 129(4), 1773-1780. https://doi.org/10.1104/pp.002170
Çakir, R. (2004). Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Research, 89(1), 1-16. https://doi.org/10/cnmwvq
Difallah, W., Benahmed, K., Draoui, B., Bounaama, F. (2017). Linear optimization model for efficient use of irrigation water. International Journal of Agronomy, 2017(1), 5353648. https://doi.org/10.1155/2017/5353648
Ding, Z., Ali, E. F., Elmahdy, A. M., Ragab, K. E., Seleiman, M. F., Kheir, A. M. (2021). Modeling the combined impacts of deficit irrigation, rising temperature and compost application on wheat yield and water productivity. Agricultural Water Management, 244, 106626. https://doi.org/10.1016/j.agwat.2020.106626
Dobor, L., Barcza, Z., Hlásny, T., Árendás, T., Spitkó, T., Fodor, N. (2016). Crop planting date matters: Estimation methods and effect on future yields. Agricultural and forest meteorology, 223, 103-115. https://doi.org/10.1016/j.agrformet.2016.03.023
Donatelli, M., Stöckle, C., Ceotto, E., Rinaldi, M. (1997). Evaluation of CropSyst for cropping systems at two locations of northern and southern Italy. European Journal of Agronomy, 6(1-2), 35-45. https://doi.org/10.1016/S1161-0301(96)02029-1
Douh, B., Boujelben, A. (2011). Improving water use efficiency for a sustainable productivity of agricultural systems with using subsurface drip irrigation for maize (Zea mays L.). J. Agric. Sci. Technol. B, 1, 881-888.
Dowswell, C. R., Paliwal, R. L., Cantrell, R. P. (2019) Maize in the Third World. CRC Press, Boca Raton.
Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M., ... Caylak, O. (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. The Journal of Agricultural Science, 151(6), 813-835. https://doi.org/10/f5q2k7
Farré, I., Faci, J. M. (2009). Deficit irrigation in maize for reducing agricultural water use in a Mediterranean environment. Agricultural water management, 96(3), 383-394. https://doi.org/10.1016/j.agwat.2008.07.002
Freitas, R. M. D., Dombroski, J. L., Freitas, F. C. D., Nogueira, N. W., Leite, T. S., Praxedes, S. C. (2019). Water use of cowpea under deficit irrigation and cultivation systems in semi-arid region. Revista Brasileira de Engenharia Agrícola e Ambiental, 23(4), 271-276. https://doi.org/10.1590/1807-1929/agriambi.v23n4p271-276
Ge, T., Sui, F., Bai, L., Tong, C., Sun, N. (2012). Effects of water stress on growth, biomass partitioning, and water-use efficiency in summer maize (Zea mays L.) throughout the growth cycle. Acta Physiologiae Plantarum, 34, 1043-1053. https://doi.org/10/cw3b2p
Getachew, F., Bayabil, H. K., Hoogenboom, G., Teshome, F. T., Zewdu, E. (2021). Irrigation and shifting planting date as climate change adaptation strategies for sorghum. Agricultural Water Management, 255, 106988. https://doi.org/10.1016/j.agwat.2021.106988
Godwin, D. C., Allan Jones, C. (1991). Nitrogen Dynamics in Soi‐Plant Systems. Modeling plant and soil systems, 31, 287-321.
Gong, D., Mei, X., Hao, W., Wang, H., Caylor, K. K. (2017). Comparison of ET partitioning and crop coefficients between partial plastic mulched and non-mulched maize fields. Agricultural Water Management, 181, 23-34. https://doi.org/10.1016/j.agwat.2016.11.016
Gonzalez-Dugo, V., Ruz, C., Testi, L., Orgaz, F., Fereres, E. (2018). The impact of deficit irrigation on transpiration and yield of mandarin and late oranges. Irrigation science, 36, 227-239. https://doi.org/10.1007/s00271-018-0579-7
Guillaume, S., Bruzeau, C., Justes, E., Lacroix, B., Bergez, J. E. (2016). A conceptual model of farmers' decision-making process for nitrogen fertilization and irrigation of durum wheat. European Journal of Agronomy, 73, 133-143. https://doi.org/10.1016/j.eja.2015.11.012
