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Optimizing sowing density-based management decisions with different nitrogen rates on smallholder maize farms in Northern Nigeria

Published online by Cambridge University Press:  18 January 2021

Adnan Aminu Adnan Open the ORCID record for Adnan Aminu Adnan [Opens in a new window] ,
Jan Diels ,
Jibrin Mohammed Jibrin ,
Alpha Yaya Kamara ,
Abdulwahab Saliu Shaibu ,
Ismail Ibrahim Garba ,
Peter Craufurd  and
Miet Maertens
Adnan Aminu Adnan*
Affiliation:
Department of Agronomy, Bayero University Kano, 70001, Kano, Nigeria Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
Jan Diels
Affiliation:
Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
Jibrin Mohammed Jibrin
Affiliation:
Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
Alpha Yaya Kamara
Affiliation:
International Institute of Tropical Agriculture, Ibadan, Nigeria. C/o IITA Ltd, Carolyn House, 26 Dingwall Road, Croydon CR9 3 EE, UK
Abdulwahab Saliu Shaibu
Affiliation:
Department of Agronomy, Bayero University Kano, 70001, Kano, Nigeria Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
Ismail Ibrahim Garba
Affiliation:
Centre for Dryland Agriculture (CDA), Bayero University Kano, 70001, Kano, Nigeria
Peter Craufurd
Affiliation:
International Maize and Wheat Improvement Center (CIMMYT) World Agroforestry Centre (ICRAF) House United Nations Avenue, Gigiri P.O.Box 1041–00621, Nairobi, Kenya
Miet Maertens
Affiliation:
Department of Earth and Environmental Sciences, Division of Soil and Water Management, KU Leuven, Celestijnenlaan 200E, 3001 Leuven, Belgium
*
*Corresponding author. Email: aaadnan.agr@buk.edu.ng
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Abstract

In this study, the CERES-Maize model was calibrated and evaluated using data from 60 farmers’ fields across Sudan (SS) and Northern Guinea (NGS) Savannas of Nigeria in 2016 and 2017 rainy seasons. The trials consisted of 10 maize varieties sown at three different sowing densities (2.6, 5.3, and 6.6 plants m−2) across farmers’ field with contrasting agronomic and nutrient management histories. Model predictions in both years and locations were close to observed data for both calibration and evaluation exercises as evidenced by low normalized root mean square error (RMSE) (≤15%), high modified d-index (> 0.6), and high model efficiency (>0.45) values for the phenology, growth, and yield data across all varieties and agro-ecologies. In both years and locations and for both calibration and evaluation exercises, very good agreements were found between observed and model-simulated grain yields, number of days to physiological maturity, above-ground biomass, and harvest index. Two separate scenario analyses were conducted using the long-term (26 years) weather records for Bunkure (representing the SS) and Zaria (representing the NGS). The early and extra-early varieties were used in the SS while the intermediate and late varieties were used in the NGS. The result of the scenario analyses showed that early and extra-early varieties grown in the SS responds to increased sowing density up to 8.8 plants m−2 when the recommended rate of N fertilizers (90 kg N ha−1) was applied. In the NGS, yield responses were observed up to a density of 6.6 plants m−2 with the application of 120 kg N ha−1 for the intermediate and late varieties. The highest mean monetary returns to land (US$1336.1 ha−1) were simulated for scenarios with 8.8 plants m−2 and 90 kg N ha−1, while the highest return to labor (US$957.7 ha−1) was simulated for scenarios with 6.6 plants m−2 and 90 Kg N ha−1 in the SS. In the NGS, monetary return per hectare was highest with a planting density of 6.6 plants m−2 with the application of 120 kg N, while the return to labor was highest for sowing density of 5.3 plants m−2 at the same N fertilizer application rates. The results of the long-term simulations predicted increases in yield and economic returns to land and labor by increasing sowing densities in the maize belts of Nigeria without applying N fertilizers above the recommended rates.

Keywords

Optimum densityContrasting environmentsNitrogenEconomic risksScenario analyses
Type
Research Article
Information
Experimental Agriculture , Volume 56 , Issue 6 , December 2020 , pp. 866 - 883
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

†

Current address: School of Agriculture and Food Sciences, The University of Queensland, Gatton 4343, Qld Australia.

