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Thesis (PhD (Food Sc))--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Maize kernels consists of two types of endosperm, a harder vitreous endosperm and a softer floury endosperm, and the ratio of the vitreous and floury endosperm present mainly determines the hardness of the kernel. Maize (Zea mays L.) is a staple food in many countries, including South Africa, and is industrially processed into maize meal using dry-milling. For optimal yield and higher quality products, hard kernels are favoured by the milling industry. Despite many maize hardness methods available, a standardised method is still lacking, furthermore, no dedicated maize milling quality method exists. Using an industrial guideline (chop percentage), a sample set of different maize hybrids was ranked based on milling performance. Unsupervised inspection (using principal component analysis (PCA) and Spearman’s rank correlation coefficients) identified seven conventional methods (hectoliter mass (HLM), hundred kernel mass (HKM), protein content, particle size index (PSI c/f), percentage vitreous endosperm (%VE) as determined using near infrared (NIR) hyperspectral imaging (HSI) and NIR absorbance at 2230 nm (NIR @ 2230 nm)) as being important descriptors of maize milling quality. Additionally, Rapid Visco Analyser (RVA) viscograms were used for building prediction models, using locally weighted partial least squares (LW-PLS). Hardness properties were predicted in the same order or better than the laboratory error of the reference method, irrespective of RVA profile being used. Classification of hard and soft maize hybrids was achieved, based on density measurements as determined using an X-ray micro-computed tomography (µCT) density calibration constructed from polymers with known densities. Receiver operating classification (ROC) curve threshold values of 1.48 g.cm-3 , 1.67 g.cm-3 and 1.30 g.cm-3 were determined for the entire kernel (EKD), vitreous (VED) and floury endosperm densities (FED), respectively at a maximum of 100% sensitivity and specificity. Classification based on milling quality of maize hybrids, using X-ray µCT derived density and volume measurements obtained from low resolution (80 µm) µCT scans, were achieved with good classification accuracies. For EKD and vitreous-to-floury endosperm ratio (V:F) measurements, 93% and 92% accurate classifications were respectively obtained, using ROC curve. Furthermore, it was established that milling quality could not be described without the inclusion of density measurements (using PCA and Spearman’s rank correlation coefficients). X-ray µCT derived density measurements (EKD) were used as reference values to build NIR spectroscopy prediction models. NIR spectra were acquired using a miniature NIR spectrophotometer, i.e. a microNIR with a wavelength range of 908 – 1680 nm. Prediction statistics for EKD for the larger sample set (where each kernel was scanned both germ-up and germ-down) was: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 and for the smaller sample set (where each kernel was scanned only germ-down): R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. The results from the larger sample set indicated that reasonable predictions can be made at the fast NIR scan rate that would be suitable for breeders as a rough screening method. / AFRIKAANSE OPSOMMING: Mieliepitte bestaan uit twee tipes endosperm, ‘n harder glasagtige endosperm en ‘n sagter melerige endosperm, en die verhouding waarin die twee tipes endosperm aangetref word, bepaal hoofsaaklik die hardheid van die pit. Mielies (Zea mays L.) is ‘n stapelvoedsel in baie lande, insluitende Suid-Afrika, en word industrieël geprosesseer na mieliemeel deur van droë-vermaling gebruik te maak. Vir optimale produksie en beter kwaliteit produkte, word harde pitte deur die meule verkies. Ongeag die beskikbaarheid van verskeie mielie hardheid metodes, ontbreek ‘n gestandardiseerde metode nog, en verder bestaan ‘n metode om mielies se maalprestasie te bepaal ook nie. ‘n Monsterstel, bestaande uit verskillende mieliebasters, is op grond van maalprestasie ingedeel deur van ‘n industriële riglyn (chop persentasie) gebruik te maak. Inspeksie sonder toesig (deur gebruik te maak van hoofkomponentanalise (HKA) en Spearman’s rangkorrelasiekoëffisiënte) het sewe onkonvensionele metodes (hektoliter massa, honderd pit massa, protein inhoud, partikel grootte indeks, persentasie glasagtige endosperm soos bepaal deur gebruik te maak van naby-infrarooi (NIR) hiperspektrale beelding en NIR absorbansie by 2230 nm) identifiseer as belangrike beskrywers van maalprestasie. Daarbenewens, is Rapid Visco Analyser (RVA) viskogramme gebruik om voorspellingsmodelle te bou deur gebruik te maak van plaaslik geweegte gedeeltelike kleinstekwadrate (PG-GKK) wat hardheidseienskappe kon voorspel met laer, of in dieselfde orde, laboratorium foute van die verwysingsmetodes, ongeag die gebruik van verskillende RVA profiele. Klassifikasie tussen harde en sagte mieliebasters was moontlik, gebasseer op digtheidsmetings soos bepaal met ‘n X-staal mikro-berekende tomografie (µBT) digtheids kalibrasie gebou vanaf polimere met bekende digthede. Ontvanger bedryf kenmerkende (OBK) kurwe drempelwaardes van 1.48 g.cm-3 , 1.67 g.cm-3 en 1.30 g.cm-3 is bepaal vir hele pit, glasagtige en melerige endosperm digthede, onderskeidelik, teen ‘n maksimum van 100% sensitiwiteit en spesifisiteit. Klassifikasie van die mieliebasters, gebasseer op maalprestasie en deur gebruik te maak van X-straal µBT afgeleide digtheid en volume metings soos verkry teen lae resolusie (80 µm) skanderings, was moontlik met goeie klassifikasie akkuraatheid. Vir heel pit digtheid en glasagtigtot-melerige endosperm verhouding metings is 93% en 92% akkurate klassifikasies verkry wanneer OBK kurwes gebruik is. Verder is dit vasgestel (deur gebruik te maak van HKA en Spearman’s rangkorrelasiekoëffisiënte) dat digtheidsmetings ingesluit moet word vir ‘n volledige beskrywing van maalprestasie. X-straal µBT afgeleide digtheid metings is gebruik as verwysings waardes om NIR spektroskopie voorspellings modelle te bou. NIR spektra is verkry deur van ‘n miniatuur NIR spektrofotometer, naamlik ‘n microNIR, bebruik te maak vanaf 908 – 1680 nm. Voorspellings statestiek vir die groter monsterstel (waar elke pit beide kiem-bo en kiem-onder geskandeer is) was vir HPD: R2 V = 0.60, RMSEP = 0.03 g.cm-3 , RPD = 1.67 en vir die kleiner monsterstel (waar elke pit was slegs kiem-onder geskandeer is) vir HPD: R2 V = 0.32, RMSEP = 0.03 g.cm-3 , RPD = 1.67. Die resultate van die groter monsterstel het aangedui dat redelike voorspellings moontlik is, teen die vinnige NIR skaderings tempo wat as rowwe vertoningsmetode geskik sal wees vir telers.

