Interactive effects of n fertilization rate, cultivars and planting date under climate change on maize (zea mays l.) yield using crop simulation and statistical downscaling of climate models
type: CreativeWork
description: Generating new information using traditional agricultural trials which are expensive is not sufficient to meeting novel agro-technologies. Planting date (PD), low soil fertility and climate change influences maize (Zea mays L.) growth and yield. Local-scale impacts of future climate change (CC) and variability (CV) on PD, N fertilizer rate (N), cultivar, maize growth, and yield is not well documented in Zambia. The impact of climate change and extreme climate indices on maize yield in AERII are carried out at large spatial scales, missing out on local-scale impacts, mitigation and adaptation potentials under which farmers operate. Statistical downscaling models such as stochastic weather generator (Long Ashton Research Station Weather Generator [LARS-WG]) and delta-based methods (Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols) have not been applied locally to assess the impact of climate change. Additionally, the AgMIP protocols have not been applied to downscale climate scenarios using Representative Concentration Paths (RCP4.5 and RCP8.5). Changes in temperature and precipitation would have a significant impact on maize phenology and yield. Two field experiments were conducted at Zambia Agriculture Research Institute at Mount Makulu (Lat: 15.550o S, Lon: 28.250o E, altitude: 1213 m) in Zambia to assess the effect of PD, N and cultivars on yield and yield parameters and to predict the impact of climate change on maize productivity in the 2050s. The irrigated experiment was arranged in a Split-plot design with maize cultivars (ZMS606, PHB30G19and PHB30B50) and N fertilizer rates (67.20, 134.40 and 201.60 kg N ha-1) as main-plots and sub-plots, respectively. These cultivars were selected as major cultivars planted by small scale farmers and their long commercial life. The rainfed experiment was a split-split plot design with PDs, cultivars, and N as the main-plots, sub-plot, and sub-subplots, respectively. Each field experiment had three…
abstract: Generating new information using traditional agricultural trials which are expensive is not sufficient to meeting novel agro-technologies. Planting date (PD), low soil fertility and climate change influences maize (Zea mays L.) growth and yield. Local-scale impacts of future climate change (CC) and variability (CV) on PD, N fertilizer rate (N), cultivar, maize growth, and yield is not well documented in Zambia. The impact of climate change and extreme climate indices on maize yield in AERII are carried out at large spatial scales, missing out on local-scale impacts, mitigation and adaptation potentials under which farmers operate. Statistical downscaling models such as stochastic weather generator (Long Ashton Research Station Weather Generator [LARS-WG]) and delta-based methods (Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols) have not been applied locally to assess the impact of climate change. Additionally, the AgMIP protocols have not been applied to downscale climate scenarios using Representative Concentration Paths (RCP4.5 and RCP8.5). Changes in temperature and precipitation would have a significant impact on maize phenology and yield. Two field experiments were conducted at Zambia Agriculture Research Institute at Mount Makulu (Lat: 15.550o S, Lon: 28.250o E, altitude: 1213 m) in Zambia to assess the effect of PD, N and cultivars on yield and yield parameters and to predict the impact of climate change on maize productivity in the 2050s. The irrigated experiment was arranged in a Split-plot design with maize cultivars (ZMS606, PHB30G19and PHB30B50) and N fertilizer rates (67.20, 134.40 and 201.60 kg N ha-1) as main-plots and sub-plots, respectively. These cultivars were selected as major cultivars planted by small scale farmers and their long commercial life. The rainfed experiment was a split-split plot design with PDs, cultivars, and N as the main-plots, sub-plot, and sub-subplots, respectively. Each field experiment had three…
author: Chisanga, Charles Bwalya
language: en
keywords: Climate change, Climate variability
date published: 2019
date modified: 2026-07-27T22:06:24Z
provider: University of Zambia
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Zambia