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This study investigated the possibility of three approaches in determination of soil moisture in the root zone. The aim of the study was to contribute to the development of soil moisture monitoring methods to better help crop best management practices. / Two fields were examined, one at the Macdonald Campus of McGill University and the other near St. Jean-sur-Richelieau. Three approaches were used; (1) a hand-held hyper-spectral sensor (350-2500 nm), (2) a Geonics RTM EM-38 conductivity meter and, (3) gravimetric soil moisture sampling. / The first experiment (at St. Jean-sur-Richelieu) investigated the possibility of monitoring soil moisture with the EM_38, in the presence of field elevation and soil texture. The second experiment (at Macdonald Campus) investigated the possibility of using hyper-spectral sensor data for determination of soil characteristics in the root zone, in the presence of such factors as (a) irrigation (main treatment), (b) nitrogen (sub-treatment), and (c) weed control (sub-sub-treatment). Statistical regression analyses and Artificial Neural Network models were used to select the best waveband region for determination of soil root zone moisture. / The coefficients of determination obtained by the statistical analyses ranged from 0.75 to 0.94. The wavebands most frequently identified by these analyses ranged from 1100 nm-1900 nm. / The performances of the ANN training models were considered acceptable (R2 from 0.6 to 0.8). The lack of sufficient data greatly impacts this approach.

This study used hyperspectral data to determine nitrogen, weed, and water stresses in a corn (Zea mays L.) field in southwestern Quebec, and incorporated these data in crop growth models for better crop growth simulation under stressful conditions. / In 2000, aerial hyperspectral images (72 wavebands, ranging from 407 to 949 nm) were acquired, and analyzed using a stepwise approach to identify wavebands useful in detecting weed and nitrogen stresses. Discriminant analysis (DA) was used to classify different weed and nitrogen treatments and their combinations. This analysis showed greater classification accuracy (nearly 75%) than those obtained with artificial neural networks (58%) or decision tree algorithms (60%), at the initial growth stages, the time when remedial actions are most needed to alleviate weed and nitrogen stresses. / To explore the possibility of improving nitrogen stress detection in corn in the presence of a confounding water stress, ground-based 2151 narrow-waveband reflectance values (350 to 2500 nm), were collected in 2002. Using DA with the chosen subset of narrow-wavebands, a classification accuracy of greater than 95% was obtained. / For crop growth monitoring, the STICS model was evaluated for yield and biomass estimation in cornfields under different stressful growth conditions using the data collected from 2000 to 2002. Measured yield, biomass, and leaf area index (LAI) were used for both calibration and validation of the model. High correlation coefficients between the measured and estimated grain yield (0.96), biomass (0.98), and LAI (0.93) indicated that the model has good potential in the simulation of corn growth. The model was also linked with LAI values estimated from the hyperspectral observations using the Support Vector Machines technique. Coupling STICS with remote sensing resulted in an overall improvement in the simulation of corn yield (6.3%) and biomass (3.7%). / A new approach was developed to apply crop growth models for yield estimation in weedy areas. The proposed method first corrects the measured/estimated LAI values in weed infested fields for weed effect, and then uses the corrected LAI values as input to the crop growth model. The results showed that the crop yield and biomass predictions were correctly simulated by this method.* / *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).

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Maize (Zea mays L.) is an important crop in the world; however, its yield is compromised by new production challenges leading to poor yield in sub-Saharan Africa. This calls for a need to enhance maize adaptation to changing climate and challenging environments. The new maize varieties should be richly endowed with high frequency of genes that confer high yield under stress and non-stress conditions. Currently, such maize is not available, prompting research into development of new germplasm lines for use in developing new hybrids. The objective of the study was to determine i) the level of genetic diversity using SSR molecular markers and phenotypic data in a set of 60 maize inbreds from the breeding program, ii) genotype by environment interaction in maize hybrids, iii) cultivar superiority, iv) combining ability effects, v) the relationship between yield and secondary traits and vi) the relevant genetic parameters that underpin genetic gains in a breeding program. To study genetic diversity present in the germplasm, phenotypic data and 30 SSR markers were used to estimate the genetic distance between the inbreds. The results indicated that inbred lines which were put in the same cluster were related by pedigree and origin. To assess the level of genotype by environment interaction (GXE) and cultivar superiority of the new germplasm lines, hybrids were planted in five environments with two replications. Data were analysed using the REML and AMMI tools in GenStat 14th edition. The results revealed significant differences between hybrids and environments for grain yield. However, GXE interaction was also significant indicating possible challenges which can be encountered in selecting new hybrids. To determine combining ability estimates two different testers were used. The REML tool from GENSTAT was used to perform the line X tester analysis. Results indicated that both additive and non-additive gene action were important for grain yield. The direct selection strategy for yield was recommended because heritability of grain yield was high. Overall, results suggested that the information on genetic diversity will assist in defining heterotic groups; which will enable effective and efficient management of the germplasm lines to produce new maize hybrids. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

