Effects of Residue-free Band Width on Soil Temperature and Water Content

1994 ◽  
Vol 37 (1) ◽  
pp. 39-49 ◽  
Author(s):  
K. J. Shinners ◽  
W. S. Nelson ◽  
R. Wang
Keyword(s):  
Soil Temperature ◽  
Water Content ◽  
Band Width ◽  
Free Band

scholarly journals Dynamics of the soil respiration response to soil reclamation in a coastal wetland

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiliang Song ◽  
Yihao Zhu ◽  
Weifeng Chen
Keyword(s):  
Soil Respiration ◽  
Soil Temperature ◽  
Soil Carbon ◽  
Water Content ◽  
Coastal Wetlands ◽  
Positive Relationship ◽  
Soil Reclamation ◽  
Diurnal Changes ◽  
Seasonal Scale ◽  
Study Sites

AbstractThe soil carbon (C) pools in coastal wetlands are known as “blue C” and have been damaged extensively owing to climate change and land reclamation. Because soil respiration (RS) is the primary mechanism through which soil carbon is released into the atmosphere at a global scale, investigating the dynamic characteristics of the soil respiration rate in reclaimed coastal wetlands is necessary to understand its important role in maintaining the global C cycle. In the present study, seasonal and diurnal changes in soil respiration were monitored in one bare wetland (CK) and two reclaimed wetlands (CT, a cotton monoculture pattern, and WM, a wheat–maize continuous cropping pattern) in the Yellow River Delta. At the diurnal scale, the RS at the three study sites displayed single-peak curves, with the lowest values occurring at midnight (00:00 a.m.) and the highest values occurring at midday (12:00 a.m.). At the seasonal scale, the mean diurnal RS of the CK, CT and WM in April was 0.24, 0.26 and 0.79 μmol CO2 m−2 s−1, and it increased to a peak in August for these areas. Bare wetland conversion to croplands significantly elevated the soil organic carbon (SOC) pool. The magnitude of the RS was significantly different at the three sites, and the yearly total amounts of CO2 efflux were 375, 513 and 944 g CO2·m−2 for the CK, CT and WM, respectively. At the three study sites, the surface soil temperature had a significant and positive relationship to the RS at both the diurnal and seasonal scales, and it accounted for 20–52% of the seasonal variation in the daytime RS. The soil water content showed a significant but negative relationship to the RS on diurnal scale only at the CK site, while it significantly increased with the RS on seasonal scale at all study sites. Although the RS showed a noticeable relationship to the combination of soil temperature and water content, the synergic effects of these two environment factors were not much higher than the individual effects. In addition, the correlation analysis showed that the RS was also influenced by the soil physico-chemical properties and that the soil total nitrogen had a closer positive relationship to the RS than the other nutrients, indicating that the soil nitrogen content plays a more important role in promoting carbon loss.

Microbial CO2 production from surface and subsurface soil as affected by temperature, moisture, and nitrogen fertilisation

Soil Research ◽  
2008 ◽  
Vol 46 (3) ◽  
pp. 273 ◽  
Author(s):  
Xiaobin Jin ◽  
Shenmin Wang ◽  
Yinkang Zhou
Keyword(s):  
Soil Temperature ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Sanjiang Plain ◽  
Co2 Production ◽  
The Sanjiang Plain ◽  
Subsurface Soil ◽  
C Mineralisation ◽  
Q10 Values

