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

Suleymanov, Azamat; Abakumov, Evgeny; Suleymanov, Ruslan; Габбасова, И. М.; Komissarov, Mikhail

doi:10.3390/ijgi10040243

Cited by 32 publications

(20 citation statements)

References 54 publications

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“…MrRTF showed a correlation of −0.21 with SOM, −0.06 with CEC. Suleymanov et al (2021) [33] reported 0.5 correlation between SOM and MrRTF and MrRTF was a key attribute for mapping soil organic carbon and thickness of humus. Ref.…”

Section: Spatial Trend Modelingmentioning

confidence: 99%

“…It is possible to generate many terrain attributes from a DEM, but it is important to limit their number to avoid redundancy, model overfitting and interpretations [32]. Suleymanov et al (2021) [33] found that of 17 generated terrain attributes, only three of them (elevation, slope, and MrRTF) were most important for predicting the spatial variability of soil properties including SOM. We focused on those terrain attributes that have a close relationship to water redistribution across a post-glaciated landscape such as at ACRE and that are commonly used in DSM.…”

Section: Terrain Attributesmentioning

confidence: 99%

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Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Rahmani

Ackerson²,

Schulze

et al. 2022

Agronomy

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Soil organic matter content (SOM) and cation exchange capacity (CEC) are important agronomic soil properties. Accurate, high-resolution spatial information of SOM and CEC are needed for precision farm management. The objectives of this study were to: (1) map SOM and CEC in a low relief area using only lidar elevation-based terrain attributes, and (2) compare the prediction accuracy of SOM and CEC maps created by universal kriging, Cubist, and random forest with Soil Survey Geographic (SSURGO) database. For this study, 174 soil samples were collected from a depth from 0 to 10 cm. The topographic wetness index, topographic position index, multi resolution valley bottom flatness, and multi resolution ridge top flatness indices generated from the lidar data were used as covariates in model predictions. No major differences were found in the prediction performance of all selected models. For SOM, the predictive models provided results with coefficient of determination (R2) (0.44–0.45), root mean square error (RMSE) (0.8–0.83%), bias (0–0.22%), and concordance correlation coefficient (ρc) (0.56–0.58). For CEC, the R2 ranged from 0.39 to 0.44, RMSE ranged from 3.62 to 3.74 cmolc kg−1, bias ranged from 0–0.17 cmolc kg−1, and ρc ranged from 0.55 to 0.57. We also compared the results to the USDA Soil Survey Geographic (SSURGO) data. For both SOM and CEC, SSURGO was comparable with our predictive models, except for few map units where both SOM and CEC were either under or over predicted.

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Section: Spatial Trend Modelingmentioning

confidence: 99%

Section: Terrain Attributesmentioning

confidence: 99%

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Rahmani

Ackerson²,

Schulze

et al. 2022

Agronomy

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“…Nevertheless, the bare soil, especially after opencast mining, is known as lacking plant nutrients, aggregation, and organic matter, which make the revegetation process difficult [4,5]. Monitoring the areas, especially in a chronosequence study, is therefore necessary to understand whether the adopted rehabilitation practices are contributing to the improvement of soil attributes and the rehabilitation of the disturbed area [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…One promising technique for rehabilitation monitoring is the use of digital image processing of remote sensing data for assessing long-to short-term landscape dynamics. In addition, it can estimate important indicators of soil quality in a cost-effective and rapid manner when compared with repeated field surveys and laboratory measurements in large areas with difficult access [7,8]. One example is the correlation between spectral indices (i.e., normalized difference vegetation index (NDVI) and bare soil index (BSI)) and soil properties such as organic carbon, nutrients, and texture [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

