scholarly journals Decomposition characteristics of long-established Salix psammophila sand barriers in an arid area, Northwestern China

BioResources ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. 5947-5963
Author(s):  
Ruidong Wang ◽  
Xia Yang ◽  
Yong Gao ◽  
Xiaohong Dang ◽  
Yumei Liang ◽  
...  
Keyword(s):  
Nutrient Release ◽  
Soil Nutrient ◽  
Decomposition Process ◽  
Northwestern China ◽  
Barrier Material ◽  
Nutrient Contents ◽  
Arid Northwestern China ◽  
Decomposition Time ◽  
Soil Nutrient Contents

Salix psammophila has been extensively used as a sand barrier material for various desertification control applications. Elucidating the long-term decomposition characteristics and nutrient cycling process of this sand barrier in desert environments is of great importance. In this study, which was conducted for 1 to 9 years, changes in the mass loss percentage and the residual percentage in the decomposition process were explored of S. psammophila sand barriers in arid Northwestern China. In addition, the S. psammophila analysis nutrient elements release rule and its influence on soil properties were evaluated. The results showed that the decomposition process of S. psammophila sand barriers exhibited a “slow-fast” trend. After decomposition time for 9 years, mass decreased remarkably, and the residual percentage was 33.6%. Further, the nutrient release characteristics differed. C, P, and K were in the release state, whereas N was in the enrichment state. The decomposition percentage of the sand barriers was significantly correlated with N, P, K, C/N, C/P, and N/P (p < 0.05). The soil nutrient contents of C, P, and K contents increased 3.43, 2.23, and 2.08 g/kg compared to the initial values, respectively. The soil nutrient contents of N contents decreased 0.19 g/kg.

Effect of long-term agricultural cultivation and land use conversion on soil nutrient contents in the Sanjiang Plain

CATENA ◽  
2013 ◽  
Vol 104 ◽  
pp. 243-250 ◽  
Author(s):  
Wei Ouyang ◽  
Yiming Xu ◽  
Fanghua Hao ◽  
Xuelei Wang ◽  
Chen Siyang ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Nutrient ◽  
Sanjiang Plain ◽  
The Sanjiang Plain ◽  
Nutrient Contents ◽  
Land Use Conversion ◽  
Soil Nutrient Contents

Predicting soil nutrient contents using Landsat OLI satellite images in rain-fed agricultural lands, northwest of Iran

2021 ◽  
Vol 193 (9) ◽  
Author(s):  
Naser Miran ◽  
Mir Hassan Rasouli Sadaghiani ◽  
Vali Feiziasl ◽  
Ebrahim Sepehr ◽  
Mehdi Rahmati ◽  
...  
Keyword(s):  
Satellite Images ◽  
Soil Nutrient ◽  
Agricultural Lands ◽  
Nutrient Contents ◽  
Northwest Of Iran ◽  
Soil Nutrient Contents

scholarly journals Effects of Planting Density on Soil Bulk Density, pH and Nutrients of Unthinned Chinese Fir Mature Stands in South Subtropical Region of China

Forests ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 351
Author(s):  
Aiguo Duan ◽  
Jie Lei ◽  
Xiaoyan Hu ◽  
Jianguo Zhang ◽  
Hailun Du ◽  
...  
Keyword(s):  
Organic Matter ◽  
Bulk Density ◽  
Soil Depth ◽  
Soil Nutrient ◽  
Chinese Fir ◽  
Planting Density ◽  
Total N ◽  
Nutrient Contents ◽  
Exchangeable Ca ◽  
Soil Nutrient Contents

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is a fast-growing evergreen conifer with high-quality timber and is an important reforestation and commercial tree species in southern China. Planting density affects the productivity of Chinese fir plantations. To study the effect of five different planting densities and soil depth on soil nutrient contents of a mature C. lanceolata plantation, the soil nutrient contents (soil depths 0–100 cm) of 36-year-old mature Chinese fir plantations under five different planting densities denoted A (1667 trees·ha−1), B (3333 trees·ha−1), C (5000 trees·ha−1), D (6667 trees·ha−1), and E (10,000 trees·ha−1) were measured in Pingxiang county, Guangxi province, China. Samples were collected from the soil surface down to a one meter depth from each of 45 soil profiles, and soil samples were obtained at 10 different soil depths of 0–10, 10–20, 20–30, 30–40, 40–50, 50–60, 60–70, 70–80, 80–90, and 90–100 cm. Twelve soil physical and chemical indicators were analyzed. The results showed that: (1) as planting density increased, the organic matter, organic carbon, total N and P, available N, effective Fe, and bulk density decreased. Soil pH, total K, and effective K increased with increasing planting density. Planting density did not significantly influence the exchangeable Ca and Mg. (2) Soil organic matter; organic carbon; total N and P; effective N, P, and K; exchangeable Ca and Mg; effective Fe content; and bulk density decreased with increasing soil depth. This pattern was particularly evident in the top 30 cm of the soil. (3) Excessively high planting density is not beneficial to the long-term maintenance of soil fertility in Chinese fir plantations, and the planting density of Chinese fir plantations should be maintained below 3333 stems·ha−1 (density A or B) to maintain soil fertility while ensuring high yields.

