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86 results for “Water Deficit”
Reduced seed set under water deficit is driven mainly by reduced flower numbers and not by changes in flower visitations and pollination
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Data from: Salicaceae endophyte inoculation alters stomatal patterning and improves the intrinsic water-use efficiency of Populus trichocarpa after a water-deficit
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The lag and cumulative response of vegetation WUE to water vapor pressure deficit on the Shiyang River Basin, northwest of China_data description
<p>Data description</p> <p>This document provides details about the data description in the manuscript. The variables are described below along with their collection methods and calculation processes:</p> <p>the lag effect: The zip file of“The lag effect” contains three files: "rET_lag_result_z", "rGPP_lag_result"_, and" rWUE_lag_result_z".</p> <p>rET_lag_result_z: "rET_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_lag_result_z: "rGPP_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_lag_result_z: "rWUE_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Cumulative effect: The zip file of“The Cumulative effect” contains three files: "rET_acc_result_z", "rGPP_acc_result"_, and" rWUE_acc_result_z".</p> <p>rET_acc_result_z: "rET_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_acc_result_z: "rGPP_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_acc_result_z: "rWUE_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Slop_Partition: The zip file of“The Slop_Partition”contains three files: "Slop_ET_Partition", "Slop_GPP_Partition"_, and" Slop_WUE_Partition".</p> <p>Slop_ET_Partition: "Slop_ET_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Evapotranspiration (ET)</p> <p>Slop_GPP_Partition: "Slop_GPP_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Gross Primary Productivity (GPP)</p> <p>Slop_WUE_Partition: " Slop_WUE_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Water Use Efficiency (WUE)</p> <p> </p> <p> </p>
Selection strategies to introgress water deficit tolerance derived from Solanum galapagense accession LA1141 into cultivated tomato (datasets)
<p>This dataset includes best linear unbiased predictors (BLUPs) used for composite interval mapping in the LA1141 × OH8245 BC<sub>2</sub>S<sub>3</sub> families (tab - BC2S3_BLUP_data_for_CIM), greenhouse data corresponding to the BC<sub>2</sub>S<sub>5</sub> advanced lines (tab - BC2S5_GH_trial), and field performance data corresponding to the BC<sub>2</sub>S<sub>5</sub> advanced lines (tab - BC2S5_Field_Trial).</p>
Data from: Genome scan identifies flowering-independent effects of barley HsDry2.2 locus on yield traits under water deficit
Increasing crop productivity under climate change requires the identification, selection and utilization of novel alleles for breeding. We analyzed the genotype and field phenotype of the barley HEB-25 multi-parent mapping population under well-watered and water-limited (WW and WL) environments for two years. A genome-wide association study (GWAS) for genotype by-environment interactions was performed for ten traits including flowering time (HEA) and plant grain yield (PGY). Comparison of the GWAS for traits per-se to that for QTL-by-environment interactions (QxE), indicates the prevalence of QxE mostly for reproductive traits. One QxE locus on chromosome 2, Hordeum spontaneum Dry2.2 (HsDry2.2), showed a positive and conditional effect on PGY and grain number (GN). The wild allele significantly reduced HEA, however this earliness was not conditioned by water deficit. Furthermore, BC2F1 lines segregating for the HsDry2.2 showed the wild allele confers an advantage over the cultivated in PGY, GN and harvest index as well as modified shoot morphology , longer grain filling period and reduced senescence (only under drought), therefore suggesting adaptation mechanism against water deficit other than escape. This study highlights the value of evaluating wild relatives in search of novel alleles and clues to resilience mechanism underlying crop adaptation to abiotic stress.
