scholarly journals Bio-Compost Influences Salinity and Plant Development by Experimenting with Greenhouse Pots

2019 ◽  
Vol 8 (2) ◽  
pp. 4847-4850
Keyword(s):  
Soil Salinity ◽  
Saline Soil ◽  
Salt Content ◽  
Soil Samples ◽  
Plant Cultivation ◽  
Crop Development ◽  
Salinity Reduction ◽  
Standard Value ◽  
Synthetic Fertilizers

This paper deals with this research “Role of compost in reduction of saline soil of Agra” [1] and “Soil salinity reduction by bio-compost” [2]. The salinity of the soil is the occurrence which raises the salt content from standard value. The method of increasing the salt content is called Salinization. In soils and water, salts usually occur. It can be these bilish Salinization. Characteristic methods, e.g., durability of minerals or progressive sea removal. It may also come into being with an artificial drainage and excessive use in agriculture of synthetic fertilizers. Salt content is a significant crisis in both plant and crop development. The aim of this research is to study salinity problems on plant cultivation and how soil management methods can prevent salinization problems. Bio-compost has been used to decrease the salinity of the land. With the use of bio-compost, Agra's estibilised soil hydrology model was decreased. Soilcompost combination of saline soil SA-1 and soil samples SA-2 electrical conductivity (EC).To assess the influence of biocompost on salinity on plant cultivation, greenhouse pot experiments were carry out on least salinity presence on both the soil by varying pH as 5.5, 6, 7 and 8. The plant chosen for this investigation was wheat ( Triticum Aestivum). The plant growth of wheat were obsevered significant at pH-6 and 7 in both SA-1 and SA-2 soil samples. The results of the study recommend biocompost have great potential to reduce soil salinity and could be highly potent alternative to chemical fertilizer and increase the growth of plant.

Hyperspectral Estimation Models for the Saline Soil Salinity in the Yellow River Delta

2015 ◽  
Vol 738-739 ◽  
pp. 197-203 ◽  
Author(s):  
Hong Yan Chen ◽  
Geng Xing Zhao ◽  
Ya Qiu Liu ◽  
Jing Chun Chen ◽  
Hong Zhang
Keyword(s):  
Soil Salinity ◽  
Yellow River ◽  
Saline Soil ◽  
Salt Content ◽  
Back Propagation ◽  
Yellow River Delta ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Soil Salt Content ◽  
Input Variables

Quantitative identification of the saline soil salinity content is a necessary precondition for the reasonable improvement and utilization of saline land, the article aimed at comparing the different quantitative analysis methods and achieving fast estimation of the saline soil salt content in the Yellow River Delta based on the visible-near infrared spectroscopy. Kenli County in Shandong Province was selected as the experimental area, firstly, the representative soil samples were selected, hyperspectral reflectance of the soil samples were measured in situ and transformed to the first deviation. Secondly the correlate spectra, the characteristic spectra and indices were firstly filtered using correlation analysis. Finally, the estimation models of soil salinity content were built using the multiple linear regression (MLR), back propagation neural network (BPNN) and support vector machine (SVM) respectively. The results indicated that the characteristic wave bands of soil salinity were 684 nm and 2058 nm. On the condition of the same input variables, the prediction precision of the SVM models was the highest, followed by the BPNN, the MLR was the lowest. The SVM model based on the first deviation of the reflectance at 684 and 2058nm had the highest precision, with the calibration R2 of 0.91 and RMSE as 0.11%, the validation R2 of 0.93, RMSE as 0.26% and RPD as 2.61, which had very good prediction accuracy of soil salt content, and was very stable and reliable. Different input variables had a great impact on the model accuracy, among of the MLR models, only the precision of the model based on characteristic spectral indices was slightly higher and could be used to estimate salt content, among of the BPNN and SVM models, the precision of the models based on characteristic spectra and indices was more high and stable significantly than the models on the correlate spectra. Therefore, for the three modeling methods of multiple linear regression, back propagation neural network and support vector machine, building the estimation model of saline soil salinity content based on characteristic spectra indices was effective.

scholarly journals Variations of Soil Salinity and Cotton Growth under Six-Years Mulched Drip Irrigation

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Wenhao Li ◽  
Zhenhua Wang ◽  
Jinzhu Zhang ◽  
Ningning Liu
Keyword(s):  
Soil Salinity ◽  
Drip Irrigation ◽  
Saline Soil ◽  
Irrigation Schedule ◽  
Salt Content ◽  
Soil Layer ◽  
Cotton Field ◽  
Soil Conditions ◽  
Quota Management ◽  
Mulched Drip Irrigation

