Research on the Spatial Variability of Soil Nutrients Prediction Based on GIS

2013 ◽  
Vol 791-793 ◽  
pp. 1681-1685
Author(s):  
Zi Han Qin
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Spatial Data ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Soil Nutrient ◽  
Growth Period ◽  
Crop Growth ◽  
Accurate Analysis ◽  
Point Line

Soil is a necessary nutrition library of crop growth, its nutrient spatial variability exists the whole crop growth period with the characteristics of long and complex, so the spatial variability of soil nutrient prediction is one of the hot research problems to be urgently solved in precision agriculture. This paper first introduces GIS and statistical analysis organic combination of research method and model, on the basis of this, based on GIS spatial data level of analysis and point-line-side buffer, we will accurate analysis of point-line-sides mutation interval through the variation Euclidean distance and distance weighting interpolation method. Finally through the comparison of mean value and T test, we can predict the approximate interval of soil nutrients spatial variability, to a certain extent, it can provide theory and technology support for the scientific prediction of soil nutrients spatial variability.

scholarly journals Co-composting Versus Direct Ponding of Sewage Sludge on Soil Nutrients Redistribution

2019 ◽  
Vol 8 (4S2) ◽  
pp. 108-111
Keyword(s):  
Sewage Sludge ◽  
Soil Nutrients ◽  
Management Practices ◽  
Sewage Treatment ◽  
Soil Nutrient ◽  
Crop Growth ◽  
Agricultural Practice ◽  
Excess Sludge ◽  
Different Depths ◽  
Nutrient Attenuation

Depriving soil fertility serves as one major cause for reduced crop growth rate and production, which in turn affects the food productivity. Various soil conservation measures to sequester the soil nutrients are adopted in developing countries locally and conventionally. In another scenario, present day sewage treatment plants (STP) across the nation are struggling to treat the receiving water quantity effectively. The fluctuations in receiving quantity of waste water resulted in untreated excess sludge dumping in nearby places. In order to synergize the excess sludge dumping in a feasible way by land management practices, the present study aims to compare two methods of sludge incorporating into soil, namely co-composting and direct ponding. Samples were collected from two different plots receiving sludge for different duration (fresh and old) at different depths (12 cm, 24 cm and 36 cm) and analyzed for various physicochemical parameters. The study also highlights the co-composting of sewage sludge with other community wastes to contribute a better analogy on nutrient redistribution. The obtained results of 17.34 mg/l total organic carbon and 1.392 mg/l total nitrogen at 36cm depth in old sludge ponding sites reveals the improved capability of sludge leachate against the soil nutrient attenuation. It finds application in agricultural practice to promote crop growth under controlled conditions of sewage sludge amendment as ponding and co-composting.

scholarly journals The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

2021 ◽  
Vol 10 (4) ◽  
pp. 243
Author(s):  
Azamat Suleymanov ◽  
Evgeny Abakumov ◽  
Ruslan Suleymanov ◽  
Ilyusya Gabbasova ◽  
Mikhail Komissarov
Keyword(s):  
Spatial Distribution ◽  
Soil Properties ◽  
Soil Nutrients ◽  
Precision Agriculture ◽  
Large Scale ◽  
Soil Nutrient ◽  
Steppe Zone ◽  
Shuttle Radar Topography Mission ◽  
Coefficient Of Determination ◽  
Support Vector

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

scholarly journals Aplikasi ESDA untuk Studi Variabilitas Spasial Hujan Bulanan di Jawa Timur

2011 ◽  
Vol 25 (2) ◽  
pp. 178
Author(s):  
I Indarto
Keyword(s):  
Spatial Variability ◽  
Spatial Data ◽  
Interpolation Method ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Spatial Data Analysis ◽  
Statistical Tool ◽  
Voronoi Map ◽  
Result Show ◽  
Inverse Distance

This article expose the spatial variability of monthly-rainfall (MR) in East Java region. Monthly rainfall data were collected from 943 pluviometres spread around the regions. Spatial statistics analysed by means of ESDA (Exploratory Spatial Data Analysis) techniques available on Geostatistical Analyst extention of ArcGIS (9.3). Statistical tools exploited to analise the data include: (1) Histogram, (2) Voronoi Map, and (3) QQ-Plot. The result show that histogram and QQ-Plot of Monthly Rainfall data are leptocurtosis. Statistical value obtained from the analysis are: minimum = 54 mm/month, average = 155,5 mm/month, maximum = 386 mm/month, and median = 150 mm/month. Other statistical value summarised are: standard deviation = 44,2 ; skewness = 0,95; and curtosis = 5,09. Finally, monthly rainfall-maps are produced by interpolating the data using Inverse Distance Weighed (IDW) interpolation method. The research demonstrate the capability and benefit of those statistical tool to describe detailed spatial variability of rainfall.

