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24 results for “cultivation conditions”
Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)
<p>This is the outcome data from our study titled "<a href="http://dx.doi.org/10.1016/j.scitotenv.2024.170481" target="_blank" rel="noopener"><em><u>Prediction of global wheat cultivation distribution under climate change and socioeconomic development</u></em></a>" which was published in The Science of The Total Environment. The present study represents a significant extension of our previous research on "<em><a href="http://dx.doi.org/10.1016/j.scitotenv.2019.06.153" target="_blank" rel="noopener">The Potential Distribution and Dynamics of Global Wheat under Multiple Climate Change Scenarios"</a></em>.</p> <p>Socioeconomic and climate change are both critical factors influencing the global distribution of crop cultivation. However, there has been limited exploration of the role of socioeconomic factors in predicting future crop cultivation distribution under climate change.</p> <p>We have proposed the MaxEnt-SPAM approach under the assumption that environmental conditions are the primary determinants of land suitability for cultivating wheat, while socioeconomic factors play a crucial role in influencing farmers' crop choices. In essence, the distribution of wheat cultivation is contingent upon maximizing potential revenue and ensuring suitability for wheat planting.</p> <p>The proposed MaxEnt-SPAM approach was utilized to estimate the distribution of wheat cultivation in three combined Representative Concentration Pathway (RCP) - Shared Socioeconomic Pathway (SSP) scenarios, namely RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3. The methodology involved estimating wheat planting suitability under future RCP scenarios using the MaxEnt model, predicting farmers' crop choices under future SSP scenarios through Time series-Backpropagation (TS-BP) models, and ultimately estimating global wheat cultivation distribution based on the SPAM model. Validation of this approach against major known datasets on the distribution of wheat cultivation demonstrated satisfactory accuracy, with a predictive accuracy exceeding 85% and a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets.</p> <p>Based on the aforementioned concept and methodology, a global wheat cultivation distribution grid (0.5 degree × 0.5 degree) was projected under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios.</p> <p>The findings suggest that RCP8.5-SSP3 may offer the most favorable conditions for wheat cultivation. Additionally, socioeconomic development significantly constrains the potential distribution of wheat cultivation, with estimated areas accounting for an average of 77% of the potential distribution determined by climatic factors under the selected RCP-SSP scenarios. Socioeconomic development appears to have a positive impact on wheat cultivation in Africa.</p> <p>Our results illustrate the influence of socioeconomic factors on crop distribution within a market economy framework, underscoring the importance of integrating socioeconomic factors and climate change for accurate predictions of crop cultivation distribution.</p> <p>We contend that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable addition to existing products. This prediction data is among the few products to consider both climate change and socio-economic development, providing a more comprehensive understanding of crop cultivation distribution dynamics.</p> <p>The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to enhance our comprehension of the dynamics and distribution of global wheat cultivation under different climate change and socio-economic development paths in the future, potentially supporting research in earth system simulation and agricultural sciences.</p> <p>The dataset for the Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) and the Maxent-SPAM approach code is stored in a zip package named SPAM_MaxEnt.zip, which contains two folders (code and data).</p> <p><strong>code: </strong></p> <p>This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information.</p> <p>The exampleData contains</p> <p>1. h_pri.tif: prior data</p> <p>2. h_res.tif: global C3 crop cultivation proportion</p> <p>Run the main programme: cross_entroy.m</p> <p><strong>data: </strong></p> <p>This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format.</p> <p><strong>1 Global distribution of the long-term wheat-</strong><strong>c</strong><strong>ultivation area fraction: </strong></p> <p>This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated.</p> <p><strong>r2s1f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario</p> <p><strong>r4s2f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario</p> <p><strong>r8s3f_sub.tif:</strong> the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario</p> <p><strong>2 </strong><strong>S</strong><strong>patial overlap between the long-term period of land suitability for wheat </strong><strong>planting </strong><strong>and wheat cultivation distribution: </strong></p> <p>This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:<strong>{1, 2, 3}</strong>, <strong>1</strong> wheat cultivation existed but was predicted to be unsuitable to plant wheat; <strong>2 </strong>presented a reduction in the wheat cultivation area compared to the land's suitability; <strong>3</strong> presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat.</p> <p><strong>com_suit_fra126.tif: </strong>the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6</p> <p><strong>com_suit_fra245.tif: </strong>the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5</p> <p><strong>com_suit_fra385.tif:</strong> the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5</p> <p><strong>3 Differences in the proportion of long-term wheat </strong><strong>c</strong><strong>ultivation: </strong></p> <p>This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability.</p> <p><strong>r2s1_f.tif:</strong> Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario</p> <p><strong>r4s2_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario</p> <p><strong>r8s3_f.tif:</strong> Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario </p> <p><strong>References:</strong></p> <p>Yaojie Yue, Puying Zhang, Yanrui Shang. The Potential Distribution and Dynamic of Global Wheat under Multiple Climate Change Scenarios. Science of the Total Environment, 2019, 688: 1308-1318.</p> <p>Xi Guo, Puying Zhang, Yaojie Yue. Prediction of global wheat cultivation distribution under climate change and socio-economic development. Science of the Total Environment, 2024, 919: 170481.</p> <p>For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014).</p> <p>Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x.</p> <p>Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003.</p> <p>You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.002</p>
Figure 3 in Influence of foliar application of glycinebetaine on Tagetes erecta L yield cultivated under salinity conditions
Figure 3. Effect of NaCl stress and glycinebetaine on total phenols content in Tagetes erecta L leaves. C = control; S1 = NaCl, 100 mM; S2 = NaCl, 150 mM; GB = Glycinebetaine, 200 mM.Values are means ± S.D (n=6). The means with the different letters are statistically different from the others based on Duncan's multiple range test at P = 0.05.
Figure 1 in Influence of foliar application of glycinebetaine on Tagetes erecta L yield cultivated under salinity conditions
Figure 1. Effect of NaCl stress and glycinebetaine on relative water content (RWC) and membrane stability index (MSI) in Tagetes erecta L leaves. C = control; S1 = NaCl, 100 mM; S2 = NaCl, 150 mM; GB = Glycinebetaine, 200 mM. Values are means ± S.D (n=6). The means with the different letters are statistically different from the others based on Duncan's multiple range test at P = 0.05.
Figure 1 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 1. Explants of a hop genotype, grown in different culture media (yellow, blue and pink). Each medium provided different development behavior for the number of nodal segments. The larger the number of nodal segments, the more new plants will be obtained.
Figure 2 in Growth regulators and their reflection on different hop genotypes cultivated under in vitro conditions
Figure 2. Estimates of the direct and indirect effects of the variables root length (RL), shoot height (SH) and number of shoots (NS) on the variable number of nodal segments (NNS) (main variable).
Cultivation of host-associated bacteria under sulfide-enriched microoxic and anoxic conditions
<p>Three images of cell colonies on culture plates inoculated with gill tissue homogenate from <em>Bathymodiolus brooksi</em>. The colonies grew under the following conditions: </p> <ul> <li>IMG_2467: microoxic, low H2S, low ammonium</li> <li>IMG_2468: microoxic, low H2S, low ammonium, organics</li> <li>IMG_2474: microoxic, low H2S, high ammonium, organics, 1 mM thiosulfate</li> </ul> <p>Image of a control plate without cultures inoculated with heat sterilized aliquot of <em>Bathymodiolus brooksi</em> gill homogenate:</p> <ul> <li>IMG_2475</li> </ul> <p>Two images of pH controls plated with homogenization medium that did not contain gill homogenate:</p> <ul> <li>IMG_2477: phenol red</li> <li>IMG_2479: bromothymol blue</li> </ul> <p>Two close up photos of colonies on agar plates</p> <ul> <li>IMG_2485</li> <li>IMG_2488</li> </ul>
Cultivation of host-associated bacteria under sulfide-enriched microoxic and anoxic conditions
<p>Cultivation protocol</p>
Supplementary material 3 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Common OTU lists and Statistical analysis : Explanation note: This file contains detected OTUs in both methods and biodiversity analysis (GLM and t-test)
Supplementary material 2 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Biodiversity workflow in R : Explanation note: Bundle of files for biodiversity analysis in R. All necessary input files and a commented script of R-commands are provided.