The lowering of salt content in the field, especially in arid areas, after consecutive application of mulched drip irrigation (MDI) is of vital importance for sustainable cotton plantation. To elucidate the effects of long-term MDI on soil properties and cotton growth, this paper systematically monitored the soil salinity, ion concentrations and the yield of cotton in the field using MDI consecutively for six years in a typical oasis in Xinjiang, China. The results showed that MDI could significantly change salt distribution in the cotton field. During the six years tested, the soil salt content using MDI declined fast at first, and then the decline rate gradually decreased. In the 1st and 2nd year, the average salt content within 0–100 cm soil layer was larger than 20 g kg−1, which belonging to the saline soil. Then the salt content decreased to 10–20 g kg−1 in the 3rd and 4th year, and the cotton field declined to heavily saline soil. After 5 years of MDI, the soil turned to non-salinized. The Cl− and SO42− equivalence ratio (CSER) also decreased with the increase of application years of MDI. Saline-alkaline land developed from chloride-sulphate solonchak (0.2 < CSER < 1) into sulphate solonchak (CSER < 0.2) after 6 years of MDI. The survival rate of the cotton increased from 1.48% (1 year of MDI) to 76.3% (6 years of MDI), and the yield increased from 72.43 kg ha−1 to 4515.48 kg ha−1. When the average CSER, SAR and the soil salinity in 0–140 cm soil layer decreased to 0.60, 0.98 (mol kg−1)0.5 and 6.25 g kg−1, farmers can achieve a balance between income and expenditure. Moreover, when CSER, SAR, and the soil salinity continuously decreased to 0.44, 0.69 (mol kg−1)0.5 and 0.77 g kg−1, the cotton yield will exceed the average production level of cotton in Xinjiang. Under the current irrigation schedule in the oasis irrigation area, the soil salinity and groundwater level after applying MDI could be conducive to cotton growth. However, this situation had also caused a waste of nearly 200 mm of water resources. Therefore, authors suggested that further research on water-saving irrigation systems suitable for different soil conditions should be carried out, and also the differential quota management in production practice should be adopted.

scholarly journals Comparative Analysis of the Spectral Response to Soil Salinity of Saline-Sodic Soils under Different Surface Conditions

2018 ◽  
Vol 15 (12) ◽  
pp. 2721 ◽  
Author(s):  
Jianhua Ren ◽  
Kai Zhao ◽  
Xiangwen Wu ◽  
Xingming Zheng ◽  
Xiaojie Li
Keyword(s):  
Soil Properties ◽  
Soil Salinity ◽  
Prediction Models ◽  
Spectral Response ◽  
Total Concentration ◽  
Salt Content ◽  
Size Fraction ◽  
Soil Samples ◽  
Sodic Soils ◽  
Cracked Soil

Desiccation cracking is a very common surface soil phenomenon of saline-sodic land. The objective of this study was to investigate the effects of salt content on the spectral reflectance of soil with and without desiccation cracks. To achieve our objective, a cracking test was performed using 17 soil samples. Following the tests, crack parameters were extracted, and correlation analysis was then performed between crack parameters and four soil properties: Na+, salinity (total concentration of ions), pH, and electric conductivity (EC). In order to select the optimum spectral measurement method and develop prediction models, spectral response to different soil properties were compared between the cracked soil samples and the comparative soil samples composed of the 2 mm particle size fraction processed by traditional methods. The results indicate that soil salinity dominated cracking propagation with a positive correlation. Since area and volume scattering are closer to what occurs in the field, a greater spectral response to soil properties was found for cracked soil samples relative to the comparative soil samples in the near-infrared and shortwave-infrared regions. The R2 of optimal linear prediction models based on the cracked soil samples were 0.74, 0.67, 0.58, and 0.67 for Na+, salinity, pH, and EC, respectively.

scholarly journals Bioremediation of polluted soil due to tsunami by using recycled waste glass

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
M. Azizul Moqsud
Keyword(s):  
Heavy Metals ◽  
Electrical Conductivity ◽  
Laboratory Experiments ◽  
Saline Soil ◽  
Salt Content ◽  
Tohoku Earthquake ◽  
Polluted Soil ◽  
Waste Glass ◽  
Excess Cost ◽  
Recycled Waste

AbstractIn this research, bioremediation of tsunami-affected polluted soil has been conducted by using collective microorganisms and recycled waste glass. The Tohoku earthquake, which was a mega earthquake in Japan triggered a huge tsunami on March 11th, 2011 that caused immeasurable damage to the geo-environmental conditions by polluting the soil with heavy metals and excessive salt content. Traditional methods to clean this polluted soil was not possible due to the excess cost and efforts. Laboratory experiments were conducted to examine the capability of bioremediation of saline soil by using recycled waste glass. Different collective microorganisms which were incubated inside the laboratory were used. The electrical conductivity (EC) was measured at different specified depths. It was noticed that the electrical conductivity decreased with the assist of the microbial metabolisms significantly. Collective microorganisms (CM2) were the highly capable to reduce salinity (up to 75%) while using recycled waste glass as their habitat.

