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Change of climate is attributed to human activity that alters the composition of the global atmosphere observed over comparable periods. The purpose of this paper is to explore smallholder farmers' perceptions of climate change and compare it with meteorological data, as well as to identify perceived adaptation barriers and examine the factors that influence the choice of adaptation options in eastern Ethiopia.

In total, 384 sample households were chosen from four districts of the zone. A cross-sectional survey was used to conduct the study. Primary data was acquired through key informant interviews, focus group discussions and semistructured interviews, whereas meteorological data was collected from the National Meteorological Service Agency of Ethiopia. A Mann–Kendall statistical test was used to analyze temperature and rainfall trends over 33 years. A multivariate probit (MVP) model was used to identify the determinants of farmers' choice of climate change adaptation strategies.

The result indicated that temperature was significantly increased, whereas rainfall was significantly reduced over the time span of 33 years. This change in climate over time was consistently perceived by farmers. Smallholder farmers use improved varieties of crops, crop diversification, adjusting planting dates, soil and water conservation practices, reducing livestock holdings, planting trees and small-scale irrigation adaptation strategies. Moreover, this study indicated that sex of the household head, landholding size, livestock ownership, access to extension, access to credit, social capital, market distance, access to climate change-related training, nonfarm income, agroecological setting and poverty status of the households significantly influence farmers’ choice of adaptation strategies.

Further research is required to evaluate the economic impact of each adaptation options on the livelihood of smallholder farmers.

Institutional variables significantly influenced how farmers adapted to climate change, and all of these issues might potentially be addressed by improving institutional service delivery. To improve farm-level adaptation, local authorities are recommended to investigate the institutional service provision system while also taking demographic and agroecological factors in to account.

This study compared farmers' perceptions with temperature and rainfall trend analysis, which has been rarely addressed by other studies. This study adopts an MVP model and indicated the adaptation strategies that complement/substitute strategies each other. Furthermore, this study discovered that the choice of adaptation options differed between poor and nonpoor households, which has been overlooked in previous climate change adaptation research.

This chapter aims to estimate the impact of the use of an innovative cultivation method on the social, economic and environmental aspects in the French region Aix-en-Provence, by using the survey data for 200 heterogeneous vegetable producers (organic and conventional). It distinguishes three types of producers in the French region Aix-en-Provence. First, conventional producers (n = 100) who used a high level of mechanization, better access to water, high yield, high labor costs. Second, certified organic producers (n = 70) who used organic technologies such as biotechnology and rotation, low yield, high organic product price compared to conventional products, a family workforce and high transport. Third, noncertified organic producers (n = 30) have used the same technologies as certified organic producers, while they sell their products at the same price as conventional products. Labor is the member of the family. These noncertified farms are marked by high operating and transport costs and low yield compared to conventional producers or certified organic producers. The results show that this cultivation method has a positive effect on the environmental aspect, however a negative one on the social and economic aspect.

The purpose of this paper is to study the corrosion behavior of Q235 steel in saturated acidic red and yellow soils.

The corrosion behavior of Q235 steel in saturated red and yellow soils was compared by weight-loss, SEM/EDS, 3D ultra-depth microscopy and electrochemical measurements.

R_p of the steel gradually increases and i_corr gradually decreases in both the red and yellow soils with time. The R_p of the steel in the red soil is lower, but its i_corr is higher than that in the yellow soil. The uniform corrosion rate, diameter and density of the corrosion pit on the steel surface in the red soil are greater than those in the yellow soil. Lower pH, higher contents of corrosive anions and high-valence Fe oxides in the red soil are responsible for its higher corrosion rates and local corrosion susceptibility.

This paper investigates the difference in corrosion behavior of carbon steel in saturated acidic red and yellow soils, which can help to understand the mechanism of soil corrosion.

Soil quality is in decline in many parts of the world, in part due to the intensification of agricultural practices. Whilst economic instruments and regulations can help incentivise uptake of more sustainable soil management practices, they rarely motivate long-term behavior change when used alone. There has been increasing attention towards the complex social factors that affect uptake of sustainable soil management practices. To understand why some communities try these practices whilst others do not, we undertook a narrative review to understand how social capital influences adoption in developed nations. We found that the four components of social capital – trust, norms, connectedness and power – can all influence the decision of farmers to change their soil management. Specifically, information flows more effectively across trusted, diverse networks where social norms exist to encourage innovation. Uptake is more limited in homogenous, close-knit farming communities that do not have many links with non-farmers and where there is a strong social norm to adhere to the status quo. Power can enhance or inhibit uptake depending on its characteristics. Future research, policy and practice should consider whether a lack of social capital could hinder uptake of new practices and, if so, which aspects of social capital could be developed to increase adoption of sustainable soil management practices. Enabling diverse, collaborative groups (including farmers, advisers and government officials) to work constructively together could help build social capital, where they can co-define, -develop and -enact measures to sustainably manage soils.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

The present study focused on examining the effect of treated wastewater (TWW) on soil chemical properties. Also, efforts were made to compare the soil chemical properties under TWW irrigation with that under groundwater (GW).

During the years 2021 and 2022, surface and subsurface soil samples were randomly collected in triplicate by using an auger fortnightly at two depths (20 and 40 cm) from the selected spot areas to represent the different types of irrigation water sources: TWW and GW. Samples of the GW and the TWW were collected for analysis.

This study examines the impact of TWW on soil characteristics and the surrounding environment. TWW use enhances soil organic matter, nutrient availability and salt redistribution, while reducing calcium carbonate accumulation in the topsoil. However, it negatively affects soil pH, electrical conductivity and sodium adsorption ratio, although remaining within acceptable limits. Generally, irrigating with TWW improves most soil chemical properties compared to GW.

