Typescript (photocopy). / Digitized by Kansas Correctional Industries / Department: Statistics.

Typescript, etc. / Digitized by Kansas Correctional Industries

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The agriculture sector in Amhara National Regional State is characterised by producing cereal crops which occupy the largest percentage (84.3%) of the total crop area cultivated in the region. As a result, it is imperative to investigate which factors influence the yields of cereal crops particularly in relation to the five major types of cereals in the study region namely barley, maize, sorghum, teff and wheat. Therefore, in this thesis, using data collected by the Central Statistical Agency of Ethiopia, various statistical methods such as multiple regression analysis were applied to investigate the factors which influence the mean yields of the major cereal crops. Moreover, a mixed model analysis was implemented to assess the effects associated with the sampling units (enumeration areas), and a cluster analysis to classify the region into similar groups of zones. The multiple regression results indicate that all the studied cereals mean yields are affected by zone, fertilizer type and crop damage effects. In addition to this, barley is affected by extension programme; maize crop by seed type, irrigation, and protection of soil erosion; sorghum and teff crops are additionally affected by crop prevention method, extension programme, protection of soil erosion, and gender of the household head; and wheat crop by crop prevention methods, extension programme and gender of the household head. The results from the mixed model analysis were entirely different from the regression results due to the observed dependencies of the cereals mean yields on the sampling unit. Based on the hierarchical cluster analysis, five groups of classes (clusters) were identified which seem to be in agreement with the geographical neighbouring positions of the locations and the similarity of the type of crops produced. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2010.

This study investigated the potential of developing in-season crop yield forecasting and mapping systems based on interpretation of airborne hyperspectral remote sensing imagery by machine learning algorithms. The data used for this study was obtained over a corn (Zea mays L.) field in eastern Canada. / The experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season. / Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study.

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A risk analysis of drought and an assessment of irrigation requirements were ascertained for a wheat (Triticum aestivum L.) crop in Parana, Brazil, using 28 years of historical weather data. Two soil moisture models, The Versatile Soil Moisture Budget (VB4) and SWACROP models, were compared using data from six wheat cropping periods. The models showed good performance in predicting soil moisture contents, but SWACROP underpredicted soil evaporation and runoff, and VB4 did not separate evapotranspiration into its components. Therefore, a new soil moisture model was proposed. In the new model, a Darcy type equation was used to calculate fluxes in the soil profile, and inputs of daily rainfall and potential evapotranspiration were partitioned during the day using simple disaggregation methods. Crop growth input parameters, interacting with weather and soil inputs, were used to calculate a detailed output of the water balance components. The validation of the model showed predictions of soil water contents and evapotranspiration in close agreement with field data. / A crop yield model based on the stress day index approach was selected from an evaluation of seven crop-water production functions using wheat field data. This model was combined with the soil moisture model to assess risks of drought during the establishment and development of non-irrigated wheat crops with different planting dates. Irrigation management strategies were simulated to identify net system delivery capacities and application frequencies that promote maximum yield with minimum requirements of water. Yield reductions in non-irrigated wheat due to water stress varied between 16%, for early plantings, to 50%, for late plantings. Maximum yields with minimum applied water was obtained by the use of low intensity (5 to 10 mm) and frequent (3 to 5 days) irrigations. System delivery capacity requirements varied from 1.5 to 3.0 mm/day, according to planting dates.

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South Africa is the largest producer of sugar in Africa and one of the ten largest sugarcane producers in the world. Sugarcane in South Africa is grown under a wide range of agro-climatic conditions. Climate has been identified as the single most important factor influencing sugarcane production in South Africa. Traditionally, sugarcane mill committees have issued forecasts of anticipated production for a region. However, owing to several limitations of such committee forecasts, more advanced technologies have had to be considered. The aim of this study has been to develop, evaluate and implement a pertinent and technologically advanced operational sugarcane yield forecasting system for South Africa. Specific objectives have included literature and technology reviews, surveys of stakeholder requirements, the development and evaluation of a forecasting system and the assessment of information transfer and user adoption. A crop yield model-based system has been developed to simulate representative crops for derived Homogeneous Climate Zones (HCZ). The system has integrated climate data and crop management, soil, irrigation and seasonal rainfall outlook information. Simulations of yields were aggregated from HCZs to mill supply area and industry scales and were compared with actual production. The value of climate information (including climate station networks) and seasonal rainfall outlook information were quantified independently. It was concluded that the system was capable of forecasting yields with acceptable accuracy over a wide range of agro-climatic conditions in South Africa. At an industry scale, the system captured up to 58% of the climatically driven variability in mean annual sugarcane yields. Forecast accuracies differed widely between different mill supply areas, and several factors were identified that may explain some inconsistencies. Seasonal rainfall outlook information generally enhanced forecasts of sugarcane production. Rainfall outlooks issued during the summer months seemed more valuable than those issued in early spring. Operationally, model-based forecasts can be expected to be valuable prior to the commencement of the milling season in April. Current limitations of forecasts include system calibration, the expression of production relative to that of the previous season and the omission of incorporating near real-time production and climate information. Several refinements to the forecast system are proposed and a strong collaborative approach between modellers, climatologists, mill committees and other decision makers is encouraged. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.