scholarly journals Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1535
Author(s):  
Tonggang Fu ◽  
Hui Gao ◽  
Jintong Liu
Keyword(s):  
Soil Salinity ◽  
Subsurface Layer ◽  
Salt Content ◽  
Spatial Prediction ◽  
Northern China ◽  
Sodium Adsorption Ratio ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Total Salt Content ◽  
Inverse Distance

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

Cattail Invasion and Persistence in a Coastal Salt Marsh: The Role of Salinity Reduction

Estuaries ◽  
1987 ◽  
Vol 10 (2) ◽  
pp. 165 ◽  
Author(s):  
Pamela A. Beare ◽  
Joy B. Zedler
Keyword(s):  
Salt Marsh ◽  
Coastal Salt Marsh ◽  
Salinity Reduction

scholarly journals SOIL SALINITY AND SAND CONTENT VARIABILITY DETERMINED BY TWO STATISTICAL METHODS IN AN IRRIGATED SALINE SOIL

1988 ◽  
Vol 68 (2) ◽  
pp. 209-221 ◽  
Author(s):  
C. Chang ◽  
T. G. SOMMERFELDT ◽  
T. ENTZ
Keyword(s):  
Soil Properties ◽  
Statistical Methods ◽  
Management Practices ◽  
Saline Soil ◽  
Salt Content ◽  
Sampling Strategy ◽  
Spatial Relationships ◽  
Sand Content ◽  
Grid Pattern ◽  
Classical Statistics

Knowledge of the variability of soluble salt content in saline soils can assist in designing experiments or developing management practices to manage and reclaim salt-affected soils. Geostatistical theory enables the use of spatial dependence of soil properties to obtain information about locations in the field that are not actually measured, but classical statistical methods do not consider spatial correlation and the relative location of samples. A study was carried out using both classical statistics and geostatistical methods to delineate salinity and sand content and their variability in a small area of irrigated saline soil. Soil samples were taken for electrical conductivity (EC) and particle size distribution determinations at 64 locations from a 20 × 25-m area, on an 8 × 8-grid pattern at depth intervals of 0–15, 15–30, 30–60, 60–90 and 90–120 cm. The high coefficient of variation (CV) values of both EC and sand content indicated that the soil was highly variable with respect to these soil properties. The semivariograms of sand content of the first two depth intervals and EC of all the depth intervals showed strong spatial relationships. Contour maps, generated by block kriging, based on spatial relationships provide estimated variances which are smaller than general variances calculated by the classical statistical method. The interpolated EC results by both ordinary and universal kriging methods were compared and were almost identical. The kriged maps can provide information useful for designing experiments and for determining soil sampling strategy. Key words: Salinity, texture, variability, geostatistics, semivariogram, kriging

scholarly journals Screening of Salt Tolerant CIP Potato Germplasm for Saline Areas

2013 ◽  
Vol 11 (1) ◽  
pp. 95-102 ◽  
Author(s):  
MH Rahman ◽  
MM Alam Patwary ◽  
H Barua ◽  
M Hossain ◽  
MM Hasan
Keyword(s):  
Soil Salinity ◽  
Agricultural Research ◽  
Saline Soil ◽  
The Other ◽  
Salt Tolerant ◽  
Agricultural Research Institute ◽  
International Potato ◽  
Local Variety ◽  
Potato Germplasm ◽  
High Level

Fifteen salt tolerant CIP (International Potato Centre) Potato genotypes along with BARI (Bangladesh Agricultural Research Institute) Alu 7 (Diamant) and one local variety viz., Dohazari Sada were evaluated at Bashkhali, Chittagong during 2011-12 to screen the suitable genotypes for cultivation in saline areas of Bangladesh. Diamant and Dohazari Sada and all of the CIP genotypes were found to grow well up to 60 DAP (Days After Planting) at saline areas having healthy plants and no senescence was noticed but after that 61-100% plants died due to high level of soil salinity (6.41dS/m) depending on genotypes. Genotype CIP 112 gave the highest yield (21.07 t/ha) and CIP 102 was comparatively less affected by soil salinity than the other genotypes. However, all the salt tolerant CIP genotypes were found to be promising in the saline soil. DOI: http://dx.doi.org/10.3329/agric.v11i1.15249 The Agriculturists 2013; 11(1) 95-102

scholarly journals Top soil salinity prediction in South-Western part of Urmia Lake with ground water data

2014 ◽  
Vol 4 (1) ◽  
pp. 57-63 ◽  
Author(s):  
N Hamzehpour ◽  
MK Eghbal ◽  
P Bogaert ◽  
N Toomanian
Keyword(s):  
Water Quality ◽  
Soil Salinity ◽  
Activity Ratio ◽  
Underground Water ◽  
Soil Samples ◽  
Urmia Lake ◽  
Quality Data ◽  
Agricultural Lands ◽  
Sodium Activity ◽  
Top Soil

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area. DOI: http://dx.doi.org/10.3329/ijarit.v4i1.21093 Int. J. Agril. Res. Innov. & Tech. 4 (1): 57-63, June, 2014

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