The Sanjiang Plain of north-east China is presently the second largest freshwater marsh in China. The drainage and use of marshes for agricultural fields occurred in the past 50 years, resulting in the increase in cultivated land from about 2.9 × 108 m2 in 1893 to 4.57 × 1010 m2 in 1994. Under human disturbance in the past half century, the environment in Sanjiang Plain has had significant change. We hypothesised that environmental factors such as soil moisture, soil temperature, and soil N levels affect the rates of soil organic C mineralisation and the nature of the controls on microbial CO2 production to change with depth through the soil profile in the freshwater marsh in the Sanjiang Plain. In a series of experiments, we measured the influence of soil temperature, soil water content, and nitrogen additions on soil microbial CO2 production rates. The results showed that Q10 values (the factor by which the CO2 production rate increases when the temperature is increased by 10°C) significantly increased with soil depth through the soil profile (P < 0.05). The average Q10 values for the surface soils were 2.7 (0–0.2 m), significantly lower than that (average Q10 values 3.3) for the subsurface samples (0.2–0.6 m) (P < 0.05), indicating that C mineralisation rates were more sensitive to temperature in subsurface soil horizons than in surface horizons. The maximum respiration rate was measured at 60% water hold capacity for each sample. The quadratic equation function adequately describes the relationship between soil respiration and soil water content, and the R2 values were > 0.80. The sensitivity of microbial CO2 production rate response to soil water content for surface soils (0–0.2 m) was slightly lower than for subsurface soils (0.2–0.6 m). The responses of actual soil respiration rates to nitrogen fertilisation were different for surface and subsurface soils. In the surface soils (0–0.2 m), the addition of N caused a slight decreased in respiration rates compared with the control, whereas, in the subsurface soils (0.2–0.6 m), the addition of N tended to increase microbial CO2 production rates, and the addition of 10 µg N/g soil treatment caused twice the increase in C mineralisation rates of the control. Our results suggested that the responses of microbial CO2 production to changes in soil moisture, soil temperature, and soil N levels varied with soil depth through the profile, and subsurface soil organic C was more sensitive to temperature increase and nitrogen inputs in the freshwater marsh of the Sanjiang Plain.

scholarly journals SURFACE RESIDUES: EFFECTS ON SOIL MOISTURE AND TEMPERATURE1

2021 ◽  
Vol 34 (4) ◽  
pp. 887-894
Author(s):  
GUSTAVO HADDAD SOUZA VIEIRA ◽  
ARILDO SEBASTIÃO SILVA ◽  
ARUN DILIPKUMAR JANI ◽  
LUSINERIO PREZOTTI ◽  
PAOLA ALFONSA VIEIRA LO MONACO
Keyword(s):  
Soil Temperature ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Depth ◽  
Block Design ◽  
Soil Disturbance ◽  
Dry Season ◽  
Depth Range ◽  
Sunn Hemp

ABSTRACT This study aimed to determine how crop residue placement and composition would affect soil water content and temperature during the dry season in the central region of Espírito Santo state, Brazil. A 19-week field study was conducted from April to August 2017. A 2 x 4 factorial study with four replications was implemented using a randomized complete block design. Factors were soil management [conventional tillage (CT) and no soil disturbance (ND)] and residue amendment [maize (Zea mays L.), sunn hemp (Crotalaria juncea L.), a maize-sunn hemp mixture, and a no amendment control]. Soil water content and temperature were measured weekly at predetermined soil depth intervals. Soil water content was higher in ND plots amended with surface residues than under all other treatments in the 0 to 0.05 m depth range. All residue amendments in this range were equally effective in conserving soil water. Surface residues reduced soil temperature by up to 8.4 °C relative to the control in ND plots. Incorporating residue amendments by CT cancelled all temperature-moderating benefits provided by surface residues. These results indicate that surface residues from cereals, legumes, or cereal/legume mixtures are equally effective in conserving soil water and moderating soil temperature during the dry season. Additional research is needed to determine how improved soil environmental conditions, generated by surface residues, would affect nutrient acquisition and crop performance.

Comparison of RZWQM2 and DNDC Models to Simulate Greenhouse Gas Emissions under Combined Inorganic/Organic Fertilization in a Subsurface-Drained Field

2020 ◽  
Vol 63 (4) ◽  
pp. 771-787
Author(s):  
Qianjing Jiang ◽  
Zhiming Qi ◽  
Chandra A. Madramootoo ◽  
Ward Smith ◽  
Naeem A. Abbasi ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Water Content ◽  
Soil Water Content ◽  
Ghg Emissions ◽  
Measured Data ◽  
Gas Emissions ◽  
N Management ◽  
Better Than

