Abstract
Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.
Highlights
Research produces larger volumes of data, owing to progress in “omics”, precision agriculture, and on-farm sensor advancements, which has resulted in a concurrent need for an advanced capacity to collect, process, and store these data
The cover crop effects on the soil carbon illustrated that rye (26.38 g C kg−1, n = 217) and legume (23.96 g C kg−1, n = 30) cover crops resulted in greater soil organic carbon (SOC) storage spatially and temporally (Figure 3A), while ryegrass (21.8 g C kg−1, n = 40) and the oat and rye mix (17.57 g C kg−1, n = 144) resulted in a lower SOC content
Combining data into a queryable and linked repository such as Agricultural Collaborative Research Outcome System (AgCROS) will require researchers’ willingness to share data, knowledge, and skills to work with the Big Data infrastructure, as well as efforts to make the necessary changes to data already collected
Summary
Research produces larger volumes of data, owing to progress in “omics”, precision agriculture, and on-farm sensor advancements, which has resulted in a concurrent need for an advanced capacity to collect, process, and store these data. Creating a database and storage system for integrating and housing diverse data sources in real time, along with well-defined metadata information (data quality), is an important step for fully utilizing Big Data for agricultural decision support tools [3,4,5,6,7]. Such a tool has been the focus of the Agricultural Collaborative Research Outcome System (AgCROS), which is an ongoing effort by the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) for a federated data repository that links diverse and disparate databases that are currently available [1]. The objectives of this manuscript are to (1) introduce agricultural researchers to the USDA PDI, which aims to provide a solution to how agricultural data are organized, accessed, and standardized for high-performance computing through highlighting three spatially focused case studies and (2) illustrate how PDI can be used to collect, process, and analyze long-term, multi-year, and complex Big Data in a systematic, quantitative review of cover crop and grazing effects on soil carbon storage
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