Agricultural Greenhouse Research Articles

Why greenhouse agricultural workers do not use pesticide protection methods? A case study

Abstract Background Exposure to pesticides in individuals working in greenhouse agriculture is an important public health problem that threatens their health more than individuals working in other agricultural jobs for the same period of time, causing an increase in mortality and morbidity rates. Applying protection methods plays an important role in reducing/preventing exposure-related health risks. It is aimed to explore the reasons why greenhouse agricultural workers do not use pesticide protection methods. Methods This case study was adopted the descriptive qualitative approach between February-March 2024. Individual in-depth face-to-face interviews were conducted with semi-structured interviews, resulting in 20 participants. The inclusion criteria for participants include being a greenhouse agricultural worker (at least 1 year), being 18 years of age or older, using pesticides, not using any personal protective equipment, and not engaging in protective behavior. Braun and Clark’s (2021) reflexive thematic analysis methodology was used to analyze the data. Results In this study generated three main themes and fourteen subthemes. The main themes identified were a) personal attitudes/ characteristics b) negative features of the physical environment, c) negative features of personal protective equipment. Conclusions The reasons why greenhouse agricultural workers do not use pesticide protection methods are individual attitudes/characteristics such as laziness and not to care about, negative characteristics of the environment such as temperature, humidity and wetness, and negative characteristics of personal protective equipment such as being heavy and inflexibility. Health institutions and health professionals serving in regions where agricultural workers should develop interventional studies, intervention programs and strategies to protect and improve the health of these individuals. Key messages • Greenhouse agriculture workers do not use any of the pesticide protection methods due to individual attitudes/characteristics, negative characteristics of environment and personal protective equipment. • Health professionals can ensure that greenhouse agricultural workers use pesticide protection methods by increasing their awareness of health risks and planning interventional studies.

Prediction of Carbon Dioxide Concentrations in Strawberry Greenhouse by Using Time Series Models

Carbon dioxide (CO2) concentrations play an important role in plant production, as they have a direct impact on both plant growth and yield. Therefore, the objectives of this study were to predict CO2 concentrations in the greenhouse by applying time series models using five datasets. To estimate the CO2 concentrations, this study was conducted over a four-month period from 1 December 2023 to 31 March 2024, in a strawberry-cultivating greenhouse. Fifteen sensors (MCH-383SD, Lutron, Taiwan) were installed inside the greenhouse to measure CO2 concentration at 1-min intervals. Finally, the dataset was transformed into intervals of 1, 5, 10, 30, and 60 min. The time-series data were analyzed using the autoregressive integrated moving average (ARIMA) and the Prophet Forecasting Model (PFM), with performance assessed through root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The evaluation indicated that the best model performance was achieved with data collected at 1-min intervals, while model performance declined with longer intervals, with the lowest performance observed at 60-min intervals. Specifically, the ARIMA model outperformed across all data collection intervals while comparing with the PFM. The ARIMA model, with data collected at 1-min intervals, achieved an R2 of 0.928, RMSE of 7.359, and MAE of 2.832. However, both ARIMA and PFM exhibited poorer performances as the interval of data collection increased, with the lowest performance at 60-min intervals where ARIMA had an R2 of 0.762, RMSE of 19.469, and MAE of 11.48. This research underscores the importance of frequent data collection for precise environmental control in greenhouse agriculture, emphasizing the critical role of short-interval data collection for accurate predictive modeling.

Temporal and spatial analysis of fertilizer application intensity and its environmental risks in China from 1978 to 2022

