| Literature DB >> 34067130 |
Evelyn Corona-López1, Alma D Román-Gutiérrez1, Elena M Otazo-Sánchez1, Fabiola A Guzmán-Ortiz2, Otilio A Acevedo-Sandoval1.
Abstract
The Water-Food Nexus (WF) has been proposed to reach equitable, balanced, and sustainable access to water and food resources in the face of the growing population demand. Therefore, developing models to assess them has become more relevant. This work systematically reviews the literature on the tools used to evaluate water and food resources between 2002 and 2020. Furthermore, it reports a critical analysis of the software used to assess the WF Nexus quantitatively. The models analyzed were Life Cycle Assessment (LCA), Common Agricultural Policy Regional Impact (CAPRI), Global Food and Water System (GFWS), Soil and Water Assessment Tool (SWAT), Water Evaluation And Planning system (WEAP), and Soil Water Atmosphere Plant (SWAP). We deduced that the following are necessary in evaluating the WF Nexus: (1) the capacity to generate future scenarios, (2) a global application, and (3) the application in case studies. The present paper is the first review to provide an overview of the software applied to evaluate WF Nexus, including the advantages and disadvantages of the tools found. They can help build sustainability criteria when designing policies that reduce water and food security risks and promote efficient water and food use.Entities:
Keywords: CAPRI; GFWS; LCA; SWAT; WEAP; WF Nexus; crops
Year: 2021 PMID: 34067130 PMCID: PMC8124841 DOI: 10.3390/ijerph18094983
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Some relevant reviews about the Water–Energy–Food Nexus (WEF).
| Focus | Description | Reference |
|---|---|---|
| Sustainability | Strengths to develop “environmental livelihood security”. | [ |
| Transdisciplinary research, public politics, and strategies for environmental management. | [ | |
| Challenges for integrating and optimizing the nexus components. Four case studies were analyzed. | [ | |
| Current state | WEF Nexus in regions. Keywords and research for stakeholders’ understanding. | [ |
| Initiatives frame with involved actors. Challenge to achieve disciplinarity and boundary-crossing endorsed by the 2030 Agenda. | [ | |
| State-of-the-art review on the concepts, research questions, and methodologies | [ | |
| WEF Nexus analytical methods for knowledge-based approaches and promotion for further approaches. | [ | |
| How the nexus approach has academically and geographically expanded | [ | |
| Social, political, and economic | The emerging literature on the WEF Nexus in the policy context | [ |
| Modeling tools to integrate policies. | [ | |
| A modeling platform for the efficiency assessment of technologies, policies, and resources management planning. | [ | |
| Circular economy approach for understanding the WEF Nexus interdependencies. | [ |
Figure 1Interaction between the Water–Food Nexus and United Nations’ Sustainable Development Goals.
Figure 2Published documents about Water–Energy–Food (WEF), Water–Energy (WE), Water–Food (WF), and others. SCOPUS analysis in 2002–2020.
Figure 3PRISMA according to [35] methodology criteria of the literature 2002–2020.
Background of models adapted in case studies to assess the Water–Food Nexus.
| Tools | Developer | Application | Advantages | Limitations | Reference |
|---|---|---|---|---|---|
| Life Cycle Assessment (LCA) | Harry E. Teasley, 1969 | Environmental impacts | Identify hotspots | Interpretation can be subjective | [ |
| Water Evaluation And Planning system (WEAP) | Jack Sieber, SEI 1988 | Assessment of water resources | Dynamic simulation of scenarios | Does not separate ground and surface water demands | [ |
| Soil & Water Assessment Tool (SWAT) | Jeff Arnold, USDA 1991 | Assess of water resources and hydrological simulation | Simulates the transport of nutrients in water and sediment | Restriction for simulate future scenarios of water availability | [ |
| Common Agricultural Policy Regional Impact Analysis (CAPRI) | ILR, UE 1997 | Impact of agricultural policies | Analysis of agricultural scenarios | Global average coverage | [ |
| Soil, Water, Atmosphere, and Plant (SWAP) | Reinder Feddes, WUR 1978 | Use of water in crops | Simulates water transport in interaction with vegetation | It does not have a graphical user interface | [ |
| Global Food and Water System (GFWS) | Quentin Grafton, 2014 | Simulation platform | Simulation platform | Simulation platform | [ |
Assessment programs for the Water–Food Nexus: case studies.
