Aranjuelo Michelena, Iker
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Aranjuelo Michelena
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Iker
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Publication 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 ElikaduraAlfalfa 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.Publication 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 GobernuaContext: 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.