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6 results for “growing location”
GPS point locations of plots, subplots, itex subplots, transects and soil sensor in the black sand extended growing season experiment, 2018 - 2023.
As a result of climate change, the Rocky Mountain Front Range is experiencing warmer summers and earlier snowmelt. Due to the importance of snow for regulating soil temperature, growing season length, and available moisture in alpine ecosystems, even small shifts in the snow-free period could have large impacts. The focus of the Black Sand Extended Growing Season Length Experiment is to examine how terrain-related differences in climate exposure influence the way alpine habitats respond to climate change via earlier snowmelt. To simulate how climate exposure may affect plant communities, NWT LTER researchers established 5 experimental sites each containing a pair 10 x 40m rectangular plots. These sites include north and south facing aspects, subalpine and alpine tundra meadows in a range of hydrological conditions (e.g. dry meadows, moist meadows, wet meadows). We accelerated snowmelt in one plot of each block by adding chemically inert black sand, while keeping the second plot as an unmanipulated control; black sand was added to these plots after snow had naturally melted. This dataset includes geolocations of individual subplots and sensors within the experiment, measured in summer 2023.
Agronomic, rheological and nutritional phenotypic data of 50 spelt varieties grown at 3 locations in Switzerland during 2 growing seasons (2021-2022)
<p>This dataset contains agronomic, rheological, and nutritional parameters of 50 winter spelt varieties tested during 2 growing seasons (2021-2022) at 3 locations in Switzerland. The dataset has been used to investigate the links between genotype and phenotype of spelt varieties, published in https://doi.org/10.1007/s10681-024-03400-8.</p> <p>The field trials were performed under the Swiss Extenso (low input) conditions, conducted by Agroscope and DSP, and under organic conditions, performed by GZPK. </p> <h3>Methods </h3> <p><em>Field trials </em></p> <div>Field trials were set up over the course of two growing seasons – 2020/2021, 2021/2022 – in three sites across the Swiss Central Plateau. The experimental sites were located in Changins (46°19′ N 6°14′ E, 455m a.s.l), Delley (46°55′ N 6°58′ E, 494m a.s.l) and Feldbach (47°14'24.00" N, 8°47'9.60" E, 410m a.s.l.).</div> <div>Each variety was grown in a plot of 7.1 m<sup>2</sup> (1.5 m*4.7 m) in Changins and Delley, and 4.5 m<sup>2</sup> (1.5 m*3 m) in Feldbach. We replicated the experiment three times per location. At each site, we used a complete randomized block design, with plots being randomized within each block. Density of sowing was 180 spikelets/m<sup>2</sup>. Plots were sowed mechanically each autumn. In Changins and Delley, the plots were mechanically fertilized with 100 kg N/ha (ammonium nitrate), applied in two splits (60 at heading stage—40 at flowering stage). In Feldbach, the fields were treated organically, and therefore no synthetic fertilizer was applied.</div> <div> </div> <div> </div> <div><em>Agronomic and morphological characteristics </em></div> <div> <p>For each plot, we recorded the heading date as the day of the year, in which 50% of the ears of the plot had fully emerged from the flag leaf. Once the plants and ears were fully developed, plant height was measured in each plot, by taking the average height in centimeters from the ground to the top of five random ears, excluding awns.</p> <p>At maturity, we harvested each plot with a combine harvester (Zürn 150, Schontal-Westernhausen, Switzerland). The harvested grains were weighed first, dehusked, sorted and cleaned with a sieve cleaner, and then weighted again. We measured specific weight and water content using a Dickey–John machine (GAC 2100). Grain yield was subsequently standardized to 15% of humidity. Protein content (%) was measured at the plot level with a near-infrared instrument (ProxiMate™, Büchi instruments). Thousand kernel weight (TKW, g), as well as kernel length and width (mm), were measured at the plot level with a Marvin seed analyzer (GTA Sensorik, Neubrandenburg, Germany).</p> <p>Additional measurements in Changins: we computed harvest index for each plot by cutting 30 individual culms just before harvest. Plants were cut just above the ground, oven-dried for 3 days at 80 °C and then weighed. We then threshed, dehusked, sieved and weighed the obtained grains. The harvest index was computed by taking the ratio of grain mass over total mass.</p> <p> </p> <p><em>Rheological characteristics </em></p> </div> <div> <p>At all sites, Zeleny sedimentation value (mL) was assessed based on the International Association for Cereal Science and Technology standard method 116/1.The analyses were performed by the analytical laboratory of DSP, Delley, at the variety level for each site—i.e., grains from the three replicates per site were pooled together and subsequently milled.