application of csm-ceres-maize for evaluation of planting ... · performance under rain-fed...

Application of CSM-CERES-Maize for

evaluation of planting date under rain-

fed conditions in the Northern Guinea

Savanna agro-ecology of Nigeria

K.O.OLUWASEMIRE and S.O. OYATOKUN

Agronomy Department, University of Ibadan, Nigeria.

Introduction

AGRICULTURE IS AN INDUSTRY THAT IS MORE DEPENDENT ON WEATHER THAN OTHER SYSTEMS FOR HARNESSING BIOLOGICAL RESOURCES SUCH AS CROP, LIVESTOCK, FORESTRY AND FISHING

CLIMATE HAS A MAJOR IMPACT ON SOILS, VEGETATION, WATER RESOURCES AND LAND USE

ISSUES REGARDING POPULATION INCREASE, DEGRADATION OF NATURAL RESOURCES AND SUSTAINABLE DEVELOPMENT CALLS FOR GREATER EFFORTS TO DESIGN FUTURE STRATEGIES OF LAND USE BASED ON SOUND UNDERSTANDING OF THE RELATIONSHIP BETWEEN CLIMATE AND AGRICULTURE

THEREFORE, IMPROVED ADAPTATION OF FOOD PRODUCTION, PARTICULARLY TO CURRENT CLIMATE VARIABILITY AND OBSERVED CLIMATE CHANGE HOLD THE KEY TO IMPROVING FOOD SECURITY FOR THE GLOBAL POPULATION.

The high level of reliance of climate sensitive activities in

agriculture such as rain-fed cultivation, limited economic

and institutional capacity to cope with and adapt to climatic

variability tend to make the Nigerian agricultural

environment particularly vulnerable to climate change.

Planting opportunities are limited by the amount and

timing of rainfall, the decision on when to plant would affect

the choice of crop cultivar and yield expectation.

Rainfall in West Africa and particularly in Nigeria is

strongly dependent on the southwest monsoon flow, which

has the unique characteristics of high seasonal, monthly and

daily variability in its moisture content and vertical depth

(Omotosho et al., 2000).

Introduction (Cont’d)

NIGERIA HAS FOUR AGRO ECOLOGICAL ZONES WITH SIXTEEN PRIMARY

CROPPING SYSTEMS IN THIRTEEN HIGH PRODUCTION STATES

SAHEL AND SUDAN

Sorghum & Millet and Sorghum or Millet

& cowpea are the primary cropping

systems in the high production states of

Kano, Katsina, and Zamfara.

GUINEA SAVANNA

Sorghum & Groundnut or Cowpea, Maize

(only) and Groundnut (only) are the primary

cropping systems in the high production

states of Niger, Kaduna, and Plateau.

DERIVED SAVANNA

Maize (only), Maize & Yam, Cowpea or Sweet

Potato and Groundnut (only) are the primary

cropping systems in the high production

states of Oyo, Nasarawa, Kogi, Benue, and

Taraba.

HUMID FOREST

Plantain (only) and Maize & Yam, Sweet

Potato or Cassava are the primary cropping

systems in the high production states of

Cross River, Ondo, and Edo

Introduction (Cont’d)

NIGERIA NATIONAL PRODUCTION IS 9-10 MILLION TONS, GROWN ON OVER 5 MILLION HECTARES ACROSS THE ENTIRE COUNTRY

Maize Production Zones Production Statistics for Top Food Crops in Nigeria

• Maize is grown in all states, however it’s only the key crop in a

few states

• Plays larger role in Guinea Savanna & Derived Savanna zones

• Where maize is important, it is often mono-cropped and

farmers are commercially oriented

• Less than 10% maize is irrigated

The objective of this study is to evaluate the

effects of planting dates and variability on maize

performance under rain-fed conditions in Zaria

area (northern Guinea savanna, Nigeria, and

to assess CERES-Maize model performance

by comparing the measured and simulated

maize phenology for improved growth,

development and yields

as well as forecasting grain yield of maize

production system

Materials and Methods

2.1 Site Description The data from a field study conducted during the 2006 rainy season at

the experimental farm of the Institute for Agricultural Research (IAR), Ahmadu Bello University (ABU), Samaru-Zaria, Nigeria (11° 09’N, 07° 38’ E;

695 m above sea level) were considered.

Weather data were recorded by an Automatic Weather Station-AWS

(Minimet, Eijkelkamp, The Netherlands) about 20m from the experimental

field.

Data provided on measured variables include air temperature, relative

humidity, wind speed and direction, net radiation and rainfall. These data

were averaged and stored at two hours interval. The mean annual rainfall

is 1016 220 mm (1928-2002) with a coefficient of variability of 18 percent

(Oluwasemire and Alabi, 2004). The rainy season starts by mid May and

ends before mid October. Mean monthly temperature varies within 22-36°C with a maximum and minimum of 38°C and 13°C, respectively.

