Using CERES-Maize model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria

Adnan, Adnan A.; Jibrin, J.M.; Kamara, Alpha Y.; Abdulrahman, B.L.; Shaibu, Abdulwahab S.

doi:10.1080/01904167.2016.1263330

Cited by 23 publications

(20 citation statements)

References 26 publications

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“…The model performance for predicting biomass and grain yield of ICSV-40 (early maturing cultivar) was overall good, indicated by the fairly low accuracy with RMSE values of 20.3 and 23.7%. The accurate prediction of grain yield and total biomass of both varieties under optimum condition at different planting dates with low RMSE values implies that the model could be used to reduce production either at high N or low N input levels, this was agreed with the opinion of Liu et al (2011) and Adnan et al (2017).…”

Section: Resultssupporting

confidence: 66%

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

Akinseye

Ajegbe

Kamara

et al. 2020

Journal of Plant Nutrition

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The Agricultural Production Systems simulator (APSIM) model was calibrated and evaluated using two improved sorghum varieties conducted in an experiment designed in a randomized complete block, 2014-2016 at two research stations in Nigeria. The results show that the model replicated the observed yield accounting for yield differences and variations in phenological development between the two sorghum cultivars. For earlymaturing cultivar (ICSV-400), the model indicated by low accuracy with root means square error (RMSE) for biomass and grain yields of 20.3% and 23.7%. Meanwhile, Improved-Deko (medium-maturing) cultivar shows the model was calibrated with low RMSE (11.1% for biomass and 13.9% for grain). Also, the model captured yield response to varying Nitrogen (N) fertilizer applications in the three agroecological zones simulated. The N-fertilizer increased simulated grain yield by 26-52% for ICSV-400 and 19-50% for Improved-Deko compared to unfertilized treatment in Sudano-Sahelian zone. The insignificant yield differences between N-fertilizer rates of 60 and 100 kgha À1 suggests 60 kgNha À1 as the optimal rate for Sudano-Sahelian zone. Similarly, grain yield increased by 23-57% for ICSV-400 and 19-59% for Improved Deko compared to unfertilized N-treatment while the optimal mean grain yield was simulated at 80 kgNha À1 in the Sudan savanna zone. In the northern Guinea savanna, mean simulated grain yield increased by 8-20% for ICSV-400 and 12-23% for Improved-Deko when Nfertilizer was applied compared to unfertilized treatment. Optimum grain yield was obtained at 40 kgha À1. Our study suggests a review of blanket recommended fertilizer rates across semi-arid environments for sorghum to maximize productivity and eliminate fertilizer losses, means of adaptation strategies to climate variability.

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Section: Resultssupporting

confidence: 66%

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

Akinseye

Ajegbe

Kamara

et al. 2020

Journal of Plant Nutrition

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“…This asserts that N is the most yield-limiting nutrient. Similarly, other studies have reported a significant response of maize to N application in the Nigerian Savannas [62][63][64]. Overall, N deficiency is recognized as the most limiting nutrient in cereal cropping system over large areas of SSA including Nigeria [65,66].…”

Section: Variability In Yield Response To Nutrient Applicationmentioning

confidence: 97%

Quantifying Variability in Maize Yield Response to Nutrient Applications in the Northern Nigerian Savanna

et al. 2018

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Diagnostic on-farm nutrient omission trials were conducted over two cropping seasons (2015 and 2016) to assess soil nutrients related constraints to maize yield in the northern Nigerian savanna agro-ecological zone and to quantify their variability. Two sets of trials were conducted side by side, one with an open pollinated maize variety (OPV) and the other one with a hybrid maize variety and each set had six equal treatments laid out in 198 farmers' fields. The treatments comprised (i) a control, (ii) a PK ('−N,' without N), (iii) an NK ('−P,' without P), (iv) an NP ('−K,' without K), (v) an NPK and (vi) an NPK + S + Ca + Mg + Zn + B ('+SMM,' NPK plus secondary macro-and micro-nutrients). Moderate to a large variability in most soil characteristics was observed in the studied fields. Consequently, cluster analysis revealed three distinct yield-nutrient response classes common for the two types of maize varieties. These define classes were fields that have (i) no-response to any nutrient, (ii) a large response to N and P and (iii) a large response to N alone. Although overall yield performance of OPV and hybrid varieties was similar, a distinct fourth class was identified for the hybrid variety, (iv) fields with a large response to N and secondary macro-and micro-nutrients. The results indicate that the large variability in soil nutrients related constraints need to be accounted for to optimize maize yield in the northern Nigerian savanna. The development of field-and area-specific fertilizer recommendations is highly needed, using simple decision support tools that consider variable soil fertility conditions and yield responses as obtained from this study.

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“…The CERES-Maize model has been shown over the years to be an important tool in evaluating crop management [3], climate change impacts [26], fertilizer recommendations [27 and 6] and yield forecasting [28]. Calibrating the newly released maize varieties currently recommended for the Nigerian maize belts will provide an important input requirement for using crop models to evaluate major production constraints including optimum stand density (OSD), appropriate varietal selection (targeting/stability analysis), choice of major partner crop (in case of mixed cropping) and fertilizer (especially N and P) managements.…”

Section: Discussionmentioning

confidence: 99%

“…In West Africa, the CERES-Maize model has been recently used by McCarthy et al [3] to evaluate climate-sensitive farm management practices in the Northern Regions of Ghana. Adnan et al [5 and 6] used the same model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria and the optimum planting dates of maize in Northern Nigeria. One of the major requirements for the use of crop simulations is calibration of Genotype Specific Parameters (GSPs).…”

Section: Introductionmentioning

confidence: 99%

Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

Adnan

Diels

Jibrin

et al. 2018

Preprint

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28Most crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide 29 the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect 30 of most modelling exercises because it requires expensive and time-consuming field experiments. 31GSPs could also be estimated using multi-year and multi locational data from breeder evaluation 32 experiments. This research was set up with the following objectives: i) to determine GSPs of 10 33 newly released maize varieties for the Nigerian Savannas using data from both calibration 34 experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the 35 accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-36 Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different 37 experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. 38 Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were 39 evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 40 120kg N ha -1 ). For the model calibration using experimental data, calculated model efficiency (EF) 41 values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. 42 Calibration of time-series data produced nRMSE below 7% while all prediction deviations were 43 below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges 44 were lower. Prediction deviations were below 17% of the means for all measured variables. Model 45 evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high 46 EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen 47 contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from 48 detailed experiments. If unavailable, data from breeder experimental trials collected from many 49 locations and planting dates can be used with lower but acceptable accuracy.

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Using CERES-Maize model to determine the nitrogen fertilization requirements of early maturing maize in the Sudan Savanna of Nigeria

Cited by 23 publications

References 26 publications

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

Quantifying Variability in Maize Yield Response to Nutrient Applications in the Northern Nigerian Savanna

Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

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