.arc/validation_packages.yml

+arc_specification: 2.0.0-draft

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assays/Harvest_Data/README.md

+#### Harvest and yield data
+
+Plots were harvested during the senescence period once genotypes reached physiological maturity- when seeds from the main panicle became hard and resistant to pressure (corresponding to a seed moisture content of about 20%). 
+
+
+A 1 m2 area consisting of two central rows was manually harvested in each plot. 
+- The plant number and height for the harvested area was recorded.
+- Plant yield was determined as the total seed weight in one linear meter of two central rows per plot. 
+
+>**Yield data are shown as the productivity in t ha−1 at a standard seed moisture content of 20%.** 
+
+- The total seed weight was divided by the number of plants sampled to obtain the seed weight per plant. 
+- The final height was measured for the same plants. 
+
+**Seed metrics:**
+
+- 1000 seed weight was determined using a seed counting machine (S-JR, DATA Technologies, Kibbutz Tzora, Israel).
+
+- The proportional weight of a 10 g seed sample retained in sieves of different mesh openings (1.7, 1.4 and 1.18 mm) after 3 min of agitation at 65 rpm. 
+
+**Panicle metrics:** 
+
+- 50 complete, intact panicles of the central plot rows were imaged with a digital RGB camera (Nikon D3100, 16–55 mm lens)
+    -  Panicle length and maximum width in the images were measured using ImageJ [1]. A ruler was included in each image for calibration purposes. 
+    
+- The drought tolerance index (DTI) and yield tolerance index (YTI) were calculated for each CLS genotype and cv Regalona as described in Ober et al. and del Pozo et al. [2,3].
+
+References
+
+[1] Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH image to imagej: 25 years of image analysis. Nat. Meth. 2012, 9, 671–675. \
+[2] Ober, E.S.; Clark, C.J.A.; Bloa, M.L.; Royal, A.; Jaggard, K.W.; Pidgeon, J.D. Assessing the genetic resources to improve drought tolerance in sugar beet: Agronomic traits of diverse genotypes under droughted and irrigated conditions. Field Crop. Res. 2004, 90, 213–234.\
+[3] del Pozo, A.; Yáñez, A.; Matus, I.A.; Tapia, G.; Castillo, D.; Sanchez-Jardón, L.; Araus, J.L. Physiological traits associated with wheat yield potential and performance under water-stress in a mediterranean environment. Front. Plant Sci. 2016, 7, 987.
\ No newline at end of file

