Using an oilseed rape x wild/weedy relative gene flow index for the monitoring of transgenic oilseed rape

To estimate the introgressive hybridisation propensity (IHP) between transgenic oilseed rape and certain of its cross-compatible wild/weedy relatives at the landscape level, a conceptual approach was developed. A questionnaire was established enclosing the successive steps to successfully achieve introgressive hybridisation. Each step was described and scored, resulting in an IHP value for each cross-compatible wild/weedy relative. This approach revealed that in Flanders (Belgium) Brassica rapa has the highest IHP value, followed by Hirschfeldia incana, Diplotaxis tenuifolia, Raphanus raphanistrum and Sinapis arvensis. Using these values, monitoring priorities can be defined within the pool of cross-compatible wild/weedy oilseed rape relatives. It is discussed how the numerical quantification may serve as a valuable tool in case-specific monitoring and general surveillance of transgenic oilseed rape

A method to search for optimal field allocations of transgenic maize in the context of co-existence

Spatially isolating genetically modified (GM) maize fields from non-GM maize fields is a robust on-farm measure to keep the adventitious presence of GM material in the harvest of neighboring fields due to cross-fertilizations below the European labeling threshold of 0.9%. However, the implementation of mandatory and rigid isolation perimeters can affect the farmers' freedom of choice to grow GM maize on their fields if neighboring farmers do not concur with their respective cropping intentions and crop plans. To minimize the presence of non-GM maize within isolation perimeters implemented around GM maize fields, a method was developed for optimally allocating GM maize to a particular set of fields. Using a Geographic Information System dataset and Monte Carlo analyses, three scenarios were tested in a maize cultivation area with a low maize share in Flanders (Belgium). It was assumed that some farmers would act in collaboration by sharing the allocation of all their arable land for the cultivation of GM maize. From the large number of possible allocations of GM maize to any field of the shared pool of arable land, the best field combinations were selected. Compared to a random allocation of GM maize, the best field combinations made it possible to reduce spatial co-existence problems, since at least two times less non-GM maize fields and their corresponding farmers occurred within the implemented isolation perimeters. In the selected field sets, the mean field size was always larger than the mean field size of the common pool of arable land. These preliminary data confirm that the optimal allocation of GM maize over the landscape might theoretically be a valuable option to facilitate the implementation of rigid isolation perimeters imposed by law.

Statistical modelling of nitrogen use efficiency of dairy farms in Flanders

In the past decade it has repeatedly been shown that agriculture is a significant source of ground-and surface water pollution. Nitrogen losses and nitrogen use efficiency (NUE) are major concerns in agricultural practice and of policy-makers. Rapid intensification of livestock production, a result of the focus on increasing productivity from the 1950s onwards, has contributed to a large increase in nutrient surpluses. Here, we performed a quantitative analysis of the variables influencing the nitrogen use efficiency in Flemish grassland-based farming systems. The analysis was based on the large dataset of the Farm Accountancy Data Network, holding technical and economic data of Flemish farms. A statistical model is proposed by performing multiple regression with several variable selection procedures. Many combinations of variables were studied in 92 models and different criteria were taken into account to select the most adequate combination of variables. This approach focuses on a deep statistical analysis and interpretation of the model. The novelty of this research is the quantification and comparison of the influence of different inputs and other variables in nitrogen use efficiency at the farm level. Our results show that, contrary to current knowledge, a higher nitrogen use efficiency was observed for farms with a higher number of 'Dairy cows ha(-1)', holding the rest of the N inputs constant. A higher stocking density is compatible with a higher agricultural sustainability. It is demonstrated that the amount of milk N produced by added cows is higher than the decrease in milk N produced by each single cow due to a reduced input of feed N per cow. The dairy cow becomes more efficient in the use of N, increasing the farm-gate NUE and the farm sustainability. In the dataset of this study, the variable 'Dairy cows ha(-1)' is more relevant than suggested by previous studies: 1.4 times more relevant than the variable 'kg of N in fertilisers ha(-1)', which is 1.5 times more relevant than 'kg of N in concentrates ha(-1)'. According to previous knowledge, the N input variables present a negative sign. Decreasing the N input in fertilisers, concentrates and by-products are recommended actions to increase the NUE. Unexpected interaction effects were found

Welke grasmat voor Brussels Airport?

Op luchthavens is wild (vogels, konijnen) erg ongewenst in het kader van de veiligheid van het vliegverkeer. In de studie beschreven in dit rapport gingen we na welke grassoorten geschikt zijn voor gebruikt op luchthavens, rekening houdend met deze wildproblematiek. De voorkeur van 9 grassoorten of mengsels voor konijnen werd onderzocht in een drie jaar durende veldproef

Variables influencing nitrogen surplus of dairy farms in Flanders

Nitrogen losses are major concerns for agriculture and policy-makers. Intensification of livestock production has contributed to an increase in nutrient surpluses. Here, we performed an exploratory analysis of the variables influencing the nitrogen surplus in Flemish dairy farms. We used the large dataset of the Farm Accountancy Data Network, holding technical and economic data of Flemish farms. A statistical model is proposed by performing multiple linear regression with several variable selection procedures. This approach focuses on a deep statistical analysis and interpretation of the model. The final model contains the following variables: N in fertilizers (kg/ha), N in concentrates (kg/ha), N in by-products (kg/ha) and N in straw (kg/ha), which refer to purchased inputs, livestock units of dairy cows per ha and percentage of arable crops. The input variables show a positive sign, indicating that the higher the nitrogen inputs, the higher the nitrogen surplus. Contrary to current knowledge, a lower nitrogen surplus was observed for farms with a higher number of livestock units of dairy cows per ha, holding the rest of the N inputs constant. A higher stocking density is compatible with a higher agricultural sustainability. The unexpected negative correlation of livestock units of dairy cows per ha with the dependent variable surplus per ha means that the higher the stocking density - under a certain limit - the lower the surplus of nitrogen will be, provided that feed inputs to the farm and cows are kept at a constant level