Fertilizers are an essential input in agriculture as they can enhance crop yields. However, their use also poses significant environmental risks. To thoroughly explore the intensity of fertilizer use and its potential threats to the ecological environment, this study analyzed the environmental risks of fertilizer use from a temporal and spatial perspective based on fertilizer application data in China from 1978 to 2022. Additionally, the contribution of fertilizer application in Chinese farmland to greenhouse gas N2O emissions was quantified using IPCC emission factor methodology. The results indicated that fertilizer application intensity and N2O emissions in China initially increased and then decreased from 1978 to 2022. Despite the implementation of various fertilizer control measures at the policy level, such as the Zero Growth of Fertilizer Action in 2015 and the Efficiency-Increasing Action for Reducing Fertilizer Use in 2022, the intensity of fertilizer application in China still exceeded international safety standards by 1.33-fold in 2022, reaching 298.79 kg/hm2. Furthermore, N2O emissions amounted to 50.17 × 104t, accounting for 16% of China's total agricultural greenhouse gas emissions that year. Correlation and regression analyses demonstrated that with increasing fertilizer application, crop production exhibits an inverted U-shaped growth trend, indicating limited effectiveness of high-intensity fertilizer use in increasing crop yields. These findings highlight the profound greenhouse effect resulting from the use of agricultural nitrogen fertilizer. Therefore, this study proposed technical and policy-level mitigation measures to address the issues caused by excessive fertilizer application, aiming to provide insights for controlling agricultural non-point source pollution and preserving the agroecological environment.

Construction of Greenhouse for Horticultural Cultivation with Soil Media and Hydroponics

Theneral background greenhouse technology is increasingly recognized as an efficient solution for sustainable agricultural practices, especially in regions with limited fertile land. Specific background In Kedungpeluk Village, where the majority of residents work as fish farmers, there are also unused and arid lands. These underutilized spaces provide an opportunity for greenhouse-based horticultural cultivation using soil media and hydroponic technology. Knowledge gap however, limited knowledge and infrastructure have prevented local farmers from effectively utilizing these lands for sustainable crop production. Aims this community engagement project aims to build and utilize a greenhouse system to cultivate horticultural plants, focusing on optimizing both soil and hydroponic methods. Results the project successfully transformed barren land into productive greenhouses that facilitate the growth of tomatoes, chilies, eggplants, pak choy, and water spinach. The greenhouse structure, designed with transparent roofing, enhances sunlight exposure, promoting optimal plant growth. Both soil and hydroponic methods showed potential for increasing crop yields, with soil cultivation being more flexible but requiring extra care for pests, while hydroponics provided faster results with minimal weed and pest interference. Novelty this project introduces a dual-method approach combining traditional soil cultivation with modern hydroponic technology in a controlled greenhouse environment. Implications the findings highlight that greenhouse farming is a viable, modern agricultural solution for rural communities, offering increased productivity and quality crops, while also addressing the challenges of limited fertile land and changing climate conditions. This project serves as a model for sustainable agriculture in similar settings. Highlights: Dual-method approach: Combining soil and hydroponic techniques in a greenhouse. Efficient land use: Transforming unused arid land for productive horticulture. Controlled environment: Optimizing plant growth through sunlight, temperature, and humidity control. Keywords: Greenhouse, Horticulture, Soil Media, Hydroponics, Sustainable Agriculture

Cherry Tomato Detection for Harvesting Using Multimodal Perception and an Improved YOLOv7-Tiny Neural Network

Robotic fruit harvesting has great potential to revolutionize agriculture, but detecting cherry tomatoes in farming environments still faces challenges in accuracy and efficiency. To overcome the shortcomings of existing cherry tomato detection methods for harvesting, this study introduces a deep-learning-based cherry tomato detection scheme for robotic harvesting in greenhouses using multimodal RGB-D perception and an improved YOLOv7-tiny Cherry Tomato Detection (YOLOv7-tiny-CTD) network, which has been modified from the original YOLOv7-tiny by eliminating the “Objectness” output layer, introducing a new “Classness” method for the prediction box, and incorporating a new hybrid non-maximum suppression. Acquired RGB-D images undergo preprocessing such as color space transformation, point cloud normal vector angle computation, and multimodal regions of interest segmentation before being fed into the YOLOv7-tiny-CTD. The proposed method was tested using an AGV-based robot in a greenhouse cherry tomato farming facility. The results indicate that the multimodal perception and deep learning method improves detection precision and accuracy over existing methods while running in real time, and the robot achieved over 80% successful picking rates in two-trial mode in the greenhouse farm, showing promising potential for practical harvesting applications.

Agricultural greenhouse extraction using multi-temporal spectral feature with decision tree algorithms in Jilin Province, China

Agricultural greenhouse extraction using multi-temporal spectral feature with decision tree algorithms in Jilin Province, China

Extraction of agricultural plastic greenhouses based on a U-Net Convolutional Neural Network Coupled with edge expansion and loss function improvement

ABSTRACT Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network’s accuracy was enhanced by 1.1% after expanding 3 × 3sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution. Implication Statement Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints.

