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Nutrient management decisions and environmental policy making must be based on sound data and proper analysis. Annual data collection and monitoring of farm and nutrient performance are wrought with uncertainties. Such uncertainties need to be addressed as it may lead to ambiguities and wrong conclusions. We developed an input-output N balance model to describe and quantify N flows in dairy farming systems. Input for this model was based on monitored data for one year (2005) from one experimental (detailed monitoring) and 14 pilot commercial dairy farms (less detailed monitoring). A Monte Carlo approach was used to quantify effects of uncertainty of input data on annual farm N surplus, soil surface N surplus and N intake during grazing, followed by a sensitivity analysis to apportion the different sources of uncertainty. Uncertainties in data input were described with probability density functions. Farm N surplus of the 14 pilot farms ranged between 81 and 294¿kg¿ha-1, soil surface N surplus between 35 and 256¿kg¿ha-1, and N intake during grazing between 27 and 108¿kg¿ha-1. The uncertainties of N flows – both relative and absolute – increased from farm N surplus (CV¿=¿8%; SD¿=¿15¿kg¿N¿ha-1) to soil surface N surplus (CV¿=¿12%; SD¿=¿16¿kg¿N¿ha-1) to N intake during grazing (CV¿=¿49%; SD¿=¿28¿kg¿N¿ha-1). Variation in uncertainty among farms in farm and soil surface N surplus and N intake during grazing was substantial and was related to the farm structure and farm characteristics such as production intensity, N fixation by clover and annual changes in stocks of roughage and manure. We found that a monitoring program based on more measurements instead of estimates and/or fixed rate values from literature will not always result in a better quantification of farm and soil surface N surplus on clover-based dairy farms. However, on farms with no N fixation, an intensive monitoring program reduced the uncertainty in farm and soil surface N surplus by 23% and the uncertainty of N intake during grazing was reduced by more than 30%. Knowledge about uncertainties of N flows is necessary to correctly interpret the N performance on dairy farms and its evolution through time. A first step is to get insights into the most uncertain N flows on a dairy farm. The next step, where possible, is to improve the estimation of the most uncertain N flows. Based on the insights from this study, these steps will underpin the validation of trends in N performance and justify decisions in environmental policy making and/or decisions for making on-farm improvements.

Malaria parasites are thought to influence mosquito attraction to human hosts, a phenomenon that may enhance parasite transmission. This is likely mediated by alterations in host odour because of its importance in mosquito host-searching behaviour. Here, we report that the human skin odour profile is affected by malaria infection. We compared the chemical composition and attractiveness to Anopheles coluzzii mosquitoes of skin odours from participants that were infected by Controlled Human Malaria Infection with Plasmodium falciparum. Skin odour composition differed between parasitologically negative and positive samples, with positive samples collected on average two days after parasites emerged from the liver into the blood, being associated with low densities of asexual parasites and the absence of gametocytes. We found a significant reduction in mosquito attraction to skin odour during infection for one experiment, but not in a second experiment, possibly due to differences in parasite strain. However, it does raise the possibility that infection can affect mosquito behaviour. Indeed, several volatile compounds were identified that can influence mosquito behaviour, including 2- and 3-methylbutanal, 3-hydroxy-2-butanone, and 6-methyl-5-hepten-2-one. To better understand the impact of our findings on Plasmodium transmission, controlled studies are needed in participants with gametocytes and higher parasite densities.

In the new Dutch decision tree for the evaluation of pesticide leaching to groundwater, spatially distributed soil data are used by the GeoPEARL model to calculate the 90th percentile of the spatial cumulative distribution function of the leaching concentration in the area of potential usage (SP90). Until now it was not known to what extent uncertainties in soil and pesticide properties propagate to spatially aggregated parameters like the SP90. A study was performed to quantify the uncertainties in soil and pesticide properties and to analyze their contribution to the uncertainty in SP90. First, uncertainties in the soil and pesticide properties were quantified. Next, a regular grid sample of points covering the whole of the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of the uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The analysis showed that the uncertainty concerning the SP90 is 10 times smaller than the uncertainty about the leaching concentration at individual point locations. The parameters that contribute most to the uncertainty about the SP90 are, however, the same as the parameters that contribute most to uncertainty about the leaching concentration at individual point locations (e.g., the transformation half-life in soil and the coefficient of sorption on organic matter). Taking uncertainties in soil and pesticide properties into account further leads to a systematic increase of the predicted SP90. The important implication for pesticide regulation is that the leaching concentration is systematically underestimated when these uncertainties are ignored.

