Modelling the Effects of Weather Conditions on Cereal Grain Contamination with Deoxynivalenol in the Baltic Sea Region.

Fusarium head blight (FHB) is one of the most serious diseases of small-grain cereals worldwide, resulting in yield reduction and an accumulation of the mycotoxin deoxynivalenol (DON) in grain. Weather conditions are known to have a significant effect on the ability of fusaria to infect cereals and produce toxins. In the past 10 years, severe outbreaks of FHB, and grain DON contamination exceeding the EU health safety limits, have occurred in countries in the Baltic Sea region. In this study, extensive data from field trials in Sweden, Poland and Lithuania were analysed to identify the most crucial weather variables for the ability of Fusarium to produce DON. Models were developed for the prediction of DON contamination levels in harvested grain exceeding 200 µg kg-1 for oats, spring barley and spring wheat in Sweden and winter wheat in Poland, and 1250 µg kg-1 for spring wheat in Lithuania. These models were able to predict high DON levels with an accuracy of 70-81%. Relative humidity (RH) and precipitation (PREC) were identified as the weather factors with the greatest influence on DON accumulation in grain, with high RH and PREC around flowering and later in grain development and ripening correlated with high DON levels. High temperatures during grain development and senescence reduced the risk of DON accumulation. The performance of the models, based only on weather variables, was relatively accurate. In future studies, it might be of interest to determine whether inclusion of variables such as pre-crop, agronomic factors and crop resistance to FHB could further improve the performance of the models.

Estimating spatially distributed SOC sequestration potentials of sustainable land management practices in Ethiopia.

The sustainable land management program (SLMP) of Ethiopia aims to improve livelihoods and create resilient communities and landscape to climate change. Soil organic carbon (SOC) sequestration is one of the key co-benefits of the SLMP. The objective of this study was to estimate the spatial dynamics of SOC in 2010 and 2018 (before and after SLMP) and identify the SOC sequestration hotspots at landscape scale in four selected SLMP watersheds in the Ethiopian highlands. The specific objectives were to: 1) comparatively evaluate SOC sequestration estimation model building strategies using either a single watershed, a combined dataset from all watersheds, and leave-one-watershed-out using Random Forest (RF) model; 2) map SOC stock of 2010 and 2018 to estimate amount of SOC sequestration and potential; 3) evaluate the impacts of SLM practices on SOC in four SLMP watersheds. A total of 397 auger composite samples from the topsoil (0-20 cm depth) were collected in 2010, and the same number of samples were collected from the same locations in 2018. We used simple statistics to assess the SOC change between the two periods, and machine learning models to predict SOC stock spatially. The study showed that statistically significant variation (P < 0.05) of SOC was observed between the two years in two watersheds (Gafera and Adi Tsegora) whereas the differences were not significant in the other two watersheds (Yesir and Azugashuba). Comparative analysis of model-setups shows that a combined dataset from all the four watersheds to train and test RF outperform the other two strategies (a single watershed alone and a leave-one-watershed-out to train and test RF) during the testing dataset. Thus, this approach was used to predict SOC stock before (2010) and after (2018) land management interventions and to derive the SOC sequestration maps. We estimated the sequestrated, achievable and target level of SOC stock spatially in the four watersheds. We assessed the impact of SLM practices, specifically bunds, terraces, biological and various forms of tillage practices on SOC using partial dependency algorithms of prediction models. No tillage (NT) increased SOC in all watersheds. The combination of physical and biological interventions ("bunds + vegetations" or "terraces + vegetations") resulted in the highest SOC stock, followed by the biological intervention. The achievable SOC stock analysis showed that further SOC stock sequestration of up to 13.7 Mg C ha--1 may be possible in the Adi Tsegora, 15.8 Mg C ha-1 in Gafera, 33.2 Mg C ha-1 in Azuga suba and 34.7 Mg C ha-1 in Yesir watersheds.

Predictions of Cu, Zn, and Cd Concentrations in Soil Using Portable X-Ray Fluorescence Measurements.

Portable X-ray fluorescence (PXRF) measurements on 1520 soil samples were used to create national prediction models for copper (Cu), zinc (Zn), and cadmium (Cd) concentrations in agricultural soil. The models were validated at both national and farm scales. Multiple linear regression (MLR), random forest (RF), and multivariate adaptive regression spline (MARS) models were created and compared. National scale cross-validation of the models gave the following R2 values for predictions of Cu (R2 = 0.63), Zn (R2 = 0.92), and Cd (R2 = 0.70) concentrations. Independent validation at the farm scale revealed that Zn predictions were relatively successful regardless of the model used (R2 > 0.90), showing that a simple MLR model can be sufficient for certain predictions. However, predictions at the farm scale revealed that the non-linear models, especially MARS, were more accurate than MLR for Cu (R2 = 0.94) and Cd (R2 = 0.80). These results show that multivariate modelling can compensate for some of the shortcomings of the PXRF device (e.g., high limits of detection for certain elements and some elements not being directly measurable), making PXRF sensors capable of predicting elemental concentrations in soil at comparable levels of accuracy to conventional laboratory analyses.

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.

Evidence for impacts of near-ambient ozone concentrations on vegetation in southern Sweden.

Substantial impacts of near-ambient ozone concentrations on agricultural crops, trees, and seminatural vegetation are demonstrated for southern Sweden. Impacts of ambient ozone levels (2-15 microL L(-1) hr annual accumulated ozone exposure over a threshold of 40 nL L(-1) [AOT40]) range from a 2%-10% reduction for trees (e.g., leaf chlorophyll, tree growth) up to a 15% reduction for crops (e.g., yield, wheat/potato). Visible leaf injury on bioindicator plants caused by ambient ozone levels has been clearly demonstrated. The humid climatic conditions in Sweden promote high rates of leaf ozone uptake at a certain ozone concentration. This likely explains the comparatively large ozone impacts found for vegetation in southern Sweden at relatively low ozone concentrations in the air. It is important that the future methods used for the representation of ozone impacts on vegetation across Europe are based on the leaf ozone uptake concept and not on concentration-based exposure indices, such as AOT40.