Hatfield, J. L., Prueger, J. H. (2015). Temperature extremes: Effect on plant growth and development. Weather and climate extremes, 10(Part A), 4-10. http://dx.doi.org/10.1016/j.wace.2015.08.001
Himanshu, S. K., Ale, S., Bordovsky, J., Darapuneni, M. (2019). Evaluation of crop-growth-stage-based deficit irrigation strategies for cotton production in the Southern High Plains. Agricultural Water Management, 225, 105782. https://doi.org/10.1016/j.agwat.2019.105782
Irmak, S., Djaman, K., Rudnick, D. R. (2016). Effect of full and limited irrigation amount and frequency on subsurface drip-irrigated maize evapotranspiration, yield, water use efficiency and yield response factors. Irrigation Science, 34, 271-286. https://doi.org/10.1007/s00271-016-0502-z
Irmak, S., Mohammed, A. T., Kukal, M. S. (2022). Maize response to coupled irrigation and nitrogen fertilization under center pivot, subsurface drip and surface (furrow) irrigation: Growth, development and productivity. Agricultural Water Management, 263, 107457. https://doi.org/10.1016/j.agwat.2022.107457
Jalota, S. K., Singh, S., Chahal, G. B. S., Ray, S. S., Panigraghy, S., Singh, K. B. (2010). Soil texture, climate and management effects on plant growth, grain yield and water use by rainfed maize–wheat cropping system: Field and simulation study. Agricultural water management, 97(1), 83-90. https://doi.org/10/bfgh95
Jasemi, M., Darabi, F., Naseri, R., Naserirad, H., Bazdar, S. (2013). Effect of planting date and nitrogen fertilizer application on grain yield and yield components in maize (SC 704). American-Eurasian Journal of Agriculture and Environmental Science, 13(7), 914-919. https://doi.org/10.5829/idosi.aejaes.2013.13.07.1991
Karandish, F., Nouri, H., Brugnach, M. (2021). Agro-economic and socio-environmental assessments of food and virtual water trades of Iran. Scientific Reports, 11(1), 15022. https://doi.org/10.1038/s41598-021-93928-9
Kaur, R., Arora, V. K. (2018). Assessing spring maize responses to irrigation and nitrogen regimes in north-west India using CERES-Maize model. Agricultural Water Management, 209, 171-177. https://doi.org/10.1016/j.agwat.2018.07.022
Kucharik, C. J. (2008). Contribution of planting date trends to increased maize yields in the central United States. Agronomy Journal, 100(2), 328-336. https://doi.org/10.2134/agronj2007.0145
Kukal, M. S., Irmak, S. (2020). Light interactions, use and efficiency in row crop canopies under optimal growth conditions. Agricultural and Forest Meteorology, 284, 107887. https://doi.org/10.1016/j.agrformet.2019.107887
Leng, G., Leung, L. R., Huang, M. (2017). Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME L and Model. Journal of Advances in Modeling Earth Systems, 9(3), 1665-1683. https://doi.org/10.1002/2016MS000885
Lenka, S., Singh, A. K. (2011). Simulating interactive effect of irrigation and nitrogen on crop yield and water productivity in maize–wheat cropping system. Current Science, 1451-1461.
Li, F., Yu, D., Zhao, Y. (2019). Irrigation scheduling optimization for cotton based on the AquaCrop model. Water resources management, 33, 39-55. https://doi.org/10.1007/s11269-018-2087-1
Li, Q., Chen, Y., Sun, S., Zhu, M., Xue, J., Gao, Z., ... Tang, Y. (2022). Research on crop irrigation schedules under deficit irrigation—A meta-analysis. Water Resources Management, 36(12), 4799-4817. https://doi.org/10.1007/s11269-022-03278-y
