Modeling Phenological Phases across Olive Cultivars in the Mediterranean.

Modeling phenological phases in a Mediterranean environment often implies tangible challenges to reconstructing regional trends over heterogenous areas using limited and scattered observations. The present investigation aimed to project phenological phases (i.e., sprouting, blooming, and pit hardening) for early and mid-late olive cultivars in the Mediterranean, comparing two phenological modeling approaches. Phenoflex is a rather integrated but data-demanding model, while a combined model of chill and anti-chill days and growing degree days (CAC_GDD) offers a more parsimonious and general approach in terms of data requirements for parameterization. We gathered phenological observations from nine experimental sites in Italy and temperature timeseries from the European Centre for Medium-Range Weather Forecasts, Reanalysis v5. The best performances of the CAC_GDD (RMSE: 4 days) and PhenoFlex models (RMSE: 5-9.5 days) were identified for the blooming and sprouting phases of mid-late cultivars, respectively. The CAC_GDD model was better suited to our experimental conditions for projecting pit hardening and blooming dates (correlation: 0.80 and 0.70, normalized RMSE: 0.6 and 0.8, normalized standard deviation: 0.9 and 1.0). The optimization of the principal parameters confirmed that the mid-late cultivars were more adaptable to thermal variability. The spatial distribution illustrated the near synchrony of blooming dates between the early and mid-late cultivars compared to other phases.

Meteorological driving forces of reference evapotranspiration and their trends in California.

Reference evapotranspiration (ETo) is a variable that helps determine atmospheric pressure on living (reference) grass to release water into the atmosphere. For this purpose, four main driving forces: air temperature, air humidity, solar radiation, and wind speed need to be measured over the well-watered reference grass. The relative influence of these driving forces is region and climate-specific, with daily and seasonal variations. A clear understanding of the dynamic interactions of ETo's driving factors can illuminate the water and energy cycles of the earth and assist modelers with more accurate predictions of ETo. In this study, Pearson correlation, mutual information, and random forest feature importance analyses have been used to evaluate the relative importance of meteorological driving forces of ETo in California. To better understand the interrelations of these variables, 1,365,823 daily data samples from 237 standardized weather stations for 36 years have been clustered into homogeneous climatic zones and analyzed. To compensate for the effects of seasonality, feature importance analysis is also conducted on seasonal and monthly clustered data. Moreover, seasonal and annual trends of ETo and its driving factors are investigated for California and homogeneous zones using the Mann-Kendall test. Our findings reveal that for annually clustered data, solar radiation is the most influential driving factor of ETo in California. However, analysis of seasonal and monthly clustered data shows that vapor pressure deficit is the most informative factor during the summer and spring, while solar radiation is more important during the colder seasons. Results of trend analysis don't suggest a consistent monotonic trend for ETo and other variables for different seasons and zones. However, it is shown that agricultural regions with heavy irrigation dependence like the Central Valley are getting warmer and drier, especially during the irrigation season. This can adversely affect the water resources, agriculture industry, and food production of California, and modeling efforts like this can be very informative for future water resources management.

Comparative Effectiveness of Two Walking Interventions on Participation, Step Counts, and Health.

PURPOSE: To (1) compare the effects of two worksite-based walking interventions on employee participation rates; (2) compare average daily step counts between conditions, and; (3) examine the effects of increases in average daily step counts on biometric and psychologic outcomes. DESIGN: We conducted a cluster-randomized trial in which six employer groups were randomly selected and randomly assigned to condition. SETTING: Four manufacturing worksites and two office-based worksite served as the setting. SUBJECTS: A total of 474 employees from six employer groups were included. INTERVENTION: A standard walking program was compared to an enhanced program that included incentives, feedback, competitive challenges, and monthly wellness workshops. MEASURES: Walking was measured by self-reported daily step counts. Survey measures and biometric screenings were administered at baseline and 3, 6, and 9 months after baseline. ANALYSIS: Analysis used linear mixed models with repeated measures. RESULTS: During 9 months, participants in the enhanced condition averaged 726 more steps per day compared with those in the standard condition (p < .001). A 1000-step increase in average daily steps was associated with significant weight loss for both men (-3.8 lbs.) and women (-2.1 lbs.), and reductions in body mass index (-0.41 men, -0.31 women). Higher step counts were also associated with improvements in mood, having more energy, and higher ratings of overall health. CONCLUSIONS: An enhanced walking program significantly increases participation rates and daily step counts, which were associated with weight loss and reductions in body mass index.