et al. 2022

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Rehabilitation is the key factor for improving soil quality and soil carbon stock after mining operations. Monitoring is necessary to evaluate the progress of rehabilitation and its success, but the use of repeated field surveys is costly and time-consuming at a large scale. This study aimed to monitor the environmental/soil rehabilitation process of an Amazonian sandstone mine by applying spectral indices for predicting soil organic carbon (SOC) stock and comparing them to soil quality index. The studied area has different chronological rehabilitation stages: initial, intermediate, and advanced with 2, 10, and 12 years of onset rehabilitation activities, respectively. Non-rehabilitated (NR) and two native forest areas (RA) were used as controls. Soil samples were analyzed for physical, chemical, and biological attributes. After determination of Normalized Difference Vegetation Index and Bare Soil Index, simple regression analysis comparing these indices with SOC stock showed a good fit (R2 = 0.82). Rehabilitated areas presented higher soil quality index (~1.50-fold) and SOC stock (~10.6-fold) than NR; however, they did not differ of RA. The use of spectral indices was effective for monitoring the soil quality in this study, with a positive correlation between the predicted SOC stock and the calculated soil quality index.

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“…В мировой практике методы цифрового картографирования эффективно используются для картографирования классов и свойств почв на основе экологических ковариат, рассчитываемых по данным дистанционного зондирования Земли Soil organic…, 2018;Suleymanov et al, 2021). Однако такой метод не подходит для крупномасштабного картографирования, когда расстояние между точками отбора проб почвы слишком мало и измеряется в сантиметрах, и при этом отсутствуют подробные данные дистанционного зондирования экологических ковариат с высоким пространственным разрешением.…”

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Effect of agrochemicals on spatio-temporal changes in soil agrochemical properties and yield of broccoli

Gopp¹

2021

POS

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The aim of the study. The aim of the study was to examine the effect of agrochemicals (mineral and organo-mineral fertilizers, plant growth regulators and ameliorants) on the spatio-temporal changes in the properties of agro-gray soil and broccoli yield using a cartographic approach. Methodology. The field experiment was conducted in 2016 at the site located near the Bykovo village in the south-east of West Siberia (54°58 '02.8" N; 83°5'21.45" E, Novosibirsk region). The objects of the study were the agro-gray soil (Luvic Retic Greyzemic Phaeozem (Siltic, Aric)) and the medium-ripe broccoli cabbage variety "Linda". The experiment scheme included the following treatments: (1) Control (without fertilizers), (2) Background (N100P60K160), (3) Background + succinic acid, (4) Background + dolomite, (5) Background + mulch, (6) Organo- mineral fertilizer "Criall". Soil samples were analyzed for the content of organic carbon, nitrates, total, mineral and organic phosphorus, as well as its mobile form; exchangeable potassium, calcium and magnesium, and pH. The dried samples of broccoli inflorescences were analyzed forthe total content of nitrogen, phosphorus, potassium, calcium and magnesium.Main results. After applying agrochemicals and harvesting broccoli, the variation coefficients of рН, mobile phosphorus, potassium and calcium increased by two or more times. The use of agrochemicals increased the yield and improved the nutritional value of broccoli inflorescences. The treatments with mineral fertilization(2, 3, 4 and 5) the treatment 2 (Background N100P60K160) provided the greatest return. The application of the studied agrochemicals resulted in the negative (acidification, binding of phosphorus, calcium and magnesium) and positive (increase in the content of elements) effects. The immobilization of nutrients (phosphorus, calcium, magnesium) in poorly soluble soil compounds and the respective decrease in the content of the elements’ mobile forms did not have a negative effect on the elemental composition of broccoli inflorescences. Thus, chemical immobilization, leading to the fixation of nutrients in the upper soil horizon, prevents their leaching by melt- and rainwater into the lower horizons.

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

Cited by 32 publications

References 54 publications

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Digital Mapping of Soil Organic Matter and Cation Exchange Capacity in a Low Relief Landscape Using LiDAR Data

Spectral and Soil Quality Index for Monitoring Environmental Rehabilitation and Soil Carbon Stock in an Amazonian Sandstone Mine

Effect of agrochemicals on spatio-temporal changes in soil agrochemical properties and yield of broccoli

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