.arc/validation_packages.yml

+arc_specification: 2.0.0-draft

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README.md

 # Quinoa_Chileanfieldtrial
 
+<p align="center">
+  <img src="Quinoaimage2.jpeg" width="32.8%" />
+  <img src="Quinoaimage5.jpeg" width="32.8%" /> 
+  <img src="Quinoaimage1.jpeg" width="32.8%" /> 
+</p>
 
+## Abstract
+
+This ARC represents the data described in the following publication:
+
+## Publication
+
+**Title:** Morphological and Physiological Traits Associated with Yield under Reduced Irrigation in Chilean Coastal Lowland Quinoa 
+
+**Authors:** Dumschott, K.★; Wuyts, N.★; Alfaro, C.; Castillo, D.; Fiorani✉, F.; Zurita-Silva✉\
+★These authors contributed equally to this work\
+✉ Corresponding authors
+
+**Information:** Plants 2022, 11, 323. https://doi.org/10.3390/plants11030323 
+
+**Abstract:** Quinoa (Chenopodium quinoa Willd.) is a genetically diverse crop that has gained popularity in recent years due to its high nutritional content and ability to tolerate abiotic stresses such as salinity and drought. Varieties from the coastal lowland ecotype are of particular interest due to their insensitivity to photoperiod and their potential to be cultivated in higher latitudes. We performed a field experiment in the southern Atacama Desert in Chile to investigate the responses to reduced irrigation of nine previously selected coastal lowland self-pollinated (CLS) lines and the commercial cultivar Regalona. We found that several lines exhibited a yield and seed size superior to Regalona, also under reduced irrigation. Plant productivity data were analyzed together with morphological and physiological traits measured at the visible inflorescence stage to estimate the contribution of these traits to differences between the CLS lines and Regalona under full and reduced irrigation. We applied proximal sensing methods and found that thermal imaging provided a promising means to estimate variation in plant water use relating to yield, whereas hyperspectral imaging separated lines in a different way, potentially related to photosynthesis as well as water use.
+
+**Keywords:** Chenopodium quinoa Willd.; field trial; hyperspectral imaging; phenotyping; quinoa; reduced irrigation; thermal imaging; yield
+
+## Investigators
+
+- **Kathryn Dumschott** (Institute of Bio- and Geosciences, Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany; Institute for Biology I, BioSC, RWTH Aachen University, 52056 Aachen, Germany)
+- **Nathalie Wuyts** (Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)
+- **Christian Alfaro** (Centro de Investigación Intihuasi (AZS), Instituto de Investigaciones Agropecuarias, La Serena 1722093, Chile; Centro de Investigación Rayentué (CA), Instituto de Investigaciones Agropecuarias, Rengo 2940000, Chile;  Centro de Investigación Quilamapu, Instituto de Investigaciones Agropecuarias, Chillán 3780000, Chile)
+- **Dalma Castillo** (Centro de Investigación Intihuasi (AZS), Instituto de Investigaciones Agropecuarias, La Serena 1722093, Chile; Centro de Investigación Rayentué (CA), Instituto de Investigaciones Agropecuarias, Rengo 2940000, Chile;  Centro de Investigación Quilamapu, Instituto de Investigaciones Agropecuarias, Chillán 3780000, Chile)
+- **Fabio Fiorani** (Institute of Bio- and Geosciences, Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany)
+- **Andrés Zurita-Silva** (Centro de Investigación Intihuasi (AZS), Instituto de Investigaciones Agropecuarias, La Serena 1722093, Chile; Centro de Investigación Rayentué (CA), Instituto de Investigaciones Agropecuarias, Rengo 2940000, Chile;  Centro de Investigación Quilamapu, Instituto de Investigaciones Agropecuarias, Chillán 3780000, Chile)****
+
+## Funding 
+
+This trial was conducted as part of the BMBF funded project "QUINOA DIVERSITY- Quinoa Phenotypic and Genotypic Diversity for Yield and Composition"
+https://www.kooperation-international.de/laender/amerika/chile/projekte-chile/detail-laendereinstiegsseite/info/biooekonomie-international-2015-quinoa-diversity-untersuchungen-zur-phaenotypischen-und-genotypisch-5
 
 ## Project Description
 
 Responses to full and reduced irrigation were investigated for nine coastal lowland self-pollinated quinoa lines (CLS-1 to CLS-9) and one commercial cultivar cv Regalona (REGA) in the field. The trial took place at the INIA-Huasco experimental center located in the southern Atacama Desert (Vallenar, Chile, 28°34′ S, 70°47′ W and 469 m.a.s.l.) during the 2019/2020 growing season (sowing on 26 September 2019, harvest on 21 February 2020).
 
