On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements.

Barometric process separation (BaPS) is an automated laboratory system for the simultaneous measurement of microbial respiration and gross nitrification rates in soil samples. To ensure optimal functioning, the sensor system, consisting of a pressure sensor, an O2 sensor, a CO2 concentration sensor, and two temperature probes, must be accurately calibrated. For the regular on-site quality control of the sensors, we developed easy, inexpensive, and flexible calibration procedures. The pressure sensor was calibrated by means of a differential manometer. The O2 and CO2 sensors were simultaneously calibrated through their exposure to a sequence of O2 and CO2 concentrations obtained by sequentially exchanging O2/N2 and CO2/N2 calibration gases. Linear regression models were best suited for describing the recorded calibration data. The accuracy of O2 and CO2 calibration was mainly affected by the accuracy of the utilized gas mixtures. Because the applied measuring method is based on the O2 conductivity of ZrO2, the O2 sensor is particularly susceptible to aging and to consequent signal shifts. Sensor signals were characterized by high temporal stability over the years. Deviations in the calibration parameters affected the measured gross nitrification rate by up to 12.5% and affected the respiration rate by up to 5%. Overall, the proposed calibration procedures are valuable tools for ensuring the quality of BaPS measurements and for promptly identifying sensor malfunctions.

High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method.

High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.

Spatial Control of Microbial Pesticide Degradation in Soil: A Model-Based Scenario Analysis.

Microbial pesticide degraders are heterogeneously distributed in soil. Their spatial aggregation at the millimeter scale reduces the frequency of degrader-pesticide encounter and can introduce transport limitations to pesticide degradation. We simulated reactive pesticide transport in soil to investigate the fate of the widely used herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in response to differently aggregated distributions of degrading microbes. Four scenarios were defined covering millimeter scale heterogeneity from homogeneous (pseudo-1D) to extremely heterogeneous degrader distributions and two precipitation scenarios with either continuous light rain or heavy rain events. Leaching from subsoils did not occur in any scenario. Within the topsoil, increasing spatial heterogeneity of microbial degraders reduced macroscopic degradation rates, increased MCPA leaching, and prolonged the persistence of residual MCPA. In heterogeneous scenarios, pesticide degradation was limited by the spatial separation of degrader and pesticide, which was quantified by the spatial covariance between MCPA and degraders. Heavy rain events temporarily lifted these transport constraints in heterogeneous scenarios and increased degradation rates. Our results indicate that the mild millimeter scale spatial heterogeneity of degraders typical for arable topsoil will have negligible consequences for the fate of MCPA, but strong clustering of degraders can delay pesticide degradation.

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.

Climate change impact on wheat and maize growth in Ethiopia: A multi-model uncertainty analysis.

Ethiopia's economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021-2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO2, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO2], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties.

Gene-Centric Model Approaches for Accurate Prediction of Pesticide Biodegradation in Soils.

Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.

Contribution of plant-induced pressurized flow to CH4 emission from a Phragmites fen.

The widespread wetland species Phragmites australis (Cav.) Trin. ex Steud. has the ability to transport gases through its stems via a pressurized flow. This results in a high oxygen (O2) transport to the rhizosphere, suppressing methane (CH4) production and stimulating CH4 oxidation. Simultaneously CH4 is transported in the opposite direction to the atmosphere, bypassing the oxic surface layer. This raises the question how this plant-mediated gas transport in Phragmites affects the net CH4 emission. A field experiment was set-up in a Phragmites-dominated fen in Germany, to determine the contribution of all three gas transport pathways (plant-mediated, diffusive and ebullition) during the growth stage of Phragmites from intact vegetation (control), from clipped stems (CR) to exclude the pressurized flow, and from clipped and sealed stems (CSR) to exclude any plant-transport. Clipping resulted in a 60% reduced diffusive + plant-mediated flux (control: 517, CR: 217, CSR: 279 mg CH4 m-2 day-1). Simultaneously, ebullition strongly increased by a factor of 7-13 (control: 10, CR: 71, CSR: 126 mg CH4 m-2 day-1). This increase of ebullition did, however, not compensate for the exclusion of pressurized flow. Total CH4 emission from the control was 2.3 and 1.3 times higher than from CR and CSR respectively, demonstrating the significant role of pressurized gas transport in Phragmites-stands.

Plant litter enhances degradation of the herbicide MCPA and increases formation of biogenic non-extractable residues in soil.

