987 resultados para Crops.


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The grass family includes some 10,000 species, and it encompasses tremendous morphological, physiological, ecological, and genetic diversity. The phylogeny of the family is becoming increasingly well understood. There were two major radiations of grasses, an early diversification leading to the subfamilies Pooideae, Bambusoideae, and Oryzoideae, and a later one leading to Panicoideae, Chloridoideae, Centothecoideae, and Arundinoideae. The phylogeny can be used to determine the direction of changes in genome arrangement and genome size.

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The adoption of genetically modified crops is becoming evermore common in United States agriculture. However, this relatively new technology carries a negative stigma and perceived risks that have resulted commonly in public disapproval. In the United States, bioengineered crops are highly regulated. The significance of environmental benefits such as decreased chemical impact, increased soil conservation, heightened carbon sequestration, decreased energy demands, and reduced air emissions, are important enough to warrant a revision to U.S. policy. The U.S. policy structure needs to be simplified and made more efficient to better facilitate the speed with which new GE products can, and should, be developed while still providing adequate mitigation of potential environmental risks such as species invasiveness and impacts on non-target species.

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Information of crop phenology is essential for evaluating crop productivity. In a previous work, we determined phenological stages with remote sensing data using a dynamic system framework and an extended Kalman filter (EKF) approach. In this paper, we demonstrate that the particle filter is a more reliable method to infer any phenological stage compared to the EKF. The improvements achieved with this approach are discussed. In addition, this methodology enables the estimation of key cultivation dates, thus providing a practical product for many applications. The dates of some important stages, as the sowing date and the day when the crop reaches the panicle initiation stage, have been chosen to show the potential of this technique.

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In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.