Seasonal denitrification rates under corn (Zea mays L.) in two Quebec soils

1997 ◽  
Vol 77 (1) ◽  
pp. 21-25 ◽  
Author(s):  
B. C. Liang ◽  
A. F. MacKenzie
Keyword(s):  
Zea Mays ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Zea Mays L ◽  
Denitrification Rate ◽  
Nitrous Oxide Emissions ◽  
Soil Core ◽  
Fertilizer N ◽  
Management Procedures

Nitrogen losses in soil through denitrification are important due to reduced agronomic effectiveness and environmental concerns with nitrous oxide emissions. Knowledge of denitrification may allow for management procedures to reduce these losses. Field experiments were conducted in 1991 and 1992 to investigate N fertilizer effects on denitrification under corn (Zea mays L.) on two soils of contrasting texture in southwestern Quebec. Soil core incubation with C2H2 was used to assess denitrification rates. Total calculated denitrification rate from April to November in 1991 and 1992 varied from 4 to 41 kg N ha−1 on a Chicot sandy clay loam and from 29 to 53 kg N ha−1 on a Ste. Rosalie clay. Denitrification rates increased linearly with increasing fertilizer N rates only in the Ste. Rosalie clay in 1991. Denitrification in the Ste. Rosalie soil was positively related to temperature and NO3− levels in April and May, moisture content from August to November, and temperature in October and November. Denitrification in the Chicot soil was positively related to soil moisture content and NO3− levels in April and May, and soil moisture content in June. Reducing soil NO3− concentrations in April and May could decrease denitrification rate in both Chicot and Ste. Rosalie soils. Key words: Denitrification, fertilizer N, temperature, moisture content

scholarly journals The role of soil moisture content on maize (Zea Mays L.) emergence and production

2019 ◽  
pp. 112-113
Author(s):  
Gabor Milics ◽  
Viliam Nagy ◽  
Tomas Orfanus ◽  
Lubomir Lichner ◽  
Peter Surda
Keyword(s):  
Zea Mays ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Zea Mays L

scholarly journals Effects of Different Planting Distance on Soil moisture content and Yield of Maize (Zea mays L.) in Tolon District of Northern Region, Ghana

2015 ◽  
Vol 5 (7) ◽  
pp. 265-277
Author(s):  
Shaibu Abdul-Ganiyu ◽  
◽  
Benjamin Osei-Mensah ◽  
Thomas A. Apusiga ◽  
Hirohiko Ishikawa ◽  
...  
Keyword(s):  
Zea Mays ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Zea Mays L ◽  
Northern Region ◽  
Planting Distance

scholarly journals Factors affecting nitrogenase activity (C2H2 reduction) associated with sorghum and millet estimated using the soil core assay

1983 ◽  
Vol 29 (8) ◽  
pp. 1063-1069 ◽  
Author(s):  
S. P. Wani ◽  
P. J. Dart ◽  
M. N. Upadhyaya
Keyword(s):  
Soil Moisture ◽  
Seasonal Variation ◽  
Moisture Content ◽  
Soil Temperature ◽  
Diurnal Variation ◽  
Host Plant ◽  
Soil Moisture Content ◽  
Nitrogenase Activity ◽  
Soil Core ◽  
Factors Affecting

Factors affecting nitrogenase activity associated with sorghum and millet roots have been studied. Plants grown in iron cores in the field and then assayed had significantly higher activity than plants cored at the time of assay. Mechanical disturbance during transportation of the cores reduced the activity significantly. Any delay between cutting off the plant top and injecting C2H2 gas led to a reduction in the level of nitrogenase activity determined. Diurnal variation in nitrogenase activity was noted but was not correlated with soil temperature. Most activity occurred at the end of the photoperiod. Seasonal variation in nitrogenase activity of plants was observed and was correlated with the ontogenetic development of the host plant, being most at flowering. A low but significant correlation existed between soil moisture content and nitrogenase activity associated with the plant.

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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