| Software | Objective | Indicator | Crop | Reported Value | Country | Reference |
|---|---|---|---|---|---|---|
| Common Agricultural Policy Regionalized Impact (CAPRI) | Agricultural and water modeling | IRWUri = Σwact CAWUri,wact × LEVLri,wact 1 | 50 agricultural products | IRWU: 3633.93 E6 m3 | United States | [ |
| Global Food and Water System (GFWS) | Assessment of food and water availability | Wkj = Rkj × Llkj × Ej 2 | wheat, rice, corn, sorghum, barley, oats, and soybeans | water consumed by crop: 4 × 10−7 m3/ha | 20 countries | [ |
| Life Cycle Assessment (LCA) | Water consumption | Crop-rotation | wheat grain | 437.5 m3/t of grain | China | [ |
| Water consumption | Water scarcity footprint (rice) = Irrigation water use (rice) × WSI 3 | paddy rice | 1.24 m3 H2Oeq/kg paddy rice | Thailand | [ | |
| Water consumption | Environmental performance | tomatoes | 147.8 m3 | Italy | [ | |
| Irrigated with groundwater and reclaimed water | GW: irrigated crops with groundwater and RW: reclaimed water | corn | GW:0.44, RW: 0.37 | China | [ | |
| Soil and Water Assessment Tool (SWAT) | Water footprint | SWt = SW0 + Σ (Rday − Qsurf − Ea − Wseep − Qgw) 4 | wheat | 1.036 m3/kg | China | [ |
| Water requirement | wstr = 1 − Et,act/Et = 1 − Wacualup/Et 5 | rice, potato, sugar beet, winter wheat, oats | Deficit irrigation (25–48%) Reduced yield (0–3.3%) | India, Germany, Chile, and Vietnam | [ | |
| Evaluation of change in irrigation systems | CPD = ΣYi × Ai/ΣVi × Ai 6 | wheat, apple, potato, tomato, sugar beet, alfalfa, and barley | Base scenario | Irán | [ | |
| Basin-scale hydrological model | WYSF: lower harvest index | grain sorghum | HVSTI: 0.45 WYSF: 0.25 | EE.UU. | [ | |
| Soil–Water-Atmosphere-Plant (SWAP) | Water cycle assessment | ∂θ/∂t = ∂/∂z [K(h) (∂h/∂z + 1)] − S(h) 7 | corn and wheat | saving water: 190 mm/yr | China | [ |
| Land management and water use | Sp(z) = Lroot (z)/∫0-Droot Lroot(z)dz 8 | grassland and corn | Holland | [ | ||
| Irrigation scheduling and groundwater recharge | C(h) ∂h/∂t = ∂/∂z [K(h)(∂h/∂z + 1)] − Sa(z) 9 | corn and wheat | optimal irrigation of 130, 260 y 390 mm in hydrological years of 25%, 50%, and 75%, respectively | China | [ | |
| Performance and water use evaluation | ∂θ/∂t = ∂/∂z [K(h) (∂h/∂z + 1)] − S(h) 7 | corn | irrigation: 229 mm–460 mm | China | [ | |
| Water Evaluation And Planning (WEAP) | Reduction of crop water requirements | ADW: Alternate Wetting and Drying | rice | 54.88 Mm3 | Philippines | [ |
| Evapotranspiration analysis | 1981–2008 and 2011–2014 | corn | 114mm | California | [ | |
| Assessment of water availability | Average annual irrigation demand for water | yams, cassava, cocoa, rice, maize and tomatoes. | ~690–748 Mm3/year | Africa | [ |
1 IRWU: Irrigation Regional Water Use. CAWU: Crop actual irrigation water use. LEVL: hectares cropped. ri: regions with irrigation. wact: total irrigated area. 2 Wkj: agricultural water for crop k in country j. R: irrigation rate for crop k in country j. LI: area of irrigated land for crop k in country j. E: water use efficiency in country j. 3 WSI: Water Stress Index. 4 SWt: final soil water content in time t. SW0: initial soil water content. Rday: the amount of precipitation on a day i. Qsurf: the amount of surface runoff on a day i. Ea amount of actual evapotranspiration on a day i. Wseep: the amount of percolation and bypass flow exiting the bottom of the soil profile in one day i. Qgw: the amount of return flow on a day i. 5 wstr: water stress. Et: maximum plant transpiration Et,act: the actual amount of transpiration. Wactualup: total plant water uptake. 6 CPD: Crop index per drop. i: crop number. n: number of cultivated crops. Yi: yield of crop i. Ai: area of crop i. Vi: consumed water volume of crop i. 7 θ: soil water content in time t. dz: the vertical coordinate taken as positive upwards (cm). K(h): is the hydraulic conductivity specified by Van Genuchten–Mualem model (cm/d). S(h): represents the water extraction by plant roots (1/d). 8 Sp(z): Stresses due to dry or wet conditions and/or high salinity concentrations may reduce. Lroot: the root length density (cm−2). Droot: the root layer thickness (cm). 9 C(h): differential soil water capacity in soil water pressure head h. t: time. Z: vertical coordinate. K: hydraulic conductivity. Sa: soil water extraction rate by plant roots