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Understanding one’s personal journey provides for effective learning, growth, and development (Madsen, 2010). Reflection on the influences and experiences of successful women leaders is essential to understanding the factors that have enabled them to obtain and sustain leadership positions in nontraditional career fields. The purpose of this qualitative study was to conceptualize and describe the personal journeys of women who became deans of agriculture. The central research question was, “Describe your personal journey to becoming a dean of agriculture?” Six women deans of agriculture were interviewed and observed in an attempt to recognize the impact their personal journeys have had in developing their leadership styles and sustaining their leadership role. Conclusions were (a) they were first born children, (b) influenced by parental qualities and spousal support, and (c) mentors recognized their gifts and talents and encouraged them to pursue advanced degrees and leadership positions.

The coach−athlete relationship mediates the relationship between sports participation and student-athlete character, health and well-being outcomes. High school athletic administrators (AAs) can provide critical leadership, mentorship and direction for coaches to optimize student-athlete performance and human development. Social and emotional learning (SEL) is an evidence-based approach to developing adult and student competencies for holistic development across the lifespan that has been primarily performed and researched in the classroom. The purpose of this research is to capture the lived experiences of AAs applying a novel SEL-based curriculum (InSideOut Initiative, ISOI) with coaches and student-athletes in high school sports.

Interviews of 10 AAs captured their lived experiences of applying SEL-based leadership and coaching and their perception of its impact on coaches and student-athletes in high school athletics.

AAs described leadership and coaching that are characterized by (1) safety, support and mentorship; (2) skill and support-based behavior modeling; (3) trusting, loving and supportive relationships; (4) self-reflection of values/beliefs and behaviors that impact self, student-athlete and culture; (5) the influence of emotions on the aforementioned; (6) the ability to have a long-term, sustainable impact on student-athletes and (7) alignment with their immediate environmental context.

The data captured in this study suggest that ISOI-trained AAs practice SEL-competent leadership and coaching. Evaluation of the novel application of SEL-based interventions in athletics will be useful to understanding their effects on participant social and emotional competencies and outcomes traditionally associated with classroom-based SEL applications.

Athletic administrator interviews describe an approach to high school sports that requires a reconceptualization of the purpose of athletics. When the high school sport operates as a curriculum, integrated opportunity for its student-athletes and athletic administrator and coach leadership aligns with this overarching philosophy, there may be increased potential for positive youth development.

The results of this research are valuable in demonstrating preliminary evidence of how SEL-based leadership and coaching is applied and impacts adult and student-athletes in a unique sport context.

This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.