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Thesis (M.Agric. (Agricultural Economics)) --University of Limpopo, 2011 / Maize is the most important cereal crop grown in South Africa. This crop is produced throughout the country under diverse environments. The study only focuses on the technical efficiency because it is an important subject in developing agriculture where resources are limited, but high population growth is very common. Technical efficiency is the ability of a farmer to obtain output from a given set of physical inputs. Farmers have a tendency of under and/or over- utilising the factors of production. The main aim of this study was to analyse the technical efficiency of small-scale maize producers in Ga-Mothiba rural community of Limpopo Province. The objective of the study was to determine the level of technical efficiency of small- scale maize producers and to identify the socio-economic characteristics that influence technical efficiency of small-scale maize producers in Ga-Mothiba. Purposive and Snowball sampling techniques were used to collect primary data from 120 small-scale farmers. Cobb-Douglas production function was used to determine the level of technical efficiency and Logistic regression model was used to analyse the variables that have influence the technical efficiency of maize production. Cobb-Douglas results reveal that small-scale farmers in Ga-Mothiba are experiencing technical inefficiency in maize production due to the decreasing return to scale, which means they are over-utilising factors of production. Logistic regression results indicate that out of 13 variables included in the analysis as socio-economic factors, 10 of them (level of education, income of the household on monthly basis, farmer`s farming experience, farm size, cost of tractor hours, fertiliser application, purchased hybrid maize seeds, membership to farmers` organisation, is maize profitable) were found to be significant and 3 (gender, age and hired labour) are non-significant. However, farm size was found to be the most significant variable at 99% level, showing a positive relationship to small- scale maize producer`s technical efficiency.Therefore, it is recommended that government should do the on-farm training since farmers mainly depend on trial and error and farmers` should have access to enough arable land and tractor services. However, farmers need to be trained on matters relating to fertiliser application, on the amount of seeds a farmer should apply per ha, and the importance of using hybrid seed.

Thesis (M.Sc. Agric.) --University of Limpopo, 2011 / Maize is the most important cereal crop grown in South Africa. This crop is produced throughout the country under diverse environments. The study only focuses on the technical efficiency because it is an important subject in developing agriculture where resources are limited, but high population growth is very common. Technical efficiency is the ability of a farmer to obtain output from a given set of physical inputs. Farmers have a tendency of under and/or over- utilising the factors of production. The main aim of this study was to analyse the technical efficiency of small-scale maize producers in Ga-Mothiba rural community of Limpopo Province. The objective of the study was to determine the level of technical efficiency of small- scale maize producers and to identify the socio-economic characteristics that influence technical efficiency of small-scale maize producers in Ga-Mothiba. Purposive and Snowball sampling techniques were used to collect primary data from 120 small-scale farmers. Cobb-Douglas production function was used to determine the level of technical efficiency and Logistic regression model was used to analyse the variables that have influence the technical efficiency of maize production. Cobb-Douglas results reveal that small-scale farmers in Ga-Mothiba are experiencing technical inefficiency in maize production due to the decreasing return to scale, which means they are over-utilising factors of production. Logistic regression results indicate that out of 13 variables included in the analysis as socio-economic factors, 10 of them (level of education, income of the household on monthly basis, farmer`s farming experience, farm size, cost of tractor hours, fertiliser application, purchased hybrid maize seeds, membership to farmers` organisation, is maize profitable) were found to be significant and 3 (gender, age and hired labour) are non-significant. However, farm size was found to be the most significant variable at 99% level, showing a positive relationship to small- scale maize producer`s technical efficiency. Therefore, it is recommended that government should do the on-farm training since farmers mainly depend on trial and error and farmers` should have access to enough arable land and tractor services. However, farmers need to be trained on matters relating to fertiliser application, on the amount of seeds a farmer should apply per ha, and the importance of using hybrid seed.