Estimating rice yield under changing weather conditions in Kenya using Ceres Rice model

1. 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 04/07/204

2. 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.

3. 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.

4. 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

5. 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)

6. 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.

7. 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.

8. 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.

9. 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.

10. Methodology Cont….

11. Methodology Cont…. The following data was collected; • Daily weather data • Soil data • Management practices • Plant profile data • Latitude of production area

12. 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),

13. 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.

14. Methodology Cont… Model calibration By determination of genetic coefficients Model Validation • RMSE • RMSEn • D – Index of agreement • R- Squared

15. Results ( Mean temperatures and solar radiation during the cropping seasons

16. Results Cont… ( Genetic Coefficients)

17. 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

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

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

20. Results Cont… ( Model validation) RMSE =0.838, RMSEn =15.027% and D= 0.875

21. Results Cont.. (Sensitivity analysis on climatic adaptations Effects of temperature change on Basmati 370 grain yield in Mwea

22. Results Cont…. Effects of temperature change on IR2793 grain yield in Ahero, Bunyala and West Kano

23. Results Cont.. Effects of change in solar Radiation on grain yield

24. Results Cont… Effects of change in CO2 on Basmati 370 grain yield in Mwea

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

26. 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.

27. 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

28. END & THANKS