Significantly enhanced energy efficiency through reflective materials integration in plant factories with artificial light

Plant factory with artificial light (PFAL) based on vertical farming is a promising technology for resource-efficient plant production, especially in urban areas or arid regions. However, the lighting system of PFAL requires a large amount of energy, which makes the total energy cost much higher than open-field and greenhouse farming. To address this issue, in this work, a cultivation system integrated with highly reflective materials is demonstrated in PFAL, which reduces light energy consumption by more than 36 %. A low-cost reflective film is fabricated with an overall reflectivity of 96.5 % in the entire photosynthetically active radiation spectrum. The effectiveness of this reflective film in reducing energy consumption is further demonstrated with optical simulation analysis and the corresponding computational fluid dynamics simulation based on an experimental cultivation rack. The corresponding field experiments of 3 different types of lettuce, Crunchy, Butterhead and Grand Rapids, under different light intensities are conducted in the cultivation unit. With reflective film properly installed, a 36 % reduction of light energy consumption is demonstrated while the dry weight and fresh weight still increase. An additional benefit is that the light reception area of lettuce becomes larger due to the more uniform lighting with the integration of reflective material. This work presents a simple yet reliable strategy for enhancing the energy efficiency in PFAL systems and can achieve the cost-effective daily production of lettuce with significantly reduced energy input.

Wind energy and hydrogen production: a new approach for green and sustainable greenhouse agriculture in Morocco

In response to the critical current energy demands and the imperative transition to green and sustainable agriculture, this study introduces an innovative approach by integrating wind energy into greenhouse agriculture. The sites of Dakhla, Agadir, Essaouira, and Kenitra, known for their intensive agricultural activities, were selected for a comprehensive evaluation. Utilising the Weibull distribution, a detailed wind potential analysis was conducted. The study's novelty lies in its combined technico-economic and environmental analysis, applied to four commercial wind turbines ranging from 1 kW to 10 kW. The findings reveal that Dakhla and Essaouira possess significant wind potential, sufficient to meet the electricity demands of agricultural greenhouses either partially or entirely. Specifically, the power densities recorded were 150.03 W/m2 and 62.29 W/m2 for Dakhla and Essaouira, respectively. The areas covered were 2 919.59 m2 and 1 120.26 m2, with a payback period of 1 year for Dakhla and 4.66 years for Essaouira using the T4 turbine. Additionally, the study explores the potential of integrating a wind/hydrogen conversion system, tested on a 100 kW turbine, which produced 6 648 kg of hydrogen in Dakhla and 4 443 kg in Essaouira. This approach allows for varied hydrogen uses, supporting sustainable agriculture.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

Performance assessment of packed bed systems for humidity control in greenhouse applications: An experimental based AI modelling approach

Optimal humidity control is essential for enhancing crop yields and ensuring favourable growth conditions in greenhouse agriculture. Packed bed devices are effective tools for regulating humidity levels; however, accurately assessing their performance, especially for temperate oceanic climates, is yet explicitly unexplored. The current paper presents a packed bed system using water as the working fluid to increase humidity during winter for greenhouse cultivation. An experimental setup is developed, and a detailed parametric study is conducted. Also, an artificial intelligence (AI) based multi-layer perceptron neural network (MLPNN) is designed to evaluate the performance of packed bed systems under varying environmental conditions with different inlet air flow rates (176 m³/hr, 286 m³/hr, 383 m³/hr, and 428 m³/hr). The results show that the system achieves a significant 50% increase in humidity ratio, transitioning from an inlet humidity ratio of 6 g/kgda to an outlet ratio of 9 g/kgda when operating with water at an average temperature of 15.7°C and a flow rate of 12.8 kg/min. The MLPNN is trained with 112 non-repeated datasets and observed that a topology of 2-10-10-1 includes 2 input neurons, 2 hidden layers with 10 neurons each, and 1 output neuron, has high prediction accuracy in estimating Δωa values for the packed bed system. The predictions closely align with experimental data, showing a maximum discrepancy within ±2.5%. This research advances the use of packed bed systems by providing a comprehensive framework for assessing and improving humidity control in greenhouse environments.

