Varieties with diverse maturity class, Striga and drought-tolerant maize varieties Soil fertility management technologies Good agronomic practices e.g. planting dates

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Simulating response of drought-tolerant
maize varieties to planting dates in
contrasting Agro-ecologies of Nigeria
Savannas
By
Abdullahi I. Tofa1,2
, A. Y. Kamara1
, U. F. Chiezey2
, B. A.
Babaji2
1
International Institute of Tropical Agriculture, Kano State,
2
Ahmadu Bello University, P.M.B. 1045, Zaria, Kaduna State
21st
Annual Symposium IARSAF

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Introduction
• Maize production in Nigeria
has increased nearly ten-fold
between1961 and 2013
• Average yield level was less
than 2.0 t/ha compared to 9.5
t/ha USA and the world
average of 5.5 t/ha
• The increased production is
mainly due to increase in
cultivated land
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
0
2000
4000
6000
8000
10000
12000
Area (000 ha)
Production (000 t)
1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015
Maizeyield(kg/ha)
0
500
1000
1500
2000
2500

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Introduction cont…
• Maize yields in Nigeria are
low due to a myriad of
reasons including:
– poor soil fertility
– moisture stress
– pests and diseases
– inappropriate agronomic
practices
Resultedto ahugegapbetween
attainableyieldandwhat is
obtainedbysmallholderfarmers

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Opportunities to increase
productivity of maize in the Nigeria
Savannas Varieties with diverse maturity class,
 Striga and drought-tolerant maize varieties
 Soil fertility management technologies
 Good agronomic practices e.g. planting dates
• Planting too early may result in crop failure due to drought and, in turn, planting too late might
reduce valuable growing time and crop yield.
• Optimum planting date allows crops to best utilized moisture, nutrients and solar radiation.

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Problem of deployment of maize production
technologies
 Reports on the performance of these technologies are largely
site specific
 To assess the performance of these technologies on large scale
could require time and expensive experiments
Dominant soils Rainfall distribution Length of growing season

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Opportunities for Use of Cropping
System Models to deploy Crop
technologies CSMs like CERES-Maize present opportunity for extrapolating
short-duration field experimental results to other years and other
locations.
 Simulate crop growth, development and yield for specific cultivars
based on the effects of weather, soil characteristics and crop
management practices.
 Multi-locational evaluation and assessment of the adaptation of a
new cultivar to a region and climate.
 Support the decision making process for cropping system
management and agricultural policy

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Objectives of the study
• To evaluate the ability of the model in simulating yield of maize
under varying planting dates in contrasting environments.

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Model Calibration and
ValidationCalibration
• Involves the modification of some model parameters such that
data simulated by the error-free model fit the observed data.
• Non-compliance may arise from sampling errors as well as from
incomplete knowledge of the system.
Validation
• Involves the confirmation that the calibrated model closely
represents the real situation
• The most commonly used statistics for model validation are
RMSE and d-index:

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Datasets for Model Calibration
& Evaluation
2 field studies were conducted during the 2015 and 2016 wet
seasons at Zaria and Iburu, Nigeria to calibrate and validate
CERES-maize model in DSSAT.
• Selected maize varieties: SAMMAZ 15 and SAMMAZ 16.
• Seasonal weather records: daily rainfall, Tmax, Tmin, SRAD
from 2015 and 2016 used to create weather files.
• Soil profile data for the 2 locations
• Top soil analysis physical and chemical properties data.
• Management practices: planting date, fertilization, weeding, etc
• Experimental data: yields, TDM e.t.c.

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Cultivar Genetic coefficients
`
Coefficient Description SAMMAZ
15
SAMMAZ
16
P1 (O
C day-1
)
Thermal time from seedling emergence to the
end of juvenile phase. 283.0 300.0
P2 (day)
Delay in development for each hour that day-
length is above 12.5 hours. 0.678 0.640
P5 (O
C day-1
)
Thermal time from silking to time of
physiological maturity. 845.8 851.1
G2 (grains ear-1
)
Maximum kernel number per
plant. 750.3 668.1
G3 (mg day-1
)
Kernel growth rate during linear grain filling
stage under optimum conditions. 6.54 6.58
PHINT (O
C day-1
)
Thermal time between successive leaf tip
appearance. 44.00 41.51
Cultivar Statistics Anthesis day
(DAP)
Physiological
maturity day
(DAP)
Grain yield
(kg /ha)
Tops weight
(kg /ha)
Harvest
index
SAMMAZ 15 D-Index 0.89 0.87 0.99 0.98 0.84
RMSE 0.50 1.22 66.3 533.4 0.02
SAMMAZ 16 D-Index 0.94 0.94 0.99 0.97 0.90
RMSE 0.71 1.22 68.3 542.9 0.01
Results
Statistical indicators
GCs allow the model to
predict differences among
different cultivars when
planted in the same
environment
vegetative, reproductive and
developmental growth
processes are sensitive to both
temperature and photoperiod
In most cases, each cultivar
has a unique photothermal
requirement to achieve each
of the developmental stages.
Cultivar specific parameters
are therefore used to define
the sensitivity of each cultivar
to day length or night length.

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Model Evaluation Results
IBURU ▲2015 ○ 2016
SAMMAZ 15

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Model Evaluation Results
ZARIA ▲2015 ○2016
SAMMAZ 15
SAMMAZ 15
SAMMAZ 16
SAMMAZ 16

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Model application to predict
maize response to planting dates
in three agro-ecological zones
• The seasonal analysis tool of DSSAT Version 4.7
• Model runs using 25 years of historical weather (1990 – 2014) data
for each location.
• Soil profile data for the 3 locations
• 9 Planting dates with 10 days intervals beginning on early June (10)
until late August (29)

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Cumulative probability of maize grain yield
in response to planting date

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Cumulative probability of maize grain yield
in response to planting date

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Cumulative probability of maize grain yield
in response to planting date

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Cumulative probability of maize grain yield
in response to planting date

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Cumulative probability of maize grain yield
in response to planting date

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Cumulative probability of maize grain yield in
response to planting date

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Conclusion
CERES–maize model was found to be a useful decision-support tool for maize
researchers in the Savanna regions of Nigeria.
The model predicted that early-June, mid and late August planting decreases mean
grain yield in the 3 agro-ecologies most especially in SS.
In SS, the best planting date was mid-June delaying planting beyond mid-June
consistently decreases grain yield.
In NGS and SGS, the best planting windows is from late-June to mid- July.
Mean grain yield was generally higher in NGS for both varieties.
SAMMAZ 15 the drought tolerant variety generally produced higher grain yield
across the three agro-ecological zones especially with delay plantings.

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Thank you!!!