Large-scale Greenhouse Research Articles

THE ROLE OF GOVERNMENT SUPPORT IN THE DEVELOPMENT OF THE CCS MARKET: EXPERIENCE OF COUNTRIES WITH A DEVELOPED OIL AND GAS INDUSTRY

The article examines the role of government support in the development of the Carbon Capture and Storage (CCS) Technology market in countries with a developed oil and gas industry. It is shown that the implementation of CCS in the oil and gas sector has significant economic and environmental importance, as it allows not only to reduce the large-scale greenhouse gas emissions of this industry but also to increase the oil recovery factor through the injection of captured CO2 into depleted reservoirs. An analysis of the cost chain of various stages of the CO2 capture, transportation, and storage process is carried out, and the most costly elements are identified. The economic aspects of using Carbon Capture, Use, and Storage (CCUS) Technology for enhanced oil recovery are considered, and the need for government subsidies to ensure the commercial attractiveness of such projects under low CO2 emission prices is justified. The global experience of using economic instruments (carbon taxation, tax incentives, grants) to stimulate CCS projects in various industrial sectors is investigated. Significant differences in the approaches of leading countries compared to Russia are revealed. Pilot initiatives of the largest Russian oil and gas companies for the implementation of CCS technologies are analyzed. Recommendations are formulated for the creation of favorable regulatory conditions and effective mechanisms of government stimulation for the widespread adoption of CCS in Russia, taking into account its specifics as a major hydrocarbon producer.

Numerical simulation study on enhanced efficiency of carbon dioxide geological storage with nanoparticles in deep saline aquifer

Geological storage of carbon dioxide (CO2) is a promising technique to reduce large-scale greenhouse gas emissions. There are some candidates for geological storage formation while deep saline aquifers have attracted tremendous attention because they have the largest potential of all possible subsurface storage sites. CO2 injected into deep saline aquifers migrates upward until it reaches a sealing formation due to its buoyancy effect, which causes poor storage efficiency. Over the past few years, some novel research has been published to utilize nanoparticles for CO2 geological storage. The addition of nanoparticles to injecting super-critical CO2 (scCO2) changes its physical properties, such as the density and viscosity, which could mitigate gravity segregation and viscous fingering. However, the studies related to these techniques are still extremely limited, and the potential advantages or their impacts are poorly understood. In this paper, we investigated the effects of the physical properties of scCO2 containing nanoparticles on storage efficiency by using a versatile commercial simulator (CMG-GEM). In particular, the effects of nanoparticle types (aluminum oxide, silicon dioxide, titanium oxide, and zinc oxide) and concentration on CO2 storage efficiency were presented quantitatively by considering possible operation scenarios. The results showed the migration of CO2 to the upper part of the aquifer was suppressed, and the storage of CO2 in the lower part of the aquifer was promoted when scCO2 was injected with nanoparticles. It also showed some effects in delaying breakthrough time and mitigating breakthrough amount when we assumed the pressure control well. It was indicated that the cumulative breakthrough amount after 30 years was reduced by 8.2% in the minimum case and by 79.3% in the maximum case. Although the proposed method still has economic challenges, the innovation in the production processes of nanoparticles or combining with added value will lead to improving feasibility.

Waste-Based Vertical Planting System Proposal to Increase Productivity in Sustainable Horticulture; “PETREE”

The problems experienced with the decrease in agricultural areas per capita against the uncontrolled population growth in the World and in Turkey are becoming increasingly evident. Especially the manpower engaged in horticulture is increasingly experiencing the problems of migration, economic reasons, and inefficient production methods. For healthy and efficient production, agricultural lands need a sequential cycle that includes cultivation and fallow periods. Agricultural lands, the salt they contain, etc. When left fallow, the soil area needs to rest to re-establish the mineral balance. It is now vital for small producers, whose production areas are gradually shrinking due to global conditions, to continue production while their lands are resting to produce more continuously and efficiently. In the face of increasing demands, decreasing production amounts and areas, the constantly increasing prices of economically simple agricultural products “fresh vegetables” and affecting the easy accessibility of local products, it has become inevitable to seek new and alternative solutions. This study includes an innovative solution proposal to increase the production efficiency of low-scale producers and individual gardeners, especially those producing in small agricultural areas. Critical problems of large-scale greenhouse systems, interactions of agricultural practices within the framework of urban life, water and energy efficiency in agriculture, and sustainability and waste management of the proposed system are examined. To meet changing conditions and maintain productivity in small areas, it is envisaged that mobile and vertical production stations, which can produce by increasing productivity even in fallow areas while the soil is resting, can be an alternative solution to the problems of small producers. The vertical planting system “PETREE” develops mobile units for sustainable agriculture by collecting 5th LT-PET packages, which have a natural lifespan of about 90 years, and reusing them as plant pots and some recycled plastic pieces as structures. With efficient and environmentally friendly design suggestions, the system also examined the possibilities of more efficient gardening with mobile production stations in small agricultural areas, and efficient and enjoyable gardening that suits the needs of urban consumers “production with local seeds and seedlings” with the increase in environmental awareness and food safety concerns.

