A Review on Food Subsystem Simulation Models for The Water-Food-Energy: Development Perspective

doi:10.21203/rs.3.rs-2317625/v1

The interactions and trade-offs between Water, Food, and Energy (WFE) have recently attracted the attention of researchers worldwide. A new approach called nexus has been used to examine these interactions in an integrated way. A significant obstacle to adopting the WFE nexus is the lack of a comprehensive and easy-to-use simulation model. By reviewing the articles in Scopus and Google Scholar databases, WFE nexus studies can be divided into two categories: simulation-based and conceptual-based studies of WFE nexus. Based on developmental perspective on food subsystem modeling in WFE nexus, the conceptual studies excluded and the modeling studies reviewed. Two points of view can be used for WFE nexus modeling: 1. Hard-link modeling and 2. Soft-link modeling. Comparing these two types of modeling showed that Hard link modeling cannot model the interrelations of the food subsystem and this shortcoming is of great importance. Agriculture is the primary source of food supply because livestock and poultry products are also indirectly dependent on agricultural products. This study reviewed the crop growth models (CGMs) used in the WFE nexus system from the development perspective. The technical characteristics of the CGMs have been evaluated according to the requirements of the CGMs. Finally, a checklist based on the criteria defined for the nexus system has been provided, which can guide researchers in choosing the appropriate simulation model for the food subsystem with the nexus approach. Also, future research suggestions were recommended to develop a food subsystem simulation model based on nexus system approach criteria.

Food simulation

Water-energy-food nexus

crop growth model

Sustainable development

Food security

Humanity is at the point in its history where it must question its ability to live without irreversibly damaging the biophysical and environmental conditions it depends on (D’Odorico et al. 2018). Increasing anthropogenic pressure is causing ecological change to become abrupt, and sustainable global development seems like a dream. With rapid international development and accelerated population growth, Water, Food, and Energy (WFE (resources have been under tremendous pressure, which has led to the problems of water scarcity, food shortages, and energy insecurity (Mahlknecht et al. 2020; Afshar et al. 2021; Liu and Zhao 2022). Remarkably, the interconnectedness of WFE subsystems is fundamental to global sustainability (Fuso Nerini et al. 2018). These problems have also been exacerbated by extreme weather conditions and the COVID-19 epidemic (Udmale et al. 2020). Previous studies have emphasized the importance that the only solution ahead is a fundamental change in understanding the management of WFE resources, and integrated systems approaches should be replaced instead of traditional approaches. However, before realizing the importance of connecting these three resources (WFE resources), management strategies usually worked for one resource independently of the others (Smajgl et al. 2016; Soleimanian et al. 2022). As a result, these strategies did not consider the internal interactions between these three inseparable sectors, and subsequently, the results obtained from the strategies used were utterly contradictory. Also, due to not paying attention to the meaningful interactions between these three inseparable sectors and wrong management, the competition over these three resources increased. The WFE nexus concept refers to the interconnections between WFE subsystems to address the aforementioned problems and offers insight into how strategies in one subsystem will influence the other subsystems and vice versa (Huang et al. 2020; Molajou et al. 2021a).

The WFE nexus is a dynamic and complex system in which WFE resources are intimately intertwined (Ma et al. 2021; Qi et al. 2022). Throughout the WFE nexus system, energy is needed to pump, transport, distribute, and treat water, and likewise, water is consumed for energy generation and processing (Roidt and Avellán 2019; Vahabzadeh et al. 2022). Much water and energy are also consumed in agricultural planting and food production (Molajou et al. 2021a; Afshar et al. 2022). WFE's nexus system is complex because of its intricate interweaving relationships. The emergency resource shortage crisis must be alleviated by clarifying the interrelationships between WFE and comprehensively managing the WFE nexus system (Ma et al. 2021).

The food subsystem is one of the essential parts of the WFE nexus system. Throughout the food subsystem, people are linked to their food as they pursue production, distribution, and consumption activities, and these activities also affect society and the environment as a whole (Ingram 2011; Schipanski et al. 2016; D’Odorico et al. 2018). One of the most critical challenges of the Food and Agriculture Organization of the United Nations (FAO) is defining projects in line with planning to feed 9 billion people by 2050 (van Dijk et al. 2021). As a result, global food production should increase by 70%, and in developed countries, the amount of food production should be doubled. According to FAO reports, in the past decades, the number of people who cannot provide food has reached one billion, of which about 65% live in Asia (United Nations 2019).

Generally, the food subsystem in the WFE nexus can be divided into four main categories: 1- Livestock products 2- Poultry products 3- Agricultural products 4- Fisheries products. Natural resources and human labor are used in producing, processing, and transporting food and in individuals' food consumption decisions. The food subsystem is therefore shaped by agricultural, trade, and food policies and food consumers' cultural, economic, and educational dimensions (Ingram 2011; Zhang et al. 2018; Bhunnoo and Poppy 2020; Mahdavian et al. 2022). Agricultural products make up the most important and diverse part of the food chain. In other words, agriculture is the primary source of food supply because livestock and poultry products are also indirectly dependent on agricultural products. In addition, agriculture relies heavily on energy, so energy is consumed directly or indirectly in agricultural lands (Tyczewska et al. 2018; Grote et al. 2021; Vahabzadeh et al. 2022). Currently, the food subsystem consumes 30% of the world's total energy (Li et al. 2021). In modern agriculture, most of the activities are mechanized and agricultural operations such as tillage, planting, irrigation, and harvesting are done with high efficiency using agricultural equipment. As a result, all these activities require energy carriers. According to earlier research, 3400 million joules of energy are needed to grow winter wheat, and 800 and 650 million joules of energy are used to cultivate and harvest the crop, respectively (Chang et al. 2016; Taghizadeh-Hesary et al. 2019).

Considering the undisputed importance of agriculture in the food subsystem, from the nexus point of view, the simulation model of crops is fundamental in the food subsystem. The crop growth model (CGM) is a model that simulates the growth stages of leaves, branches, and roots gradually over time, like a natural plant. The CGMs can calculate crop growth, development, and yield by solving the governing equations of soil, plant, weather, and management measures (such as irrigation, fertilizer, pesticides, Etc.)(van Keulen et al. 1982; di Paola et al. 2016). Therefore, a crop simulation model predicts the results of a specific management or environmental condition. One of the applications of crop simulation models is to understand better the performance of different parts of the crop growth process. Also, by changing various parameters such as seed type, soil type, weather conditions, irrigation amount, type and amount of fertilizer, pesticides, Etc., the amount and manner of the effect of those parameters can be calculated. As a result, these models can perform thousands of test plans for different products during their growth period, which takes place in a minimal time (Steduto et al. 2009; Siad et al. 2019).