HighlightsRZWQM2 was compared with DNDC to predict greenhouse gas emissions.RZWQM2 was applied to simulate the greenhouse gas emissions under manure application.RZWQM2 performed better than DNDC in simulating soil water content and CO2 emissions.Abstract. N management has the potential to mitigate greenhouse gas (GHG) emissions. Process-based models are promising tools for evaluating and developing management practices that may optimize sustainability goals as well as promote crop productivity. In this study, the GHG emission component of the Root Zone Water Quality Model (RZWQM2) was tested under two different types of N management and subsequently compared with the Denitrification-Decomposition (DNDC) model using measured data from a subsurface-drained field with a corn-soybean rotation in southern Ontario, Canada. Field-measured data included N2O and CO2 fluxes, soil temperature, and soil moisture content from a four-year field experiment (2012 to 2015). The experiment was composed of two N treatments: inorganic fertilizer (IF), and inorganic fertilizer combined with solid cattle manure (SCM). Both models were calibrated using the data from IF and validated with SCM. Statistical results indicated that both models predicted well the soil temperature, but RZWQM2 performed better than DNDC in simulating soil water content (SWC) because DNDC lacked a heterogeneous soil profile, had shallow simulation depth, and lacked crop root density functions. Both RZWQM2 and DNDC predicted the cumulative N2O and CO2 emissions within 15% error under all treatments, while the timing of daily CO2 emissions was more accurately predicted by RZWQM2 (RMSE = 0.43 to 0.54) than by DNDC (RMSE = 0.60 to 0.67). Modeling results for N management effects on GHG emissions showed consistency with the field measurements, indicating higher CO2 emissions under SCM than IF, higher N2O emissions under IF in corn years, but lower N2O emissions in soybean years. Overall, RZWQM2 required more experienced and intensive calibration and validation, but it provided more accurate predictions of soil hydrology and better timing of CO2 emissions than DNDC. Keywords: CO2 emission, Corn-soybean rotation, Inorganic fertilization, Manure application, N2O emission, Process-based modeling.

scholarly journals Effects of Soil Temperature, Water Content, Species, and Fertilization on Soil Respiration in Bamboo Forest in Subtropical China

Forests ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 99 ◽  
Author(s):  
Houxi Zhang ◽  
Zhuangzhuang Qian ◽  
Shunyao Zhuang
Keyword(s):  
Soil Respiration ◽  
Soil Temperature ◽  
Water Content ◽  
Management Practices ◽  
Clay Content ◽  
Bamboo Forest ◽  
Subtropical China ◽  
Bivariate Model ◽  
Natural Factor ◽  
Change Pattern

Understanding the change pattern of soil respiration (SR) and its drivers under different bamboo species and land management practices is critical for predicting soil CO2 emission and evaluating the carbon budget of bamboo forest ecosystems. A 24-month field study was performed in subtropical China to monitor SR in experimental plots of local bamboo (Phyllostachys glauca) without fertilization (PG) and commercial bamboo (Phyllostachys praecox) with and without fertilization (PPF and PP, respectively). The SR rate and soil properties were measured on a monthly timescale. Results showed that the SR rate ranged from 0.38 to 8.53 µmol CO2 m−2s−1, peaking in June. The PPF treatment had higher SR rates than the PP and PG treatments for most months; however, there were no significant differences among the treatments. The soil temperature (ST) in the surface layer (0–10 cm) was found to be the predominant factor controlling the temporal change pattern of the monthly SR rate in the PG and PP treatments (i.e., those without fertilization). A bivariate model is used to show that a natural factor—comprised of ST and soil water content (SWC)—explained 44.2% of the variation in the monthly SR rate, whereas biological (i.e., bamboo type) and management (i.e., fertilization) factors had a much smaller impact (less than 0.1% of the variation). The annual mean SR showed a significant positive correlation with soil organic matter (SOM; r = 0.51, P < 0.05), total nitrogen (TN; r = 0.47, P < 0.05), total phosphorus (TP; r = 0.60, P < 0.01), clay content (0.72, P < 0.05) and below-ground biomass (r = 0.60*), which altogether explain 69.0% of the variation in the annual SR. Our results indicate that the fertilization effect was not significant in SR rate for most months among the treatments, but was significant in the annual rate. These results may help to improve policy decisions concerning carbon sequestration and the management of bamboo forests in China.

