An integration of soil characteristics by using GIS based Geostatistics and multivariate statistics analysis Sultan Batheri block, Wayanad District, India

Arumugam, Thangavelu; Kinattinkara, Sapna; Nambron, Drisya; Velusamy, Sampathkumar; Shanmugamoorthy, Manoj; Pradeep, T.; Mageshkumar, P.

doi:10.1016/j.uclim.2022.101339

Cited by 13 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nevertheless, it is worth noting that [58] highlighted that prediction accuracy can still be maintained even when RMSSE deviates from 1, as long as it falls within the tolerance interval of 1 + 3 √ 2/N; 1 − 3 √ 2/N. As per the methods proposed by [59,60], the ME and RMSSE were computed using the following formulas:…”

Section: Mapping Of Spatial Variabilitymentioning

confidence: 99%

“…The PCA was conducted using the IBM SPSS 18.0 statistical software. Among the numerous PCs, those with eigenvalues greater than or equal to 1 were considered to explain the variance within the entire data set and to facilitate the development of MZ classes [30,60,62,63]. The highest factor loadings were considered the most representative of system variables [22].…”

Section: Multivariate Analysis and Delineation Of Management Zonesmentioning

confidence: 99%

See 1 more Smart Citation

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

Salem,

Schott,

Piaskowski

et al. 2024

Sustainability

View full text Add to dashboard Cite

This research study underscores the importance of effectively managing soil nutrients in a site-specific manner to enhance crop productivity while considering the spatial variability of the soil. The objective is to identify subfields with similar soil characteristics, referred to as management zones (MZs), to promote sustainable land utilization. This study was conducted in two central pivot fields located in Southern Idaho, USA, where barley and sugar beets were grown. Soil samples were collected from each field in a grid pattern and analyzed for various chemical properties. These properties included soil pH, organic matter, cation exchange capacity, excess lime, electrical conductivity, total inorganic nitrogen, phosphorus, potassium, calcium, magnesium, zinc, iron, manganese, copper, and boron. Descriptive statistics and normality assessments were performed, and the coefficient of variation was calculated to assess the heterogeneity of soil properties, revealing significant variability. To determine the spatial variability of soil properties, ordinary kriging was used revealing diverse spatial patterns for each location and soil variable examined with moderate to strong spatial dependence. To develop the MZs, a combination of principal component analysis and fuzzy k-means clustering was utilized, and specific parameters that represented the overall variability of soil properties in each field were identified. Based on the identified parameters, two clusters were created in each field. The first management zone (MZ1) exhibited lower values of soil pH, excess lime content, and electrical conductivity compared to the MZ2. Consequently, higher crop productivity was observed in MZ1 in both fields. The biomass yields of barley and sugar beets in MZ1 surpassed those in MZ2. This study highlights the effectiveness of the methodology employed to delineate MZs, which can be instrumental in precise soil nutrient management and maximizing crop productivity.

show abstract

Section: Mapping Of Spatial Variabilitymentioning

confidence: 99%

Section: Multivariate Analysis and Delineation Of Management Zonesmentioning

confidence: 99%

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

Salem,

Schott,

Piaskowski

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…In this case, the closer the mean absolute percentage error (MAPE) and root mean square error (RMSE) are to 0, and the closer the R 2 is to 1, the better the model performance is. 36 3 | RESULTS AND DISCUSSION…”

Section: Model Evaluation Indicators and Environmentmentioning

confidence: 99%

Prediction of key quality attributes in Salvia miltiorrhiza standard decoction using a Gaussian process regression model

