Analysis and Evaluation of Energy, Economic and Environmental Impact Indicators of Horticultural and Greenhouse Production Systems in Iran using PRISMA Method

The aim of this study was to compare the energy flow in greenhouse and open-field cucumber production systems in Iran. For this purpose, data were collected by using a face-to-face questionnaire performed with 100 cucumber farmers (50 farmers for greenhouse systems and 50 farmers for open-field cucumber systems) in summer 2010. Farmers were selected by random sampling method in Tehran and Kermanshah province of Iran. The results revealed that total energy consumption amount in greenhouse systems was 11709452.43 MJha-1 while in open-field systems it was 78476.33 MJha-1. The highest share of total input energy in greenhouse cucumber systems was recorded for diesel fuel with 99% while the highest share of input energy in open-field cucumber systems was observed for electricity power with 38%. Energy use efficiency in greenhouse systems (0.017) was lower than open-field systems (0.33). Energy productivity and specific energy in greenhouse production systems were 0.02 KgMJ-1 and 46.84 MJKg-1, respectively while that in open-field production systems were 0.41 KgMJ-1 and 2.38 MJKg-1, respectively. Accordingly, net energy in open-field cucumber production systems (-52171.93 MJha-1) was higher than greenhouse cucumber production systems (-11509452.43 MJha-1). Key words: Greenhouse, open-field, energy use efficiency.

Although the physiological principles involved in the growth of greenhouse and field crops are not basically different, the development of models for greenhouse crops to some extent has followed its own way. This is mainly due to the specific characteristics of the crops and of the greenhouse production systems involved. Many important greenhouse crops are multi-harvest crops, where the balance between vegetative and generative growth is an aspect of major concern to growers. Moreover, most products have a high water content and they are sold fresh. In food crops, taste is a valuable crop property. In ornamentals, shape and colour are important characteristics that put certain demands on the output of models. More generally, quality issues (e.g., shelf or vase life) often have to be approached in a different way than with field crops. Last but not least, the huge number of species is problematic for crop modellers in horticulture. Modern greenhouse production systems provide the grower with a highly advanced, but expensive system for controlling the aerial and root environments of the crop. Through this control system growers are able to control the production process in great detail. The high added value obtained in greenhouses and the high quality requirements go together with a great deal of human interference in the production process: either directly, by pruning and training, or indirectly, by using various organisms for pest control and pollination. Crop management and the interaction of pests and diseases with the crop are both aspects that make special demands on crop models. It is a major challenge for greenhouse growers to make the best possible use of the available options to achieve high productivity at the moment when products are required in the market. In addition, this must be accomplished while reducing the environmental impact by emissions of CO2, nutrients, and biocides and at minimum cost. To optimise greenhouse production systems, crop models are needed. But, they also have to be integrated into more complex models of the nursery as a whole to address planning, scheduling, and logistics. For policy makers, there is a need for models at regional or national scale that help them to decide on measures related to environmental issues or economic development. Models on product quality and integration of models from different disciplines to simulate nurseries and whole product chains are some of the important and challenging developments in greenhouse simulation over the last few years. Also, the implementation of models into practice is a hot issue generating many new and complex research questions.

ENVIRONMENTAL SYSTEM ANALYSIS FOR HORTICULTURAL CROP PRODUCTION

Uganda has a largely agrarian based economy with 85% of its nearly 35 million people living in rural areas and 80% of its labor force engaged in agricultural production as their primary form of livelihood. The agriculture sector also accounts for 40 percent of GDP and 85% of export earnings with 90% of this being generated by crop production. Horticultural production is one of the fastest growing agricultural sub-sectors with a growth rate of 20% per year. It contributes to value addition, income diversification and foreign exchange earnings through exports (UIA, 1999). Horticultural production in Uganda is dominated by small scale producers (2ha. or less) who produce for both local and export markets. The most important horticultural crops in the vegetable category include tomato, green beans, cowpea, pepper, onion, crucifers, and Amaranthas spp. Because of ravages of pests and diseases on these moderate to high value crops, pesticides are among the key inputs on these crops. The increased use of chemical pesticides on horticultural crops has raised a number of economic, ecological and health concerns. Economic concerns arise from the over reliance and use of chemical pesticides which increase the costs of production. Indiscriminate use of pesticides has resulted in ecological problems of common pests developing resistance, elimination of natural enemies and other beneficial arthropods, and environmental pollution. Human health concerns focus on risks from shortcomings in protective clothing, large deviations from recommended doses/situations, and excessive run-off into the soil and water sources. These concerns are exacerbated by poorly regulated internal markets for pesticides that have fostered usage of banned or outdated products; creating a situation that if not stopped will negatively impact on horticultural exports to countries with more stringent regulatory requirements for fresh crop produce. Meeting these food safety requirements has become a major challenge for the fresh produce export sector of many African countries. To ensure and maintain export compliance, grower and consumer safety, and environmental integrity; farmers, government and development partners are developing programmes designed to improve pesticides usage, regulation and management on horticultural crops. In this chapter, three important horticultural crops grown in Uganda-

