WHAT IS THE VALUE OF BT CORN?

A common perception is that the value of Bt corn arises from two components-Bt corn increases expected profit and reduces profit variability. This perception encourages farmers and the policy makers to add a risk benefit to estimates of the value of Bt corn to account for the variability reduction. However, a conceptual model generates a useful decomposition of the value of Bt corn and a condition determining the impact of Bt corn on profit variability. An empirical model finds that Bt corn increases profit variability and thus decreases the value of Bt corn by 10-25% depending on risk preferences.Crop Production/Industries,

Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings.Comment: in the Proceedings of 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining 201

Bean leaf beetles: a current and historical perspective

In 2002, bean leaf beetle populations in Iowa reached their highest levels in 14 years (figure, left). In Iowa, this increase in beetle populations has been partly fueled by weather conditions that favor winter survival, such as mild temperatures (2001-2002: second mildest winter on record) or snow cover (2000-2001: snow cover for 99 consecutive days in central Iowa). The increase in beetle populations has followed the trend for warmer weather during the previous six winters (figure, right)

Bean leaf beetles and soybean planting date

Considering the enormous bean leaf beetle populations in recent years, many soybean growers are interested in options for managing this pest. Cultural control, such as planting date, could be very useful for managing bean leaf beetle. Studies conducted by Larry Pedigo and Mike Zeiss at Iowa State University (1998-1992) quantified the effects of soybean planting date on bean leaf beetle abundance, soybean pod injury, and soybean yield

Fast Matrix Factorization for Online Recommendation with Implicit Feedback

This paper contributes improvements on both the effectiveness and efficiency of Matrix Factorization (MF) methods for implicit feedback. We highlight two critical issues of existing works. First, due to the large space of unobserved feedback, most existing works resort to assign a uniform weight to the missing data to reduce computational complexity. However, such a uniform assumption is invalid in real-world settings. Second, most methods are also designed in an offline setting and fail to keep up with the dynamic nature of online data. We address the above two issues in learning MF models from implicit feedback. We first propose to weight the missing data based on item popularity, which is more effective and flexible than the uniform-weight assumption. However, such a non-uniform weighting poses efficiency challenge in learning the model. To address this, we specifically design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) technique, for efficiently optimizing a MF model with variably-weighted missing data. We exploit this efficiency to then seamlessly devise an incremental update strategy that instantly refreshes a MF model given new feedback. Through comprehensive experiments on two public datasets in both offline and online protocols, we show that our eALS method consistently outperforms state-of-the-art implicit MF methods. Our implementation is available at https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure

Management decisions for bean leaf beetles and bean pod mottle virus

Yogi Berra said, If you come to a fork in the road, take it. Many soybean producers will be at that fork in a couple of weeks, trying to decide whether or not to spray overwintered bean leaf beetles, and determining how to manage bean pod mottle virus. The dilemma is that some overwintered bean leaf beetles may transmit bean pod mottle virus and not knowing where in Iowa the problem is most likely to occur, what percentage of beetles are transmitting the virus, or when to spray can greatly complicate management decisions

Recent bean leaf beetle and bean pod mottle virus research

Soybean growers face a dilemma when considering management options for bean leaf beetles and bean pod mottle virus. Rayda Krell recently completed a research program at Iowa State University that focused on immediate solutions for this pest problem. This article summarizes her research from which we suggest some short-term management options

Revisiting an integrated approach to bean leaf beetle and bean pod mottle virus management

This article originally appeared in the 2005 ICM newsletter. However, the significance of the bean leaf beetle and bean pod mottle virus has not diminished in recent years. There is still the potential of economic damage from either or both pests. We have recently completed a three-year study that examines the complex issues of managing these two pests, but the data are still being analyzed. We also have identified potential field tolerance to virus disease. Growers are encouraged to query seed dealers regarding tolerance of varieties to virus disease. Ultimately, this will likely be the best management tool for disease control. In the meantime, we give you our best recommendations as we understand the situation in Iowa