Reliability of Rain-Fed Maize Yield Simulation Using LARS-WG Derived CMIP5 Climate Data at Mount Makulu, Zambia

Chisanga, Charles Bwalya and Phiri, Elijah and Chinene, Vernon R. N. (2020) Reliability of Rain-Fed Maize Yield Simulation Using LARS-WG Derived CMIP5 Climate Data at Mount Makulu, Zambia. Journal of Agricultural Science, 12 (11). p. 275. ISSN 1916-9752

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Abstract

The impact of climate change on crop growth and yield can be predicted using crop simulation models. A study was conducted to assess the reliability and uncertainty of simulated maize yield for the near future in 2050s at Mount Makulu (latitude = 15.550o S, longitude = 28.250o E, altitude = 1213 m), Zambia. The Long Ashton Research Station Weather Generator (LARS-WG) was used to generate baseline (1980-2010) and future (2040-2069) climate scenarios for two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). Results showed that mean temperature would increase by 2.09oC (RCP 4.5) and 2.56oC (RCP 8.5) relative to the baseline (1980-2010). However, rainfall would reduce by 9.84% (RCP 4.5) and 11.82% (RCP 8.5). The CERES-Maize model simulated results for rainfed maize growth showed that the simulated parameters; days after planting (DAP), biomass and grain yield would reduce from 2040-2069/1980-2010 under both RCP4.5 and RCP8.5 scenarios. The LARS-WG was successfully for our location can be used in generating climate scenarios for impact studies to inform policy, stakeholders and decision makers. Adaptation strategies to mitigate for the potential impact of climate change includes several sowing dates, cultivar selection that are efficient at using nitrogen fertilizer and planting new cultivars breeds that will thrive under low root soil water content and higher temperatures.

Item Type: Article
Subjects: STM One > Agricultural and Food Science
Depositing User: Unnamed user with email support@stmone.org
Date Deposited: 10 May 2023 07:04
Last Modified: 19 Jun 2024 12:14
URI: http://publications.openuniversitystm.com/id/eprint/1044

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