Overuse of chemical and organic fertilizers in greenhouse (GH) crop production may cause the accumulation of heavy metals in soils and risks to human health. The aims of this study were to compare physical and chemical properties of GH with open-field (OF) soils, to clarify the buildup of heavy metals and phosphorus (P) in soils, and to assess the risks of selected heavy metals in soils and cucumber (Cucumis sativus L.) and tomato (Lycopersicon esculentum Mill.) from GH vegetables in Hamedan, western Iran. The average total and Olsen P of GH soils were significantly higher than the OF soils for both vegetables. The order of total and available heavy metal content in tomato GH soils has been set as zinc (Zn) > nickel (Ni) > chromium (Cr) > lead (Pb) > copper (Cu) > cadmium (Cd) and Zn > Cr > Cu > Pb > Ni > Cd, respectively. The same order was found for cucumber GH soils, except that the position of Pb and Cu was changed. The results indicated that in both GH cucumber and tomato soils, the mean content of total and available Zn, available Cu, Ni, and Pb, was extra than in OF soils. There were no significant differences between average total Cr, Cu, Ni, and Pb in GH and OF soils. Tomato vegetables had higher heavy metal content and transfer factors, particularly for Cr than cucumber vegetables. According to the health risk indices, Cr and Pb represented a high potential risk for health through cucumber and tomato consumption. There were limited Cd, Cu, Pb, and Zn inputs from the irrigation waters, while the input of Cr and Ni may be important. However, the amount of manure application and heavy metal content of the manures was significant.

The Chinese government made a commitment to achieve a 40–45 % reduction in carbon emissions per unit of gross domestic product (GDP) by 2020 compared with 2005. Most provinces followed the national commitment due to unified task of 40–45 % reduction in carbon emissions. However, different industrial structures, energy consumption structures and natural resources endowment of each province vary the emission abatement costs. Each province should take the carbon dioxide abatement cost into consideration for the carbon dioxide reduction target. Data envelopment analysis (DEA) and linear programming (LP) methods were used to measure the marginal abatement cost in previous studies. In this paper, we built a quadratic parametric directional distance function (DDF) to measure the carbon dioxide marginal abatement cost of Chinese provinces. To overcome the flaw of ignoring random errors in previous research, this paper compared results of stochastic frontier analysis (SFA) method and DEA method. Because DEA method only considers the inefficiency and SFA method can distinguish the random error from inefficiency, the result of the average carbon dioxide marginal abatement cost of each province calculated by SFA was 55 % lower than DEA method. As the random error may be introduced by chosen function form, Spearman test and paired sample T test were used to test the correlation of two methods’ MAC results. The results show that the ranking order MAC results sequence of SFA method and DEA method is highly correlated. But the MAC value of SFA and DEA methods has significant difference. As half of the error comes from the random error, the MAC results calculated by SFA method are more precise than DEA method. So SFA method is more appropriate than DEA in this paper. This result reinforces the feasibility of the Chinese government carbon dioxide emission reduction target. However, this study proved that the carbon dioxide emissions and marginal abatement cost varied from province to province. Furthermore, there was no distinct correlation between carbon dioxide emissions and the marginal abatement cost. On the contrary, the marginal abatement cost was related to the industrial structures, energy consumption structures and natural resources endowment of each province. Therefore, two policy suggestions are proposed as CO2 emission reduction principle: First, central government should establish CO2 emission reduction targets based on MAC and local economic affordability. Second, resource endowments and embodied carbon transfer should be considered.

Vehicle crashes are amongst the major causes of mortality and results in losses of lives and properties. A large number of the vehicle crashes occur on rural roads. Accidents become more noteworthy in two-lane roads due to going and coming traffic. Therefore, prediction of crashes and their causes are considerably important to reduce the number and severity of the accidents. The safety index is a suitable quantity for determination of road safety degree. It informs us to study the number of accidents in a specific road and time. In this study, safety index of two-lane rural roads is predicted by Artificial Neural Network (ANN), Radial Basis Function Neural Networks (RBFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithms using MATLAB software. The number of causes which ends to an accident is related to some parameters. We chose seven new parameters as inputs to the ANN, RBFNN and ANFIS methods that are geometric and statistical values of the roads and one output variable that is the safety index of segments of two-lane rural roads. 5 roads in Ilam Province, Iran, were selected for the case study to train, validate and test the proposed estimation models. Finally, the results show that, it is possible to predict the safety index of two-lane rural roads with a high correlation coefficient and a low mean square error (MSE) in relation to real values. The ANN method has a higher correlation coefficient and lower MSE in comparison to RBFNN and ANFIS methods. The achieved correlation coefficient and MSE for validation of the ANN approach are 0.94 and 0.0086 respectively, and correlation coefficient of 0.845 and MSE of 0.019 for all data.

