INTRODUCTION

•Land is not only one of the most defining social, political and development issues in Southern Africa, but is the most intractable element.

•After the inception of Land reform in Zimbabwe; Sugarcane production was transferred to new land owners in the in Eastern lowveld, Mkwasine Estate.

•

Study Aim

: To monitor and evaluate sugarcane production trends using normalized difference vegetation index (NDVI) derived from SPOT vegetation as a proxy for sugarcane growth and production model.

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Utility of Vegetation Indices: NDVI

that uses the visible and near is a numerical indicator infrared bands of the electromagnetic spectrum to measure reflectance. Its results are used as an indicator of plant biomass and greenness.

STUDY AREA

• •

Location

: Mkwasine Estate Eastern lowveld of Zimbabwe.

Characteristics

: Low summer precipitation of less than 450mm per annum and high temperatures around 35 ˚C.

• Sugarcane is thus grown under irrigation as the conditions are conducive for the growth of the crop.

TREND ANALYSIS OF SMALL SCALE COMMERCIAL SUGARCANE PRODUCTION IN POST RESETTLEMENT AREAS OF MKWASINE ZIMBABWE, USING HYPER TEMPORAL SATELLITE IMAGERY. Shingirirai Mutanga

1

,Keneilwe Hlahane

1

, Abel Ramoelo

2

, Tichatonga Gonah

3

1

Africa Institute of South Africa, Pretoria(AISA),

2

South Africa, Council for Scientific and Industrial Research(CSIR) , Pretoria, South Africa,

3

Department of Water Affairs, Pretoria, (DWA) South Africa

.

SPOT vgt NDVI

Download 1998-2010

METHODS

Field Points Geo-referencing and De clouding Stratified Random Sampling Plot Coordinates Extraction: Zonal Statistics Mean NDVI per Sugar Cane Plot

Trend Analysis using R Programming Language



Decompose using Moving Average.



Computation of seasonal figures.



Determination of error component



Auto Correlation Coefficient.

Land Sat Images

Classification

Extraction of Sampling Plots

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Ground truthing

: Collection of sugarcane production data in the field using stratified random sampling based on whether the farmers produced sugarcane or other crops.

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Sample Size

: Twenty sugarcane plots. Coordinates for each plot were taken using Global Positioning System (GPS).

•

Extraction

: Zonal Statistics, Mean NDVI per Sugar Cane Plot.

•

Trend Analysis

using R Programming Language.

•

Time series analysis

was undertaken using the moving average computed in R programming language to monitor sugarcane production.

• Sugarcane production levels data, was obtained from the cane supply sections of Mkwasine milling plants.

RESULTS

The general trend shows a more or less constant pattern of the NDVI between 1999 and mid 2001 before it fell to its lowest for the period during the first few months of 2002. The NDVI rose again to lower peaks towards 2003 and maintained the lower peaks to 2005 after which it rose to the highest peak of the study period around the end of 2005. The NDVI fell again during the beginning of 2006 and maintained lower levels throughout 2007 and 2008 before it started rising gradually. The 2001 to 2002 and 2007 till 2008 seasons were characterised by continuously low NDVI levels.

Explanations for the changes in the NDVI for the study period: • The main reason for the probable significant fluctuations is the land reform that was carried out in the area and country at large from the beginning of 2000.

• Poor rains received in the 2008 to 2009 agricultural season might have been a contributing factor in the depressed NDVI trend during that season as depicted by the NDVI trend.

• Long political instability in Zimbabwe could have contributed to this through reduced investor confidence in the farming sector due to an uncertain operating environment for the farmers leading to a decline in sugarcane hectares and a continuous low NDVI trends.

RESULTS

The above figure shows the autocorrelation and the partial autocorrelation estimation derived from the time series data.

CONCLUSION

• Hyper-temporal satellite imagery (SPOT Vegetation), can be used to estimate sugarcane production trends over the years.

• Main findings indicated a declining trend with a few years of improved production over the 11 year period under investigation.

• The study identified a wide range of factors that could possibly help explain the general production trend among which includes land reform, economic factors and climate variables.

• This study recommends a comparative analysis of the trend in production before and after the land reform in order to ascertain the net benefit of the programme.