References

Abubakar, A.W. and Manga, A.A. (2017). Effect of plant population on the growth of Hybrid-Maize (Zea Mays L.) in The Northern Guinea Savanna of Nigeria. International Journal on Advances in Chemical Engineering and Biological Sciences, 4(1), 134–141. doi: 10.15242/IJACEBS.C0417007.Google Scholar
Adnan, A.A., Jibrin, J.M., Kamara, A.Y., Abdulrahman, B.L., Shaibu, A.S. and Garba, I.I. (2017a). CERES–maize model for determining the optimum planting dates of early maturing maize varieties in Northern Nigeria. Frontiers in Plant Science, 8, 1–14. doi: 10.3389/fpls.2017.01118.CrossRefGoogle ScholarPubMed
Adnan, A.A., Jibrin, J.M., Kamara, A.Y., Abdulrahman, B.L. and Shaibu, A.S. (2017b). Using CERES-Maize model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria. Journal of Plant Nutrition 40(7), 1066–1082. doi: 10.1080/01904167.2016.1263330.CrossRefGoogle Scholar
Adnan, A.A., Diels, J., Jibrin, J.M., Kamara, A.Y., Craufurd, P., Shaibu, A.S., Mohammed, I.B. and Tonnang, Z.E.H. (2019). Options for calibrating CERES-maize genotype specific parameters under data-scarce environments. PLoS One. Edited by S. Rutherford. Public Library of Science, 14(2), e0200118. doi: 10.1371/journal.pone.0200118.CrossRefGoogle ScholarPubMed
de Aguiar, P.F., Bourguignon, B., Khots, M.S., Massart, D.L. and Phan-Than-Luu, R. (1995). D-optimal designs. Chemometrics and Intelligent Laboratory Systems 30(2), 199–210. doi: 10.1016/0169-7439(94)00076-X.CrossRefGoogle Scholar
Al-Naggar, A.M.M., Shabana, R.A., Mohamed, M.M.A. and Tarek, H.A. (2015). Maize response to elevated plant density combined with lowered N-fertilizer rate is genotype-dependent. Crop Journal. Crop Science Society of China and Institute of Crop Science, CAAS, 3(2), 96–109. doi: 10.1016/j.cj.2015.01.002.CrossRefGoogle Scholar
Amouzou, K.A., Naab, J.B., Lamers, J.P.A. and Becker, M. (2018). CERES-Maize and CERES-Sorghum for modeling growth, nitrogen and phosphorus uptake, and soil moisture dynamics in the dry savanna of West Africa. Field Crops Research. Elsevier, 217, 134–149. doi: 10.1016/J.FCR.2017.12.017.CrossRefGoogle Scholar
Archontoulis, S.V., Miguez, F.E. and Moore, K.J. (2014). A methodology and an optimization tool to calibrate phenology of short-day species included in the APSIM PLANT model: Application to soybean. Environmental Modelling & Software. Elsevier, 62(62), 465–477.CrossRefGoogle Scholar
Badu-Apraku, B., Oyekunle, M., Obeng-Antwi, K., Osuman, A.S., Ado, S.G., Coulibaly, N., Yallou, C.G., Abdulai, M., Boakyewaa, G.A. and Didjeira, A. (2012). Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. The Journal of Agricultural Science. Cambridge University Press, 150(4), pp. 473–483. doi: 10.1017/S0021859611000761.CrossRefGoogle Scholar
Bänziger, M. and Lafitte, H.R. (1997). Efficiency of secondary traits for improving maize for low-nitrogen target environments. Crop Science. Crop Science Society of America, 37(4), 1110. doi: 10.2135/cropsci1997.0011183X003700040013x.CrossRefGoogle Scholar
Bhatt, P.S. (2012). Response of sweet corn hybrid to varying plant densities and nitrogen levels. African Journal of Agricultural Research, 7(46), 6158–6166. doi: 10.5897/AJAR12.557.Google Scholar
Chisanga, C.S., Phiri, E., Chizumba, S. and Sichingabula, H. (2014). Evaluating CERES-Maize Model Using Planting Dates and Nitrogen Fertilizer in Zambia. Journal of Agricultural Science, 7(3), 79–97. doi: 10.5539/jas.v7n3p79.CrossRefGoogle Scholar
Edwards, J.T., Purcell, L.C. and Vories, E.D. (2005). Light Interception and Yield Potential of Short-Season Maize (Zea mays L.) Hybrids in the Midsouth, Reproduced from Agronomy Journal.Google Scholar
