Abstract: Extension professionals are expected to help disseminate agricultural technologies, information, knowledge and skills to farmers. In order to develop valuable and long-lasting extension services, it is essential to understand the methods of extension that farmers find most beneficial. This understanding helps adopt improved practices, overcome barriers, provide targeted interventions and continuously improve agricultural extension programs. Thus, assessing factors affecting farmers' choice of agricultural extension methods is essential for developing extension methods that comply with farmers' needs and socio-economic conditions. Therefore, we analyzed the factors affecting farmers' preferences in extension methods, using cross-sectional data collected from 300 households in two sample districts and 16 Kebelles in Ethiopia between September 2019 and March 2020. Four extension methods, including training, demonstration, office visits and phone calls were considered as outcome variables. We fitted a multivariate probit model to estimate the factors that influence farmers' choice of extension methods. The results of the study showed that the number of dependents in the household head, formal education and membership of Idir (an informal insurance program a community or group runs to meet emergencies) were negatively associated with farmers' choices to participate in different extension methods compared to no extension. On the other hand, the sex of the household head, farm experience, participation in non-farm activities, monetary loan access, owning a mobile phone, radio access and membership of cooperatives were found to have a statistically significant positive impact on farmers' choices of extension methods. Based on these findings, the government and the concerned stakeholders should take farmers' socio-economic and institutional traits into account when selecting and commissioning agricultural extension methods. This could help to develop contextually relevant extension strategies that are more likely to be chosen and appreciated by farmers. Furthermore, such strategies can aid policymakers in designing extension programs that cater to farmers' needs and concerns. In conclusion, farmers' socio-economic and institutional affiliation should be taken into consideration when selecting agricultural extension methods.

Abstract: We report a plasma‐assisted CO₂hydrogenation to CH₃OH over Fe₂O₃/γ‐Al₂O₃catalysts, achieving 12% CO₂conversion and 58% CH₃OH selectivity at a temperature of nearly 80°C atm pressure. We investigated the effect of various supports and loadings of the Fe‐based catalysts, as well as optimized reaction conditions. We characterized catalysts by X‐ray powder diffraction (XRD), hydrogen temperature programmed reduction (H₂‐TPR), CO₂and CO temperature programmed desorption (CO₂/CO‐TPD), high‐resolution transmission electron microscopy (HRTEM), scanning transmission electron microscopy (STEM), x‐ray photoelectron spectroscopy (XPS), Mössbauer, and Fourier transform infrared<bold>(</bold>FTIR). The XPS results show that the enhanced CO₂conversion and CH₃OH selectivity are attributed to the chemisorbed oxygen species on Fe₂O₃/γ‐Al₂O₃. Furthermore, the diffuse reflectance infrared Fourier transform spectroscopy (DRIFTs) and TPD results illustrate that the catalysts with stronger CO₂adsorption capacity exhibit a higher reaction performance.<italic>In situ</italic>DRIFTS gain insight into the specific reaction pathways in the CO₂/H₂plasma. This study reveals the role of chemisorbed oxygen species as a key intermediate, and inspires to design highly efficient catalysts and expand the catalytic systems for CO₂hydrogenation to CH₃OH.

Abstract: Although grazing of dairy cows is an integral part of dairy farming in many European countries, farmers today more often choose for zero-grazing systems, where cows are housed throughout the year. Some studies already compared grazing and zero-grazing systems for specific issues such as labor efficiency, environmental impact, or animal welfare. In our study, we perform a more integrated evaluation, considering relevant ecological, economic, and social aspects. This allows for a balanced and more complete comparison of the sustainability performance of the two production methods. We evaluated ten intensive grazing and ten zero-grazing specialized Flemish dairy farms on the use of nutrients and energy, productivity and profitability, labor input, and animal welfare. In addition, we put special effort in formulating useful management advice for farmers. Therefore, we combined a detailed analysis of the sustainability indicators with an intensive interaction and discussion with farmers and farm advisors. Results show that, on average, the zero-grazing farms performed significantly worse from an ecological and economic point of view. This fact is explained mainly due to a less efficient use of concentrates and byproducts. Social sustainability performance did not differ significantly between the two groups. As a result, the integrated sustainability performance was significantly lower for the zero-grazing group. This finding shows that a further shift from intensive grazing to zero-grazing can move dairy farming in Flanders further away from sustainability. An important advice to improve the ecological and economic performance of zero-grazing farms is to optimize cows' rations to include more forages and optimize forage production and use. More detailed site- and case-specific management advice for farmers of both groups was provided during a discussion meeting. We consider this an essential additional step to any sustainability evaluation, since progress can only be made when monitoring results are translated into practical measures.

