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Abstract
Assessing the effects of land use and land cover (LULC) on ecosystem service values (ESVs) is critical for public understanding and policymaking. This study evaluated the impacts of LULC dynamics on ESVs in Chandigarh city of India. The assessment of LULC changes was performed by analyzing the satellite imagery of the study area for the years 1990 and 2020 with different band combinations in ArcGIS (10.8 version software). In addition, we analyzed ecosystem services changes which were based on the LULC classes of the study area. Five LULC classes were identified in the present study area (Water bodies, forest and vegetation, built-up, agriculture and shrubland and open spaces). The results demonstrated (from 1990 to 2020) that the forest cover and agricultural areas decreased by 4.19% and 37.01%, respectively, whereas the built-up area substantially increased by 104.61%. Overall, ESV decreased by 2.54% from 1990 to 2020 due to rapid urbanization. The combination of LULC and ecosystem services valuation can increase our understanding of different issues of an urban ecosystem. Hence, we recommend the integration of LULC and ecosystem services valuation as a tool that could provide information to policymakers, urban planners and land managers for sustainable use in future.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience ; Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.

International audience Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfill these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20 and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green LAI, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.