Ecological Informatics: Overview

Recknagel, Friedrich

doi:10.1016/b978-008045405-4.00170-1

Cited by 3 publications

(4 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the 30-40 cm soil layer, there were even fewer pores and the soil porosities were lower than that of other soil layer (Figures 1-3), possibly because the application of SAP in the upper layer led to a higher soil water content, which caused a larger concentration of crop roots to grow into this layer, in turn leading to a lower root density in the bottom ploughed layer at 30-40 cm. Furthermore, another possible cause is the reduced activity of microorganisms and soil fauna because of low soil water and crop root presence, which caused the porosity of this soil layer to be lower than that of other soil layers (Recknagel, 2008). However, compared to the control, the pore density under SAP treatment was higher in the 40-100 cm soil layer, and the presence of macropores and earthworm burrows suggest earthworm activity (Figure 1).…”

Section: Discussionmentioning

confidence: 99%

Effect on Soil Properties and Crop Yields to Long-Term Application of Superabsorbent Polymer and Manure

Yang

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

Intensive farming leads to soil degradation, and poses a serious threat to agricultural yield sustainability and soil ecology. Superabsorbent polymer (SAP) and organic manure (OM) are amendments that can remediate the soil, and improve soil structure and soil pore characteristics, thereby enhancing soil quality and promoting crop growth. These benefits may be enhanced through long-term continuous application of the amendments. X-ray computed tomography (CT) was used to determine the number, connectivity, and locations of pores (160–1,000 μm, >1,000 µm) in the soil at depths of 0–100 cm. Soil organic carbon content, proportion of water-stable macroaggregates (>0.25 mm) (WSM), soil bulk density, field water capacity, available water content, wilting point content, and saturated hydraulic conductivity were also determined. Compared to the control, long-term SAP and OM treatments significantly increased the number of 160–1,000 µm and >1,000 µm soil pores (5–20 cm depths), >1,000 µm and >160 µm soil porosity (at 0–20 cm depths), total organic carbon (10–20 cm and 40–50 cm), field water capacity (0–20 cm), available water content (0–20 cm), wilting point (10–30 cm), saturated water content (10–70 cm), saturated hydraulic conductivity (0–90 cm), and WSM (0–30 cm and 40–50 cm) while decreasing the soil bulk density (0–70 cm). Compared to OM, SAP had greater beneficial effects on the total organic carbon, soil field water capacity, available water content, saturated water content, saturated hydraulic conductivity, and WSM in soil at 0–20 cm depth. The improvements to soil quality resulted in an increase in the yields of wheat and maize. Therefore, although SAP and OM were both effective at improving soil properties and increasing the economic yield of wheat and maize, the effectiveness of SAP was greater than that of OM.

show abstract

Section: Discussionmentioning

confidence: 99%

Effect on Soil Properties and Crop Yields to Long-Term Application of Superabsorbent Polymer and Manure

Yang

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Time lag inputs were used to improve model performance and obtain optimum operation capacity. Friedrich Recknage [20] believes that ANN models can attain a high generalization degree as well as forecasting accuracy after training using time lag time series data. His research has shown that input sensitivity curves using a supervised feedback ANN, combined with component planes using a non-supervised ANN, is an effective approach.…”

Section: Effect Of Climate Change On Hydrological-ecological Environmmentioning

confidence: 99%

Application of artificial neural networks in global climate change and ecological research: An overview

et al. 2010

View full text Add to dashboard Cite

Fields that employ artificial neural networks (ANNs) have developed and expanded continuously in recent years with the ongoing development of computer technology and artificial intelligence. ANN has been adopted widely and put into practice by researchers in light of increasing concerns over ecological issues such as global warming, frequent El Niño-Southern Oscillation (ENSO) events, and atmospheric circulation anomalies. Limitations exist and there is a potential risk for misuse in that ANN model parameters require typically higher overall sensitivity, and the chosen network structure is generally more dependent upon individual experience. ANNs, however, are relatively accurate when used for short-term predictions; despite global climate change research favoring the effects of interactions as the basis of study and the preference for long-term experimental research. ANNs remain a better choice than many traditional methods when dealing with nonlinear problems, and possesses great potential for the study of global climate change and ecological issues. ANNs can resolve problems that other methods cannot. This is especially true for situations in which measurements are difficult to conduct or when only incomplete data are available. It is anticipated that ANNs will be widely adopted and then further developed for global climate change and ecological research. global change, ecology, artificial neural network, nonlinear problem Citation: Liu Z L, Peng C H, Xiang W H, et al. Application of artificial neural networks in global climate change and ecological research: An overview.In recent years, global climate change has become a focus of attention for governments, scientists, and the public at large. Related research in numerous domains has been conducted and remarkable progress made in recent years. Global climate change is primarily a result of global warming, frequent El Niño and La Niña events, and atmospheric circulation anomalies. The global average temperature has increased by approximately 0.6°C over the past 100 years and is projected to continue to rise at a rapid rate. In addition, global atmospheric CO 2 concentrations have risen by nearly 38% since the pre-industrial period and are projected to surpass 700 μmol mol -1 by the end of this century [1].Taking "global warming" as a prominent indicator, climate change may effectively alter ecosystem structure and function. Over the years researchers throughout the world have undertaken numerous observations and experiments, and have accumulated and analyzed much data on climate change. It is necessary to make full use of this data to provide reasonable modeling predictions in relation to the effects of climate change for scientific usage and government policymaking initiatives. Artificial neural networks (ANNs) are a unique tool for the special training of data, model calibration, simulation, and prediction. It has developed into an effective method of study. The neural network possesses a strong capacity to learn and is a convenient tool that can handle compl...

show abstract

“…It corresponds and partially overlaps with the well-established disciplines bioinformatics and ecological modeling but is taking its distinct shape and scope. A comparison between ecological informatics and bioinformatics in Recknagel (2008) concluded that even though both are based on the same computational technology their focus is different. Bioinformatics focuses very much on determining gene function and interaction, protein structure and function as well as phenotypes of organisms utilizing DNA microarray, genomic, physiological and metabolic data.…”

Section: Introductionmentioning

confidence: 99%

“…Comparing between ecological modeling and ecological informatics Recknagel (2008) stated that even though both rely on similar ecological data they adopt different approaches in utilizing the data. Whilst ecological modeling typically processes ecological data top down by ad hoc designed statistical or mathematical concepts, ecological informatics infers ecological relationships from ecological data patterns bottom up by computational techniques.…”

Section: Introductionmentioning

confidence: 99%

Current Scope, Case Studies and Future Directions of Ecological Informatics

Recknagel

2013

J ENV INFORM

Self Cite

View full text Add to dashboard Cite

Ecological data management, analysis and synthesis as well as information processing and transfer in ecology are considered feature areas of ecological informatics. The paper presents case studies for analysis and synthesis of ecological data by means of unsupervised artificial neural networks and hybrid evolutionary algorithms to assist in computational bioindication of wetland water quality and early warning of cyanobacteria blooms in lakes and reservoirs. Integrated analysis and synthesis of ecological and genomics data, hybrid model libraries generic for ecosystem categories as well as internet-based data and model sharing are identified as key research areas of ecological informatics.

show abstract

Ecological Informatics: Overview

Cited by 3 publications

References 81 publications

Effect on Soil Properties and Crop Yields to Long-Term Application of Superabsorbent Polymer and Manure

Effect on Soil Properties and Crop Yields to Long-Term Application of Superabsorbent Polymer and Manure

Application of artificial neural networks in global climate change and ecological research: An overview

Current Scope, Case Studies and Future Directions of Ecological Informatics

Contact Info

Product

Resources

About