scholarly journals Morpho-Physiological and Molecular Evaluation of Drought and Recovery in Impatiens walleriana Grown Ex Vitro

Plants ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1559
Author(s):  
Marija Đurić ◽  
Angelina Subotić ◽  
Ljiljana Prokić ◽  
Milana Trifunović-Momčilov ◽  
Aleksandar Cingel ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Potential ◽  
Soil Moisture Content ◽  
Growth Parameters ◽  
Proline Content ◽  
Antioxidant Enzyme Activities ◽  
Ex Vitro ◽  
Drought Effect ◽  
Impatiens Walleriana

This study was carried out to examine the drought effect on development, physiological, biochemical and molecular parameters in Impatiens walleriana grown ex vitro. Experiment design included three treatments: Control plants—grown under optimal watering (35%–37% of soil moisture content), drought-stressed plants—non-irrigated to reach 15% and 5% of soil moisture content and recovery plants—rehydrated for four days to reach optimal soil moisture content. Drought reduced fresh weight, total leaf area, as well as dry weight of I. walleriana shoots. Drought up-regulated expression of abscisic acid (ABA) biosynthesis genes 9-cis-epoxycarotenoid dioxygenase 4 (NCED4) and abscisic aldehyde oxidase 2 (AAO2) and catabolic gene ABA 8′-hydroxylase 3 (ABA8ox3) which was followed by increased ABA content in the leaves. Decrement in water potential of shoots during the drought was not accompanied with increased amino acid proline content. We detected an increase in chlorophyll, carotenoid, total polyphenols and flavonols content under drought conditions, as well as malondialdehyde, hydrogen peroxide and DPPH (1,1′-diphenyl-2-picrylhydrazyl) activity. Increased antioxidant enzyme activities (superoxide dismutase, peroxidase and catalase) throughout drought were also determined. Recovery treatment was significant for neutralizing drought effect on growth parameters, shoot water potential, proline content and genes expression.

scholarly journals The Regulatory Mechanism of 2-Acetyl-1-Pyrroline Biosynthesis in Fragrant Rice (Oryza sativa L.) Under Different Soil Moisture Contents

2021 ◽  
Vol 12 ◽  
Author(s):  
Haowen Luo ◽  
Meiyang Duan ◽  
Leilei Kong ◽  
Longxin He ◽  
Yulin Chen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Growth Parameters ◽  
Oryza Sativa L ◽  
Dry Weight ◽  
Transcript Level ◽  
Fragrant Rice ◽  
Moisture Contents ◽  
Significant Difference

2-acetyl-1-pyrroline (2-AP) is the key compound of rice aroma. However, the responses of 2-AP biosynthesis in fragrant rice under different soil moisture and the corresponding mechanism are little known. The present study evaluated the effects of different soil moisture on 2-AP biosynthesis through a pot experiment. Four soil moisture contents, that is, 50% (SM50), 40% (SM40), 30% (SM30), and 20% (SM20), were adopted, and SM50 treatment was taken as control. The pots were weighed and watered to maintain the corresponding soil moisture content. The results showed no significant difference in growth parameters (plant height, stem diameter, and plant dry weight) among all treatments. Compared with SM50, SM40, SM30, and SM20 treatments significantly (p<0.05) increased 2-AP content by 32.81, 23.18, and 53.12%, respectively. Between 20 to 90% higher proline content was observed in SM40, SM30, and SM20 treatments than in SM50. Enzymes including proline dehydrogenase, ornithine transaminase, and 1-pyrroline-5-carboxylate synthetase exhibited lower activities with soil moisture declined. Higher diamine oxidase activity was observed in SM40, SM30, and SM20 treatments compared with SM50, and real-time PCR analyses showed that transcript level of DAO1 was greatly increased under low soil moisture treatments, especially in SM20 treatment. Transcript levels of PRODH, DAO2, DAO4, DAO5, OAT, P5CS1, and P5CS2 decreased or maintained in SM40, SM30, and SM20 treatments compared with SM50. We deduced that low soil moisture content enhanced 2-AP biosynthesis mainly by upregulating the expression of DAO1 to promote the conversion from putrescine to 2-AP.

scholarly journals Robusta coffee transpiration rate in smallholder coffee plantations on Inceptisols of Malang, East Java

2021 ◽  
Vol 9 (1) ◽  
pp. 3165-3173
Author(s):  
Jiyanti Yana Saputri ◽  
Sugeng Prijono ◽  
Budi Prasetya
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Water Potential ◽  
Soil Water ◽  
Transpiration Rate ◽  
Rainy Season ◽  
Soil Moisture Content ◽  
Soil Water Potential ◽  
Dry Season ◽  
Sunlight Intensity

Climate change and the erratic and uneven rainfall distribution are the causes of reduced water available in the soil for plant needs to the transpiration process. This study aimed to determine coffee transpiration rate on dry land with rain harvesting techniques during the dry season, transition season, and rainy season and the factors that influence it. This study used field observation and laboratory analysis with two treatments, i.e. a bench terrace as a control (P1) and an L-shaped silt pit (P2). The variables observed were soil moisture content, transpiration rate, soil water potential, leaf water potential, and microclimate, especially temperature and sunlight intensity. The results showed that the transpiration rate of coffee plants was significantly different in the two treatments. The highest transpiration rate was found in P2 as much as 13.17 mm week-1 or equivalent to 1.88 mm day-1 during the dry season. Application of the L-shaped silt pit (P2) increased soil moisture content compared to the control (P1). This increase was followed by an increase in soil water potential and leaf water potential, which could reach the highest values of 0.18 bar and 0.49 bar, respectively. The transpiration decreases with the change of seasons from the dry season to the transitional season and the rainy season. This decrease is caused by changes in the microclimate, especially the temperature and sunlight intensity. Both are the most variables that affect the rate of transpiration.

Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Yellow River ◽  
The Yellow River

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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