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Article
Publication date: 24 December 2020

Xuesong Cao, Xican Li, Wenjing Ren, Yanan Wu and Jieya Liu

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Abstract

Purpose

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Design/methodology/approach

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

Findings

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R2 = 0.924, and the average relative error is 6.608%.

Practical implications

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

Originality/value

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

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Article
Publication date: 20 October 2011

Li Xi‐can, Yu Tao, Wang Xiao, Yuan Zheng and Shang Xiao‐dong

The purpose of this paper is to establish the grey‐weighted relationship prediction pattern of the soil organic matter content spectral inversion under the uncertainties…

Abstract

Purpose

The purpose of this paper is to establish the grey‐weighted relationship prediction pattern of the soil organic matter content spectral inversion under the uncertainties between soil organic matter contents and spectral characteristics and the theory of grey system.

Design/methodology/approach

At first, according to grey‐weighted distance, a new grey relationship model is presented. Second, in order to make full use of the information of grey relationship sequences, the maximum grey relationship discrimination principle is improved and then the soil organic matter content spectral inversion pattern is put forward based on weighted grey recognition theory. A numeric example of Hengshan County in Shanxi Province is also computed in the last part of the paper.

Findings

The results are convincing: not only that soil organic matter content spectral inversion pattern based on the weighted grey recognition theory is valid, but also the model's prediction accuracy is higher; the sample's average prediction accuracy is 94.917 per cent.

Practical implications

The method exposed in the paper can be used at soil organic matter content hyper‐spectral inversion and even for other similar forecast problems.

Originality/value

The paper succeeds in realising both prediction pattern and application of soil organic matter content hyper‐spectral inversion by using the newest developed theories: weighted grey recognition theory.

Details

Grey Systems: Theory and Application, vol. 1 no. 3
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 23 August 2013

Li Xi‐can, Yuan Zheng and Zhang Guangbo

This paper attempts to establish the grey GM(0,N) estimation model of the soil organic matter content spectral inversion under the uncertainties between soil organic matter

Abstract

Purpose

This paper attempts to establish the grey GM(0,N) estimation model of the soil organic matter content spectral inversion under the uncertainties between soil organic matter contents and spectral characteristics and the theory of grey system.

Design/methodology/approach

At first, based on the uncertainty of the relationship between the soil organic matter content and spectral characteristics, using the ordered grey accumulation generation and grey GM(0, N) model to establish hyper‐spectral grey estimation model of soil organic matter content. Second, the presented model is used to estimate soil organic matter of Hengshan County in Shanxi province in the last part of the paper.

Findings

The results are convincing: not only that soil organic matter content spectral inversion grey GM(0, N) model based on the ordered grey accumulation generation theory is valid, but also the model's prediction accuracy is higher, with the sample's average prediction accuracy being 93.662 per cent.

Practical implications

The method exposed in the paper can be used on soil organic matter content hyper‐spectral inversion and even for other similar forecast problems.

Originality/value

The paper succeeds in realising both prediction pattern and application of soil organic matter content hyper‐spectral inversion by using the newest developed theories: grey GM(0, N) model based on the ordered grey accumulation generation.

Details

Grey Systems: Theory and Application, vol. 3 no. 2
Type: Research Article
ISSN: 2043-9377

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Article
Publication date: 26 September 2009

Koen Mondelaers, Joris Aertsens and Guido Van Huylenbroeck

This paper aims to perform a meta‐analysis of the literature comparing the environmental impacts of organic and conventional farming and linking these to differences in…

Abstract

Purpose

This paper aims to perform a meta‐analysis of the literature comparing the environmental impacts of organic and conventional farming and linking these to differences in management practises. The studied environmental impacts are related to land use efficiency, organic matter content in the soil, nitrate and phosphate leaching to the water system, greenhouse gas emissions and biodiversity.

Design/methodology/approach

The theoretic framework uses the driver‐state‐response framework and literature data were analysed using meta‐analysis methodology. Meta‐analysis is the statistical analysis of multiple study results. Data were obtained by screening peer reviewed literature.

Findings

From the paper's meta‐analysis it can conclude that soils in organic farming systems have on average a higher content of organic matter. It can also conclude that organic farming contributes positively to agro‐biodiversity (breeds used by the farmers) and natural biodiversity (wild life). Concerning the impact of the organic farming system on nitrate and phosphorous leaching and greenhouse gas emissions the result of the analysis is not that straightforward. When expressed per production area organic farming scores better than conventional farming for these items. However, given the lower land use efficiency of organic farming in developed countries, this positive effect expressed per unit product is less pronounced or not present at all.