Abstract: Development of a clear understanding of the relationship between the availability of dam-driven irrigation water and crop revenue is important in poverty reduction and food security process. As a result, large research efforts are devoted to understanding the relationship between the availability of irrigation water and crop revenue. However, earlier studies do have several limitations. For example, without considering its indirect effect, prior studies focused solely on the direct effect of availability of irrigation water on crop revue. In this study, using a structural equation model analysis, the direct and indirect effect of availability of dam-driven irrigation water on crop revenue is decomposed and quantified specifically for the Koga irrigation scheme, located in the Mecha district of Amhara region in Ethiopia. A primary data set was collected from a randomly selected sample of 450 households in the Koga irrigation scheme. More than half of the households (254) are supported by the Koga Dam irrigation water during the dry season, and the other 196 households depended only on rainfall. The results of the study showed that, in addition to its direct effect, the availability of irrigation water indirectly affected crop revenue through receptivity of the farmers to use modern farm inputs. Around 27 percent of the total effect of dam-driven irrigation water on crop revenue was mediated by farmers’ receptivity to use yield-enhancing modern farm inputs. The results of this study suggested that the availability of irrigation water is essential to improve both crop revenue and receptivity of the farmers to use modern farm inputs. This finding also drives a strategic framework that the receptivity of the farmers to use modern farm inputs is crucial for utilizing the positive effects of irrigation water availability on crop revenue.

Abstract: Farms contribute heavily to overall sustainability. To date, various frameworks, approaches, methods and indicators have been developed to appraise how much corporations (firms, farms) contribute to sustainability. Among these, the Sustainable Value (SV) method helps to determine by which economic entity resources should be used in order to achieve the highest contributions to sustainability, by comparing company's efficiency of capital use against the efficiency of a benchmark. In this work we argue that the SV benchmark does not include certain crucial environmental concepts, such as the carrying capacity of an ecosystem or the multiple vital functions provided by ecosystem services. Thus, it is not a suitable reference for companies to evaluate their contribution to sustainability. As an alternative benchmark to standard SV applications, we propose a farm whose resource use productivity is correlated to the fulfilment of pre-determined environmental constraints: the Environmentally Sustainable Farm (ESF). To design this farm, we created an agro-environmental farm modelling framework that includes: (i) methods and approaches to assess indicators concerning specific environmental issues; (ii) an integrated ecological-economic model based on linear programming. This farm modelling framework is employed to determine both the environmental performance and the economic returns of two farming systems, conventional (CFS) and organic (OFS), which are compared using the Sustainable Value approach. Despite the fact that the OFS environmentally outperforms the CFS, none of them reaches the sustainable performances achieved by the ESF, both performing a negative Sustainable Value. The environmental constraints forced the ESF towards management choices that, although decreasing the economic performance, increase its environmental sustainability behaviour. Hence, the ESF is a viable benchmark that can be used to evaluate llllfarms' environmental sustainability. (C) 2015 Elsevier Ltd. All rights reserved.

Abstract: Several indicators and methods are already applied for sustainability assessment in agriculture. The links between sustainability indicators, agricultural management and policies are not well explained. The aim of this study is to combine biophysical and monetary sustainability assessment tools to support agriculture policy decision-making. Three methodological steps are considered: i) the environmental impacts of farms are assessed using terrestrial acidification, freshwater eutrophication, soil and freshwater ecotoxicity as well as natural land transformation; ii) the most relevant indicators of agriculture damages on ecosystems quality are aggregated into an index; iii) the farms' index scores are integrated with farm assets, land and labor, into the Sustainable Value approach (SVA), as indicator of natural resources used by farms. As a case study, the methodology was applied to arable farms with and without animal husbandry of the “Alta Murgia” National Park. The crop farms, in our sample, have a higher sustainable value using their economic and environmental resources. Mixed farms need to improve their resources use efficiency. Although crop farms have lower land-use efficiency compared to mixed farms, our results suggest supporting, by means of policy options, the specialized crop farms that, on average, perform better in terms of ecosystems quality preservation. Finally, we find that Life Cycle Assessment (LCA) to soundly measure the environmental impacts clearly enriches the SVA. (C) 2016 Elsevier Ltd. All rights reserved.

