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
Leaf δ15N as a physiological indicator of the responsiveness of N2-fixing alfalfa plants to elevated CO2, temperature and low water availability
(Frontiers Media, 2015) Ariz Arnedo, Idoia; Cruz, Cristina; Neves, Tomé; Irigoyen, Juan J.; García Olaverri, Carmen; Nogués, Salvador; Aparicio Tejo, Pedro María; Aranjuelo Michelena, Iker; Estatistika eta Ikerketa Operatiboa; Natura Ingurunearen Zientziak; Estadística e Investigación Operativa; Ciencias del Medio Natural; IdAB. Instituto de Agrobiotecnología / Agrobioteknologiako Institutua; Gobierno de Navarra / Nafarroako Gobernua
The natural 15N/14N isotope composition (δ15N) of a tissue is a consequence of its N source and N physiological mechanisms in response to the environment. It could potentially be used as a tracer of N metabolism in plants under changing environmental conditions, where primary N metabolism may be complex, and losses and gains of N fluctuate over time. In order to test the utility of δ15N as an indicator of plant N status in N2-fixing plants grown under various environmental conditions, alfalfa (Medicago sativa L.) plants were subjected to distinct conditions of [CO2] (400 vs. 700 μmol mol−1), temperature (ambient vs. ambient +4°C) and water availability (fully watered vs. water deficiency—WD). As expected, increased [CO2] and temperature stimulated photosynthetic rates and plant growth, whereas these parameters were negatively affected by WD. The determination of δ15N in leaves, stems, roots, and nodules showed that leaves were the most representative organs of the plant response to increased [CO2] and WD. Depletion of heavier N isotopes in plants grown under higher [CO2] and WD conditions reflected decreased transpiration rates, but could also be related to a higher N demand in leaves, as suggested by the decreased leaf N and total soluble protein (TSP) contents detected at 700 μmol mol−1 [CO2] and WD conditions. In summary, leaf δ15N provides relevant information integrating parameters which condition plant responsiveness (e.g., photosynthesis, TSP, N demand, and water transpiration) to environmental conditions.
Open Access
Physiological, hormonal and metabolic responses of two alfalfa cultivars with contrasting responses to drought
(MDPI, 2019) Soba Hidalgo, David; Zhou, Bangwei; Arrese-Igor Sánchez, César; Munné Bosch, Sergi; Aranjuelo Michelena, Iker; Zientziak; Institute for Multidisciplinary Research in Applied Biology - IMAB; Ciencias; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
Alfalfa (Medicago sativa L.) is frequently constrained by environmental conditions such as drought. Within this context, it is crucial to identify the physiological and metabolic traits conferring a better performance under stressful conditions. In the current study, two alfalfa cultivars (San Isidro and Zhong Mu) with different physiological strategies were selected and subjected to water limitation conditions. Together with the physiological analyses, we proceeded to characterize the isotopic, hormone, and metabolic profiles of the different plants. According to physiological and isotopic data, Zhong Mu has a water-saver strategy, reducing water lost by closing its stomata but fixing less carbon by photosynthesis, and therefore limiting its growth under water-stressed conditions. In contrast, San Isidro has enhanced root growth to replace the water lost through transpiration due to its more open stomata, thus maintaining its biomass. Zhong Mu nodules were less able to maintain nodule N2 fixing activity (matching plant nitrogen (N) demand). Our data suggest that this cultivar-specific performance is linked to Asn accumulation and its consequent N-feedback nitrogenase inhibition. Additionally, we observed a hormonal reorchestration in both cultivars under drought. Therefore, our results showed an intra-specific response to drought at physiological and metabolic levels in the two alfalfa cultivars studied.
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.

Aranjuelo Michelena, Iker

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