Experimental Studies on Effect of Water and Soil quality on Crop Yield

Suresh, K.; Nagesh, M.

doi:10.1016/j.aqpro.2015.02.161

Cited by 19 publications

(5 citation statements)

References 5 publications

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“…A farmer's income depends on the crop yield which, in turn, relies on irrigation water availability and quality [70]. Application of saline irrigation water causes degradation of soil fertility, and crop problems can develop [71].…”

Section: Resultsmentioning

confidence: 99%

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

et al. 2021

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Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

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Section: Resultsmentioning

confidence: 99%

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

et al. 2021

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“…To some degree, most of the soils are capable of adsorbing and detoxifying many pollutants to harmless levels through chemical and biochemical processes. Polluted water and soil pose a serious threat to plants, affecting the yield [13]. Furthermore, soil has the ability to adsorb pesticides in the humus and clay contents [14].…”

Section: Soil and Pesticide Pollutionmentioning

confidence: 99%

Effect of PCP Pesticide Contamination on Soil Quality

Ammeri¹,

Hidri²,

Hassen³

2021

Soil Contamination - Threats and Sustainable Solutions

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In recent years, soil contamination with pesticides has become a crucial news issue with serious short- and long-term effects on human health and its environment. Pesticides play a significant role in the success of modern farming and food production. These compounds have potential for toxicity and adverse effects on human health and ecological soil systems. Pentachlorophenol (PCP) is one of the most recalcitrant chemicals polluting the environment for its stable aromatic ring system and chloride content. Nowadays, many sites are contaminated with this substance. In these areas, concentrations may stay high for a long time because of slow degradation in the soil due to the negative effects that PCP has on soil microbial populations. Bioremediation of PCP contaminated sites can be realized introducing directly, into a contaminated system, microorganisms able to consume selectively the target compound (bioaugmentation) or increasing the microbial indigenous population by addiction of nutrients in form of organic and/or inorganic fertilizers and biosolids (biostimulation). In the present chapter, we present an overview of the effect of PCP pesticide contamination on soil microbial populations (density and diversity), enzymatic activity and physicochemical parameters. Additionally, the bioremediation process will be detailed.

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“…In the worst-case scenario, such water resources could be contaminated. Hence, the prolonged consumption of such water resources has the potential to result in a diverse array of health problems to the human ( Fahad et al., 2020 ), livestock ( Giri et al., 2020 ), and crops ( Suresh and Nagesh, 2015 ).…”

Section: Introductionmentioning

confidence: 99%