Energy Generation Intensity (EGI) of Solar Updraft Tower (SUT) Power Plants Relative to CSP Plants and PV Power Plants Using the New Energy Simulator “Aladdin”

The current investigation provides information about solar updraft tower power plants, SUTPPs (also called solar chimney power plants, SCPPs), which form a unique method of solar-powered electricity production through a ducted wind turbine driven by induced airflow as a result of solar heating. The investigation is conducted using numerical modeling via the system-level simulation tool Aladdin (developed and released freely by the Institute for Future Intelligence, IFI) for solar energy systems, wind energy systems, or the built environment. The Aladdin energy simulator is first evaluated here by comparison with published experimental and numerical results corresponding to the historical 50 kW prototype SUTPP that was successfully tested in Manzanares (Spain) between 1982 and 1989. This prototype has a height of about 195 m for the chimney (the updraft tower) and a radius of about 122 m for the solar heat absorber (the solar air collector or the greenhouse). Next, various climate and performance characteristics are investigated and contrasted for nine different locations around the world with a similar latitude of 24°, which is within the sunbelt, assuming that the same Manzanares SUTPP prototype geometry is employed in these locations. These nine locations are Muscat (Oman), Al Jawf (Libya), Riyadh (Saudi Arabia), Karachi (Pakistan), Ahmedabad (India), Havana (Cuba), Culiacán (Mexico), Dhaka (Bangladesh), and Baise (China). The energy generation intensity (EGI) for the Manzanares-type solar updraft tower power plant in these nine examined locations was between 0.93 kWh/m2 per year (in Baise) and 2.28 kWh/m2 per year (in Muscat). Also, Muscat had the smallest seasonality index (maximum-to-minimum monthly electric output) of 1.90, while Baise had the largest seasonality index of 4.48. It was found that the main limitation of the overall SUTPP energy conversion efficiency is the chimney efficiency (the process of accelerating the air after entering the chimney). This study concludes that solar updraft towers (SUTs) cannot compete with existing mature and modular renewable energy alternatives, particularly photovoltaic (PV) panels, if the aimed use is commercial utility-scale electricity generation. Instead, SUTs may become attractive and achievable if viewed as hybrid-use projects by serving primarily as a large-scale greenhouse area for agricultural applications while secondarily allowing energy harvesting by generating clean (emissions-free) electricity from the incoming solar radiation heat.

Beneficial impact of multi-bacterial inoculation on growth of selected Brassicaceae seedlings in a greenhouse culture

Modern agriculture requires the development of new eco-friendly and cost efficient methods of crop production. In this study we tested if engineering Brassica microbiome by supplementation it with an inoculum composed of selected microorganisms can accelerate seedling growth and be a substitute for inorganic fertilizers. We hypothesised that the multiorganismal inoculum will be more efficient and affect a wide range of plants compared to inoculation with a single microorganism. We developed and examined several new multispecies inocula in the cultivation of different Brassica oleracea, B. rapa, Eruca sativa and Lactuca sativa cultivars. Every microorganism used for biotization was isolated from Arabidopsis arenosa (Brassicaceae) and tested for plant growth promoting properties. Of all the inocula tested, one (T4) containing only bacteria (Chryseobacterium lathyri, Bacillus cereus, Lysinibacillus fusiformis, Pseudomonas protegens) proved to be particularly efficient in increasing the fresh biomass of most B. oleracea and B. rapa cultivars cultured in the growth chamber. None of the bacteria used in the inoculum was able to activate plant growth individually. Thus, T4 inoculum was evaluated in a large-scale greenhouse culture of young Brassica vegetables. The results confirmed the positive effect of inoculation on the growth of selected Brassica crops, but it differed according to the plant genotype. What is more, the T4 inoculation proved to be as effective for increasing plant biomass as synthetic fertilizers. None of the inocula tested improved the growth of plants from genus other than Brassica.

