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Abstract of the M. Sc. Thesis
Title:The effect of Land and Management aspect on Maize Yield.
| Maize is the most preferred staple food in Mozambican and demand increases as population grows. Since the arable area is limited, the productivity has to be increased. One of the methods to enhance yield is by minimising the yield gap and yield variability in farmers' fields through identifying specific land and management factors that causes to Maize yield gap at regional level. The aim of this study was to identify the biophysical factors at field level that causes maize yield gaps in Limpopo during the 2002/03 cropping season using Comparative Performance Analysis (CPA). The study was carried out through a field survey; the quantified land use analysis approach was used to carry out the yield gap analysis. Data on land and land use such as regarding soil texture, varieties, land preparation, sowing/planting, thinning, weeding, harvesting actual and expected yield and farmers' perceptions on yield differences and management like application of FYM, chemical fertilizers, application of pesticides were collected through interviews. Significant land and management parameters were selected through descriptive statistics. Tukey's pair-wise comparison was applied to identify significant mean differences for nominal parameters. Stepwise forward linear multiple regressions was applied to select constraints for yield variability and to derive the production model; it explains 57 % of the encountered yield variability. Note that the model excludes farmers' perceptions on reported yield gap causes like drought, pests and diseases. The data on these parameters proved not logical; statistical analysis showed unexpected opposite relationship. A quantitative production function is derived and used to determine the 'mean' and 'best' values for each explanatory parameter; impact estimates by yield constraints and individual contribution to the overall yield gap. The identified yield constraints and their relative contribution to the yield gap are: light texture soil (27%), Plot size (30%), more seeds per plant hill (30%) and no thinning practiced (13%). The production model was then tested using a separate data set of a previous season to check its value. The model proved significant with an adj. R2 of 43% (p=0.001). Therefore, the model is fit to estimate and quantify yield constraints across years of maize in the Limpopo valley. These findings feature the fact that farmers operate at a rather low technology level, and are still at the early level of acquiring proper production skills. |