The soil temperature and moisture regimes are Isohyperthermic and

Typic Tropustic (Uyovbisere et al., 1984).

Experimental Procedures The predicted onset window (dates) of growing season for Zaria from

NIMET range from 13th – 23rd May 2006 at a probability level of 70%.

Four planting dates were adopted viz: planting date within one week

before the predicted window, (PD1); planting date early within the

predicted window, (PD2); planting date late within the window, (PD3) and

planting date within the week after the window, (PD4). These

corresponded to May 12, 2006 (PD1), May 16, 2006 (PD2), May 22, 2006

(PD3) and May 27, 2006 (PD4).

The experiment was laid out as a Randomized Complete Block Design,

with planting date as the treatment and replicated 4 times. 2 seeds of

maize were planted per hole and later thinned ten days after emergence to

one plant per stand. The maize variety was SAMMAZ 14 (Obatanpa)

Fertilizer application rates were 60kg N, 30 kg P and 30 kg K ha-1; applied

12 days after planting when soil was moist enough from rain water. The

fertilizer used was N.P.K 15:15:15 fertilizer brand.

Thirty five days after planting, the maize was top dressed with urea (46%

N) at the rate of 60 kg N ha-1. The plant spacing was 75cm x 25cm.

Plant Sampling and Measurements Records of crop emergence and daily records of weather parameters

(maximum air temperature, minimum air temperature, relative

humidity, wind speed and rainfall) were collected close to the

experimental site.

Records of date of planting, date of emergence (>50%), date of

thinning, plant population after thinning were recorded. Destructive

plant sampling commenced from emergence and was subsequently

carried out at two week intervals. Plant stands from plot areas outside

the designated final harvest area were sampled, oven-dried and

weighed to obtain their dry matter.

Whole plant leaves were also detached and measured for leaf area.

Yield and yield parameters recorded included days to first bud

appearance, first flower appearance, 50% tasseling, 50% silking, 50%

flowering, 50% cob filling and physiological maturity.

Others included date of harvest, Plant stand per plot, number of leaves

on stem, number of grains per maize cob, grain weight per plot at

harvest and haulm/stover weight per plot at harvest.

Evaluation of the model The CSM-CERES-Maize model was calibrated with the data

obtained from the field experiment for the treatment that consisted

of four planting dates of SAMMAZ 14 (Obatanpa) maize variety.

The cultivar coefficients were determined sequentially, starting

with phenological parameters followed by the grain filling

parameters and finally total biomass and grain yield (Hunt and

Boote, 1998).

The experimental data collected were used for model evaluation.

As part of the calibration and evaluation process the simulated data

for physiological maturity date, leaf area index, dry shoot weight,

grain yield and harvest index were compared with the observed

values.

Statistical analysis of model data and maize yield prediction The statistical index used for model calibration and evaluation is the root

mean square error (RMSE) method (Wallach and Goffinet, 1987). A simple way of

expressing error is to express root mean square error as percentage of means of

observation often referred to as normalized RMSE (n-RMSE)

The simulation is considered excellent with a PE less than 10 %, good if

greater than 10% and less than 20%, fair if greater than 20% and less than 30%,

and poor if greater than 30% (Jamieson et al., 1991).

The equations are as written below:

RMSE = √ [ ∑ (Pi-Oi)2/n] (1)

PE = RMSE / Ō x 100 (2)

Where RMSE = Root mean square error, Pi = predicted value, Oi = observed

value, Ō = mean of observed value, n = number of replicates/locations, ∑ =

Summation sign and √ = Square root sign.

Maize yield forecasts were computed using 15 years (2007-2022) daily weather

data generated from MarkSimTM GCM-DSSAT weather file generator using the

Representative Concentration Pathway - RCP 2.6 (RCP 3PD) climate change

scenario and used to predict maize performance over 2007-2022 period. The RCP

2.6 was developed by the IMAGE modeling team of the PBL Netherlands

Environmental Assessment Agency.