This study investigated the possibility of using ground-based remotely sensed hyperspectral observations with a special emphasis on detection of water, weed and nitrogen stresses contributing towards in-season decision support for precision crop management (PCM). / A three factor split-split-plot experiment, with four randomized blocks as replicates, was established during the growing seasons of 2003 and 2004. Corn (Zea mays L.) hybrid DKC42-22 was grown because this hybrid is a good performer on light soils in Quebec. There were twelve 12 x 12m plots in a block (one replication per treatment per block) and the total number of plots was 48. Water stress was the main factor in the experiment. A drip irrigation system was laid out and each block was split into irrigated and non-irrigated halves. The second main factor of the experiment was weeds with two levels i.e. full weed control and no weed control. Weed treatments were assigned randomly by further splitting the irrigated and non-irrigated sub-blocks into two halves. Each of the weed treatments was furthermore split into three equal sub-sub-plots for nitrogen treatments (third factor of the experiment). Nitrogen was applied at three levels i.e. 50, 150 and 250 kg N ha-1 (Quebec norm is between 120-160 kg N ha-1). / The hyperspectral data were recorded (spectral resolution = 1 nm) mid-day (between 1000 and 1400 hours) with a FieldSpec FR spectroradiometer over a spectral range of 400-2500 run at three growth stages namely: early growth, tasseling and full maturity, in each of the growing season. / There are two major original contributions in this thesis: First is the development of a hyperspectral data analysis procedure for separating visible (400-700 nm), near-infrared (700-1300 nm) and mid-infrared (1300-2500 nm) regions of the spectrum for use in discriminant analysis procedure. In addition, of all the spectral band-widths analyzed, seven waveband-aggregates were identified using STEPDISC procedure, which were the most effective for classifying combined water, weed, and nitrogen stress. The second contribution is the successful classification of hyperspectral observations acquired over an agricultural field, using three innovative artificial intelligence approaches; support vector machines (SVM), genetic algorithms (GA) and decision tree (DT) algorithms. These AI approaches were used to evaluate a combined effect of water, weed and nitrogen stresses in corn and of all the three AI approaches used, SVM produced the best results (overall accuracy ranging from 88% to 100%). / The general conclusion is that the conventional statistical and artificial intelligence techniques used in this study are all useful for quickly mapping combined affects of irrigation, weed and nitrogen stresses (with overall accuracies ranging from 76% to 100%). These approaches have strong potential and are of great benefit to those investigating the in-season impact of irrigation, weed and nitrogen management for corn crop production and other environment related challenges.

