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Browsing by Author "Westra, Phil, committee member"

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    Candidate gene identification for glyphosate resistance and rapid cell death in Ambrosia trifida
    (Colorado State University. Libraries, 2024) Sparks, Crystal Devona, author; Gaines, Todd, advisor; Dayan, Franck, committee member; Beffa, Roland, committee member; Nishimura, Marc, committee member; Westra, Phil, committee member
    Glyphosate is one of the most widely used herbicides worldwide due to favorable chemical characteristics and availability of compatible transgenic biotechnology in crops. Resistance to glyphosate has evolved in many weed species capable of significant yield reduction in top production systems globally. One such species is Ambrosia trifida (giant ragweed), a monoecious broadleaf with imperfect flowers native to North America where it is highly competitive in corn, soybean, and cotton production. Some glyphosate resistant populations of A. trifida also display a rapid response with cell death in the mature leaves within 24-48 hours after treatment with glyphosate. Transcriptomic analysis revealed differential expression of multiple gene families associated with known glyphosate resistance mechanisms such as ATP-binding cassette (ABC) transporters and aldo-keto reductases. Gene ontology analysis showed an enrichment of many genes related to phytohormone response to biotic and abiotic stress in the differentially expressed genes. This could be related to a novel glyphosate resistance mechanism or a signaling cascade involved in the rapid cell death response. The A. trifida genome contains two loci of the glyphosate target site gene 5-enolpyruvylshikimate-3-phosphate-synthase (EPSPS), with a previously reported Pro106Ser mutation in EPSPS2. This locus showed up-regulation by three hours after treatment. Trait mapping revealed three genomic regions associated with glyphosate resistance and a single interval associated with the rapid response. Along with phenotypic segregation ratios, this indicates that resistance and rapid response traits are genetically independent and multiple genes likely contribute to resistance.
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    Remote sensing and apparent electrical conductivity to characterize soil water content
    (Colorado State University. Libraries, 2016) de Lara, Alfonso, author; Khosla, Raj, advisor; Longchamps, Louis, committee member; Westra, Phil, committee member
    Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency while maintaining or increasing grain yield. As part of site-specific farming, PI needs to be explored, tested, and evaluated which continues to be a research issue. Neutron probes (NPs) have consistently been used for studies as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related with root water uptake. Crop yield has not been evaluated on a basis of SWC as explained by NPs in time and at different depths. One among many challenges in implementing PI is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. However, the accuracy of this method has not been properly studied at the field scale. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC (3) to assess the potential of ECa measurement to characterize spatial distribution of SWC at field scale, and (4) to determine whether soil properties coupled with ECa could further improve the characterization of the SWC. For objectives 1 and 2, the study was conducted on maize (Zea Mays L.) irrigated in two fields in northern Colorado. Soil water data was collected at five soil depths (30, 60, 90, 120 and 150 cm), 21 and 12 times at Site I and II, respectively. Three vegetation indices were calculated on seven dates (Emergence to R3). Maize grain yield was harvested at the physiological maturity at each NPs location. Automated model selection of SWC readings to assess maize yield consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). For objectives 3 and 4, the study was conducted on two fields located in northeastern Colorado. In-field SWC was measured using neutron probes at 41 and 31 locations for Site I and II respectively. Soil ECa measurements were acquired using Geonics EM38-MK2 unit. In addition, cation exchange capacity, clay, organic matter and salt content were coupled with soil ECa to estimate SWC. Data analysis was performed using the statistical software R. Statistical correlations and multiple linear regressions were obtained from the properties that were statistically significant (p value < 0.05). Results of the study showed that the SWC readings at the 90 cm depth had the highest correlations with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status right before the reproductive growth stage (V9). Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management. Likewise, the SWC was found to be statistically different across ECa derived zones, indicating that ECa was able to accurately characterize average differences in SWC across management zones. Organic matter and salt content significantly improved the SWC assessment when combined with the ECa. The development of prescription maps for variable rate irrigation should be tailor made depending on the specific field characteristics influencing SWC.