Liao, Q., Ding, R., Du, T., Kang, S., Tong, L., Li, S. (2022). Stomatal conductance drives variations of yield and water use of maize under water and nitrogen stress. Agricultural Water Management, 268, 107651. https://doi.org/10.1016/j.agwat.2022.107651
Linker, R., Kisekka, I. (2017). Model-based deficit irrigation of maize in Kansas. Transactions of the ASABE, 60(6), 2011-2022. https://doi.org/10.13031/trans.12341
Linker, R., Sylaios, G., Tsakmakis, I., Ramos, T., Simionesei, L., Plauborg, F., Battilani, A. (2018). Sub-optimal model-based deficit irrigation scheduling with realistic weather forecasts. Irrigation Science, 36, 349-362. https://doi.org/10.1007/s00271-018-0592-x
Lobell, D. B., Bänziger, M., Magorokosho, C., Vivek, B. (2011). Nonlinear heat effects on African maize as evidenced by historical yield trials. Nature climate change, 1(1), 42-45. https://doi.org/10.1038/nclimate1043
Long, S. P., Ainsworth, E. A., Leakey, A. D., Nosberger, J., Ort, D. R. (2006). Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations. Science, 312(5782), 1918-1921. https://doi.org/10.1126/science.1114722
Lu, J., Sun, G., McNulty, S. G., Amatya, D. M. (2005). A comparison of six potential evapotranspiration methods for regional use in the southeastern United States 1. JAWRA Journal of the American Water Resources Association, 41(3), 621-633. https://doi.org/10.1111/j.1752-1688.2005.tb03759.x
Luo, Q., Hoogenboom, G., Yang, H. (2022). Uncertainties in assessing climate change impacts and adaptation options with wheat crop models. Theoretical and Applied Climatology, 149(1), 805-816. https://doi.org/10.1007/s00704-022-04086-5
Ma, L., Ahuja, L. R., Islam, A., Trout, T. J., Saseendran, S. A., & Malone, R. W. (2017). Modeling yield and biomass responses of maize cultivars to climate change under full and deficit irrigation. Agricultural Water Management, 180, 88-98. https://doi.org/10.1016/j.agwat.2016.11.007
Marsal, J., Stöckle, C. O. (2012). Use of CropSyst as a decision support system for scheduling regulated deficit irrigation in a pear orchard. Irrigation Science, 30, 139-147. https://doi.org/10.1007/s00271-011-0273-5
Mendiburu, F. de (2021). agricolae: Statistical Procedures for Agricultural Research
Mirhashemi, S. H. (2022). Use of two hybrid algorithms in the investigation and prediction of the values of five quantitative traits of guar beans in different deficit irrigation methods. Applied Water Science, 12(12), 273. https://doi.org/10.1007/s13201-022-01789-y
Monteith, J. L. (1965). Evaporation and environment. In: Symposia of the society for experimental biology. Cambridge University Press (CUP) Cambridge, pp 205–234.
Montoya, F., Camargo, D., Domínguez, A., Ortega, J. F., Córcoles, J. I. (2018). Parametrization of Cropsyst model for the simulation of a potato crop in a Mediterranean environment. Agricultural Water Management, 203, 297-310. https://doi.org/10.1016/j.agwat.2018.03.029
Morsy, M., El Afandi, G., Naif, E. (2018). Cropsyst simulation and response of some wheat cultivars to late season drought. Italian Journal of Agrometeorology, 1, 15-24. https://doi.org/10.19199/2018.1.2038-5625.015
Mugiyo, H., Mhizha, T., Chimonyo, V. G., & Mabhaudhi, T. (2021). Investigation of the optimum planting dates for maize varieties using a hybrid approach: A case of Hwedza, Zimbabwe. Heliyon, 7(2). https://doi.org/10.1016/j.heliyon.2021.e06109
Naz, S., Fatima, Z., Iqbal, P., Khan, A., Zakir, I., Ullah, H., ... Ahmad, S. (2022). An introduction to climate change phenomenon. Building climate resilience in agriculture: theory, practice and future perspective, 3-16.