scholarly journals Spatio-temporal variability of soil nutrients and the responses of growth during growth stages of winter wheat in the north of China

2018 ◽  
Author(s):  
Su Bao-wei ◽  
Zhao Geng-xing ◽  
Dong Chao
Keyword(s):  
Winter Wheat ◽  
Spatial Correlation ◽  
Soil Nutrients ◽  
Growth And Development ◽  
Soil Nutrient ◽  
Growth Period ◽  
Nutrient Content ◽  
Wheat Growth ◽  
Filling Stage ◽  
Jointing Stage

AbstractStudy on soil nutrient variability and its effect on the growth and development of crop under traditional tillage mode is the foundation to comprehensively implement the precision agriculture policy on the field scale and crop excellent management. In this paper, a winter wheat field of 28.5 hm2 under traditional cultivation model in Tianzhuang town of Huantai county was selected as the research area. Sampling by mesh point method (60×60m), the variation characteristics of soil available nitrogen (AN), available phosphorus (AP) and available potassium (AK) in the before sowing, reviving stage, jointing stage, filling stage of winter wheat were analyzed by the method of geostatistics and GIS. At the same time, Pearson correlation analysis was used to study the response of wheat growth and development to soil nutrient variation. As the growth period goes by, 1) each nutrient showed low-high-low and moderate variability. AN and AK had the highest content at reviving stage, while AP at jointing stage, as turning point. The order of variability of each nutrient was AN>AP>AK. 2) The difference of nutrient variation first increased and then decreased, and showed medium to strong spatial correlation.Three nutrients in the before sowing stage were strong spatial correlation, and the reviving stage was medium spatial correlation, to the jointing and filling stages, AN was medium spatial correlation, AP and AK were strong spatial correlation. The spatial correlation of each nutrients was the weakest in the reviving stage, and AN was the strongest in the before sowing stage, while AP and AK were strongest in the jointing stage. The spatial correlation of each soil nutrients decreased from before sowing to reviving stage, jointing stage to filling stage, and the spatial correlation increased from reviving stage to jointing stage. 3) Soil nutrient content first increased and then decreased, and the grade of nutrient gradually decreased. 4) The correlation between soil nutrients and wheat growth was gradually increasing. AN had the highest correlation with wheat growth, followed by AK and AP lowest. The effect of soil nutrients on the growth of wheat at the reviving stage was higher than that of the current nutrient, and the growth of wheat at jointing stage was mainly influenced by the current nutrient, while the growth of wheat at the filling period was both influenced by the nutrient content of the last and the current period; the date to supplement fertilizer should be postponed properly. In this study, the soil nutrient dynamics and its influence on the growth of wheat during the winter wheat growth period under the traditional field model were well understood, which could provide a theoretical basis for the precision management of soil nutrients in the northern winter wheat area with relatively uniform planting environment and cultivation management.

The Nutrients Spatial Variability of Black Soil Based on GIS

2014 ◽  
Vol 694 ◽  
pp. 580-583
Author(s):  
Yue Ling Zhao ◽  
Hai Yan Han ◽  
Li Ying Cao ◽  
Gui Fen Chen
Keyword(s):  
Information Technology ◽  
Spatial Distribution ◽  
Soil Nutrients ◽  
Production Technology ◽  
Precision Agriculture ◽  
Agricultural Production ◽  
Soil Nutrient ◽  
Black Soil ◽  
Critical Determinant ◽  
Speed Up

The precision agriculture (PA) is the end product of the modern high tech's information technology and the agricultural production technology union. The soil nutrients are not only an important component of soil research, but also a critical determinant of its productivity. Some soil nutrients spatial distribution pictures were established based on important factors that affect crops production. The soil nutrient situation was understood by the paper in Jilin province black soil. The results can realized some information opening and sharing and helped some farmer and manager to understand some soil nutrient spatial distribution. They can speed up the development of Jilin province’s precision agriculture.

scholarly journals Assessment of Potato Farmland Soil Nutrient Based on MDS-SQI Model in the Loess Plateau

2021 ◽  
Vol 13 (7) ◽  
pp. 3957
Author(s):  
Yingying Xing ◽  
Ning Wang ◽  
Xiaoli Niu ◽  
Wenting Jiang ◽  
Xiukang Wang
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Fertility ◽  
Soil Quality ◽  
Soil Nutrients ◽  
Organic Matter Content ◽  
Soil Nutrient ◽  
Matter Content ◽  
Soil Organic Matter Content ◽  
Farmland Soil

