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There is a growing need for developing high-throughput tools for crop phenotyping that would increase the rate of genetic improvement. In most cases, the indicators used for this purpose are related with canopy structure (often acquired with RGB cameras and multispectral sensors allowing the calculation of NDVI), but using approaches related with the crop physiology are rare. High-resolution hyperspectral remote sensing imagery provides optical indices related to physiological condition through the quantification of photosynthetic pigment and chlorophyll fluorescence emission. This study demonstrates the use of narrow-band indicators of stress as a potential tool for phenotyping under rainfed conditions using two airborne datasets acquired over a wheat experiment with 150 plots comprising two species and 50 varieties (bread and durum wheat). The flights were performed at the early stem elongation stage and during the milking stage. Physiological measurements made at the time of flights demonstrated that the second flight was made during the terminal stress, known to largely determine final yield under rainfed conditions. The hyperspectral imagery enabled the extraction of thermal, radiance, and reflectance spectra from 260 spectral bands from each plot for the calculation of indices related to photosynthetic pigment absorption in the visible and red-edge regions, the quantification of chlorophyll fluorescence emission, as well as structural indices related to canopy structure. Under the conditions of this study, the structural indices OPEN ACCESS Remote Sens. 2015, 7 13587(i.e., NDVI) did not show a good performance at predicting yield, probably because of the large effects of terminal water stress. Thermal indices, indices related to chlorophyll fluorescence (calculated using the FLD method), and carotenoids pigment indices (PRI and CAR) demonstrated to be better suited for screening complex traits such as crop yield. The study concludes that the indicators derived from high-resolution thermal and hyperspectral airborne imagery are efficient tools for field-based phenotyping providing additional information to standard NDVI imagery currently used.

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Durum wheat is an important crop in the Mediterranean Rim, and it is deeply rooted in the history and tradition of this region. Recently, several studies that examined DNA markers on Mediterranean landrace collections have successfully elucidated the pathways of this crop across the Mediterranean Rim, but the historical frame is still rather diffuse. This paper aims at tracing the historical evolution of durum wheat throughout the Mediterranean Rim since its commencement as a crop until present times. A search was carried out through archaeological references where durum wheat remains were found. Historical descriptions about cultivation of this crop, references to products made from its grain, and articles interpreting DNA marker information from Mediterranean landraces were also consulted. The present article also examines the currently available durum wheat genetic resources.Durum wheat was domesticated in the Levant area. Phoenicians, Greeks, and above all Romans were active in the expansion and success of durum cultivation in all Mediterranean Rim that started displaced emmer by the mid first millennium BCE. Early Arab empire expanded in the area of durum wheat cultivation promoting food types based on semolina (dry pasta and couscous). Up to 1955 most durum areas in this area were planted with landraces, but several breeding programs were initiated in Italy, and later at CIMMYT and at ICARDA. Landrace collection and conservation efforts were carried out along the Mediterranean Rim countries to preserve the legacy of this crop.

This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication model is typical of monitoring applications, an important application domain sensor networks target. The main idea behind in-network aggregation is that, rather than sending individual data items from sensors to sinks, multiple data items are aggregated as they are forwarded by the sensor network. Through simulations, we evaluate the performance of different in-network aggregation algorithms, including our own cascading timers, in terms of the trade-offs between energy efficiency, data accuracy and freshness. Our results show that timing, i.e., how long a node waits to receive data from its children (downstream nodes in respect to the information sink) before forwarding data onto the next hop (toward the sink) plays a crucial role in the performance of aggregation algorithms for applications that generate data periodically. By carefully selecting when to aggregate and forward data, cascading timers achieves considerable energy savings while maintaining data freshness and accuracy. We also study in-network aggregation's cost-efficiency using simple mathematical models.