The aim of this study was to investigate farm-level economic and technical factors that are associated with the use of antibiotics on pig farms. Identification of such factors, like farm size and net farm result, may help to increase epidemiological knowledge and to specify farm advice and policy making to reduce inappropriate use of antibiotics. The study used over 300 farm-year records collected during 2004 to 2007 from pig farms in the Netherlands. Data included economic and technical factors as well as antibiotic administration. Data were statistically analyzed for factors associated with antibiotic use, both for fattening pig and sow farms (piglets only), separately. The response variable was the average number of daily dosages per average pig year. Statistical analysis was performed on 16 and 19 potential explanatory factors for the fattening pig and sow farms, respectively. The results showed that, both on the fattening pig and sow farms, the average use of antibiotics increased from 2004 to 2006, but decreased during 2007, but the effect of year was not significant (P > 0.05). Use of antibiotics varied between individual farms. Large farm repeatability for the use of antibiotics in the different years was found. Factors associated (P 0.05) related to the on-farm use of antibiotics. Therefore, it is recommended to focus future research on the potential role of socioeconomic factors associated with antibiotic use on pig farms.

GeoPEARL is a spatially distributed model describing the fate of pesticides in the soil–plant system. It calculates the drainage of pesticides to local surface waters and the leaching into groundwater. GeoPEARL plays an important role in the evaluation of Dutch pesticide policy plans. This study analysed how uncertainties in soil and pesticide properties propagate through GeoPEARL for three representative pesticides. The GeoPEARL output considered is the 90th percentile of the spatial distribution of the temporal median of the leaching concentration (P90). The uncertain pesticide properties are the coefficient of sorption on organic matter and the half-life of transformation in soil. Both were assumed uncorrelated in space and were represented by lognormal probability distributions. Uncertain soil properties considered were horizon thickness, texture, organic matter content, hydraulic conductivity and the water retention characteristic. Probability distributions were derived from meta-data stored in the Dutch soil information system. A regular grid sample of 258 points covering the agricultural area in the Netherlands was randomly selected. At the grid nodes, realizations from the probability distributions of uncertain inputs were generated and used as input to a Monte Carlo uncertainty propagation analysis. The results show large uncertainties in P90, with interquartile ranges larger than the median for all three pesticides. Taking input uncertainty into account also leads to a systematic shift of the P90 towards greater values. Stochastic sensitivity analysis showed that the pesticide half-life is the main source of uncertainty and that the coefficient of sorption to organic matter and uncertainty in soil organic matter contribute to a lesser extent. Uncertainty contributions from other soil properties were negligible. These results suggest that improved assessment of soil properties will only marginally improve the accuracy of the predicted pesticide leaching. Instead, more accurate assessment of the pesticide properties, in particular the pesticide half-life, is required. This is, however, difficult, because the pesticide half-life depends on highly variable soil microbial properties in a way that is as yet poorly understood.

Kohl, J., de Haas, B. H., Kastelein, K., Burgers, S. L. G. E., and Waalwijk, C. 2007. Population dynamics of Fusarium spp. and Microdochium nivale in crops and crop residues of winter wheat. Phytopathology 97:971-978. Naturally occurring populations of Fusarium avenaceum, F. culmorum , F. graminearum, F. poae, and Microdochium nivale were studied in two field experiments from anthesis in June 2003 until harvest in crops of winter wheat, and subsequently during 10 months after harvest until June 2004 on their residues exposed on the soil surface under field conditions. The dynamics of the different pathogens were estimated by quantifying the amount of DNA present in wheat tissues using TaqMan-polymerase chain reaction. While colonization of grain by Fusarium spp. and M. nivale was low, high amounts of DNA of F. avenaceum, F. graminearum, and F. culmorum were found in ear residues, internodes, and nodes of the mature crop. Amounts of DNA of pathogens decreased significantly during the following 10 months in residues of internodes and nodes, but not in residues of stem bases. Knowledge on population dynamics of pathogens will help to develop preventive measures aimed at reduction of inoculum sources of head blight pathogens.