assays/Imaging/README.md

+## Imaging
+
+
+#### Thermal Infrared Imaging
+Thermal infrared imaging data were acquired using a CAT S60 smartphone equipped with a FLIR Lepton longwave infrared micro thermal camera module (https://www.catphones.com/ (accessed on 5 November 2019)). 
+    
+- Images had a resolution of 320 × 480 pixels. 
+
+- Two images per plot were taken to cover the complete plot surface. 
+
+- Images overlapped at the center of the plot, where a fastened piece of crumpled aluminum foil (40 cm<sup>2</sup>) and a leaf covered with petroleum jelly used as a dry reference could be included in both images. 
+
+>###### Sampling time
+>- Images were acquired at the visible inflorescence stage of development for all plots in both the FI and RI treatments in the afternoon of 46 and 47 days after sowing.
+>- Images were acquired in the afternoons of 48, 49 and 50 days after sowing for all plots of blocks 2 and 5, 1 and 4, and 3 and 6, respectively. 
+>- At 47 days after sowing, all plots were also imaged in the late morning when the leaves were still wet with morning dew. 
+
+
+
+- The derived temperature data were used as a wet reference. 
+
+- Images were processed in R using the ‘Thermimage’ package [1]. 
+
+- Settings for air temperature and relative humidity at the time of imaging were obtained from the collected environment data. 
+
+- Emissivity was set to 1 in the conversion of image data for measuring the mean temperature of the crumpled aluminum foil, which represented the reflected temperature. This was then applied together with an emissivity value of 0.96 to obtain the leaf temperature data. 
+
+- An ImageJ macro was used to semi-automatically determine regions of interest (ROIs) in the images and corresponding temperature data for the piece of aluminum foil, the dry reference leaf and patches of sunlit, exposed soil. The temperature data of these ROIs were then excluded from the image to obtain the mean leaf temperature data. 
+
+
+- Thermal index 1 (TI1) was calculated as follows:
+
+
+$$TI1 = dTwet.m − dTm$$
+
+
+The dTwet.m is the mean of the difference between the temperature of the wet leaves per plot at 47 days after sowing and the ambient air temperature at the time of imaging. The dTm is the difference between the mean leaf temperature per plot and the ambient air temperature at the time of imaging [2,3].
+
+References:\
+[1] Tattersall, G.J. Thermimage: Thermal Image Analysis. Available online: https://CRAN.R-project.org/package=Thermimage \
+[2] Maes, W.H.; Steppe, K. Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: A review. J. Exp. Bot. 2012, 63, 4671–4712.\
+[3] Perich, G.; Hund, A.; Anderegg, J.; Roth, L.; Boer, M.P.; Walter, A.; Liebisch, F.; Aasen, H. Assessment of multi-image unmanned aerial vehicle based high-throughput field phenotyping of canopy temperature. Front. Plant Sci. 2020, 11, 150.
+
+
+
+#### Hyperspectral Imaging
+
+Hyperspectral image data were acquired using a Specim IQ (Specim Ltd., Oulu, Finland), a handheld push broom camera system with integrated operating system and controls [1]. The Specim IQ measures reflectance in the visible and near-infrared, i.e., from 400 to 1000 nm, with a spectral resolution (FWHM) of 7 nm, 204 spectral bands, and a spatial resolution of 512 × 512 pixels2. The camera was mounted on a tripod at a height that allowed a complete individual plot to be captured in the image. 
+
+>###### Sampling time
+>- The plots of blocks 1 and 4, and 2 and 5 were imaged at 49 and 48 DAS, respectively, between 15:00 and 16:00. 
+>- The plots of blocks 3 and 6 were imaged at 49 DAS between 16:00 and 17:00. 
+
+>###### Repetitions
+>As blocks represented differences in the date and time of imaging, they were referred to as repetitions:
+>- Blocks 1 and 4 assigned to repetition 1, 
+>- Blocks 2 and 5 to repetition 2, and 
+>- Blocks 3 and 6 to repetition 3. 
+
+
+- Plots were always captured in treatment pairs (RI after FI or vice versa). 
+
+- Each dataset contained a white reference tile, which was imaged simultaneously for data calibration. A dark reference, representing sensor noise without incoming light, was recorded automatically before each capture.
+
+
+- Upon image data acquisition, the Specim IQ integrated software allows for the selection of the white reference tile in the image based on its high reflectance values, in addition to automated calibration to obtain relative reflectance data. However, we noted that the white reference tile itself was not selected alone in some images, as other elements with high reflectance were present, such as pieces of crumpled aluminum foil (used for the measurement of stem water potential and thermal imaging). The calibration procedure was therefore redone in R after threshold-based selection of the white reference tile pixels using an ImageJ macro. All other hyperspectral data processing and analysis steps were also executed in R.
+
+
+- Plant pixels were segmented from the background using the Normalized Difference Vegetation Index (NDVI, Table S1) and a threshold level, which also excluded inflorescences and specular reflection. Shaded background and shaded plant parts with low reflectance were removed using a threshold in near-infrared (838 nm) and green (554 nm) wavelengths, respectively. Spectra were smoothed on the pixel level using the Savitzky–Golay smoothing filter [2] with a third order polynomial and a window size of 11 using the R package ‘prospectr’ [3].
+
+- A total of 41 published vegetation indices (VIs, Table S1) were calculated. By means of a cluster analysis, genotypes were grouped based on the similarity of VI data within the FI and RI treatment. Agglomerative hierarchical clustering was applied on the scaled VI mean observations for repetitions 1 to 3 using the ‘agnes’ function of the R package ‘cluster’ [4]. The trees were cut at five clusters. Pearson correlation coefficients were calculated to describe the linear relationships between traits measured at the visible inflorescence stage, plant morphology and performance traits measured at harvest, and VIs. In addition, differences in relative reflectance between genotypes and treatments, independent of VIs, were analyzed for a selection of wavelengths and wavelength bands. A selection was used because of the high degree of correlation or collinearity in the relative reflectance of mostly adjacent wavelengths. A Pearson correlation coefficient was calculated between the relative reflectance of all wavelengths. A threshold of 0.8 was then applied to split up the wavelength range in groups of high correlation. One wavelength was selected for further analysis per group. This yielded five wavelengths, 476 nm, 554 nm, 616 nm, 679 nm and 724 nm, in the blue, green, orange, red and red-edge regions of the spectrum, respectively. Furthermore, reflectance in the near-infrared region (NIR) was averaged and included in the selection.
+
+References:
+
+[1] Behmann, J.; Acebron, K.; Emin, D.; Bennertz, S.; Matsubara, S.; Thomas, S.; Bohnenkamp, D.; Kuska, M.T.; Jussila, J.; Salo, H.; et al. Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors 2018, 18, 441.\
+[2] Savitzky, A.; Golay, M.J.E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 1964, 36, 1627–1639.\
+[3] Stevens, A.; Ramirez-Lopez, L. Miscellaneous Functions for Processing and Sample Selection of Vis-NIR Diffuse Reflectance Data. Available online: https://github.com/l-ramirez-lopez/prospectr (accessed on 30 November 2020).\
+[4] Maechler, M.; Rousseeuw, P.; Struyf, A.; Hubert, M.; Hornik, K.; Studer, M.; Roudier, P.; Gonzalez, J.; Kozlowski, K.; Schubert, E.; et al. “Finding Groups in Data”: Cluster Analysis Extended Rousseeuw et al. Available online: https://cran.r-project.org/web/packages/cluster/cluster.pdf (accessed on 26 February 2021).
+

