Agriculture today places great strains on biodiversity, soils, water and the atmosphere, and these strains will be exacerbated if current trends in population growth, meat and energy consumption, and food waste continue. Thus, farming systems that are both highly productive and minimize environmental harms are critically needed. How organic agriculture may contribute to world food production has been subject to vigorous debate over the past decade. Here, we revisit this topic comparing organic and conventional yields with a new meta-dataset three times larger than previously used (115 studies containing more than 1000 observations) and a new hierarchical analytical framework that can better account for the heterogeneity and structure in the data. We find organic yields are only 19.2% (+3.7%) lower than conventional yields, a smaller yield gap than previous estimates. More importantly, we find entirely different effects of crop types and management practices on the yield gap compared with previous studies. For example, we found no significant differences in yields for leguminous versus non-leguminous crops, perennials versus annuals or developed versus developing countries. Instead, we found the novel result that two agricultural diversification practices, multi-cropping and crop rotations, substantially reduce the yield gap (to 9 + 4% and 8 + 5%, respectively) when the methods were applied in only organic systems. These promising results, based on robust analysis of a larger meta-dataset, suggest that appropriate investment in agroecological research to improve organic management systems could greatly reduce or eliminate the yield gap for some crops or regions.
& PurposeEarthPy makes commonly performed spatial data exploration tasks easier for scientist by building upon functions in the widely used packages: Rasterio and GeoPandas. EarthPy is designed for users who are new to Python and spatial data with a focus on scientific data. When a user is working with spatial data for research, there are a suite of data exploration activities that are often performed including:
The field of data science and associated diversity of domain specific applications is rapidly growing. Simultaneously, given the growing volume of data, science is becoming more interdisciplinary and compute-intensive. Yet data science and domain science have traditionally been taught separately. Given the cross-discipline demand for data science skills, it is important to consider what is taught, how it is taught, and who has access to these skills. This paper presents a model for teaching earth and environmental data science (EDS) that fuses data science skills with science domain knowledge, understanding of scientific data types and structures, and the communication and collaboration skills needed to work in interdisciplinary team environments. Our model also empowers a diversity of students through supporting student-directed learning using hybrid in-person and online classrooms, project-based learning to make content relatable and build student confidence, and open education resources to make curriculum more broadly accessible. Our model is founded upon evaluation and assessment which allows us to identify student pain points and iteratively improve as needed. This model could be applied to existing data science and science courses in an effort to support the workforce demand for skills at the intersection of science and data science.
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