With the advancement of computers and the increase in the ability of calculations, the simulation of crops takes place in the shortest time. For this reason, the acceptance of CGMs has increased in recent years(O. Rauff and Bello 2015). However, this point should be noted that the agricultural products and food subsystem is highly dependent on energy and water for the production of agricultural food products(el Gafy et al. 2017); Therefore, the simulation models of the food subsystem should be developed in such a way that shows the interactions of the food subsystem with water and energy in the nexus system. With the development of such a model, water and energy consumption will be determined in the simulation model to define different cultivation patterns. As a result, macro-management decisions will be made with a broader vision. First of all, this review introduces two types of simulation models of the WFE nexus system. In the next step, the framework related to the CGMs, in which the mandatory nexus criteria are applied, is defined. Then, food subsystem simulation models for crop growth, which are used in the nexus system simulations, have been technically reviewed from the development perspective. It is worth mentioning that in some cases, the CGMs which have not been used in the WFE nexus system have been assessed. Finally, a checklist is provided based on the appropriate choice of CGMs that can be used in the nexus system.

Using the nexus system approach, there are two types of connections within each WFE subsystem. In the first category, there is a relationship between the subsystem's internal components in the sense that changes to one variable of the subsystem's sector result in changes to another variable. This category of relationships is known as interrelation. In the food subsystem, for instance, the relationship between fertilizer and yield is regarded as an interrelation because the yield is affected by changes in fertilizer consumption (Stewart et al. 2005; Molajou et al. 2021a).

The second category of connections, known as interactions, comprises connections between subsystem components. These interactions give meaning to the WFE nexus system approach. For instance, the quantitative and qualitative return flow from agricultural farms to water bodies is considered an interaction. It is also important to note that the variables exchanged between subsystems are known as nexus variables. The values of the nexus variables at each time step depend on the state of other subsystems, and these variables facilitate interactions. The relevant subsystems simulate the nexus variables and export them as input to other subsystems (Molajou et al. 2021b; Afshar et al. 2022).

Moreover, interrelations and interactions affect one another. In other words, in the WFE nexus simulation models, interrelations may influence interactions with another subsystem by altering the interrelation between two components of the same subsystem. For instance, by increasing a crop's fertilizer consumption, its water consumption is decreased, and its energy consumption is increased (Zhou et al. 2011; Ati et al. 2012; Afshar et al. 2022). In this example, the amount of fertilizer consumption and yield are regarded as an interrelation, whereas the amount of energy and water consumption in relation to the food subsystem is regarded as interactions (See Fig. 1).

What is depicted in Fig. 1 is a simple example of the complex relationship between interrelations and interactions of food subsystem within the WFE nexus. It shows that the management of fertilizer consumption is not only effective in food production, but also has an effect on water and energy consumption. Therefore, it is necessary to pay attention to both interrelations and interactions for the analysis of trade-offs and synergies in the WFE nexus. As a result, in order to understand to what extent, the food subsystem can cause changes in the water and energy subsystems, it is first necessary to identify and model the important interrelations within the food subsystem.

The review methodology of food subsystem simulation in the comprehensive nexus system simulation models with a development perspective is shown in Fig. 2.

Step 1

In this step, Scopus and Google Scholar databases were searched to find articles related to the nexus system. First, the keyword "nexus" was searched, and subsequently, many articles were found due to the broad scope of its research. Then, the keyword "nexus" was combined with the phrase "water," "energy," and "food." In addition, the search was limited to the field of environmental science.

Step 2

In this stage, the found articles were explored according to whether these studies examine the nexus system in a conceptual or modeling way. Studies that have conceptually investigated the nexus system were excluded, and simulation articles were included.

Step 3

In the third step, the nexus simulation studies were categorized into soft and hard links. Also, the authors carefully investigated the studies carried out to simulate the nexus system in several stages. The performance, advantages, and disadvantages of each simulation category were carefully studied.

Step 4

By this step, Considering the dependencies of the food subsystem on water and energy, the required criteria of nexus for food simulation were carefully selected by the nexus research group under the supervision of the group's supervisor. Then, a conceptual framework of the food subsystem was presented to simulate crop growth according to defined criteria. Finally, by detailed examination of the CGMs of the WFE nexus system was recognized, and according to the defined criteria to be linked in the holistic nexus system simulation, their technical characteristics were carefully examined.

Step 5

Finally, A checklist for food subsystem simulators was provided to check their suitability or inappropriateness to be applied in holistic WFE nexus system simulation models. It is worth mentioning that the simulation that can address the most nexus criteria is selected, and since this review study was examined from the development perspective, it is suggested that the rest of the nexus criteria be developed in future research works.

According to the literature conducted, we can categorize and name the simulation of the WFE nexus system in two ways: soft link and hard link. In the soft link simulation, the food subsystem modeled with an exist previous CGMs, such as DSSAT and in some studies it is linked to water model software, such as WEAP to evaluation of water and food subsystems (Amjath-Babu et al. 2019), and as a result of defining the scenario in one subsystem, the responses of the other two subsystems are evaluated in the context of the WFE nexus system. The interaction between subsystems is one-way, and its implementation and modeling are less time-consuming. Soft link simulation studies adequately address interrelation by using a model for the subsystem under study, but their primary flaw is that they cannot account for interactions sufficiently.

In hard link simulation, the WFE subsystems are programmed in a single programming language environment and Integrated formulation for all subsystems within WFE nexus approach; usually, simulating the WFE nexus system with this approach is complex and time-consuming. In this approach, the interaction between the subsystems is two-way, and usually, the definition of the scenario for all three subsystems is done simultaneously, and this is the meaning of the multicentricity of the nexus system (Daher and Mohtar 2015; Wicaksono and Kang 2019). It should be noted that their main problem is that they cannot address interrelations in the WFE subsystems.

According to the various categories of nexus system simulators and the concepts presented for interrelation and interaction in section 2 and the presented categories hard link and soft link in this section, the literature that were carried out to investigate food subsystem modeling with the nexus approach is shown in Table 2.

Table 1

The pros and cons of types of WFE nexus system simulation models
	Soft link	Hard link
Interconnectedness	Unilateral linkage	Bilateral linkage
Advantages	• Less time-consuming to model the WFE nexus simulation model • Demonstrates how one subsystem affects or is affected by other subsystems • Further evaluation of the interrelations of the investigated subsystem	• WFE subsystems can be linked to each other holistically • Simulating the nexus system comprehensively from the food perspective • Import/export nexus variables to/from other subsystems • Further evaluation of the interactions of the investigated subsystem
Disadvantages	• Cannot reflect the mutual impacts of WFE subsystems • Defining the scenario in one subsystem, then obtaining feedback from the other two subsystems, and vice versa.	• Costs a lot of time modeling to link WFE subsystems • Requires a massive number of data•

