Version 1
: Received: 30 January 2024 / Approved: 30 January 2024 / Online: 31 January 2024 (02:59:01 CET)
How to cite:
Chisanga, C.B.; Chilambwe, A.; Kipkulei, H...K.; Mubanga, K.H. Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia. Preprints2024, 2024012143. https://doi.org/10.20944/preprints202401.2143.v1
Chisanga, C.B.; Chilambwe, A.; Kipkulei, H...K.; Mubanga, K.H. Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia. Preprints 2024, 2024012143. https://doi.org/10.20944/preprints202401.2143.v1
Chisanga, C.B.; Chilambwe, A.; Kipkulei, H...K.; Mubanga, K.H. Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia. Preprints2024, 2024012143. https://doi.org/10.20944/preprints202401.2143.v1
APA Style
Chisanga, C.B., Chilambwe, A., Kipkulei, H...K., & Mubanga, K.H. (2024). Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia. Preprints. https://doi.org/10.20944/preprints202401.2143.v1
Chicago/Turabian Style
Chisanga, C.B., Harison . K Kipkulei and Kabwe H. Mubanga. 2024 "Climate Change Impact on Management Practices of Maize Yield: Case of Mount Makulu, Zambia" Preprints. https://doi.org/10.20944/preprints202401.2143.v1
Abstract
Abstract: Long-term rainfall, temperature and solar radiation time series data are required to simulate crop yield and yield variability. A field experiment conducted at Mount Makulu was used to simulate the interactive effect of planting dates (SD1, SD2, SD3), maize varieties (PIO30G19, PIO30B50, ZMS606), and nitrogen fertilizer application levels (N1 = 66; N2 = 132; N3 = 198 kg N ha-1) on strategic and economic assessment. Statistical downscaled climate datasets from three GCMs from 1971-2000, 2010-2039, 2040-2069, and 2070-2099 using Representative Concentration Pathways (RCP4.5, RCP8.5) were utilized as DSSAT v4.7 inputs. The Seasonal analysis Program of the DSSAT model was used to simulate the impacts of climate change on maize yield. Results show increasing trends in temperature while there is variability in rainfall. The biophysical analysis showed varied grain yield responses to sowing date, maize cultivars and N application rates. The Mean-Gini analysis showed that PIO30B50 had an efficient late sowing data (SD3) with an application of 132 and 168 kg N ha-1 under both scenarios. Further, PIO30G19 at SD3 with 198 kg N ha-1 would be the most dominant management option for maize grain yield under future climate scenarios from 2010-2099. This research emphasizes the urgency for tailored adaptation actions and collaborative efforts along the maize value chain to mitigate future yield losses and sustain food security. Increasing maize grain yield requires implementing adaptation strategies such as varying sowing dates, adopting late-maturing varieties with high thermal heat requirements under future climate scenarios.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
