In research portfolio planning contexts, an estimate of research policy and project synergies/tradeoffs (i.e. covariances) is essential to the optimal leveraging of institution resources. The data by which to make such estimates generally do not exist. Research institutions may often draw on domain expertise to fill this gap, but it is not clear how such ad hoc information can be quantified and fed into an optimal resource allocation workflow. Drawing on principal components analysis, I propose a method for "reverse engineering" synergies/tradeoffs from domain expertise at both the policy and project level. I discuss extensions to other problems and detail how the method can be fed into a research portfolio optimization workflow. I also briefly discuss the relevance of the proposed method in the context of the currently toxic relations between research communities and the donors that fund them.

Globally, there has been an explosion of data generation in agriculture. With such a deluge of data available, it has become essential to create solutions that organize, analyze, and visualize it to gain actionable insights, which can guide farmers, scientists, or policy makers to take better decisions that lead to transformative actions for agriculture. There is a plethora of digital innovations in agriculture that implement big data techniques to harness solutions from large amounts of data, however, there is also a significant gap in access to these innovations among stakeholders of the value chains, with smallholder's farmers facing higher risks. Open data platforms have emerged as an important source of information for this group of producers but are still far from reaching their full potential. While the growing number of such initiatives has improved the availability and reach of data, it has also made the collection and processing of this information more difficult, widening the gap between those who can process and interpret this information and those who cannot. The Crop Observatorie s are presented in this article as an initiative that aims to harmonize large amounts of crop-specific data from various open access sources to build relevant indicators for decision making. Observatories are being developed for rice, cassava, beans, plantain and banana, and tropical forages, containing information on production, prices, policies, breeding, agronomy, and socioeconomic variables of interest. The Observatories are expected to become a lighthouse that attracts multi-stakeholders to avoid “not see the forest for the trees” and to advance research and strengthen crop economic systems. The process of developing the Observatories , as well as the methods for data collection, analysis, and display, is described. The main results obtained by the recently launched Rice Observatory ( www.riceobservatory.org ), and the about to be launched Cassava Observatory are presented, contextualizing their potential use and importance for multi-stakeholders of both crops. The article concludes with a list of lessons learned and next steps for the Observatories , which are also expected to guide the development of similar initiatives. Observatories , beyond presenting themselves as an alternative for improving data-driven decision making, can become platforms for collaboration on data issues and digital innovations within each sector.

Recent advances in approaches to quantitative strategic foresight have enabled new insights into understanding potential futures of the agriculture sector. Quantitative foresight approaches facilitate understanding of different plausible scenarios, especially as related to both endogenous and exogenous factors (e.g., global markets and climate change). These approaches tend to be macroeconomic in nature and resolve trends relative to coarse-grained drivers. In order translate these outputs into strategies that realistically benefit producers across scale, finer resolution and context specific understanding is needed. This paper offers perspective on how foresight analysis can be combined with more pointed assessment of the specific policies, institutions and market requirements needed create more inclusive agricultural investment strategies.

A few studies have reported some of the costs associated with bringing to market genetically-modified (GM) crops but no comprehensive studies exist on the real cost of the entire process of developing and releasing one GM variety by a not-for-profit institution in a developing country for sustainable agriculture. Despite the lack of documented studies, it is commonly assumed that such an undertaking is cost prohibitive, based on mere hearsay, and on two private sector cost assessments. The present study assesses the costs and the time expenditures to two not-for-profit programs, one lead by CIP and the other by Cornell University, of developing a late blight resistant (LBr) potato variety for release in one developing country. CIP's costs run to $1.6 million over eight years, while Cornell's costs amount to $1.4 million over nine years. Exogenous disturbances might result in insignificant increases in cost, but can increase time expenditure significantly. A sensitivity analysis revealed that the total cost is markedly influenced by technical parameters determining the production and identification of the pre-commercial LBr transgenic event.