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Quality protein maize (QPM) has enhanced levels of two essential amino acids, lysine and tryptophan compared to normal maize. This makes QPM an important cereal crop in communities where maize is a staple crop. The main abiotic factor to QPM production is drought stress. Little information is available on the effect of drought stress on QPM. Therefore, the objectives of this study were to: (i) conduct diversity analysis of QPM inbred lines using morpho-agronomic and simple sequence repeat markers, (ii) screen available QPM inbred lines and F1 progeny for tolerance to seedling drought stress, (iii) determine the combining ability and type of gene action of QPM inbred lines for tolerance to seedling drought stress, grain yield and endosperm modification. The study was conducted in South Africa, at the University of Fort Hare. Morphological characterisation of 21 inbred lines was done using quantitative and qualitative traits. A randomised complete block design with three replicates was used for characterizing the inbred lines in the field. Genstat statistical software, version 12 (Genstat ®, 2009) was used for analysis of variance (ANOVA) and descriptive statistics. Analysis of variance was performed on all quantitative data for morphological traits. Data for qualitative traits was tabulated in their nominal classes. Traits that contributed most to the variation were days to anthesis, days to silking, anthesis-silking interval, plant height, number of kernel rows, ear length and grain yield. Cluster analysis grouped the inbred lines into three main clusters. The first cluster was characterised by tall and average yielding lines, while the second cluster showed the least anthesis-silking interval, and had the highest yield. Cluster three consisted of lines that were early maturing, but were the least yielding. Genetic distances between maize inbred lines were quantified by using 27 simple sequence repeat markers. The genetic distances between genotypes was computed using Roger’s (1972) genetic distances. Cluster analysis was then carried out using the neighbour-joining tree method using Power Marker software version 3.25. A dendrogram generated from the genetic study of the inbred lines revealed three groups that concurred with expectations based upon pedigree data. These groups were not identical to the groups generated using morpho-agronomic characterisation. Twenty one QPM inbred lines were crossed using a North Carolina design II mating scheme. These were divided into seven sets, each with three inbred lines. The three inbred lines in one set were used as females and crossed with three inbred lines in another set consisting of males. Each inbred line was used as a female in one set, and as a male in a second set. Sixty three hybrids (7 sets x 9 hybrids) were formed and evaluated in October 2011, using a 6x8 alpha-lattice incomplete block design with three replicates under glasshouse and optimum field conditions. A randomised complete block design with three replicates was used for the 21 parental inbred lines. Traits recorded for the glasshouse study were, canopy temperature, chlorophyll content, leaf roll, stem diameter, plant height, leaf number, leaf area, fresh and dry root and shoot weights. Data for the various traits for each environment, 25 percent (stress treatment) and 75 percent (non-stress) of field capacity, were subjected to analysis of variance using the unbalanced treatment design in Genstat statistical package Edition 12. Where varietal differences were found, means were separated using Tukey’s test. Genetic analyses for grain yield and agronomic traits were performed using a fixed effects model in JMP 10 following Residual Maximum Likelihood procedure (REML). From the results, inbred lines that were not previously classified into heterotic groups and drought tolerance categories were classified based on their total dry weight performance and drought susceptibility index. Inbred lines L18, L9, L8, L6 and L3, in order of their drought tolerance index were the best performers under greenhouse conditions and could be recommended for breeding new varieties that are tolerant to seedling drought stress. Evaluation of maize seedlings tolerant to drought stress under glasshouse conditions revealed that cross combination L18 x L11 was drought tolerant, while cross L20 x L7 was susceptible. Total dry weight was used as the major criteria for classifying F1 maize seedlings as being resistant or susceptible. General combining ability effects accounted for 67.43 percent of the genetic variation for total dry weight, while specific combining ability effects contributed 37.57 percent. This indicated that additive gene effects were more important than non-additive gene action in controlling this trait. In the field study (non-drought), the experimental design was a 6x8 alpha lattice incomplete block design with three replicates. On an adjacent field a randomised complete block design with three replicates was used to evaluate the parental inbred lines. The following variables were recorded: plant height, ear height, ears per plant, endosperm modification, days to silking and days to anthesis, anthesis-silking interval, number of kernels per row, number of rows per ear and grain yield. General analyses for the incomplete lattice block design and randomised complete block design for hybrid and inbred data respectively were performed using JMP 10 statistical software. Means were separated using the Tukey's test. Genetic analyses of data for grain yield and agronomic traits were conducted using a fixed effects model using REML in JMP 10. The importance of both GCA (51 percent) and SCA (49 percent) was observed for grain yield. A preponderance of GCA existed for ear height, days to anthesis, anthesis-silking interval, ears per plant and number of kernels per row, indicating that predominantly, additive gene effects controlled hybrid performance under optimum field conditions. The highest heritability was observed for days to silking (48.27 percent) suggesting that yield could be improved through selection for this trait. Under field conditions, variation in time to maturity was observed. This implies that these inbred lines can be recommended for utilisation in different agro-ecologies. Early maturing lines such as L18 can be used to introduce earliness in local cultivars, while early maturing single crosses such as L18 x L2, L5 x L9, L3 x L4 and L2 x L21 could be recommended for maize growers in drought prone areas such as the former Ciskei. Single crosses L18xL11, L16xL18, L8xL21 and L9xL6 had good tolerance to seedling drought stress. On the other hand, single crosses L18xL11 and L11xL13 had high grain yield and good endosperm modification. All these single crosses could be recommended for commercial production after evaluation across locations in the Eastern Cape Province. Alternatively they can be crossed with other superior inbreds to generate three or four way hybrids, which could then be evaluated for potential use by farmers in the Eastern Cape.

MSCAGR (Plant Production) / Department of Plant Production / Smallholder farmers in Sub-Saharan Africa still obtain low grain yields in maize largely due to low soil fertility. The soils are inherently low in nitrogen (N) that is required for the proper development of the maize plant. Currently there are no commercial cultivars for low N tolerance locally. The combining ability approach can be used as a tool for breeding desirable cultivars. In order to improve grain yield in maize, it is important to consider ear prolificacy which is a major yield component. Therefore this study was designed to estimate combining ability in maize. Exotic germplasm from the International Maize and Wheat Improvement Center and the Institute of Tropical Agriculture as well as the local germplasm from the Agricultural Research Council was used in the study to generate crosses. One hundred and two crosses were evaluated together with a standard commercial check under low N and optimum N conditions. The specific objectives of the study were to determine general and specific combining ability for prolificacy among local and exotic inbred lines and evaluate the response of prolific hybrids to low N conditions. The hybrids were planted in the 2014/2015 summer season under irrigation in Potchefstroom, Cedara and Taung in field plots consisting of 0.75m x 0.25m spacing in a 0.1 alpha lattice design replicated twice. Data for agronomic attributes were recorded and subjected to analysis of variance using SAS version 9.1.3. Genetic correlations were analyzed using the Principal Components Analysis and factor analysis based on the correlation analysis and major traits. The results showed variation in agronomic performance among the inbred lines and their F1 hybrids. Inbred lines including TZEI63, T1162W, L15 and L17 showed positive GCA estimates for ear prolificacy at the different locations. Specific combining ability for prolific hybrids was positive at all locations and environments. The GCA:SCA ratio was close to unity; indicating that the number of ears per plant showed highly significant (P<0.01) correlation with grain yield. The hybrids showed ear prolificacy under the low N conditions. This trait can be used effectively in stress tolerance maize breeding programmes. / NRF