By Viacheslav I. Adamchuk, Richard B. Ferguson, Gary W. Hergert (auth.), Erich-Christian Oerke, Roland Gerhards, Gunter Menz, Richard A. Sikora (eds.)
Precision farming is an agricultural administration method utilizing worldwide navigation satellite tv for pc structures, geographic details platforms, distant sensing, and knowledge administration structures for optimizing using meals, water, seed, insecticides and effort in heterogeneous box occasions. This booklet, generated by way of scientists from a large diversity of medical disciplines, offers broad info at the state of the art of analysis on precision crop security and up to date advancements in site-specific program applied sciences for the administration of weeds, arthropod pests, pathogens and nematodes. It supplies the reader an up to date and in-depth evaluation of either uncomplicated and utilized examine advancements. The chapters speak about I) biology and epidemiology of pests, II) new sensor applied sciences, III) functions of multi-scale sensor structures, IV) sensor detection of pests in becoming vegetation, V) spatial and non-spatial info administration, VI) impression of pest heterogeneity and VII) targeted mechanical and chemical pest regulate. Examples for using applied sciences are given for the administration of pests which allow growers to alter program timing and dosage, and to optimize pesticide combinations or depth of mechanical weeding in line with the spatial and temporal variability of pests.
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Extra info for Precision Crop Protection - the Challenge and Use of Heterogeneity
Example text
1 lists ranges of semivariogram models (measures of soil spatial structure) and coefficients of variation for several influential soil attributes (Mulla and McBratney 2000). Soil properties can vary over time as well as space. Also, some properties are highly dynamic, changing rapidly with time, while other properties are relatively static, varying little from year to year. In assessing spatial variability, temporal variability can sometimes prove to be an important factor. Dynamic properties may change at different temporal scales as well.
There is a need to develop metrics for evaluating and monitoring Integrated Pest Management (IPM) performance (Hamerchlag and Kaplan 2007). Remote sensing, when coupled with GPS and GIS technologies, has the potential to assess crop health (rather than disease intensity) over time and space, with greater accuracy and precision (Nutter 1990, 2001, Nutter et al. 2009). Therefore, the integration of remote sensing, GPS, and GIS technologies represents a new paradigm in that disease management strategies and tactics could, in the future, be evaluated and deployed based upon the capability of a disease management program to produce and maintain (protect) healthy green leaf area (Lathrop and Pennypacker 1980, Nutter 1989, 1999, 2001).
Interpolated weed maps were reclassified based on weed infestation levels (Gerhards et al. 1997). Density classes were equal for all species in this study to facilitate the analysis of overlay maps. A weed treatment map was created to provide a decision rule for the patch sprayer (Fig. 2). 3 Temporal and Spatial Dynamics of Weed Populations The dynamics of weed populations are influenced by the biological characteristics of weed species, farming practices such as tillage, crop rotation, time of seeding, harvesting competitiveness of the crop and direct weed control methods as well as soil 20 R.