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According to EC regulations the deliberate release of genetically modified (GM) crops into the agro-environment needs to be accompanied by environmental monitoring to detect potential adverse effects, e.g. unacceptable levels of gene flow from GM to non-GM crops, or adverse effects on single species or species groups thus reducing biodiversity. There is, however, considerable scientific and public debate on how GM crops should be monitored with sufficient accuracy, discussing questions of potential adverse effects, agro-environmental variables or indicators to be monitored and respective detection methods; Another basic component, the appropriate number and location of monitoring sites, is hardly considered. Currently, no consistent GM crop monitoring approach combines these components systematically. This study focuses on and integrates spatial agro-environmental aspects at a landscape level in order to design monitoring networks. Based on examples of environmental variables associated with the cropping of Bt-Maize (Zea maize L.), herbicide-tolerant (HT) winter oilseed rape (Brassica napus L.), HT sugar beet (Beta vulgaris L.), and starch-modified potato (Solanum tuberosum L.), we develop a transferable framework and assessment scheme that comprises anticipated adverse environmental effects, variables to be measured and monitoring methods. These we integrate with a rule-based GIS (geographic information system) analysis, applying widely available spatial area and point information from existing environmental networks. This is used to develop scenarios with optimised regional GM crop monitoring networks.

Background In-field measurement of yield and growth rate in pasture species is imprecise and costly, limiting scientific and commercial application. Our study proposed a LiDAR-based mobile platform for non-invasive vegetative biomass and growth rate estimation in perennial ryegrass ( Lolium perenne L.). This included design and build of the platform, development of an algorithm for volumetric estimation, and field validation of the system. The LiDAR-based volumetric estimates were compared against fresh weight and dry weight data across different ages of plants, seasons, stages of regrowth, sites, and row configurations. Results The project had three phases, the last one comprising four experiments. Phase 1: a LiDAR-based, field-ready prototype mobile platform for perennial ryegrassrecognition in single row plots was developed. Phase 2: real-time volumetric data capture, modelling and analysis software were developed and integrated and the resultant algorithm was validated in the field. Phase 3. LiDAR Volume data were collected via the LiDAR platform and field-validated in four experiments. Expt.1: single-row plots of cultivars and experimental diploid breeding populations were scanned in the southern hemisphere spring for biomass estimation. Significant ( P < 0.001) correlations were observed between LiDAR Volume and both fresh and dry weight data from 360 individual plots (R 2 = 0.89 and 0.86 respectively). Expt 2: recurrent scanning of single row plots over long time intervals of a few weeks was conducted, and growth was estimated over an 83 day period. Expt 3: recurrent scanning of single-row plots over nine short time intervals of 2 to 5 days was conducted, and growth rate was observed over a 26 day period. Expt 4: recurrent scanning of paired-row plots over an annual cycle of repeated growth and defoliation was conducted, showing an overall mean correlation of LiDAR Volume and fresh weight of R 2 = 0.79 for 1008 observations made across seven different harvests between March and December 2018. Conclusions Here we report development and validation of LiDAR-based volumetric estimation as an efficient and effective tool for measuring fresh weight, dry weight and growth rate in single and paired-row plots of perennial ryegrass for the first time, with a consistently high level of accuracy. This development offers precise, non-destructive and cost-effective estimation of these economic traits in the field for ryegrass and potentially other pasture grasses in the future, based on the platform and algorithm developed for ryegrass. Electronic supplementary material The online version of this article (10.1186/s13007-019-0456-2) contains supplementary material, which is available to authorized users.

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