The role of citizen science in addressing grand challenges in food and agriculture research

The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.This working group was partially funded from the NCSU Plant Sciences Initiative, College of Agriculture and Life Sciences ‘Big Ideas’ grant, National Science Foundation grant to R.R.D. (NSF no. 1319293), and a United States Department of Food and Agriculture-National Institute of Food and Agriculture grant to S.F.R., USDA-NIFA Post Doctoral Fellowships grant no. 2017-67012-26999.http://rspb.royalsocietypublishing.orghj2018Forestry and Agricultural Biotechnology Institute (FABI

Machine learning and data mining methodology to predict nominal and numeric performance body weight values using Large White male turkey datasets

SUMMARY: Large biological datasets with many variables and a small number of biological replicates (“omics” sciences and industry data) are challenging to analyze with traditional inferential statistics. Statistical models can be applied to data containing more observations than variables, and they are strongly suited for this purpose. However, the power to detect actual differences is reduced when the number of comparisons exceeds the number of experimental replicates or observations. Machine learning (ML) allows researchers to evaluate treatments groups or multiple categories of variables with fewer observations. Thus, it has become a tool used to predict phenomena and evaluate relationships within datasets that are less suited for traditional statistics. Data mining (DM) helps researchers to identify the most critical variables in an ML predictive model and can be used akin to “statistical significance” for interpretation. This current effort aimed to develop ML and DM methodologies while applying them to predict Large White male turkey body weight (BW). Data from a previously reported study were used. Bird BW, weekly BW gain (BWG), feed intake (FI), feed conversion ratio (FCR), small intestine pH, cloacal temperature, density, microbiome taxa, litter content of Mn and Zn, were used as variables for the ML analysis. A total of 253 variables were used in ML and DM analysis. BW and FI at 18 wk were classified as low, objective, and high based on a 5% for BW and 3% for FI margin of the Aviagen male turkey objectives for ML analysis. The WEKA 3.8.5 Experimenter tool used various classification and regression algorithms with a 10-fold cross-validation system to predict 18 wk BW based on input data. A single algorithm made the most practical model, from 3 models constructed, with a correlation of 0.73 and a root square error of 0.26 based only on turkey 14 wk BW. In conclusion, these ML and DM tools could be applied to turkey research and production systems by analyzing large datasets to predict growth performance