Table 2

Hard link and soft link simulation literature for Food subsystem in the context of the WFE nexus system
Cite	Interconnection	Scale	WFE Model	Water Model	Food Model	Agricultural Energy demand	Multi-Crop	Research priority
(Giampietro et al. 2009)	Hard-Link	Nasional	MuSIASEM	Data-based	Data-based	-	+	exposing players to the main trade-offs and co-benefits by using a quantitative framework of relationships between the nexus elements based on environmental footprint indicators.
(Davies and Simonovic 2010)	Hard-Link	Global	ANEMI	Data-based	Data-based	-	+	evaluate interactions between climate system, carbon, nutrient, water demand and water supply development, hydrologic cycles, land use, food production, sea-level rise, energy production, population dynamics, economy
(Howells et al. 2013)	Soft-Link	Nasional	CLEWs	Process-based (WEAP)	Process-based (WEAP-FAO56)	+	-	uses a module-based method to quantitatively and simultaneously examine the interconnectedness of land, energy, and water resource systems (as well as climate) inside a modeling framework that incorporates specific models from other tools. Between sectoral models, the tool iteratively transfers data.
(Daher and Mohtar 2015)	Hard-Link	National	Nexus Tool 2.0	Data-based	Data-based	+	+	evaluating scenarios and identifying sustainable national resource allocation strategies
(Shrestha et al. 2015)	Soft-Link	irrigation district		-	Process-based (AquaCrop)	+	+	assessing the WFE nexus when evaluating the performance of groundwater-based irrigation systems
(de Vito et al. 2017)	Hard-Link	irrigation district		-	Process-based (CropWat)	-	+	proposing an index-based approach to assessing irrigation practices, environmental impacts, and economic aspects
(Martinez-Hernandez et al. 2017)	Hard-Link	Local	NexSym	Data-based	Data-based	-	+	Integrates ecosystem, technological, and WFE production and consumption models for modeling of local techno-ecological interactions
(Yan et al. 2018)	Soft-Link	National		-	Process-based (DSSAT)	+	-	assessing sweet sorghum-based ethanol potential due to possible energy–water conflicts
(Anderson et al. 2018)	Soft-Link	Irrigation district		-	Process-based (DSSAT)	-	-	Evaluation of fertilizer use and plant population on energy use, water use, and greenhouse gases
(Wicaksono and Kang 2019)	Hard-Link	National	WFESiM	Data-based	Data-based	-	+	simulating the resource connections and two-way feedback analysis
(Momblanch et al. 2019)	Soft-Link	River Basin		Process-based (WEAP)	Process-based (WEAP-FAO56)	-	+	evaluation impacts of climate change and alternative socio-economic development scenarios
(Hatamkhani and Moridi 2019)	Soft-Link	River Basin		Process-based (WEAP)	Process-based (WEAP-FAO56)	-	+	developing a simulation-optimization model to best trade-off between hydropower and agricultural development
(Liu et al. 2019)	Soft-Link	National		Process-based (DBH)	Process-based (EPIC)	-	+	assessment of trade-off between food production and water requirement and hydropower potential
(Sadegh et al. 2020)	Hard-Link	Country	EWF Nexus Tool	Data-based	Data-based	-	+	Estimating the amount of water resources required in a country to produce various types of food and energy, the amount of energy needed to supply a particular amount of water or agricultural product, and CO2-equivalent emissions
(Cuberos Balda and Kawajiri 2020)	Soft-Link	Regional		-	Process-based (WOFOST)	-	+	estimating the potential for biomass and bioenergy and talking about the trade-off between food and energy
(Ren et al. 2021)	Soft-Link	Pluvial Plain		Data-based	Process-based (DSSAT)	-	+	predicting the effects of cropping adjustments on groundwater sustainability and food production
(Shen et al. 2022)	Soft-Link	irrigation district		-	Process-based (AquaCrop)	+	+	exploring the optimal arrangement of agronomic and water-saving measures in farmland systems under the water-energy-food nexus

According to Table 2, hard link and soft link food simulation models of the WFE nexus system fall into two categories: data-based and process-based respectively. Data-based models are models that utilize data intensity. In other words, the quantity of water and energy required to produce one mass unit of wheat. Process-based CGMs and observe the effects of various agricultural inputs and climate variables on it. In addition, food modeling with the nexus approach has been performed at various spatial scales, including irrigation districts, basins, and the national level with different data-based and process-based approaches, this shows that it is possible to model both approaches in a variety of scales, also In some studies, a single crop was used to investigate the nexus effects of WFE, whereas in other studies, multiple crops were used to investigate various cropping patterns with varying nexus indices so, it is necessary to evaluate both hard and soft connection approaches. based on several studies that simulate the WFE nexus system with soft and hard link modeling approaches, the advantages and disadvantages of types of nexus system simulations are shown in Table 1.

As a result of comparing the models of hard link and soft link that were examined in Table 1 and Table 2, it can be concluded that Due to the importance of the interrelation of the food subsystem in a nexus analysis of different cropping patterns, it influences the interactions with the water and energy subsystems. Thus, the process-based food models utilized by soft link nexus simulators become crucial.

The interactions between the WFE subsystem and the nexus variables circulation among them are according to Fig. 3. The nexus variables stand for the variables which are exchanged between WFE subsystems, so examination and evaluation of interaction between WFE subsystems because these interactions are one of the most important factors that determine what interrelations should be modeled (Afshar et al. 2022).

As shown in Fig. 3, the modeling of the food subsystem should be able to show the effects of the amount of allocated water as well as the effects of water salinity on food production and also be able to model the amount of water returned to the water subsystem. Also, in relation to the energy subsystem, it should be able to consider and model the amount of energy required for the food subsystem in the agricultural inputs and agricultural machinery sector.

In previous studies, sufficient attention has not been paid to the food subsystem with the nexus approach. The food subsystem highly depends on the water and energy subsystems in producing food byproducts. In the previously developed models, the nexus variables received by the food subsystem from the two water and energy subsystems have not been considered simultaneously. Also, the interactions that occur in previous modeling for the simulation of the food subsystem with the nexus approach have been considered offline. However, in some research, the food subsystem is considered online to achieve a better and more accurate response, and the interaction of the subsystems is two-way. Therefore, the first important step in framing the framework of the food subsystem with the nexus approach is to recognize and identify the relevant requirements related to water and energy subsystems.

In previous research, food subsystem simulation models did not address the existing interactions with water and energy subsystems in hard-link and soft-link simulations in a comprehensive manner. However, a holistic framework that can incorporate these crucial interactions into CGMs is required. The nexus variables received from the water and energy subsystems by considering the WFE nexus approach in a holistic manner are shown in Fig. 4. The nexus variables received in the food subsystem act as constraints, and the food subsystem provides its response, which is the yield of crops, return water, water, and energy consumption.

On the other hand, there are interrelationships within the food subsystem (such as the relationship between fertilizers and crop growth rate) whose effects can influence the interaction of food with other subsystems if they are not addressed (such as the energy subsystem). In this way, ignoring the effects of used fertilizer can result in ignoring the energy required to produce used fertilizer. The framework of the food subsystem in the comprehensive simulation of the WFE nexus system is shown in Fig. 5.

The framework in Fig. 5 illustrates the crop growth process from cultivation to the harvesting stage, considering its interactions and interrelations with water and energy. in the following, the existent CGMs used in the WFE nexus simulation models will be reviewed according to the defined mandatory criteria for the nexus system within the novel framework. It should be noted that considering that this review study is from the perspective of food simulation development, other models that have not been used in the nexus system simulators will be reviewed and evaluated based on nexus simulation criteria.

