Early-life physical performance predicts the aging and death of elite athletes.

Athleticism and the mortality rates begin a lifelong trajectory of decline during early adulthood. Because of the substantial follow-up time required, however, observing any longitudinal link between early-life physical declines and late-life mortality and aging remains largely inaccessible. Here, we use longitudinal data on elite athletes to reveal how early-life athletic performance predicts late-life mortality and aging in healthy male populations. Using data on over 10,000 baseball and basketball players, we calculate age at peak athleticism and rates of decline in athletic performance to predict late-life mortality patterns. Predictive capacity of these variables persists for decades after retirement, displays large effect sizes, and is independent of birth month, cohort, body mass index, and height. Furthermore, a nonparametric cohort-matching approach suggests that these mortality rate differences are associated with differential aging rates, not just extrinsic mortality. These results highlight the capacity of athletic data to predict late-life mortality, even across periods of substantial social and medical change.

Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data.

Four species of grass generate half of all human-consumed calories. However, abundant biological data on species that produce our food remain largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we assemble and analyse a continent-wide database of field experiments spanning 10 years and hundreds of thousands of machine-phenotyped populations of ten major crop species. Training an ensemble of machine learning models, using thousands of variables capturing weather, ground sensor, soil, chemical and fertilizer dosage, management and satellite data, produces robust cross-continent yield models exceeding R2 = 0.8 prediction accuracy. In contrast to 'black box' analytics, detailed interrogation of these models reveals drivers of crop behaviour and complex interactions predicting yield and agronomic traits. These results demonstrate the capacity of machine learning models to interrogate large datasets, generate new and testable outputs and predict crop behaviour, highlighting the powerful role of data in the future of food.

A multiple species, continent-wide, million-phenotype agronomic plant dataset.

A critical shortage of 'big' agronomic data is placing an unnecessary constraint on the conduct of public agronomic research, imparting barriers to model development and testing. Here, we address this problem by providing a large non-relational database of agronomic trials, linked to intensive management and observational data, run under a unified experimental framework. The National Variety Trials (NVTs) represent a decade-long experimental trial network, conducted across thousands of Australian field sites using highly standardised randomised controlled designs. The NVTs contain over a million machine-measured phenotypic observations, aggregated from density-controlled populations containing hundreds of millions of plants and thousands of released plant varieties. These data are linked to hundreds of thousands of metadata observations including standardised soil tests, fertiliser and pesticide input data, crop rotation data, prior farm management practices, and in-field sensors. Finally, these data are linked to a suite of ground and remote sensing observations, arranged into interpolated daily- and ten-day aggregated time series, to capture the substantial diversity in vegetation and environmental patterns across the continent-spanning NVT network.

Errors as a primary cause of late-life mortality deceleration and plateaus.

Several organisms, including humans, display a deceleration in mortality rates at advanced ages. This mortality deceleration is sufficiently rapid to allow late-life mortality to plateau in old age in several species, causing the apparent cessation of biological ageing. Here, it is shown that late-life mortality deceleration (LLMD) and late-life plateaus are caused by common demographic errors. Age estimation and cohort blending errors introduced at rates below 1 in 10,000 are sufficient to cause LLMD and plateaus. In humans, observed error rates of birth and death registration predict the magnitude of LLMD. Correction for these sources of demographic error using a mixed linear model eliminates LLMD and late-life mortality plateaus (LLMPs) without recourse to biological or evolutionary models. These results suggest models developed to explain LLMD have been fitted to an error distribution, that ageing does not slow or stop during old age in humans, and that there is a finite limit to human longevity.

Plane inclinations: A critique of hypothesis and model choice in Barbi et al.

This study highlights how the mortality plateau in Barbi and colleagues can be generated by low-frequency, randomly distributed age-misreporting errors. Furthermore, sensitivity of the late-life mortality plateau in Barbi and colleagues to the particular age range selected for regression is illustrated. Collectively, the simulation of age-misreporting errors in late-life human mortality data and a less-specific model choice than that of Barbi and colleagues highlight a clear alternative hypothesis to explanations based on evolution, the cessation of ageing, and population heterogeneity.