scholarly journals Effects of Water and Salt Movement On Photosynthetic Production Characteristics and Yield Formation of Cotton On Coastal Saline Land

2021 ◽  
Author(s):  
Guoyi Feng ◽  
Qian Zhang ◽  
Chitao Sun ◽  
Xiaopeng Lei ◽  
Shulin Wang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Water Content ◽  
Soil Water ◽  
Rainy Season ◽  
Reproductive Stage ◽  
Cotton Field ◽  
Alkali Stress ◽  
Photosynthetic Production ◽  
Yield Formation ◽  
Saline Land

Abstract Background In order to study the effects of spatial distribution and yearly migration variations of soil water and salt in coastal saline land on photosynthetic production and yield formation of cotton, spatial distribution characteristics of water content, salinity, and pH in soil at 0–200 cm depths in 3 cotton fields that were similar in locality but differed markedly in degree of salinization were determined in April through October, and photosynthetic characteristics and photosynthate accumulation of cotton were also determined. Results The study shows that, the slightly salinized cotton field had lower soil salinity and pH, where soil water content was lower in rainy season (July–August), and at the late reproductive stage (September–October), soil water content was markedly higher than that in the moderately salinized cotton field, where cotton suffered smaller salt-alkali stress, photosynthetic production matched well with hydrothermal resource, the sink organ had a long photosynthate accumulation time and was at the active material accumulation stage for a long time; salt-alkali stress to the moderately salinized cotton field was relieved in rainy season, but at the early reproductive stage (April–June) and the late reproductive stage of cotton, salt-alkali stress remained evident, and photosynthetic production fit more poorly with the rich photothermal resource stage; the severely salinized cotton field subjected to prolonged high salt-alkali stress resulted in low levels of photosynthetic production capacity and yield. Conclusions For the severely salinized cotton field, salt inhibition and other relevant agronomic actions should be taken with greater efforts; for the moderately salinized cotton field, water and fertilizer management should be tightened at the early reproductive stage of cotton; for the slightly salinized cotton field, high-quality efficient cotton production should be developed.

scholarly journals Spatial distribution of soil water content in potato horizontal-ridge profile under various ridge dimensions

2019 ◽  
Vol 406 ◽  
pp. 012025
Author(s):  
Krissandi Wijaya ◽  
Purwoko Hari Kuncoro ◽  
Ardiansyah ◽  
Poppy Arsil ◽  
Hilda Ary ani
Keyword(s):  
Spatial Distribution ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content

scholarly journals Spatial distribution of top soil water content in an experimental catchment of Southeast Brazil

2011 ◽  
Vol 68 (3) ◽  
pp. 285-294 ◽  
Author(s):  
Carlos Rogério de Mello ◽  
Léo Fernandes Ávila ◽  
Lloyd Darrell Norton ◽  
Antônio Marciano da Silva ◽  
José Márcio de Mello ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Time Domain Reflectometry ◽  
Water Infiltration ◽  
Local Factor ◽  
Top Soil ◽  
Over Time

Soil water content is essential to understand the hydrological cycle. It controls the surface runoff generation, water infiltration, soil evaporation and plant transpiration. This work aims to analyze the spatial distribution of top soil water content and to characterize the spatial mean and standard deviation of top soil water content over time in an experimental catchment located in the Mantiqueira Range region, state of Minas Gerais, Brazil. Measurements of top soil water content were carried out every 15 days, between May/2007 and May/2008. Using time-domain reflectometry (TDR) equipment, 69 points were sampled in the top 0.2 m of the soil profile. Geostatistical procedures were applied in all steps of the study. First, the spatial continuity was evaluated, and the experimental semi-variogram was modeled. For the development of top soil water content maps over time a co-kriging procedure was used having the slope as a secondary variable. Rainfall regime controlled the top soil water content during the wet season. Land use was also another fundamental local factor. The spatial standard deviation had low values under dry conditions, and high values under wet conditions. Thus, more variability occurs under wet conditions.

scholarly journals Application of Ground-Penetrating Radar and a Combined Penetrometer–Moisture Probe for Evaluating Spatial Distribution of Soil Moisture and Soil Hardness in Coastal and Inland Windbreaks

Geosciences ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 238
Author(s):  
Kenta Iwasaki ◽  
Makoto Tamura ◽  
Hirokazu Sato ◽  
Kazuhiko Masaka ◽  
Daisuke Oka ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Ground Penetrating Radar ◽  
Soil Compaction ◽  
Soil Moisture Condition ◽  
Soil Hardness ◽  
Ground Penetrating