Data from: The shift from plant–plant facilitation to competition under severe water deficit is spatially explicit
The stress-gradient hypothesis predicts a higher frequency of facilitative interactions as resource limitation increases. Under severe resource limitation, it has been suggested that facilitation may revert to competition, and identifying the presence as well as determining the magnitude of this shift is important for predicting the effect of climate change on biodiversity and plant community dynamics. In this study, we perform a meta-analysis to compare temporal differences of species diversity and productivity under a nurse plant (Retama sphaerocarpa) with varying annual rainfall quantity to test the effect of water limitation on facilitation. Furthermore, we assess spatial differences in the herbaceous community under nurse plants in situ during a year with below-average rainfall. We found evidence that severe rainfall deficit reduced species diversity and plant productivity under nurse plants relative to open areas. Our results indicate that the switch from facilitation to competition in response to rainfall quantity is nonlinear. The magnitude of this switch depended on the aspect around the nurse plant. Hotter south aspects under nurse plants resulted in negative effects on beneficiary species, while the north aspect still showed facilitation. Combined, these results emphasize the importance of spatial heterogeneity under nurse plants for mediating species loss under reduced precipitation, as predicted by future climate change scenarios. However, the decreased water availability expected under climate change will likely reduce overall facilitation and limit the role of nurse plants as refugia, amplifying biodiversity loss.
Hyperspectral imaging dataset of potato plants exposed to water-deficit condition
<p><strong>An experiment:</strong></p> <ul> <li>Greenhouse experiment under controlled environmental conditions.</li> <li>Conducted at the Agricultural Institute of Slovenia (Ljubljana, Slovenia). </li> <li>From April to August 2021.</li> <li>A night/day temperature of 21 °C/15 °C; relative humidity of 60%, and photoperiod of 14h.</li> <li>28 cultivars of KIS Krka and 18 of KIS Savinja grown from tubers in 5-litre pots.</li> <li>5 weeks after planting, half plants of both cultivars were randomly assigned to either water-deficient or well-watered groups. </li> <li>The water-deficient group was exposed to a limited water irrigation regime, i.e., up to 50% of substrate saturation field capacity. </li> <li>The soil moisture was surveilled using tensiometers (14.04.04 Jett Fill tensiometers, Eijkelkamp, Giesbeek Netherlands).</li> <li>Throughout the duration of the experiment, the matric potential of the soil was maintained within the range -0,01 MPa to -0,025 MPa for well-watered plants, and -0,05 MPa to -0,07 MPa for water-deficient plants. </li> </ul> <p> </p> <p><strong>Hyperspectral imaging: </strong></p> <ul> <li>Every week after the deficit was introduced.</li> <li>Total of 5 imaging sessions were performed.</li> <li>The imaging sessions took place in a dark room, where cameras were positioned at a 3 m distance from the potato plants, together with calibrated halogen lamps.</li> <li>Hyperspectral images were acquired in the VNIR (visible to near infrared) and SWIR (short-wave infrared) spectral regions. </li> <li>Hyspex (Norsk Elektro Optikk, Oslo Norway) push-broom cameras VNIR-1600 (400–988 nm, 160 bands, bandwidth 3.6 nm) and SWIR-384 (950–2500 nm, 288 bands, bandwidth 5.4 nm) were used.</li> </ul> <p> </p> <p><strong>Files:</strong></p> <ul> <li> <p><strong>File structure:</strong></p> </li> </ul> <p> 📂 imagings<br> ├── 📁 imaging-1<br> │ ├── 📄 0_1_0__KK-K-04_KS-K-05_KK-S-03__imaging-1__1-22_20000_us_2x_HSNR02_ 2022-05-11T104633_corr_rad_f32.hdr<br> │ ├── 📄 0_1_0__KK-K-04_KS-K-05_KK-S-03__imaging-1__1-22_20000_us_2x_HSNR02_2022-05-11T104633_corr_rad_f32.img<br> │ └── 📄 ...<br> ├── 📁 imaging-2<br> │ └── 📄 ...<br> ├── 📁 imaging-3<br> │ └── 📄 ...<br> ├── 📁 imaging-4<br> │ └── 📄 ...<br> └── 📁 imaging-5<br> └── 📄 ...</p> <p> </p> <ul> <li> <p><strong>Description of a name:</strong></p> </li> </ul> <p>A_B_C__L1_L2_L3__imaging-X__ID.img -> image file</p> <p>A_B_C__L1_L2_L3__imaging-X__ID.hdr -> header file belonging to an image file</p> <p> </p> <p>A - index of original raw hyperspectral image</p> <p>B - index of an object on the image (of a particular potato plant)</p> <p>C - index of slice extracted from the image</p> <p>L - labels of plants on the image</p> <p>X - index of the imaging session</p> <p>ID - string identifier</p> <p> </p> <ul> <li> <p><strong>Description of labels (L):</strong></p> </li> </ul> <p>V-T-N (e.g. KK-K-04)</p> <p> </p> <p>V - variety (KK - KIS Krka or KS - KIS Savinja)</p> <p>T - treatment (K - control or S, drought)</p> <p>N - index of a particular plant</p> <p> </p> <ul> <li> <p><strong>Image properties:</strong></p> </li> </ul> <p>Width of the image: 64</p> <p>Height of the image: 64</p> <p>Number of spectral bands: 448</p> <p>Spectral range: 410nm - 2510nm</p> <p>Image values are expressed in reflectance</p> <p> </p> <p><strong>Additional links:</strong></p> <p>Code where the dataset was used for the entire analysis could be found here:</p> <p>https://github.com/Manuscripts-code/Potato-plants-drought--plants-2024</p> <p> </p>
