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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: [email protected]
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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.

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