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Article
Publication date: 27 June 2023

Wei Yang, Luu Quoc Phong, Tracy-Anne De Silva and Jemma Penelope

This study aims to understand New Zealand sheep farmers’ readiness toward sustainability transition by assessing their intentions of transition and adoption of sustainability…

Abstract

Purpose

This study aims to understand New Zealand sheep farmers’ readiness toward sustainability transition by assessing their intentions of transition and adoption of sustainability tools, with information collection considered to mediate the intention–adoption relationship.

Design/methodology/approach

Based on the data collected from a survey of New Zealand sheep farmers in 2021, the empirical analysis was developed to investigate farmers’ perceptions of and attitudes toward readiness to move toward a sustainability transition. Structural equation modeling associated with principal component analysis was used to empirically test the theory of planned behavior constructs.

Findings

The results show that pressure from the public and the sheep industry, and the perceived controls of transition drive the intention of sustainability transition; farmers with higher intention of sustainability transition are found to be more likely to adopt sustainability tools. However, there is an attitude–behavior gap, wherein positive attitudes toward sustainability transition may not lead to a higher likelihood of adopting sustainability tools. There is no evidence of the mediating role of information collection on the intention–adoption relationship, while a positive effect was found in information collection on the adoption of sustainability tools.

Practical implications

The empirical evidence indicates that policymakers need to help increase the awareness of sustainable production and help farmers overcome barriers to achieving sustainable production by finding ways to turn intentions into adoption.

Originality/value

Being the first attempt to empirically assess farmers’ readiness toward sustainability transition, the study fills the gap of limited understanding of the link between sustainability transition intention and sustainable tools adoption in sustainability transition.

Article
Publication date: 26 February 2024

Zhuang Zhang and You Hua Chen

Numerical literature shows that agricultural insurance can affect pesticide investments, but few of them are devoted to explain how agricultural insurance affects farmers’…

Abstract

Purpose

Numerical literature shows that agricultural insurance can affect pesticide investments, but few of them are devoted to explain how agricultural insurance affects farmers’ selection on green or traditional pesticides. This paper aims to develop a theoretical model about how agricultural insurance influences on green pesticides selections and tests our conclusions by using the data from China land economic survey (CLES) from 2020 to 2021.

Design/methodology/approach

We employ probit model to capture the effects of agricultural insurance on green pesticides adoption.

Findings

We indicate that green pesticides have a stronger effect on stabilizing yield and increasing income than traditional pesticides, but there are still risks disturbing farmers’ decisions on green pesticides usage. By providing premium subsidies after the farmers are affected by natural risk, agricultural insurance improves the farmers’ expected income and encourages farmers to use green pesticides. Further, we further confirm these conclusions by considering different scenarios such as climate risks, farmers’ entrepreneurship and credit constraints. We find that the effects are more salient if croplands are under higher natural risks and, farmers are equipped with entrepreneurship and formal credit. This paper implies that the agricultural insurance decoupled with green technologies also have salient positive effects on agricultural pollution control.

Originality/value

The potential contributions of this paper can be outlined in three aspects in detail. Firstly, this paper aims to revel the effects of agricultural insurance on pesticide selection by structuring a general theoretical model. By using the CLES data from 2020 to 2021, we confirm that agricultural insurance increases the probability for adopting green pesticides. Secondly, this paper discusses the effects of farmers’ characteristics on the results and finds that if farmers have entrepreneurship, the effects of agricultural insurance on green pesticide usage will be more salient. Thirdly, it uncovers some practices in China, which will supply experiences for other developing countries. For example, this paper further demonstrates that “insurance + credit” plan the present Chinese government carried out will be an important measure for strengthening effects of agricultural insurance on green pesticides usage. Moreover, it shows that decouple agricultural policies will also guide farmers to use green technologies eventually if the technologies are reliable and farmers can afford.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 18 January 2023

Payam Najafi, Akram Eftekhari and Alireza Sharifi

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as…

Abstract

Purpose

In the past three decades, remote sensing-based models for estimating crop yield have addressed critical problems of general food security, as the unavailability of grains such as rice creates serious worldwide food insecurity problems. The main purpose of this study was to compare the potential of time-series Landsat-8 and Sentinel-2 data to predict rice yield several weeks before harvest on a regional scale.