The development of a method to easily investigate the spatial distribution of soil moisture and soil hardness in tree windbreaks is necessary because these windbreaks often decline due to inappropriate soil moisture condition and soil compaction. This research examined the applicability of ground-penetrating radar (GPR) and a combined penetrometer–moisture probe (CPMP) for evaluating the spatial distribution of soil moisture and soil hardness in four windbreaks with different soil characteristics. A GPR-reflecting interface was observed at a less permeable layer in a coastal windbreak and at a depth affected by soil compaction in an inland windbreak with andosol. The spatial distribution of the groundwater table could also be evaluated by examining the attenuation of GPR reflection in a coastal windbreak. In contrast, GPR was not applicable in an inland windbreak with peat because of high soil water content near the soil surface. The CPMP could detect vertical distributions of soil hardness and soil water content regardless of soil type. The CPMP was useful for interpreting GPR profiles, and GPR was useful for interpolating the information about the horizontal distribution of soil moisture and soil hardness between survey points made with the CPMP. Thus, the combination of GPR and a CPMP is ideal for examining the two-dimensional spatial distribution of soil moisture and soil hardness at windbreaks with soils for which both methods are applicable.

scholarly journals Spatial variability of soil penetration resistance influenced by season of sampling

Bragantia ◽  
2010 ◽  
Vol 69 (suppl) ◽  
pp. 163-173 ◽  
Author(s):  
Juan José Bonnin ◽  
José Manuel Mirás-Avalos ◽  
Kléber Pereira Lanças ◽  
Antonio Paz González ◽  
Sidney Rosa Vieira
Keyword(s):  
Spatial Distribution ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Depth ◽  
Indicator Kriging ◽  
Data Series ◽  
Soil Resistance ◽  
Inverse Distance ◽  
Cone Index

The aim of this work was to analyze the spatial distribution of soil compaction and the influence of soil water content on the resistance to penetration. The latter variable was described by the cone index. The soil at the study site was a Nitisol and the cone index data were obtained using a penetrometer. Soil resistance was assessed at 5 different depths, i.e. 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and deeper than 40 cm, whereas soil water content was measured at 0-20 cm and 20-40 cm. Soil water conditions varied during the different samplings. Coefficients of variation for cone index ranged from 16.5% to 45.8% while those for soil water content varied from 8.96% to 21.38%. Results suggested a high correlation between soil resistance, as assessed by the cone index, and soil depth. However, the expected relation with soil water content was not observed. Spatial dependence was observed in 31 out of 35 data series, both cone index and soil water content. This structure was fitted to exponential models with nugget effect varying from 0 to 90% of the sill value. Four of the data series showed a random behaviour. Inverse distance technique was used in order to map the distribution of the variables when no spatial structure was observed. Ordinary kriging showed a smoothing of the maps compared to those from inverse distance weighing. Indicator kriging was used to map the cone index spatial distribution for recommendation of further soil management.

Factors regulating spatial distribution of soil water content in small semiarid catchments

2001 ◽  
Vol 253 (1-4) ◽  
pp. 211-226 ◽  
Author(s):  
A Gómez-Plaza ◽  
M Martı́nez-Mena ◽  
J Albaladejo ◽  
V.M Castillo
Keyword(s):  
Spatial Distribution ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content

scholarly journals Spatial Variation of Soil Moisture and its Effect on Lint Yield in a Deficit Irrigated Cotton Field

2017 ◽  
Author(s):  
Yujin Wen
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Management Practice ◽  
Lint Yield ◽  
Cotton Field ◽  
Spatial Correlations ◽  
Regulated Deficit Irrigation ◽  
Contour Map

ABSTRACTAs the population increases in Southwest Texas in recent years, the urban water demand is drastically increasing. Regulated deficit irrigation (RDI) is expected to be one of the potential water management practice for saving water while maintaining crop yield. A field experiment was conducted at the AgriLIFE Research center in Uvalde in summer 2008 to examine the water saving potential. Seven irrigation schemes and four varieties were assigned to the experimental field to test their effects on lint yield. As the spatial correlation of the soil moisture/ soil water content were suspected, a spatial analysis on lint yield and soil water content was conducted. The analysis results showed that: 1) The soil water contents showed spatial correlations, while the lint yield did not. 2) The relationship between lint yield and soil water content can be described by linear model better than spatial autocorrelation model. 3) The variogram fit of the soil water content showed a completed curve; the contour map generated using ordinary kriging illustrated the irrigation schemes effect well, and gradient effect was suspected. The variogram of the lint yield could not be fitted by a completed curve, which indicated that the sample field was not large enough to determine the variance function. Further study is needed to determine the slope effect on soil water content, and to improve the contour map precision of the lint yield.

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

2020 ◽  
Vol 36 (3) ◽  
pp. 375-386
Author(s):  
Ruixiu Sui ◽  
Earl D. Vories
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Irrigation Scheduling ◽  
Cotton Field ◽  
List Type ◽  
Soil Moisture Sensors

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

Spatial distribution of surface soil water content under different vegetation types in northwest Guangxi, China

2012 ◽  
Vol 69 (8) ◽  
pp. 2699-2708 ◽  
Author(s):  
Wanxia Peng ◽  
Tongqing Song ◽  
Fuping Zeng ◽  
Kelin Wang ◽  
Hu Du ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Surface Soil ◽  
Vegetation Types
Sign in / Sign up

Export Citation Format

Share Document