Delivery of pollen to forsythia flower pistils autonomously and precisely using a robot arm

Autonomous pollination robots have received significant attention in recent years. It is expected that these robots will assist farmers in pollinating plants effectively. Despite employing various pollination methods, delivering pollen autonomously and precisely to pistils (the reproductive organs of flowers) remains a challenge. This is due to the small size of pistils, as well as difficulty in their rotation and orientation identification. Under this background, we present a novel method for detecting the orientation and area of the rotated pistil, known as PSTL_Orient, which we combine with servoing techniques and flower detection to develop an autonomous pollination robot. The experimental results demonstrate the superiority of our proposed PSTL_Orient in comparison to existing state-of-the-art methods in the detection of the orientation and rotation of pistil anchors. Additionally, the proposed pollination robot can achieve a 91.08 % accuracy in detecting flowers and an 86.19 % success rate in pollinating the small pistils of forsythia flowers. By being mounted on a ground vehicle, the proposed robotic arm system can help farmers in practical large-scale greenhouse environments.

CFD Model Verification and Aerodynamic Analysis in Large-Scaled Venlo Greenhouse for Tomato Cultivation

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using computational fluid dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to design and verify the 3D CFD model for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification of the CFD models was conducted using the improved grid independence test (GIT) and wall Y+ approaches. The findings revealed that a grid resolution of 0.8 m and a first-layer height of 0.04 m were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. Subsequently, the aerodynamic characteristics and regional ventilation efficiency were analyzed in a 3D greenhouse model for developing a new large-scale greenhouse complex.

Evaluating the Efficacy of the Fermentation Formula of Bacillus velezensis Strain Tcb43 in Controlling Cucumber Powdery Mildew

Powdery mildew is a prevalent disease that affects cucumber crops, leading to yield reductions and declines in quality. Bacillus sp. strain Tcb43 culture medium was shown to be capable of inhibiting fungal spore germination in previous reports. In this study, the identification of the Tcb43 strain was updated to Bacillus velezensis by using whole-genome sequencing. The strain exhibited tolerance to a wide range of temperatures (12–40 °C), salinities (7–10%), and pH levels (ranging from 5 to 11). Additionally, Tcb43 demonstrated insensitivity to most tested fungicides. A new fermentation formula, Tcb43FBSO, was developed by adding 0.25% soybean oil to the fermented formula (Tcb43FB). This new formula exhibited a shelf life of up to 12 months with the decrease in bacterial count from 5.35 × 108 to 1.97 × 108 cfu/mL. Greenhouse assays showed that the treatment of potted cucumber plants with a 100-fold dilution (100×) of Tcb43FBSO for four weeks resulted in a significant reduction (64.64%) of cucumber powdery mildew compared to the mock group. In large-scale greenhouse trials, the treatment of cucumber plants with 200× of Tcb43FBSO for 5 weeks effectively suppressed powdery mildew disease, with a control rate that reached 76.6% compared to the mock group. These findings highlight the potential of Tcb43 as a biocontrol agent for managing cucumber powdery mildew and suggest its promising application in agriculture.

Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China

Rapid and accurate agricultural greenhouse extraction with remote sensing imagery is essential for providing spatial information for precision agriculture. Benefiting from local spatial perception, deep learning based object extraction methods have achieved satisfactory performances in extracting geo-objects. However, they fail to work for large-scale greenhouse extraction since the objects are sparsely distributed in the background and densely distributed in the foreground, where the local spatial perception causes redundant computation and false detection problems. In this paper, we propose a layout attention network (LANet) framework for large-scale greenhouse extraction using remote sensing imagery, which replaces the local spatial perception with spatial layout perception, i.e., a sparse global layout to identify the sparse background and a dense local layout to identify the dense foreground. To address the shortcoming of the sparse background, which leads to redundant computation, a sparse global layout awareness module is formulated as a scene classifier. This accommodates the global layout attention map of the global scene features by adopting a layout-shared convolutional neural network (CNN) backbone for generating class-agnostic layout priors and global channel attention for aggregating discriminative global layout features, ensuring robust sparse background identification. Then, to alleviate the problem of the dense foreground, which causes false detection, a dense local layout awareness module is proposed to incorporate the local layout attention map and rotated region of interest (RRoI) features. The RRoI features are then further embedded to guide the initial RRoIs for object location refinement by aligning the initial RRoI locations in a layout-sensitive attention mechanism and achieving semantic enhancement by taking the local layout density as a semantic prior to assign a reliable class score map. The experimental results obtained on an agricultural greenhouse benchmark dataset and a large-scale agricultural greenhouse extraction dataset illustrate that the proposed framework can outperform the state-of-the-art object extraction methods in both speed and accuracy, and has a high generalization ability for large-scale dense object extraction.

Multitemporal greenhouse mapping for high-resolution remote sensing imagery based on an improved YOLOX

Plastic greenhouses (PGs), as safeguard agricultural facilities, have changed the cultivation structure of agriculture, to a certain extent, and can seriously impact the environment. Exploring the temporal characteristics of greenhouses in terms of quantity and area is significant for sustainable agricultural development and environmental protection. Pixel or object-based large-scale greenhouse mapping has difficulty meeting the demand for the simultaneous extraction of greenhouse quantity and area. Multibranch task model can provide the number and area information of greenhouse. However, there are various labeling methods and model between different branches, which lead to the deviation between quantity and area results. This paper proposes a model that is based on improved you only look once X (YOLOX) and kullback–leibler divergence (KLD), called YOLOX and KLD plus (YXLD + ), to obtain spatiotemporally consistent greenhouse mapping. The new model still has stable performance in dense scenes, with a 5.7 % improvement in average precision compared to BBAVectors. Finally, a multitemporal greenhouse map of Zhoukou City was obtained using imagery provided by Google Earth (GE). Overall, the distribution of greenhouses in Zhoukou City remained stable between 2012 and 2017. However, the number and area of greenhouses in local areas has increased rapidly in the past five years, with the number of greenhouses increasing by 47.08 % and the area expanding by 41.38 %. Greenhouses' distribution and changing characteristics concentrate on the gently sloping areas from the northwest to the southeast. These results provide an objective perspective on the development of the local agricultural cultivation structure, which is of significant importance for the promotion of agricultural modernization.

System for determining the job status of individual laborers in a large-scale greenhouse

To obtain the work status of individual laborers by drawing the work progress map and determining the work speeds of individual laborers in a large-scale greenhouse, we developed a simple system comprising smartphone and web-based server applications, using smartphones, two-dimensional (2D) barcodes (called QR codes), a cloud server, and a personal computer. Using the smartphone applications, the laborers read the QR codes on their smartphones when they started or ended the designated work. The timestamps obtained when reading the QR codes were sent to the cloud server. The work progress map was drawn and the work speeds of individual laborers were estimated by the web-based server application in the cloud server based on the length of the gutter and the time difference between the start and end of the designated work. In the field trial, we placed QR codes at the end of the gutters (556 gutters) in a model greenhouse where bell pepper plants were grown in an area of 3 ha. The work progress map and work speeds of individual laborers were obtained rapidly and remotely in the greenhouse based on the collected data. Because the system has a simple structure, its installation is relatively easy, indicating lower cost than that of the commercially available systems.

Identification of cassava germplasms resistant to two-spotted spider mite in China: From greenhouse large-scale screening to field validation.