Design/methodology/approach

To this end, the sum of normalized difference vegetation index (NDVI)-based models created the best agreement with actual yield data at the golden time window of six weeks before harvest when rice grains were in milky and mature growth stages. The application of nine other vegetation indicators was also investigated in the golden time window in comparison to NDVI.

Findings

The findings of this study demonstrate the viability of identifying locations with poor and superior performance in terms of production management approaches through a rapid and economical solution for early rice grain yield assessment. Results indicated that while some of those, such as enhanced vegetation index (EVI) and optimized soil adjusted vegetation index, were able to estimate rice yield with high accuracy, NDVI is still the best indicator to predict rice yield before harvest. However, experiments can be conducted in different regions in future studies to evaluate the generalizability of the approach.

Originality/value

To achieve this objective, the authors considered the following purposes: using Sentinel-2 time-series data, determining the appropriate growth stage for estimating rice yield and evaluating different vegetation indices for estimating rice yield.

Details

Aircraft Engineering and Aerospace Technology, vol. 95 no. 5
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 17 July 2023

Kazi Moshiur Rahman, Hadi Miyanaji and Christopher B. Williams

In binder jetting, the interaction between the liquid binder droplets and the powder particles defines the shape of the printed primitives. The purpose of this study is to explore…

Abstract

Purpose

In binder jetting, the interaction between the liquid binder droplets and the powder particles defines the shape of the printed primitives. The purpose of this study is to explore the interaction of the relative size of powder particles and binder droplets and the subsequent effects on macro-scale part properties.

Design/methodology/approach

The effects of different particle size distribution (5–25 µm and 15–45 µm) of stainless steel 316 L powders and droplet sizes (10 and 30 pL) on part density, shrinkage, mechanical strength, pore morphology and distribution are investigated. Experimental samples were fabricated in two different layer thicknesses (50 and 100 µm).

Findings

While 15–45 µm samples demonstrated higher green density (53.10 ± 0.25%) than 5–25 µm samples (50.31 ± 1.06%), higher sintered densities were achieved in 5–25 µm samples (70.60 ± 6.18%) compared to 15–45 µm samples (65.23 ± 3.24%). Samples of 5–25 µm also demonstrated superior ultimate tensile strength (94.66 ± 25.92 MPa) compared to 15–45 µm samples (39.34 ± 7.33 MPa). Droplet size effects were found to be negligible on both green and sintered densities; however, specimens printed with 10-pL droplets had higher ultimate tensile strength (79.70 ± 42.31 MPa) compared to those made from 30-pL droplets (54.29 ± 23.35 MPa).

Originality/value

To the best of the authors’ knowledge, this paper details the first report of the combined effects of different particle size distribution with different binder droplet sizes on the part macro-scale properties. The results can inform appropriate process parameters to achieve desired final part properties.

Details

Rapid Prototyping Journal, vol. 29 no. 8
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 22 March 2024

Yumei Zhang, Ming Lei, Xiangmin Lan, Xiangyang Zhang, Shenggen Fan and Ji Gao

As one of its major strategies, China has made a new plan to further expand High Standard Farmland (HSF) to all permanent basic farmland (80% of total farmland) for grain security…

Abstract

Purpose

As one of its major strategies, China has made a new plan to further expand High Standard Farmland (HSF) to all permanent basic farmland (80% of total farmland) for grain security over the next decade. Yet, what will be the impact of farmland infrastructure investment on agrifood systems? The paper aims to systematically evaluate the multiple effects (food security, economy, nutrition and environment) of expanding HSF construction under the context of the “Big Food vision” using an interdisciplinary model.

Design/methodology/approach

An interdisciplinary model – AgriFood Systems Model, which links the China CGE model to diet and carbon emission modules, is applied to assess the multiple effects of HSF construction on agrifood systems, such as food security and economic development, residents’ diet quality and carbon emissions. Several policy scenarios are designed to capture these effects of the past HSF investment based on counterfactual analysis and compare the effects of HSF future investment at the national level under the conditions of different land use policies – restricting to grain crops or allowing diversification (like vegetables, and fruit).