Data from: Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes
<p>Grain filling is a critical process for improving crop production under adverse conditions caused by climate change. Here, using a quantitative method, we quantified post-anthesis source-sink relationships of a large data set to assess the contribution of remobilized pre-anthesis assimilates to grain growth for both biomass and nitrogen. The data set came from 13 years' semi-controlled field experimentation, in which six bread wheat genotypes were grown at plot scale under contrasting temperature, water, and nitrogen regimes. On average, grain biomass was ~10% higher than post-anthesis aboveground biomass accumulation across regimes and genotypes. Overall, the estimated relative contribution (%) of remobilized assimilates to grain biomass became increasingly significant with increasing stress intensity, ranging from virtually nil to 100%. This percentage was altered more by water and nitrogen regimes than by temperature, indicating the greater impact of water or nitrogen regimes relative to high temperatures under our experimental conditions. Relationships between grain nitrogen demand and post-anthesis nitrogen uptake were generally insensitive to environmental conditions, as there was always significant remobilization of nitrogen from vegetative organs, which helped to stabilize the amount of grain nitrogen. Moreover, variations in the relative contribution of remobilized assimilates with environmental variables were genotype-dependent. Our analysis provides an overall picture of post-anthesis source-sink relationships and pre-anthesis assimilate contributions to grain filling across (non-)environmental factors, and highlights that designing wheat adaption to climate change should account for complex multi-factor interactions.</p>
Terrestrial water storage deficits during the GRACE and GRACE-FO gap
<p>This datasets provide predictions of the terrestrial water storage anomalies (TWSAs) during the 11-month gap (July 2017-May 2018) between the GRACE satellite and its follow-on GRACE-FO. The predictions were obtained by a hydroclimatic data-driven Bayesian convolutional neural network driven. The training period is April 2002-March 2014. Based on the predicted TWSAs, the terrestrial water storage deficit (WSD) dataset and its standardized version, the WSD index (WSDI) dataset, are also provided. </p> <p>The GIF animation depicts the GRACE and BCNN TWSAs in the 70 testing months (April 2014-June 2017 and June 2018-December 2020) and the 11-month gap (July 2017-May 2018).</p>
Stronger ROS Scavenging Supports Brother Better than Sister Sibling over Water Deficit in Artificial-bred Poplar Hybrids
<p>Figures in main manuscript of 'Stronger ROS Scavenging Supports Brother Better than Sister Sibling over Water Deficit in Artificial-bred Poplar Hybrids' submited to Forests.</p>
Segregation data of maize populations exposed to water-deficit and defoliation stress
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Data from: Source-sink relationships during grain filling in wheat in response to various temperature, water deficit and nitrogen deficit regimes
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Data from: Genome scan identifies flowering-independent effects of barley HsDry2.2 locus on yield traits under water deficit
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Data from: The shift from plant–plant facilitation to competition under severe water deficit is spatially explicit
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The generalizability of water-deficit on bacterial community composition; Site-specific water-availability predicts the bacterial community associated with coast redwood roots
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Data from: Attenuated accumulation of jasmonates modifies stomatal responses to water deficit
To determine whether drought-induced root jasmonate [jasmonic acid (JA) and jasmonic acid-isoleucine (JA-Ile)] accumulation affected shoot responses to drying soil, near-isogenic wild-type (WT) tomato (Solanum lycopersicum cv. Castlemart) and the def-1 mutant (which fails to accumulate jasmonates during water deficit) were self- and reciprocally grafted. Rootstock hydraulic conductance was entirely rootstock dependent and significantly lower in def-1, yet def-1 scions maintained a higher leaf water potential as the soil dried due to their lower stomatal conductance (gs). Stomatal sensitivity to drying soil (the slope of gsversus soil water content) was low in def-1 self-grafts but was normalized by grafting onto WT rootstocks. Although soil drying increased 12-oxo-phytodienoic acid (OPDA; a JA precursor and putative antitranspirant) concentrations in def-1 scions, foliar JA accumulation was negligible and foliar ABA accumulation reduced compared with WT scions. A WT rootstock increased drought-induced ABA and JA accumulation in def-1 scions, but decreased OPDA accumulation. Xylem-borne jasmonates were biologically active, since supplying exogenous JA via the transpiration stream to detached leaves decreased transpiration of WT seedlings but had the opposite effect in def-1. Thus foliar accumulation of both ABA and JA at WT levels is required for both maximum (well-watered) gs and stomatal sensitivity to drying soil.