Organic growers need information on vari- ety performance under their growing conditions. A 4-year onion variety research project was carried out to investigate whether setting up a variety testing system combining conventional and organic variety trials is feasible and efficient rather than organizing separate variety trials under the two management systems. During 4 years commercial onion cultivars were tested at a certified organic and a non-organic location. Both systems were managed without chemical pest, disease and sprouting control, but differed in fertility manage- ment (organic manure in autumn versus synthetic fertilizer), soil cultivation and weed management (mechanical weeding versus application of herbicide). Management system significantly affected plant den- sity, thickness of neck, and proportion of small and large bulbs. Variety 9 management system interactions were significant for bulb uniformity, earliness, proportion of large bulbs, dormancy and relative storage success but did not change the ranking of the varieties. We conclude that organic growers can profit from a more conscious variety choice when conventionally fertilised trials would refrain from using pesticides, fungicides, herbi- cides and sprout inhibitors. However, this would require an adaptation of the management protocol in such a way that trials might no longer represent conditions of conventional farmers. Furthermore, assessments of leaf erectness, disease resistance to downy mildew and leaf blight should be included in the protocols for organic use. We advocate better communication between breeders and growers on specific variety characteristics contributing to improving yield stability under low- input, organic growing conditions.

Data on soil meso- and macrofauna from winter wheat fields in the Netherlands under conventional and integrated management were analysed to explain their population dynamics and to evaluate their contribution to carbon and nitrogen dynamics. To compare the functioning of the soil fauna in both systems, respiration and mineralisation were calculated on a daily basis, and canonical correspondence analysis was used to analyse the response of soil fauna to abiotic factors and to biotic factors of the previous sample date. Mainly because of the occurrence of earthworms, which were absent in the conventional system, soil fauna was clearly more important in the integrated system: integrated/conventional ratios of biomass and respiration were 58.43 and 6.49, respectively for soil meso- and macrofauna. An indication of a shift of bacterivorous to fungivorous fauna was found in the integrated system under the assumption that earthworms were 80% microbivorous and 20% saprophagous, but bot when it was assumed that they were 100% saprophagous. It was calculated that meso- and macrofauna mineralised 3.3 kg N ha −1 year −1 in the conventional and 39.8 kg N ha −1 year −1 in the integrated system, or 4.3% and 39.4% of the total in situ mineralisation, respectively. Total faunal mineralisation was 49.1% and 86.6% of the total in situ mineralisation in conventional and integrated systems, respectively. It was found that in both systems, seasonal factors played an important role in explaining biomass dynamics and that in the course of time, the upper layer in the integrated system increased in importance. It appeared from the analyses that food relations may differ in the two systems, with probably more complicated and diverse relations occurring in the integrated system. Analysis of the results of this study in comparison with the results of a food web approach showed general agreement; the largest deviations occurred on lower trophic levels, which may be associated with the greater sensitivity of the food web approach to the accuracy of parameter estimates.

Nitrate leaching in intensive grassland- and silage maize-based dairy farming systems on sandy soil is a main environmental concern. Here, statistical relationships are presented between management practices and environmental conditions and nitrate concentration in shallow groundwater (0.8 m depth) at farm, field, and point scales in The Netherlands, based on data collected in a participatory approach over a 7-yr period at one experimental and eight pilot commercial dairy farms on sandy soil. Farm milk production ranged from 10 to 24 Mg ha(-1). Soil and hydrological characteristics were derived from surveys and weather conditions from meteorological stations. Statistical analyses were performed with multiple regression models. Mean nitrate concentration at farm scale decreased from 79 mg L(-1) in 1999 to 63 in 2006, with average nitrate concentration in groundwater decreasing under grassland but increasing under maize land over the monitoring period. The effects of management practices on nitrate concentration varied with spatial scale. At farm scale, nitrogen surplus, grazing intensity, and the relative areas of grassland and maize land significantly contributed to explaining the variance in nitrate concentration in groundwater. Mean nitrate concentration was negatively correlated to the concentration of dissolved organic carbon in the shallow groundwater. At field scale, management practices and soil, hydrological, and climatic conditions significantly contributed to explaining the variance in nitrate concentration in groundwater under grassland and maize land. We conclude that, on these intensive dairy farms, additional measures are needed to comply with the European Union water quality standard in groundwater of 50 mg nitrate L(-1). The most promising measures are omitting fertilization of catch crops and reducing fertilization levels of first-year maize in the rotation.

