The document describes using the Agricultural Production Systems Simulator (APSIM) model to simulate the response of two maize varieties to applied nitrogen in two agro-ecologies in northern Nigeria. The model was calibrated using data from experiments in three locations and validated using data from two additional locations. The calibrated model accurately predicted flowering dates, maturity dates, and grain yields. The model was then used to simulate maize yields over a 27-year period under varying nitrogen application rates. The results showed that optimum nitrogen rates varied by location and variety, suggesting the need for location-specific fertilizer recommendations accounting for weather, soil type, and variety.

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Understanding the response of drought-tolerant
maize varieties to nitrogen application in the
Nigeria savannas using APSIM Model
By
Aloysius Beah1,2, A.Y. Kamara2, J.M. Jibrin1 and A.I.Tofa2
1Bayero University Kano
2IITA, Kano, Nigeria

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Maize production in the Nigeria savannas
 Maize is an important food security crop in Nigeria produced on a
land area of 5.6million ha.
 Annual maize production is 7.2 million MT
 Maize yields in Nigeria are generally low and vary significantly from
one location to another due to several reasons including:
– Poor soil fertility and low nutrient availability particularly
Nitrogen
– Variable climate (Climate varies from one location to another)
– Low and often unpredictable rainfall patterns
– Low use of improved inputs including seeds and N fertilizers
– Pests and diseases

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Maize production in the Nigeria savannas
 Poor soil fertility and low nutrient availability have been pointed out
as the most serious biophysical limitations to maize production in the
Nigeria Savannas.
 Nitrogen is the main driving force to produce large yields because
nitrogen is vitally important and it is required in large amounts.
Nitrogen deficiency on maize

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Status of fertilizer recommendation in Nigeria
 Fertilizer recommendations in Nigeria are presently based on agro
ecologies and are uniform for all maize varieties
 The published N, P, and K recommendation per hectare is 120 kg N,
60 kg phosphorus pentoxide (P2O5), and 60 kg dipotassium oxide
(K2O) for all open-pollinated maize varieties across the Sahel, Sudan
and Northern Guinea Savannas

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 Response of maize to applied N is largely
dependent on soil type, climate conditions and
maize variety
 The blanket fertilizer recommendations for maize
does not account for differences in soil type and
site specific differences in the micro-climates
 To account for soil type and weather conditions
would require the establishment of several trials
across the country which may be expensive and
time consuming
Dominantsoils
Rainfall
distribution
Slopeclasses
Limitations of blanket fertilizer recommendation

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Use of Crop and cropping systems models as Decision
Support Tools for Fertilizer Recommendation
 Decision support tools such as crop and cropping systems models can
be used to predict the response of maize to fertilizer nutrients on
different soils under variable climate conditions

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Objectives of the study
• Calibrate and evaluate the Agricultural Production Systems Simulator
(APSIM) model for simulating the response of two maize cultivars
(2009 EVDT and IWDC2) to applied nitrogen.
• Simulate the long term response of maize to applied N in two agro-
ecologies in northern Nigeria

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The APSIM Model
• APSIM version 7.6 is a processed based model that has been used to
study crop response to different management options, environmental
conditions and genetic yield potentials.
• APSIM has specific modules embedded in the software which are
linked and operate together.
• It runs at a daily time step and simulates crop growth and
development, yield, soil water and nitrogen dynamics either for single
crop or crop rotations in response to climatic and management
changes.
• APSIM is capable of carrying out simulation studies for various
farming systems and weed competition

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Model Calibration
➢ Calibration
• Model calibration involves the modification of some model
parameters such that data simulated by the error-free model fits
the observe data.
• Calibration Experiment
• Trial sites (three Agricultural Research Farm) representing two
different agro-ecologies
– BUK Kano
– ABCOA Danbatta
– IAR Samaru Zaria

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Model Calibration
➢ Input data for model calibration
• Daily climatic conditions
– Rain- rainfall (mm),
– solar radiation, MJ/m2,
– maxT and (minT)
• Soil profile information
– Physical and Chemical properties
• Crop growth parameters
– Phenology, morphology, yield and yield component

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Result of model calibration
Parameters Unit EVDT IWDC2
Estimated days from end of juvenile stage to flower initiation oC 20 20
Thermal time from seedling emergence to end of juvenile stage oC 190 240
Thermal time from flowering stage to maturity oC 540 870
Maximum head grain number 670 720
Thermal time from flag leaf development to flowering oC 8.0 8.0
Thermal time from flowering to start of grain filling oC 120 120
Thermal time from maturity to harvest oC 1 1
Genetic coefficients for two maize cultivars 2009EVDT and IWDC2
calibrated in APSIM

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Result of model calibration
Location 2009 EVDT IWDC2
Flowering Maturity Flowering Maturity
Simulated Observed Simulated Observed Simulated Observed Simulated Observed
Kano 50 51 80 81 56 56 103 103
Danbatta 47 49 76 79 53 54 98 101
Zaria 53 54 85 82 62 59 112 105
Model calibration indices
RMSE(days)a 1.41 2.52 0.71 4.24
RMSEn (%)b 2.75 3.12 1.83 4.12
R2(1:1) c 0.99 0.94 0.99 0.97
CRM 0.97 0.99 1.0 1.0

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Model Validation
➢ Model validation
• This involves confirming that the calibrated model closely represent
the real situation.
• Experiment for model validation
– 2 locations (Kubwa-Abuja ) in Southern Guinea Savanna and
(Samaru-Zaria) in the Northern Guinea Savanna under rain-fed
conditions for two years
– 5 nitrogen rates were used (0, 30, 60, 90, 120 kgNha-1)

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Statistical indices for model validation

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Result of Model validation
Abuja-Grain yield
The model accurately predicted grain yield (Kgha-1) of both varieties with low RMSE-values
(below 10% of mean) and high r-square (above 0.8).

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Results of Model validation
Samaru-Zaria
The model accurately predicted grain yield (Kgha-1) of both varieties with low RMSE-
values (below 10% of mean) and high r-square (above 0.8).

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Model application to simulate maize yield response to N
• 2 locations
– Kano in the Sudan Savanna
– Zaria in the Northern Guinea Savanna

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Model application to simulate maize yield response to
N
• Input data for the long term simulation
– Daily maximum and minimum temperatures, rainfall
and solar radiation corresponding to the period 1990-
2016
– Soil profile for each location
– Simulations were run under rain-fed conditions, varying
nitrogen application

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Cummulative probability of maize yield response to N
Samaru- Zaria

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Cumulative probability of maize yield response to N
BUK-Kano

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Conclusion
• The model was reasonably calibrated and evaluated for the two
varieties with all statistical indices within the acceptable range.
• In Zaria 2009 EVDT did not significantly respond to N applied at rates
higher than 90 kg N ha-1 while IWD C2 produced optimum yield at the
rate of 120 kg N ha-1.
• However, in Kano, both varieties did not respond significantly to N
applied beyond 90 Kg N ha-1.
• The results suggests the need to have a range of fertilizer
recommendations to be applied based on seasonal weather forecast,
soil type and the variety.

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