Developmental and water deficit-induced changes in hydraulic properties and xylem anatomy of tomato fruit and pedicel
<p><span>Xylem water transport from the parent plant into the fruit plays a crucial role in fruit growth, development, and quality formation. Current research on fruit hydraulics has attempted to partition the hydraulic resistance of the pathway over development. However, no consensus has been reached and this question has not been addressed in the context of changing plant and fruit water status under water deficit. We rigorously investigated the developmental changes of hydraulic property of the fruit and pedicel under well-irrigated condition and water deficit based on hydraulic measurements, fruit rehydration, dye tracing, light and electron microscopy, and flow modeling. A decline in water transport capacity did not occur in the pathway prior to the fruit, but within the fruit itself, which might lie in the xylem and/or outside-xylem pathway. The developmental pattern of pathway hydraulic resistance was not significantly influenced by water deficit. The changed xylemic water flow between the fruit and the parent plant due to a reduced driving force under water deficit could explain the reduced fruit water accumulation. This work provides new insights into the understanding of xylem water transport in fleshy fruits and its sensitivity to water deficit from a hydraulics perspective.</span></p>
Figure 2. Initial fluorescence - F0 in Shading minimizes the effects of water deficit in Campomanesia xanthocarpa (Mart.) O. Berg seedlings
Figure 2. Initial fluorescence - F0 (a) and the potential quantum efficiency of photosystem II – Fv/Fm (b) of Campomanesia xanthocarpa seedlings as a function of continuous irrigation (CI) and intermittent (II) conditions, shading (0, 30, and 70%) and experimental period (Start: T0, 1st and 2nd Photosynthesis Zero: P0, 1st and 2nd Recovery: REC and END). Uppercase letters compare the same shading and irrigation conditions in different experimental periods. Lowercase letters compare the same irrigation condition and period in different shading. The asterisk compares irrigation conditions in the same shading and period. The means of shading were compared by the Tukey test, the experimental periods by the Scott Knott test, and the irrigation conditions by the Bonferroni T test. In all cases, 5% probability was used.
Figure 1 in Does silicon help to alleviate water deficit stress and in the recovery of Dipteryx alata seedlings?
Figure 1. Dynamics of photosynthesis (A) in D. alata seedlings produced under different water regimes (I: Irrigated; II: combined intermittent irrigation without and with 0.75 and 1.50 Si) in different evaluation periods (T0: zero time; P0: photosynthesis close to zero; REC: recovery: END: end of evaluations).
Figure 4 in Does silicon help to alleviate water deficit stress and in the recovery of Dipteryx alata seedlings?
Figure 4. Leaf area (a), relative water content of WRC leaves (b) and Dickson quality index DQI (c and d) in D. alata seedlings produced under different water regimes (I: Irrigated; II: combined intermittent irrigation without and with 0.75 and 1.50 of Si) in different evaluation periods (T0: time zero; P0: photosynthesis close to zero; REC: recovery: END: end of evaluations). Capital letters compare water regimes within each assessment period (Tukey; p <0.05); Lowercase letters compare the evaluation periods within each water regime (Tukey; p <0.05).
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Allen Brain Atlas
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