Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

Neto, Alfredo Bonini; Bonini, C. S. B.; Reis, André Rodrigues dos; Piazentin, Jhonatan Cabrera; Coletta, Luiz Fernando Sommaggio; Putti, Fernando Ferrari; Heinrichs, Reges; Moreira, Adônis

doi:10.1080/00103624.2019.1635144

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…Soil pH and V (%) in the two studied soil layers showed positive correlations higher than 0.6, as well as P with OM in the first soil layer. This behavior confirms the relationship between soil variables and shows that the OM contribution is important for the increment of nutrients in the soil, as well as the pH correction in the increase in base saturation, in agreement with Bonini [20]. Legend: P = phosphorus, OM = organic matter, pH, V = base saturation in the 0.00-0.10 m layer; P2 = phosphorus, OM2 = organic matter, pH2, V2 = base saturation in the 0.10-0.20 m layer.…”

Section: Resultssupporting

confidence: 88%

“…Soil degradation has negatively altered the quality of soil chemical properties, compromising plant development, as it alters the organic matter content, increasing the challenge in soil management; that is, it makes it difficult to enrich and maintain this chemical attribute [15][16][17][18]. Research has been carried out aiming to contribute with techniques for a higher addition of organic matter to soils using plant species, mineral fertilizers, and residues [7,19,20].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it creates a favorable environment for the development of organisms that will act to improve the soil's chemical and physical qualities. The action of soil organisms causes an improvement in soil structure by increasing cementing agents and mechanical action [20].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evolution of Soil Chemical Fertility in an Area under Recovery for 30 Years with Anthropic Intervention

Lourencetti,

Bonini,

Andreotti

et al. 2023

Sustainability

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The investigation and application of recovery techniques associated with the use of qualitative and quantitative indicators enable the ecological restoration of these sites. In this context, the main difficulty consists of establishing the A horizon, capable of supporting the emergence of other horizons, choosing appropriate species, and adding organic matter to the soil in a balanced way. Thus, this study aimed to evaluate over time (1992 to 2022) the chemical properties of a stripped oxisol that has been in the recovery process for 30 years, using liming, gypsum, and plant species. All treatments were cultivated with Urochloa decumbens (Stapf.) in 1999 and tree species in 2010, and the contents of phosphorus, organic matter, pH, and base saturation. ANOVA and Scott–Knott test (5%), Pearson correlation, and response surface analysis were performed for each studied soil attribute. The results showed that the treatments with green manure + limestone + gypsum had the highest values of organic matter compared to the recovery treatments and that the treatments have been efficient in soil recovery. Therefore, the soil undergoing recovery showed an increase in P, OM, pH, and base saturation until 2011, and the response surface method was the most efficient in analyzing the results over time.

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Section: Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of Soil Chemical Fertility in an Area under Recovery for 30 Years with Anthropic Intervention

Lourencetti,

Bonini,

Andreotti

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the operation or diagnosis stage of supervised training, the ability of the network to predict a target class or value of an attribute is tested on a dataset that was not used in the training stage. Therefore, under a supervised approach, ANNs learn by adjusting their weights during training as a function of input data whose outputs are known (Bonini Neto et al., 2019; Souza et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction of soybean yield cultivated under subtropical conditions using artificial neural networks

et al. 2023

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Mathematical models that incorporate biotic and abiotic attributes are important tools for improving fertilizer use efficiency and reducing production costs for soybean [Glycine max (L.) Merrill] crop. In this study, artificial neural networks (ANNs) were used to estimate soybean grain yield (GY) under subtropical conditions in Brazil from plant morphological and nutritional data collected from 16 cultivars in two growing seasons. The ANNs were adequately trained, with a mean squared error of approximately 10−5 between the outputs obtained (via ANN) and desired (via experimental field), equivalent to a mean percentage error of 70.1 kg ha−1 (1.6%), confirming their efficacy as a tool to estimate GY. Smaller plant height, higher foliar calcium, magnesium and chlorophyll concentrations, and greater numbers of grains per pod and branches per plant were associated with higher GY, whereas oil content, crude protein content, and foliar manganese and potassium concentrations had no predicted effects on GY.

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“…Automation of classification algorithms allows more time-efficient and robust classification in comparison to the conventional manual approach [11]. Recently developed algorithms for soil classification cover a wide range of applications, most notably measurement of soil surface roughness [12], prediction of soil fertility [13] and estimation of degraded soils [14]. In addition, mapping of soil texture variability in both horizontal and vertical dimensions has been a subject of interest in previous studies [15,16].…”

Section: Introductionmentioning

confidence: 99%

Delineation of Soil Texture Suitability Zones for Soybean Cultivation: A Case Study in Continental Croatia

et al. 2020

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Soil texture is a vital criterion in most cropland suitability analyses, so an accurate method for the delineation of soil texture suitability zones is necessary. In this study, an automated method was developed and evaluated for the delineation of these zones for soybean cultivation. A total of 255 soil samples were collected in the Continental biogeoregion of Croatia. Three methods for interpolation of clay, silt and sand soil content were evaluated using the split-sample method in five independent random repetitions. An automated algorithm for soil texture classification based on the United States Department of Agriculture (USDA) in 12 classes was performed using Python script. Suitability classes for soybean cultivation per soil texture class were determined according to previous agronomic and soybean land suitability studies. Ordinary kriging produced the highest accuracy of tested interpolation methods for clay, silt and sand. Highly suitable soil texture classes for soybean cultivation, loam and clay loam, were detected in the northern part of the study area, covering 5.73% of the study area. The analysis of classification results per interpolation method indicated a necessity of the evaluation of interpolation methods as their performance depended on the normality and stationarity of input samples.

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Automatic Recovery Estimation of Degraded Soils by Artificial Neural Networks in Function of Chemical and Physical Attributes in Brazilian Savannah Soil

Cited by 8 publications

References 17 publications

Evolution of Soil Chemical Fertility in an Area under Recovery for 30 Years with Anthropic Intervention

Evolution of Soil Chemical Fertility in an Area under Recovery for 30 Years with Anthropic Intervention

Prediction of soybean yield cultivated under subtropical conditions using artificial neural networks

Delineation of Soil Texture Suitability Zones for Soybean Cultivation: A Case Study in Continental Croatia

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