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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #372069

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: A grass growth model adapted to urban areas: a case study with bahiagrass (Paspalum notatum flugee) in San Carlos, Brazil

item ESCOBAR-SILVA, E. - Universidade Federal De Sao Carlos
item BOURSCHEIDT, V. - Universidade Federal De Sao Carlos
item Daughtry, Craig
item Kiniry, James
item BACKES, A.R. - Federal University - Brazil

Submitted to: Environmental Modelling & Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/20/2022
Publication Date: 4/26/2022
Citation: Escobar-Silva, E., Bourscheidt, V., Daughtry, C.S., Kiniry, J.R., Backes, A. 2022. A grass growth model adapted to urban areas: a case study with bahiagrass (Paspalum notatum flugee) in San Carlos, Brazil. Environmental Modelling & Software. 73:127583.

Interpretive Summary: Urban population growth over the next several decades will present enormous challenges for urban planning and management and, consequently, for human health and well-being. Urban green spaces (such as, public parks, greenways, street trees, and patches of natural vegetation) can provide a wide range of environmental and social benefits. Managing these urban green spaces is challenging. Process-based vegetation growth models for urban environments are needed for managing these green spaces. A general vegetation growth model was adapted to urban green space conditions and embedded in a geographic information system (GIS). This work provided spatial monitoring of grass growth and will serve as the basis for creating applications which will display grass conditions over time and space in urban areas. This research results will support the ecological integrity of urban green spaces and increase the quality of life for urban populations.

Technical Abstract: In Brazil, grasses are widely grown in urban public areas, including in lawns, parks squares, roadsides, and edges of waterways. However, grass growth is often highly complex to predict for an adequate mowing schedule. This work aims to implement a growth model adapted to urban areas, which allows spatial monitoring of urban green spaces (UGS) in geographic information systems (GIS). The model was developed in Python based on available models. It simulates the daily dynamics of leaf area index (LAI), biomass, evapotranspiration and soil water content, from local to regional scales, whether mowed or not. The model simulates active growth and dormancy. Water and temperature stresses are considered as primary environmental limits to growth. A case study using bahiagrass (Paspalum notatum Flügge) as input to the model is presented within two scenarios, one considering UGS as a single area and a second one considering several areas independently. São Carlos, Brazil, was adopted as the study site. The findings obtained in the analysis suggest that the model implementation was suitable for its purpose and it will be an important tool for grass mowing processes of UGS.