The Application of GIS Techniques in Soil Testing and Fertilizer Recommendations: Part II the Distribution of Soil Nutrients in Hainan, China

2012 ◽  
Vol 610-613 ◽  
pp. 3697-3701
Author(s):  
Zhi Yuan Wei ◽  
Deng Feng Wang ◽  
Zhi Ping Qi
Keyword(s):  
Spatial Analysis ◽  
Soil Nutrients ◽  
Arable Land ◽  
Soil Nutrient ◽  
Hainan Island ◽  
Nutrient Elements ◽  
Available N ◽  
Nutrient Contents ◽  
Fertilizer Recommendations ◽  
Soil Nutrient Contents

The research on the distribution of soil nutrient contents in arable land regionally is the basis for the proper fertilization. The study, taking Hainan Island as the study area, analyzes the distribution and variation of soil nutrient contents in arable land spatially and temporally by applying GIS spatial analysis. As the study shows, from the 1980s to the year of 2005, the TN, TP, TK, and Available N contents in soil of arable land have declined to certain degree, while Available P and Available K kept increasing. Generally, the soil nutrients have declined in quality during last two decades but remain in a mediate level. Spatial analysis can reflect the distribution characteristics of the regional soil nutrient elements in an objective manner.

scholarly journals The effect of long-term fertilization on the 0.01 M CaCl2 extractable nutrient content of a meadow soil

2012 ◽  
pp. 73-79
Author(s):  
Emese Bertáné Szabó
Keyword(s):  
Recommendation System ◽  
Arable Land ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Limit Values ◽  
Nutrient Contents ◽  
Ammonium Lactate ◽  
Supply Capacity ◽  
Mineralizable Nitrogen

During my research, I studied the 0.01 M CaCl2 extractable NO3--N, NH4+-N, Norg, P and K contents of the soil samples originated from a long term fertilisation trial in the experimental site Hajdúböszörmény. Relationships among the soil nutrient contents, the agronomic nutrient balances of the 2009 year, and fertilization were studied. From the results of the study it was concluded as follows:– Fertilization significantly increased the CaCl2 extractable NO3--N, NH4+-N, and K contents of soil.– Norg fraction increased as a function of the increasing yield. Hence, it can be assumed that the greater the produced yield, the more the stubble and root residues remain on the arable land. These organic residues can result significant increase in the Norg content of soils.– The CaCl2 extractable P and K contents were compared with the calculated P and K limit values. According to these, the experimental soil has a good phosphorus and lower potassium supply capacity. These results are in accordance with the results of the conventional Hungarian fertilization recommendation system.– It can be stated that the 0.01 M CaCl2 is able to determine not just inorganic N forms but Norg fraction as well that characterize the easily mineralizable nitrogen reserves. The results proved that AL-P and -K (ammonium lactate acetic acid, traditional Hungarian extractant) are in good agreement with the P and K reserves, but it is important from the aspect of environmental protection and plant nutrition to measure the easily soluble and exchangeable K-, and P-contents of soil. 0.01 M CaCl2 method is recommended for this.

scholarly journals Prediction of Soil Nutrient Contents Using Visible and Near-Infrared Reflectance Spectroscopy

2019 ◽  
Vol 8 (10) ◽  
pp. 437 ◽  
Author(s):  
Yiping Peng ◽  
Li Zhao ◽  
Yueming Hu ◽  
Guangxing Wang ◽  
Lu Wang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Regional Scale ◽  
Soil Nutrient ◽  
Hyperspectral Data ◽  
Estimation Accuracy ◽  
Soil Productivity ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Nutrient Contents ◽  
Soil Nutrient Contents

Quickly and efficiently monitoring soil nutrient contents using remote sensing technology is of great significance for farmland soil productivity, food security and sustainable agricultural development. Current research has been conducted to estimate and map soil nutrient contents in large areas using hyper-spectral techniques, however, it is difficult to obtain accurate estimates. In order to improve the estimation accuracy of soil nutrient contents, we introduced a GA-BPNN method, which combined a back propagation neural network (BPNN) with the genetic algorithm optimization (GA). This study was conducted in Guangdong, China, based on soil nutrient contents and hyperspectral data. The prediction accuracies from a partial least squares regression (PLSR), BPNN and GA-BPNN were compared using field observations. The results showed that (1) Among three methods, the GA-BPNN provided the most accurate estimates of soil total nitrogen (TN), total phosphorus (TP) and total potassium (TK) contents; (2) Compared with the BPNN models, the GA-BPNN models significantly improved the estimation accuracies of the soil nutrient contents by decreasing the relative root mean square error (RRMSE) values by 15.9%, 5.6% and 20.2% at the sample point level, and 20.1%, 16.5% and 47.1% at the regional scale for TN, TP and TK, respectively. This indicated that by optimizing the parameters of BPNN, the GA-BPNN provided greater potential to improving the estimation; and (3) Soil TK content could be more accurately mapped by the GA-BPNN method using HuanJing-1A Hyperspectral Imager (HJ-1A HSI) (manufacturer: China Aerospace Science and Technology Corporation; Beijing, China) data with a RRMSE value of 20.37% than the soil TN and TP with the RRMSE values of 40.41% and 34.71%, respectively. This implied that the GA-BPNN model provided the potential to map the soil TK content for the large area. The research results provided an important reference for high-accuracy prediction of soil nutrient contents.

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