A crop simulation model simulates the growth stages of leaves, branches, and stems gradually over time. In crop simulation models, crops' growth, development, and yield are calculated by solving the governing equations of the soil, plant, weather, and management operations (such as irrigation, fertilizer, and pesticides). Hence, crop simulation is used to evaluate the effects of specific management practices or environmental conditions (Jones et al. 2017). Crop simulation models are used to understand the performance of different parts of the crop growth process. It is also possible to calculate how different parameters such as seed type, soil type, weather conditions, irrigation volume, type and amount of fertilizer, pesticides, and other management variables affect the crop. This way, these models can implement thousands of scenarios for different crops during their growth period within a brief period. Due to computer advancements and the increased ability to calculate, crop simulation can occur in the shortest amount of time. In this way, it is not only economical but also very time-efficient. As a result, crop simulation models have become more prevalent in recent years (Whisler et al. 1986).

Agricultural products can be simulated using many different modeling approaches. The most famous approach is to classify crops based on their growth processes. Accordingly, crop simulation models are classified into two categories based on this type of classification:

6.1. Statistical models

Statistical models are the first method of simulating crops. These models calculate how and how much the crop reacts to changes in various parameters based on historical data sets. Biophysical and biological processes that exist between variables are not taken into account in statistical models. Therefore, these models are also called experimental models. The basic assumption in this method is that the data used are samples of a population that can be used for prediction in different conditions (Jones et al. 2017).

In most cases, the results of statistical models cannot be used for conditions different from the conditions of the samples. Therefore, these models cannot be used to predict and calculate crop growth results under the effect of climate change, pests, increasing CO₂ concentration beyond the range of historical data, Etc. Regression and Artificial Neural Networks are examples of methods used from this point of view. Also, combining these models with models that consider crop growth processes makes it possible to check and predict different conditions with the input data(Dourado-Neto et al. 1998).

6.2. Dynamic models

A dynamic model. In contrast to a statistical model, dynamic models can consider different conditions on plant growth stages. Dynamic models define functions to represent different states and conditions. In dynamic models, functions express the state of different plant parts, so it is possible to investigate how different policies or decisions affect the crop's response. The output of these models is the state of plant parts over time. However, it should be noted that all models are empirical to calculate some processes, which means that even dynamic models use empirical relationships to calculate parameters after dividing crop growth into more detailed processes (Whisler et al. 1986; Jones et al. 2017). Dynamic models are divided into mechanical and functional models depending on the extent to which the model examines the processes in more detail.

6.2.1. Mechanistic models

These models are also called explanatory models. These models try to make connections between variables using the cause-effect relationship. The fundamental processes in soil and plants are simulated in these models to obtain the desired output values. The fundamental processes in soil and plants are simulated in these models to obtain the desired output values. For example, in mechanistic models to simulate photosynthesis, processes such as receiving sunlight, absorbing CO₂ and converting it into biomass, dividing biomass between different plant components, and consuming biomass in plant respiration are considered (Ritchie and Alagarswamy 2002). Mechanistic models deal with parameters that change instantaneously, and their changes must be calculated on a microscopic temporal scale. For example, to calculate photosynthesis and plant transpiration, effective parameters change hourly, and mechanistic models must alter these parameters for the simulation. Since these models require a lot of input information, they usually have the minimal application (Ritchie and Alagarswamy 2002). Among the examples of mechanical models, we can refer to the biochemical model of photosynthetic CO₂ absorption in leaves presented by Farquhar (Farquhar et al. 1980).

6.2.2. Functional models

Another dynamic model is the functional model. In these models, unlike mechanistic models, very detailed processes of the crop growth process are not considered, but cause-effect relationships between parameters are still considered to simulate the product. For example, these models use only the amount of daily solar radiation and soil water stress to calculate photosynthesis. Therefore, these models can be used to simulate the crop on a daily time scale. Furthermore, functional models usually require less input information than mechanistic models. Therefore, the functional models are applied to the food subsystem simulation from the WFE nexus system perspective. Among the examples of functional models, we can mention the DSSAT, APSIM, EPIC, CropSyst, Etc., which have been compared (Ritchie and Alagarswamy 2002; Jones et al. 2017).

7.1. AquaCrop

The AquaCrop model was presented as an alternative to the FAO publication, Irrigation and Drainage No. 33, for estimating crop yield based on water supply and management measures. The primary basis of this model is to calculate the crop yield based on the function of the amount of water consumption under different irrigation conditions, including; Rainfed, low, and stress-free irrigation. AquaCrop model inputs include; Weather data, crop, soil, and management characteristics. Management characteristics mean the same environment in which the crop is grown. In this model, the processes of water infiltration, water drainage out of the root zone, growth of leaves and roots, evapotranspiration rate, biomass production, and crop yield are simulated (Raes et al. 2009; Steduto et al. 2009).

The simulation steps in the AquaCrop model are as follows: first, the amount of crop transpiration is calculated, and then the amount of biomass produced is obtained using Eq. 1:

$$\text{B}=\text{W}\text{P}\times \sum \text{T}\text{r}$$

1

B, WP, and Tr are produced biomass, water productivity of biomass produced (biomass produced per cumulative transpiration of crop unit), and crop transpiration, respectively. The biomass water productivity parameter must be available to convert transpiration into produced biomass. This parameter is normalized based on the amount of atmospheric evaporation and CO2 concentration. The model can calculate the crop growth process for different places and times. The cover canopy is used instead of the leaf area index to calculate crop transpiration. By doing this, the amount of plant transpiration can be separated from soil evaporation (Steduto et al. 2009).

The crop yield is calculated using the amount of biomass produced and the harvest index. At a particular time, the harvest index increases linearly and continues for several days after physiological maturity. The reaction of the crop to the lack of water is applied in the model through four correction coefficients, all of which are a function of the percentage of water in the soil. Correction coefficients are obtained according to the four crucial stages of crop growth: leaf opening, transpiration, stomatal control, and harvest index. It should be noted that the harvest index can act both positively and negatively according to the plant's stress level and time (Steduto et al. 2009).

7.2. APSIM

The Agricultural Production Systems Simulator (APSIM) model with a module-oriented structure is presented by the Agricultural Production Systems Research Unit in Australia. This model was developed to simulate the biological process of agricultural systems. Several modules are included in the model, including crops, pastures, orchards, soil, and management activities. Several important variables have been considered in this model, including soil pH, water, nitrogen, phosphorus, and erosion. A daily time scale simulates key processes in the crop module. Several input parameters are required, including daily weather data, soil characteristics, and crop management practices (Keating et al. 2003). In the APSIM model, each farm is regarded as a point, and its characteristics are expressed as the average of the entire farm. When the land features are very scattered, the study area can be converted into a series of fields, and each field is entered as a point in this model. Accordingly, this work incorporates the different characteristics of a region into the model (Keating et al. 2003).

7.3. WOFOST

The WOrld FOod STudies (WOFOST) model for simulating crop growth was developed by the World Center for Food Studies in Wageningen, the Netherlands (Todorovic et al. 2009). This model describes crops' growth and annual production using physical parameters such as crop type, soil type, and hydrological and weather conditions during the growing season. The specific point of this model from other models is that the growth process is simulated based on carbon. It uses the Eq. 2 to calculate the growth rate:

$${\Delta }W={C}_{e}\times (A-{R}_{m})$$

2

ΔW, C_e, A, and R_m are the growth rate, biomass absorption coefficient produced by the plant, The amount of gross absorption of CO₂, and the amount of CO₂ absorption that is not used for growth (maintenance absorption), respectively. Therefore, the WOFOST model is designed to calculate the production potential for each type of agricultural product in different soil and climate conditions. Also, this model considers the main limitations in crop production such as sunlight, temperature, water, nitrogen, phosphorus, and potassium nutrients. The spatial scale of this model is lumped and considers the studied area as a point and the time scale daily (van Diepen et al. 1989).

