| Literature DB >> 31921246 |
Justus Wesseler1, Hidde Politiek1, David Zilberman2.
Abstract
New plant breeding technologies (NPBTs) are increasingly used for developing new plants with novel traits. The science tells us that those plants in general are as safe as than those once developed using "conventional" plant breeding methods. The knowledge about the induced changes and properties of the new plants by using NPBTs is more precise. This should lead to the conclusion that plants developed using NPBTs should not be regulated differently than those developed using "conventional" plant breeding methods. This contribution discusses the economics of regulating new plant breeding technologies. We first develop the theoretical model and elaborate on the different regulatory approaches being used and compare their advantages and disadvantages. Then we provide a perspectives on EU regulation around mutagenesis-based New Plant Breeding Techniques (NPBT), formed by new insights from a survey among Dutch plant breeding companies. The survey measures the attitude of breeding companies towards the ruling of the EU Court of Justice that subjected the use of CRISPR-Cas in the development of new plant varieties under the general EU regulations around GMOs. The results show that plant breeders experience a financial barrier because of the ruling, with perceived negative impact on competitiveness and investments in CRISPR-Cas as a result. The degree of negative impact differs however significantly among seed-sectors and company sizes. One of the most striking results was the relative optimism of companies in the sector about more lenient legislation in the next five years, despite the stated negative effects.Keywords: CRISPR-Cas; Court of Justice of the European Union; Dutch plant breeders; genetically modified organism; impact; new plant breeding technologies; plant breeding sector; regulation
Year: 2019 PMID: 31921246 PMCID: PMC6932994 DOI: 10.3389/fpls.2019.01597
Source DB: PubMed Journal: Front Plant Sci ISSN: 1664-462X Impact factor: 5.753
Number of CRISPR Patent Families by Technical Fields and EU Share.
| Technical field | Total number | European Union | |
|---|---|---|---|
| No. | % | ||
| Agricultural | 374 | 18 | 4.8 |
| Industrial | 192 | 23 | 12.0 |
| Medical | 614 | 19 | 3.1 |
| Technical improvement | 1,052 | 76 | 7.7 |
Source: based on data published in Martin-Laffon et al. (2019). Regional identification of patent applications has been done by first priority date.
Distribution of the population (sample) across the defined categories of seed sectors and company sizes.
| Seed potato | Vegetables | Agriculture | Fruit trees |
| Other | Multi | Total | Share (%) | |
|---|---|---|---|---|---|---|---|---|---|
| Micro (<10 empl.) | 2 (3) | 3 (1) | 1 (0) | 2 (0) | 1 (1) | 9 (5) | 13 (15) | ||
| Small (10 to 49 empl.) | 3 (1) | 10 (4) | 4 (2) | 1 (0) | 5 (0) | 3 (2) | 1 (0) | 27 (9) | 38 (27) |
| Medium (50 to 249 empl.) | 1 (1) | 4 (2) | 2 (2) | 2 (2) | 3 (0) | 0 (2) | 12 (9) | 17 (27) | |
| Large (250+ empl.) | 4 (2) | 8 (5) | 8 (3) | 2 (0) | 1 (0) | 1 (0) | 24 (10) | 33 (30) | |
| Total | 10 (7) | 25 (12) | 15 (7) | 1 (0) | 9 (2) | 9 (2) | 3 (3) | 72 (33) | |
| Share (%) | 14 (21) | 35 (36) | 21 (21) | 1 (0) | 13 (6) | 13 (6) | 4 (9) | 100 (100) |
Categorization is based on company profile as publicly provided by the companies. Company-size categories as defined in EU recommendation 2003/361. Differences in company sizes are the result of the categorization by companies themselves, as reported in brackets, as opposed to the categorization based on publicly available information.
Figure 1Overview of average results of the survey statements on a five-point Likert-scale, excluding not relevant (0) responses ranging from (1) strongly disagree to (5) strongly agree. Graph panel (A) gives the results differentiated by company size, whereas panel (B) differentiates the results on seed sector. Statistical results graph panel (A): a* = two-sided significant difference micro from rest (P < 0.05) and two-sided significant different distributions among all groups (P < 0.05). Statistical results graph panel (B): b = two-sided significant difference potato from all (P < 0.05). c = two-sided significant difference vegetable from all (P < 0.05). d = two-sided significant different potato from all (P < 0.05).