Noreldin, T., Ouda, S., Mounzer, O., Abdelhamid, M. T. (2015). CropSyst model for wheat under deficit irrigation using sprinkler and drip irrigation in sandy soil. Journal of Water and Land Development. https://doi.org/10.1515/jwld-2015-0016
R Core Team (2021) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria
Ranum, P., Peña‐Rosas, J. P., Garcia‐Casal, M. N. (2014). Global maize production, utilization, and consumption. Annals of the new York academy of sciences, 1312(1), 105-112. https://doi.org/10/gnpkx9
Rivington, M., Matthews, K. B., Bellocchi, G., Buchan, K., Stöckle, C. O., Donatelli, M. (2007). An integrated assessment approach to conduct analyses of climate change impacts on whole-farm systems. Environmental Modelling & Software, 22(2), 202-210. https://doi.org/10.1016/j.envsoft.2005.07.018
Robinson, D., Hayes, A., & Couch, S. (2021). broom: Convert statistical objects into tidy tibbles. R package version 0.7, 5.
Rocha, J., Carvalho-Santos, C., Diogo, P., Beça, P., Keizer, J. J., Nunes, J. P. (2020). Impacts of climate change on reservoir water availability, quality and irrigation needs in a water scarce Mediterranean region (southern Portugal). Science of the Total Environment, 736, 139477. https://doi.org/10.1016/j.scitotenv.2020.139477
Sariyev, A., Barutcular, C., Acar, M., Hossain, A., El Sabagh, A. (2020). Sub-surface drip irrigation in associated with H2O2 improved the productivity of maize under clay-rich soil of Adana, Turkey. PHYTON-INTERNATIONAL JOURNAL OF EXPERIMENTAL BOTANY, 89(3). https://doi.org/10.32604/phyton.2020.09142
Sharda, V., Gowda, P. H., Marek, G., Kisekka, I., Ray, C., Adhikari, P. (2019). Simulating the impacts of irrigation levels on soybean production in Texas high plains to manage diminishing groundwater levels. JAWRA Journal of the American Water Resources Association, 55(1), 56-69. https://doi.org/10.1111/1752-1688.12720
Sommer, R., Wall, P. C., Govaerts, B. (2007). Model-based assessment of maize cropping under conventional and conservation agriculture in highland Mexico. Soil and Tillage Research, 94(1), 83-100. https://doi.org/10.1016/j.still.2006.07.007
Stockle, C. O., Cabelguenne, M., Debaeke, P. (1997). Comparison of CropSyst performance for water management in southwestern France using submodels of different levels of complexity. European Journal of Agronomy, 7(1-3), 89-98. https://doi.org/10.1016/S1161-0301(97)00033-6
Stockle, C. O., Campbell, G. S. (1989). Simulation of crop response to water and nitrogen: an example using spring wheat. Transactions of the ASAE, 32(1), 66-0074. https://doi.org/10.13031/2013.30964
Stöckle, C. O., Donatelli, M., Nelson, R. (2003). CropSyst, a cropping systems simulation model. European journal of agronomy, 18(3-4), 289-307. https://doi.org/10.1016/S1161-0301(02)00109-0
Stockle, C. O., Kiniry, J. R. (1990). Variability in crop radiation-use efficiency associated with vapor-pressure deficit. Field Crops Research, 25(3-4), 171-181. https://doi.org/10.1016/0378-4290(90)90001-R
Stockle, C. O., Nelson, R. (1994). Cropping Systems Simulation: Model Users Manual (Version 1. 02. 00). Biological Systems Engineering Department, Washington State University 167.
Tollenaar, M., Bruulsema, T. W. (1988). Efficiency of maize dry matter production during periods of complete leaf area expansion. Agronomy Journal, 80(4), 580-585. https://doi.org/10.2134/agronj1988.00021962008000040008x
Tsimba, R., Edmeades, G. O., Millner, J. P., Kemp, P. D. (2013). The effect of planting date on maize grain yields and yield components. Field Crops Research, 150, 135-144.https://doi.org/10.1016/j.fcr.2013.05.028