Abstract: Assumptions on the perceptions of risks, made in agricultural economics literature, are recognized to be over-simplistic. For example most studies assume that risks are independent and static, while in reality most risks are interlinked and dynamic. We propose an alternative method to identify and present risk perception, closer to the actual comprehension of risk by farmers. Grounded theory is used to investigate the perceptions of risk by farmers while avoiding prior assumptions. Main findings are: (i) farmers have difficulty to rank or score probability and impact of risks in a (semi)quantitative manner; (ii) farmers attach different meanings to risk, when the focus shifts between, uncertain event, probability or value at stake and; (iii) farmers perceive risks as being interrelated. Based on these findings, we propose that farmers' risk perception can be best understood as a network of interrelated notions of uncertain events, their effects and uncertain outcomes. Furthermore, cognitive mapping is suggested to elucidate and present these networks. We test cognitive mapping, exploring dairy farmers' risk perception, and demonstrate the appropriateness of this methodology for capturing the complexity and context of perceived risk. Advantages are: (i) the qualitative approach, (ii) the focus on interrelations and context, (iii) the applicability at farm level, (iv) the farmer-driven rather than researcher-driven perspective, and (v) the elucidation of the polyvalent use of the risk concept. Cognitive maps can be used as a communication tool, a risk management tool, and a tool to stimulate bi-directional learning amongst farmers, policy makers, researchers and extension agents. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract: In Ethiopia, there is a renewed interest in agricultural cooperatives as an institutional tool to improve the welfare of smallholder farmers. One of the pathways through which cooperatives benefit their members is scale economies. However, the establishment of cooperatives in Ethiopia seems to pay little attention to the size of the organizations. This article aims at investigating the effect of size on cost efficiency of agricultural cooperatives. More specifically, the purpose is to examine whether a single cooperative can serve a given number of farmers at a lower cost than two or more smaller cooperatives could. We employ the concept of cost subadditivity to compare the cost efficiency of large versus small cooperatives, and by extension unilateral actions. We estimate a flexible production technology using cross-sectional cooperative-level data. Findings show that costs would drop by 78% to 181% if farmers join hands in relatively large rather than small cooperatives.

Abstract: The cooperative landscape in Ethiopia is very heterogeneous with a mixture of remains of the pre-1991 government-controlled system and new post-1991 bottom-up collective action initiatives. This heterogeneity, coupled with a large growth in the number of cooperatives in the country, offers an interesting perspective to study the determinants of the (in)efficiency of cooperatives. In this paper, we analyse the performance of Ethiopian agricultural cooperatives, focusing on the degree of technical (in)efficiency and its determinants. We use the stochastic frontier approach in which we account for heteroskedasticity and the monotonicity of production functions, presenting a methodological improvement with respect to previous technical efficiency studies. The results show that NGO- and government-initiated cooperatives are less efficient than community-initiated ones, implying that governments and NGOs should not interfere too strongly in cooperative formation. Cooperatives with a high degree of heterogeneity in members' participation are found to be about 98% less efficient, while cooperatives that have paid employees are 33% more efficient. Besides, results show that cooperatives in Ethiopia function more efficiently if they incentivize committee members through monetary compensation.

Abstract: Over the last decade, scanning transmission electron microscopy has become one of the most powerful tools to characterise nanomaterials at the atomic scale. Often, the ultimate goal is to retrieve the three-dimensional structure, which is very challenging since small species are typically sensitive to electron irradiation. Nevertheless, measuring individual atomic positions is crucial to understand the relation between the structure and physicochemical properties of these (nano)materials. In this review, we highlight the latest approaches that are available to reveal the 3D atomic structure of small species. Finally, we will provide an outlook and will describe future challenges where the limits of electron microscopy will be pushed even further.

Abstract: By combining statistical parameter estimation and model-order selection using a Bayesian framework, the maximum a posteriori (MAP) probability rule is proposed in this chapter as an objective and quantitative method to detect atom columns from high-resolution scanning transmission electron microscopy (HRSTEM) images. The validity and usefulness of this approach is demonstrated to both simulated and experimental annular dark-field (ADF) STEM images, but also to simultaneously acquired annular bright-field (ABF) and ADF STEM image data.

Abstract: An efficient model-based estimation algorithm is introduced to quantify the atomic column positions and intensities from atomic-resolution (scanning) transmission electron microscopy ((S)TEM) images. This algorithm uses the least squares estimator on image segments containing individual columns fully accounting for overlap between neighboring columns, enabling the analysis of a large field of view. To provide end-users with this well-established quantification method, a user friendly program, StatSTEM, is developed which is freely available under a GNU public license. In this chapter, this efficient algorithm is applied to three different nanostructures for which the analysis of a large field of view is required.