Crop physiology calibration in the CLM

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites

Implementation of a hybrid healthcare model in rheumatic musculoskeletal diseases: 6-months results of the multicenter Digireuma study

Abstract Objectives Rheumatic and musculoskeletal diseases (RMDs) require a tailored follow-up that can be enhanced by the implementation of innovative tools. The Digireuma study aimed to test the feasibility of a hybrid follow-up utilizing an electronic patient reported outcomes (ePROs)-based monitoring strategy in patients with RMDs. Methods Adult patients with rheumatoid arthritis (RA) and spondyloarthritis (SpA) were recruited for a 6-month bicentric prospective follow-up consisting of face-to-face and digital assessments. Patients were asked to report disease-specific ePROs on a pre-established basis, and could also report flares, medication changes, and recent infections at any time. Four rheumatologists monitored these outcomes and contacted patients for interventions when deemed necessary. Results from face-to-face and digital assessments were described. Results Of 56 recruited patients, 47 (84%) submitted any ePROs to the digital platform. Most patients with RA were female (74%, median age of 47 years), while 48% of patients with SpA were female (median age 40.4 years). A total of 3,800 platform visits were completed, with a median of 57 and 29 visits in patients with RA and SpA, respectively. Among 52 reported alerts, 47 (90%) needed contact, of which 36 (77%) were managed remotely. Adherence rates declined throughout the study, with around half of patients dropping out during the 6 months follow-up. Conclusion The implementation of a hybrid follow-up in clinical practice is feasible. Digital health solutions can provide granular knowledge of disease evolution and enable more informed clinical decision making, leading to improved patient outcomes. Further research is needed to identify target patient populations and engagement strategies