The fate of agrochemicals in paddy rice-aquaculture systems in Northern Vietnam

During the last decades, high population growth and export-oriented economics have led to a tremendous intensification of rice production in North Vietnam, which in turn has significantly increased the amount of agrochemicals applied in rice cropping systems. Since pesticides are toxic by design, there is a natural concern on the impact of their presence in the environment on human health and environmental quality. In North Vietnam, lowland and upland paddy rice production systems were identified to be the major non-point source of agrochemical pollution to surface and ground water, which are often directly used for domestic purposes. Hence, the quantification and forecast of pesticide losses to ground and surface water from paddy rice fields is of indispensable concern and a prerequisite for assesing the potential environmental exposure and risk of water pollution across vulnerable landscapes. In Son La province, North Vietnam, paddy rice fields and fish ponds are linked, that is, irrigation water is first used in paddy fields, before it flows to the adjacent fish pond. The aim of the present study was to measure and simulate the water regime and the transport of agrochemicals in integrated farming systems in North Vietnam and to evaluate the potential risk of water pollution across the landscape. During two consecutive cropping seasons (2008) we intensively measured the water regime (inflow, outflow, water level, soil moisture) of an integrated paddy field - fish pond system and the concentrations of two applied pesticides (Dimethoate, Fenitrothion) in various system components (paddy water, soil water, pond water, soil sediment, inflow and outflow water). Pesticide samples were processed by solid phase extraction and analyzed in Hohenheim by GC and mass spectrometry. In our presentation we will focus on measurement results indicating that under the current management and pesticide application practices a considerable amount of pesticides is lost from the paddy field to the adjacent pond and further to the receiving stream and/or to the ground water

Comparison of simple models for total nitrogen removal from agricultural runoff in FWS wetlands

Free water surface (FWS) wetlands can be used to treat agricultural runoff, thereby reducing diffuse pollution. However, as these are highly dynamic systems, their design is still challenging. Complex models tend to require detailed information for calibration, which can only be obtained when the wetland is constructed. Hence simplified models are widely used for FWS wetlands design. The limitations of these models in full-scale FWS wetlands is that these systems often cope with stochastic events with different input concentrations. In our study, we compared different simple transport and degradation models for total nitrogen under steady- and unsteady-state conditions using information collected from a tracer experiment and data from two precipitation events from a full-scale FWS wetland. The tanks-in-series model proved to be robust for simulating solute transport, and the first-order degradation model with non-zero background concentration performed best for total nitrogen concentrations. However, the optimal background concentration changed from event to event. Thus, to use the model as a design tool, it is advisable to include an upper and lower background concentration to determine a range of wetland performance under different events. Models under steady- and unsteady-state conditions with simulated data showed good performance, demonstrating their potential for wetland design

Anpassung der Barometrischen Prozess-Separation (BaPS) für die Anwendung in kalkhaltigen Böden

Es wird ein neues Konzept zur Anpassung der Barometrischen Prozess-Separation (BaPS) an alkalische Bodenbedingungen vorgestellt und es werden erste vorläufige Ergebnisse präsentiert. Das methodische Problem der BaPS in kalkhaltigen Böden besteht im zunehmenden Einfluss der CO2-Lösung in der Bodenlösung (Kalk-Kohlensäure-Gleichgewicht) auf die Gasbilanzrechnungen. Dies wirkt sich auf die Bestimmung der C- und N- Umsatzraten aus. Anstelle den ?CO2aq-Term mit dem Massenwirkungsgesetz für CO2 anzu-nähern, soll er nun mit der Fumigations-CO2-Injektions Methode experimentell bestimmt und charakterisiert werden

Proceedings of the 4th bwHPC Symposium

The bwHPC Symposium 2017 took place on October 4th, 2017, Alte Aula, Tübingen. It focused on the presentation of scientific computing projects as well as on the progress and the success stories of the bwHPC realization concept. The event offered a unique opportunity to engage in an active dialogue between scientific users, operators of bwHPC sites, and the bwHPC support team

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

Similar estimates of temperature impacts on global wheat yield by three independent methods

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.<br/

The chaos in calibrating crop models

Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of system models and has an important impact on simulated values. Here we propose and illustrate a novel method of developing guidelines for calibration of system models. Our example is calibration of the phenology component of crop models. The approach is based on a multi-model study, where all teams are provided with the same data and asked to return simulations for the same conditions. All teams are asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.HighlightsWe propose a new approach to deriving calibration recommendations for system modelsApproach is based on analyzing calibration in multi-model simulation exercisesResulting recommendations are holistic and anchored in actual practiceWe apply the approach to calibration of crop models used to simulate phenologyRecommendations concern: objective function, parameters to estimate, software usedCompeting Interest StatementThe authors have declared no competing interest

Multi-model ensemble simulations to assess the impact of climate change on agro-ecosystems

Global climate change will alter the water, nitrogen and carbon cycles of agroecosystems. To predict future agricultural production under climate change, numerical soil-crop models are used. These soil-crop models can represent the complex and coupled processes of agroecosystems in a deterministic manner for a given environment. The projections made by soilcrop models suffer from two kinds of uncertainty: (1) epistemic uncertainty and (2) parameter uncertainty. Additionally, it is assumed that the parameterization is applicable to other environments. Therefore, this study has two major aims. The first aim is to quantify the above-mentioned uncertainties simultaneously by combining two methods: multi-model ensemble modeling and Bayesian statistics. The multi-model ensemble allows to quantify epistemic uncertainty by comparing individual model outputs. This has been demonstrated in many studies. Bayesian methods are common to assess credible parameter intervals for highly nonlinear process models. The second aim of this study is to provide a framework for assessing the robustness of the parametrization of soil-crop models. Therefore, a preliminary numerical study was conducted to test different calibration schemes and to investigate parameters sensitivities in dependence of the environment. The soil-crop modelling software ExpertN 3.0 will be used to set up a multi-model ensemble with eight soilcrop models. The model output will be analyzed by comparison with data from two sites, five soil types and two crops gathered by the DFG Research Unit 1695 since 2010. To achieve the second aim a global sensitivity analyses was conducted to rank the input factors for each soil-crop model. The result of the global sensitivity analyses will clarify the impact of model input on model output in regard to environment, model combinations, and extent. Additionally, different calibration schemes will be tested to identify the method yielding the most robust parametrization. We used a Latin Hypercube sampling scheme. In total, the whole study requires 1,000,000 CPU hours. We expect that the results will enable us to develop a generally applicable and feasible strategy of how soil-crop models have to be set up to produce reliable predictions of agroecosystem behavior under climate change

Noah-MP simulated surface energy fluxes and temperature for a generic crop, early covering crops (ECC) and late covering crops (LCC) for Kraichgau region, southwest Germany

The site under study is the agricultural field belonging to the farm "Katharinentalerhof". The field is located north of the city of Pforzheim (48.920N, 8.700E). The central research site is a part of the Kraichgau region, southwest Germany. Simulations were performed for a generic crop, early covering crops (ECC) and late covering crops (LCC). We also considered different ECC-LCC ratios. Additionally, we tested the performance of the Noah-MP on latent heat flux (LE) data measured with the Eddy Covariance (EC) technique. For the simulation with Noah-MP, we used measured GVF and LAI data. The USGS land use dataset was used. The vegetation type index was set to 2 (Dryland cropland and Pasture) and soil type index to 4 (Silt loam). The model was forced with half-hourly weather data (wind speed, wind direction, temperature, humidity, pressure, precipitation, downwelling longwave and shortwave radiation) measured from 2011 to 2012. Simulations were initialized with a spin up period of one year (2011) and run with a time step of 1800 seconds