Sources of pesticide losses to surface waters and groundwater at field and landscape scales

Pesticide residues in groundwater and surface waters may harm aquatic ecosystems and result in a deterioration of drinking water quality. EU legislation and policy emphasize risk management and risk reduction for pesticides to ensure long-term, sustainable use of water across Europe. Different tools applicable at scales ranging from farm to national and EU scales are required to meet the needs of the various managers engaged with the task of protecting water resources. The use of computer-based pesticide fate and transport models at such large scales is challenging since models are scale-specific and generally developed for the soil pedon or plot scale. Modelling at larger scales is further complicated by the spatial and temporal variability of agro-environmental conditions and the uncertainty in predictions. The objective of this thesis was to identify the soil processes that dominate diffuse pesticide losses at field and landscape scales and to develop methods that can help identify 'high risk' areas for leaching. The underlying idea was that pesticide pollution of groundwater and surface waters can be mitigated if pesticide application on such areas is reduced. Macropore flow increases the risk of pesticide leaching and was identified as the most important process responsible for spatial variation of diffuse pesticide losses from a 30 ha field and a 9 km² catchment in the south of Sweden. Point-sources caused by careless handling of pesticides when filling or cleaning spraying equipment were also a significant source of contamination at the landscape scale. The research presented in this thesis suggests that the strength of macropore flow due to earthworm burrows and soil aggregation can be predicted from widely available soil survey information such as texture, management practices etc. Thus, a simple classification of soils according to their susceptibility to macropore flow may facilitate the use of process-based models at the landscape scale. Predictions of a meta-model of the MACRO model suggested that, at the field scale, fine-textured soils are high-risk areas for pesticide leaching. Uncertainty in pesticide degradation and sorption did not significantly affect predictions of the spatial extent of these high-risk areas. Thus, site-specific pesticide application seems to be a promising method for mitigating groundwater contamination at this scale

Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic

Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1–0.5 Mg C ha−1 y−1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5–1.5 Mg C ha−1 y−1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions

Soil Quality Changes Following Forest Clearance in Bengkulu, Sumatra

Intense destruction and degradation of tropical forests is recognized as one of the environmental threats and tragedies. These have increased the need to assess the effects of subsequent land-use following forest extraction on soil quality. Therefore, the objective of this study is to evaluate the impacts of land-use type on soil quality properties in Bengkulu Province, Sumatra. Soil samples were collected from adjacent sites including natural secondary forest, bare land, cultivated land and grassland. The results show that land-use following forest clearance lowered saturated hydraulic conductivity (85%), porosity (10.50%), soil water content at field capacity (34%),C organic (27%), N total (26%), inorganic N (37%), soil microbial biomass C (32%), mineralizable C (22%), and particulate organic matter (50%), but slightly increased water soluble organic C. Specific respiration activity rates increased about 14% in cultivated soils compared to natural forest soils, indicating greater C turnover per labile C pool in the form of soil microbial biomass, thus decreased biologically active soil organic matter. Forest conversion tends to reduce the C,ffg/Crer for all deforested sites. All of deforested areas relatively have infertile soil, with the worst case found in cultivated field. The C^g/Crd of cultivated fields was about 24% less than that of remnant forest (1.07). Grassland apparently maintains only slightly higher soil C levels than the bare land. On average, degradation index of soil following forest clearance was 35% with the highest deterioration occurred in the bare land (38%). Fallowing the fields by naturally growth of Imperata cylindrica for about 15 yr in abandoned land after 3-5 years of cultivation did not improve the soil quality. Moreover, forest clearance has an impact on soil quality as resulted in the loss of a physically protected organic matter and reduction in some labile C pools, thus declined biological activity at disturbed ecosystems

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980-2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070-2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070-2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where "no severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. In contrast, underestimation of situations where "severe biomass loss + increased N leaching" occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios

Aggregation of soil and climate input data can underestimate simulated biomass loss and nitrate leaching under climate change

