Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed

Patterns of pollen and nectar foraging specialization by bumblebees over multiple timescales using RFID

The ecological success of social insects is frequently ascribed to improvements in task performance due to division of labour amongst workers. While much research has focused on improvements associated with lifetime task specialization, members of colonies can specialize on a given task over shorter time periods. Eusocial bees in particular must collect pollen and nectar rewards to survive, but most workers appear to mix collection of both rewards over their lifetimes. We asked whether bumblebees specialize over timescales shorter than their lifetime. We also explored factors that govern such patterns, and asked whether reward specialists made more foraging bouts than generalists. In particular, we described antennal morphology and size of all foragers in a single colony and related these factors to each forager's complete foraging history, obtained using radio frequency identification (RFID). Only a small proportion of foragers were lifetime specialists; nevertheless, >50% of foragers specialized daily on a given reward. Contrary to expectations, daily and lifetime reward specialists were not better foragers (being neither larger nor making more bouts); larger bees with more antennal olfactory sensilla made more bouts, but were not more specialized. We discuss causes and functions of short and long-term patterns of specialization for bumblebee colonies.University of Arizona Graduate & Professional Student Council; University of Arizona Center for Insect Science; National Science Foundation [IOS-0921280]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Indirect effects drive coevolution in mutualistic networks

Ecological interactions have been acknowledged to play a key role in shaping biodiversity. Yet a major challenge for evolutionary biology is to understand the role of ecological interactions in shaping trait evolution when progressing from pairs of interacting species to multispecies interaction networks. Here we introduce an approach that integrates coevolutionary dynamics and network structure. Our results show that non-interacting species can be as important as directly interacting species in shaping coevolution within mutualistic assemblages. The contribution of indirect effects differs among types of mutualism. Indirect effects are more likely to predominate in nested, species-rich networks formed by multiple-partner mutualisms, such as pollination or seed dispersal by animals, than in small and modular networks formed by intimate mutualisms, such as those between host plants and their protective ants. Coevolutionary pathways of indirect effects favour ongoing trait evolution by promoting slow but continuous reorganization of the adaptive landscape of mutualistic partners under changing environments. Our results show that coevolution can be a major process shaping species traits throughout ecological networks. These findings expand our understanding of how evolution driven by interactions occurs through the interplay of selection pressures moving along multiple direct and indirect pathways