In general, almost all of the soil’s chemical properties were improved by irrigating with TWW rather than GW. Following that, wastewater is used to irrigate the soil. Additionally, the application of gypsum to control the K/Na and Ca/Na ratios should be considered under long-term TWW and GW usage in this study area in order to control the salt accumulation as well as prevent soil conversion to saline-sodic soil in the future. However, more research is needed to thoroughly investigate the long-term effects of using TWW on soil properties as well as heavy metal accumulation in soil.

This paper aims to present physical and geotechnical study in terms of experimental field and laboratory investigations of the subgrade soils in severely damaged and highly degraded road section with numerous potholes between Chencha to Ezo towns of Ethiopia needs to be addressed for a robust pavement.

Eighteen soil samples were collected from 18 km road stretch at a kilometer interval by considering variation and composition of soils along the road alignment. The field density with dry density, natural moisture content, consistency limit, compaction and California Bearing Ratio (CBR) were determined.

Soils were classified predominantly as silty-clay that replicates its expansive nature, characterized as bad to medium subgrade. The average optimum moisture content and maximum dry density are 17.18% and 1.83 g/cc, whereas the average CBR and swell as 8.40% and 1.49%, respectively. The investigated results indicated that the indispensable way for a stable and durable road subgrade in the existing silty clayey soil requires a capping layer. The results were uploaded into ArcGIS platform to create interactive maps for spatial distribution, composition and strength of the subgrade properties.

Experimental investigation of subgrade soils by scientific procedures and presenting important properties through integrated approach using ArcGIS Mapping for the road pavement design and construction purpose of under developed areas like Chencha-Ezo. ArcGIS-based mapping of all required and numerical subgrade properties with a single click using ArcGIS tool is the main significance and contribution of this study. To the best of the authors’ knowledge, this paper is original, and all the references are properly cited.

Natural gas hydrate (NGH) has been regarded as one of the most important resources due to NGH's large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Therefore, developing a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior is very important and necessary. The purpose of this article is to develop and solve a multi-phase, strong nonlinearity and large-scale fully coupled thermal-hydro-mechanical–chemical (THMC) model for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition.

In this paper, a multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH dissociation. The fully coupled THMC model is solved by using a fully implicit finite element method, in which the gas pressure, water pressure, temperature and displacement are taken as basic unknown variables. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

A multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

Some assumptions are made to make the model tractable, including (1) the composition gas of hydrate is pure methane; (2) the gas-liquid multi-phase flow in the pore obeys Darcy's law; (3) hydrate occurs on the surface of soil particles, both of them form the composite consolidation material; (4) the small-strain assumption is applied to composite solid materials, which are treated as skeletons and cannot be moved; (5) momentum change caused by phase change is not considered.

NGH has been regarded as one of the most important resources due to its large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Most of the existing studies decouple the process with solid deformation and seepage behavior, but the accuracy of the numerical results will be sacrificed to certain extent. Therefore, it is very important and necessary to develop a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior.

NGH, widely distributed in shallow seabed or permanent frozen region, has the characteristics of high energy density and high combustion efficiency (Yan et al., 2020). A total of around 7.5 × 1,018 m³ has been proved to exist around the world and 1 m³ of NGH can release about 160–180 m³ of natural gas (Kvenvolden and Lorenson) under normal conditions. Safely and sustainably extracting NGH commercially can effectively relieve global energy pressure and contribute to achieving carbon reduction goals.

The novelty of the present work lies in mainly two aspects. First, a fully coupled THMC model is developed for studying the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and solid deformation during NGH dissociation. Second, the numerical solution is obtained by using a fully implicit finite element method (FEM) and is validated against experimental data.

The purpose of this study is rangeland biomass estimation and its spatial–temporal dynamics. Remote sensing has been a significant method for estimating biomass in recent years. The connection between vegetation index and field biomass will be used to assign probabilities, but in some cases, it does not provide acceptable results because of soil background and geographical and temporal variability.

In this study, the normalized difference red-edge (NDRE) index was used to calculate the rangeland biomass in comparison to five vegetation indices. Field measurements of biomass of natural rangeland in the West of Iran were taken in 2015, 2018 and 2021, and SENTINEL-2 data were used for analysis.

The results indicated that the overall advantage of NDRE stems from the fact that it adjusts for changes in leaf water content while overcoming the detrimental effects of soil substrate heterogeneity, both of these factors have a significant impact on pasture biomass. These results suggest that an NDRE-based biomass estimation model might be useful for estimating and monitoring biomass in large rangelands with significant geographical and temporal variability.

Identifying the best vegetation index to establish a vegetation-based biomass regression model for rangelands in large areas with different climatic conditions, plant compositions and soil types is the overall aim of this study.

Reducing wear of undercarriage track propulsion systems used in heavy construction equipment decreases the maintenance costs and increases the equipment's life. Therefore, understanding key factors that affect the wear rate is critical. This study is an attempt to predict undercarriage wear.

This research analyzes a sample of track-type dozers in the eastern half of North Carolina (NC), USA. Sand percentage in the soil, precipitation level, temperature, machine model, machine weight, elevation above sea level and work type code are considered as factors influencing the wear rate. Data are comprised of 353 machines. Machine model and work code data are categorical. Sand percentage, elevation, machine weight, average temperature and average precipitation are continuous. ANOVA is used to test the hypothesis.

The study found that only sand percentage has a significant impact on the wear rate. Consequently, a regression model is developed.

The regression model can be used to predict undercarriage wear and bushing life in soils with different sand percentages. This is demonstrated using a hypothetical scenario for a construction company.

This work is useful in managing maintenance intervals of undercarriage tracks and in bidding construction jobs while predicting machine operating expense for each specific job site soil makeup.