Original value

Given the recent growth of organic farming and the general perception that organic farming is more environment friendly than its conventional counterpart, it is interesting to explore whether it meets the alleged benefits. By combining several studies in one analysis, the technique of meta‐analysis is powerful and may allow the generation of more nuanced findings and the generalisation of those findings.

Details

British Food Journal, vol. 111 no. 10
Type: Research Article
ISSN: 0007-070X

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Article
Publication date: 5 January 2015

Shiva Kumar Shrestha

Temporary and permanent decline in the productive capacity of the land due to natural and human-induced activities such as soil erosion, changing cropping practices and…

Abstract

Purpose

Temporary and permanent decline in the productive capacity of the land due to natural and human-induced activities such as soil erosion, changing cropping practices and less use of organic matter (OM) has been the greatest challenge faced by mankind in recent years, particularly in the hills and mountains of Nepal. Hence, the purpose of this paper is to examine the effectiveness of sustainable soil management practices to mitigate desertification process in the hills of Nepal.

Design/methodology/approach

Promotion of sustainable soil management (SSM) practices through a decentralised agriculture extension approach by involving all the stakeholders in a participatory way.

Findings

SSM practices mainly: OM management, fodder and forage promotion, increased biomass production systems, integrated plant nutrition systems, and bioengineering for soil and water conservation are identified as the most appropriate and relevant technologies in mitigating the desertification process without deteriorating land quality, particularly conserving the top-soils effectively and efficiently in the hills and mountains of the country.

Research limitations/implications

This research is focus on the overall effect of SSM practices due to time and budget constraints. There is scope for doing research on the different aspects of SSM practices and the extent of their effect on different soil parameters (chemical, biological and physical).

Practical implications

SSM interventions clearly indicated that there is significant impact in increasing soil fertility, conserving fertile top-soils and mitigating physical, chemical and biologic desertification processes. These are possible through maintaining and improving the soil organic matter, which is the most important indicator for soil health. SSM practices have resulted in an increase of up to 30 per cent in crop yield compared to yields without SSM practices. This might be due to the improvement in SOC which improves soil texture, increases nutrient supply from organic source and conserves water quality, thus, improving soil quality.

Social implications

This has created awareness among farmers. Hence, farmers are mitigating pH through increased use of organic manures, where there is less availability of agriculture lime and they are far from road access.

Originality/value

SSM practices significantly contributes to combat soil desertification in the hills of Nepal.

Details

World Journal of Science, Technology and Sustainable Development, vol. 12 no. 1
Type: Research Article
ISSN: 2042-5945

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Article
Publication date: 1 October 1996

Vlasta Drevenkar, Sanja Fingler, Zlatko Fröbe and Zelimira Vasilic

Reviews compound physico‐chemical properties and water and soil properties influencing the transport and distribution of organochlorine pesticides, polychlorinated…

Abstract

Reviews compound physico‐chemical properties and water and soil properties influencing the transport and distribution of organochlorine pesticides, polychlorinated biphenyls (PCBs) and chlorophenols in a water and soil environment. As highly hydrophobic compounds of low water solubility, organochlorine pesticides and PCBs are rapidly and strongly sorbed by most soils and sediments. The sorption of weakly acidic chlorophenols comprises both molecular and ionic forms and depends not only on the soil/sediment organic matter content but also on the pH and ionic strength of the aqueous phase. Briefly describes the analytical methods for trace analysis of organocholorine pesticides, PCBs and chlorophenols in water and soil/sediment samples. Presents some results of those micropollutants’ analysis in surface, ground and drinking waters, soils, river sediments and wet depositions in Croatia.

Details

Environmental Management and Health, vol. 7 no. 4
Type: Research Article
ISSN: 0956-6163

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Article
Publication date: 3 October 2012

Elkhtab Mohamed Abdalla, Sulieman Hammad Nasser Ali, Sarra Ahmed Mohamed Saad and Ibrahim Saeed Ibrahim

The purpose of this paper is to investigate the influence of two decomposition processes, namely, composting and vermicomposting, on the chemical composition of the…

Abstract

Purpose

The purpose of this paper is to investigate the influence of two decomposition processes, namely, composting and vermicomposting, on the chemical composition of the finished products of a mixture of: cotton residues; soil and cotton residues; farmyard; soil.