assays/MiniPam/README.md

+#### Chlorophyll fluorescence
+
+Fluorescence data was collected using a MINI-PAM-II fluorometer (Walz Heinz GmbH, Effeltrich,
+Germany). 
+
+**To conduct measurements:**
+
+- The leaf clip was attached to a leaf and measurements of
+photosynthetically active radiation (PAR) and the photosynthetic efficiency of photosystem II (PhiPSII) were
+recorded. 
+
+- This was repeated ~10 times per plot. 
+
+- Each FI plot was followed by its RI plot counterpart. 
+
+>**Plots of blocks 1 and 4 were measured 49 days after sowing between 14:45 and 15:45. Plots of blocks 2 and 5 were
+measured 48 days after sowing between 15:00 and 17:00. All blocks were measured 50 days after sowing
+between 10:24 and 12:49.**
+
+The fluorescence kinetics of saturation pulse analyses for measurements were inspected using WinControl-3 Software (version 3.29-rev.1112) (Walz Heinz GmbH, Effeltrich, Germany).
+Measurements that did not pass inspection were discarded from subsequent analyses.
+PhiPSII values were clustered according to genotype, treatment and PAR level using custom R scripts.
+
+**PAR was divided into four levels:**
+- low (<500 μmol photons m -2 s -1 ),\
+- mid-low (500-1000 μmol photons m -2 s -1 ),
+- mid-high (1000-1500 μmol photons m -2 s -1 ) and
+- high (>1500 μmol photons m -2 s -1 ). 
+
+Agglomerative hierarchical clustering was applied on the scaled PhiPSII means for all plots using the ‘agnes’ function of the R package ‘cluster’ [1] with a cut-off of five clusters.
+
+Reference:
+
+[1] Maechler, M.; Rousseeuw, P.; Struyf, A.; Hubert, M.; Hornik, K.; Studer, M.; Roudier, P.; Gonzalez, J.; Kozlowski, K.; Schubert, E.; et al. “Finding Groups in Data”: Cluster Analysis Extended Rousseeuw et al. Available online: https://cran.r-project.org/web/packages/cluster/cluster.pdf (accessed on 26 February 2021).
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assays/Phenology/README.md