In order to calculate crop production, the model first determines the potential yield based on types of crops, planting times, and weather conditions. In this case, water and nutrients are assumed to be completely supplied to the crop. As a result, the amount of crop that can be obtained depends on soil conditions, the amount of water supplied to the crop, and the number of nutrients in the soil. There is an essential distinction between this amount and the actual yield because pests and diseases are not considered (de Wit et al. 2019). The Soil-Water-Atmosphere-Plant (SWAP) model is designed to simulate soil, water, atmosphere, and crop, using the WOFOST model to simulate the crop section (Uniyal and Dietrich 2021).

7.4. EPIC

This model was developed in the early 1980s to evaluate the relationship between soil erosion and the efficiency of agricultural production as the erosion efficiency index calculator. Therefore, there was a need for crop simulation to evaluate the efficiency of crop production. In the following years, the simulation of many essential management processes in agriculture was expanded in this model. As a result, the project's name was changed to the integrated climate of environmental policies over time. In recent years, this model has also considered aspects of agricultural sustainability, such as wind erosion, water quality, supply, soil quality, Etc. In addition, this model includes several management evaluations, including irrigation, drainage, fertilization, rotating crops, pesticides, and tillage (Wang et al. 2022).

There are several components to the EPIC model, including climate simulation, hydrology, sedimentation, erosion, nutrient cycling, pesticides, crop growth, tillage, economics, and plant environmental control. The model runs on a daily time scale. However, it should be noted that some processes, such as penetration rate, are calculated on a smaller scale than daily. This model is written in FORTRAN language and has a graphical user interface (Wang et al. 2022).

7.5. DSSAT

The DSSAT model was developed by the international network of scientists cooperating with the international network of crop transfer benchmark projects to simulate crop models. When developing this model, researchers felt the void of a comprehensive model for soil, climate, crops, and management to make better decisions about where to grow different crops. For this purpose, the DSSAT model was developed. As a result, a framework was provided to research the performance of the system and its components so that researchers can predict the system's behavior under specific conditions. As a result, the DSSAT model helps decision-makers make decisions requiring complex analysis in the shortest time by reducing time and human resources (Jones et al. 2003).

Before the development of the DSSAT model, there were models for simulating products. However, these models used different input data with different structures. Therefore, the environment and structure specific to that model was needed to use each. In the DSSAT model, by harmonizing the input data and the structure of the existing crop simulation models, a framework was provided for those models to work together consistently. For example, the CERES model was used to simulate corn and wheat, the SOYGRO model was used to simulate soybeans, and the PNUTGRO model was used to simulate peanuts in the past. The DSSAT model uses these models to simulate the crop (Jones et al. 2003).

In addition to crop simulation, the DSSAT model also simulates soil and atmosphere. Based on CROPGRO and CERES simulator models and the module-oriented structure, a new simulator model named DSSAT-CSM was built. This model simulates crops using climate, genetics, water in the soil, carbon, and nitrogen in the soil, and management in any place modules. So, the simulation is done with the minor input data [38]. DSSAT-CSM crop simulation model on a uniform land surface under defined or mandated management measures (a set of management operations whose values are not known in advance and are carried out when needed) and also taking into account water changes in the soil, nitrogen, and carbon in the soil, simulates the processes of growth, development, and productivity of crops (Jones et al. 2003).

7.6. CropSyst

CropSyst is a multi-year, multi-crop, daily time step cropping systems simulation model developed to serve as an analytical tool to study the effect of climate, soils, and management on cropping systems productivity and the environment. It simulates soil water budgets, soil-plant nitrogen budgets, crop phenology, canopy and root growth, biomass production, crop yield, residue production and decomposition, soil erosion by water, and salinity. The processes of crop rotation, cultivar selection, irrigation, nitrogen fertilization, soil, and irrigation water salinity, tillage operations, and residue management are all influenced by weather, soil characteristics, crop characteristics, and cropping system management options (Stöckle et al. 2003).

7.7. Cropwat

The CropWat is a computer program to calculate crop water and irrigation requirements from climatic and crop data. Furthermore, the program allows the development of irrigation schedules for different management conditions and calculating water supply for varying cropping patterns.

The criteria selected for the food subsystem simulation with the nexus approach should be chosen in a way related to the interactions of the water and energy subsystem. In other words, those criteria are required to simulate the food subsystem. Mandatory criteria for the food subsystem include fertilizer effect, salinity effect, energy consumption, water requirement, return flow, modeling accuracy, and data.

The amount of fertilizer used is effective in the crop's growth and affects the crop's performance based on its consumption. On the other hand, the amount of fertilizer used in crop growth is related to energy and water subsystems. Energy is used to produce fertilizer in petrochemical factories, and the amount of fertilizer used affects the quality of water resources. Therefore, it is necessary to apply the effect of fertilizer in the food subsystem due to its interactions with water and energy. In addition, as a result of irrigation and allocation of water to agricultural lands, the irrigation water may have some concentration of salt that enters the root zone and affects the growth rate of crops. Therefore, applying the effect of salinity stress is also one of the required criteria of the food subsystem simulation with the nexus approach that should be considered because leaching in the root zone increases the irrigation requirement of the crop.

Furthermore, the food subsystem in crop production is heavily dependent on allocated water and energy. Energy is consumed in different stages of cultivating, protecting, harvesting, and post-harvesting, such as agricultural machinery operation, groundwater pumping, and fertilizer consumption. On the other hand, crops need irrigation water to grow and consume it through the evapotranspiration process. It is worth mentioning that the irrigation water is unavoidably returned to the water bodies and affects the availability of water. The modeling accuracy and the amount of input data to crop growth simulators are essential criteria for the food subsystem with the WFE nexus system approach because the comprehensive WFE nexus simulation model is more likely to be implemented at least at the watershed scale. Therefore, the CGMs should require fewer data and have more accuracy to be able to be scaled up (Siad et al. 2019).

Based on the defined essential criteria for the food subsystem simulation with the nexus system approach, the famous and used CGMs have been explored and evaluated in the comprehensive simulation of the WFE nexus system. It should be noted that these CGMs are suitable for their use in the comprehensive WFE nexus system simulation with the hard link approach. As mentioned in section 3, hard link simulation models help employers and managers in making holistic decisions due to addressing two-way interactions. The provided checklist is shown in Table 3.