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

Synchronous monitoring agricultural water qualities and greenhouse gas emissions based on low-cost Internet of Things and intelligent algorithms

The effect of cow manure amendment and the ratio of gold mine tailings on sweet potato gold phytomining

Artisanal and small-scale gold mining has commonly used the mercury amalgamation method for gold ore extraction. The processing waste or tailings often contain gold and other metals. This study examined the effects of cow manure amendment and the ratio of gold mine tailings on sweet potato (Ipomoea batatas L.) gold phytomining. The pot experiment was conducted in the Faculty of Agriculture greenhouse at Padjadjaran University in Indonesia. The sweet potato MZ119 clone (an orange-fleshed sweet potato) was planted on a polybag (40 x 40 cm) consisting of 10 kg of planting media, which was a mixture of gold mine tailing and soil. The design used was a completely randomized design factorial with three replications. The first factor was ratios of gold mine tailing consisting of three levels, namely 30%, 50%, 70%, and 100% (w/w). The second factor was doses of organic manure, namely without cow manure, 250 g pot-1, 500 g pot-1, and 750 g pot-1. The results demonstrated that the application of cow dung and the gold mine tailings ratio (w/w) had a significant (p = 0.05) effect on plants' dry weight biomass, gold accumulation, and gold phytomining by sweet potato. The highest gold mass yield of 0.066 mg gold plant-1 was achieved by a 50% (w/w) gold mine tailing ratio and 750 g pot-1 cow manure treatment.

Sustainable and Low-Input Techniques in Mediterranean Greenhouse Vegetable Production

In the modern agricultural landscape, numerous challenges, such as climate change, diminishing arable lands, and the reduction of water resources, represent significant threats. The Mediterranean greenhouse farming model relies on low-input strategies to maximize both yield and quality. Its protected horticulture is essential for the year-round cultivation of high-value crops, ensuring efficient and sustainable production. In the realm of future agricultural strategies, leveraging internet-based approaches emerges as a pivotal factor for real-time and remote control of various agricultural parameters crucial for crop growth and development. This approach has the potential to significantly optimize agronomic inputs, thereby enhancing the efficiency of targeted vegetable production. The aim of the present review is to underscore the challenges related to the intensive greenhouse production systems emphasizing various strategies leading to low-input greenhouse vegetable production. The goal is to promote more sustainable and resource-efficient approaches in the cultivation of greenhouse vegetables. This review highlights several key strategies for optimizing the greenhouse environment, including efficient water management through conservation tillage, drainage water reuse, and selecting the most appropriate irrigation systems and timing. Additionally, light modulation and temperature control—using solar energy for heating and pad-and-fan systems for cooling—are crucial for enhancing both crop performance and resource efficiency. The review also explores low-input agronomical strategies, such as pest and disease control—including solarization and optimized integrated pest management (IPM)—as well as fertilization and advanced growing techniques. These approaches are essential for sustainable greenhouse farming.

Simultaneously reducing methane emissions and arsenic mobility by birnessite in flooded paddy soil: Overlooked key role of organic polymerisation

Flooding of paddy fields enhances methane (CH4) emissions and arsenic (As) mobilisation, which are crucial issues for agricultural greenhouse gas emissions and food safety. Birnessite (δ-MnO2) is a common natural oxidant and scavenger for heavy metals. In this study, birnessite was applied to As-contaminated paddy soil. The capacity for simultaneously alleviating CH4 emissions and As mobility was explored. Soil microcosm incubation results indicated that birnessite addition simultaneously reduced CH4 emissions by 47 %–54 % and As release by 38 %–85 %. The addition of birnessite decreased the dissolved organic carbon (DOC) contents and altered its chemical properties. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) results showed that birnessite reduced the labile fractions of proteins, carbohydrates, lignins, tannins, and unsaturated hydrocarbons, however, increased the abundance of condensed aromatic structures, suggesting the polymerisation of dissolved organic matter (DOM) by birnessite. The degradation of labile fractions and the polymerisation of DOM resulted in an inventory of recalcitrant DOM, which is difficult for microbes to metabolise, thus inhibiting methanogenesis. In contrast, birnessite addition increased CH4 oxidation, as the particulate methane monooxygenase (pmoA) gene abundance increased by 30 %. The enhanced polymerisation of DOM by birnessite also increased As complexation with organics, leading to the transfer of As to the organic bound phase. In addition, the decrease in ferrous ion [Fe(II)] concentrations with birnessite indicated that the reductive dissolution of Fe oxides was suppressed, which limited the release of arsenite [As(III)] under reducing conditions. Furthermore, birnessite decreased As methylation and shaped the soil microbial community structure by enriching the metal-reducing bacterium Bacillus. Overall, our results provide a promising method to suppress greenhouse gas emissions and the risk of As contamination in paddy soils, although further studies are needed to verify its efficacy and effectiveness under field conditions.