Predicting areas of severe biomass loss and increased N leaching risk under climate change is critical for applying appropriate adaptation measures to support more sustainable agricultural systems. The frequency of annual severe biomass loss for winter wheat and its coincidence with an increase in N leaching in a temperate region in Germany was estimated including the error from using soil and climate input data at coarser spatial scales, using the soil-crop model CoupModel. We ran the model for a reference period (1980–2010) and used climate data predicted by four climate model(s) for the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5. The annual median biomass estimations showed that for the period 2070–2100, under the RCP8.5 scenario, the entire region would suffer from severe biomass loss almost every year. Annual incidence of severe biomass loss and increased N leaching was predicted to increase from RCP4.5 to the 8.5 scenario. During 2070–2100 for RCP8.5, in more than half of the years an area of 95% of the region was projected to suffer from both severe biomass loss and increased N leaching. The SPEI3 predicted a range of 32 (P3 RCP4.5) to 55% (P3 RCP8.5) of the severe biomass loss episodes simulated in the climate change scenarios. The simulations predicted more severe biomass losses than by the SPEI index which indicates that soil water deficits are important in determining crop losses in future climate scenarios. There was a risk of overestimating the area where “no severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. In contrast, underestimation of situations where “severe biomass loss + increased N leaching” occurred when using coarser aggregated input data. Larger annual differences in biomass estimations compared to the finest resolution of input data occurred when aggregating climate input data rather than soil data. The differences were even larger when aggregating both soil and climate input data. In half of the region, biomass could be erroneously estimated in a single year by more than 40% if using soil and climate coarser input data. The results suggest that a higher spatial resolution of especially climate input data would be needed to predict reliably annual estimates of severe biomass loss and N leaching under climate change scenarios.Peer reviewe

SOIL QUALITY CHANGES FOLLOWING FOREST CLEARANCE IN BENGKULU, SUMATRA

Intense destruction and degradation of tropical forests is recognized as one of the environmental threats and tragedies. These have increased the need to assess the effects of subsequent land-use following forest extraction on soil quality. Therefore, the objective of this study is to evaluate the impacts of land-use type on soil quality properties in Bengkulu Province, Sumatra. Soil samples were collected from adjacent sites including natural secondary forest, bare land, cultivated land and grassland. The results show that land-use following forest clearance lowered saturated hydraulic conductivity (85%), porosity (10.50%), soil water content at field capacity (34%),C organic (27%), N total (26%), inorganic N (37%), soil microbial biomass C (32%), mineralizable C (22%), and particulate organic matter (50%), but slightly increased water soluble organic C. Specific respiration activity rates increased about 14% in cultivated soils compared to natural forest soils, indicating greater C turnover per labile C pool in the form of soil microbial biomass, thus decreased biologically active soil organic matter. Forest conversion tends to reduce the C,ffg/Crer for all deforested sites. All of deforested areas relatively have infertile soil, with the worst case found in cultivated field. The C^g/Crd of cultivated fields was about 24% less than that of remnant forest (1.07). Grassland apparently maintains only slightly higher soil C levels than the bare land. On average, degradation index of soil following forest clearance was 35% with the highest deterioration occurred in the bare land (38%). Fallowing the fields by naturally growth of  Imperata cylindrica  for about 15 yr in abandoned land after 3-5 years of cultivation did not improve the soil quality. Moreover, forest clearance has an impact on soil quality as resulted in the loss of a physically protected organic matter and reduction in some labile C pools, thus declined biological activity at disturbed ecosystems. Keywords: Degradation index / forest / Imperata cylindrica grassland / soil quality/ soil organic matte

Documentation of model components EXPAMOD and CAPRI

ISBN no.: 978-90-8585-129-

Critical review of the impacts of grazing intensity on soil organic carbon storage and other soil quality indicators in extensively managed grasslands

Acknowledgements This work contributes to the N-Circle project (grant number BB/N013484/1), and CINAg (BB/N013468/1) Virtual Joint Centres on Agricultural Nitrogen (funded by the Newton Fund via UK BBSRC/NERC), U-GRASS (grant number NE/M016900/1), the Belmont Forum/FACCE-JPI DEVIL project (grant number NE/M021327/1), Soils-R-GGREAT (grant number NE/P019455/1), ADVENT (grant number NE/M019713/1), Sêr Cymru LCEE-NRN project, Climate-Smart Grass and the Scottish Government’s Strategic Research Programme.Peer reviewedPublisher PD