Yield gap of the double-crop system of main-season soybean with off-season maize in Brazil

The succession of main-season soybean (Glycine max (L.) Merr.) with off-season maize (Zea mays L.) is an important Brazilian agricultural system contributing to increased grain production without the need for crop land expansion. Yield-gap studies that identify the main factors threatening these crops are pivotal to increasing food security in Brazil and globally. Therefore, the aim of the present study was to determine, for the soybean–off-season-maize succession, the magnitude of the grain and revenue yield gap (YG) caused by water deficit (YGW) and suboptimal crop management (YGM), and to propose strategies for closing these gaps in different Brazilian regions. The ensemble of three previously calibrated and validated models (FAO-AZM, DSSAT and APSIM) was used to estimate yields of soybean and off-season maize for 28 locations in 12 states for a period of 34 years (1980–2013). Water deficit is the biggest problem for soybean and off-season maize crops in the regions of Cocos (state of Bahia), Buritis (Minas Gerais) and Formosa (Goiás), where the YGW accounted for ~70% of total YG. The YGM revealed that locations in the central region of Brazil, mainly in the state of Mato Grosso, presented an opportunity to increase yields of soybean and off-season maize, on average, by 927.5 and 909.6 5 kg ha–1, respectively. For soybean, YGM was the main cause of total YG in Brazil, accounting for 51.8%, whereas for maize, YGW corresponded to 53.8% of the total YG. Our results also showed that the choice of the best sowing date can contribute to reducing soybean YGW by 34–54% and off-season maize YGW by 66–89%.

Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs

This article presents the state-of-the-art in optimal solution methods for decentralized partially observable Markov decision processes (Dec-POMDPs), which are general models for collaborative multiagent planning under uncertainty. Building off the generalized multiagent A* (GMAA*) algorithm, which reduces the problem to a tree of one-shot collaborative Bayesian games (CBGs), we describe several advances that greatly expand the range of Dec-POMDPs that can be solved optimally. First, we introduce lossless incremental clustering of the CBGs solved by GMAA*, which achieves exponential speedups without sacrificing optimality. Second, we introduce incremental expansion of nodes in the GMAA* search tree, which avoids the need to expand all children, the number of which is in the worst case doubly exponential in the node's depth. This is particularly beneficial when little clustering is possible. In addition, we introduce new hybrid heuristic representations that are more compact and thereby enable the solution of larger Dec-POMDPs. We provide theoretical guarantees that, when a suitable heuristic is used, both incremental clustering and incremental expansion yield algorithms that are both complete and search equivalent. Finally, we present extensive empirical results demonstrating that GMAA*-ICE, an algorithm that synthesizes these advances, can optimally solve Dec-POMDPs of unprecedented size.

Shift of holes in spectral lines due to time dependent perturbation

The radiative decay of an atom with two excited states, one decaying and the other non-decaying, coupled by a time dependent perturbation, is investigated. The hole in the emission line is found to be shifted by an amount equal to the frequency of the time dependent perturbation. The practical importance of this effect is discussed. It is also noticed that the two methods of solution, one by making the rotating-wave approximation and the other by a perturbation expansion, yield different results, especially in the case when the two excited states are strongly coupled.