Findings

The investments in HSF offer a promising solution for addressing the challenges of food and nutrition security, economic development and environmental sustainability. Without HSF construction, grain production and self-sufficiency would decline significantly, while the agricultural and agrifood systems’ GDP would decrease. The future investment in the HSF construction will further increase both grain production and GDP, improve dietary quality and reduce carbon emissions. Compared with the policy of limiting HSF to planting grains, diversified planting can provide a more profitable economic return, improve dietary quality and reduce carbon emissions.

Originality/value

This study contributes to better informing the impact of land infrastructure expanding investment on the agrifood systems from multiple dimensions based on an interdisciplinary model. We suggest that the government consider applying diversified planting in the future HSF investment to meet nutritional and health demands, increase household income and reduce carbon emissions.

Details

China Agricultural Economic Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-137X

Keywords

Article
Publication date: 21 March 2023

Rajat Kumar, Mahesh Kumar Gupta, Santosh Kumar Rai and Vinay Panwar

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a…

Abstract

Purpose

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a critical AGS has also been observed which shows that the Hall–Petch relationship behaves inversely.

Design/methodology/approach

Nanoscale deformation of polycrystalline nanocopper has been done in this study with the help of an embedded atom method (EAM) potential. Voronoi construction method has been employed for creating four polycrystals of nanocopper with different sizes. Statistical analysis has been used to examine the observations with emphasis on the polycrystal size effect on melting point temperature.

Findings

The study has found that the key stress values (i.e. elastic modulus, yield stress and ultimate tensile stress) are significantly influenced by the considered parameters. The increase in strain rate is observed to have an increasing impact on mechanical properties, whereas the increase in temperature degrades the mechanical properties. In-depth analysis of the deformation mechanism has been studied to deliver real-time visualization of grain boundary motion.

Originality/value

This study provides the relationship between required grain size variations for consecutive possible variations in mechanical properties and may help to reduce the trial processes in the synthesis of polycrystalline copper based on different temperatures and strain rates.

Details

Multidiscipline Modeling in Materials and Structures, vol. 19 no. 3
Type: Research Article
ISSN: 1573-6105

Keywords

Article
Publication date: 24 November 2023

Yuling Ran, Wei Bai, Lingwei Kong, Henghui Fan, Xiujuan Yang and Xuemei Li

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Engineering Computations, vol. 41 no. 1
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 9 October 2023

Zeeshan Ahmed, Mishal Khosa, Shafique Ur Rehman and Abdulaziz Fahmi Omar Faqera

The environmental sustainability of manufacturing firms may begin with employees' green initiatives. Consequently, there is a need to examine how green human resource management…

Abstract

Purpose

The environmental sustainability of manufacturing firms may begin with employees' green initiatives. Consequently, there is a need to examine how green human resource management (GHRM) promotes green creativity among manufacturing employees. This study aims to ascertain whether manufacturing employees' environmental-felt responsibility (EFR) and work engagement with eco-initiatives (WEEI) serve as a serial mediation mechanism for the relationship between GHRM and green creativity. Further, the quality of green communication (QGC) moderated the link of GHRM with EFR and WEEI.

Design/methodology/approach

The data were garnered from 408 managers in Pakistani manufacturing firms and analysed using partial least squares structural equation modelling.

Findings

The findings revealed a significant and positive association of GHRM with green creativity, EFR and WEEI. Similarly, EFR and WEEI demonstrated significant and positive relationships with green creativity. Furthermore, EFR and WEEI mediated the relationship between GHRM and green creativity. Moreover, this relationship was also serially mediated by EFR and WEEI. Additionally, QGC moderated the relationship of GHRM with EFR and WEEI.

Originality/value

Anchored on the self-determination theory integrated with a resource-based view, this study provides novel empirical evidence by investigating the mechanisms and boundary conditions between GHRM and green creativity nexus.