FAO (2018). Production Year Book. Rome: Food and Agriculture Organization of the United Nations.Google Scholar
Gijsman, A.J., Hoogenboom, G., Parton, W.J. and Kerridge, P.C. (2002). Modifying DSSAT crop models for low-input agricultural systems using a soil organic matter–residue module from CENTURY. Agronomy Journal. American Society of Agronomy, 94(3), 462. doi: 10.2134/agronj2002.4620.CrossRefGoogle Scholar
Gungula, D.T., Kling, J.G. and Togun, A.O. (2003). CERES-maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agronomy Journal. American Society of Agronomy, 95(4), 892–899. doi: 10.2134/AGRONJ2003.8920.CrossRefGoogle Scholar
Hashemi, A.M., Herbert, S.J. and Putnam, D.H. (2005). Yield response of corn to crowding stress. Agronomy Journal. American Society of Agronomy, 97(3), 839. doi: 10.2134/agronj2003.0241.CrossRefGoogle Scholar
Holzworth, D.P., Huth, N.I., deVoil, P.G., Zurcher, E.J., Herrmann, N.I., McLean, G., Chenu, K. and Erik, J. (2014). APSIM – Evolution towards a new generation of agricultural systems simulation. Environmental Modelling & Software. Elsevier Science Publishers B. V., 62(C), 327–350. doi: 10.1016/j.envsoft.2014.07.009.CrossRefGoogle Scholar
Hoogenboom, G., Porter, C.H., Boote, K.J., Shelia, V., Wilkens, P.W., Singh, U., White, J.W., Asseng, S., Lizaso, J.I., Moreno, L.P., Pavan, W., Ogoshi, R., Hunt, L.A., Tsuji, G.Y. and Jones, J.W. (2019). Decision Support System for Agrotechnology Transfer (DSSAT). Gainesville, FL: DSSAT Foundation.Google Scholar
IITA (2018). Scaling up innovations. IITA 2019 Annual Report. Ibadan, Nigeria. International Institute of Tropical Agriculture (IITA). Available online at www.iita.org/annual-reports.Google Scholar
Iyanda, R.A., Pranuthi, G., Dubey, S.K. and Tripathi, S.K. (2014). Use of dssat ceres maize model as a tool of identifying potential zones for maize production in Nigeria. International Journal of Agricultural Policy and Research, 2(February), 69–75.Google Scholar
Jagtap, S., Abamu, F. and Kling, J. (1999). Long-term assessment of nitrogen and variety technologies on attainable maize yields in Nigeria using CERES-maize. Agricultural Systems. Elsevier, 60(2), 77–86. doi: 10.1016/S0308-521X(99)00019-0.CrossRefGoogle Scholar
Jagtap, S.S., Alabi, R.T. and Adeleye, O. (1998). The influence of maize density on resource use and productivity; An experimental and simulation study. African Crop Science Journal, 6(3), 259–272. doi: 10.4314/acsj.v6i3.27799.CrossRefGoogle Scholar
Jamieson, P.D., Porter, J.R. and Wilson, D.R. (1991). A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Research. Elsevier, 27(4), 337–350. doi: 10.1016/0378-4290(91)90040-3.CrossRefGoogle Scholar
Jibrin, J.M., Kamara, A.Y. and Ekeleme, F. (2012). Simulating planting date and cultivar effects on dryland maize production using CERES-maize model. African Journal of Agricultural Research, 7(40), 5530–5536. doi: 10.5897/AJAR12.1303.Google Scholar
Jones, C.A., Kiniry, J.R. and Dyke, P.T. (1986). CERES-Maize : a simulation model of maize growth and development. Texas, USA: Texas A & M University Press.Google Scholar
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J. and Ritchie, J.T. (2003). The DSSAT cropping system model. European Journal of Agronomy, 235–265. doi: 10.1016/S1161-0301(02)00107-7.CrossRefGoogle Scholar
Jones, J.W., Hoogenboom, G., Porter, C.H., Boote, K.J., Batchelor, W.D., Hunt, L.A., Wilkens, P.W., Singh, U., Gijsman, A.J. and Ritchie, J.T. (2010). Decision Support System for Agrotechnology Transfer Version 4.5. Volume 3. DSSAT v4.5: ICASA Tools.Google Scholar
