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- ItemEffects of the COVID-19 Pandemic on Food Security and Agriculture in Iran: A Survey(Basel : MDPI AG, 2021) Rad, Abdullah Kaviani; Shamshiri, Redmond R.; Azarm, Hassan; Balasundram, Siva K.; Sultan, MuhammadThe consequences of COVID-19 on the economy and agriculture have raised many concerns about global food security, especially in developing countries. Given that food security is a critical component that is affected by global crises, beside the limited studies carried out on the macro-impacts of COVID-19 on food security in Iran, this paper is an attempt to address the dynamic impacts of COVID-19 on food security along with economic and environmental challenges in Iran. For this purpose, a survey was conducted with the hypothesis that COVID-19 has not affected food security in Iran. To address this fundamental hypothesis, we applied the systematic review method to obtain the evidence. Various evidences, including indices and statistics, were collected from national databases, scientific reports, field observations, and interviews. Preliminary results revealed that COVID-19 exerts its effects on the economy, agriculture, and food security of Iran through six major mechanisms, corresponding to a 30% decrease in the purchasing power parity in 2020 beside a significant increase in food prices compared to 2019. On the other hand, the expanding environmental constraints in Iran reduce the capacity of the agricultural sector to play a crucial role in the economy and ensure food security, and in this regard, COVID-19 forces the national programs and budget to combat rising ecological limitations. Accordingly, our study rejects the hypothesis that COVID-19 has not affected food security in Iran.
- ItemEffect of 1-Methyl Cyclopropane and Modified Atmosphere Packaging on the Storage of Okra (Abelmoschus esculentus L.) : Theory and Experiments(Basel : MDPI, 2020) Kanwal, Rabia; Ashraf, Hadeed; Sultan, Muhammad; Babu, Irrum; Yasmin, Zarina; Nadeem, Muhammad; Asghar, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Ahmad, Nisar; Imran, Muhammad A.; Zhou, Yuguang; Ahmad, RiazOkra possesses a short shelf-life which limits its marketability, thereby, the present study investigates the individual and combined effect of 1-methylcyclopropene (1-MCP) and modified atmosphere packaging (MAP) on the postharvest storage life of okra. The treated/ untreated okra samples were stored at ambient (i.e., 27 °C) and low (i.e., 7 °C) temperatures for eight and 20 days, respectively. Results revealed that the 1-MCP and/or MAP treatment successfully inhibited fruit softening, reduction in mucilage viscosity, and color degradation (hue angle, ∆E, and BI) in the product resulting in a longer period of shelf-life. However, MAP with or without 1-MCP was more effective to reduce weight loss in okra stored at both ambient and cold storage conditions. Additionally, ascorbic acid and total antioxidants were also retained in 1-MCP with MAP during cold storage. The 1-MCP in combination with MAP effectively suppressed respiration rate and ethylene production for four days and eight days at 27 °C and 7 °C temperature conditions, respectively. According to the results, relatively less chilling injury stress also resulted when 1-MCP combined with MAP. The combined treatment of okra pods with 1-MCP and MAP maintained the visual quality of the product in terms of overall acceptability for four days at 20 °C and 20 days at 7 °C.
- ItemInvestigating Solid and Liquid Desiccant Dehumidification Options for Room Air-Conditioning and Drying Applications(Basel : MDPI, 2020) Naik, B. Kiran; Joshi, Mullapudi; Muthukumar, Palanisamy; Sultan, Muhammad; Miyazaki, Takahiko; Shamshiri, Redmond R.; Ashraf, HadeedThis study reports on the investigation of the performance of single and two-stage liquid and solid desiccant dehumidification systems and two-stage combined liquid and solid desiccant dehumidification systems with reference to humid climates. The research focus is on a dehumidification system capacity of 25 kW designed for room air conditioning application using the thermal models reported in the literature. RD-type silica gel and LiCl are used as solid and liquid desiccant materials, respectively. In this study, the application of proposed system for deep drying application is also explored. Condensation rate and moisture removal efficiency are chosen as performance parameters for room air conditioning application, whereas air outlet temperature is chosen as performance parameter for deep drying application. Further, for a given range of operating parameters, influences of air inlet humidity ratio, flow rate, and inlet temperature on performance parameters of the systems are investigated. In humid climatic conditions, it has been observed that a two-stage liquid desiccant dehumidification system is more effective for room air conditioning application, and two-stage solid desiccant dehumidification system is more suitable for deep drying application in the temperature range of 50 to 70 °C, while single-stage solid desiccant and two-stage combined liquid and solid desiccant dehumidification systems are more effective for low temperature, i.e., 30 to 50 °C deep drying application.
- ItemIoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato(Basel : MDPI, 2020) Rezvani, Sayed Moin-eddin; Abyaneh, Hamid Zare; Shamshiri, Redmond R.; Balasundram, Siva K.; Dworak, Volker; Goodarzi, Mohsen; Sultan, Muhammad; Mahns, BenjaminOptimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
- ItemEnergy Systems and Applications in Agriculture(Basel : MDPI, 2022) Sultan, Muhammad; Mahmood, Muhammad Hamid; Ahamed, Md Shamim; Shamshiri, Redmond R.; Shahzad, Muhammad Wakil[No abstract available]
- ItemEffect of In Vitro Digestion on the Antioxidant and Angiotensin-Converting Enzyme Inhibitory Potential of Buffalo Milk Processed Cheddar Cheese(Basel : MDPI AG, 2021) Shaukat, Amal; Nadeem, Muhammad; Qureshi, Tahir Mahmood; Kanwal, Rabia; Sultan, Muhammad; Kashongwe, Olivier Basole; Shamshiri, Redmond R.; Murtaza, Mian AnjumThe purpose of this study was to develop an in-vitro digestion protocol to evaluate the antioxidant potential of the peptides found in processed cheddar cheese using digestion enzymes. We first studied antioxidant and angiotensin-converting enzyme (ACE) inhibition and antioxidant activities of processed cheddar cheese with the addition of spices e.g., cumin, clove, and black pepper made from buffalo milk and ripened for 9 months. Then we conducted an in vitro digestion of processed cheddar cheese by gastric and duodenal enzymes. Freeze-dried water (WSE) and ethanol-soluble fractions (ESE) of processed cheddar cheese were also monitored for their ACE inhibition activity and antioxidant activities. In our preliminary experiments, different levels of spices (cumin, clove, and black pepper) were tested into a cheese matrix and only one level 0.2 g/100 g (0.2%) based on cheese weight was considered good after sensory evaluation. Findings of the present study revealed that ACE-inhibitory potential was the highest in processed cheese made from buffalo milk with the addition of 0.2% cumin, clove, and black pepper. A significant increase in ACE-inhibition (%) of processed cheddar cheese, as well as its WSE and ESE, was obtained. Lower IC50 values were found after duodenal phase digestion compared to oral phase digestion.
- ItemEvaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application(Basel : MDPI, 2022) Hussain, Ghulam; Aleem, Muhammad; Sultan, Muhammad; Sajjad, Uzair; Ibrahim, Sobhy M.; Shamshiri, Redmond R.; Farooq, Muhammad; Usman Khan, Muhammad; Bilal, MuhammadThe study aims to investigate Maisotsenko cycle evaporative cooling assisted solid desiccant air‐conditioning (M‐DAC) system for agricultural storage application. Conventional air‐conditioning (AC) systems used for this application are refrigeration‐based which are expensive as they consume excessive amount of primary‐energy. In this regard, the study developed a lab‐scale solid silica gel‐based desiccant AC (DAC) system. Thermodynamic performance of the developed system was investigated using various adsorption/dehumidification and desorption/regeneration cycles. The system possesses maximum adsorption potential i.e., 4.88 g/kg‐DA at higher regeneration temperature of 72.6 °C and long cycle time i.e., 60 min: 60 min. Moreover, the system’s energy consumption performance was investigated from viewpoints of maximum latent, sensible, and total heat as well as latent heat ratio (LHR), which were found to be 0.64 kW, 1.16 kW, and 1.80 kW, respectively with maximum LHR of 0.49. Additionally, the study compared standalone DAC (S‐ DAC), and M‐DAC system thermodynamically to investigate the feasibility of these systems from the viewpoints of temperature and relative humidity ranges, cooling potential (Qp), and coefficient of performance (COP). The S‐DAC system showed temperature and relative humidity ranging from 39 °C to 48 °C, and 35% to 66%, respectively, with Qp and COP of 17.55 kJ/kg, and 0.37, respectively. Conversely, the M‐DAC system showed temperature and relative humidity ranging from 17 °C to 25 °C, and 76% to 98%, respectively, with Qp and COP of 41.80 kJ/kg, and 0.87, respectively. Additionally, the study investigated respiratory heat generation rate (Qres), and heat transfer rate (Qrate) by agricultural products at different temperature gradient (∆T) and air velocity. The Qres and Qrate by the products were increased with ∆T and air velocity, respectively, thereby generating heat loads in the storage house. Therefore, the study suggests that the M‐DAC system could be a potential AC option for agricultural storage application.
- ItemSoil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review(Basel : MDPI, 2022) Rasheed, Muhammad Waseem; Tang, Jialiang; Sarwar, Abid; Shah, Suraj; Saddique, Naeem; Khan, Muhammad Usman; Imran Khan, Muhammad; Nawaz, Shah; Shamshiri, Redmond R.; Aziz, Marjan; Sultan, MuhammadThe amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.
- ItemSimulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate(Basel : MDPI AG, 2022) Aziz, Marjan; Rizvi, Sultan Ahmad; Sultan, Muhammad; Bazmi, Muhammad Sultan Ali; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Imran, Muhammad A.AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.
- ItemScientific Irrigation Scheduling for Sustainable Production in Olive Groves(Basel : MDPI AG, 2022) Aziz, Marjan; Khan, Madeeha; Anjum, Naveeda; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Balasundram, Siva K.; Aleem, MuhammadThe present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.