EFFECT OF SOIL WATER AND TEMPERATURE ON CORN (Zea mays L.) ROOT GROWTH DURING EMERGENCE

1986 ◽  
Vol 66 (1) ◽  
pp. 51-58 ◽  
Author(s):  
H. W. CUTFORTH ◽  
C. F. SHAYKEWICH ◽  
C. M. CHO
Keyword(s):  
Root Growth ◽  
Soil Temperature ◽  
Water Content ◽  
Soil Water ◽  
Zea Mays L ◽  
Rate Sensitivity ◽  
Corn Hybrids ◽  
Soil Water Stress ◽  
Soil Temperatures ◽  
Water Contents

Root growth between germination and emergence for the corn hybrids Pioneer 3995, Northrup King 403 and Pride 1108 was studied. Soil temperatures of 15, 19, 25 and 30.5 °C and a range of soil water contents were used. Decreases in soil temperature and water content both decreased root growth rate. Sensitivity to water content decreased with decreasing soil temperature. All three hybrids responded to soil temperature in the same way. By contrast, Pioneer 3995 was less sensitive to soil water stress than was Northrup King 403, while Pride 1108 was the most sensitive. Key words: Soil water, soil temperature, root growth (early), corn

Influence of Soil Temperature, Soil Moisture, and Seed Burial Depth on the Emergence of Round-Leaved Mallow (Malva pusilla)

Weed Science ◽  
1990 ◽  
Vol 38 (6) ◽  
pp. 518-521 ◽  
Author(s):  
Robert E. Blackshaw
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Water Content ◽  
Environmental Conditions ◽  
Control Strategies ◽  
Cultural Control ◽  
Burial Depth ◽  
Moisture Regime ◽  
Seed Burial ◽  
Rate Of Emergence

A study was conducted under controlled environmental conditions to determine the effect of soil temperature, soil moisture, and depth of seed burial on the emergence of round-leaved mallow. Emergence occurred from 5 to 30 C but was optimal at 15 to 20 C. Soil moisture had a greater effect than soil temperature on percentage emergence. Emergence progressively declined below a soil water content of −0.28 MPa, with less than 20% emergence attained at −1.03 to −1.53 MPa. In contrast, rate of emergence of round-leaved mallow was affected more by soil temperature than by moisture. A decrease in temperature from 30 to 5 C increased the time to reach 50% emergence by 10 to 12 days over the moisture regime of this study. Emergence was greatest at depths of 0.5 to 2 cm. No emergence occurred at 8 cm or below. The potential of using the findings of this study to develop cultural control strategies for round-leaved mallow is discussed.

scholarly journals Application of Random Forest and ICON Models Combined with Weather Forecasts to Predict Soil Temperature and Water Content in a Greenhouse

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1176
Author(s):  
Yi-Zhih Tsai ◽  
Kan-Sheng Hsu ◽  
Hung-Yu Wu ◽  
Shu-I Lin ◽  
Hwa-Lung Yu ◽  
...  
Keyword(s):  
Water Management ◽  
Machine Learning ◽  
Random Forest ◽  
Soil Temperature ◽  
Water Content ◽  
Numerical Models ◽  
Volumetric Water Content ◽  
Forest Model ◽  
Greenhouse Experiments ◽  
Machine Learning Methods

Climate change might potentially cause extreme weather events to become more frequent and intense. It could also enhance water scarcity and reduce food security. More efficient water management techniques are thus required to ensure a stable food supply and quality. Maintaining proper soil water content and soil temperature is necessary for efficient water management in agricultural practices. The usage of water and fertilizers can be significantly improved with a precise water content prediction tool. In this study, we proposed a new framework that combines weather forecast data, numerical models, and machine learning methods to simulate and predict the soil temperature and volumetric water content in a greenhouse. To test the framework, we performed greenhouse experiments with cherry tomatoes. The numerical models and machine learning methods we selected were Newton’s law of cooling, HYDRUS-1D, the random forest model, and the ICON (inferring connections of networks) model. The measured air temperature, soil temperature, and volumetric water content during the cultivation period were used for model calibration and validation. We compared the performances of the models for soil temperature and volumetric water content predictions. The results showed that the random forest model performed a more accurate prediction than other methods under the limited information provided from greenhouse experiments. This approach provides a framework that can potentially learn best water management practices from experienced farmers and provide intelligent information for smart greenhouse management.

Sign in / Sign up

Export Citation Format

Share Document