+#### Phenology
+
+Over the course of the growing season, plots were regularly inspected and phenology was determined visually according to the extended BBCH scale [1].
+
+
+
+**BBCH table: from Phenological growth stages of quinoa (Chenopodium quinoa Willd.) based on the BBCH scale. DOI: 10.1111/aab.12358**
+| BBCH Code (two digit) | Description |
+| :---: | ------ |
+|Principal growth stage 0: germination 
+| 00 | Dry seed |
+| 01| Initiation of seed imbibition |
+|03 | Seed imbibition completed
+|05 |Radicle emergence from seed| 
+|07 |Emergence of hypocotyl |
+|08 | Hypocotyl with cotyledons growing towards soil surface||09 | Emergence of cotyledons through soil| 
+| Principal growth stage 1: leaf development|
+|10 | Cotyledons fully emerged|
+|11 | First pair of leaves visible|
+|12 | Second pair of leaves visible|
+|1. | Coding continues with the same scheme|
+|19 | Nine pair of leaves visible. If required, coding can continue following the same scheme.|
+|Principal growth stage 2: formation of side shoots|
+|20 | Visible lateral buds or expanded leaves without lateral stems| 
+|21 | One side shoot visible| 
+|22 | Two side shoots visible| 
+|2. | Coding continues with the same scheme|
+|29 | Nine side shoots visible. If required, coding can continue following the same scheme.|
+|Principal growth stage 3: stem elongation (omitted)| |
+|Principal growth stage 4: development of harvestable vegetative parts (omitted)|
+|Principal growth stage 5: inflorescence emergence| |
+|50 | Inflorescence present but still enclosed by leaves|
+|51 | Leaves surrounding inflorescence separated, inflorescence is visible from above|
+|59 | Inflorescence visible, but all the flowers are still closed|
+|Principal growth stage 6: flowering|
+|60 | Beginning of anthesis: main inflorescence flowers with first extruded anthers|
+|67 | Early end of anthesis: main inflorescence flowers with first senesced anthers|
+|69 | Complete anthesis: main inflorescence flowers with senesced anthers|
+|Principal growth stage 7: fruit development|
+|70 | Fruit set: ovary thickening and first visible grains in the main stem|
+|Principal growth stage 8: ripening| | 
+|81 | Milky grain, easily crushed with fingernails, liquid content and green pericarp|
+|85 | Thick grain, easily crushed with fingernails, white pasty content, green, beige, red or black pericarp|
+|89 | Ripe grain, difficult to crush with fingernails, dry content, the grain has a beige, red or black colour on its outside. Ready to harvest.|
+|Principal growth stage 9: senescence| |
+|91 | Only basal leaves are dry|
+|93 | Leaves of the first half portion of the plant, starting from the base, are dead|
+|95 | All leaves are dead; stem colour turns from yellow to brown|
+|97 | Plant dead and dry|
+|99 | Harvested product |
+
+
+
+Reference:
+
+[1] Sosa-Zuniga, V.; Brito, V.; Fuentes, F.; Steinfort, U. Phenological growth stages of quinoa (Chenopodium quinoa) based on the BBCH scale. Ann Appl Biol 2017, 171, 117-124. DOI: 10.1111/aab.12358
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assays/RelativeWaterContent/README.md

+#### Leaf relative water content
+
+
+- An approximately 1 cm 3 section of leaf was sampled from each plot
+and transferred to a pre-weighed Eppendorf tube. 
+
+- The tubes were then reweighed, filled with MilliQ water
+and left at 4 °C overnight. 
+
+- The following morning, the turgid leaf section was removed from the tube, carefully dried with a paper towel and reweighed. 
+
+- Samples were then placed back into the emptied
+Eppendorf tubes and placed in an oven at approximately 70 °C overnight. 
+
+- Once leaf sections were completely
+dry, the tubes containing the leaf samples were reweighed. 
+
+
+- The empty tube weight was subtracted from the
+fresh weight and dried weight measurements and relative water content (RWC, %) was calculated using the
+equation:
+
+$$RWC = \frac{fresh~weight - dry~weight}{turgid~weight - dry~weight} ~x ~100 $$ 
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