Supplementary material 4 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Master data sheet : Explanation note: Spreadsheet file containing information about read abundances of operational taxonomic units (OTUs) and sample metadata. Here, data were prepared for subsequent biodiversity analysis in R.
Supplementary material 1 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Bioinformatics pipeline : Explanation note: This file provides all steps and commands necessary for quality filtering and demultiplexing of raw paired fastq sequences.
Figure 5 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 5 - Relative abundance distribution of fungal leaf-inhabiting endophytes of beech among the five main trophic guilds as revealed by analysis with FUNGuild (Nguyen et al. 2016). A compares the two localities for each trophic guild on the basis of Illumina data B compares the two localities for each trophic guild on the basis of cultivation data.
Figure 4 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 4 - Relative abundance of fungal leaf-inhabiting endophytes of beech among the five main trophic guilds as revealed by analysis with FUNGuild (Nguyen et al. 2016). A compares the two methods for each trophic guild and unassigned data. B displays the trophic guilds and unassigned taxa for Illumina data, C for cultivation data. Abbreviations in [B and C]: U = Unassigned, P = Pathotrophs, PSa = Patho-Saprotrophs, PSy = Patho-Symbiotrophs, Sa = Saprotrophs, Sy = Symbiotrophs
Figure 2 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 2 - . Principal coordinate analysis (PCoA) of fungal leaf-inhabiting endophytes of beech display strongly differing assemblages obtained with Illumina sequencing and cultivation. Both methods revealed differing mycobiomes from valley and from mountain leaves, although these differences were less pronounced for cultivation data. Abbreviations: IM = Illumina data from mountain samples, IV = Illumina data from valley samples, CM = cultivation data from mountain samples, CV = cultivation data from valley samples
Figure 3 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 3 - Abundance distribution of the 20 most abundant orders of fungal leaf-inhabiting endophytes of beech on a logarithmic scale. Three of the five most abundant orders from high-throughput sequencing were also most abundant in cultivation data.
Figure 1 from: Siddique AB, Khokon AM, Unterseher M (2017) What do we learn from cultures in the omics age? High-throughput sequencing and cultivation of leaf-inhabiting endophytes from beech (Fagus sylvatica L.) revealed complementary community composition but similar correlations with local habitat conditions. MycoKeys 20: 1-16. https://doi.org/10.3897/mycokeys.20.11265
Figure 1 - Diversity indexes and accumulation curves for a Illumina and b cultivation data of fungal leaf-inhabiting endophytes of beech. Except of the accumulation curves of cultivation data, both methods revealed a clear and partly significant trend of higher fungal diversity at the valley site.
Cultivating Well-being in Severe Psychiatric Conditions
ClinicalTrials.gov study NCT04768959. IPD Sharing: YES. Countries: 1. Publications: 0.
Transcriptional variation in the yeast Yarrowia lipolytica depending on substrate and pH conditions applied in bioreactor cultivations
GEO Series GSE151659. Yarrowia lipolytica. 35 samples. Type: Expression profiling by high throughput sequencing.
Transcript profiling of transgenic rice lines where the OsMADS26 gene is over-expressed or down growing cultivated in standard or osmotic stress condition
GEO Series GSE52640. Oryza sativa. 24 samples. Type: Expression profiling by array.
Adaptation of Pseudomonas aeruginosa to cultivation in standard laboratory conditions
GEO Series GSE146906. Pseudomonas aeruginosa. 32 samples. Type: Expression profiling by high throughput sequencing.
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