Rice (Oryza sativa L.) supports half of the human population. However, predominant rice consumption leads to malnutrition due to mineral deficiencies. The research goal was to support identification of genes responsible for the uptake/accumulation of potassium (K), iron (Fe), zinc (Zn) and molybdenum (Mo), thus promoting the breeding for rice with high grain concentrations of these elements. Prior studies identified rice genotypes with high grain-K, -Fe, -Zn or -Mo concentrations that were hypothesized to be due to differences in root traits. The research objective was to identify root traits associated with these elements. These traits could be bases for identifying genes. The first study determined if these genotypes showed similar accumulation patterns in leaves as in grains, which would hint at influences of the roots and enable identifying distinct root traits and possible genes in vegetative growth stages. The second study determined if root traits of high grain-Mo genotypes displayed an acid-tolerance mechanism as these genotypes originated from Malaysia where acidic soils strongly adsorb Mo making it unavailable for plants. The third study identified root trait differences of high grain-K, -Fe, -Zn and -Mo genotypes in hydroponics media, while the fourth determined root trait differences in these genotypes in sand-culture media including a 1-Naphthalene Acetic Acid (NAA) seed treatment for perturbation. The first study identified several high grain-Mo genotypes with similar Mo accumulation patterns in V4 to V6 stage-leaves as in grains, suggestive of a root influence. The second study established that gross morphological and physiological root traits of a high grain-Mo genotype were not part of an acid-tolerance mechanism. Neither the third nor fourth study identified root traits related to shoot K, Fe, Zn or Mo concentration, however positive associations of seedling vigor traits with several beneficial elements, including K, and negative associations with numerous toxic elements were established. Lack of correlation with root traits suggests other mechanisms (e.g. active uptake transporters) instead control the observed grain accumulation differences. Based on the fourth study, either direct effects of NAA on element uptake/transfer or indirect effects on soil pH and redox potential altered tissue Fe and Zn levels.

Doctor of Philosophy / Genetics - Plant Pathology / Jesse A. Poland / The global population is estimated to reach 9.1 billion by 2050. Together with climate change, insuring food security for this population presents a significant challenge to agriculture. In this context, a large number of breeding objectives must be targeted. The focus of the work presented here is to explore genomic approaches for tapping exotic germplasm for valuable alleles to increased yield, disease resistance and abiotic stress tolerance. The loss of genetic diversity in bread wheat (Triticum aestivum L.) due to bottlenecks during polyploidization, domestication and modern plant breeding can be compensated by introgressing novel exotic germplasm. Here, the potential of genomic selection (GS) for rapid introgression of synthetic derived wheat is evaluated in field trials. Overall, the GS models had moderate predictive ability. However, prediction accuracies were lower than expected likely due to complex and confounding physiological effects. As such, implementation of rapid cycle GS for introgression of exotic alleles is possible but might not perform very well with synthetic derived wheat. Disease resistance is another important trait affecting grain yield. Stem rust (Puccinia graminis f. sp. tritici) has historically caused severe yield loss of wheat worldwide. In a quantitative trait loci (QTL) mapping study with a synthetic-derived mapping population, QTLs for resistance to stem rust races TRTTF and QTHJC were identified on chromosomes 1AS, 2BS, 6AS and 6AL. Some of these genes could be new resistance genes and useful for marker-assisted selection (MAS). In addition to food insecurity through lack of sufficient source of calories, nutrient deficiency is considered the ‘hidden hunger’ and can lead to serious disorders in humans. Through biofortification, essential nutrients are increased in staple crops for improved quality of food and human health. A high-throughput elemental profiling experiment was performed with the same synthetic derived mapping population to study the wheat ionome. Twenty-seven QTL for different elements in wheat shoots and two QTL in roots were identified. Four “hotspots” for nutrient accumulation in the shoots were located on chromosomes 5AL, 5BL, 6DL and 7AL. Overall, exotic germplasm is a valuable source of favorable alleles, but improved breeding methodologies are needed to rapidly utilize this diversity.

<p> Significant portions of nitrogen (40–60%), phosphorus (80–90%) and potash (30–50%) applied in agricultural fields are not taken up by plants, causing serious issues for farmers and the environment. Fertilizer losses result in greater fertilizer input costs and the cost of fertilizer is projected to increase due to limited ore resources and increasing fossil fuel prices. Moreover, excess fertilizer application can contaminate water and air, resulting in human health problems. Leaching fertilizers also induce eutrophication, acid rain and global climate change. Therefore, developing crops with high nutrient uptake efficiency is important for economic and environmental sustainability of agriculture. Crop improvement depends on efficiency and accuracy of genotyping and phenotyping. Genotyping has improved in recent years and is generally efficient and accurate. In contrast, improvements in phenotyping lag far behind. Lack of high-throughput (efficient, accurate and inexpensive) phenotyping (HTP) methods limit the speed of genetic improvement. As a result, there is an increasing interest in development of HTP for predicting crop nutrient status. My research addresses whether hyperspectral data in the visible-near-infrared range (HS-VNIR) acquired by a handheld device or an unmanned aerial vehicle (UAV) can be used for predicting maize nutrient status. Proximal and remote sensing data coupled with ground reference measurements of hybrid maize nutrient status were collected in fertilizer strip trials conducted at Purdue Agricultural Centers located throughout Indiana. Statistical models were developed to predict nutrient status based on HS-VNIR with coefficients of determination of cross-validation [R² (CV)] used to evaluate the performance of the predictive models. Models with acceptable goodness-of-fit [R² (CV) > 0.30] were considered satisfactory. These studies demonstrated that models developed using handheld proximal sensing data performed adequately for predicting N, K, Mg, Ca, P, S, Mn, Zn and B. Similarly, models developed using UAV-based HS-VNIR could be used to predict N, K, Mg, Ca, P, S, Mn, Zn and B. Models that combine proximal and remote sensing data also performed well with predictions of N, K, Mg, Ca, P, S, Mn, Zn and B. In conclusion, handheld or UAV-based hyperspectral imaging can provide corn breeders with HTP data on the status of all macronutrients (N, K, Mg, Ca, P, S) and some micronutrients (Mn, Zn, B). Deployment of this technology may provide a valuable tool to support development of cultivars with improved nutrient uptake efficiencies.  </p>