Kamara, A.Y., Menkir, A., Kureh, I., Omoigui, L.O. and Ekeleme, F. (2006). Performance of old and new maize hybrids grown at high plant densities in the tropical Guinea savanna. International Journal of the Faculty of Agriculture and Biology, 1(1), 41–48.Google Scholar
Kamara, A.Y., Ekeleme, F., Chikoye, D. and Omoigui, L.O. (2009). Planting Date and Cultivar Effects on Grain Yield in Dryland Corn Production. Agronomy Journal, 101(1), 91. doi: 10.2134/agronj2008.0090.CrossRefGoogle Scholar
Kamara, A.Y., Ewansiha, S.U. and Menkir, A. (2014). Assessment of nitrogen uptake and utilization in drought tolerant and Striga resistant tropical maize varieties. Archives of Agronomy and Soil Science, 60(2), 195–207. doi: 10.1080/03650340.2013.783204.CrossRefGoogle Scholar
Keating, B.A., Carberry, P.S., Hammer, G.L., Probert, M.E., Robertson, M.J., Holzworth, D., Huth, N.I., Hargreaves, J.N.G., Meinke, H., Hochman, Z., McLean, G., Verburg, K., Snow, V., Dimes, J.P., Silburn, M., Wang, E., Brown, S., Bristow, K.L., Asseng, S., Chapman, S., McCown, R.L., Freebairn, D.M. and Smith, C.J. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy. Elsevier, 18(3–4), 267–288. doi: 10.1016/S1161-0301(02)00108-9.CrossRefGoogle Scholar
Legates, D.R. and McCabe, G.J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. doi: 10.1029/1998WR900018.CrossRefGoogle Scholar
Litchfield, J.A. (1999). Inequality: Methods and Tools, World Bank’s Web Site on Inequality, Poverty, and Socio-economic Performance.Google Scholar
Liu, W. and Tollenaar, M. (2009). Response of yield heterosis to increasing plant density in maize. Crop Science, 49(5), 1807–1816.CrossRefGoogle Scholar
MacCarthy, D.S., Adiku, S.G.K., Freduah, B.S., Gbefo, F. and Kamara, A.Y. (2017). Using CERES-Maize and ENSO as decision support tools to evaluate climate-sensitive farm management practices for maize production in the Northern Regions of Ghana. Frontiers in Plant Science, 8(January), 1–31. doi: 10.3389/fpls.2017.00031.CrossRefGoogle ScholarPubMed
Mason, S.C. and D’croz-Mason, N.E. (2002). Agronomic practices influence maize grain quality. Journal of Crop Production. Taylor & Francis Group, 5(1–2), 75–91. doi: 10.1300/J144v05n01_04.CrossRefGoogle Scholar
Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D. and Veith, T.L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. American Society of Agricultural and Biological Engineers, 50(3), 885–900. doi: 10.13031/2013.23153.CrossRefGoogle Scholar
Muoneke, C.O., Ogwuche, M.A.O. and Kalu, B.A. (2007). Effect of maize planting density on the performance of maize/soybean intercropping system in a guinea savannah agroecosystem. Journal of Agricultural Research, 2(December), 667–677.Google Scholar
NAERLS and FDAE (2017). Agricultural Performance Survey Of 2016 Wet Season in Nigeria. Zaria, Nigeria.Google Scholar
O’Neill, P.M., Shanahan, J.F., Schepers, J.S. and Caldwell, B. (2004). Agronomic responses of corn hybrids from different eras to deficit and adequate levels of water and nitrogen. Agronomy Journal. American Society of Agronomy, 96(6), 1660. doi: 10.2134/agronj2004.1660.CrossRefGoogle Scholar
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L. and Mouazen, A.M. (2016). Wheat yield prediction using machine learning and advanced sensing techniques, 121, 57–65. doi: 10.1016/j.compag.2015.11.018.CrossRefGoogle Scholar
Pereira, H.R., Meschiatti, M.C., Matos, P.R.C. and Blain, G.C. (2018). On the performance of three indices of agreement: An easy-to-use r-code for calculating the willmott indices. Bragantia, 77(2), 394–403. doi: 10.1590/1678-4499.2017054.CrossRefGoogle Scholar
Priestly, C.H.B. and Taylor, R. (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81–92. doi: 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2.2.3.CO;2>CrossRefGoogle Scholar