Tularam, G. A., Hassan, O. M. (2016). Water availability and food security: Implication of people’s movement and migration in sub-Saharan Africa (SSA). In: Thangarajan M, Vijay Singh P(eds) Groundwater assessment, modeling, and management, CRC Press, 405–426. https://doi.org/10.1201/9781315369044
Umair, M., Shen, Y., Qi, Y., Zhang, Y., Ahmad, A., Pei, H., Liu, M. (2017). Evaluation of the CropSyst model during wheat-maize rotations on the North China Plain for identifying soil evaporation losses. Frontiers in plant science, 8, 1667. https://doi.org/10.3389/fpls.2017.01667
Van Donk, S. J., Petersen, J. L., Davison, D. R. (2013). Effect of amount and timing of subsurface drip irrigation on corn yield. Irrigation Science, 31(4), 599-609. https://doi.org/10.1007/s00271-012-0334-4
Vories, E. D., Tacker, P. L., Lancaster, S. W., Glover, R. E. (2009). Subsurface drip irrigation of corn in the United States Mid-South. Agricultural Water Management, 96(6), 912-916. https://doi.org/10.1016/j.agwat.2008.12.004
Wang, J., Chen, R., Yang, T., Wei, T., Wang, X. (2021). A computationally-efficient finite element method for the hydraulic analysis and design of subsurface drip irrigation subunits. Journal of Hydrology, 595, 125990. https://doi.org/10.1016/j.jhydrol.2021.125990
Wang, Y. P., Zhang, L. S., Mu, Y., Liu, W. H., Guo, F. X., Chang, T. R. (2020). Effect of a root‐zone injection irrigation method on water productivity and apple production in a semi‐arid region in North‐Western China. Irrigation and drainage, 69(1), 74-85. https://doi.org/10.1002/ird.2379
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C., ... Dunnington, D. (2021). ggplot2: Create elegant data visualisations using the grammar of graphics. 2021.
Williams, J. J., Henry, W. B., Varco, J. J., Reynolds, D. B., Buehring, N. W., Krutz, L. J., ... Boykin, D. L. (2018). Deficit irrigation, planting date, and hybrid selection impacts on a Mid‐South corn production system. Crop, Forage & Turfgrass Management, 4(1), 1-8. https://doi.org/10.2134/cftm2018.07.0052
Wu, Y., Du, T., Yuan, Y., Shukla, M. K. (2018). Stable isotope measurements show increases in corn water use efficiency under deficit irrigation. Scientific Reports, 8(1), 14113. https://doi.org/10.1038/s41598-018-32368-4
Yadav, A., Upreti, H., Singhal, G. D. (2024). Crop water stress index and its sensitivity to meteorological parameters and canopy temperature. Theoretical and Applied Climatology, 155(4), 2903-2915. https://doi.org/10.1007/s00704-023-04768-8
Zhang, H., Han, M., Comas, L. H., DeJonge, K. C., Gleason, S. M., Trout, T. J., Ma, L. (2019). Response of maize yield components to growth stage‐based deficit irrigation. Agronomy Journal, 111(6), 3244-3252. https://doi.org/10.2134/agronj2019.03.0214
Zhang, H., Ma, L., Douglas-Mankin, K. R., Han, M., Trout, T. J. (2021). Modeling maize production under growth stage-based deficit irrigation management with RZWQM2. Agricultural Water Management, 248, 106767. https://doi.org/10.1016/j.agwat.2021.106767
Zhao, C., Liu, B., Piao, S., Wang, X., Lobell, D. B., Huang, Y., ... Asseng, S. (2017). Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of sciences, 114(35), 9326-9331. https://doi.org/10.1073/pnas.1701762114
Zou, Y., Saddique, Q., Ali, A., Xu, J., Khan, M. I., Qing, M., ... Siddique, K. H. (2021). Deficit irrigation improves maize yield and water use efficiency in a semi-arid environment. Agricultural Water Management, 243, 106483. https://doi.org/10.1016/j.agwat.2020.106483