-![Quinoaplots](quinoa_trialplots.png)
+<p align="center" width="100%">
+     <img width=70% src="studies/ExperimentalSetup/quinoa_trialplots.png">
+</p>
 
-#### This trial was conducted as part of the BMBF funded project "QUINOA DIVERSITY- Quinoa Phenotypic and Genotypic Diversity for Yield and Composition"
-https://www.kooperation-international.de/laender/amerika/chile/projekte-chile/detail-laendereinstiegsseite/info/biooekonomie-international-2015-quinoa-diversity-untersuchungen-zur-phaenotypischen-und-genotypisch-5
+## Data Overview
 
-## Publication
+Data included:
+
+- Agronomic traits at harvest
+- Imaging data
+     - Thermal infrared imaging
+     - Hyperspectral imaging
+- Chlorophyll fluorescence
+- Phenology
+- Relative water content
+- Weather Data
+
+## ARC Structure
+
+This ARC is organized as follows:
+
+* **Study:** Description of the field trial, including genotypes used, soil characteristics, and Experimental design.
+* **Assays:** Each assay represents a specific technical analysis performed to characterize quinoa responses within the trial.
 
-Title: Morphological and Physiological Traits Associated with Yield under Reduced Irrigation in Chilean Coastal Lowland Quinoa 
+### Study
 
-Authors: Dumschott, K.★; Wuyts, N.★; Alfaro, C.; Castillo, D.; Fiorani, F.; Zurita-Silva
-★These authors contributed equally to this work
+- ExperimentalSetup [README](https://git.nfdi4plants.org/usadellab/quinoa_chileanfieldtrial/-/tree/main/studies?ref_type=heads)
 
-Information: Plants 2022, 11, 323. https://doi.org/10.3390/plants11030323 
 
-Abstract: Quinoa (Chenopodium quinoa Willd.) is a genetically diverse crop that has gained popularity in recent years due to its high nutritional content and ability to tolerate abiotic stresses such as salinity and drought. Varieties from the coastal lowland ecotype are of particular interest due to their insensitivity to photoperiod and their potential to be cultivated in higher latitudes. We performed a field experiment in the southern Atacama Desert in Chile to investigate the responses to reduced irrigation of nine previously selected coastal lowland self-pollinated (CLS) lines and the commercial cultivar Regalona. We found that several lines exhibited a yield and seed size superior to Regalona, also under reduced irrigation. Plant productivity data were analyzed together with morphological and physiological traits measured at the visible inflorescence stage to estimate the contribution of these traits to differences between the CLS lines and Regalona under full and reduced irrigation. We applied proximal sensing methods and found that thermal imaging provided a promising means to estimate variation in plant water use relating to yield, whereas hyperspectral imaging separated lines in a different way, potentially related to photosynthesis as well as water use.
 
-Keywords: Chenopodium quinoa Willd.; field trial; hyperspectral imaging; phenotyping; quinoa; reduced irrigation; thermal imaging; yield

assays/Chlorophyll_Fluorescence/README.md

+# Chlorophyll fluorescence
+
+## Assay Description and Instrumentation
+
+Fluorescence data was collected using a MINI-PAM-II fluorometer (Walz Heinz GmbH, Effeltrich,
+Germany). 
+
+
+<p float="left">
+  <img src="Quinoaimage4.jpeg" width="40%" />
+</p>
+
+### Protocol
+
+- 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.**
+
+### Calibration and Quality Control
+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.
+
+### Data Processing
+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.
+
+### Software and tools
+
+-  WinControl-3 Software (version 3.29-rev.1112) (Walz Heinz GmbH, Effeltrich, Germany)
+- R (with package 'cluster') [1]
+
+### Assay Date
+
+This assay was performed at the visible inflorescence stage of development (BBCH 59); 46-50 days after sowing
+
+#### 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).
\ No newline at end of file

assays/Harvesting/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).
+