Utilization of resistant germplasm is considered as an effective, economical and eco-friendly strategy for cassava pest management. Tetranychus urticae, known as the two-spotted spider mite (TSSM), is a devastating pest in Asian cassava planting countries as well as in China. However, the resistant levels of abundant cassava germplasms to TSSM remains largely unknown. To fill this knowledge gap, we conducted screening of 202 cassava germplasm for resistance to TSSM in China based on the classification of mite damage phenotype, under both greenhouse and field conditions. The three rounds of large-scale greenhouse experiments had identified two highly resistant (HR) varieties (C1115 and MIANDIAN), five resistant (R) varieties (SC5, SC9, SC15, COLUMBIA-4D and LIMIN) and five highly susceptible (HS) varieties (KU50, BREAD, SC205, TMS60444 and BRA900), besides, these 'HR' and 'R' varieties would significantly repress the normal development and reproduction of TSSM. In addition, the 12 cassava varieties selected from the greenhouse screening were further subjected to consecutive five years of field validation at Danzhou, Wuming and Baoshan. The seven resistant varieties not only exhibited stable TSSM-resistance performance across the three field environments, but also possessed the same resistant levels as the greenhouse identification, while the resistant varieties SC5 was an exception, which was identified as moderate resistant in Baoshan, indicating the variety-environment interaction may affect its resistance. Furthermore, regional yield estimation suggested that the higher the resistance level was, the better capacity in reducing the yield losses. This study demonstrated that the TSSM-resistant varieties could be considered as ideal materials in mite control or in future breeding programme of mite-resistant cassava plant.

A Convolutional Neural Network for Large-Scale Greenhouse Extraction from Satellite Images Considering Spatial Features

Deep learning-based semantic segmentation technology is widely applied in remote sensing and has achieved excellent performance in remote sensing image target extraction. Greenhouses play an important role in the development of agriculture in China. However, the rapid expansion of greenhouses has had a series of impacts on the environment. Therefore, the extraction of large-scale greenhouses is crucial for the sustainable development of agriculture and environmental governance. It is difficult for existing methods to acquire precise boundaries. Therefore, we propose a spatial convolutional long short-term memory structure, which can fully consider the spatial continuity of ground objects. We use multitask learning to improve the network’s ability to extract image boundaries and promote convergence through auxiliary loss. We propose a superpixel optimization module to optimize the main-branch results of network semantic segmentation using more precise boundaries obtained by advanced superpixel segmentation techniques. Compared with other mainstream methods, our proposed structure can better consider spatial information and obtain more accurate results. We chose Shandong Province, China, as the study area and used Gaofen-1 satellite remote sensing images to create a new greenhouse dataset. Our method achieved an F1 score of 77%, a significant improvement over mainstream semantic segmentation networks, and it could extract greenhouse results with more precise boundaries. We also completed large-scale greenhouse mapping for Shandong Province, and the results show that our proposed modules have great potential in greenhouse extraction.

Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments

This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A rail-guided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results.

Establishing a large-scale Greenhouse Gas Removal sector in the United Kingdom by 2030: First mover dilemmas

In June 2019 the UK legislated a 2050 net-zero emissions target. This will require the realisation of a technical Greenhouse Gas Removal (GGR) sector potentially generating over 60 MtCO2 pa of negative emissions by 2050. In October 2021, the UK pledged that at least 5 MtCO2 of engineered negative emissions be deployed by 2030. At present less than 0.5 ktCO2 pa of engineered negative emissions are deployed. The 10,000-fold scale-up to 2030 will require the co-ordinated engagement of first movers to establish and realise at least one GGR value chain. The 120,000-fold scale up to 2050 will require the integration of multiple GGR value chains with existing infrastructure systems and substantive societal engagement to enhance positive social outcomes. This scaling is fundamental to the UK, and arguably international efforts, to address the worst impacts of climate change.A series of exploratory exercises have been undertaken to identify the financial and non-financial barriers to the establishment of a UK multi-MtCO2 pa scale GGR sector from a first mover perspective. This is the first synthesis of first mover drivers in the UK GGR sector.The key findings include: (1) The GGR sector represents a multi-billion pound opportunity in 2050; and (2) inspite of this opportunity - as the incentive, policy, regulatory and governance ecosystem presently stands - first movers face too much risk, uncertainty and a multiplicity of dilemmas to commit substantive investments in establishing the UK GGR multi-MtCO2 pa sector.