Horticultural greenhouse production in circumpolar regions (>60° N latitude), but also at lower latitudes, is dependent on artificial assimilation lighting to improve plant performance and the profitability of ornamental crops, and to secure production of greenhouse vegetables and berries all year round. In order to reduce energy consumption and energy costs, alternative technologies for lighting have been introduced, including light-emitting diodes (LED). This technology is also well-established within urban farming, especially plant factories. Different light technologies influence biotic and abiotic conditions in the plant environment. This review focuses on the impact of light quality on plant–microbe interactions, especially non-phototrophic organisms. Bacterial and fungal pathogens, biocontrol agents, and the phyllobiome are considered. Relevant molecular mechanisms regulating light-quality-related processes in bacteria are described and knowledge gaps are discussed with reference to ecological theories.

Extreme bushfire is having considerable negative effects on the sustainability of agricultural landscapes in various parts of the world. Fire-induced damages to tree crops have led to significant effects on perennial horticultural production systems with associated lower returns and decline in economic sustainability. Australia is one of the most fire-prone countries in the world and contributes to global horticultural production with production forecast level estimated at $18.2 billion in 2023–24, according to the Australian Department of Agriculture, Fisheries and Forestry. Bushfire-related damages to horticultural production may however threaten this promising potential. This review provides a commentary on the history, scale and impacts of extreme bushfires in Australia. The effects of bushfire on horticulture, including soil nutrient availability, fruit tree physiology and carbohydrate sink-source dynamics are discussed. Given the increasing frequency and severity of bushfires as a result of climate change, the negative effects of heat and fire damage on fruit tree production are expected to increase. Based on the Australian experience with bushfires in horticultural landscapes, this review outlines proactive responses for minimising bushfire impacts on horticultural production in temperate regions, with particular reference to the Rosaceae family. Adaptation strategies must be planned and set up before orchard establishment and should include defensible space or safety zones around the orchard, as well as internal and external fuel reduction strategies for the orchard lifespan.

Horticultural production occurs in various production systems, dominated by greenhouse and open-field production. During the last decade, alternative production systems with more advanced technologies, such as LED lighting and artificial intelligence, have started to appear, e.g., plant factories with artificial lighting. This opens up new opportunities where increased attention from venture capitalists and investors highlights food-tech as an innovative field of interest. Technological development can also accelerate possibilities, mainly for firms producing in greenhouses, if they can adopt relevant knowledge and innovations from other production systems. Another aspect is the increased interest in start-up initiatives and businesses in urban settings, e.g., urban farming, vertical farming, aquaponics, or rooftop greenhouses, to mention a few models. In parallel, low-tech initiatives are developing, e.g., market gardening and small-scale artisan production, which can also be important niches for the sustainable production of vegetables. The innovative production systems often use alternative food networks and different business models, e.g., Community Supported Agriculture or Product Service Systems, often with shorter supply chains. These different initiatives are also associated with positive movements influencing society and increasing consumers’ awareness of sustainable food production. However, the fact that new actors are entering the market could also create tensions between urban and rural contexts due to the different backgrounds of business owners. This is further accelerated by the different conditions for the firms, e.g., depending on support and policies from the innovation system and society in general.

Knowing water and energy consumption patterns sets the baseline for understanding their drivers and assessing the perfomance of potential measures to increase efficiency and/or reliability. However, these patterns can vary substantially depending on the building caracteristics, on the building users and use, on the cultural, social, economic, environmental context in which the building is located, among many other factors. This article presents a general methodological framework for characterizing water and energy consumption patterns in buildings based on the evaluation of the proportions of the surface areas of each space type, proposing indicators of water and energy use, by end use per square meter and by space type. Universities, in most cases, represent large water and energy consumers with distintive consumption drivers and patterns which have received limited attention when compared to other types of buildings (e.g., residential). The methodological framework proposed was applied to the buildings of the Paricarana Campus of Federal University of Roraima (UFRR), Brazil, providing one of the few examples in the literature reporting water and energy consumption in university buildings in tropical climates. The findings have shown that teaching rooms and administration rooms are the main source consumers, representing 48% and 49% of the institution's energy and water consumption, respectively. Air conditioning is the biggest energy consumption (63%), while personal use represents 72% of the total water consumption in a building. The toilets represent a large water consumption in a university building (46.40%). Comparing different buildings uses, the central library is the highest source consumption, due to the longest operating time and the highest occupational density. The methodological proposal intends to be a useful tool to support managers and decision-makers to understand the dynamics of consumption and then propose effective practices to reduce water and energy uses, as well as providing reference data for comparison with other educational institutions.