Qian, C., Yang, Y., Xiujie, G., Yubo, J., Yang, Z., Zhongliang, Y., Yubo, H., Liang, L., Zhenwei, S. and Weijian, Z. (2016). Response of grain yield to plant density and nitrogen rate in spring maize hybrids released from 1970 to 2010 in Northeast China. Crop Journal. Elsevier B.V., 4(6), 459–467. doi: 10.1016/j.cj.2016.04.004.CrossRefGoogle Scholar
Rezzoug, W., Gabreille, B., Suleiman, A. and Benabdeli, K. (2008). Application and evaluation of the DSSAT-wheat in the Tiaret region of Algeria. African Journal of Agricultural Research 4 (3), 284–296.Google Scholar
Ritter, A. and Muñoz-Carpena, R. (2013). Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments. Journal of Hydrology. Elsevier, 480, 33–45. doi: 10.1016/J.JHYDROL.2012.12.004.CrossRefGoogle Scholar
Robertson, M.J., Carberry, P.S., Huth, N.I., Turpin, J.E., Probert, M.E., Poulton, P.L., Bell, M., Wright, G.C., Yeates, S.J. and Brinsmead, R.B. (2002). Simulation of growth and development of diverse legume species in APSIM. Australian Journal of Agricultural Research. CSIRO Publishing, 53(4), 429. doi: 10.1071/AR01106.CrossRefGoogle Scholar
Van Roekel, R.J. and Coulter, J.A. (2011). Agronomic responses of corn to planting date and plant density. Agronomy Journal. American Society of Agronomy, 103(5), 1414. doi: 10.2134/agronj2011.0071.CrossRefGoogle Scholar
Sangoi, L., Gracietti, M.A., Rampazzo, C. and Bianchetti, P. (2002). Response of Brazilian maize hybrids from different eras to changes in plant density. Field Crops Research, 79(1), 39–51. doi: 10.1016/S0378-4290(02)00124-7.CrossRefGoogle Scholar
Sani, B.M., Oluwasemire, K.O. and Mohammed, H.I. (2008). Effect of irrigation and plant density on the growth, yield and water use efficiency of early maize in the Nigerian Savanna. Journal of Agricultural and Biological Science, 3(2), 121–131.Google Scholar
SAS (2018). JMP® 14 Documentation Library. Cary, NC: SAS Institute Inc.Google Scholar
Shaibu, A.S., Adnan, A.A. and Jibrin, J.M. (2016). Stability performance of extra early maturing maize (Zea mays L.) varieties under high and low nitrogen environments in Sudan Savanna. Cogent Food & Agriculture. Cogent, 2(1), 1–12. doi: 10.1080/23311932.2016.1231456.CrossRefGoogle Scholar
Tollenaar, M., Deen, W., Echarte, L. and Liu, W. (2006). Effect of crowding stress on dry matter accumulation and harvest index in maize. Agronomy Journal, 98(4), 930. doi: 10.2134/agronj2005.0336.CrossRefGoogle Scholar
Tollenaar, M. and Lee, E.A. (2002). Yield potential, yield stability and stress tolerance in maize. Field Crops Research, 75(2–3), 161–169. doi: 10.1016/S0378-4290(02)00024-2.CrossRefGoogle Scholar
Tovihoudji, P.G., Akponikpè, P.B.I., Agbossou, E.K. and Bielders, C.L. (2019). Using the DSSAT model to support decision making regarding fertilizer microdosing for maize production in the sub-humid region of Benin. Frontiers in Environmental Science, 7(February), 1–15. doi: 10.3389/fenvs.2019.00013.CrossRefGoogle Scholar
USAID (2019) FEWSNET.Google Scholar
USDA-Soil Conservation Service (1972). Soil series of the United States, Puerto Rico and the Virgin Islands: their taxonomic classification. in Supplement to Agricultural Handbook No. 436. Washington DC.Google Scholar
Willmott, C.J., Ackleson, S.G., Davis, R.E., Feddema, J.J., Legates, D.R., O’Donnell, J. and Rowe, C.M. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, 90, 8995–9005.CrossRefGoogle Scholar
Yang, J.M., Yang, J.Y., Liu, S. and Hoogenboom, G. (2014). An evaluation of the statistical methods for testing the performance of crop models with observed data. Agricultural Systems. Elsevier, 127, 81–89. doi: 10.1016/J.AGSY.2014.01.008.CrossRefGoogle Scholar