A global mapping template for natural and modified habitat across terrestrial Earth
یک الگوی نقشه برداری جهانی برای زیستگاه طبیعی و اصلاح شده در سراسر زیستگاه های زمینی-2020
The IPBES Global Assessment proposed ﬁve key interventions to tackle the drivers of nature deterioration. One of these proposals was to take pre-emptive and precautionary actions in regulatory and management institutions and businesses. Performance standards are tools that can be used to help achieve these interventions. The most inﬂuential standard is Performance Standard 6 (PS6) of the International Finance Corporation (IFC), part of the World Bank Group. Institutions like the IFC invest in the private sector in developing countries, including in the infrastructure, agribusiness, forestry, oil, gas and mining sectors, all of which have the potential to cause large environmental impacts. A core element of PS6 outlines the need for the consideration of “natural and modiﬁed habitat” within investment screening processes. Here we use freely available data layers in combination to develop a new global layer that identiﬁes natural and modiﬁed habitat. It is aligned with the IFC PS6 deﬁnitions of natural and modiﬁed habitat. However, we propose this layer as an output that can be used beyond the IFC and could be integrated into the investment decision making of global and regional banks, or the decision making of international corporations.
Keywords: International Finance Corporation Performance | Standard 6 | Biodiversity safeguards | Natural habitat | Modified habitat | Investment screening | Environmental risk
Renewable energy diversification: Considerations for farm business resilience
متنوع سازی انرژی تجدیدپذیر: ملاحظاتی برای انعطاف پذیری مشاغل مزرعه-2020
With a varied landscape, Wales is resource rich in terms of wind and water and a suitable location to develop many different forms of sustainable energy. Whilst farm businesses face increasing challenges in terms of economic stability and traditional production methods, this paper considers the role of renewable energy production as a form of diversification. The study adopts mixed methods as a means of undertaking an in-depth investigation into the role of renewable energy generation in supporting agribusinesses in Wales. Initially a questionnaire obtained 118 responses from farmers in Wales. Subsequently, 15 follow-up semi-structured interviews with farmers were conducted to further investigate the issues from the initial questionnaire. The theoretical contribution of this paper is a segmentation of farmer businesses which allows for distinctions to be made of different attitudes to off-farm income and the adoption of renewable energy sources. Five farm types were identified, varying in relation to farm characteristics, attitudes to diversification, access to renewable energy and resource allocation. These farm types highlight the need for specific policies towards facilitating the increase in renewable energy along with sustaining farming incomes. Furthermore the research provides valuable information to the farming industry on opportunities in renewable energy production, particularly for farmers and farm businesses who are considering diversification strategies.
Keywords: Green economy | Farm diversification | Agribusiness | Entrepreneurship | Renewable energy
Sustainable business models and eco-innovation: A life cycle assessment
مدل های تجاری پایدار و نوآوری در محیط زیست: ارزیابی چرخه زندگی-2020
Eco-innovative business models are prominent elements of the development of sustainable production and consumption systems in organizations of all sizes, especially for small and medium enterprises, where a key challenge is to direct eco-innovation strategies toward the goals of their business model. Therefore, using product life cycle assessment, this research analyzed the alignment between the sus- tainable business model and the eco-innovative strategies of a Brazilian company in the veterinary homeopathy pharmaceutical industry. Disregarding the controversial discussion about homeopathy, this activity has shown signiﬁcant growth, having a representative economic importance in the animal protein production chain. The research adopted a case study method for one of Brazil’s leading com- panies in this activity. Data were collected through interviews, process analysis and company records. The results were built through quantitative and qualitative techniques that demonstrated that the eco- innovations developed by the company are directed toward the creation of new production methods and, above all, new products. The management model was framed in the “adopt a management role” archetype, in accordance with the literature. It was found that eco-innovation strategies are important for the development of the company’s business model and that this alignment is possible only when there is a management system and investments in the company’s ability to eco-innovate in product, process and organizational structure.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Agribusiness | Sustainable development indicators | Organizational innovation | Veterinary medicine
Evaluating machine learning performance in predicting injury severity in agribusiness industries
ارزیابی عملکرد یادگیری ماشینی در پیش بینی شدت جراحات در صنایع کشاورزی-2019
Although machine learning methods have been used as an outcome prediction tool in many fields, their utilization in predicting incident outcome in occupational safety is relatively new. This study tests the performance of machine learning techniques in modeling and predicting occupational incidents severity with respect to accessible information of injured workers in agribusiness industries using workers’ compensation claims. More than 33,000 incidents within agribusiness industries in the Midwest of the United States for 2008–2016 were analyzed. The total cost of incidents was extracted and classified from workers’ compensation claims. Supervised machine learning algorithms for classification (support vector machines with linear, quadratic, and RBF kernels, Boosted Trees, and Naïve Bayes) were applied. The models can predict injury severity classification based on injured body part, body group, nature of injury, nature group, cause of injury, cause group, and age and tenure of injured workers with the accuracy rate of 92–98%. The results emphasize the significance of quantitative analysis of empirical injury data in safety science, and contribute to enhanced understanding of injury patterns using predictive modeling along with safety experts’ perspectives with regulatory or managerial viewpoints. The predictive models obtained from this study can be used to augment the experience of safety professionals in agribusiness industries to improve safety intervention efforts.
Keywords: Injury severity classification | Injury severity prediction | Machine learning
Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018
Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization
Price risk perceptions and management strategies in selected European food supply chains: An exploratory approach
ادراک قیمت ریسک و راهبردهای مدیریتی در زنجیره تامین مواد غذایی انتخاب شده در اروپا: رویکرد اکتشافی-2017
Agricultural prices in European food markets have become more volatile over the past decade exposing agribusinesses to risk and uncertainty. This study goes beyond the farm stage and explores through interviews the price risk perceptions and management strategies in multiple stages of the food supply chain. Respondents were farmers, wholesalers, processors, and retailers in six European food supply chains. Results show that price risk management strategies in EU food chains are diverse and well beyond traditional instruments such as futures and forward contracts. We further find that deviations of prices by more than 10–15% from expected levels were perceived as price volatility by a majority of the chain actors. This study provides new insights on price risk management, a deeper understanding of price risk perceptions and highlights the interrelation of price risk management decisions with other business decisions.
Keywords:Price risk|Perceptions|Management strategies|European union|Exploratory|Interviews|Food supply chains