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In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

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

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.

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%.

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

The service environment of urban polyethylene (PE) pipes has a crucial influence on their long-term safety and performance. Based on the application and structural performance analysis of PE pipe failure cases, this study aims to investigate the impact of organic substances in the soil on the aging behavior of PE pipes by designing organic solutions with different concentrations, which are based on the composition of organic substances in the soil environment, and periodic immersion tests.

Soil samples in the vicinity of the failed pipes were analyzed by gas chromatography-mass spectrometry, sensitive organic substances were screened and soaking solutions of different concentrations were designed. After the soaking test, the PE pipe samples were analyzed using differential scanning calorimetry, Fourier-transform infrared spectroscopy and other testing methods.

The performance difference between the outer surface and the middle of the cross section of PE pipes highlights the influence of the soil service environment on their aging. Different organic solutions can have varying impacts on the aging behavior of PE pipes when immersed. For instance, when exposed to amine organic solutions, PE pipes may have an increased weight and decreased material yield strength, although there is no reduction in their thermal or oxygen stability. On the contrary, when subjected to ether organic solutions, the surface of PE pipe specimens may be affected, leading to a reduction in material fracture elongation and a decrease in their thermal and oxygen stability. Furthermore, immersion in either amine or ether organic solutions may result in the production of hydroxyl and other aging groups on the surface of the material.

Understanding the potential impact of organic substances in the soil environment on the aging of PE pipe ensures the long-term performance and safety of urban PE pipe. This research approach will provide valuable insights into improving the durability and reliability of urban PE pipes in soil environments.

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.

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.

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.

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.

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.

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

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.

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).

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.

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.

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

This paper analysed survey data of 715 rice-producing households in China to assess the determinants of adoption of five mutually exclusive soil conservation practices (SCPs) and their impact on rice yield and chemical fertiliser use.

The multinomial endogenous treatment effects model was used to account for selection bias and endogeneity arising from both observed and unobserved heterogeneity.

Farms that adopted SCPs as a package experienced an increase in rice yield and decrease in chemical fertiliser use. Adoption of SCPs as a package led to a 12.0% increase in yield and 15.2% decrease in chemical fertiliser use; these results have policy implications for the non-point source pollution control in the agricultural sector. In contrast, adoption of straw retention only significantly reduced yield by 4.9% and increased chemical fertiliser use by 18.1%.

The authors evaluate and compare multi-type of SCPs, such as straw retention, deep tillage and use of organic fertiliser, separately or in combination, and their impacts on smallholder farmers’ rice yield and chemical fertiliser usage.

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.

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.

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.

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.