Design/methodology/approach

Composting experiments were done over six months to prepare four different mixtures as follows: cotton residues+soil (C); cotton residues+soil+earthworms (C+E); cotton residues+soil+farmyard manure (C+F); and cotton residues+soil+farmyard manure+earthworms (C+F+E). Electrical conductivity, pH, nitrate-N, ammonium-N, ash, total phosphorus, total nitrogen, total organic carbon, carbon: nitrogen ratio, total potassium and trace elements (Mn, Fe, Cu and Zn) were determined on monthly-based samples.

Findings

Significant differences (p < 0.05) in organic carbon, nitrate-N, nitrogen, phosphorus, and potassium content were recorded in vermicompost compared to compost. In general, results indicated that vermicompost had a significant effect compared to compost and a positive effect on the chemical properties of the finished products.

Originality/value

This research work was carried out by four researchers from two institutions concerned with agricultural production and environmental aspects related to soil productivity. The paper emphasizes on production of organic fertilizers with good quality and monitoring of composting process for better management practices of agricultural wastes in Sudan.

Details

World Journal of Science, Technology and Sustainable Development, vol. 9 no. 4
Type: Research Article
ISSN: 2042-5945

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Book part
Publication date: 4 December 2020

Sneha Kumari, Vidya Kumbhar and K. K. Tripathy

The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil

Abstract

The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.

Details

Application of Big Data and Business Analytics
Type: Book
ISBN: 978-1-80043-884-2

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Article
Publication date: 1 May 2003

G. Pepin, H. Baroudi and M. Nomine

Supporting many policies of contaminated sites management, the risk assessment methodology needs a deep knowledge of the characteristics of the contamination of soils

Abstract

Supporting many policies of contaminated sites management, the risk assessment methodology needs a deep knowledge of the characteristics of the contamination of soils. Physico‐chemical analysis has to take in account the heterogeneity and the specificity of the soil. A better knowledge of the interactions between the chemical substances and the matrix should be developed and its influence on the measurement chain shall be determined. In particular, the influence on the final result of each of the analytical steps (pre‐treatment, extraction, purification, analysis) has to be quantified. An experimental protocol has been designed to study the influence of the type of pre‐treatment chosen in relation to the characteristics of the matrix of real contaminated soils by PCB and PAH. The results show and quantify how the texture, the granulometry, the water content and the total organic carbon (TOC) content may affect the analytical result depending on the analytical pre‐treatment‐step chosen.

Details

Management of Environmental Quality: An International Journal, vol. 14 no. 2
Type: Research Article
ISSN: 1477-7835

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Article
Publication date: 2 November 2015

Ghangela Jones, Cesar Escalante and Hofner Rusiana

Organic outputs have been increasing at much lower rates than growth in consumer demand. Organic farmers’ debt aversion hinders them from obtaining business funds through…

Abstract

Purpose

Organic outputs have been increasing at much lower rates than growth in consumer demand. Organic farmers’ debt aversion hinders them from obtaining business funds through borrowing. The purpose of this paper is to clarify that the farmers’ reluctance to use debt as a funding option can be more attributed to gaps in existing borrower-lender relationships, beyond sustainability principles.

Design/methodology/approach

Empirical evidence collected from organic farmers and farm lenders establish differing expectations and perceptions that reinforce the organic farmers’ debt aversion. The farm lender survey data set was analyzed using the Heckman approach applied to two lenders’ decisions: their interest in lending to organic farm borrowers and loan amounts approved for successful loan applicants. The econometric results were reconciled with the compiled inputs provided by organic farmers interviewed.

Findings

Results validate the farmers’ lower reliance on loans due to suspicions that lenders lack knowledge and consideration of organic farming conditions and principles. Farm lenders must depart from employing a uniform credit risk appraisal model and adopt borrower-specific versions of the model, but not necessarily delineating organic-conventional farming dichotomy that may not substantially affect credit risk measurement. Organic farms, on the other hand, need to better understand the credit risk appraisal principles and use their inherent business strengths to compete for loans with conventional farms without any special consideration.

Practical implications

Borrower-lender relationships can improve if information gaps between lenders and borrowers can be minimized with more extensive outreach education efforts. Better relationships would increase organic farms’ credit access to effectively address an impending supply gap in an expanding industry.

Originality/value

To the knowledge, a specific focus on organic farms in understanding farm borrower-lender relationships has never been explored in literature.

Details

Agricultural Finance Review, vol. 75 no. 4
Type: Research Article
ISSN: 0002-1466

Keywords

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