Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage)

The effects of tillage on soil properties, crop productivity, and global greenhouse gas emissions have been discussed in the last decades. Global ecosystem models have limited capacity to simulate the various effects of tillage. With respect to the decomposition of soil organic matter, they either assume a constant increase due to tillage or they ignore the effects of tillage. Hence, they do not allow for analysing the effects of tillage and cannot evaluate, for example, reduced tillage or no tillage (referred to here as “no-till”) practises as mitigation practices for climate change. In this paper, we describe the implementation of tillage-related practices in the global ecosystem model LPJmL. The extended model is evaluated against reported differences between tillage and no-till management on several soil properties. To this end, simulation results are compared with published meta-analyses on tillage effects. In general, the model is able to reproduce observed tillage effects on global, as well as regional, patterns of carbon and water fluxes. However, modelled N fluxes deviate from the literature values and need further study. The addition of the tillage module to LPJmL5 opens up opportunities to assess the impact of agricultural soil management practices under different scenarios with implications for agricultural productivity, carbon sequestration, greenhouse gas emissions, and other environmental indicators

The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage

No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions

Tilling the earth; modelling global N2O emissions caused by tillage

Agriculture is the largest contributor of non-CO2 anthropogenic greenhouse gas emissions (GHG). Agricultural based mitigation strategies (e.g. no-tillage) are identified to reduce emissions from agricultural soils through improved agricultural management. Global ecosystem models that are usually used for finding the potential of agricultural based mitigation strategies are limited, because processes related to agricultural management are currently underrepresented in global ecosystem models. The aim of this thesis is to contribute to the representation of agricultural management in global ecosystem models, so that the potential of agricultural based mitigation practices can be better quantified. Therefore, this thesis first addressed how processes related to agricultural management can be described in global ecosystem models, with a focus on processes related to tillage and N2O emissions. This analysis resulted in a standardized framework that can be followed to incorporate other agricultural management practices in global ecosystems as well. After indicating how processes related to tillage can be described, they were implemented into the global ecosystem model LPJmL. Subsequently, the extended LPJmL model was evaluated on its performance on various fluxes (including N2O and CO2) at the global scale and for a number of experimental sites. Finally, the uncertainty caused by the upscaling of soil input data when assessing tillage effects on N2O emissions were addressed. LPJmL was not capable of accurately simulating tillage effects on N2O emissions. Hence, the potential of mitigating N2O emissions through tillage management cannot be well assessed. However, the implementation of the more detailed tillage-related mechanism into the global ecosystem model LPJmL improved the ability to understand agricultural management options for agricultural mitigation of CO2 emissions, climate change adaptation and reducing environmental impacts. The work in this thesis concludes that as processes related to agricultural management can be incorporated into global ecosystem models by following the standardized framework, and data-scarcity on agricultural management does not necessarily limit the evaluation of the extended model, there is no general barrier to extend global ecosystem models by modules for the representation of agricultural management

Options to model the effects of tillage on N₂O emissions at the global scale

Strategies on agricultural management can help to reduce global greenhouse gas (GHG) emissions. However, the potential of agricultural management to reduce GHG emissions at the global scale is unclear. Global ecosystem models often lack sufficient detail in their representation of management, such as tillage. This paper explores whether and how tillage can be incorporated in global ecosystem models for the analysis of nitrous oxide (N2O) emissions. We identify the most important nitrogen processes in soils and their response to tillage. We review how these processes and tillage effects are described in field-scale models and evaluate whether they can be incorporated in the global-scale models while considering the data requirements for a global application. The most important processes are described in field-scale models and the basic data requirements can be met at the global scale. We therefore conclude that there is potential to incorporate tillage in global ecosystem models for the analysis of N2O emissions. There are several options for how the relevant processes can be incorporated into global ecosystem models, so that generally there is potential to study the effects of tillage on N2O emissions globally. Given the many interactions with other processes, modelers need to identify the modelling approaches that are consistent with their modelling framework and test these.</p

Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage)

The effects of tillage on soil properties, crop productivity, and global greenhouse gas emissions have been discussed in the last decades. Global ecosystem models have limited capacity to simulate the various effects of tillage. With respect to the decomposition of soil organic matter, they either assume a constant increase due to tillage or they ignore the effects of tillage. Hence, they do not allow for analysing the effects of tillage and cannot evaluate, for example, reduced tillage or no tillage (referred to here as "no-till") practises as mitigation practices for climate change. In this paper, we describe the implementation of tillage-related practices in the global ecosystem model LPJmL. The extended model is evaluated against reported differences between tillage and no-till management on several soil properties. To this end, simulation results are compared with published meta-analyses on tillage effects. In general, the model is able to reproduce observed tillage effects on global, as well as regional, patterns of carbon and water fluxes. However, modelled N fluxes deviate from the literature values and need further study. The addition of the tillage module to LPJmL5 opens up opportunities to assess the impact of agricultural soil management practices under different scenarios with implications for agricultural productivity, carbon sequestration, greenhouse gas emissions, and other environmental indicators.</p