Medical homes: cost effects of utilization by chronically ill patients.

OBJECTIVES: The impact of primary care practices adopting the patient-centered medical home (PCMH) model is analyzed by comparing per member per month (PMPM) costs and utilization among commercial HMO members with chronic illnesses in PCMH and non-PCHM practices in the Philadelphia area. Transforming primary care practices to conform to the PCMH model has shown early promise in reducing costs and improving outcomes, and chronically ill patients' frequent contact with the healthcare system and costly care make them ideal targets for such health system reforms. STUDY DESIGN AND METHODS: The impact of the PCMH model on PMPM costs was analyzed using a generalized linear regression model to adjust for age, gender, and baseline cost. The impact of the PCMH model on utilization per 1000 rates was analyzed with the Poisson regression model, adjusting for baseline differences in age, gender, and risk score. RESULTS: After accounting for differences at baseline, PCMH practices achieved lower total, inpatient, and specialist PMPM costs, as well as lower relative utilization of hospital admissions and specialist visits. CONCLUSIONS: These findings suggest that policy makers should maintain or expand incentives to adopt PCMH reforms and that targeting chronically ill patients may be the most effective way to leverage the benefits of PCMH adoption.

Patient-centered medical home impact on health plan members with diabetes.

OBJECTIVE: To compare costs and utilization for patients with diabetes enrolled in patient-centered medical home (PCMH) practices and non-PCMH practices. DESIGN: Commercial Health Maintenance Organization members with diabetes who enrolled between 2008 and 2011 in 26 Pennsylvania-based PCMH practices that were recognized by the National Committee for Quality Assurance in 2009 were compared with similar patients in 97 non-PCMH primary care practices. A difference-in-differences longitudinal research design was used to analyze differences between both groups on per-member, per-month costs and utilization. The statistical models controlled for baseline practice and patient-level characteristics through 2-step propensity score matching. The regression analysis on program effect further controlled for within-practice variation. Sensitivity analyses were also conducted on patients with type 1 and type 2 diabetes separately, and a third analysis was limited to diabetic patients enrolled in practices within Philadelphia. RESULTS: Adoption of the PCMH reduced overall medical costs for diabetic patients by 21% in year 1. This reduction was driven largely by inpatient costs, which fell by 44%. Reductions in emergency department visits, outpatient costs, and specialist visits were also seen in subsequent years among patients enrolled in PCMH practices. Additional sensitivity analyses indicated that adoption of the PCMH model yielded similar results when analyzing patients with type 2 diabetes as well as for diabetic patients enrolled in PCMH practices located within the city of Philadelphia. CONCLUSIONS: The cost of care for patients with diabetes can be reduced by securing care at a PCMH practice. Immediate results were seen in reduction of inpatient costs, which indicate that these patients enrolled in PCMH practices were using less costly inpatient services.

Surface renewal: an advanced micrometeorological method for measuring and processing field-scale energy flux density data.

Advanced micrometeorological methods have become increasingly important in soil, crop, and environmental sciences. For many scientists without formal training in atmospheric science, these techniques are relatively inaccessible. Surface renewal and other flux measurement methods require an understanding of boundary layer meteorology and extensive training in instrumentation and multiple data management programs. To improve accessibility of these techniques, we describe the underlying theory of surface renewal measurements, demonstrate how to set up a field station for surface renewal with eddy covariance calibration, and utilize our open-source turnkey data logger program to perform flux data acquisition and processing. The new turnkey program returns to the user a simple data table with the corrected fluxes and quality control parameters, and eliminates the need for researchers to shuttle between multiple processing programs to obtain the final flux data. An example of data generated from these measurements demonstrates how crop water use is measured with this technique. The output information is useful to growers for making irrigation decisions in a variety of agricultural ecosystems. These stations are currently deployed in numerous field experiments by researchers in our group and the California Department of Water Resources in the following crops: rice, wine and raisin grape vineyards, alfalfa, almond, walnut, peach, lemon, avocado, and corn.