Soil nutrients are essential nutrients provided by soil for plant growth. Most researchers focus on the coupling effect of nutrients with potato yield and quality. There are few studies on the evaluation of soil nutrients in potato fields. The purpose of this study is to investigate the soil nutrients of potato farmland and the soil vertical nutrient distributions, and then to provide a theoretical and experimental basis for the fertilizer management practices for potatoes in Loess Plateau. Eight physical and chemical soil indexes were selected in the study area, and 810 farmland soil samples from the potato agriculture product areas were analyzed in Northern Shaanxi. The paper established the minimum data set (MDS) for the quality diagnosis of the cultivated layer for farmland by principal component analysis (PCA), respectively, and furthermore, analyzed the soil nutrient characteristics of the cultivated layer adopted soil quality index (SQI). The results showed that the MDS on soil quality diagnosis of the cultivated layer for farmland soil included such indicators as the soil organic matter content, soil available potassium content, and soil available phosphorus content. The comprehensive index value of the soil quality was between 0.064 and 0.302. The SPSS average clustering process used to classify SQI was divided into three grades: class I (36.2%) was defined as suitable soil fertility (SQI < 0.122), class II (55.6%) was defined as moderate soil fertility (0.122 < SQI < 0.18), and class III (8.2%) was defined as poor soil fertility (SQI > 0.186). The comprehensive quality of the potato farmland soils was generally low. The proportion of soil nutrients in the SQI composition ranged from large to small as the soil available potassium content = soil available phosphorus content > soil organic matter content, which became the limiting factor of the soil organic matter content in this area. This study revolves around the 0 to 60 cm soil layer; the soil fertility decreased gradually with the soil depth, and had significant differences between the respective soil layers. In order to improve the soil nutrient accumulation and potato yield in potato farmland in northern Shaanxi, it is suggested to increase the fertilization depth (20 to 40 cm) and further study the ratio of nitrogen, phosphorus, and potassium fertilizer.

scholarly journals Temporal stability of electrical conductivity in a sandy soil

2016 ◽  
Vol 30 (3) ◽  
pp. 349-357 ◽  
Author(s):  
Aura Pedrera-Parrilla ◽  
Eric C. Brevik ◽  
Juan V. Giráldez ◽  
Karl Vanderlinden
Keyword(s):  
Electrical Conductivity ◽  
Spatial Variability ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Precision Agriculture ◽  
Temporal Stability ◽  
Principal Component ◽  
Model Parameters ◽  
Soil Spatial Variability

Abstract Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

Integrated Evaluation on Soil Nutrients in Spring Maize (Zea mays) Field Subjected to Limited Irrigation

2013 ◽  
Vol 389 ◽  
pp. 67-72
Author(s):  
Heng Jia Zhang ◽  
Jun Hui Li
Keyword(s):  
Soil Nutrients ◽  
Irrigation Management ◽  
Soil Nutrient ◽  
Fuzzy Mathematics ◽  
Available Nitrogen ◽  
Maize Field ◽  
Spring Maize ◽  
Significant Difference ◽  
One Year ◽  
The Membership Function

An experiment was conducted to explore the integrated evaluation on soil nutrients in spring maize field subjected to limited irrigation (LI) in oasis region. The soil organic matter (SOM), soil total and available nitrogen (STN and SAN) and phosphorus (STP and SAP), and soil available potassium (SAK) in 0~40 cm increment at harvest of maize subjected to LI were selected as the evaluation factors to calculate the weighing coefficient of each soil nutrient and the IEI for soil nutrients using the membership function in fuzzy mathematics. At maize harvest, differences were not significant (p>0.05) in SOM, STN, STP, SAP, and SAK within 0~40 cm increment among treatments and CK, but significant difference (p<0.05) was found in SAN, with the maximum SAN maintained in MI5, which was respectively 187.3%, 96.8%, and 41.2% higher over MI2 valued the minimum, MI1, and CK. The IEI was improved by 12.4% to 22.3% in all the other treatments and CK compared to the minimum marked in MI4, with the maximum valued in MI3 treatments. Therefore, after one year experiment, the optimized irrigation management was maintained in MI3 treatment due to its maximum IEI in all the LI regimes.

Study on Spatial Variability of Soil Nutrients in Ebinur Lake Wetlands in China

2015 ◽  
Vol 73 ◽  
pp. 59-63 ◽  
Author(s):  
Yonghui Wang ◽  
Li Zhang ◽  
Yimiti Haimiti
Keyword(s):  
Spatial Variability ◽  
Soil Nutrients ◽  
Ebinur Lake ◽  
Lake Wetlands
Sign in / Sign up

Export Citation Format

Share Document