Using a simulation model to assess the performance of early maturing maize varieties in the Nigeria Savannas

www.iita.org A member of CGIAR consortium

Using a simulation model to assess the performance of early maturing maize varieties in the Nigeria

Savannas

Alpha Y. Kamara and Jibrin M. Jibrin

23 November 2015 (R4D Week 2015)

A member of the CGIAR Consortium www.iita.org

Using a simulation model to

assess the performance of

early maturing maize varieties

in the Nigeria Savannas

Alpha Y. Kamara1, and Jibrin M. Jibrin2

1International Institute of Tropical Agriculture, Ibadan, Nigeria 2Centre for Dryland Agriculture, Bayero University, PMB 3011, Kano, Nigeria


Agroecological zones in Nigeria


Introduction • Maize production in

Nigeria has increased

nearly ten-fold

between1961 and 2013

• The increased production

is mainly due to increase

in cultivated land

• Average yield levels only

increased from about 0.8

t/ha in 1961 to about 2.0

t/ha in 2013

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

Ma

ize

yie

ld (

kg/h

a)

0

500

1000

1500

2000

2500

Data source: FAOstat3.fao.org


• Maize yields in Nigeria are generally low and vary significantly from one

location to another due to a myriad of reasons including:

– poor soil fertility

– moisture stress

– pests and diseases

– inappropriate agronomic practices


Highlights of biophysical constraints


Nitrogen deficiency on maize

Poor soil fertility


Opportunities to increase productivity of

maize in the Nigeria Savannas

There is usually huge gap between attainable

yield and what is obtained by smallholder farmers

Availability of Striga and drought-tolerant maize

varieties/hybrids

Availability of maize varieties/hybrids of diverse

maturity class

Availability of soil fertility management

technologies

Manipulating plant population and planting dates

Improved cropping sequence


Problem of deployment of maize

production technologies

The need to conduct costly and timely adaptation

trials over a wide areas to enable us make useful

recommendations of production technologies

The production environment is variable in terms of

soil, topography, and climate, socio-economics

conditions


Dominant soils Rainfall distribution Slope classes

Biophysical Map


Rainfall and length of growing season

decreases from the south northward


• Crop growth models simulate crop growth, development and yield for specific cultivars based on the effects of weather, soil characteristics and crop management practices.

• Support the decision making process for cropping system management and agricultural policy

• Multi-locational evaluation and assessment of the adaptation of a new cultivar to a region and climate

• Explore crop response to numerous alternative management practices under specific environmental conditions,

• Extrapolation and scaling-out of results obtained in trial sites

Opportunities for Use of Cropping System

Models to deploy Crop technologies


Project Target Areas


• On-station experiments were conducted in 2013 and 2014 to generate genotype-specific parameters for calibrating the CERES-Maize model using 5 maize varieties developed by IITA

• Data from on-station trials and 33 on-farm trials were used for model validation.

Variety P1 P2 P5 G2 G3 PHINT (odays) (days) (o

days) (# grains)

(mg day-

1) oC day tip-1

2009 TZEE 191 0.60 830 635 7.0 38.9

2009 EVDT 205 0.69 860 652 7.8 40.0

IWD C2 SYN 215 0.75 870 804 8.5 39.9

TZECOMP5 212 0.75 863 700 7.8 38.9

TZLCOMP1SYN 309 0.56 889 894 8.8 41.9 Calibration: comparison between observed and simulated grain yields of 2009 EVDT and 2009 TZEE


• Information on soil types, topography, drainage and climate were collected and mapped in the two States

• Areas within the Savannas of Kano and Kaduna States were divided into climate-soil zones in which various production scenarios (variety x planting time x N application rates) were modeled.


Maps of simulated yields of 2009 TZEE in Kano State as an extension tool for

discussing the effect of planting dates and N fertilizer rates

Earl

y Ju

ne

pla

nti

ng

Mid

Ju

ne

pla

nti

ng

Earl

y Ju

ly p

lan

tin

g

Mid

Ju

ly p

lan

tin

g

• The outputs produced include yield maps and yield gap graphs, which will assist extension workers, researchers and policy makers in making decisions on alternative crop and soil management interventions and ex-ante evaluations of various production scenarios

Legend

1500 - 2000

2001 - 2500

2501 - 3000

3001 - 3500

3501 - 4000

4001 - 4500

4501 - 5000

5001 - 5500

<1500

Grain Yield Kg/ha


• Comparisons between observed and simulated yields under water limiting (Yw) and non-water limiting (Yp) scenarios were made.

• Observed on-farm yields (Yf) were compared with the potential yields (Yp and Yw) of the various varieties under different production environments.

• There is room for increasing the yield of 2009 EVDT from about 3233 kg/ha to about 4382 kg/ha under water-limited (rain-fed) condition, and up to 5132 kg/ha with irrigation

• There is also potential to increase the average yield of 2009TZEE by more than 1500kg/ha.

Assessment of yield gaps in 2014 in Kano State based on site specific weather, soil properties, planting dates and other management data on farmers’ fields

Gra

in y

ield

(kg

ha

-1)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2009EVDT

Field

Gra

in y

ield

9kg

ha

-1)

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

On-farm yield

Simulated yield gap

Yw (3991 kg/ha)

Yf (3153 kg/ha)

Yp (4734 kg/ha)

Yw (4383 kg/ha)

Yf (3233 kg/ha)

Yp (5132 kg/ha)

2009TZEEW

On-farm yield

Simulated yield gap


Looking ahead (TAMASA and SARD-SC)

1. Use modelling tools to develop nutrient

management system for maize

1. Develop a tool to help in targetting and

deployment of maize hybrids and OPVs in

specific locations and climate


Looking ahead • Development of fertilizer recommendation tool

– Calibrating Nutrient Expert for Maize (developed by IPNI) • NOTs across various ecologies

• Collection of secondary data layers

• Development of Variety Tool for targeting varieties to specific environments – Calibration trials to generate cultivar coefficients for 10

OPV and 10 hybrids

– Collection of secondary data layers

• Assessment of yield gaps across maize growing areas – Panel survey

– Harvest survey


– Generate Cultivar Coefficients for other grain

crops (cowpea and soybean) for targetting

production domains and management

practices


Thank You

Science

Using a simulation model to assess the performance of early maturing maize varieties in the Nigeria Savannas