Microprocessor Control of the Electric Drive of Variable Radiation Installation and Ensuring of Operation Reliability

Introduction. The introduction of energy-saving technologies has become particularly relevant in recent years, when energy prices have been constantly increasing. This is particularly important for greenhouse farms, where the main condition for growing plants indoors is the creation of artificial climatic conditions: temperature, humidity, and lighting.Problem Statement. Given the light and the dark stages of photosynthesis, it becomes possible to use a variable light field in the plant-growing photoculture, which is created by light installations with variable radiation. The development of an energy-saving electric drive and automated control systems for the parameters of artificial climatic conditions when growing plants indoors results in reducing energy consumption and increasing the reliability of the technological process in greenhouses of agro-industrial complex.Purpose. Implementation of energy-saving variable illumination irradiating systems with an automated control system for greenhouse farms.Materials and Methods. A lighting installation with rotation of the irradiator in the vertical and horizontal planes with a discrete electric drive and microprocessor control has been used.Results. The current state of variable irradiating systems with energy-saving radiation sources has been analyzed. The use of a discrete electric drive with an automated control system has been proposed. The installation with a DNAT-400 radiation source and a ZHO-01 reflector enables irradiating an area of up to 100 m2. The maximum exposure to photosynthetically active radiation is 30 W/m2, and the irradiation irregularity does not exceed 20%. At the same time, electricity savings reach about 10 thousand kWh per season. The yield and quality of products remain unchanged.Conclusions. The results of the research have confirmed the feasibility of practical implementation of variable irradiation system for energy efficiency of greenhouse operations.

Advancing Sustainable Cyber-Physical System Development with a Digital Twins and Language Engineering Approach: Smart Greenhouse Applications

In recent years, the integration of Internet of Things technologies in smart agriculture has become critical for sustainability and efficiency, to the extent that recent improvements have transformed greenhouse farming. This study investigated the complexity of IoT architecture in smart greenhouses by introducing a greenhouse language family (GreenH) that comprises three domain-specific languages designed to address various tasks in this domain. The purpose of this research was to streamline the creation, simulation, and monitoring of digital twins, an essential tool for optimizing greenhouse operations. A three-stage methodology was employed to develop the GreenH DSLs, a detailed metamodel for enhanced smart monitoring systems. Our approach used high-level metamodels and extended Backus–Naur form notation to define the DSL syntax and semantics. Through a comprehensive evaluation strategy and a selected language usability metrics, the expressiveness, consistency, readability, correctness, and scalability of the DSL were affirmed, and areas for usability improvement were highlighted. The findings suggest that GreenH languages hold significant potential for advancing digital twin modeling in smart agriculture. Future work should be aimed at refining usability and extending its application range. The anticipated integration with additional model-drive engineering and code generation tools will improve interoperability and contribute to digital transformation in the smart greenhouse domain and promote more sustainable food production systems.

Social Economic Challenges of Adaptation to Climate Change in Masinga Sub-County, Kenya