Table 3

The guidance for selecting suitable CGMs under the WFE nexus system approach
Crop simulation models	Open source	Integration	Modeling Accuracy	Low Data	Return flow	Water stress	Fertilizer stress	Salinity stress	Temperature stress	Water requirement	Energy consumption
DSSAT	✓	×	✓	×	✓	✓	✓	×	✓	✓	×
EPIC	✓	✓	✓	✓	✓	✓	✓	×	✓	✓	×
WOFOST	✓	✓	✓	×	×	✓	×	×	✓	✓	×
Cropwat	✓	✓	×	✓	×	✓	×	×	×	✓	×
APSIM	✓	×	✓	×	✓	✓	✓	✓	✓	✓	×
CropSyst	×	✓	✓	✓	×	✓	✓	✓	✓	✓	×
AquaCrop	✓	✓	✓	✓	✓	✓	✓	✓	✓	✓	×

DSSAT and APSIM are complete CGMs that provide accurate simulations of crops. These CGMs are open-source and assess salinity, fertilizer, and water stress on the crop. These models are not suitable for applying to nexus system simulation because, theoretically, they provide unique equations for each crop. Due to the large volume of input data, unique CGM for each crop, and inability to scale up when used in holistic nexus models, WFE nexus simulation models increase running time when used in larger spatial scales, such as watersheds.

The CropSyst model is software-based and cannot be linked with the water and energy subsystems since open-source models must be linked together for modeling the WFE nexus system with the hard link approach. On the other hand, this model cannot address the return flow from agricultural lands to water bodies, which is one of the significant interactions between water and food subsystems.

A significant variable that affects the interactions between all three WFE subsystems in the comprehensive nexus simulation is salinity in the root zone of crops. The higher the salt concentration in the root area, the more leaching is required. Therefore, with the increase in salt concentration, the water required for crop production increases and the trade-off between energy production and agriculture systems under the nexus system increases. The Cropwat, EPIC, and WOFOST models are CGMs that do not consider salinity. Therefore, the mentioned models are inappropriate for use in the WFE nexus system simulation.

It is worth noting that none of the CGMs evaluates the energy consumption of the crop, while to provide a suitable cropping pattern for an area with the nexus approach, each crop's energy consumption must be calculated separately. AquaCrop is one of the most suitable food simulation models in the WFE nexus. The AquaCrop model can address salinity, fertilizer, temperature, and water stresses and evaluate the amount of water returned to water bodies. Alternatively, this model can reduce the simulation time by taking a small amount of data and providing a specific relationship for all crops. More importantly, scaling this model up and running it on spatial scales such as watersheds is possible.

In the modern industrial world, crop production is highly dependent on water and energy. On the other hand, ignoring the interactions of the food subsystem with water and energy causes the unsustainable development of the agricultural production system. Therefore, WFE subsystems should be addressed as a single system so that the effects of action in one subsystem can be easily assessed in other subsystems. Recent research has focused on the interactions between WFE. This interaction has been examined in an integrated manner using a new approach called the "nexus system." This study has tried to identify the food subsystem simulations applied in the nexus system context. The CGMs were reviewed from the development perspective so that their strengths and weaknesses were examined. Finally, the most suitable food subsystem simulation was selected to develop it with the criteria defined by the nexus system approach.

The food subsystem has been modeled in the literature as the soft and hard links in the WFE nexus system simulations. In soft link simulations, food subsystem software is coupled with other subsystems, and interactions are addressed in a one-way manner. In this category of simulations, the answers are not more accurate; consequently, comprehensive decision-making does not happen. In hard link simulations, WFE subsystems are programmed in a programming environment. In this category of simulations, the interactions are two-way, scenarios can be defined for all three subsystems simultaneously, and the effects of each can be observed in the integrated WFE nexus system.

The CGMs were carefully evaluated based on the defined criteria for the food subsystem and its novel framework with the nexus approach. Finally, the AquaCrop simulation model was selected as the most suitable open-source simulation to be developed for the integrated WFE nexus system. The AquaCrop model was the most appropriate for the holistic nexus system simulation in several ways. Since the water simulation model is usually used on the watershed scale, therefore, the food simulation model should be able to be scaled up from the farm scale to the watershed. On the other hand, in nexus system simulations, where water is a limited resource, different scenarios can be explored by allocating water to the food and energy subsystems.

An example would be allocating a specified amount of water to energy and food to evaluate these subsystems' output. According to the mentioned issues, the AquaCrop model can be scaled up on the watershed scale and is also a water-driven simulation model. It is suggested to develop the AquaCrop simulation model with the nexus approach in future research works as follows:

Simulating and seeing the simultaneous effects of water, salinity, and fertilizer stress on crop yield.
Scaling up the simulation model to adapt to the scales of the water and energy subsystem in the integrated nexus system.
Analyzing the energy consumption of crops during the stages of cultivating, protecting, harvesting, and post-harvesting according to different cultivation patterns.

Conflict of interest statement

Not applicable.

Ethical Approval:

The authors declare that there is no conflict of interest.

Consent to Participate:

The authors declare that they agree with the participate of the journal.

Consent to Publish:

The authors declare that they agree with the publication of this paper in this journal.

Author contribution

Hossein Akbari Variani: Conceptualization, original draft, visualization, writing and editing

Abbas Afshar: Supervision, conceptualization, original draft, review, and editing

Masoud Vahabzadeh: Conceptualization, original draft, visualization, writing and editing

Amir Molajou: Conceptualization, original draft, visualization, writing and editing

Funding: Not applicable.

Data availability: The authors declare that the data are not available and can be presented upon the request of the readers.