Details

Primary Language

English

Subjects

Agricultural Systems Analysis and Modelling, Agricultural Water Management, Conservation and Improvement of Soil and Water Resources

Journal Section

Research Article

Authors

Taofeek Samuel Wahab ^*
0000-0001-9192-5946
Nigeria

Alkhan Sarıyev
0000-0003-2832-0113
Türkiye

Mert Acar
0000-0002-9971-4470
Türkiye

Celaleddin Barutçular
0000-0003-3583-9191
Türkiye

Ahmet Çilek
0000-0002-6781-2658
Türkiye

Publication Date

September 27, 2025

Submission Date

May 2, 2025

Acceptance Date

August 25, 2025

Published in Issue

Year 2025 Volume: 9 Number: 3

DOI

https://doi.org/10.31015/2025.3.7

IZ

https://izlik.org/JA42WB26ZZ

APA

Wahab, T. S., Sarıyev, A., Acar, M., Barutçular, C., & Çilek, A. (2025). Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst. International Journal of Agriculture Environment and Food Sciences, 9(3), 690-705. https://doi.org/10.31015/2025.3.7

AMA

1.Wahab TS, Sarıyev A, Acar M, Barutçular C, Çilek A. Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst. int. j. agric. environ. food sci. 2025;9(3):690-705. doi:10.31015/2025.3.7

Chicago

Wahab, Taofeek Samuel, Alkhan Sarıyev, Mert Acar, Celaleddin Barutçular, and Ahmet Çilek. 2025. “Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation Using Sowing Date and Temperature in CropSyst”. International Journal of Agriculture Environment and Food Sciences 9 (3): 690-705. https://doi.org/10.31015/2025.3.7.

EndNote

Wahab TS, Sarıyev A, Acar M, Barutçular C, Çilek A (September 1, 2025) Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst. International Journal of Agriculture Environment and Food Sciences 9 3 690–705.

IEEE

[1]T. S. Wahab, A. Sarıyev, M. Acar, C. Barutçular, and A. Çilek, “Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst”, int. j. agric. environ. food sci., vol. 9, no. 3, pp. 690–705, Sept. 2025, doi: 10.31015/2025.3.7.

ISNAD

Wahab, Taofeek Samuel - Sarıyev, Alkhan - Acar, Mert - Barutçular, Celaleddin - Çilek, Ahmet. “Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation Using Sowing Date and Temperature in CropSyst”. International Journal of Agriculture Environment and Food Sciences 9/3 (September 1, 2025): 690-705. https://doi.org/10.31015/2025.3.7.

JAMA

1.Wahab TS, Sarıyev A, Acar M, Barutçular C, Çilek A. Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst. int. j. agric. environ. food sci. 2025;9:690–705.

MLA

Wahab, Taofeek Samuel, et al. “Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation Using Sowing Date and Temperature in CropSyst”. International Journal of Agriculture Environment and Food Sciences, vol. 9, no. 3, Sept. 2025, pp. 690-05, doi:10.31015/2025.3.7.

Vancouver

1.Taofeek Samuel Wahab, Alkhan Sarıyev, Mert Acar, Celaleddin Barutçular, Ahmet Çilek. Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst. int. j. agric. environ. food sci. 2025 Sep. 1;9(3):690-705. doi:10.31015/2025.3.7

Abstracting & Indexing Services

Google Scholar | Crossref DOI Registry

All content published in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Authors retain copyright of their work and grant the journal a non-exclusive right to publish, reproduce, and distribute the articles within an open-access framework.

Web: dergipark.org.tr/jaefs E-mail: editorialoffice@jaefs.com Phone / WhatsApp: +90 850 309 59 27

Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst

Optimizing Maize Yield under Subsurface Drip and Deficit Irrigation using Sowing Date and Temperature in CropSyst

Abstract

Keywords

Supporting Institution

Project Number

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

Abstracting & Indexing Services

© International Journal of Agriculture, Environment and Food Sciences