Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

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Given its high nutritional value and capacity to grow in harsh environments, quinoa has significant potential to address a range of food security concerns. Monitoring the development of phenotypic traits during field trials can provide insights into the varieties best suited to specific environmental conditions and management strategies. Unmanned aerial vehicles (UAVs) provide a promising means for phenotyping and offer the potential for new insights into relative plant performance. During a field trial exploring 141 quinoa accessions, a UAV-based multispectral camera was deployed to retrieve leaf area index (LAI) and SPAD-based chlorophyll across 378 control and 378 saline-irrigated plots using a random forest regression approach based on both individual spectral bands and 25 different vegetation indices (VIs) derived from the multispectral imagery. Results show that most VIs had stronger correlation with the LAI and SPAD-based chlorophyll measurements than individual bands. VIs including the red-edge band had high importance in SPAD-based chlorophyll predictions, while VIs including the near infrared band (but not the red-edge band) improved LAI prediction models. When applied to individual treatments (i.e. control or saline), the models trained using all data (i.e. both control and saline data) achieved high mapping accuracies for LAI (R2 = 0.977–0.980, RMSE = 0.119–0.167) and SPAD-based chlorophyll (R2 = 0.983–0.986, RMSE = 2.535–2.861). Overall, the study demonstrated that UAV-based remote sensing is not only useful for retrieving important phenotypic traits of quinoa, but that machine learning models trained on all available measurements can provide robust predictions for abiotic stress experiments.

Increases in seawater temperature and reduction in light quality have emerged as some of the most important threats to marine coastal communities including seagrass ecosystems. Temperate seagrasses, including Zostera marina, typically have pronounced seasonal cycles which modulate seagrass growth, physiology and reproductive effort. These marked temporal patterns can affect experimental seagrass responses to climate change effects depending on the seasons of the year in which the experiments are conducted. This study aimed at evaluating how seasonal acclimatization modulates productivity and biochemical responses of Zostera marina to experimental warming and irradiance reduction. Seagrass shoots were exposed to different temperatures (6, 12, 16, 20, and 24°C), combined with high (180 μmol photons m–2 s–1) and low (60 μmol photons m–2 s–1) light conditions across four seasons (spring: April, summer: July, and autumn: November 2015, and winter: January 2016). Plants exhibited similar temperature growth rates between 16 and 20°C; at 24°C, a drastic reduction in growth was observed; this was more accentuated in colder months and under low irradiance conditions. Higher leaf growth rates occurred in winter while the largest rhizomes were reached in experiments conducted in spring and summer. Increases in temperature induced a significant reduction in polyunsaturated fatty acids (PUFA), particularly omega-3 (n-3 PUFA). Our results highlight that temperate seagrass populations currently living under temperature limitation will be favored by future increases in sea surface temperature in terms of leaf and rhizome productivity. Together with results from this study on Z. marina from a temperate region, a wider review of the reported impacts of experimental warming indicates the likely reduction in some compounds of nutritional importance for higher trophic levels in seagrass leaves. Our results further demonstrate that data derived from laboratory-based studies investigating environmental stress on seagrass growth and acclimation, and their subsequent interpretation, are strongly influenced by seasonality and in situ conditions that precede any experimental exposure.

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