Thus, the node assessment is conducted for the initial and current state of the farms that utilize the open field and hydroponic system as a production method in order to analyze the water and energy consumption. Two scenarios are conducted in order to investigate the change within energy and water consumption for the four farms. Agrico Agricultural Development, Global Farm and Al-Safwa Farm represent the first scenario that analyzes the water and energy consumption using the hydroponic greenhouse to produce tomato and groundwater for their water requirements. The second scenario is Al- Sulaiteen Agricultural & Industrial Complex  that uses RO to desalinate and treat brackish water within the hydroponic greenhouse. Along with the available growing method, the effect of seasonality on tomato farms is also considered. All meteorological data are extracted from the worldwide weather database for 13 weather stations that are mapped on Qatar map using ArcGIS. The following sections describe the required models and equations used in assessing energy and water consumption of the farming industry with the assumption that they are tomato farms. All equations utilized in this case study are illustrated below in Table 4.To estimate the overall water consumed by a tomato farm, two growing methods along with evapotranspiration factor per tomato crop are considered in this case study. Where the ET factor describes the amount of water lost from plant leaves  to the atmosphere, in addition to the amount that evaporates mostly from the soil surface after rain or irrigation . The amount of water lost through evapotranspiration is calculated using the Penman-Monteith equation, as recommended by the FAO .

The Penman-Monteith equation identified a set of weather parameters, specifically temperature , relative humidity ,ebb flow table mean daily radiation , wind speed  and soil density , where it produces a reference evapotranspiration rate  that can be used to compare water loss between hydroponic greenhouse and open field agriculture for various crops in various season. The atmospheric parameters are obtained from a weather database. Yet, in the full Penman-Monteith equation, other variables are assumed to be either as constant or negligible. The term of mean net daily radiation  in the Penman-Monteith equation describes the light-diffusing properties of the greenhouse covering. To describe this light diffusion, Rn term in the open-air equation is set to 1, while in the greenhouse equation it is set to a light diffusion rate for a specific greenhouse that is covered by a Polyethylene film. Hence, greenhouse covers that are rated with 88% light diffusion will have a value of 0.88 for the Rn term . It is important to note that the Rn values for open fields and greenhouses do not fully elaborate the solar radiation component. Furthermore, when soil heat density  is compared to  it will be relatively small and can be approximated at zero when the ground is covered in vegetation . Once the water savings in the open field and hydroponic greenhouse are estimated using the Penman-Monteith equation, the effect of crop characteristics that distinguish a typical field crop from the grass reference is implemented.To calculate the energy consumed by the tomato farms, two growing methods in addition to the source of water requirement are considered, as it has an impact on the overall energy consumption within a farm. Table 5 illustrates the main energy factors that are compiled from Jadidi and Sabouhi  study, in order to represent the total energy consumed in open field production. However, in the case of hydroponic greenhouse production, four energy components are included in the energy analysis: energy consumed by reverse osmosis plant; the use of electricity in the pumping of groundwater ; energy used during supplemental lighting and cooling loads.

The first component in the hydroponic greenhouse production is the annual energy consumption of brackish desalination plants, which is calculated using mass balance equations along with the specific energy consumption , the capacity and availability of the desalination plants . The SEC of desalinated water is one of the most critical factors characterizing the performance of the water supply . The SEC of various desalination processes and technologies is established in previous literature indicating a value with a range of 0.5 – 3 kWh/m3 for brackish water reverse osmosis . In addition, studies demonstrated that when specific data is not available for plant availability , then Pa is set as 90% . The second energy component is the energy consumed by electricity when pumping groundwater for irrigation. The shallow and renewable groundwater system in Qatar is composed mainly of two major aquifers, the northern aquifer and the southern aquifer, where the northern groundwater aquifer is the most important to Qatar’s developing agricultural sector. It is shallow with a range of 10–40 m deep and covers approximately 19% of Qatar’s land area. The southern aquifer and other secondary basins are smaller in size with diminished water quality and higher salinity levels. The agricultural activities in Qatar are very limited and are concentrated in the northern parts of the country . In this study, all the farms are located in the Northern Basin . Hence, the maximum depth of northern groundwater aquifer is used to determine the total energy consumed by pumping irrigation water. An irrigation pumping plant has three main components: power unit, pump and pump drive. In which, the overall pumping plant efficiency is a combination of the efficiencies of each separate component. It is assumed that all farms require energy to pump irrigation water and that all of the pumps use electricity as a power source, which has an average of 72–77% pumping efficiency . Hence, the groundwater aquifer depth , and its mass  along with pumping efficiency are considered in the energy calculation. Then, the average water requirement for tomato crop per season is used in conjunction with the estimated yield values for a tomato to estimate the energy use related to pumping in units of kJ/kg/y.