</p> <p>Additional measurements in Changins were done for each variety, based on a pooled sample of the three replicates. Extensograph properties of the obtained dough were assessed according to ICC standard method 114/1; area under curve (energy, cm2), resistance to extension at 5 cm extension (EE), and extensibility of the dough (mm) were measured. The analyses were performed by the accredited laboratory “Versuchsanstalt für Getreideverarbeitung” based in Austria (<a href="https://www.vfg.or.at/">https://www.vfg.or.at/</a>).</p> <p> </p> <p><em>Nutritional characteristics </em></p> </div> <div> </div> <div>We assessed the structure of starch (amylose content) and the fatty acid composition for each variety in Changins. These analyses were done by pooling grains from the three replicates in Changins and milling them. The amylose and amylopectin contents of starch were determined enzymatically via an assay based on the precipitation of amylopectin complexes with the lectin concanavalin A, according to K-Amy 06/18. The fatty acid composition was analyzed by GC-FAME, via in situ transesterification, according to the method of Ampuero Kragten et al. (<a title="Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330&ndash;337" href="https://link.springer.com/article/10.1007/s10681-024-03400-8#ref-CR36">2014</a>). These analyses were performed at the accredited analytical laboratory of Agroscope, Posieux.</div> <div> </div> <div>Kragten SA, Collomb M, Dubois S, Stoll P (2014) Determination of fatty acid composition in feed: analytical methods. Agrarforschung Schweiz 5(9):330–337</div> <div> </div> <div> </div> <div><em>DNA extraction & Genotyping </em></div> <div> </div> <div>DNA was extracted from all cultivars, and sent to TraitGenetics (SGS institute Frenius, Gatersleben DE) for SNP genotyping on the 25 K XT Infinium array for wheat.</div> <div> </div> <div> </div>
Quantitative assessment of trace and macro element compositions of Cassava (Manihot esculenta) storage roots enriched with Β-Carotene as influenced by genotypes and growing locations
Cassava's important mineral contents depends on some factors, including genetic and growing locational factors. The study aimed to evaluate the influence of genotype and growing locations on the mineral concentrations in yellow-fleshed cassava root genotypes. Twenty-five pipeline yellow-fleshed cassava genotypes and three white-fleshed varieties (check samples) were planted at five different experimental fields for two seasons, each representing the major agroecological zones in Nigeria. Standard laboratory protocols were employed in the sampling to ensure zero contamination, and the trace and macro elements were determined using the inductively coupled plasma optical emission spectroscopic method (ICPOES). The trace and macro elements identified in all the genotypes and varieties investigated were Fe, Mn, B, Cu, Mo, Co, Ni, Zn, and Al; Ca, Mg, Na, K. P, and S respectively. Genotype and growing location had a highly significant (p < 0.05) effect on all the trace elements except Ti and Cr. However, there was no interactive effect between genotype and growing location on all the trace elements except for Pb and Zn. Among the explanatory variables, the variable growing location was the most influential on macro and trace elements. Conclusively, genotypes 01/1442 and 01/1273 have outstanding trace and macro element concentrations.
Quantitative assessment of trace and macro element compositions of Cassava (Manihot esculenta) storage roots enriched with Β-Carotene as influenced by genotypes and growing locations
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Data from: Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity and synchronization in growing cells
Oscillations in pollen tubes have been reported for many cellular processes, including growth, extracellular ion fluxes, and cytosolic ion concentrations. However, there is a shortage of quantitative methods to measure and characterize the different dynamic regimes observed. Herein, a suite of open-source computational methods and original algorithms were integrated into an automated analysis pipeline that we employed to characterize specific oscillatory signatures in pollen tubes of Arabidopsis thaliana (Col-0). Importantly, it enabled us to detect and quantify a Ca2+ spiking behaviour upon growth arrest and synchronized oscillations involving growth, extracellular H+ fluxes, and cytosolic Ca2+, providing the basis for novel hypotheses. Our computational approach includes a new tip detection method with subpixel resolution using linear regression, showing improved ability to detect oscillations when compared to currently available methods. We named this data analysis pipeline 'Computational Heuristics for Understanding Kymographs and aNalysis of Oscillations Relying on Regression and Improved Statistics', or CHUKNORRIS. It can integrate diverse data types (imaging, electrophysiology), extract quantitative and time-explicit estimates of oscillatory characteristics from isolated time series (period and amplitude) or pairs (phase relationships and delays), and evaluate their synchronization state. Here, its performance is tested with ratiometric and single channel kymographs, ion flux data, and growth rate analysis.
Data from: Oscillatory signatures underlie growth regimes in Arabidopsis pollen tubes: computational methods to estimate tip location, periodicity and synchronization in growing cells
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