ABSTRACT
Vegetation Condition Index (VCI), a function of Normalised Difference Vegetation Index
(NDVI) forms a very critical model for climate modelling, detecting crop yield productivity
and yield prediction. It approximates the weather (rainfall) component in NDVI value and
allows quantifying the impact of weather (rainfall) on maize crop. The thrust of this research
study was to detect land suitability potential for maize crop based on VCI-drydekads in
Mberengwa District, Ward 36 in view of recurrent food insecurity in this study area. Maize
crop performance is a function of critical variables which include rainfall and soil nutrients.
A test of the relationship between average VCI and remotely-sensed seasonal rainfall was
performed using an empirical regression model. A significant relationship (p
80%) for all the five sample growing seasons was observed between mean VCI and remotelysensed
rainfall amount. Subsequently, VCI was used to detect maize crop land suitability by
calculating the number of VCI-drydekads under rain-fed maize Land Utilisation Type (LUT)
along with soil analysis in Geographic Information System (GIS). Results showed that Ward
36 experienced the worst maize crop failure based on the subdued VCI (%) and the high
number of VCI-drydekads experienced and soil constraints hence factor rated currently not
suitable (n1) and marginally suitable (s3) respectively subject to Food and Agricultural
Organisation (FAO) land suitability guidelines. Based on FAO land suitability framework,
VCI and soil maps which were generated for Ward 36 exposed severe limitations in terms of
subdued rainfall totals and pedological constraints that yielded exceedingly high VCIdrydekads.
This apparently disqualified its land suitability potential for maize crop
production (LUT). The study therefore recommends that Agricultural Research and
Extension Services (AREX) should evaluate the feasibility of relying on maize crop judging
by the results of this research and advise resettled A1 farmers accordingly on the best
diversified investment farming such as drought-tolerant varieties like sorghum and millet.
Government should consider allocating bigger plots to A1 farmers for commercial livestock
production (LUT) such as cattle, sheep and goats under subsidised loan schemes, inputs,
technical support, and establish potential market network.
Hove., N (2021). Land Suitability Analysis From Space For Crop Farming: The Case Of Ward 36, Mberengwa District In Zimbabwe By Hove John (R103505M). Afribary. Retrieved from https://track.afribary.com/works/land-suitability-analysis-from-space-for-crop-farming-the-case-of-ward-36-mberengwa-district-in-zimbabwe-by-hove-john-r103505m
Hove., Norlene "Land Suitability Analysis From Space For Crop Farming: The Case Of Ward 36, Mberengwa District In Zimbabwe By Hove John (R103505M)" Afribary. Afribary, 02 May. 2021, https://track.afribary.com/works/land-suitability-analysis-from-space-for-crop-farming-the-case-of-ward-36-mberengwa-district-in-zimbabwe-by-hove-john-r103505m. Accessed 25 Nov. 2024.
Hove., Norlene . "Land Suitability Analysis From Space For Crop Farming: The Case Of Ward 36, Mberengwa District In Zimbabwe By Hove John (R103505M)". Afribary, Afribary, 02 May. 2021. Web. 25 Nov. 2024. < https://track.afribary.com/works/land-suitability-analysis-from-space-for-crop-farming-the-case-of-ward-36-mberengwa-district-in-zimbabwe-by-hove-john-r103505m >.
Hove., Norlene . "Land Suitability Analysis From Space For Crop Farming: The Case Of Ward 36, Mberengwa District In Zimbabwe By Hove John (R103505M)" Afribary (2021). Accessed November 25, 2024. https://track.afribary.com/works/land-suitability-analysis-from-space-for-crop-farming-the-case-of-ward-36-mberengwa-district-in-zimbabwe-by-hove-john-r103505m