Amendment of soils with plant residues is common practice for improving soil quality. In addition to stimulated microbial activity, the supply of fresh soluble organic (C) from litter may accelerate the microbial degradation of chemicals in soils. Therefore, the aim of this study was to test whether the maize litter enhances degradation of 4-chloro-2-methylphenoxyacetic acid (MCPA) and increases formation of non-toxic biogenic non-extractable residues (bioNERs). Soil was amended with 13C6-MCPA and incubated with or without litter addition on the top. Three soil layers were sampled with increasing distance from the top: 0-2 mm, 2-5 mm and 5-20 mm; and the mass balance of 13C6-MCPA transformation determined. Maize litter promoted microbial activity, mineralization of 13C6-MCPA and bioNER formation in the upper two layers (0-2 and 2-5 mm). The mineralization of 13C6-MCPA in soil with litter increased to 27% compared to only 6% in the control. Accordingly, maize addition reduced the amount of extractable residual MCPA in soil from 77% (control) to 35% of initially applied 13C6-MCPA. While non-extractable residues (NERs) were <6% in control soil, litter addition raised NERs to 21%. Thereby, bioNERs comprised 14% of 13C6-MCPA equivalents. We found characteristic differences of bioNER formation with distance to litter. While total NERs in soil at a distance of 2-5 mm were mostly identified as 13C-bioNERs (97%), only 45-46% of total NERs were assigned to bioNERs in the 0-2 and 5-20 mm layers. Phospholipid fatty acid analysis indicated that fungi and Gram-negative bacteria were mainly involved in MCPA degradation. Maize-C particularly stimulated fungal activity in the adjacent soil, which presumably facilitated non-biogenic NER formation. The plant litter accelerated formation of both non-toxic bioNERs and non-biogenic NERs. More studies on the structural composition of non-biogenic NERs with toxicity potential are needed for future recommendations on litter addition in agriculture.

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.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

The impact of chemical pollution on the resilience of soils under multiple stresses: A conceptual framework for future research.

Soils are faced with man-made chemical stress factors, such as the input of organic or metal-containing pesticides, in combination with non-chemical stressors like soil compaction and natural disturbance like drought. Although multiple stress factors are typically co-occurring in soil ecosystems, research in soil sciences on this aspect is limited and focuses mostly on single structural or functional endpoints. A mechanistic understanding of the reaction of soils to multiple stressors is currently lacking. Based on a review of resilience theory, we introduce a new concept for research on the ability of polluted soil (xenobiotics or other chemical pollutants as one stressor) to resist further natural or anthropogenic stress and to retain its functions and structure. There is strong indication that pollution as a primary stressor will change the system reaction of soil, i.e., its resilience, stability and resistance. It can be expected that pollution affects the physiological adaption of organisms and the functional redundancy of the soil to further stress. We hypothesize that the recovery of organisms and chemical-physical properties after impact of a follow-up stressor is faster in polluted soil than in non-polluted soil, i.e., polluted soil has a higher dynamical stability (dynamical stability=1/recovery time), whereas resilience of the contaminated soil is lower compared to that of not or less contaminated soil. Thus, a polluted soil might be more prone to change into another system regime after occurrence of further stress. We highlight this issue by compiling the literature exemplarily for the effects of Cu contamination and compaction on soil functions and structure. We propose to intensify research on effects of combined stresses involving a multidisciplinary team of experts and provide suggestions for corresponding experiments. Our concept offers thus a framework for system level analysis of soils paving the way to enhance ecological theory.

Evidence for the importance of litter as a co-substrate for MCPA dissipation in an agricultural soil.

Environmental controls of 2-methyl-4-chlorophenoxyacetic acid (MCPA) degradation are poorly understood. We investigated whether microbial MCPA degraders are stimulated by (maize) litter and whether this process depends on concentrations of MCPA and litter. In a microcosm experiment, different amounts of litter (0, 10 and 20 g kg(-1)) were added to soils exposed to three levels of the herbicide (0, 5 and 30 mg kg(-1)). The treated soils were incubated at 20 °C for 6 weeks, and samples were taken after 1, 3 and 6 weeks of incubation. In soils with 5 mg kg(-1) MCPA, about 50 % of the MCPA was dissipated within 1 week of the incubation. Almost complete dissipation of the herbicide had occurred by the end of the incubation with no differences between the three litter amendments. At the higher concentration (30 mg kg(-1)), MCPA endured longer in the soil, with only 31 % of the initial amount being removed at the end of the experiment in the absence of litter. Litter addition greatly increased the dissipation rate with 70 and 80 % of the herbicide being dissipated in the 10 and 20 g kg(-1) litter treatments, respectively. Signs of toxic effects of MCPA on soil bacteria were observed from related phospholipid fatty acid (PLFA) analyses, while fungi showed higher tolerance to the increased MCPA levels. The abundance of bacterial tfdA genes in soil increased with the co-occurrence of litter and high MCPA concentration, indicating the importance of substrate availability in fostering MCPA-degrading bacteria and thereby improving the potential for removal of MCPA in the environment.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Modelling the fate of pesticides in paddy rice-fish pond farming systems in northern Vietnam.

BACKGROUND: In Vietnam, paddy rice fields have been identified as a major non-point source of pesticide pollution of surface- and groundwater which is often directly used for domestic purposes. One strategy to assess the risk of pesticide pollution is to use process-based models. Here, we present a new model developed for simulating short-term pesticide dynamics in combined paddy rice field-fish pond farming systems. The model was calibrated using the Gauss-Marquardt-Levenberg algorithm and validated against measured pesticide concentrations of a paddy field-fish pond system typical for northern Vietnam. RESULTS: In the calibration period, model efficiencies were 0.82 for dimethoate and 0.87 for fenitrothion. In the validation period, modelling efficiencies slightly decreased to 0.42 and 0.76 for dimethoate and fenitrothion, respectively. Scenario simulations revealed that a field closure period of 1 day after pesticide application considerably reduces the risk of pond and surface water pollution. CONCLUSION: These results indicate that the proposed model is an effective tool to assess and evaluate management strategies, such as extended field closure periods, aiming to reduce the loss of pesticides from paddy fields.