scholarly journals The Effect of Soil Sampling Density and Spatial Autocorrelation on Interpolation Accuracy of Chemical Soil Properties in Arable Cropland

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2430
Author(s):  
Dorijan Radočaj ◽  
Irena Jug ◽  
Vesna Vukadinović ◽  
Mladen Jurišić ◽  
Mateo Gašparović
Keyword(s):  
Spatial Autocorrelation ◽  
Soil Properties ◽  
Agricultural Land ◽  
Interpolation Method ◽  
Accuracy Assessment ◽  
Soil Sampling ◽  
Potassium Oxide ◽  
Sampling Density ◽  
Interpolation Accuracy ◽  
Chemical Soil

Knowledge of the relationship between soil sampling density and spatial autocorrelation with interpolation accuracy allows more time- and cost-efficient spatial analysis. Previous studies produced contradictory observations regarding this relationship, and this study aims to determine and explore under which conditions the interpolation accuracy of chemical soil properties is affected. The study area covered 823.4 ha of agricultural land with 160 soil samples containing phosphorus pentoxide (P2O5) and potassium oxide (K2O) values. The original set was split into eight subsets using a geographically stratified random split method, interpolated using the ordinary kriging (OK) and inverse distance weighted (IDW) methods. OK and IDW achieved similar interpolation accuracy regardless of the soil chemical property and sampling density, contrary to the majority of previous studies which observed the superiority of kriging as a deterministic interpolation method. The primary dependence of interpolation accuracy to soil sampling density was observed, having R2 in the range of 56.5–83.4% for the interpolation accuracy assessment. While this study enables farmers to perform efficient soil sampling according to the desired level of detail, it could also prove useful to professions dependent on field sampling, such as biology, geology, and mining.

PEMANFAATAN LIMBAH JAGUNG MENJADI PUPUK ORGANIK UNTUK PENYUBURAN LAHAN PERTANIAN

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Daru Mulyono
Keyword(s):  
Agricultural Land ◽  
Organic Fertilizer ◽  
Economic Value ◽  
Soil Microorganism ◽  
Appropriate Technology ◽  
Inorganic Fertilizer ◽  
Plant Materials ◽  
Chemical Soil ◽  
Stem Leaf

The use of maize waste plant materials (stem, leaf, and husk cover) have high economic value to be processed become organic fertilizer for agricultural land fertilizer. Maize have several and quite high contents of macro and micro nutrients. This activity was hoped that the farmers can overcome the increasing price of inorganic fertilizer recently and furthermore farmers can reap higher income. Beside higher income the use of organic fertilizer can improve the nature and behaviourof land through improving of soil chemical, soil physical, and soil microorganism. Therefore, the appropriate technology for processing of maize become organic fertilizer is very important to be diffused or socialized to farmers.Keywords: fertilizer, maize waste

Potential Waste Application of Several Industries Segments in Brazilian Agriculture: Effects on Physical and Chemical Soil Properties

2021 ◽  
pp. 1-24
Author(s):  
Marcos Renan Besen ◽  
Michel Esper Neto ◽  
Bruno Maia Abdo Rahmen Cassim ◽  
Evandro Antonio Minato ◽  
Tadeu Takeyoshi Inoue ◽  
...  
Keyword(s):  
Soil Properties ◽  
Chemical Soil ◽  
Physical And Chemical

scholarly journals Biochar Improves Maize Growth but Has a Limited Effect on Soil Properties: Evidence from a Three-Year Field Experiment

2021 ◽  
Vol 13 (7) ◽  
pp. 3617
Author(s):  
Agnieszka Medyńska-Juraszek ◽  
Agnieszka Latawiec ◽  
Jolanta Królczyk ◽  
Adam Bogacz ◽  
Dorota Kawałko ◽  
...  
Keyword(s):  
Low Temperature ◽  
Field Experiment ◽  
Soil Properties ◽  
High Efficiency ◽  
Crop Yields ◽  
Temperate Climate ◽  
Soil Conditions ◽  
Maize Growth ◽  
Chemical Soil ◽  
Physical And Chemical

Biochar application is reported as a method for improving physical and chemical soil properties, with a still questionable impact on the crop yields and quality. Plant productivity can be affected by biochar properties and soil conditions. High efficiency of biochar application was reported many times for plant cultivation in tropical and arid climates; however, the knowledge of how the biochar affects soils in temperate climate zones exhibiting different properties is still limited. Therefore, a three-year-long field experiment was conducted on a loamy Haplic Luvisol, a common arable soil in Central Europe, to extend the laboratory-scale experiments on biochar effectiveness. A low-temperature pinewood biochar was applied at the rate of 50 t h−1, and maize was selected as a tested crop. Biochar application did not significantly impact the chemical soil properties and fertility of tested soil. However, biochar improved soil physical properties and water retention, reducing plant water stress during hot dry summers, and thus resulting in better maize growth and higher yields. Limited influence of the low-temperature biochar on soil properties suggests the crucial importance of biochar-production technology and biochar properties on the effectiveness and validity of its application in agriculture.

Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger
Keyword(s):  
Spatial Autocorrelation ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Accuracy Assessment ◽  
Small Scale ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Management Tools ◽  
Seabed Topography ◽  
Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

Research about Influencing Factor on Interpolation Accuracy in Data Exchange of Fluid-Structure Interaction

2013 ◽  
Vol 318 ◽  
pp. 100-107
Author(s):  
Zhen Shen ◽  
Biao Wang ◽  
Hui Yang ◽  
Yun Zheng
Keyword(s):  
Data Exchange ◽  
Shape Function ◽  
Interpolation Method ◽  
Matching Condition ◽  
Optimal Interpolation ◽  
Complex Interpolation ◽  
Interpolation Function ◽  
Interpolation Methods ◽  
Interpolation Accuracy ◽  
Lower Accuracy

Six kinds of interpolation methods, including projection-shape function method, three-dimensional linear interpolation method, optimal interpolation method, constant volume transformation method and so on, were adoped in the study of interpolation accuracy. From the point of view about the characterization of matching condition of two different grids and interpolation function, the infuencing factor on the interpolation accuracy was studied. The results revealed that different interpolation methods had different interpolation accuracy. The projection-shape function interpolation method had the best effect and the more complex interpolation function had lower accuracy. In many cases, the matching condition of two grids had much greater impact on the interpolation accuracy than the method itself. The error of interpolation method is inevitable, but the error caused by the grid quality could be reduced through efforts.

Vegetation Effects on Soil Properties in the Monteagle Sandy Loam Series: Implications for Land‐use Change

2017 ◽  
Author(s):  
Allison Neil
Keyword(s):  
Land Use ◽  
Organic Matter ◽  
Land Use Change ◽  
Soil Properties ◽  
Soil Carbon ◽  
Sugar Maple ◽  
Agricultural Land ◽  
Deciduous Forest ◽  
Coniferous Forest ◽  
The Impact

Soil properties are strongly influenced by the composition of the surrounding vegetation. We investigated soil properties of three ecosystems; a coniferous forest, a deciduous forest and an agricultural grassland, to determine the impact of land use change on soil properties. Disturbances such as deforestation followed by cultivation can severely alter soil properties, including losses of soil carbon. We collected nine 40 cm cores from three ecosystem types on the Roebuck Farm, north of Perth Village, Ontario, Canada. Dominant species in each ecosystem included hemlock and white pine in the coniferous forest; sugar maple, birch and beech in the deciduous forest; grasses, legumes and herbs in the grassland. Soil pH varied little between the three ecosystems and over depth. Soils under grassland vegetation had the highest bulk density, especially near the surface. The forest sites showed higher cation exchange capacity and soil moisture than the grassland; these differences largely resulted from higher organic matter levels in the surface forest soils. Vertical distribution of organic matter varied greatly amongst the three ecosystems. In the forest, more of the organic matter was located near the surface, while in the grassland organic matter concentrations varied little with depth. The results suggest that changes in land cover and land use alters litter inputs and nutrient cycling rates, modifying soil physical and chemical properties. Our results further suggest that conversion of forest into agricultural land in this area can lead to a decline in soil carbon storage.

Study on Lidar Data Interpolation Method Based on GA-BP

2012 ◽  
Vol 588-589 ◽  
pp. 1312-1315
Author(s):  
Yi Kun Zhang ◽  
Ming Hui Zhang ◽  
Xin Hong Hei ◽  
Deng Xin Hua ◽  
Hao Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Interpolation Method ◽  
Linear Interpolation ◽  
Lidar Data ◽  
Data Interpolation ◽  
Interpolation Model ◽  
Genetic Method ◽  
Interpolation Accuracy ◽  
Linear Interpolation Method

Aiming at building a Lidar data interpolation model, this paper designs and implements a GA-BP interpolation method. The proposed method uses genetic method to optimize BP neural network, which greatly improves the calculation accuracy and convergence rate of BP neural network. Experimental results show that the proposed method has a higher interpolation accuracy compared with BP neural network as well as linear interpolation method.

Effect of Soil Sampling Density on Detected Spatial Variability of Soil Organic Carbon in a Red Soil Region of China

Pedosphere ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 207-213 ◽  
Author(s):  
Dong-Sheng YU ◽  
Zhong-Qi ZHANG ◽  
Hao YANG ◽  
Xue-Zheng SHI ◽  
Man-Zhi TAN ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Red Soil ◽  
Soil Sampling ◽  
Sampling Density
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