Afshar A, Khosravi M, Molajou A (2021) Assessing Adaptability of Cyclic and Non-Cyclic Approach to Conjunctive use of Groundwater and Surface water for Sustainable Management Plans under Climate Change. Water Resources Management 2021 35:11 35:3463–3479. https://doi.org/10.1007/S11269-021-02887-3
Afshar A, Soleimanian E, Akbari Variani H, et al (2022) The conceptual framework to determine interrelations and interactions for holistic Water, Energy, and Food Nexus. Environment, Development and Sustainability 24:8 24:10119–10140. https://doi.org/10.1007/S10668-021-01858-3
Amjath-Babu TS, Sharma B, Brouwer R, et al (2019) Integrated modelling of the impacts of hydropower projects on the water-food-energy nexus in a transboundary Himalayan river basin. Appl Energy 239:494–503. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.01.147
Anderson R, Keshwani D, Guru A, et al (2018) An integrated modeling framework for crop and biofuel systems using the DSSAT and GREET models. Environmental Modelling & Software 108:40–50. https://doi.org/https://doi.org/10.1016/j.envsoft.2018.07.004
Ati AS, Iyada AD, Najim SM (2012) Water use efficiency of potato (Solanum tuberosum L.) under different irrigation methods and potassium fertilizer rates. Annals of Agricultural Sciences 57:99–103. https://doi.org/10.1016/J.AOAS.2012.08.002
Bhunnoo R, Poppy GM (2020) A national approach for transformation of the UK food system. Nature Food 2020 1:1 1:6–8. https://doi.org/10.1038/s43016-019-0019-8
Chang Y, Li G, Yao Y, et al (2016) Quantifying the Water-Energy-Food Nexus: Current Status and Trends. Energies 2016, Vol 9, Page 65 9:65. https://doi.org/10.3390/EN9020065
Cuberos Balda M, Kawajiri K (2020) The right crops in the right place for the food-energy nexus: Potential analysis on rice and wheat in Hokkaido using crop growth models. J Clean Prod 263:121373. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.121373
Daher BT, Mohtar RH (2015) Water–energy–food (WFE) Nexus Tool 2. 0: Guiding integrative resource planning and decision-making. Water Int 40:748–771. https://doi.org/10.1080/02508060.2015.1074148
Davies EGR, Simonovic SP (2010) ANEMI: A new model for integrated assessment of global change. Interdisciplinary Environmental Review 11:127–161. https://doi.org/10.1504/ier.2010.037903
de Vito R, Portoghese I, Pagano A, et al (2017) An index-based approach for the sustainability assessment of irrigation practice based on the water-energy-food nexus framework. Adv Water Resour 110:423–436. https://doi.org/https://doi.org/10.1016/j.advwatres.2017.10.027
de Wit A, Boogaard H, Fumagalli D, et al (2019) 25 years of the WOFOST cropping systems model. Agric Syst 168:154–167. https://doi.org/https://doi.org/10.1016/j.agsy.2018.06.018
di Paola A, Valentini R, Santini M (2016) An overview of available crop growth and yield models for studies and assessments in agriculture. J Sci Food Agric 96:709–714. https://doi.org/10.1002/JSFA.7359
D’Odorico P, Davis KF, Rosa L, et al (2018) The global food-energy-water Nexus. Reviews of Geophysics 56:456–531. https://doi.org/10.1029/2017rg000591
Dourado-Neto D, Teruel DA, Reichardt K, et al (1998) Principles of crop modeling and simulation: I. Uses of mathematical models in agricultural science. Sci Agric 55:46–50
el Gafy I, Grigg N, Reagan W (2017) Dynamic Behaviour of the Water–Food–Energy Nexus: Focus on Crop Production and Consumption. Irrigation and Drainage 66:19–33. https://doi.org/10.1002/IRD.2060
Farquhar GD, von Caemmerer S, Berry JA (1980) A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149:78–90. https://doi.org/10.1007/BF00386231
Fuso Nerini F, Tomei J, To LS, et al (2018) Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nat Energy 3:10–15. https://doi.org/10.1038/s41560-017-0036-5
Giampietro M, Mayumi K, Ramos-Martin J (2009) Multi-scale integrated analysis of societal and ecosystem metabolism (MuSIASEM): Theoretical concepts and basic rationale. Energy 34:313–322. https://doi.org/10.1016/J.ENERGY.2008.07.020
Grote U, Fasse A, Nguyen TT, Erenstein O (2021) Food Security and the Dynamics of Wheat and Maize Value Chains in Africa and Asia. Front Sustain Food Syst 4:317. https://doi.org/10.3389/FSUFS.2020.617009/BIBTEX
Hatamkhani A, Moridi A (2019) Multi-Objective Optimization of Hydropower and Agricultural Development at River Basin Scale. Water Resources Management 33:4431–4450. https://doi.org/10.1007/s11269-019-02365-x
Howells M, Hermann S, Welsch M, et al (2013) Integrated analysis of climate change, land-use, energy and water strategies. Nat Clim Chang 3
Huang D, Li G, Sun C, Liu Q (2020) Exploring interactions in the local water-energy-food nexus (WFE-Nexus) using a simultaneous equations model. Science of The Total Environment 703:135034. https://doi.org/10.1016/J.SCITOTENV.2019.135034
Ingram J (2011) A food systems approach to researching food security and its interactions with global environmental change. Food Security 2011 3:4 3:417–431. https://doi.org/10.1007/S12571-011-0149-9
Jones JW, Antle JM, Basso B, et al (2017) Brief history of agricultural systems modeling. Agric Syst 155:240–254. https://doi.org/https://doi.org/10.1016/j.agsy.2016.05.014
Jones JW, Hoogenboom G, Porter CH, et al (2003) The DSSAT cropping system model. European Journal of Agronomy 18:235–265. https://doi.org/https://doi.org/10.1016/S1161-0301(02)00107-7
Keating BA, Carberry PS, Hammer GL, et al (2003) An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18:267–288. https://doi.org/https://doi.org/10.1016/S1161-0301(02)00108-9
Li J, Cui J, Sui P, et al (2021) Valuing the synergy in the water-energy-food nexus for cropping systems: a case in the North China Plain. Ecol Indic 127:107741. https://doi.org/https://doi.org/10.1016/j.ecolind.2021.107741
Liu S, Zhao L (2022) Development and synergetic evolution of the water–energy–food nexus system in the Yellow River Basin. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-20405-9
Liu W, Yang H, Tang Q, Liu X (2019) Understanding the Water–Food–Energy Nexus for Supporting Sustainable Food Production and Conserving Hydropower Potential in China. Front Environ Sci 7:
Ma Y, Li YP, Zhang YF, Huang GH (2021) Mathematical modeling for planning water-food-ecology-energy nexus system under uncertainty: A case study of the Aral Sea Basin. J Clean Prod 308:127368. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.127368
Mahdavian SM, Ahmadpour Borazjani M, Mohammadi H, et al (2022) Assessment of food-energy-environmental pollution nexus in Iran: the nonlinear approach. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-19280-1
Mahlknecht J, González-Bravo R, Loge FJ (2020) Water-energy-food security: A Nexus perspective of the current situation in Latin America and the Caribbean. Energy 194:116824. https://doi.org/https://doi.org/10.1016/j.energy.2019.116824
Martinez-Hernandez E, Leach M, Yang A (2017) Understanding water-energy-food and ecosystem interactions using the nexus simulation tool NexSym. Appl Energy 206:1009–1021. https://doi.org/10.1016/J.APENERGY.2017.09.022
Molajou A, Afshar A, Khosravi M, et al (2021a) A new paradigm of water, food, and energy nexus. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-021-13034-1
Molajou A, Pouladi P, Afshar A (2021b) Incorporating Social System into Water-Food-Energy Nexus. Water Resources Management 35:4561–4580. https://doi.org/10.1007/s11269-021-02967-4
Momblanch A, Papadimitriou L, Jain SK, et al (2019) Untangling the water-food-energy-environment nexus for global change adaptation in a complex Himalayan water resource system. Science of The Total Environment 655:35–47. https://doi.org/https://doi.org/10.1016/j.scitotenv.2018.11.045
