Generally, improvement in the soil health of pasturelands can result in amplified ecosystem services which can help improve the overall sustainability of the system. The extent to which specific best management practices have this effect has yet to be established. A farm-scale study was conducted in eight beef-pastures in the Southern Piedmont of Georgia, from 2015 to 2018, to assess the effect of strategic-grazing (STR) and continuous-grazing hay distribution (CHD) on soil health indicators and runoff nitrate losses. In 2016, four pastures were converted to the STR system and four were grazed using the CHD system. Post-treatment, in 2018, the STR system had significantly greater POXC (by 87.1, 63.4, and 55.6 mg ha−1 at 0–5, 5–10, and 10–20 cm, respectively) as compared to CHD system. Soil respiration was also greater in the STR system (by 235 mg CO2 m-2 24 h−1) and less nitrate was lost in the runoff (by 0.21 kg ha−1) as compared to the CHD system. Cattle exclusion and overseeding vulnerable areas of pastures in STR pastures facilitated nitrogen mineralization and uptake. Our results showed that the STR grazing system could improve the sustainability of grazing systems by storing more labile carbon, efficiently mineralizing soil nitrogen, and lowering runoff nitrate losses.

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In recent years, there has been increased interest in symbolic data analysis, including for exploratory analysis, supervised and unsupervised learning, time series analysis, etc. Traditional statistical approaches that are designed to analyze single‐valued data are not suitable because they cannot incorporate the additional information on data structure available in symbolic data, and thus new techniques have been proposed for symbolic data to bridge this gap. In this article, we develop a regularized convex clustering approach for grouping histogram‐valued data. The convex clustering is a relaxation of hierarchical clustering methods, where prototypes are grouped by having exactly the same value in each group via penalization of parameters. We apply two different distance metrics to measure (dis)similarity between histograms. Various numerical examples confirm that the proposed method shows better performance than other competitors.

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