Zou,

Zhang,

Zhang

et al. 2024

Phytochemical Analysis

View full text Add to dashboard Cite

IntroductionNonstationary, nonlinear mass transfer in traditional Chinese medicine (TCM) extraction poses challenges to correlating process characteristics with quality parameters, particularly in defining clear parameter ranges for the process.ObjectivesThe aim of the study was to provide a solution for quality consistency analysis in TCM preparation processes.Materials and methodsSalvia miltiorrhiza was taken as an example for 15 batches of standard decoction. Using aqueous extract, alcoholic extract, and the content of salvianolic acid B as herb material key quality attributes, multiple nonlinear regression, Gaussian process regression, and artificial neural network models were employed to predict the key quality attributes including the paste yield, the content of salvianolic acid B, and the transfer rate. The evaluation criteria were root mean square error, mean absolute percentage error, and R2.ResultsThe Gaussian process regression model had the best prediction effect on the paste yield, the content of salvianolic acid B, and the transfer rate, with R2 being 0.918, 0.934, and 0.919, respectively. Utilizing Gaussian process regression model confidence intervals, along with Shewhart control and intervals optimized through process capability index analysis, the quality control range of the standard decoction was determined as follows: paste yield, 25.14%–33.19%; salvianolic acid B content, 2.62%–4.78%; and transfer rate, 56.88%–64.80%.ConclusionThis study combined the preparation process of standard decoction with the Gaussian process regression model, accurately predicted the key quality attributes, and determined the quality parameter range by using process analysis tools, providing a new idea for the quality consistency standard of TCM processes.

show abstract

“…Metode overlay dengan memberikan bobot setiap variabel dan memberikan skor terhadap atribut di dalamnya (Pratiwi, Supriatna & Manessa, 2021). Skor yang diberikan sesuai dengan kriteria dan analisis tujuan yang diinginkan (Arumugam et al, 2023), dalam penelitian ini semakin besar skor maka akan semakin sesuai (Tabel 2). Untuk melakukan analisis spasial weighted overlay, semua parameter yang telah dispasialkan dan diberikan skor harus dalam format raster.…”

Section: Metodeunclassified

Analisis Spasial Penentuan Lokasi Sekolah Baru SMA/SMK Sederajat (Studi Kasus: Kota Cilegon)

Ainiyah

2023

gdk

View full text Add to dashboard Cite

Memperoleh pendidikan merupakan hak setiap anak Indonesia, pemerintah memiliki kewajiban untuk memberikan fasilitas yang layak agar setiap anak Indonesia mendapatkan pendidikan dengan layak. Dalam melakukan pembangunan suatu sekolah terdapat kriteria yang harus dipenuhi sesuai dengan peraturan yang ada yaitu Peraturan Menteri Pendidikan Nasional Nomor 24 tahun 2007. Kriteria pendirian sekolah salah satunya yaitu jarak antar sekolah minimal 3 km. Analisis yang dilakukan yaitu dengan menggunakan Sistem Informasi Geografis (SIG). Berdasarkan analisis spasial yang dilakukan diperoleh bahwa hanya satu SMA yang berada di radius lebih dari 3 km dari sekolah lainnya, sedangkan 24 sekolah lainnya memiliki jarak kurang dari 3 km. Perkembangan kota Cilegon berpusat di tenggara, sehingga perkembangan urban area dan juga sekolah juga berpusat di area tersebut. Kesesuaian lokasi untuk menentukan lokasi pembangunan sekolah baru menggunakan metode weighted overlay dengan menggunakan parameter jarak antar sekolah, sempadan sungai sebagai constraint, serta kemiringan tanah. Lokasi yang sesuai untuk pembangunan sekolah baru adalah di sebelah barat daya Kota Cilegon dengan luas total 4,4 km 2 dan merupakan tutupan lahan sawah serta ladang.

show abstract

An integration of soil characteristics by using GIS based Geostatistics and multivariate statistics analysis Sultan Batheri block, Wayanad District, India

Cited by 13 publications

References 27 publications

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis

Prediction of key quality attributes in Salvia miltiorrhiza standard decoction using a Gaussian process regression model

Analisis Spasial Penentuan Lokasi Sekolah Baru SMA/SMK Sederajat (Studi Kasus: Kota Cilegon)

Contact Info

Product

Resources

About