Browsing by Author "Mazango, Joseph Tauyanarwo"

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    Mapping wheat water use, yield gap and productivity based on climate and satellite data in the Berg River Catchment
    (Stellenbosch : Stellenbosch University, 2020-12) Mazango, Joseph Tauyanarwo; Munch, Zahn; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: The world population is forecast to increase substantially in the coming decades and doubling of current food production to meet expected demand is envisaged. The Western Cape (WC) of South Africa (SA) is a dryland winter wheat production area with average yield levels below the national average. It was important to ascertain whether the current wheat production levels are optimal or potential exists to increase yields. The study intended to establish wheat yields, estimate seasonal water use and subsequently compute study area wheat yield gaps and water productivity. The study was implemented in part of the Berg river catchment area for the 2016 season winter wheat crop. The Penman Monteith (PM) and Hargreaves Samani (HS) reference ET0 were compiled and computed respectively using Python scripts and compiled into a Julian day (JD) indexed databases. The daily distributed reference crop evapotranspiration ET0PM and ET0HS surfaces were interpolated using the spline with tension interpolator implemented in ArcMap. Seasonal distributed wheat crop water use (ETc) was determined from Sentinel 2 (S2) and Landsat 8 (L8) NDVI modelled crop coefficients and ET0. Wheat crop yield surfaces were modelled from L8 and S2 scenes of the 4th and 5th of August 2016 respectively. From the statistical analysis of validated yield data, the within study area, regional and globally benchmarked wheat yield gaps were estimated. The at-station generated ET0HS were validated based on ET0P Musing the absolute and relative difference statistical measures. The results indicated that climate station ET0HS deviated from the monthly ET0PM values in the year 2016 with the magnitude of the differences higher in some stations and certain months. Although the ET0HS and ET0PM were generally in close range of each other, the ET0HS values were consistently higher in nearly all the months, except in January, March and December. The climate station ET0HS and ET0PM differences although large in absolute terms had a low annual RMSE of 0.426 mm.day-1. The averaged percent relative difference (RD %) indicated that ET0HS estimates were within 20% of the ET0PM.The results indicate that the ET0HS adequately estimates reference crop evapotranspiration in the study area. Both the S2 and L8 based crop coefficients (KcS2 and KcL8) were low at the start of the season at 0.236 and 0.185 level, and increased to a maximum of 0.954 and 0.66 mm.day-1 by mid-season on JD 217, respectively. Thereafter the Kc values decreased. The computed seasonal KcS2 and KcL8 values and plotted curves conform to those indicated in the literature, validating the NDVI based Kc in crop water use estimation studies. However, the KcS2 had consistently higher values than those estimated using the Landsat 8 NDVI (KcL8).The seasonal wheat crop water use patterns indicated that the ET0HS and the KcS2 based ETc consistently overestimated crop water use when compared to the ET0PM and KcL8 based crop ETc. Study area wide crop water use data based on PM and HS methodologies, and S2 and L8 modelled Kc were extracted using the zonal statistic as table tool in ArcMap. The median statistic values indicated that the season wheat crop water use was generally low and less than the expected values cited in literature. This was likely due to the drought conditions during the 2016 winter wheat growing season. The range in ETc values obtained were due to differences observed at the level of ET0 and Kc computation. The L8 modelled crop coefficients had lower values than the S2coefficients. In addition, the ET0HS had consistently higher vales than the ET0PM.The best wheat yield metrics were obtained from the L8 based wheat yield maximum statistic with RMSE less than one t.ha-1 while the corresponding S2 based wheat yield estimate showed a RMSE of 3 t.ha-1. The mean wheat yield for S2 was relatively more accurate than the L8 sensor based wheat yield mean statistic with lower RMSE of 1.408 compared to 1.865 t.ha-1. For both S2 and L8 the median maximum yield statistic results were higher than the corresponding mean of the maximum statistic values.The mean-based within study area wheat yield gaps gave comparable results using S2 and L8 imagery of 1.51 and 1.78 t.ha-1 respectively. Similarly the median-based statistic was computed giving yield gaps of 1.36 and 1.47 t.ha-1 respectively. These were compared with yield gaps obtained using mean SA irrigated, SA dryland and Northern Cape irrigated wheat study area median of maximum zonal statistics of 2.39, -1.51 and 3.65 t.ha-1 respectively. These results indicated that the study area had yields that were higher than the national dryland average and that an additional 3.65 t.ha-1 to current yield level could be attained in the WC with irrigation. The observed study area water productivity metrics of 0.39 to 0.43 kg.m-3 established using the L8 yields to ETcPML8 and ETcHSL8 mean statistics respectively, indicated the existence of wheat production augmentation potential considering the possible 2kg.m-3 attained in high wheat production regions. The corresponding S2 yield to ETcPMS2 and ETcHSS2 WP were 1.89 and 1.79 kg.m-3 respectively, which was more than 4 times the L8 estimates. The study results showcased the utility of geographic information systems (GIS), remote sensing (RS) and tabular climate data in determining wheat water use and yield characteristics. The crop performance characterisation has been specified in United Nations sustainable development goals (SDGs). Studies of this nature are important decision support tools in the planning and management for sustainable irrigation projects to meet the escalating world food demand.