National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China

Greenhouses have revolutionized farming all over the world. To estimate vegetable yields, greenhouse mapping using high spatial resolution (HSR) remote sensing imagery is very important. Although automatic greenhouse mapping methods have been proposed, they are often applied in limited small-scale areas (i.e. a parcel, a city, or a province). Large-scale greenhouse mapping (i.e. national-scale) faces the diversity of greenhouses in different areas, the difficulty of the simultaneous extraction of the number and area of greenhouses, and the dense spatial distribution of greenhouses. In this paper, to solve the problem of large-scale greenhouse mapping, a novel data-driven deep learning framework is proposed, which we refer to as the dense object dual-task deep learning (DELTA) framework. The dual-task learning module simultaneously extracts the number and area of greenhouses by adopting a greenhouse area extraction branch and a greenhouse number extraction branch. A high-density-biased sampler module is proposed to select more samples in areas with a dense distribution, so that the trained model is more effective at dense greenhouse extraction. Six regions in China were selected for evaluation, which obtained a performance increment of 1.8% in mean average precision (mAP) when compared with Faster R-CNN. Finally, the whole of China was taken as the research area, and remote sensing image tiles at a 1-m spatial resolution from all over China were obtained. All the images were captured by different sensors and downloaded from open-source sites or purchased. The experimental results indicate that more than 13 million greenhouses were extracted in China.

A climate-based analysis of photosynthetically active radiation availability in large-scale greenhouses across China

Photosynthetically Active Radiation (PAR) is critically required for sustaining plant and vegetable growth. This study investigated PAR availabilities in two typical large-scale greenhouses using an advanced method of climate-based solar modelling in China. Seven Chinese locations in terms of daylight (solar) climate zones and latitudes were studied. The PAR prediction was conducted via RADIANCE (ray-tracing solar and light simulation package). Key findings were: 1) A climate-based analysis was proved as more practical than the methods based on only clear sky and solar geometries. 2) A ray-tracing solar modelling could effectively predict PAR levels at specific positions (e.g. vertical planes), which could benefit the development of vertical farming activities. 3) The PAR availability could receive insignificant impact from roof configuration in these large-scale greenhouses. 4) For the approach of vertical farming, the vertical arrangement of planting system could be significantly affected by greenhouse orientations, while horizontally no such effect can be found. These findings could be developed into design strategies to support greenhouse planning.

Research of the Mathematical Model of Heated Greenhouses

Large-scale greenhouse production is currently developing along the path of introducing technologies for intensive cultivation of vegetable crops and using automated control systems for technological processes based on micro and minicomputers. One of important technological processes is watering and feeding plants with mineral fertilizers. The need to automate this process is associated with laboriousness of process of preparing solutions, accurately maintaining given concentration of substances in them, timely supply and uniform dosing over the entire area of greenhouse under various disturbances of external environment. The article shows a technological scheme of a heated greenhouse of the simplest design. A method for constructing balance models is presented; model can be transformed for more complex schemes. Three main models in spacestate (continuous, discrete, operator) are considered. Information about processes is recorded discretely, therefore, we first identify discrete form of the model, then we obtain parameters of the discrete form. Further, using equation of constraints, a matrix of discrete and continuous forms, we obtain transfer functions for the internal airtemperature. Due to simplicity of setting parameters, it can be easily reproduced for many different designs and systems of greenhouse complexes, as well as for various scenarios of changing environmental conditions. Proposed balanced dynamic models allow the most complete use of computers for studying heat consumption modes of heated greenhouses, as well as for synthesis of temperature controllers. In this case, vector-matrix algebra is used as a mathematical apparatus, standard programs for which are widely used on computers for various purposes.

Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.

What really drives the deployment of renewable energy? A global assessment of 118 countries

Climate change poses unprecedented risks to human society. Ample scientific evidence suggests that greenhouse gas emissions are the primary contributor to climate change. Large-scale greenhouse gas emissions are mainly caused by the consumption of fossil fuels. Thus, promoting renewable energy is one key strategy to mitigate these emissions and combat climate change. In this study, we investigate some potential driving forces of renewable energy deployment. We analyze panel data from 118 countries worldwide from 1995 to 2015. Results show that countries that are more vulnerable to climate change and have less carbon-intensive economies deploy higher shares of renewable energy in their total energy consumption. However, carbon tax has a statistically insignificant influence on renewable energy deployment. This study complements the literature by exploring the impact of climate change vulnerability, carbon intensity, and carbon taxes on renewable energy deployment. The findings imply that countries become more supportive of renewable energy when their economies are less intertwined with fossil fuels and when relevant policies are in place to provide incentives on renewables.