Results and Discussion

Profile

depth

SMH SLL DUL SSAT RGF SSKS SBD Clay Silt Sand Textura

l Class

pH TOC TN Avail

P

0 -18 AP1 0.133 0.257 0.357 0.9 2.92 1.63 150 440 410 L 5.7 0.60 0.11 -99

18 -33 AP2 0.169 0.291 0.361 0.5 5.47 1.71 210 440 370 L 5.5 0.24 0.17 -99

33 – 57 BT1 0.214 0.334 0.363 0.2 0.97 1.64 310 380 310 CL 5.6 0.26 0.06 -99

57 - 84 BT2 0.196 0.318 0.366 0.1 1.87 1.50 430 320 250 C 5.8 0.17 0.4 -99

84 – 112 BT3 0.178 0.298 0.357 0.1 9.82 1.41 430 320 250 C 5.9 0.17 0.4 -99

112 – 141 BT4 0.152 0.277 0.367 0.0 3.00 1.45 390 360 250 CL 5.5 0.13 0.2 -99

141 - 180 BT5 0.240 0.358 0.373 0.0 3.25 1.45 390 360 250 CL 5.6 0.13 0.2 -99

Table 1: Physico-chemical and Hydrological properties of the soil

profile in the study area

L- Loam; C - Clay; CL - Clay loam; SMH – Soil morphological horizon; SLL – Soil water lower limit

(permanent wilting point); DUL – Drained upper limit of soil (field capacity); SSAT – Soil saturation;

RGF – Soil root growth factor; SSKS – Hydraulic conductivity; SBD – Soil bulk density; -99 - Data not

available TOC - Total Organic Content, TN - Total Nitrogen,

Variables Values

P1=Thermal time from emergence to the end of

the juvenile phase (degree days calculated with a

base temperature of 8°C)

280.0

P2= Delay in completing flower induction (d) for

each hour increase in photoperiod above which

development proceeds at a maximum rate (12.5 h)

0.600

P5= Thermal time from silking to physiological

maturity (degree days calculated with a base

temperature of 8°C).

680.0

G2= Potential number of kernels per plant 650.0

G3= Potential kernel growth rate during the

linear grain filling stage 8.80

PHINT= Phyllochron, thermal time between the

appearance of consecutive leaf tips (degree days

calculated with a base temperature of 8°C)

75.00

Table 2: Genetic Coefficient of Maize variety, SAMMAZ 14

Variable Simulated Measured % error (PE)

1st Planting Date

Physiological maturity day (dap) 98 96 2.08

Yield at harvest maturity (kg [dm] 4018 4134 2.81

Tops weight at maturity (kg [dm]/ha 8787 8289 6.01

Leaf area index, maximum 1.53 1.54 0.65

Harvest index at maturity 0.46 0.43 6.98

2nd Planting Date






3rd Planting Date





Table 3. Main growth and development variables at

different planting dates

Variable Simulated Measured % error (PE)

4th Planting Date






Table 3 contd.: Main growth and development variables at different

planting dates

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8L

eaf

are

a i

nd

ex

Predicted year

12-May

16-May

22-May

27-May

Fig. 1. Trend of predicted maize maximum leaf area over

years as influenced by planting date

0

2000

4000

6000

8000

10000

12000S

ho

ot

weig

ht

(kg

/ha)

Predicted year

12-May

16-May

22-May

27-May

Fig. 2. Trend of predicted maize shoot dry weight over the

years as influenced by planting date

85

90

95

100

105

110

115

120

Days t

o p

hysio

log

ical

matu

rity

Predicted year

12-May

16-May

22-May

27-May

Fig. 3. Trend of predicted days to physiological maturity of maize

over the years as influenced by planting date

0

1000

2000

3000

4000

5000

6000

Gra

in y

ield

(kg

/ha)

Predicted year

12-May

16-May

22-May

27-May

Fig. 4. Trend of predicted maize yield over the years as influenced by

planting date

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Harv

est

ind

ex

Predicted year

12-May

16-May

22-May

27-May

CONCLUSIONS The CSM-CERES-Maize model was able to accurately simulate phenology

and yield of SAMMAZ grown during the rainy season in a northern Guinea

savanna agroecological environment in Nigeria. In general, total biomass

and LAI were also reasonably well simulated.

For both rainfed maize production, average grain yield decreased with

later planting dates.

This study also showed that the CSM-CERES-Maize model can be a

promising tool for yield forecasting for rain-fed maize varieties grown in

the Nigerian Guinea savanna within predicted planting windows.

This information is considered to be timely and useful for decision

makers, farmers, climate-smart agriculture with veritable platform for

precision agricultural practitioners and weather index insurance

providers.

Further research is needed to apply this methodology to different

locations in order to be able to make practical decisions with respect to

grain stock management. Additional model calibration and evaluation

might also be needed for other varieties of high value rain-fed crops

varieties used by farmers.

Acknowledgements

The Commission for Agricultural Meteorology

(CAgM), World Meteorological Organization (WMO)

and the Korea Meteorological Administration

(KMA) are highly appreciated for this inaugural

workshop and for the invitation and sponsorship

in respect of my participation.

The Nigerian Meteorological Agency (NIMET) for

providing information and opportunity to

participate in this workshop.

Thank you all for listening.

application of csm-ceres-maize for evaluation of planting ... · performance under rain-fed...

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