Article
Publication date: 23 September 2022

Shahrooz Sadeghi Borujeni, Gursimran Singh Saluja and Vasily Ploshikhin

This study aims at compensating for sintering deformation of components manufactured by metal binder jetting (MBJ) technology.

Abstract

Purpose

This study aims at compensating for sintering deformation of components manufactured by metal binder jetting (MBJ) technology.

Design/methodology/approach

In the present research, numerical simulations are used to predict sintering deformation. Subsequently, an algorithm is developed to counteract the deformations, and the compensated deformations are morphed into a CAD model for printing. Several test cases are designed, compensated and manufactured to evaluate the accuracy of the compensation calculations. A consistent accuracy measurement method is developed for both green and sintered parts. The final sintered parts are compared with the desired final shape, and the accuracy of the model is discussed. Furthermore, the effect of initial assumptions in the calculations, including green part densities, and green part dimensions on the final dimensional accuracy are studied.

Findings

The proposed computational framework can compensate for the sintering deformations with acceptable accuracy, especially in the directions, for which the used material model has been calibrated. The precise assumption of green part density values is important for the accuracy of compensation calculations. For achieving tighter dimensional accuracy, green part dimensions should be incorporated into the computational framework.

Originality/value

Several studies have already predicted sintering deformations using numerical methods for MBJ parts. However, very little research has been dedicated to the compensation of sintering deformations with numerical simulations, and to the best of the best of the authors' knowledge, no previous work has studied the effect of green part properties on dimensional accuracy of compensation calculations. This paper introduces a method to omit or minimize the trial-and-error experiments and leads to the manufacturing of dimensionally accurate geometries.

Details

Rapid Prototyping Journal, vol. 29 no. 3
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 21 March 2023

Lakhwinder Singh, Sangyul Ha, Sanjay Vohra and Manu Sharma

Modeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the…

Abstract

Purpose

Modeling of material behavior by physically or microstructure-based models helps in understanding the relationships between its properties and microstructure. However, the majority of the numerical investigations on the prediction of the deformation behavior of AA2024 alloy are limited to the use of phenomenological or empirical constitutive models, which fail to take into account the actual microscopic-level mechanisms (i.e. crystallographic slip) causing plastic deformation. In order to achieve accurate predictions, the microstructure-based constitutive models involving the underlying physical deformation mechanisms are more reliable. Therefore, the aim of this work is to predict the mechanical response of AA2024-T3 alloy subjected to uniaxial tension at different strain rates, using a dislocation density-based crystal plasticity model in conjunction with computational homogenization.

Design/methodology/approach

A dislocation density-based crystal plasticity (CP) model along with computational homogenization is presented here for predicting the mechanical behavior of aluminium alloy AA2024-T3 under uniaxial tension at different strain rates. A representative volume element (RVE) containing 400 grains subjected to periodic boundary conditions has been used for simulations. The effect of mesh discretization on the mechanical response is investigated by considering different meshing resolutions for the RVE. Material parameters of the CP model have been calibrated by fitting the experimental data. Along with the CP model, Johnson–Cook (JC) model is also used for examining the stress-strain behavior of the alloy at various strain rates. Validation of the predictions of CP and JC models is done with the experimental results where the CP model has more accurately captured the deformation behavior of the aluminium alloy.

Findings

The CP model is able to predict the mechanical response of AA2024-T3 alloy over a wide range of strain rates with a single set of material parameters. Furthermore, it is observed that the inhomogeneity in stress-strain fields at the grain level is linked to both the orientation of the grains as well as their interactions with one another. The flow and hardening rule parameters influencing the stress-strain curve and capturing the strain rate dependency are also identified.

Originality/value

Computational homogenization-based CP modeling and simulation of deformation behavior of polycrystalline alloy AA2024-T3 alloy at various strain rates is not available in the literature. Therefore, the present computational homogenization-based CP model can be used for predicting the deformation behavior of AA2024-T3 alloy more accurately at both micro and macro scales, under different strain rates.

Details

Multidiscipline Modeling in Materials and Structures, vol. 19 no. 3
Type: Research Article
ISSN: 1573-6105

Keywords

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