Open Access
Onfield estimation of quality parameters in alfalfa through hyperspectral spectrometer data
(Elsevier, 2024) Gámez Guzmán, Angie Lorena; Vatter, Thomas; Santesteban García, Gonzaga; Araus, José Luis; Aranjuelo Michelena, Iker; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura
Alfalfa is a forage of vast importance around the world. In the past, near-infrared spectroscopy (NIRS) technique have been explored in the lab to determine quality traits such as fibre content in dried and ground material. During the last decade, portable hyperspectral devices have emerged as a tools for in-field prediction, of not only crop yield but also a large range of quality and physiological markers. The objective of this study was to estimate quality parameters in an alfalfa crop using hyperspectral data acquired from a full-range (350–2500 nm) spectrometer under field conditions. Reflected spectra were measured in single leaves as well as at the canopy level, then reflectance was related to target parameters such as biomass, leaf pigments, sugars, protein, and mineral contents. Due to their large effect on crop quality parameters, meteorological conditions and phenological stages were included as predictors in the models. We found that meteorological and phenological variables improved the accuracies and percentage of variance explained (R2) for most of the parameters evaluated. Based on R2 values, the best prediction models were obtained for biomass (0.71), sucrose (0.65), flavonoids (Flav) (0.56) and nitrogen (0.70) with normalized root mean squared errors of 0.196, 0.32, 0.087 and 0.08, respectively. These parameters were associated mainly with visible (VIS) (approx. 350–700 nm) and near infrared (NIR) (700–1250 nm) regions of the spectrum. Regarding mineral composition, the best prediction models were developed for P (0.51), B (0.50) and Zn (0.44), associated with the short-wave infra-red (SWIR) region (1250–2500 nm). The results of this study demonstrated the potential of hyperspectral techniques to be used as a base for performing initial evaluations in the field of quality traits in alfalfa crops.
Open Access
Alfalfa yield estimation using the combination of Sentinel-2 and meteorological data
(Elsevier, 2025-03-19) Gámez Guzmán, Angie Lorena; Segarra, Joel; Vatter, Thomas; Santesteban García, Gonzaga; Araus, José Luis; Aranjuelo Michelena, Iker; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura; Institute for Multidisciplinary Research in Applied Biology - IMAB; Gobierno de Navarra / Nafarroako Gobernua
Context: Alfalfa (Medicago sativa L.) is one of the world's most important forages for livestock feeding. Timely yield estimates could provide information to guide management decisions to improve production. Since alfalfa crops typically undergo multiple harvests in a year and demonstrate rapid regrowth, satellite remote sensing techniques present a promising solution for alfalfa monitoring. Objective: To generate alfalfa yield estimation models at three phenological stages (early vegetative, late vegetative, and budding stages) using vegetation indices (VIs) derived from satellite Sentinel-2 images and their combination with meteorological data. Methods: We analyzed fields located in Navarre (northern Spain) over two consecutive seasons (2020 and 2021). To generate the yield estimation models, we applied a conventional multilinear regression and two machine learning algorithms (Least Absolute Shrinkage and Selection Operator - LASSO and Random Forest - RF). Results: Regardless of the statistical approach, the three phenological stages were not optimal when either VIs or meteorological data were used singularly as the predictor. However, the combination of VIs and meteorological data significantly improved the yield estimations, and in the case of LASSO model reached percentages of variance explained (R2) and normalized root mean square error (nRMSE) of R2= 0.61, nRMSE= 0.16 at the budding stage, but RF reached a R2= 0.44, nRMSE= 0.22 at the late vegetative stage, and R2= 0.36, nRMSE= 0.24 at the early vegetative stage. The most suitable variables identified were the minimum temperature, accumulated precipitation, the renormalized difference vegetation index (RDVI) and the normalized difference water index (NDWI). The RF model achieved more accurate yield estimations in early and late vegetative stages, but LASSO at bud stage. Conclusion: These models could be used for alfalfa yield estimations at the three phenological stages prior to harvest. The results provide an approach to remotely monitor alfalfa fields and can guide effective management strategies from the early development stages.
Open Access
Assessing the evolution of wheat grain traits during the last 166 years using archived samples
(Nature Research, 2020) Mariem, S.B.; Gámez Guzmán, Angie Lorena; Larraya Reta, Luis María; Fuertes Mendizabal, Teresa; Cañameras, Nuria; Araus, José Luis; Aranjuelo Michelena, Iker; McGrath, Steve P.; Hawkesford, Malcolm J.; González Murua, Carmen; Gaudeul, Myriam; Medina, Leopoldo; Paton, Alan; Cattivelli, Luigi; Fangmeier, Andreas; Bunce, James; Tausz-Posch, Sabine; Macdonald, Andy J.; Agronomia, Bioteknologia eta Elikadura; Institute for Multidisciplinary Research in Applied Biology - IMAB; Agronomía, Biotecnología y Alimentación
The current study focuses on yield and nutritional quality changes of wheat grain over the last 166 years. It is based on wheat grain quality analyses carried out on samples collected between 1850 and 2016. Samples were obtained from the Broadbalk Continuous Wheat Experiment (UK) and from herbaria from 16 different countries around the world. Our study showed that, together with an increase in carbohydrate content, an impoverishment of mineral composition and protein content occurred. The imbalance in carbohydrate/protein content was specially marked after the 1960’s, coinciding with strong increases in ambient [CO2] and temperature and the introduction of progressively shorter straw varieties. The implications of altered crop physiology are discussed.
Open Access
Foliar heavy metals and stable isotope (δ13C, δ15N) profiles as reliable urban pollution biomonitoring tools
(Elsevier, 2021) Soba Hidalgo, David; Gámez Guzmán, Angie Lorena; Úriz, Naroa; Ruiz de Larrinaga, Lorena; González Murua, Carmen; Becerril, José María; Esteban Terradillos, Raquel; Serret, Dolors; Araus, José Luis; Aranjuelo Michelena, Iker; Agronomía, Biotecnología y Alimentación; Agronomia, Bioteknologia eta Elikadura
Anthropogenic heavy metal pollution is an important health issue in urban areas, and therefore rapid and inexpensive monitoring in time and space is desirable. This study aimed (i) to assess the suitability of Tilia cordata leaves as a valuable heavy metal bioindicator, including seasonal changes in concentrations and (ii) to evaluate the use of leaf carbon and nitrogen isotope composition (δ13C and δ15N) as novel indicators of urban heavy metal pollution. Leaves were collected from three different pollution intensity locations (Bilbao, Vitoria, and Muskiz) in the Basque Country (northern Spain). Analysis of leaf heavy metals related to traffic emissions and δ13C and δ15N determinations were carried out during July-October 2018. Leaf samples from Bilbao, the most populated and traffic-intense location, showed the highest concentration of heavy metals (mainly from polluted air). Additionally, the two urban areas, Bilbao and Vitoria, showed stronger correlation between these heavy metals, indicating a traffic-related source of emissions. The source of contamination (soil or air) in relation to elements and optimal sampling time is discussed herein. On the other hand, Pearson correlation analysis revealed significant trends between leaf δ13C and δ15N and the studied heavy metals, especially Pb, Cr and Cd, supporting the hypothesis of δ13C and δ15N as tools to distinguish locations according to their heavy metal pollution levels. To our knowledge, this is the first time that δ13C and δ15N have been used as monitoring tools in heavy metal pollution and consequently more research is still needed to calibrate this tool through extensive vegetation screening.

Gámez Guzmán, Angie Lorena

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