Submitted to: New Crops National Conference Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 2/28/2007
Publication Date: 10/30/2007
Citation: White, J.W., Dierig, D.A., Tomasi, P., Salywon, A., and Nath, D. Harnessing information technologies for more efficient crop development. In: Janick, J. and Whipkey, A. (eds.). Issues in New Crops and New Uses. ASHS Press, Alexandria, VA, pp. 8-18. Interpretive Summary: Agricultural research develops new crops in order to broaden opportunities for farmers to diversify their production, increase their profitability, better protect the environment or conserve natural resources, and satisfy demand for specific products, such as specialty oils or low-allergy latex. To compete with established crops such as wheat, corn and soybean, however, potential new crops must convincingly be shown to reliably satisfy diverse expectations of growers, producers, regulators and other stakeholders. Increasingly, quantitative data are displacing anecdotes and personal judgments, so researchers must manage their data efficiently, including ensuring that information can be passed from one software tool to another. This paper briefly reviews how three types of tools, databases, crop simulation models, and geographic information systems (GIS) have increased the efficiency of agricultural research. The International Crop Information System (ICIS) is a software system for managing agricultural research data and includes tools for tracking pedigrees and selections, preparing fieldbooks, and describing individual experiments. ICIS implementations were recently created for two new oil seed crops, lesquerella ((Lesquerella spp., Brassicaceae) and vernonia (Vernonia galamensis Less., Asteraceae). This effort confirmed that ICIS can reliably document the diverse breeding methods used in developing new crops. Examples from modeling and GIS demonstrate the benefits from ensuring that data can readily be shared among software tools. Researchers should strive for effective, integrated data management, while avoiding overly complex software systems that do not support analytical needs.
Technical Abstract: Candidate new crops are scrutinized by researchers, producers, processors, marketing specialists, and others. Increasingly, quantitative data are displacing anecdotes and personal judgments. This paper examines how more integrated management of information can assist development of new crops, with examples from data management per se, simulation modeling, and geographic information systems (GIS). The International Crop Information System (ICIS) is a software system for managing agricultural research data and includes tools for tracking pedigrees and selections, preparing fieldbooks, and describing individual experiments. ICIS implementations were recently created for two new oil seed crops, lesquerella ((Lesquerella spp., Brassicaceae) and vernonia (Vernonia galamensis Less., Asteraceae). The effort confirmed that ICIS can fully document methods used in plant breeding that range from initial germplasm introductions to formation of tetraploids in order to obtain fertile progeny from interspecific crosses. Simulation models, which integrate knowledge on physiology, genetics, soil chemistry, and climatology, can predict both crop performance and effects of the crop on the environment, such as water and nutrient requirements and impacts on soil organic matter. Model outputs are no better than the data used to calibrate and run the simulations, so again, robust data management facilitates development and effective use of models. Stakeholders must understand where a promising crop can best be produced, considering both biological potential and access to processing facilities or markets. GIS can provide this geographic context but requires spatially explicit data on crop performance, climate, soils, and location of markets. Thus, component tools such as databases, models and GIS can contribute tremendously in the development and promotion of new crops, but realizing their full potential requires integration across disciplines and software tools. This goal is best reached by using systems such as ICIS to provide a core level of data integration and then utilizing other tools to exchange data readily, and appropriate levels of standardization.