Kristin Piikki

Spatial and temporal patterns of pesticide concentrations in streamflow, drainage and runoff in a small Swedish agricultural catchment

A better understanding of the dominant source areas and transport pathways of pesticide losses to surface water is needed for targeting mitigation efforts in a more cost-effective way. To this end, we monitored pesticides in surface water in an agricultural catchment typical of one of the main crop production regions in Sweden. Three small sub-catchments (88-242ha) were selected for water sampling based on a high-resolution digital soil map developed from proximal sensing methods and soil sampling; one sub-catchment had a high proportion of clay soils, another was dominated by coarse sandy soils while the third comprised a mix of soil types. Samples were collected from the stream, from field drains discharging into the stream and from within-field surface runoff during spring and early summer in three consecutive years. These samples were analyzed by LC-MS/MS for 99 compounds, including most of the polar and semi-polar pesticides frequently used in Swedish agriculture. Information on pesticide applications (products, doses and timing) was obtained from annual interviews with the farmers. There were clear and consistent differences in pesticide occurrence in the stream between the three sub-catchments, with both the numbers of detected compounds and concentrations being the largest in the area with a high proportion of clay soils and with very few detections in the sandy sub-catchment. Macropore flow to drains was most likely the dominant loss pathway in the studied area. Many of the compounds that were detected in drainage and stream water samples had not been applied for several years. This suggests that despite the predominant role of fast flow pathways in determining losses to the stream, long-term storage along the transport pathways also occurs, presumably in subsoil horizons where degradation is slow.

Performance Evaluation of Proximal Sensors for Soil Assessment in Smallholder Farms in Embu County, Kenya.

Four proximal soil sensors were tested at four smallholder farms in Embu County, Kenya: a portable X-ray fluorescence sensor (PXRF), a mobile phone application for soil color determination by photography, a dual-depth electromagnetic induction (EMI) sensor, and a LED-based soil optical reflectance sensor. Measurements were made at 32–43 locations at each site. Topsoil samples were analyzed for plant-available nutrients (N, P, K, Mg, Ca, S, B, Mn, Zn, Cu, and Fe), pH, total nitrogen (TN) and total carbon (TC), soil texture, cation exchange capacity (CEC), and exchangeable aluminum (Al). Multivariate prediction models of each of the lab-analyzed soil properties were parameterized for 576 sensor-variable combinations. Prediction models for K, N, Ca and S, B, Zn, Mn, Fe, TC, Al, and CEC met the setup criteria for functional, robust, and accurate models. The PXRF sensor was the sensor most often included in successful models. We concluded that the combination of a PXRF and a portable soil reflectance sensor is a promising combination of handheld soil sensors for the development of in situ soil assessments as a field-based alternative or complement to laboratory measurements.

Producing nitrogen (N) uptake maps in winter wheat by combining proximal crop measurements with Sentinel-2 and DMC satellite images in a decision support system for farmers

ABSTRACT Responsive fertilisation of winter wheat (Triticum aestivum L.) is often adopted, with N applied two or three times between the developmental stages of tillering and booting. Satellite-based decision support systems (DSS) providing vegetation index maps calculated from satellite data are available to aid farmers in adjusting the topdressing nitrogen (N) rate site-specifically to the current season and to variations in growth conditions within the field. One example is the freely available CropSAT DSS used in Scandinavia, which provides farmers with raster maps of the modified soil-adjusted vegetation index (MSAVI2) calculated mainly from data obtained from satellites Sentinel-2 (ESA, EU) and DMC (DMCii Ltd, Guildford, UK). This study investigated the possibility of calibrating MSAVI2 maps with data from handheld proximal sensor measurements of N uptake covering the main agricultural regions in Sweden during growth stages Z30-45 on the Zadok scale, in order to facilitate farmers’ decisions on N rate. More than 200 N-sensor measurements acquired during 2015 and 2016 in seven different winter wheat cultivars were combined with MSAVI2 values from CropSAT. It was found that N uptake could be predicted in a general, national model, i.e. for sites and dates other than those for which the calibration model was parameterised, with a mean absolute error of 11–15 kg N ha−1. A cultivar-specific model performed better than this general model, but a regional model showed no improvement compared with the model parameterised with national data. Vegetation indices calculated from the two narrow bands of Sentinel-2 in the red edge-near infrared region of the crop canopy reflectance spectrum proved to be promising alternatives to the broadband index MSAVI2. Based on the results, we suggest that data from a monitoring programme involving handheld N sensor measurements can be integrated with a satellite-based DSS to upscale N uptake information.