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Soba Hidalgo, David

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Soba Hidalgo

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David

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2020 - 20222
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    PublicationOpen Access
    Photosynthetic metabolism under stressful growth conditions as a bases for crop breeding and yield improvement
    (MDPI, 2020) Morales Iribas, Fermín; Ancín Rípodas, María; Fakhet, Dorra; González Torralba, Jon; Gámez Guzmán, Angie Lorena; Seminario Huárriz, Amaia; Soba Hidalgo, David; Ben Mariem, Sinda; Garriga, Miguel; Aranjuelo Michelena, Iker; Agronomia, Bioteknologia eta Elikadura; Institute for Multidisciplinary Research in Applied Biology - IMAB; Agronomía, Biotecnología y Alimentación
    Increased periods of water shortage and higher temperatures, together with a reduction in nutrient availability, have been proposed as major factors that negatively impact plant development. Photosynthetic CO2 assimilation is the basis of crop production for animal and human food, and for this reason, it has been selected as a primary target for crop phenotyping/breeding studies. Within this context, knowledge of the mechanisms involved in the response and acclimation of photosynthetic CO2 assimilation to multiple changing environmental conditions (including nutrients, water availability, and rising temperature) is a matter of great concern for the understanding of plant behavior under stress conditions, and for the development of new strategies and tools for enhancing plant growth in the future. The current review aims to analyze, from a multi-perspective approach (ranging across breeding, gas exchange, genomics, etc.) the impact of changing environmental conditions on the performance of the photosynthetic apparatus and, consequently, plant growth.
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    PublicationOpen Access
    Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models
    (Springer, 2022) Buchaillot, María Luisa; Soba Hidalgo, David; Shu, Tianchu; Liu, Juan; Aranjuelo Michelena, Iker; Araus, José Luis; Runion, G. Brett; Prior, Stephen A.; Kefauver, Shawn C.; Sanz Saez, Álvaro; IdAB. Instituto de Agrobiotecnología / Agrobioteknologiako Institutua
    One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.