Knowing water and energy consumption patterns sets the baseline for understanding their drivers and assessing the performance of potential measures to increase efficiency and/or reliability. These patterns can vary substantially depending on the building characteristics, on the building users and use, on the cultural, social, economic, environmental context in which the building is located, among many other factors. This article presents a general methodological framework for characterizing water and energy consumption patterns in buildings based on the evaluation of the characteristics of the equipments and appliances, as well as the type of users and the activities developed in each type of room. This allows estimating water and energy use, by end use per square meter and by roomtype. The methodological framework proposed was applied to the buildings of the Paricarana Campus of Federal University of Roraima (UFRR), Brazil, providing one of the few examples in the literature reporting water and energy consumption in university buildings in tropical climates. Universities, in most cases, represent large water and energy consumers with distinctive consumption drivers and patterns which have received limited attention when compared to other types of buildings (e.g., residential). The findings have shown that teaching rooms and administration rooms are the main consumers, representing 48% and 49% of the institution's energy and water consumption, respectively. Air conditioning is the biggest energy consumption (63%), while personal use represents 72% of the total water consumption in a building. The toilets represent a large water consumption in a university building (46.40%). Comparing different building uses, the central library is the highest consumer, due to the longest operating time and the highest occupational density. The methodological proposal intends to be a useful tool to support managers and decision-makers to understand the dynamics of consumption and then propose effective practices to reduce water and energy uses, as well as providing reference data for comparison with other educational institutions.

Aims: Every year, a significant number of individuals lose their lives due to cancer or undergo challenging treatments. Indeed, the development of an effective cancer prediction method holds great importance in the field of healthcare. Methods: Machine learning methods have played a significant role in advancing cancer prediction models. In this context, this study focuses on exploring the potential of two machine learning methods: Artificial neural network (ANN) and adaptive-network-based fuzzy inference system (ANFIS) for cancer prediction. In this study, two different types of cancer, ovarian cancer and lung cancer, are taken into consideration. For the prediction of ovarian cancer, three specific biomarkers, namely human epididymis protein 4 (HE4), carbohydrate antigen 125 (CA-125), and carcinoembryonic antigen (CEA), are used to develop a prediction model. For the prediction of lung cancer, six different variables are utilized in the development of both the ANN and ANFIS methods. Results: The findings demonstrated that the proposed methods had an accuracy rate of at least 93.9% in predicting ovarian cancer. With an accuracy rate of at least 89%, the proposed methods predicted lung cancer. Also, the proposed ANN method outperforms the ANFIS method in terms of predictive accuracy for both ovarian cancer and lung cancer. Conclusion: This study suggests that the ANN method provides more reliable and accurate predictions for these specific cancer types based on the chosen variables or biomarkers. This study highlights the potential of machine learning methods, particularly ANN, in improving cancer prediction models and aiding in the early detection and effective management of ovarian and lung cancers.

Artificial Intelligence (AI) is currently used by people working in different fields of science, also the need for Smart Grids in electrical networks requires the use of intelligent technologies. Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) methods are the creation of AI. For the first time, AI methods will be used to determine the Load Current (LC) of distribution network’s buses. The objective is to test and compare ANN and ANFIS methods in order to predict LC in every bus of the I3E 33 bus system and to evaluate the possibility of using them in systems where iterative methods do not converge and also to obtain the predicted values in real time. For this comparison, two discrepancies indicators have been used to test the reliability of the two methods. Generally, the obtained results from the study have been good enough to recommend the employment of AI techniques to solve difficult problems that lack data or have large and complicated systems. In addition, ANFIS method is more precise and requires less time for training and predicting compared with ANN method, indicating that combining two or more intelligent methods can be beneficial.

This paper investigates energy consumption patterns using panel data of final energy consumption and economic variables from 1970 to 2010 for more than 100 countries and regions. Although previous economic studies address the role of economic development and income as determinant factors of energy consumption, we examine how physical factors such as climate affects energy consumption level and patterns. By integrating economic and engineering approaches, we demonstrate statistical analyses showing that climate determines not only energy consumption levels, but also the relationship between energy consumption and income levels over time. While climate affects energy consumption patterns, our results suggest that energy technology and price policy are significant in determining whether a country can decouple energy consumption from economic growth.