Advances in high throughput and affordable phenotyping for adapting maize and wheat to climate change
[eng] Supplying sufficient food to an increasing population is one of the most important challenges over the next century. To meet this demand, crop productivity will need to increase while it is being threatened by climate change effects like the increase of temperatures and the intensity of drough...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de Barcelona |
| Repositorio: | Dipòsit Digital de la UB |
| OAI Identifier: | oai:diposit.ub.edu:2445/183903 |
| Acceso en línea: | https://hdl.handle.net/2445/183903 http://hdl.handle.net/10803/673705 |
| Access Level: | acceso abierto |
| Palabra clave: | Fenotip Blat Blat de moro Teledetecció Drons Productivitat agrícola Phenotype Wheat Corn Remote sensing Drone aircraft Agricultural productivity |
| Sumario: | [eng] Supplying sufficient food to an increasing population is one of the most important challenges over the next century. To meet this demand, crop productivity will need to increase while it is being threatened by climate change effects like the increase of temperatures and the intensity of drought periods. Improving crop performance is key for an efficient adaptation to these challenging growing conditions, with crop breeding being one of the pillars. In that sense selecting more productive varieties for specific environments requires a better understanding of plant acclimation to stress conditions, including efficient phenotyping approaches. Plant phenotyping research pursues the development of new methods with high-throughput capacity and affordable to characterize non-destructively plant traits of interest. The main focus of this thesis was to develop and study versatile and precise methodologies with high-throughput capacity in order to improve crop performance assessments, while saving time and costs in the phenotyping tasksof two of the most important cereal crops: maize and wheat. The use of unmanned aerial vehicles (UAV) equipped with imaging sensors (including RGB, multispectral and thermal) permits covering simultaneously hectares of experimental fields fast, precisely, and in a non-destructive way. However, ground evaluations may still be an alternative in terms of cost and spatial resolution. The performance of these methodologies to assess genotypic differences in grain yield was evaluated in maize and wheat under different agronomical and environmental growing conditions such as nutrient deficiency, conservation agriculture, drought and heat stress. On one side, maize studies were performed in trials in Zimbabwe focused on the evaluation of genotypes under either low and normal phosphorus conditions or the application of conservation agriculture together with different top-dressing nitrogen fertilization regimes, to overcome the nutrient poverty of soils. In these studies, vegetation indices, related to parameters informing on the above-ground biomass and assessed during early stages of development, performed well as grain yield indicators. Moreover, during more advanced phenological stages, indices informing on the leaf and the canopy color were the traits that reported a better association with grain yield and N content in leaves. For the case of wheat, evaluations were performed in different latitudes in Spain covering a range of environments and grown under different management conditions, and sampling was performed during the reproductive stage (heading, anthesis and grain filling). In general terms, biomass indicators, such as canopy green biomass inferred from vegetation indices, together with water status indicators, such as canopy temperature, were the most critical traits predicting GY. The delay of senescence in water-limited environments and the photosynthetic efficiency measured by multispectral indices like the photochemical reflectance index (PRI) during anthesis were also relevant traits for GY under the rainfed and late-planting trials, respectively. |
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