Soil tillage does not lead to homogeneously mixed soil layers. Depending on the interaction between the physical condition of the soil and the tillage implement, a repetitive, layered pattern of accumulated crop residues or other freshly added organic matter can often be observed. Such a non-homogeneous pattern may affect subsequent biological activity, the rates of decomposition, and the chances for root development to occur close to the sites of mineralization in the next growing season. Patches of organic matter, soil cracks and roots were mapped on clear plastic (polythene) sheets on a soil profile wall. Maps were analysed for spatial correlation of the various mapped items. Small samples of the patches of crop residues and freshly added organic matter, and of surrounding (control) soil were analysed for the abundances of bacteria and protozoa. A statistical test for synlocation of roots and cracks was developed, on the basis of measured root densities as a function of the distance to the nearest crack. A significant increase of root density (by a factor two) close to the crack was found. For organic matter patches a similar synlocation with cracks was indicated. Patches with freshly added organic matter contained twice as many bacteria and five times as many protozoa as the control soil. Patches with recent crop residues contained four times as many protozoa, but equal densities of bacteria as control soil. Only a small part of the spatial variability in the abundance of soil organisms, encountered when using a random sampling scheme, is accounted for by distinguishing these patches. Nevertheless, knowledge of the location and activity of these “hot spots” may help in understanding the process of decomposition. The present data indicate a certain degree of synlocation of roots and sites of increased n mineralization, but its effect on plant nutrition and on N use efficiency is probably small.

Dit boek helpt onderzoekers statistiek toe te passen in hun onderzoek. Theoretisch (wiskundige) details zijn hierbij zoveel mogelijk achterwege gebleven. De in dit boek besproken methoden sluiten aan bij het onderzoek van Wageningen UR. De methoden worden toegelicht aan de hand van voorbeelden.

International audience; Predictions of deoxynivalenol (DON) content in wheat at harvest can be useful for decision making by stakeholders of the wheat feed and food supply chain. The objective of the current research was to develop quantitative predictive models for DON in mature winter wheat in The Netherlands for two specific groups of end-users. One model was developed for use by farmers in underpinning Fusarium spp. disease management, specifically the application of fungicides around wheat flowering (model A). The second model was developed for industry and food safety authorities, and considered the entire wheat cultivation period (model B). Model development was based on observational data collected from 425 fields throughout the Netherlands in the period 2001-2008. For each field, agronomical information, climatic data and DON levels in mature wheat were collected. Using multiple regression analyses, the set of biological relevant variables that provided the highest statistical performance was selected. The two final models include the following variables: region, wheat resistance level, spraying, flowering date, several climatic variables in the different stages of wheat growing, and length of the period between flowering and harvesting (model B only). The percentage of variance accounted for was 64.4 % and 65.6 % for model A and B, respectively. Model validation showed high correlation between the predicted and observed DON levels. The two models may be applied by various groups of end-users to reduce DON contamination in wheat derived feed and food products and, ultimately, reduce animal and consumer health risks.

In the coming decade, European dairy farms are obliged to realize a balance between phosphor (P) inputs to their farmland (in inorganic fertilizers and manure) and outputs (in crop products), the so-called P-equilibrium fertilization. The objective of the present study is to analyze the long-term effects of P-equilibrium fertilization on soil-P status (total soil-P and available soil-P), crop yield and P leaching on dry sandy soil, using data from experimental dairy farm 'De Marke', where P-equilibrium fertilization has been applied since 1989. For grassland, P availability is expressed in P-Al and for arable land in Pw. Total and available P status were monitored in the upper topsoil (layer 0-0.2 m). Total soil-P was also monitored in the lower topsoil (layer 0.2-0.4 m) and in the subsoil (0.4-0.6 m). From 1989 to 2006, Pw and P-Al (means of all farmland) decreased by 26 and 25%, respec- tively. In the same period, mean total-P content of the farmland decreased by 16%. There was a large variation in initial P status (1989) of the various plots. The rate of decline in all soil-P indicators was positively correlated to their initial values. In plots with the lowest initial values, P status did not change, while in plots with high initial values it tended to stabilize at lower levels. At equilibrium-P fertilization, Pw is estimated to stabilize at 20. This is lower than the recommended P status of Dutch soils used for maize cropping. P-Al is estimated to stabilize at 30-40, which corresponds to the current recommendations for grassland. The data show that at P-equilibrium fertil- ization, soil available-P status is higher in a maize-ley rotation than in permanent grassland. The decline in total P and available P did not affect crop yield, nor did it affect the P concentration in groundwater, but at 'De Marke', P emission to groundwater is generally low. The results obtained suggest that P-equilibrium fertilization can be compatible with efficient crop production.

This paper describes the analysis of an agent-based model’s sensitivity to changes in parameters that describe the agents’ cultural background, relational parameters, and parameters of the decision functions. As agent-based models may be very sensitive to small changes in parameter values, it is of the essence to know for which changes the model is most sensitive. A long-standing metamodeling-based approach of sensitivity analysis is applied to the agent-based model. The analysis is differentiated for homogeneous and heterogeneous agent populations. Intrinsic stochastic effects of the agent-based model are taken into account. The paper describes how an appropriate regression model has been selected and analyses the parameter’s variance contributions in general and in specific cultural settings.

