Estimating rice yield under changing weather conditions

in Kenya using Ceres Rice model

By: W.O. Nyang’au, B.M. Mati, K. Kalamwa R.K. Wanjogu, L. Kiplagat

Presented at:

NIB AND COLLABORATORS RESEARCH FINDINGS AND PROPOSALS WORKSHOP AT KSMS

04/07/20421/04/23 1

Introduction

•Agriculture is always vulnerable to unfavourable

weather events and climate conditions.

•Despite technological advances such as improved crop

varieties and irrigation systems, weather and climate

are important factors, which play a significant role to

agricultural productivity.21/04/23 2

Introduction Cont…

• In past years, Kenya has experienced food shortages

arising from declining farm productivity owing to low

fertility levels, high input costs and unreliable

weather in the face of a rising population.

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Introduction Cont….

•Understanding rice production in relation to weather changes is of great importance to boost food productivity.

•Crop growth simulation models provide the means to qualify the effects of climate, soil and management on crop growth, productivity and sustainability of agricultural production

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Introduction Cont…..

• These tools can reduce the need for expensive and

time-consuming field trials and could be used to

analyze yield gaps in various crops including rice

( Pathak, 2005)

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Objective

• To assess the effects of change in weather conditions

(temperature, solar radiation and atmospheric CO2

concentration) in Kenya on Basmati 370 and IR 2793-

80-1 grain yield cultivated under System of rice

Intensification using the CERES modeling system.21/04/23 6

Methodology

Description of the study area

• The study was conducted in the four national irrigation

schemes in Kenya namely; Mwea in Central province

region, Ahero in Nyanza province region, Bunyala in

Western province region and West Kano in Nyanza

province region. 21/04/23 7

Methodology cont…

Material, Methods and Data collection

Plant material

•Basmati 370 and IR 2793-80-1 rice varieties were used

in this study. This is because they are the two

commonly grown varieties in Kenya.

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Methodology Cont….

Field selection and design

• From each of the four irrigation schemes under study,

two SRI farmers were randomly selected and their

farms used as research fields. The rice profile and

management practices from nursery till harvest were

monitored.21/04/23 9

Methodology Cont….

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Methodology Cont….

The following data was collected;

•Daily weather data

• Soil data

•Management practices

•Plant profile data

• Latitude of production area21/04/23 11

Methodology Cont….

Input files were created to run the model: •Weather file (FILE.WTH)• Soil file (FILES)•Rice management file (FELEX).• Experimental data file (FILEA) with measured data.•Genetic coefficients file (FILEC),

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Methodology Cont…

Data Analysis

The CERES-Rice model version 4.5 of the DSSAT modeling system

which is an advanced physiologically based rice crop growth

simulation model was used to predict rice (Basmati 370 and IR2793-

80-1) growth, development, and response to various climatic

conditions prevailing in the four irrigation schemes.

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Methodology Cont…

Model calibrationBy determination of genetic coefficientsModel Validation•RMSE•RMSEn•D – Index of agreement•R- Squared21/04/23 14

Results ( Mean temperatures and solar radiation during the cropping seasons

Scheme Mean Tmax (oC)

Mean Tmin (oC)

Mean solar rad (MJ/m2 )

Mwea 27.4 19.2 16.5

Ahero 30.1 17.0 21.1

West Kano 27.4 17.4 18.2

Bunyala 29.4 17.8 19.821/04/23 15

Results Cont… ( Genetic Coefficients)

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Results Cont.. ( Genetic Coefficients ..)• P1 - Time from seedling emergence to the end of juvenile phase

(GDD). • P2O - Optimum photoperiod • P2R - Rate of photo-induction • P5 - Time from grain filling to physical maturity• G1 - Maximum spikelet number coefficient. • G2 - Maximum possible single grain size under stress free conditions. • G3 -scalar vegetative growth coefficient for tillering relative to IR64. • G4 defines the temperature tolerance scalar coefficient

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Results Cont.. (Main growth and development variables for Basmati 370 under SRI in Mwea irrigation scheme, Kenya.

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Results Cont.. ( Main Dev. Variables for IR2793)

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Results Cont… ( Model validation)

RMSE =0.838, RMSEn =15.027% and D= 0.875

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Results Cont.. (Sensitivity analysis on climatic adaptations

Effects of temperature change on Basmati 370 grain yield in Mwea

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Results Cont….

Effects of temperature change on IR2793 grain yield in Ahero, Bunyala and West Kano

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Results Cont..

Effects of change in solar Radiation on grain yield

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Results Cont…

Effects of change in CO2 on Basmati 370 grain yield in Mwea

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Results Cont… ( Effects of C02 on IR2793 grain yield in Ahero, West Kano & Bunyala

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Conclusion and Recommendation

• Therefore to improve on rice production under

System of Rice Intensification in Kenya, proper

understanding of the prevailing weather conditions

and regular monitoring is necessary.•

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Acknowledgement

•NIB- For funding the project• JKUAT Community•All staff and farmers of Mwea, Ahero, Bunyala and

West Kano irrigation scheme•Prof. Gerrit of Washington University, USA for his

comprehensive support towards acquisition of the DSSAT

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END&

THANKS

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