Assessing Soil Quality Index Under Different Sugarcane Monoculture Periods and Soil Orders

Kusumawati, Anna; Hanudin, Eko; Purwanto, Benito Heru; Nurudin, Makruf

doi:10.1080/00103624.2022.2112213

Cited by 2 publications

(3 citation statements)

References 49 publications

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“…x -value of each indicator; x min -minimum value of the indicator; x max -maximum value of the indicator (Andrews et al 2002;Askari and Holden 2015;Kusumawati et al 2023).…”

Section: Methodsmentioning

confidence: 99%

“…In contrast, optimum rating curves are used when indicators positively affect soil quality to a certain extent, and negative beyond that limit. The more curves for assessing soil quality are better for indicators of C-Min, aggregate stability, available K, CEC, C-POM, total N, organic C, available P, and C-Mic (Nabiollahi et al 2017;Kusumawati et al 2023). Soil quality assessment curves are slightly better for BD parameters as published by Nabiollahi et al (2017).…”

Section: Types Of Assessment Curves and Formulas For Each Indicatormentioning

confidence: 99%

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Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

Meitasari,

Hanudin,

Purwanto

2024

Soil & Water Res.

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This study aims to compare the soil quality indexing model by adding and weighting the soil under different land uses and slope positions on the southwest slope of Mount Merapi, Indonesia. Soil sampling was carried out based on a landscape analysis divided into four geomorphological units (slopes): upper, middle, lower and foot slopes. The research design was nested where the soil sample was located (surface soil 0-30 cm). Based on the research results, soil quality indices (SQI) of forest on the upper slopes is very high. SQI of dry fields on the middle, lower and foot slopes is low to medium. SQI of mixed gardens on the middle and lower slopes is low to medium. SQI of snake fruit land on the middle, lower and foot slopes is medium to high. SQI of grassland on the lower slopes is medium to high, and SQI of paddy fields on the foot slopes is medium to high. Weighted soil quality index (SQIw) has a higher correlation (R 2 = 0.90) and can predict soil quality better than the adding soil quality index (SQIa) model (R 2 = 0.76). Indicators that most influence soil quality are the percentage of sand, total N, C-POM, C-Min, pH, and aggregate stability, that indicators are entirely influenced by organic matter, site-specific management to maintain SQI by maintaining organic matter. The selected indicators in this study can be used to determine the SQI in similar areas.

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“…x -value of each indicator; x min -minimum value of the indicator; x max -maximum value of the indicator (Andrews et al 2002;Askari and Holden 2015;Kusumawati et al 2023).…”

Section: Methodsmentioning

confidence: 99%

Section: Types Of Assessment Curves and Formulas For Each Indicatormentioning

confidence: 99%

Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

Meitasari,

Hanudin,

Purwanto

2024

Soil & Water Res.

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“…In the work of Anna et al [11], soil quality index (SQI) was measured using principal component analysis (PCA) method and also showed that long, the long term sugarcane monoculture significantly affects on the value of SQI. In the work of Ofelia et al [12], eleven different soil physicochemical parameters with an average accuracy of 73% using only the data of potassium, calcium and CEC as input parameters was achieved and also, it was possible to determine the soil potential with respect to hydrogen, phosphorous, boron, manganese, zinc, organic matter, calcium, magnesium, sulphur, copper, including soil texture with the help of machine learning models KNN and linear regression algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

CNN and LSTM Approach for Soil Nutrient analysis for Sugarcane Crop

2024

Preprint

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A new paradigm has been adopted in agricultural techniques, tools, and technology. To guarantee the implementation of site-specific crop management, which includes soil nutrient treatments according to crop requirements, precision agriculture is crucial. Soil nutrients are a major component in determining the growth of precision agriculture, which has gained global attention. One of the main challenges is more effective nutrient content detection as it guides well-planned nutrient-level-boosting routines. Many approaches, such using research labs and additional mobile labs, haven't shown to be very useful in helping farmers control their soil fertility. Taking advantage of the breakthroughs is severely hampered by factors like lack of knowledge and distance from research centers. In the work proposed, LSTM based RNN is employed to predict the Ph and nutrient values of the soil measured through microcontroller using sensor from the agricultural field. At the same time, RELU-CNN approach is applied to the soil images for measuring the same values. The values obtained from both the approaches are compared against each other so that the method may be made directly available to the farmers for evaluating the nutrient level of the soil and take necessary action. The approaches are measured in terms of quality parameters Recall, F1-Score, Accuracy, Precision and RMS value.

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Assessing Soil Quality Index Under Different Sugarcane Monoculture Periods and Soil Orders

Cited by 2 publications

References 49 publications

Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

Comparison of two soil quality assessment models under different land uses and topographical units on the southwest slope of Mount Merapi

CNN and LSTM Approach for Soil Nutrient analysis for Sugarcane Crop

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