The purpose of our study was to assess the role of soil quality parameters in leek production and to assess their importance relative to nitrogen (N) applied as fertilizer. We selected seven (2004) and seven (2005) fields on leek farms in the southern sand district of the Netherlands and measured physical and chemical soil properties. Three N rates (0, 90 and 360 kg N ha -1 as calcium ammonium nitrate; denoted as N 0 , N 90 , N 360 ) were given at each site. Leek (Allium porrum L. 'Kenton') was planted in June-July and harvested next spring. Measured response variables were shoot biomass yield (gross and net, fresh and dry) and shoot N-yield (gross, net) at harvest. Pooled data from both years were analyzed by linear regression. N uptake from unfertilized soil (U 0 ), and topsoil properties soluble organic N (N S0 ), soil organic matter content (SOM), total nitrogen (N tot ) and water content at field capacity (W fc ) all had large and significant impacts on biomass yield and N yield. These five properties (X i ) were correlated and were therefore used alternately in regression models. Effects of soil properties found by regression refer to a shift in the regressor from its 25% to its 75% percentile value, and are expressed here relative to mean yields (both years, all treatments). This normalization facilitates direct comparison with fertilizer effects. Normalized effects of X i variables on biomass yield and N yield were between +0.10 and +0.20. Effects of fertilizer application at N 90 were about +0.10 (biomass yield) and +0.20 (N yield). At N 360 effects were +0.10 to +0.20 (biomass yield) and +0.30 to +0.40 (N-yield). So while N fertilizer strongly promoted N-uptake relative to growth, soil properties X i affected growth and N yield more evenly. With shifts in X i variables, dry matter produced per kg additional N uptake was 1.49 to 1.77 times larger than with extra N uptake resulting from fertilizer application at N 90 . This indicates that soil properties X i promoted yield not only via enhanced N supply. Besides effects of X i properties and N fertilizer, we found significant effects of year, soil texture, pH and inorganic soil N at planting, on biomass yield. Texture parameters F sfine (50-210 μm) and M 50 (median of particle size in 50-2000 μm fraction) had large and additive positive effects on net fresh yield. Apparent recovery of fertilizer N (ANR) averaged 0.35 at N 90 , and 0.17 at N 360 . ANR decreased with higher N so and increased with higher W fc .

Groundwater quality management relies more and more on models in recent years. These models are used to predict the risk of groundwater contamination for various land uses. This paper presents an assessment of uncertainties and sensitivities to input parameters for a regional model. The model had been set up to improve and facilitate the decision-making process between stakeholders and in a groundwater quality conflict. The stochastic uncertainty and sensitivity analysis comprised a Monte Carlo simulation technique in combination with a Latin hypercube sampling procedure. The uncertainty of the calculated concentrations of nitrate leached into groundwater was assessed for the various combinations of land use, soil type, and depth of the groundwater table in a vulnerable, sandy region in The Netherlands. The uncertainties in the shallow groundwater were used to assess the uncertainty of the nitrate concentration in the abstracted groundwater. The confidence intervals of the calculated nitrate concentrations in shallow groundwater for agricultural land use functions did not overlap with those of non-agricultural land use such as nature, indicating significantly different nitrate leaching in these areas. The model results were sensitive for almost all input parameters analyzed. However, the NSS is considered pretty robust because no shifts in uncertainty between factors occurred between factors towards systematic changes in fertilizer and manure inputs of the scenarios. In view of these results, there is no need to collect more data to allow science based decision-making in this planning process. © 2008 Elsevier B.V. All rights reserved.

Nitrogen (N) remaining as inorganic ('mineral') soil N at crop harvest (N(minH)) contributes to nitrate leaching. N(minH) data from 20 (grass) and 78 (maize) experiments were examined to identify main determinants of N(minH). N-rate (A) explained 51% (grass) and 34% (maize) of the variance in N(minH). Best models included in addition crop N-offtake (U), offtake in unfertilised plots (U(0)), and N(minH) in unfertilised plots (N(minH,0)) and then explained up to 75% of variance. At low N-rates where apparent N recovery rho keeps to its initial value rho(ini), N(minH) keeps to its base level N(minH,0). At N-rates that exceed the value A(crit) where rho drops below rho(ini), N(minH) rises above N(minH,0) by an amount proportional to (rho(ini)-rho)A. About 80% of (rho(ini)-rho)A was found as N(minH,) in grass as well as in maize. The fraction (1-rho(ini))A does not appear to contribute to N(minH) at low N-rates (Aor =A(crit)) or at high N-rates (AA(crit)).