Keywords
LandSupport; ARMOSA; crop-growht model; Ecosystem Service; production systems
Start Date
5-7-2022 12:00 PM
End Date
8-7-2022 9:59 AM
Abstract
Many tools are nowadays available for ecosystem services evaluation, including those referring to provisioning services (Grêt-Regamey et al., 2017). In the case of the geospatial approach developed in the H2020 LandSupport (LS) project, we decided to use the concept of equivalent wheat to compare potential provisioning services in the geospatial domain. In fact, the estimation of the ecosystem service provided by soils could be highly variable in function of the change in crops and then the specific land use and management. In addition, the choice of using equivalent wheat to analyze different areas allows to express the ecosystem service in terms of its potential. Our approach was applied in Telesina Valley, an area of about 200 km2 in the south of Italy. We firstly used the ARMOSA crop model for calculating the wheat yield in about 45 soil-climate different conditions characterizing the local spatial variability. Once the equivalent wheat yield was calculated by the ARMOSA model, a first attempt to express wheat-equivalent production in monetary terms was made through the Gross Margin procedure. To illustrate, equivalent wheat yield was multiplied by wheat market price and, then, Variable Costs, including labor costs, were subtracted. Market price and Variable Costs were computed from the Italian Farm Accountancy Data Network (FADN) that allowed to select farms producing wheat in the same areas where ARMOSA crop model was applied. This allowed to calibrate the economic of wheat production on real production systems. This procedure was implemented within the “Ecosystem Services - Biomass Potential Productivity” tools within the LS spatial decision support system, which allows to obtain maps representing the patterns of yearly average wheat yield and the estimated gross margin, in which the variability can be attributable to different production potentials due to soils and climatic differences.
The quantification and valuation of food provision in the H2020 LandSupport project
Many tools are nowadays available for ecosystem services evaluation, including those referring to provisioning services (Grêt-Regamey et al., 2017). In the case of the geospatial approach developed in the H2020 LandSupport (LS) project, we decided to use the concept of equivalent wheat to compare potential provisioning services in the geospatial domain. In fact, the estimation of the ecosystem service provided by soils could be highly variable in function of the change in crops and then the specific land use and management. In addition, the choice of using equivalent wheat to analyze different areas allows to express the ecosystem service in terms of its potential. Our approach was applied in Telesina Valley, an area of about 200 km2 in the south of Italy. We firstly used the ARMOSA crop model for calculating the wheat yield in about 45 soil-climate different conditions characterizing the local spatial variability. Once the equivalent wheat yield was calculated by the ARMOSA model, a first attempt to express wheat-equivalent production in monetary terms was made through the Gross Margin procedure. To illustrate, equivalent wheat yield was multiplied by wheat market price and, then, Variable Costs, including labor costs, were subtracted. Market price and Variable Costs were computed from the Italian Farm Accountancy Data Network (FADN) that allowed to select farms producing wheat in the same areas where ARMOSA crop model was applied. This allowed to calibrate the economic of wheat production on real production systems. This procedure was implemented within the “Ecosystem Services - Biomass Potential Productivity” tools within the LS spatial decision support system, which allows to obtain maps representing the patterns of yearly average wheat yield and the estimated gross margin, in which the variability can be attributable to different production potentials due to soils and climatic differences.
Stream and Session
false