The importance of management information and soil moisture representation for simulating tillage effects on N₂O emissions in LPJmL5.0-tillage

No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund-Potsdam-Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions.</p

What patients want to know, and what we actually tell them: The ABIDE project

Background:We studied to what degree and at whose initiative 25 informational topics, formerly identified as important, are discussed in diagnostic consultations. Methods: Audio recordings of clinician–patient consultations of 71 patients and 32 clinicians, collected in eight Dutch memory clinics, were independently contentcoded by two coders. The coding scheme encompassed 25 informational topics. Results: Approximately half (Mdn = 12) of the 25 topics were discussed per patient during the diagnostic process, with a higher frequency among individuals receiving a dementia diagnosis (Mdn = 14) compared to others (Mdn = 11). Individual topics ranged from being discussed with 2/71 (3%) to 70/71 (99%) of patients. Patients and/or care partners rarely initiated topic discussion (10%). When they did, they often enquired about one of the least frequently addressed topics. Conclusion: Most patients received information on approximately half of the important informational topics. Providing the topic list to patients and care partners beforehand could allow consultation preparation and stimulate participation

What patients want to know, and what we actually tell them: The ABIDE project

Background:We studied to what degree and at whose initiative 25 informational topics, formerly identified as important, are discussed in diagnostic consultations. Methods: Audio recordings of clinician–patient consultations of 71 patients and 32 clinicians, collected in eight Dutch memory clinics, were independently contentcoded by two coders. The coding scheme encompassed 25 informational topics. Results: Approximately half (Mdn = 12) of the 25 topics were discussed per patient during the diagnostic process, with a higher frequency among individuals receiving a dementia diagnosis (Mdn = 14) compared to others (Mdn = 11). Individual topics ranged from being discussed with 2/71 (3%) to 70/71 (99%) of patients. Patients and/or care partners rarely initiated topic discussion (10%). When they did, they often enquired about one of the least frequently addressed topics. Conclusion: Most patients received information on approximately half of the important informational topics. Providing the topic list to patients and care partners beforehand could allow consultation preparation and stimulate participation

Biomarker-based prognosis for people with mild cognitive impairment (ABIDE) : a modelling study

Background: Biomarker-based risk predictions of dementia in people with mild cognitive impairment are highly relevant for care planning and to select patients for treatment when disease-modifying drugs become available. We aimed to establish robust prediction models of disease progression in people at risk of dementia. Methods: In this modelling study, we included people with mild cognitive impairment (MCI) from single-centre and multicentre cohorts in Europe and North America: the European Medical Information Framework for Alzheimer's Disease (EMIF-AD; n=883), Alzheimer's Disease Neuroimaging Initiative (ADNI; n=829), Amsterdam Dementia Cohort (ADC; n=666), and the Swedish BioFINDER study (n=233). Inclusion criteria were a baseline diagnosis of MCI, at least 6 months of follow-up, and availability of a baseline Mini-Mental State Examination (MMSE) and MRI or CSF biomarker assessment. The primary endpoint was clinical progression to any type of dementia. We evaluated performance of previously developed risk prediction models—a demographics model, a hippocampal volume model, and a CSF biomarkers model—by evaluating them across cohorts, incorporating different biomarker measurement methods, and determining prognostic performance with Harrell's C statistic. We then updated the models by re-estimating parameters with and without centre-specific effects and evaluated model calibration by comparing observed and expected survival. Finally, we constructed a model combining markers for amyloid deposition, tauopathy, and neurodegeneration (ATN), in accordance with the National Institute on Aging and Alzheimer's Association research framework. Findings: We included all 2611 individuals with MCI in the four cohorts, 1007 (39%) of whom progressed to dementia. The validated demographics model (Harrell's C 0·62, 95% CI 0·59–0·65), validated hippocampal volume model (0·67, 0·62–0·72), and updated CSF biomarkers model (0·72, 0·68–0·74) had adequate prognostic performance across cohorts and were well calibrated. The newly constructed ATN model had the highest performance (0·74, 0·71–0·76). Interpretation: We generated risk models that are robust across cohorts, which adds to their potential clinical applicability. The models could aid clinicians in the interpretation of CSF biomarker and hippocampal volume results in individuals with MCI, and help research and clinical settings to prepare for a future of precision medicine in Alzheimer's disease. Future research should focus on the clinical utility of the models, particularly if their use affects participants' understanding, emotional wellbeing, and behaviour. Funding: ZonMW-Memorabel