Climate in Masinga sub-county has been changing at a momentum which is unprecedented in the recent human history. This area consists mainly small-scale farmers who depend on rain to carry out their farming activities. Coping strategies to the changing climate in the area has faced various challenges ranging from high poverty, low levels of education, age, gender among others. This study investigated how social-economic challenges affecting adaptation strategies on climate change among the households living in Masinga sub-county, in Kenya. The main objectives of the study included; 1) To examine the adaptation strategies employed by households in Masinga sub-county 2) To determine the main factors that affect household in adaptation to climate change in Masinga sub-county 3) To investigate the environmental implications of adaptation to climate change in Masinga sub-county. Essentially, variables such as education levels, age, gender, were assessed. In order to gain an understanding on adaptation strategies used by households in Masinga sub-county, the study was conducted in all six divisions. Various methods of sampling were used in the survey which included stratified random sampling based on the number of households proportionately in the six divisions. In essence, 384 household heads were sampled through stratified random sampling. The study employed specific data collection tools in order acquire data from the six divisions; structured questionnaires, interviews schedules, Focused Group Discussions (FGDs) and direct observation were used. Qualitative data was collected through; interviews, Desk research, Focus Group Discussions comprising six to eight sampled through stratified random sampling and purposive sampling. The households coping strategies, influence of adaptation strategy to climate change and environmental implications of household adaptation was analyzed through both qualitative and quantitative interpretation. Data from survey was organized and analyzed using descriptive and inferential statistics. The results were subjected to statistical package for social sciences (SPSS) to determine their reliability. When applying descriptive statistics, the results were presented in tables, bar graphs, and charts. In this study Chi-squire was used because it was easier to compare the results from a two dimensional perspective. These results after analysis by chi-square test, the p-value was found to be .001, thus, smaller than the significance level (α = .05). Using Chi-square tests, this study established that there is strong evidence of an association between the education level and the implementation of green-house farming (Χ2 (3) = 1.064, p = 0.001). Further analysis by chi-square test indicated that the p-value was found to be .008, thus, less than the significance level (α = .05). This study established that early warning as a strategy to adaptation to climate change was not common amongst households in the area. Further research is suggested on impact of charcoal burning on climate change adaptation and cost implications in semi- arid areas. The study findings will enable the government and other relevant stakeholders to ensure that early warning system information is able to reach the local residents at all times

Optimization framework of clean heat and CO2 supply for agricultural greenhouses exploiting industrial symbiosis

The rising demand for greenhouse cultivation poses a challenge in providing environmentally friendly energy to maintain favorable greenhouse indoor conditions. This research presents an optimization model to identify cost-optimal symbiotic pathways to replace the import of three key greenhouse crops (tomato, cucumber, lettuce) with local greenhouse production. The proposed solutions involve greenhouses that can meet these crops' heat and CO2 demands via industrial symbiosis. Switzerland is investigated as the case study, and the (waste) heat suppliers are municipal solid waste incinerators, cement production plants and biogas plants. Upgrading biomethane production plants are also considered potential CO2 suppliers. The objective is to minimize the discounted cost when replacing 25%–100 % of vegetable imports with local agricultural greenhouses, considering availability of suitable land as well as waste heat and CO2 suppliers. Optimization results suggest prioritizing northeast and west Switzerland for greenhouse development, due to the availability of suitable land and proximity of waste heat and CO2 suppliers. Waste incinerators could provide 50%–70 % of the necessary heat, while Organic Rankine cycle in cement plants could generate 63 % of the electricity of supplementary lighting demand of greenhouses. While tailored for Switzerland, the optimization framework's general formulation can be adapted also to other regions to optimize greenhouses' heat and CO2 demands. The optimization code is provided open source for associated applications.

Pilot study on liquid desiccant systems in greenhouse for water saving and climatecontrol

In regions with hot and arid climates, meeting the growing demands for irrigation and food production is crucial. Greenhouse farming offers a promising solution, significantly enhancing agricultural water efficiency by producing high-value crops with reduced freshwater consumption compared to traditional methods. However, the reliance on freshwater-dependent evaporative cooling systems in greenhouses poses a significant challenge, consuming up to 80 % of total freshwater usage. This challenge underscores the urgent need for innovative solutions to reduce freshwater use while maintaining optimal greenhouse conditions.This study proposes integrating a desiccant system with conventional evaporative cooling to improve humidity control and reduce freshwater consumption. This innovative approach enables greenhouse establishment in humid coastal areas previously deemed unsuitable, offering a sustainable alternative to traditional cooling methods. The proposed system includes liquid desiccant, desiccant pads, a chiller, and a fan, aiming to reduce or eliminate water use during nighttime cooling cycles. Additionally, the system has the potential to harvest water from ambient air, further reducing overall water consumption. Through this case study, we address the critical issue of freshwater overuse in greenhouse agriculture, presenting a solution that could revolutionize water management practices in arid and semi-arid regions.