O. Rauff K, Bello R (2015) A Review of Crop Growth Simulation Models as Tools for Agricultural Meteorology. Agricultural Sciences 06:1098–1105. https://doi.org/10.4236/AS.2015.69105
Qi Y, Farnoosh A, Lin L, Liu H (2022) Coupling coordination analysis of China’s provincial water-energy-food nexus. Environmental Science and Pollution Research 29:23303–23313. https://doi.org/10.1007/s11356-021-17036-x
Raes D, Steduto P, Hsiao TC, Fereres E (2009) AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description. Agron J 101:438–447. https://doi.org/https://doi.org/10.2134/agronj2008.0140s
Ren D, Yang Y, Hu Y, Yang Y (2021) Evaluating the potentials of cropping adjustment for groundwater conservation and food production in the piedmont region of the North China Plain. Stochastic Environmental Research and Risk Assessment 35:117–128. https://doi.org/10.1007/s00477-019-01713-y
Ritchie JT, Alagarswamy G (2002) Overview of crop models for assessment of crop production. In: Effects of climate change and variability on agricultural production systems. Springer, pp 43–68
Roidt M, Avellán T (2019) Learning from integrated management approaches to implement the Nexus. J Environ Manage 237:609–616. https://doi.org/https://doi.org/10.1016/j.jenvman.2019.02.106
Sadegh M, AghaKouchak A, Mallakpour I, et al (2020) Data and analysis toolbox for modeling the nexus of food, energy, and water. Sustain Cities Soc 61:102281. https://doi.org/10.1016/J.SCS.2020.102281
Schipanski ME, MacDonald GK, Rosenzweig S, et al (2016) Realizing Resilient Food Systems. Bioscience 66:600–610. https://doi.org/10.1093/BIOSCI/BIW052
Shen Q, Niu J, Liu Q, et al (2022) A resilience-based approach for water resources management over a typical agricultural region in Northwest China under water-energy-food nexus. Ecol Indic 144:109562. https://doi.org/https://doi.org/10.1016/j.ecolind.2022.109562
Shrestha S, Adhikari S, Babel MS, et al (2015) Evaluation of groundwater-based irrigation systems using a water–energy–food nexus approach: a case study from Southeast Nepal. Journal of Applied Water Engineering and Research 3:53–66. https://doi.org/10.1080/23249676.2014.1001881
Siad SM, Iacobellis V, Zdruli P, et al (2019) A review of coupled hydrologic and crop growth models. Agric Water Manag 224:105746. https://doi.org/10.1016/J.AGWAT.2019.105746
Smajgl A, Ward J, Pluschke L (2016) The water–food–energy Nexus—Realising a new paradigm. J Hydrol (Amst) 533:533–540. https://doi.org/10.1016/j.jhydrol.2015.12.033
Soleimanian E, Afshar A, Molajou A (2022) A review on water simulation models for the WFE Nexus: development perspective. Environmental Science and Pollution Research 29:79769–79785. https://doi.org/10.1007/s11356-022-19849-w
Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—The FAO Crop Model to Simulate Yield Response to Water: I. Concepts and Underlying Principles. Agron J 101:426–437. https://doi.org/10.2134/AGRONJ2008.0139S
Stewart WM, Dibb DW, Johnston AE, Smyth TJ (2005) The Contribution of Commercial Fertilizer Nutrients to Food Production. Agron J 97:1–6. https://doi.org/10.2134/AGRONJ2005.0001
Stöckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. European Journal of Agronomy 18:289–307. https://doi.org/https://doi.org/10.1016/S1161-0301(02)00109-0
Taghizadeh-Hesary F, Rasoulinezhad E, Yoshino N (2019) Energy and Food Security: Linkages through Price Volatility. Energy Policy 128:796–806. https://doi.org/10.1016/J.ENPOL.2018.12.043
Todorovic M, Albrizio R, Zivotic L, et al (2009) Assessment of AquaCrop, CropSyst, and WOFOST Models in the Simulation of Sunflower Growth under Different Water Regimes. Agron J 101:509–521. https://doi.org/https://doi.org/10.2134/agronj2008.0166s
Tyczewska A, Woźniak E, Gracz J, et al (2018) Towards Food Security: Current State and Future Prospects of Agrobiotechnology. Trends Biotechnol 36:1219–1229. https://doi.org/10.1016/J.TIBTECH.2018.07.008
Udmale P, Pal I, Szabo S, et al (2020) Global food security in the context of COVID-19: A scenario-based exploratory analysis. Progress in Disaster Science 7:100120. https://doi.org/https://doi.org/10.1016/j.pdisas.2020.100120
United Nations (2019) The Sustainable Development Goals Report 2019. In: United Nations. https://unstats.un.org/sdgs/report/2019/The-Sustainable-Development-Goals-Report-2019.pdf. Accessed 20 Nov 2022
Uniyal B, Dietrich J (2021) Simulation of Irrigation Demand and Control in Catchments – A Review of Methods and Case Studies. Water Resour Res 57:e2020WR029263. https://doi.org/https://doi.org/10.1029/2020WR029263
Vahabzadeh M, Afshar A, Molajou A (2022) Energy simulation modeling for water-energy-food nexus system: a systematic review. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-022-24300-1
van Diepen CA, Wolf J, van Keulen H, Rappoldt C (1989) WOFOST: a simulation model of crop production. Soil Use Manag 5:16–24. https://doi.org/https://doi.org/10.1111/j.1475-2743.1989.tb00755.x
van Dijk M, Morley T, Rau ML, Saghai Y (2021) A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat Food 2:494–501. https://doi.org/10.1038/s43016-021-00322-9
van Keulen H, Penning De Vries FWT, Drees EM (1982) A summary model for crop growth. In: Simulation of plant growth and crop production
Wang Z, Ye L, Jiang J, et al (2022) Review of application of EPIC crop growth model. Ecol Modell 467:109952. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2022.109952
Whisler FD, Acock B, Baker DN, et al (1986) Crop Simulation Models in Agronomic Systems. In: Brady NC (ed) Advances in Agronomy. Academic Press, pp 141–208
Wicaksono A, Kang D (2019) Nationwide simulation of water, energy, and food nexus: Case study in South Korea and Indonesia. Journal of Hydro-environment Research 22:70–87. https://doi.org/10.1016/J.JHER.2018.10.003
Yan X, Jiang D, Fu J, Hao M (2018) Assessment of Sweet Sorghum-Based Ethanol Potential in China within the Water–Energy–Food Nexus Framework. Sustainability 10:. https://doi.org/10.3390/su10041046
Zhang WJ, John MG, Bassi AM, et al (2018) Systems thinking: an approach for understanding ‘eco-agri-food systems.’ TEEB for Agriculture & Food: Scientific and Economic Foundations
Zhou J bin, Wang C yang, Zhang H, et al (2011) Effect of water saving management practices and nitrogen fertilizer rate on crop yield and water use efficiency in a winter wheat–summer maize cropping system. Field Crops Res 122:157–163. https://doi.org/10.1016/J.FCR.2011.03.009

A Review on Food Subsystem Simulation Models for The Water-Food-Energy: Development Perspective

Status:

Journal Publication

Version 1

Abstract

Figures

1. Introduction

2. Interrelations And Interactions In The Wfe Nexus System

3. Methodology For Developing Food Subsystem Simulation Under The Wfe Nexus System

4. Types Of Wfe Nexus Simulation Modeling

5. Food Subsystem Simulation Framework Within The Nexus System Approach

6. Classification Of Crop Simulation Models

6.1. Statistical models

6.2. Dynamic models

6.2.1. Mechanistic models

6.2.2. Functional models

7. Investigating Functional Crop Simulation Models

7.1. AquaCrop

7.2. APSIM

7.3. WOFOST

7.4. EPIC

7.5. DSSAT

7.6. CropSyst

7.7. Cropwat

8. Essential Criteria For Food Subsystem Under Wfe Nexus System

9. Providing Guidance To Select Appropriate Food Simulation For The Nexus System

10. Discussion

Conclusion

Declarations

References

Status:

Journal Publication

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