The third energy component is related to the energy used in supplemental artificial lighting, or what is known as energy per mole of photons , which is determined by assuming a 24- h photoperiod and a recommended daily Photosynthetically Active Radiation  of 0.4–0.5 mol/m2/s of both natural and supplemental light for optimal tomato production . It is also assumed that half of the required radiation could be obtained from natural lighting, and a wavelength of 400 – 700 nm. The resulting value is then used in conjunction with the estimated yield values for hydroponic greenhouse growing tomato to calculate the energy demand from supplemental lighting in units of kJ/kg/y. The energy use related to cooling loads is estimated by calculating the design heat load of a greenhouse in Qatar. The standard heat transfer equation is used to determine the fourth energy component. For the purpose of estimations, 20 °C is used as the set point temperature of the greenhouse, as this is the optimal temperature for tomato cultivation . Polyethylene is one of the most common materials used in greenhouse construction, and the overall heat transfer coefficient for this material is used in the calculations. For comparison purposes, all greenhouses are assumed to be closet to Al Khor weather station, in the North of Qatar; therefore, the average monthly temperatures for this region are used to estimate the temperatures external to the greenhouse. The final energy estimates are used in conjunction with the estimated yield values for a tomato to create a metric, in units of kJ/kg/y. Together, the estimates of energy use related to the supplemental lighting, water pumps, and cooling loads are combined to produce an overall estimate of energy use for tomato production in Qatar in units of kJ/kg/y .The methodology presented in this study is applied to a case study considering open-field agriculture, conventional greenhouses, and hydroponic greenhouses in Qatar. Nine risk factors comprising of temperature, humidity, solar radiation, soil quality , groundwater depth, groundwater recharge rate, groundwater salinity, and groundwater pH are selected to perform the AHP method for open-field agriculture and conventional greenhouse.

However, the soil factors are eliminated from the analysis in the case of the hydroponic greenhouse as it is a soil-less growing method. Based on the analysis, weather factors such as temperature, solar radiation and humidity have the highest impact open-field agriculture, where their importance percentages that contribute to increasing the risk are 18.527%, 16.860% and 15.785% respectively. These results indicate that using an open field to grow and produce various types of crops will have a high risk of losing the harvest because of harsh weather conditions that make it much more challenging in managing the risk of weaker yield, thus many countries have shifted to a conventional greenhouse. This would allow farmers to have more environmental control over their growing crops. Temperature, humidity, irrigation and lighting process will be efficiently managed; thus, the yield can be 10–12 times higher when compared to open field cultivation, making crops much healthier and reliable. In the case of a conventional greenhouse, since the weather factors are controlled, it would have the lowest impact in comparison to groundwater factors. The results indicate that the groundwater salinity, pH and depth are equally important with a percentage of 18.12%. Similarly, Table 6 illustrates the importance weights for seven risk factors that affect hydroponic greenhouses. Since the plants in hydroponic farming are grown in a nutrient rich liquid solution instead of soil, all soil factors are eliminated and water factors have the highest impact on the farming condition, with a relative weight of 0.27503, 0.22404 and 0.13965 for groundwater salinity, groundwater pH and solar radiation respectively. The detailed results generated from the AHP method specifying the relative weights for open-field agriculture and conventional greenhouse are demonstrated in Appendix B. To validate the assumptions that are made for the AHP method, the Consistency Index  for an open field, conventional greenhouse and hydroponic greenhouse is estimated to be 0.03875, 0.09107 and 0.05632. Hence, the Consistency Ratio  is 2.7%, 6.3% and 4.3% respectively. Based on the literature, hydroponic grow table the CR should be less than 10%, therefore all assumptions validated the literature and the inconsistency within the subjective judgments is acceptable. When overlaying the locations of the four farms on the three risk maps generated from ArcGIS as illustrated in Fig. 8, the following can be deduced; when using open field and conventional greenhouse, the three farms – SAIC, Global and Al Sawfa farms – are expected to be located in low-risk areas .

However, AGRICO is situated in a high-risk area . Although both cases lead to the same result, there remains some variation in the overall risk maps, demonstrating that the amount of risk varies according to the utilized growing method. This is especially demonstrated in the third case which is the hydroponic greenhouse. Fig. 1 indicates that by transitioning the growing method from conventional greenhouse to hydroponic greenhouse, three farms will be located in an approximately high-risk area. This could be due to the fact that the quality of groundwater in these areas is poor, and thus the greenhouses fully depend on the water resource. The results are summarized in Table 7. The variability in risk maps obtained from ArcGIS is also assessed for the different seasons. Fig. 9 illustrates that there is a very slight variation between summer and winter. This is due to protected farming in conventional greenhouses, in addition to the AHP, allocating smaller weights to external weather conditions, such as temperature and humidity.Finally, the node assessments are conducted to assess the performance of the four farming industries in Qatar in terms of water and energy consumption, and by considering that all farms utilize both open-field agriculture and hydroponic greenhouse as growing methods. In addition, it is assumed that these farms have technologically evolved over time from open fields to hydroponic greenhouses for tomato production. Fig. 10 is an example of average weather data in the summer. The same maps are created for the rest of the seasons. These maps illustrate the variation within weather data  in different locations per different seasons.